esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
###### Abstract
Compared with the traditional machine learning method, it has the following characteristics: deep learning can simplify or even omit the pre-processing of data, and directly use the original data for model training; deep learning is composed of multi-layer neural networks, which solve the defects in the traditional machine learning methods of artificial feature extraction and optimization. So far, deep learning has been extensively adopted for surface defect detection. For example, in [23], Deep Belief Network (DBN) was adopted to obtain the mapping relationship between training images of solar cells and non-defect templates, and the comparison between reconstructed images and defect images was used to complete the defect detection of the test images. [PERSON] et al. employ a deep convolution neural network (CNN) to identify concrete cracks in complex situations, e.g. strong spots, shadows and ultra-thin cracks, and proves that the deep CNN outperformed the traditional tools like Canny edge detector and Sobel edge detector [24]. [PERSON] et al. detect various types of defects on hub surfaces with residual network (ResNet)-101 as the base net and the faster region-based CNN (Faster R-CNN) as the detector and achieves a high mean average precision of 86.3% [25]. The above studies fully verify the excellent performance of deep learning in detecting surface defects. Nevertheless, there are few studies on product surface defect detection using several main target detection networks in recent years, such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector) [26] and so on. The detection performance of these networks in surface defect detection needs to be further verified and optimized. This paper presents a surface defect detection method based on MobileNet-SSD. By optimizing the network structure and parameters, this method can meet the requirements of real-time and accuracy in actual production. It was verified in the filling line and the results show that our method can automatically locate and classify the defects on the surface of the products.
## 2 Materials and Methods
### Characterization of PES ENMs  
The surface structures of the PES nanofibrous mats and ENMs were analyzed with a field emission scanning electron microscope (FE-SEM) (S-4800, Hitachi, Tokyo, Japan). The average diameter of the nanofibers was measured using FE-SEM images and Image J 1.48v software (National Institutes of Health, Bethesda, MD, USA). To calculate the average diameter in each sample, 30 nanofibers were randomly selected within three images. The thickness of PES ENMs was measured using a dial thickness gauge (Mitutoyo 7301, Mitutoyo, Kanagawa Japan). Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy (Varian 660-IR, Bruker, Billerica, USA) was utilized to identify the chemical surface property of the PES ENMs according to the LiCl addition.  
To compare the pore structures of the PES ENMs with the different fiber diameters, the pore size distribution of the PES ENMs was evaluated using a capillary flow porometer (Porous Materials Inc., New York, NY, USA). The PES ENMs were soaked with the wetting liquid, Galwick(tm) (Welcron, Seoul, Korea) and then put in the test sell with an effective diameter of 1.5 cm. Nitrogen gas was injected to overcome the capillary force of the liquid in the pores and flowed through the pores, increasing the differential pressure slowly. The relation between pore size and the corresponding gas pressure is obtained by the Young-Laplace Equation (1):  
\[\mathrm{r}=\frac{2\gamma}{\Delta P}\cos\theta \tag{1}\]  
where r is the radius of the pore, \(\Delta P\) is the differential gas pressure, \(\gamma\) is the surface tension of the wetting liquid, Galwick(tm) (\(15.9\times 10^{-5}\) N/cm = 15.9 dyn/cm), and \(\theta\) is the wetting angle. The bubble point pore diameter represents the largest pore size in the membrane. The mean flow pore diameter indicates that half of the gas flow passes through pores larger than the mean pore size and that the remaining half of the gas flow passes through pores smaller than the mean pore size [13]. The porosity  
\begin{table}
\begin{tabular}{c c c c} \hline  & **ENM 30** & **ENM 30-0.01** & **ENM 30-0.10** \\ \hline PES concentration (\% (_w/v_)) & 30 & 30 & 30 \\ LiCl concentration (\% (_w/v_)) & 0 & 0.01 & 0.10 \\ Applied voltage (kV) & 13.0 & 15.0 & 21.0 \\ Feed rate (mL/h) & 0.5 & 0.3 & 0.3 \\ Tip-to-collector distance (cm) & 20 & 20 & 20 \\ Humidity (\%) & 40–50 & 40–50 & 40–50 \\ \hline \end{tabular}
\end{table}
Table 1: Fabrication conditions of the three different polyetheresulfone (PES) electrospun nanofibrous membranes (ENMs).  
measurements of the PES ENMs were performed using a mercury porosimeter (AutoPore III 9420, Micrometrics, Norcross, GA, USA).
