esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Surface defect detection aims to recognize samples that exhibit dissimilar properties when compared with defect-free samples. Surface defect detection commonly occurs in many industrial applications, such as steel defect detection [1], textile defect detection [2], and glass bottle bottom defect detection [3]. The defects of industrial products that occur during manufacturing have negative effects on the product quality and even functionality in certain cases. To guarantee the quality of final products, the defective products must be detected and removed manually or automatically.  
In the past decades, many traditional methods of surface defect detection have been developed such as statistics-based methods [4; 5; 6; 7], spectrum-based methods [8; 9; 10], and model-based methods [11; 12; 13]. These traditional detection algorithms rely on the features of artificial design, which are limited to specific defect modes. Therefore, they cannot handle detection of texture defects that involve complex textures or any new defect mode.  
Recently, Convolutional Neural Networks (CNN) have been widely applied in industrial defect detection task and achieved promising performance. [PERSON] et al. [14] applied CNN for the first time to detection of steel surface defects. Later, more and more methods based on CNN have been proposed. The CNN-based methods have reached better defect detection results than traditional algorithms through the design of efficient network structure. However, their network structures were streamlined which cannot extract the information of the input image completely. In addition, although they used some data augmentation methods to increase the number of training samples, these methods are too simple to significantly improve the detection performance. Most importantly, they did not consider the imbalance of positive and negative samples. Generally speaking, the number of negative samples (defect-free samples) is much larger than positive samples (defective samples). In this case, the network learns more features of negative samples, resulting in a decrease in the detection accuracy of positive samples.  
To tackle these problems, this paper makes the following contributions:  
1. an efficient network called MobileNet-v2-dense is proposed. It starts from MobileNet-v2 and innovates on a multi-scale cascade structure to improve defect detection performance;
2. a two-stage data augmentation method is proposed for network training. This method can effectively increase the number of data sets and improve detection performance;
3. to solve the imbalance between positive and negative samples, an asymmetric loss function is proposed, which can make the network pay more attention to the loss of positive samples.  
The rest of the paper is organized as follows. Section 2 reviews some related work on surface defect detection. Section 3 describes the proposed MobileNet-v2-dense network in detail, including the data augmentation method and asymmetric loss function. To evaluate the model and compare the overall performance, the experimental results are presented in Section 4. Section 5 summarizes the work.
## 2 Materials and Methods  
### Experimental Setup  
A laboratory scale aerobic, submerged type MBR system, which closely simulates the filtration operation of full-scale MBR through, constant flux operation with scheduled periodic backwashing,was designed and constructed (Figure 1). The MBR tank has an effective volume of 20 L, in which a hollow fiber custom-made membrane module is submerged. The membranes, supplied by ZENON Environmental Inc., Oakville, Canada, are made of hydrophilized polyvinylidenefluoride. Their nominal pore size is 0.04 um and the membrane module has an effective filtration area of 0.174 m\({}^{2}\). A coarse air bubble diffuser is installed under the membrane module to supply oxygen to biomass and to provide agitation hindering the deposition of bio-flocs on the membrane surface; the air flow rate is approx. 8 Ndm\({}^{3}\)/min, and the mean dissolved oxygen concentration is significantly higher than 2.0 mg/L, to ensure aerobic conditions.  
The wastewater is fed to the MBR through a piston pump (Fluid Metering Inc., Syosset, New York, USA). A pH-meter with integrated temperature sensor (713 type pH-meter, Metrohm Ltd., Herisau, Switzerland) is used to monitor the pH and temperature of the MBR. A piston pump (Fluid Metering Inc., Syosset, New York, USA) is employed for constant flux operation, whereas a pressure transducer (Tecsis GmbH, Offenbach, Germany), is connected at the outlet of the membrane module to monitor the Trans-Membrane Pressure (TMP) as an indicator of membrane fouling. A typical backwashing protocol of 3 min filtration followed by 1 min backwashing with tap water, at the same backwashing flux as the filtration mode, is applied throughout the start-up period. Computer-based data acquisition hardware (PCL-818L, Advantech Co., Taipei, Taiwan), and the software GeniDAQ 4.25 (Advantech Co., Taipei, Taiwan) have been used for monitoring the system. Two level sensors activate the permeate pump on and off in order to keep constant the level of the bioreactor at 10 \(\pm\) 0.5 L.
