esa-sceva/satcom-chunk-collection huggingface dataset:
# RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks
## 3 Method  
The framework of RT-GAN is shown in Figure 1, which can be divided into two parts: the rail track segmentation network RT-seg and the track discriminator, which are represented by \(\mathcal{G}\) and \(\mathcal{D}\), respectively. The input of RT-seg is an RGB-3 channel image of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method (Authors)** & **Year** & **Generator** & **Generated Target** \\ \hline SS-GAN ([PERSON] et al. [22]) & 2016 & LargeFOV} & semantic segmentation image \\ cGANDA ([PERSON] et al. [23]) & 2018 & FCN-8s & shift feature map between datasets \\ CyCADA ([PERSON] et al. [24]) & 2018 & FCN-8s & style transfer image \\ MS-GAN ([PERSON] et al. [26]) & 2018 & U-Net & semantic segmentation image \\ bi-SSIS ([PERSON] et al. [25]) & 2019 & DeepLabV2 & style transfer image \\ FISS-GAN ([PERSON] et al. [27]) & 2021 & U-Net & edge image and semantic \\ \hline RT-GAN (ours) & 2022 & RT-seg & semantic segmentation image \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of state-of-the-art GAN-based semantic segmentation methods.  
arbitrary size, and the output is a pixel-level semantic segmentation image with the same size. Suppose the input image is \(I\) and the output result is \(C_{cls}\),  
\[C_{cls}=\mathcal{G}(I). \tag{2}\]  
The input of the track discriminator contained 3 parts: the input image, the ground truth image and the output of RT-seg, and its output is the veracity scores of the generated image. In each training, the inference of the discriminator is executed twice, and two different scores are output based on the output result of RT-seg and the ground truth as input, respectively. Suppose the ground truth image is \(C_{gt}\) and the output score is \(F\), then, the output of the track discriminator is as follows:  
\[F=(F_{cls},F_{gt})=\mathcal{D}(I,C_{cls},C_{gt}). \tag{3}\]  
The RT-seg and track discriminator are explained in detail next.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 5 The Framework of Proposed Unsupervised Anomaly Detection Model
### The Fourth Defence Line  
This defence line is designed to learn the execution time pattern for each operation function type \(FN\) and use this pattern to detect malicious operations that deviate from the normal range.  
**Lear execution time pattern.** The learning approach for this defence line is similar to that of the third line. The difference is that the dataset used in this line shows the execution time of each operation type. This line also uses the normal distribution model to describe execution time patterns and describes the pattern for each operation type as a set of models.  
**Detect anomalies.** The detection method is also similar to the approach used in the third defence line. This line relies on the operation type \(FN\) rather than the transaction edge to retrieve the execution time modes. This means that the operation time interval defence line can only be placed after the operation sequence line; however, this defence line can be placed anywhere after the first line. If the execution time satisfies at least one model, this line classifies it as a normal operation. Otherwise, this defence line reports it as malicious, with detailed causes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(\text{IP}_{\textit{mt}}\)} & \multicolumn{2}{c}{\(\text{\emph{Add}}_{\textit{slp}}\)} & \multirow{2}{*}{\(\text{\emph{PC}}\)} & \multirow{2}{*}{\(\text{\emph{Reg}}_{\textit{start}}\)} & \multirow{2}{*}{\(  
This unsupervised anomaly detection model uses four defence lines to identify abnormal operations and attacks. This model classifies an operation as normal only if it satisfies all defence lines; otherwise, this model reports it as abnormal, with detailed causes.
# Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning
## 5 Experiments and Results  
### Evaluation Metrics  
Let True Positive (TP) denote the number of correctly classified vulnerable samples, False Positive (FP) denotes the number of falsely classified clean samples, False Negative (FN) denotes the number of falsely classified vulnerable samples, and Ture Negative (TN) denotes the number of correctly classified clean samples. To measure vulnerability detection results, five metrics were used. False Positive Rate \((\text{FPR})~{}=~{}\text{FP}/(\text{FP}+\text{TN})\) denotes the proportion of falsely classified clean samples in all clean samples. False Negative Rate \((\text{FNR})~{}=~{}\text{FN}/(\text{FN}+\text{TP})\), denotes the proportion of falsely classified vulnerable samples in all vulnerable samples. Precision (P) = TP/(TP + FP), denotes the proportion of correctly classified vulnerable samples in all samples that are classified as vulnerable. Recall \((\text{R})=\text{TP}/(\text{TP}+\text{FN})\), represents the ability of a classifier to discover vulnerabilities from all vulnerable samples. F1-Measure \((\text{F1})~{}=~{}2\cdot\text{P}\cdot\text{R}/(\text{P}+\text{R})\) measure the ability of both Precision and Recall. In the experimental results, the classifiers with low FPR, FNR and high P, R and F1 metrics have excellent performance.
## 1 Introduction
A more efficient description, which preserves the accuracy to a high degree, can be obtained by coupling machine learning (ML) techniques to existing ED calculations [22]. To this end, we recently described the charge localization in randomly shaped quantum dots using an image-to-image translation technique based on conditional generative adversarial networks (cGANs) [23]. In fact, due to their versatility and power, machine learning techniques have become suitable candidates to approach demanding computational tasks in the field of quantum systems, statistical physics and even condensed matter. For example, artificial neural networks were trained to classify quantum states [24] and solve many-body problems mapped on spin systems in one and two dimensions [25], while Deep Boltzmann machines were shown to possess high efficiency in the representation of many-body states [26]. In the realm of solid-state physics, the idea of tuning electronic devices to the optoelectronic and transport properties that are most useful is highly appealing. To this end, deep learning methods have been employed to identify the optimal point defects in 2D materials with promising applications in quantum and neuromorphic computing [27]. Artificial intelligence (AI) in the form of reinforcement learning has also been widely explored as a viable method to reveal new physics and improve established numerical and experimental methods. For example, AI models were used to simulate the design of photonic experiments with entangled states [28], discover quantum error correction strategies [29; 30] and generate elementary gate sequences from a quantum algorithm, therefore making it suitable for hardware implementation [31]. In this paper, we explore multiple configurations of neuromorphic electron waveguides, taking into account a rigorous description of the many-electron states, based on the ED method. We focus on the localization of the charge within the dendritic-like structures and investigate the control of electronic states by external fields. In this context, the curvature and branching of the electron waveguides are key aspects. In connection with the high-throughput ED calculations, a cGAN method is implemented in order to achieve potential-to-charge mappings. Furthermore, the charge distribution can be extracted from the tunneling current measured in a perpendicular direction. The proposed reconfigurable device structure enables an efficient design of QIs, aided by image-to-image translation techniques.
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 3 Design and Principle of Path Programming
### Tabu Search (TS)
#### 3.2.2 Improving TS to Improve the Cross-Route Path  
The algorithm of improved TS combined with 2-opt was used to improve the cross-route path in this research. By moving based on 2-opt, the nodal lines were swapped between different paths to improve the current paths. As shown in Figure 7, the method for a cross-route path swap was different from the one used for the single path. Exchanging the unfavorable (route crossed) swap nodal lines of (4,6) and (5,2) creates two different possible paths: (4,5) and (6,2) as well as (4,2) and (6,5). Compared to the former, the latter also changes its direction. Thus, 2-opt can reverse the direction of the paths.  
The key point of the improved TS is to set the \(S_{lmt}\) variable, which contains solutions exceeding the maximum distance limitation, and the solution exceeding \(S_{lmt}\) is removed from the candidate list to ensure that all path solution values are within range. This limitation may result in an empty set for the candidate list, blocking all solutions from entering the next generation; therefore, an original Tabu list was adopted with \(S_{lmt}\), the solution set containing all solutions, even those exceeding the maximum distance limitation. With the loosened criteria, the search also selected the \"distance of the longest traveling path\" as the solution of the minimum value.  