# A Site-Specific Response Analysis: A Case Study in Hanoi, Vietnam
## 3 Site Response Analysis Methodology
### Soil Profiles  
A total of three districts in Hanoi were selected for the analyses. Two soil profiles were selected from Cau Giay District (District 1), one from Hoang Mai District (District 2), and three soil profiles were selected from Hai Ba Trung District (District 3). The typical soil stratigraphy includes fill, sand, clay, and gravel. Due to the lack of shear wave velocity (\(V_{S}\)) profile measurements, the in situ standard penetration test blow count (SPT-N) measurements were used to evaluate the \(V_{S}\) profiles. The correlation between SPT-N and \(V_{S}\) proposed by [PERSON] et al. [27] was used to determine the \(V_{S}\) profiles. This correlation was also employed by [PERSON] et al. [28] for Vietnamese soil conditions, expressed as:  
\[Vs=77.13N^{0.377} \tag{7}\]  
The depth to bedrock of the profiles range from 33 m to 75 m. Natural periods of the sites range from 0.64 to 1.39 s. The time-average shear wave velocity to 30 m depth (\(V_{\text{S30}}\)) of the sites range from 160 m/s to 202 m/s. Sites are classified as types C and D according to the Vietnamese design code. The bedrock is considered at the point where the \(V_{\text{S}}\) reached to 800 m/s, according to the bedrock definition in TCVN 9386: 2012. The detail of soil profiles is shown in Table 4 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Soil Profile**} & \multicolumn{2}{c}{**Soil Class (According to**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Notation**} \\  & **EC-8 and TCVN 9386: 2012)** & & \\ \hline P1 & C & \multicolumn{2}{c}{Cau Giay District} & \multirow{2}{*}{1-C} \\ P2 & C & \multicolumn{2}{c}{Cau Giay District} & \multirow{2}{*}{1-C} \\ P3 & D & Hoang Mai District (District 2) & & 2-D \\ P4 & C & Hai Ba Trung District (District 3) & & 3-C \\ P5 & D & Hai Ba Trung District & & 3-D \\ P6 & D & Hai Ba Trung District & & 3-D \\ \hline \hline \end{tabular}
\end{table}
Table 4: Soil profile properties.  
Figure 4: Site response analysis procedure in this study.
# Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage +
## 4 Conclusions  
In this work we presented an AE monitoring system with a resonant sensor that is able to detect the damaged area in an injection molding tool. We made a close comparison of AE signals captured from a new mold and a damaged mold under the same injection molding process conditions.  
The most useful information about the injection molding tool integrity based on AE bursts is gained during the filling and holding stage of the injection molding process, when the mold pressure is high enough.  
We introduced time domain, frequency domain, and wavelet packet transform descriptors to classify feature vectors of AE bursts during the injection molding cycle. A 10-dimensional subset feature vector with real-valued explanatory variables is proposed for classification of AE burst signals.  
Based on SFS, the feature subset consists mainly of normalization values of the energy of wavelet packets, which turned out to be the most informative for prediction of damaged areas in the injection molding tool.  
Neural network pattern recognition results show very good prediction probability of tool damage and very good prediction of process-orientated signals during the filling and holding phases of the injection molding cycle.  
The research work was partly financed by the European Union, European Social Fund, and by the Slovenian Ministry for Higher Education, Science and Technology.  
All the authors conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data and wrote the paper; [PERSON] performed the experiments and contributed materials and injection molding machine; [PERSON] led the research work and reviewed the work.  
The authors declare no conflict of interest.