## 1 Introduction
In detail, the radiation tolerances of the consumer communication IC and single-chip microcomputer are evaluated, and the possibility of applying the small communication module in space is examined, assuming installation in a nanosatellite, considering the measures for stable operation. We also evaluate the Doppler shift tolerance to confirm the module sensitivity when the frequency is shifted by the high-speed movement of the satellite. Finally, we design and develop a communication substrate that can be mounted on a CubeSat using devices with high-space-utilization potential; this design is demonstrated in orbit using a low-Earth-orbit (LEO) satellite. The remainder of this paper is organized as follows. Section 2 describes the wireless communication system developed in this work and the testing methodology. Section 3 presents and analyzes the test results and discusses the feasibility for satellite application. Section 4 discusses the performance of the proposed communication component based on a real-space demonstration using CubeSat. Finally, Section 5 summarizes the conclusions of this study and discusses future work.
## 2 Experiment One: Localisation Thresholds for Natural Sound Sources
### Results  
#### 2.2.1 The Effect of Presentation Method  
Figure 4 shows the median localisation thresholds for each stimulus at each ICTD for both presentation methods. The medians have been plotted with notch edges, which is a method suggested by [PERSON] et al. (2013). In general, there is considerable overlap between the notch edges for each presentation method, which, according to ([PERSON], 2013), indicates that pairs of stimuli are not significantly different from one another with 95% confidence. However, it is clear that in some cases the overlap between notches is minimal (e.g., conga at 1 ms, quartet at 10 ms). In order to analyse this further, the results for vertical stereophonic and quadraphonic presentation were compared for each stimulus using Wilcoxon tests. The critical p value was 0.05. According to this analysis, the effect of stimulus presentation was only significant for the Oboe with ICTD of 0 ms (\(p\) = 0.036). However, it is clear from Figure 4 that there is a large overlap between the notch edges for this stimulus. In addition to this, the effect size \(r\) calculated based on [PERSON] (2013) was 0.49, which is not considered as being a large effect ([PERSON], 2013). It could be argued then that the significant effect identified in the Wilcoxon test was a type-I error, being a false positive when in fact there is no true effect ([PERSON], 2013). It can therefore be concluded that the effect of presentation method on the localisation thresholds obtained was not significant.
# Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity
## 2 System Model
### Performance Metrics  
We consider coverage and \(\alpha\)-fair throughput as our performance metric in this work. The coverage is defined as the probability of user receiving SINR greater than the minimum threshold as in [21]. We consider \(\alpha\)-fairness in this work. The utility function of \(\Lambda_{\alpha}(x)\) with respect to general variable \(x\) as in [21] is written as follows.  
\[\Lambda_{\alpha}(x)=\begin{cases}\ \frac{x^{1-\alpha}}{1-\alpha},&\alpha>0,\ \alpha\
eq 1,\\ log(x)&\alpha=1.\end{cases} \tag{13}\]  
where \(\alpha\) is the fairness parameter. When \(\alpha=1\), (13) leads to proportional fairness. As \(\alpha\) increases, the worst case user's rate of the system improves. \(\alpha\rightarrow\infty\) corresponds to max-min fairness where then minimum rate of the user in the system is maximized. The optimization problem formulated for RIS-assisted DC systems is presented in the next section.
## 2 Materials and Methods
### Methods
#### 2.2.4 Accuracy Assessment  
The confusion matrix is a standard tool for accuracy evaluation, typically represented as an (n \(\backslash\)times n) matrix [60]. In machine learning, the confusion matrix serves to assess  
Figure 3: Technical flowchart.  