Therefore, 2-opt was applied to improve cross-route paths, and the length of the Tabu list, the stopping criteria, and the initial solution were set at this time. Then, the improved TS was executed to determine the optimal multi-paths.
## 4 Experiments and Results
### Dataset  
Extensive experiments are performed on the nuScenes dataset [35], a multi-modal dataset for autonomous driving, which provides challenging urban driving scenarios using the full suite of sensors from a real autonomous vehicle. It offers annotated images, bounding boxes, and point-cloud radar data suitable for object detection, tracking, and forecasting tasks related to autonomous driving, everyday objects, and humans using cameras and radar sensors. Figure 10 provides a sample from the nuScenes dataset.
## 5 Conclusions  
To study the reasonable grouting thickness and water-cement ratio of the surrounding rock of the double arch tunnel in the Haicang Tunnel, the ground settlement, deformation of the vault, and adjacent pipeline under different grouting parameters were studied through laboratory tests, literature analysis, and numerical simulation. The following conclusions were obtained.  
(1) The viscosity of cement slurry decreased with the increase of water-cement ratio. The initial setting time of cement slurry increased linearly with the increasing water-cement ratio. The slurry bleeding rate gradually increased with time and tended to be stable after reaching a certain value, and the greater the water-cement ratio, the greater the slurry bleeding rate. The strength of the sample decreased linearly with the increase of the water cement ratio.  
(2) Frictional angle \(\varphi\) and cohesive force c can be determined by water-cement ratio of slurry and UCS of rock mass before grouting.  
(3) With the increase of reinforcement layer thickness and the decrease of cement slurry water-cement ratio, the ground settlement, vault displacement, plastic zone area, and pipeline deformation continued to decrease, but the reduction range increased first and then decreased.  
(4) When the grouting reinforcement layer thickness h = 1.5 m and the water-cement ratio \(\eta\) = 0.85, the tunnel grouting reinforcement effect was best and more economical.  
J.W.: conceptualization, funding acquisition, writing--original draft, writing--revision; A.C.: writing--original draft; Z.W.: software; H.W.: funding acquisition; X.L., H.L. and Y.S.: validation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Municipal Science and Technology Project (18 DZ1201301; 19 DZ1200900); Xiamen Road and Bridge Group (XM2017-TZ0151; XM2017-TZ0117); the project of Key Laboratory of Impact and Safety Engineering (Ningbo University), Ministry of Education (CJ202101); Shanghai Municipal Science and Technology Major Project (2021 SHZDZX0100) and the Fundamental Research Funds for the Central Universities; Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources of the People's Republic of China (No. KLLSMP202101), Suzhou Rail Transit Line 1 Co. Ltd., China Railway 15 Bureau Group Co. ltd.  
The data presented in this study are available on request from the corresponding author.  
Figure 24: Relationship between the maximum displacement of pipeline and water–cement ratio \(\eta\).
