esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposed a defect detection method based on CNN networks and wavelet transform, which can achieve a very high level of accuracy only with a small amount of training data. The method is evaluated on the DAGM2007 dataset, micro-surface defect database and KolektorSDD dataset, and the results show that it has a good detection ability for most defective images and can locate the location of the defect. In addition, by comparing the wavelet model with the non-wavelet model, it can be found that this method has faster learning speed and detection accuracy than traditional detection methods. The method used in this paper to extract defect patches with random windows greatly saves the time of data preprocessing. There are also sliding window methods and judgment strategies that can effectively extract image features and make defect judgments. These methods can also be applied to other detection fields. In the task of small target detection, researchers can increase the proportion of the target area by extracting patches from the original image, and can also use wavelet analysis to improve the performance of the CNN model. At the same time, this method also has some room for improvement. The detection ability of this method is not good for the image with great background texture difference. The reason is the random-window method only uses a little information from non-defective images, which is not enough for CNN training. For the images with great background texture difference, such as Class 1 and Class 4 in DAGM2007 dataset, the sliding-window method proposed in the Ref. [2] can be used to extract non-defective patches. In addition, only using the low-frequency sub-images may lose some important information for the image which is changing rapidly, such as Class 8. It can consider using high-frequency images or using high-frequency and low-frequency fusion to process training data. As for the threshold setting in the judgment strategy, we do not propose specific rules, but set them based on experience. In the follow-up research, additional experiments can be set up for different application scenarios and defect sizes to select appropriate thresholds. Conceptualization, M.D. and K.L. ; methodology, K.L. ; software, K.L. and D.L. ; investigation, K.L. and D.L. ; resources, M.D. ; data curation, D.L. ; writing--original draft preparation, K.L. ; writing--review and editing, D.L. and J.X. ; visualization, K.L. and D.L; supervision, M.D. ; project administration, M.D. ; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This research was funded by The Key Research Projects of Henan Higher Education Institutions [grant number 18A510017]; the Henan Postdoctoral Research Project [grant number 001701004]; the Major science and technology special plan of Henan Province [grant number 201111212300]; and Henan province natural science fund item [grant number 212300410291]. Not applicable. This publication is supported by multiple datasets, which are openly available.
###### Abstract  
The industrial use of ultrasonic flaw classification using neural networks in weldments must overcome many challenges. A major constraint is the use of numerous systems, including a combination of transducers and equipment. This causes high complexity in the datasets used in the training of neural networks, which decreases performance. In this study, the performance of a neural network was enhanced using signal processing on an ultrasonic weldment flaw dataset to achieve system invariance. The dataset contained 5839 ultrasonic flaw signals collected by various types of transducers connected to KrautKramer USN60. Every signal in the dataset was from 45 FlawTech/Sonspection weldment specimens with five types of flaw: crack, lack of fusion, slag inclusion, porosity, and incomplete penetration. The neural network used in this study is a residual neural network with 19 layers. The performance evaluation of the same network structure showed that the original database can achieve 62.17% \(\pm\) 4.13% accuracy, and that the invariant database using the system invariant method can achieve 91.45% \(\pm\) 1.77% accuracy. The results demonstrate that using a system invariant method for ultrasonic flaw classification in weldments can improve the performance of a neural network with a highly complex dataset.  
weldments; ultrasonic testing; flaw classification; system invariant; residual neural network +
Footnote †: journal: Medical Engineering
A large amount of data must be collected to ensure the performance of artificial intelligence (AI). It is highly difficult to collect a large amount of real inspection data in the ultrasonic testing research field. Many studies collect data from specimens to solve this problem; however, there is a limit on the amount of data that one research group can collect. This works well for systems with little variation, such as those that use an ultrasonic transducer from the same manufacturer. However, inspectors use various types of transducer and equipment for industrial ultrasonic testing. The limitation of dataset size can be a problem in this scenario for the following reasons. First, the AI may not sufficiently train signals from each system. Second, flaw signals from various systems may interfere with the training process, finding global minimum. To solve this problem, this study proposes a system invariant technique to reduce the complexity of ultrasonic weldment flaw databases and compares the performance of two different databases, one without a system invariant method and the other with a system invariant method, using the same neural network architecture.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference  
Whenever astronomers have looked at the cosmos through radio wavelengths, they have been greeted by a large diversity of radio signals created by humanity. These signals, while undoubtedly important for the progress of civilisation, are a nuisance in detecting any faint radio signal from the Universe. This so-called \"radio frequency interference\" (RFI) can be broadly classified into two distinct categories: broad-band and narrow-band RFI. Broad-band RFI refers to the spurious signals that affect the entire band of observations. Narrow-band RFI is limited in its spectral coverage and can be caused by communications at specific frequencies or any strong periodic signal that is terrestrial in nature. Any signal that is not astrophysical is treated as RFI in radio astronomy. Hence, various tools and techniques have been devised over the last 50 years to reduce the deleterious effects of RFI on fainter signatures from FRBs.  