the performance of classification models, particularly in supervised learning [61], while it is often referred to as a similarity or matching matrix in unsupervised learning. In image accuracy assessment, the confusion matrix is primarily used to compare the classification results with the actual ground-truth values [62]. It provides a means of displaying the classification accuracy. The matrix is generated by comparing the position and class of each ground-truth pixel with its corresponding position and classification in the classified image. To assess the accuracy of the fallow farmland extraction results, this study uses ENVI 5.6 software to load a validation sample set and verifies the results using the confusion matrix method. Based on the confusion matrix, the overall accuracy (Overall Accuracy), producer's accuracy (Producer's Accuracy), user's accuracy (User's Accuracy), and Kappa coefficient (Kappa, (k)) are then calculated to evaluate the classification performance [63]. The formulas for calculating these metrics are as follows [62; 64]:  
\[\text{Overall Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+ \text{FP}+\text{FN}} \tag{6}\]  
\[\text{Prod Accuracy}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[\text{User Accuracy}=\frac{\text{TN}}{\text{TN}+\text{FN}} \tag{8}\]  
\[\text{Pe}=\frac{\left(\text{TP}+\text{FN}\right)*\left(\text{TP}+\text{FP} \right)+\left(\text{FN}+\text{TN}\right)*\left(\text{FP}+\text{TN}\right)}{ \left(\text{TP}+\text{TN}+\text{FP}+\text{FN}\right)^{2}} \tag{9}\]  
\[\text{Kappa Coefficient}=\frac{\text{P}0-\text{Pe}}{1-\text{Pe}} \tag{10}\]  
In the formula, P0 is equal to the overall accuracy, TP stands for true positives (correctly classified abandoned farmland), TN stands for true negatives (correctly classified non-abandoned farmland), FP stands for false positives (non-abandoned farmland incorrectly classified as abandoned), and FN stands for false negatives (abandoned farmland incorrectly classified as non-abandoned).
## 4 Discussion
### Research Limitations  
We used remote sensing image data to assess climate change in Central Asia and address changes in ecosystem resistance and resilience characteristics under different climate events. The spatial resolution of satellite remote sensing images is one limitation of this study. Another limitation lies in the lack of ground truth data for verification purposes. This study also lacks information on the relationship between water demand and carbon uptake. The normal climatic threshold range of the ecosystem was not quantified, and we do not know precisely the time at which an ecosystem begins to experience water limitations. These issues warrant attention in our future research. The parameters used in the calculation of MODIS productivity data were the same for all land use types; however, there are differences among different ecosystem types. We could not distinguish the areas dominated by C3 and C4 vegetation; however, the water shortage sensitivities of C3 and C4 plants differ in arid areas. We did not exclude the effects of wildfires and human logging in the calculation of resilience. Despite these limitations, this study used remote sensing technology to calculate long-term climate change sequences and to investigate the stability of Central Asia ecosystems under different climate events.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 2 Related Work
### Typical Image SR Algorithms  
In generally, there are three categories for SR techniques. Earlier interpolation methods, such as bicubic interpolation and Lanczos resampling [11] can predict the center pixel using neighboring pixels. Although these interpolation methods are very fast, the edge information cannot be recovered effectively. To avoid edge artifacts, learning-based approaches [12; 13; 14] can improve the resolution with the help of prior knowledge. However, the prediction relies significantly on prior knowledge. This makes the effect in reconstruction drop dramatically while processing complex images. Due to its powerful learning ability, the deep-learning [15; 16; 17; 18; 19] algorithms have shown greater degrees of performance than the traditional methods in SR tasks. As the pioneer CNN model for SR, there are three convolutional blocks in SRCNN: feature extraction, non-linear mapping, and reconstruction. They learn implicit mapping through the CNN model and use this mapping to recover the HR image from an interpolated image. Unfortunately, considering the limitations of the network layers, there is some characteristic information that is not well introduced for the further improvement of image quality. Under its powerful calculating capabilities, an increasing number of CNN-based methods have attracted attention in SR tasks. These include Fast Super Resolution Convolutional Neural Networks (FSRCNN), Very Deep Convolutional Network (VDSR), Deeply Recursive Convolutional Network (DRCN), and Deep Recursive Residual Network (DRRN). Meanwhile, [PERSON] et al. [9] proposed a GAN that has become more popular and well-known in the deep-learning field. Various kinds of GANs have been proposed in recent years. Under the guidance of network structure, the Laplacian Pyramid of Generative Adversarial Network (LapGAN) produces sharp images using the Laplacian pyramid [20]. The Deep Convolutional Generative Adversarial Network (DCGAN) has demonstrated great feature representations using fully convolutional networks in the generator [21], instead of deterministic spatial pooling functions. Even more excellent and effective are the perceptual loss functions, including a content loss and an adversarial loss in Super-Resolution using a Generative Adversarial Network (SRGAN) [22], which achieves state-of-the-art performance. In addition, other deep-learning algorithms also achieve good results, such as the DRCN [23], InfoGAN [24], CGAN [25], and CycleGAN [26].