## 3 Results
### Mineral Profile  
The use of fertilization with both frans and cattle manure resulted in a significant increase in the potassium level in the collected plant material. The opposite effect, i.e., a decrease in the content as a result of fertilization, was observed for calcium. In the case of magnesium, sodium, copper and zinc, no differences were noted in their concentration between different fertilizers. The iron content in the drought conditions was higher where HI frans was use (Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mineral** & **Fertilizer** & \begin{tabular}{c} **Dose** \\ **(g/L)** \\ \end{tabular} & \
\begin{tabular}{c} **Wetered** \\ **(mg/g)** \\ \end{tabular} &
\begin{tabular}{c} **Drought** \\ **(mg/g)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Ca} & Control & - & 95.1 \(\pm\) 6.4 a,A & 82.3 \(\pm\) 7.8 a,B \\  & Cattle manure & 10 & 78.7 \(\pm\) 6.3 c,B & 84.4 \(\pm\) 6.0 a,A \\  & HI frans & 10 & 83.7 \(\pm\) 7.3 bc,A & 75.2 \(\pm\) 5.5 b,A \\  & HI frans & 12.5 & 87.4 \(\pm\) 8.0 b,A & 78.8 \(\pm\) 5.5 b,A \\ \hline \multirow{4}{*}{Mg} & Control & - & 36.9 \(\pm\) 3.1 a,A & 34.2 \(\pm\) 2.8 a,A \\  & Cattle manure & 10 & 33.2 \(\pm\) 2.8 a,A & 32.1 \(\pm\) 2.8 a,A \\  & HI frans & 10 & 31.7 \(\pm\) 2.7 a,A & 30.8 \(\pm\) 2.4 a,A \\  & HI frans & 12.5 & 34.4 \(\pm\) 2.8 a,A & 32.4 \(\pm\) 3.0 a,A \\ \hline \multirow{4}{*}{K} & Control & - & 37.6 \(\pm\) 2.5 b,B & 49.5 \(\pm\) 3.8 b,A \\  & Cattle manure & 10 & 57.8 \(\pm\) 4.0 a,A & 60.2 \(\pm\) 5.0 a,A \\  & HI frans & 10 & 56.3 \(\pm\) 4.7 a,A & 59.9 \(\pm\) 4.2 a,A \\  & HI frans & 12.5 & 68.7 \(\pm\) 5.0 a,A & 65.5 \(\pm\) 4.4 a,A \\ \hline \multirow{4}{*}{Na} & Control & - & 27.9 \(\pm\) 1.5 a,A & 25.7 \(\pm\) 1.6 a,A \\  & Cattle manure & 10 & 25.9 \(\pm\) 1.8 a,A & 24.6 \(\pm\) 1.5 a,A \\  & HI frans & 10 & 23.7 \(\pm\) 1.5 a,A & 25.0 \(\pm\) 1.6 a,A \\  & HI frans & 12.5 & 23.1 \(\pm\) 1.6 a,A & 24.5 \(\pm\) 1.5 a,A \\ \hline \multirow{4}{*}{Cu} & Control & - & 0.28 \(\pm\) 0.04 a,A & 0.22 \(\pm\) 0.03 a,A \\  & Cattle manure & 10 & 0.20 \(\pm\) 0.04 a,A & 0.25 \(\pm\) 0.03 a,A \\  & HI frans & 10 & 0.22 \(\pm\) 0.04 a,A & 0.28 \(\pm\) 0.04 a,A \\  & HI frans & 12.5 & 0.18 \(\pm\) 0.03 a,B & 0.31 \(\pm\) 0.04 a,A \\ \hline \multirow{4}{*}{Fe} & Control & - & 0.37 \(\pm\) 0.02 a,B & 0.43 \(\pm\) 0.02 b,A \\  & Cattle manure & 10 & 0.38 \(\pm\) 0.02 a,A & 0.39 \(\pm\) 0.02 b,A \\  & HI frans & 10 & 0.33 \(\pm\) 0.02 a,B & 0.47 \(\pm\) 0.03 b,A \\ \cline{1-1}  & HI frans & 12.5 & 0.39 \(\pm\) 0.02 a,B & 0.73 \(\pm\) 0.02 a,A \\ \hline \end{tabular}
\end{table} 
Table 4: Lettuce mineral profile under different fertilizer and water conditions.
# Kernel Identification of Non-Linear Systems with General Structure
## 2 Problem Statement
### Examples
#### 2.2.3 Finite Memory Bilinear System  
Another important and often met in application special case is the bilinear system with finite order \(m\). It is described by the formula  
\[y_{k}=\sum_{l_{0}J_{1},...,l_{m-1}:l_{i\geq 0}\cap\sum_{i}l_{i}\leq m}c_{l_{0}J_{1 },...J_{m-1}}\prod_{i=0}^{m-i}u_{k-i}^{l_{i}}+z_{k} \tag{10}\]  
i.e., for \(m=1\) we get  
\[y_{k}=c_{0,0}+c_{1,0}u_{k}+c_{0,1}u_{k-1}, \tag{11}\]  
for \(m=2\) we have that  
\[y_{k}=c_{0,0}+c_{1,0}u_{k}+c_{0,1}u_{k-1}+c_{1,1}u_{k}u_{k-1}+c_{2,0}u_{k}^{2}+ c_{0,2}u_{k-1}^{2}. \tag{12}\]  
Since \(y_{k}\) does not depend on \(u_{k-m},u_{k-m-1},...\), for \(s\geq m\) we get \(\mathcal{F}\left(\{u_{k-i}\}_{i=0}^{\infty}\right)=\mathcal{F}\left(\{\overline {u}_{k-i}\}_{i=0}^{\infty}\right)\), and obviously \(\Delta\left(s\right)=0\).  