A key feature that differentiates an astrophysical radio signal from a terrestrial one is the dispersion measure (DM). As radio waves travel through the intergalactic and interstellar plasma, they encounter free electrons that act like a refractive material, causing radio waves at lower frequencies to be delayed with respect to the higher frequencies. This manifests itself as a delay at the receiving antenna, observed at a band between \(v_{1}\) and \(v_{2}\) as  
\[\Delta t=4.15\left(v_{1}^{-2}-v_{2}^{-2}\right)\,\mathrm{DM\,ms}, \tag{1}\]  
where,  
\[\mathrm{DM}=\int_{0}^{l}n_{e}\,dl. \tag{2}\]  
Here, \(n_{e}\) is the electron density and \(l\) is the distance to the source. The DM is a measurable quantity that is proportional to the distance to the source. Astronomers can use this attribute to identify RFI, as any terrestrial or satellite RFI will have a DM of 0 pc cm\({}^{-3}\). Below, we list a few of the popular RFI excision techniques used in time-domain radio data, ordered by the level of complexity.
## 3 Results and Discussion
### Results of the Characterization of Raw and Sintered Samples
#### 3.2.3 Results of the Leaching Tests of the Raw and Sintered Samples
Shrinkage and expansion occur sequentially with the increase in temperature from RT to 800 \({}^{\circ}\)C, after which shrinkage can be observed due to prolonged heating at 800 \({}^{\circ}\)C.  
The dilatometry plot clearly shows that waste percentage addition in the compositions slightly reduced the temperature corresponding to the onset of shrinkage. However, each GC sample, while being heat-treated at 800 \({}^{\circ}\)C for 8 h, exhibited very little linear dimensional change, where the shrinkage or expansion was in the order of magnitude of only around 0.5%. This can be explained by the above-discussed phase composition of the GC samples. Namely, according to the performed XRD analysis, several crystal phases of clay minerals were identified: quartz, as the most abundant, but also albite, wollastonite, and zeolite minerals. As is commonly known, one of the main characteristics of quartz from the thermal point of view is its low thermal expansion coefficient (CTE = 5 \(\times\) 10\({}^{-7}\)/\({}^{\circ}\)C), i.e., exceptional thermal stability at temperatures up to 1100 \({}^{\circ}\)C [35]. Wollastonite is similar: it has a very low expansion rate (CTE = 7 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C) and good thermal shock resistance after heating up to 800 \({}^{\circ}\)C [36]. Also, pure albite is reported to be thermally stable below a temperature of 1000 \({}^{\circ}\)C [37]. Moreover, temperatures at which structural changes in most zeolite minerals take place are at 800 \({}^{\circ}\)C or above [38].
## 4 Results and Discussion
### Identification Results for Multiple Surface Defects on Various Arc Magnets  
The models trained by knowledge distillation were applied to test all data of the testing set in our dataset to verify our models' ability to identify different defect types. Depending on our previously prepared and expanded dataset, a total of 1871 data points, covering eight categories of images for defective arc magnets and one category of that for defect-free magnets, were tested by our model. The amount of data per category ranged from 200 to 217 in order to allow for a relative data balance between different categories, avoiding considerable specificity in the testing results. The confusion matrix in Figure 8a illustrates the identification results of our model for each data item in all categories. As can be seen, each category of data representing defective arc magnets was identified with 100% accuracy, confirming that \(A_{D}\) is also at 100%. This ability to identify different surface defects is entirely consistent with the teacher network corresponding to the transferred YOLOv5s before network pruning and knowledge distillation. Moreover, it overcomes the  
Figure 7: Results of the knowledge distillation for the student network: (**a**) the contribution of the introduced weight factor \(\lambda\) to the accuracy improvement; (**b**) the mAP and loss of student network during the training process; (**c**) the output visualization of the specific layer in the networks before and after knowledge distillation.  
problem that the \(A_{D}\) of the previously retrained and pruned transferred YOLOv5s could not reach 100%.  