## 4 Current Approaches to Measure Resilience in Smart ICI
### Indicator-Based Approach
#### 4.1.3 The SmartResilience RIL Method  
Similar to the REWI method, the Smart Resilience Level (RIL) [39] method employs _issues_ and _indicators_ at the two lowest levels of the structure, while _phases_ are utilized at the next higher level, as opposed to themes in LIOH and contributing success factors in REWI. For each phase, significant issues are identified, guiding the implementation of indicators to evaluate these issues and assign estimation values accordingly. The hierarchical model comprises six distinct levels, as depicted in Figure 3.  
The \(RIL\) method relies on character scores ranging from E to A, where A is the best and E is the worst, assigned to each level. Furthermore, each score is associated with a weighted value corresponding to a specific resilience level (RIL). For instance, a weighted value score between 0-1 corresponds to level E, while a weighted value between 1-2 signifies resilience level D, and so on. The quantification process of the method involves initially selecting the area of interest, such as a smart city. Subsequently, specific smart ICI components within the area must be chosen, along with identifying the most relevant threats for each SCI. The next step requires considering each phase of the event for every threat and determining the issues within each phase. A comprehensive investigation of the indicators for each issue should be conducted, followed by defining the range values for each indicator. Finally, values are assigned to the indicators, and the overall \(RIL\) calculations are completed. This approach primarily aims to measure _preparedness_ to identify potential issues before they arise.
# 5G NPN Performance Evaluation for I4.0 Environments
## 3 Hybrid 5G and I4.0 Concept Architecture
### IIoT Edge  
Information systems that support production processes (e.g., ERP, MES, SCADA) rely on data collected from the shop floor. In many cases, these data are still stored locally and are integrated over the infrastructure manually; however, automated solutions that allow for data integration are tending to become a common solution in practice. Until recently, only non-standard data collection systems designed for specific problems were available on the market. In case of larger production compositions, where various different machines and controllers were used, custom system integration solutions had to be developed in order to integrate all data. With the emergence of the I4.0 initiative, significant progress has been achieved in converging IT and OT environments. Data collection software now uses web protocols and protocols for Industrial Ethernet (e.g., Profinet, OPC UA) in order to display and manipulate data and manage IoT devices, significantly easing data collection efforts while providing rapid adaptability and scalability. All of this enables factory automation solutions that are more intelligent, distributed, collaborative, and easier to integrate. The I4.0 edge can offer turnkey functionalities, dynamic asset management, and the necessary interfaces to onboard, test, and validate various innovative apps, while ensuring uninterrupted system operations and availability. This holistic system and its extensions can achieve (i) digital twinning of the operational protocols present on the factory floor (i.e., PLC, ERP, MES, SCADA) following an OT-as-a-Service (OTaaS) approach, and creating a validation framework for the various new services to be audited; and (ii) secure deployment of the developed services on the factory floor, initiating continuous monitoring of the OT process chain.
## 3 Codewords Analysis
### Non-Structured Codewords
 \right..\]  
Note that, the non-structured codewords of \(\textbf{H}_{a}^{T}(D)\) and \(\textbf{H}_{b}^{T}(D)\) are not involved in generating the codewords in the set \(\{\textbf{v}_{1}(D),\textbf{v}_{2}(D),\textbf{v}_{3}(D),\textbf{v}_{4}(D), \textbf{v}_{5}(D)\}\). However, the \((21,3,5)\) Tanner LDPC-CC has another base structured codeword of weight \(24\), _i.e._,  