Considered example falls into the more general class of Volterra representation. Presented approach works without the knowledge of parametric representation. As regards applicability to the class (1), for \(m<\infty\) we have finite memory system, i.e., \(\Delta(s)=0\), as \(s>m\). Moreover, since the input is assumed to be bounded (\(\left|u_{k}\right|<u_{\max}\)), resulting mapping \(\mathcal{F}()\) fulfills Lipschitz condition (as ordinary polynomial on compact support).
## 2 System Configuration and Methods of Analysis
### Gaussian Filter Image Analysis Method  
The authors of this paper have proposed a real-time image analysis system for solar panel fault detection with UAV previously [23]. Three different filtering methodologies were compared in that study, and the Gaussian filter was found to be the most suitable for detecting the high temperature spots on solar panels. In this study, Gaussian filter was  
Figure 3: Proposed algorithm flow chart.  
Figure 2: System description showing main components of the equipment used.  
adopted for the pre-treatment of the IR image, and the temperature distribution of the panel was presented with a histogram and a cumulative curve.  
The pixel in an IR image is composed of different ratios of RGB. In response to this, converting the image from RGB to grayscale becomes the first step. This procedure not only enhances the resolution of the image but also facilitates color judgment of the image. Synchronously, filters are necessarily applied to eliminate noise and reduce the sharpness of the image. In fact, filtering techniques are adopted to pre-process the image prior to the detection of faults. In this study, Gaussian filters are applied to reduce noise and other unnecessary details in IR images. Normally, defects such as hot spots and cracks can affect the temperature uniformity on the solar panel module and result in different colors in the IR image. However, noise may also occur in the IR image that real defects may be misinterpreted. Normal components and defective areas on the solar panel module, without the interference of noise, can be clearly distinguished under the filtered image, as shown in Figure 4. The original IR image was grayscale-processed follow by a division of black and white pixels of the image, as shown in Figure 4b, representing cold areas and hot spots on the surface of the solar module respectively [8]. To show the defect more clearly, it is necessary to convert it to an RGB image as shown in Figure 4c. After the conversion, the green area shows up to represent cold zone and the red area the hot zone. The correspondence between color and temperature is shown in the color bar. The lowest temperature in this image is 35 \({}^{\circ}\)C and the hottest area is at 55 \({}^{\circ}\)C.  
Figure 4a is the original IR image which was grayscale-processed. Basically the panel is of uniform temperature with color of yellowish orange but some areas with bright color are also displayed, indicating that higher temperature regions are distributed in the panel. Figure 5b shows an image converted to grayscale after Gaussian filtering.  
Figure 4: Preprocess of the IR image. (**a**) IR origin image. (**b**) Gaussian filtered grayscale image. (**c**) Gaussian filtered color image.
# Artificial Intelligence (AI) Applied in Civil Engineering
## 2 Contributions
### Machine Learning in Identification Problems  
In recent years, deep learning-based detection methods have been successfully applied to pavement crack detection problems. In this field, [PERSON] et al. [15] propose a method to improve the accuracy of crack identification by combining a semantic segmentation and edge detection model. Their work is inspired by the U-Net semantic segmentation network and holistically nested edge detection network. A side-output part is added to the U-Net decoder that performs edge extraction and deep supervision. A network model is proposed, combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales. The model can also be used for other tasks that need both semantic segmentation and edge detection. On the topic of concrete structures, [PERSON] and [PERSON] [16] propose an improved self-organizing mapping (SOM) neural network (NN) model to solve the problem of intelligent detection of damage to modern concrete structures under complex constraints. The method is based on a small number of samples and the use of a self-developed 3D laser scanning system. The improved SOM model method fully combines the network topology and its unique image features and can accurately identify structural damage, contributing to the realization of high-precision intelligent health monitoring of damage to modern concrete structures.  
In railway engineering, the performance of the passing train and the structural state of the track bed are common concerns regarding the safe operation of the subway. Monitoring the vibration response of the track bed structure and identifying abnormal signals within it can help address these concerns. In this direction, [PERSON] et al. [17] propose an unsupervised learning-based methodology for identifying the abnormal signals of the track beds detected by the ultra-weak fiber optic Bragg grating sensing array. The experimental results demonstrate that the established unsupervised learning network and the selected metric for quantifying error sequences can serve the threshold selection well, based on the receiver operating characteristic curve.
## 2 System Model
### Frame Structure  
The frame structure adopts the 5G frame structure based on the 3 GPP specifications [31]. The frame length is 10 ms, which consists of ten subframes of 1 ms duration. Each subframe contains \(N_{slot}^{subframe,\mu}N_{symb}^{slot}\) OFDM symbols, where \(N_{symb}^{slot}\) denotes number of OFDM symbols per slot. \(N_{slot}^{subframe,\mu}=2^{\mu}\) represents the number of slots contained in each subframe. \(\mu\) represents the subcarrier spacing configuration. In the frequency domain, a resource block contains 12 consecutive subcarriers. The subcarrier spacing can be represented as \(\Delta f=2^{\mu}15\) [kHz].
# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 4 Training Details
### Alternate Training Strategy  
According to the research made by [PERSON] [27] and [PERSON] [44], the features learned by deep neural networks can be applicable to some related tasks even those distant ones. Compared with obtaining object detection data, people are always having difficulties to access massive segmentation data. Taking the KITTI dataset [23; 24; 28] as an example, the dataset includes 7481 training images for detection while only 289 training images for road segmentation and 200 training images for multi-class pixel-wise semantic segmentation (call \"pixel-wise segmentation\" later). The cause of this situation is the case that the cost is much higher to label segmentation datasets. To address this problem, an alternate training strategy was created in this paper. It firstly feeds detection data into the model, computes the detection loss function and completes the corresponding backward propagation. Following this, feed segmentation data into the model, then compute the segmentation loss function and accomplish the relevant backward propagation. And repeat these two procedures alternately. This \"one batch of segmentation data feeding with one batch of detection data feeding\" method is called the \"1-1 Alternation\" strategy. Likewise, \"1-N Alternation\" means one batch of segmentation data feeding with N batches of detection data feeding. To make it clear, the process is displayed in Figure 1. The rightward arrows indicate the forward process while the leftward arrows indicate the backward propagation process. Different colors (red and blue) are used to identify the alternate strategy here. For instance, feed segmentation data through blue-rightward arrows (forward process) and adjust the parameters by the red-leftward arrows (backward process), and the adjusted model is next used for the detection task following the red-rightward arrows. Using the \"1-N Alternation\" strategy, the OFA-Net is able to locate an acceptable point in the high dimensional parameter space where the model performs well on both tasks.  
The performance of the \"1-N Alternation\" strategy is also tested to seek out the proper mixture ratio of detection data and segmentation data, where \(N\) is equal to 1, 2, and 5. Finally, convergence speed, IOU (Intersection Over Union) for segmentation, precision for detection, and some additional related indicators are measured.
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
## 5 Experimental Verification  
Experiments were conducted to verify the measurement performance of the proposed dual-scan scanning method. Because temperature-induced deformation of the measurement modules may affect measurement accuracy [28], the measurement tests were performed in an air-conditioned workshop, with temperature controlled at \(20\pm 2\)\({}^{\circ}\)C. The reference systems were also working in a laboratory with the temperature controlled at \(20\pm 1\)\({}^{\circ}\)C. In order to minimize the influence of temperature variation, the data capture processes were completed within 3 min.  
An area of \(1\) mm \(\times\) 1 mm on an additive manufacturing sample was scanned to verify the areal measurement method as discussed in Section 3. A line scan of 25 mm was conducted to verify the long-travel surface tracing strategy discussed in Section 4.
## 4 Circuit
### Design Procedure  
As already discussed in Section 1, the matching circuit is primarily responsible for maximizing the power transfer from the antenna to the conditioning circuit. However, since the half-wave Cockcroft-Walton multiplier has a strong non-linear behavior, the design of the matching circuit can become a complicated task. The multiplier circuit is highly sensitive to slight variations in the input power, frequency and other terms. Specifically, the design of the matching circuit depends of five parameters: input power, input frequency, antenna impedance, load (type of sensor), and the number of stages \(N\) in the HWCW multiplier. Fortunately, the input power and the input frequencies can be estimated according to the power spectrum acquired by the Archimedean spiral antenna. We also know that the antenna impedance has a theoretical value of 188 \(\Omega\). Thus, the complexity of the design can be reduced to only two dependent terms: the number of stages \(N\) in the multiplier circuit and the load impedance. The design of the matching circuit is as follows. The antenna impedance is replaced by a series equivalent impedance \(Z_{s}\) placed after the generator.  
The value of \(Z_{s}\) that maximizes the output power \(P_{o}\) is then sought via an optimization process. Power transfer is then maximized when the matching circuit is able to transform the antenna impedance \(Z_{ant}\) (188 \(\Omega\)) into the optimum equivalent impedance \(Z_{s}\). Subsequently, the components that conform the matching circuit (\(L\) matching network) are determined in order to provide the desired impedance transformation. The design procedure for a single stage of the HWCW multiplier is clearly depicted in Figure 13.  
Figure 13: Design procedure of the matching circuit. Obtaining the optimum source impedance \(Z_{s}\) (a) and implementing the matching circuit (b).