For defect-free arc magnets, there were two misidentifications, including one with a blowhole and one with a crack, such that the accuracy was only 99%. The existing misidentification rate was most likely caused by the enhanced sensitivity to defective arc magnet recognition and the weakened recognition ability of defect-free magnets in knowledge distillation, but the misidentification rate of 1%, as well as the arbitrary defect recognition rate of 100%, fully met the conventional accuracy requirements and could be widely accepted by the actual production. Figure 8b further shows realistic scenarios of identifying different surface defects on arc magnets from their images. It follows that our model is extremely capable of accurately identifying the surface defects, regardless of the number and type of defects in the same image. These results demonstrate that the student network, after knowledge distillation, fully inherited the accurate recognition performance of the teacher network for all defects, compensating for the accuracy loss given by pruning the teacher network as the student network. It is noteworthy that the student network model generated by the knowledge distillation process we designed is more oriented towards accurately identifying surface defects. Its 100% accuracy is reflected in the ability to confirm both the presence of surface defects on an arc magnet and the type of the corresponding defects.
## 3 Application to a Case Study
### Establishment of Finite-Element Model  
For the general finite-element model by ANSYS, PIPE59 elements were used for the jacket above the mud surface, SHELL181 elements were used for the observation platform, BEAM4 elements were used for the platform beams and diagonal braces, and PIPE16 elements were used for the jacket below the mud surface. For numerical analysis, the model parameters must be defined properly [34; 35], and the size of the model as well as the mesh size should be described [36; 37], and the size of finite element mesh is 0.3 m with the 2-node linear rod element acceptable by ANSYS. The established finite-element model is shown in Figure 6.  
In practice, the constraint conditions of the piles and soil will be changed by external factors, and this boundary is an important factor causing changes in the structural frequency. Therefore, it is of great significance to adopt appropriate boundary conditions. As shown in Figure 7, the soil reaction of a pile in the API specifications is divided into axial and lateral components to describe the establishment of the finite-element model under pile-soil interactions. The axial soil reaction includes the axial-transfer load reaction and the pile-tip load reaction. The horizontal spring constant is determined by the p-x curve, the vertical spring constant is determined by the t-z curve, and the end spring constant is determined by the Q-z curve [27].  
Figure 6: Finite-element model of the offshore platform.  
Figure 7: Pile-soil interaction model [30].  
This paper only gives a brief description of the soft clay, for further details, please refer to the API specification. The p-x curve reflects the relationship between the soil force at the depth \(z\) below the mud surface and the lateral deflection \(x\) of the pile foundation at that depth under the action of a horizontal load. In soft clay, the p-x curve can generally be divided according to the load properties under static loads (\(z<z_{r}\)) and under cyclic loads (\(z>z_{r}\)).  
Under a static load, the curve can be expressed as:  
\[\frac{P}{P_{\text{u}}}=\left\{\begin{array}{ll}0.5\Big{(}\frac{x}{x_{c}} \Big{)}^{\frac{1}{3}}&(0\leq x/x_{c}\leq 8)\\ 1&(x/x_{c}\geq 8)\end{array}\right. \tag{8}\]  
where \(P_{\text{u}}\) is the ultimate soil resistance (unit: kPa); and \(x_{c}\) can be determined according to \(x_{c}=2.5\varepsilon_{c}\times D\), in which \(\varepsilon_{c}\) is the strain at \(1/2\) the maximum stress when an undrained compression test of an undisturbed soil sample is carried out in the laboratory and \(D\) is the pile diameter (unit: mm). Under long-term cyclic loads, the curve can be determined according to Table 3.  