\[\textbf{v}_{6}(D)= D^{5}+D^{30}+D^{49}+D^{60}+D^{65}+D^{72}+D^{74}+D^{77}+D^{83}+\] \[D^{96}+D^{101}+D^{102}+D^{104}+D^{119}+D^{127}+D^{132}+D^{134}+\] \[D^{136}+D^{147}+D^{153}+D^{167}+D^{171}+D^{174}+D^{202}\]  
which cannot be obtained using Lemma 1. Unlike the codewords \(\{\textbf{v}_{1}(D),\textbf{v}_{2}(D),\textbf{v}_{3}(D),\textbf{v}_{4}(D), \textbf{v}_{5}(D)\}\), the codeword \(\textbf{v}_{6}(D)\) requires both structured and non-structured codewords for each super code to convert, _i.e._,  
\[\left\{ \begin{array}{ll}\textbf{v}_{6}(D)=&D^{5\cdot 0}\textbf{v}_{a}^{5}(D)+D ^{5\cdot 18}\textbf{v}_{a}^{8}(D)+D^{5\cdot 11}\textbf{v}_{a}^{10}(D)+D^{5 \cdot 23}\textbf{v}_{a}^{11}(D)+\\ &D^{5\cdot 5}\textbf{v}_{a}^{15}(D)+D^{5\cdot 6}\textbf{v}_{a}^{19}(D)+D^{5 \cdot 18}\textbf{v}_{a}^{20}(D)\\ &\textbf{v}_{6}(D)=&D^{5\cdot 4}\textbf{v}_{b}^{1}(D)+D^{5\cdot 18}\textbf{v}_{b}^{1}(D)+D ^{5\cdot 6}\textbf{v}_{b}^{5}(D)+D^{5\cdot 12}\textbf{v}_{b}^{5}(D)+\\ &D^{5\cdot 4}\textbf{v}_{b}^{11}(D)+D^{5\cdot 18}\textbf{v}_{b}^{11}(D)+D^{5 \cdot 0}\textbf{v}_{b}^{12}(D)+D^{5\cdot 6}\textbf{v}_{b}^{12}(D)\end{array} \right.\]  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{\(\textbf{H}_{a}^{T}\)**(_D_)**} & \multicolumn{2}{c}{\(\textbf{H}_{b}^{T}\)**(_D_)**} \\ \hline \(\textbf{v}_{a}^{1}(D)\rightarrow\textbf{v}_{a}^{12}(D)\) & \(\textbf{v}_{a}^{13}(D)\rightarrow\textbf{v}_{a}^{22}(D)\) & \(\textbf{v}_{b}^{1}(D)\rightarrow\textbf{v}_{b}^{12}(D)\) \\ \hline Structured & Non-Structured & Structured \\ \hline \([3,12,19,38,59,87]\) & \([3,12,32,36,52,56]\) & \([2,26,29,46,77,84]\) \\ \([3,12,23,36,72,76]\) & \([3,12,15,19,47,54]\) & \([2,9,43,63,92,119]\) \\ \([4,17,21,24,32,56]\) & \([5,33,35,42,72,106]\) & \([2,26,63,118,136,167]\) \\ \([4,8,21,23  
Since the non-structured codewords of the super codes are always involved in the formation of the corresponding non-structured codewords of the mother code, and the non-structured codewords can be deleted by modifying the monomials in \(\textbf{H}^{T}(D)\), we conjecture that eliminating the non-structured codewords in the super codes can decrease the number of non-structured codewords in the mother code.
## 3 Simulation Model  
### Test Bed Model  
To compare the fault handling performance between HB- and FB-MMCs, a test bed was modeled in Matlab/Simulink, as shown in Figure 2. A bipolar point-to-point HVDC link was modeled using 40-level HB- and FB-MMCs. In the steady state, the MMC was operated with constant active and reactive power control. The reference voltage of the DC link was set at 80 kV, and the length of the transmission line was 100 km. The pole-to-pole fault was generated at 0.2 s.  
Table 1 presents a summary of the HVDC system parameters. After the occurrence of the DC fault, the main controller trips the HB and FB converters within 500 microseconds to protect the converter. By performing this process, the DC fault contribution from the submodule capacitor can be prevented in HB-MMCs. The configuration of submodules in FB-MMCs does not allow the discharge of the DC side capacitor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Voltage converter (VSC) HVDC type & Bipolar HB\(-\)/FB-MMC \\ AC source voltage (rectifier side) & 154 kV \\ Number of submodules per arm & 40 \\ Equivalent capacitance & 10 uF \\ Current-limiting reactor & 20 mH \\ Transformer power rating & 450 MVA \\ Transformer voltage ratio & 154 kV/100 kV \\ DC cable resistance & 0.0133 \(\Omega\)/km \\ DC cable inductance & 0.8273 mH/km \\ DC cable capacitance & 0.0139 uF/km \\ Length of transmission line & 100 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of high-voltage direct-voltage (HVDC) system parameters.  
Figure 2: Test bed model in Matlab/Simulink. Converters are composed of 40-level HB- and FB-MMCs. The pole-to-pole DC fault is introduced at a variable distance from the rectifier.