The pile-soil interaction model used COMBIN39 spring elements, and PIPE16 elements were used for the steel-pipe piles. Since no actual information about the bottom layer is known, soil-layer information presented in a previous report was used for this analysis [38]. Finally, the finite element model of offshore platform with pile-soil interaction is established, and the first ten modal frequencies of the model are listed in Table 4.
## 2 Outline of the Rocket and the Launcher
### Telemetry and Data Loggers  
Telemetry and data loggers were developed to aid low-cost, commercial-grade MEMS (Micro Electro Mechanical Systems) sensors, and devices were selected. To achieve high reliability, the same two telemetry and data loggers were mounted with mechanical and electrical independence because commercial-grade MEMS do not have high reliability. Mechanical independence enables easy replacement at failure, and electrical independence prevents EMC (Electromagnetic Compatibility) trouble. Telemetry measured air pressure and GPS position coordinates during flight, and it downlinked to the ground by using a LoRa (Long Range) communication protocol, a type of LPWA (Low Power Wide Area) device using a 920 MHz band. The data logger recorded information, such as acceleration, geomagnetism, and gyro, and was picked up when the rocket was recovered. Both units were mounted on the measurement module shown in Figure 1. Because the altitude of the rocket can be estimated from the air pressure during flight, it can be measured in real time from the downlink signal. The GPS signal was used to identify the position of the rocket when it landed on the sea. The telemetry and data loggers had initially been developed at Kanagawa University and flew in 2017 [26] and 2018; they were made more compact, lightweight, and reliable by the Fullheart Japan Company.
## 2 Materials and Methods
### Measurements  
The angular sensor was fixed to the dorsal central part of the right thumb's proximal phalanx along its bone axis using tape (Figure 3). A conical model was adopted to represent  
Figure 2: Validation of the angle sensor used for reproducing thumb movements.  
thumb movement [14; 15; 16; 17; 18; 19]. In this procedure, the subject was instructed to minimize movement at the MP and IP joints. As a result, the CMC joint was considered the center of abduction and pronation of the thumb, and it was assumed that the marker trajectories of the CMC, MP, and IP joints would align on the same straight line and concentric sphere. Following the approach taken in previous research, the apex of the cone was assumed to be the CMC joint of the thumb, the second metacarpal was considered the base axis, the MP joint of the index finger was treated as the center at the base of the cone, and the IP joint of the thumb was assumed to carry out circular motion, forming the base of the cone around the MP joint of the index finger. The rotation angle, denoted as \(\theta\), was defined using \(0^{\circ}\) as the rotation angle from the plane of the palm (Figure 4). The participants were allowed to practice before the actual recording trial. Each participant sat with their wrist in a neutral position and performed four abduction movements between \(\theta=0^{\circ}\) and \(90^{\circ}\) at the maximum abduction position of the right thumb while keeping the MP and IP joints as rigid as possible. They also performed two clockwise (from proximal to distal) and two counterclockwise rotations at a self-selected speed (Figure 4). The initial and final hand positions were recorded with the thumb fully extended, abducted, and oriented vertically and horizontally. The sensor recorded radial abduction, palmar abduction, and pronation. During the thumb abduction movements, a tablet device (Surface Pro 8, Microsoft Corporation, Redmond, WA) was positioned 50 cm above the table, 100 cm from the participant, and at a \(45^{\circ}\) angle with respect to the palm. Videos were recorded in 1080p HD at 30 frames per second by a designated examiner (E.Y.) (Figure 5).  
While our previous research [19] focused only on radial and palmar abduction, this study expanded the scope by including pronation as an additional parameter. This broader focus provided a more comprehensive evaluation of thumb movements and represents a novel aspect of this research.
# Community Detection Based on Graph Representation Learning in Evolutionary Networks
## 3 Preliminaries  
### Definitions  
Given a network \(G=(V,E)\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) is the set of nodes, \(E=\{e_{1},e_{2},\ldots,e_{n}\}\) is the set of edges. The adjacency matrix \(A=\left[a_{ij}\right]_{n\times n}\) represents the connection relationship between nodes, the value of the corresponding element in the matrix indicates whether the edge exists, if there is an edge between node \(v_{i}\) and node \(v_{j}\), then \(a_{ij}=1\); otherwise \(a_{ij}=0\). In this paper, we take the adjacency matrix \(A\) as the closest relation matrix in the network to describe the similarity between nodes in the graph and their proximity relation.  
(1) Node proximity  
Given a network \(G=(V,E)\), \(u\), \(v\in V\), the proximity \(Nei(u,v)\) between node \(u\) and node \(v\) is defined as  
\[Nei(u,v)=e^{\alpha(2-l)}, \tag{1}\]  
where \(l\) is the shortest path length from node \(u\) to node \(v\), \(l\geq 2\), \(\alpha\) is the degree of attenuation, and the value range is \((0,\,1)\). If the path length l increases, the proximity decreases, and \(\alpha\) controls the degree of proximity attenuation. The larger the value of \(\alpha\), the faster the proximity relationship between the nodes decays.  
(2) Proximity matrix  
Given a network \(G=(V,E)\), \(M=\left[m_{ij}\right]_{n\times n}\) is a matrix corresponding to the network \(G\), where we use the proximity formula to calculate the proximity between the two corresponding nodes \(v_{i}\) and \(v_{j}\) in \(M\), \(m_{ij}=Nei\big{(}v_{i},v_{j}\big{)}\), with \(v_{i},v_{j}\in V\); then, we call \(M\) the proximity matrix of \(G\).
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 2 Mathematical Model and Control Method
### Mathematical Model of the \(\alpha\)-\(\beta\) Coordinate System  
[PERSON] transformation was carried out on Equation (1) to obtain the state equation of the three-phase inverter in the two-phase stationary coordinate system, and then Laplace transformation was carried out to obtain the transfer function in the two-phase stationary coordinate system, as shown in Equations (2) and (3).  
\[\left\{\begin{array}{ll}\left[\begin{array}{c}u_{\alpha}\\ u_{\beta}\end{array}\right]=\left[\begin{array}{c}u_{\text{C}\alpha}\\ u_{\text{C}\beta}\end{array}\right]+L_{1}p\left[\begin{array}{c}i_{1\alpha} \\ i_{1\beta}\end{array}\right]\\ \left[\begin{array}{c}u_{\text{C}\alpha}\\ u_{\text{C}\beta}\end{array}\right]=\left[\begin{array}{c}u_{\beta\alpha}\\ u_{\beta\beta}\end{array}\right]+L_{2}p\left[\begin{array}{c}i_{2\alpha}\\ i_{2\beta}\end{array}\right]\\ \left[\begin{array}{c}i_{1\alpha}\\ i_{1\beta}\end{array}\right]=\left[\begin{array}{c}i_{2\alpha}\\ i_{2\beta}\end{array}\right]+Cp\left[\begin{array}{c}u_{\text{C}\alpha}\\ u_{\text{C}\beta}\end{array}\right]\end{array}\right. \tag{2}\]  
Figure 1: LCL-type three-phase grid-connected inverter topology.  
\(p=\frac{\mathrm{d}}{\mathrm{d}t}\), and Laplace transformation was carried out on Equation (2) to obtain the mathematical model in the s domain, as shown in Equation (3):  
\[\left\{\begin{array}{l}\left[\begin{array}{c}u_{a}\\ u_{\beta}\\ u_{C\beta}\end{array}\right]=\left[\begin{array}{c}u_{Ca}\\ u_{C\beta}\end{array}\right]+L_{1}s\left[\begin{array}{c}i_{1a}\\ i_{1\beta}\end{array}\right]\\ \left[\begin{array}{c}u_{Ca}\\ u_{C\alpha}\\ u_{C\beta}\end{array}\right]=\left[\begin{array}{c}u_{ga}\\ u_{g\beta}\end{array}\right]+L_{2}s\left[\begin{array}{c}i_{2a}\\ i_{2\beta}\end{array}\right]\\ \left[\begin{array}{c}i_{1a}\\ i_{1\beta}\end{array}\right]=\left[\begin{array}{c}i_{2a}\\ i_{2\beta}\end{array}\right]+Cs\left[\begin{array}{c}u_{Ca}\\ u_{C\beta}\end{array}\right] \tag{3}\]  
As can be seen from Equation (3), there is no coupling in the two-phase static coordinate system three-phase LCL grid-connected inverter. This advantage can be fully utilized to decouple the active power and reactive power. The LCL type three-phase grid-connected inverter will be directly controlled in a two-phase stationary coordinate system.