## 2 Soil-Borne Plant Pathogens and Microbiota Determine Disease Suppression
### Soil Microbiome Influences Disease Suppression
#### 3.2.1 Contribution of Bacterial and Archaeal Communities  
Most of the assessment studies have been focused on soil bacteria because the composition of the bacterial and archaeal microbiomes is one of the major factors that drive suppression [147]. In DNA microarray studies for the DSS, conclusions have been drawn by higher signals from non-pathogenic strains of the genera _Streptomyces_, _Bradyrhizobium_, _Burkholderia_[148; 149] and _Nitrospira_[150], whereas the DCS exhibited higher signals from _Acidobacterium_ spp., _Pseudomonas_ spp., _Agrobacterium tumefaciens_ and _Jantibobacterium_ spp., [151; 152]. Bacteria are known for their inherent ability to produce large numbers of such bioactive secondary metabolites as 2,4-diacetyl phylloroglucinol (2,4-DAPG) released by fluorescent _Pseudomonas_ spp., which inhibits development of take-all decline in wheat and barley by defense plant roots [153]. Similarly, the archaeal community is also neglected in biocontrol and disease suppression, though it is only a part of the rhizosphere microbiome [48]. These authors differentiated the DCS from the DSS by the lower abundance of _Actinobacterria_ (_Streptomyces_ spp.) specific archaea and micro-eukaryotes in the conducive soils than in the suppressive ones. In a PhyloChip-based metagenomics study of the rhizosphere microbiome, authors detected strong suppressive characteristics in soils containing more abundances of the phylotypes _Proteobacteria_, _Firmicutes_ and _Actinobacteria_[48]. Beneficial microbiota for suppressing pathogens in the land management generally belong to the families _Xylariaceae_ and _Lactobacillusaceae_, and to the genus _Bacillus_[54; 154]; while _Enterobacter_ spp., _Flavobacterium blustinum_, _Pseudomonas fluorescens_, _Pseudomonas putida_ and _Streptomyces griseus_ were sourced by compost [155].
## Application of Decision-Tree Model to Groundwater Productivity-Potential Mapping  
**[PERSON] 1,2 and [PERSON] 3,*  
1 Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahang-no, Yuseong-gu, Daejeon 305-350, Korea; E-Mail: [EMAIL_ADDRESS]  
2 Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 305-350, Korea  
3 Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 200-701, Korea  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-33-250-6731; Fax: +82-33-250-5600.  
Academic Editor: [PERSON]  
_Received: 10 July 2015 / Accepted: 22 September 2015 / Published: 30 September 2015_  
**Abstract:** For the sustainable use of groundwater, this study analyzed groundwater productivity-potential using a decision-tree approach in a geographic information system (GIS) in Boryeong and Pohang cities, Korea. The model was based on the relationship between groundwater-productivity data, including specific capacity (SPC), and its related hydrogeological factors. SPC data which is measured and calculated for groundwater productivity and data about related factors, including topography, lineament, geology, forest and soil data, were collected and input into a spatial database. A decision-tree model was applied and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The resulting groundwater-productivity-potential (GPP) maps were validated using area-under-the-curve (AUC) analysis with the well data that had not been used for training the model. In the Boryeong city, the CHAID and QUEST algorithms had accuracies of 83.31% and 79.47%, and in the Pohang city, the CHAID and QUEST algorithms had accuracies of 86.18% and 80.00%. As another validation, the GPP maps were validated by comparing the actual SPC data. As the result, in the Boryeong city, the CHAID and QUEST algorithms had accuracies of 96.55% and 94.92% and in the Pohang city, the CHAID and QUEST algorithms had accuracies of 87.88% and 87.50%. These results indicate that decision-tree models can be useful for development of groundwater resources.  
keywords: groundwater; productivity; GIS; decision tree; Korea
## 2 Channel width Quantization Method Based on Deep Learning
### Comparison with Other Algorithms  
In 2017, [PERSON] proposed a model preference method based on Minimum Data Event Distance (MDevD) [31]. This method builds a set of MDevD attributes by calculating the MDevD of conditional data events in each candidate model. The candidate models are then evaluated and ranked based on the mean and variance of the MDevD attributes. The smaller the mean and variance of the MDevD attributes, the higher the compatibility of the corresponding candidate model with the conditional data. The model preference method based on the CNN method used in this paper is compared with the MDevD-based method to test the practicality and reliability of the method in this paper.  
A comparative study was conducted using the 2D data in Section 2.3. Firstly, we randomly selected 1% of the data from four different channel models A, B, C, and D as the conditioned data, and then calculated the candidate models E, F, G, and H by applying the MDevD attribute of the conditioned data, respectively (Figure 6). The smaller the average value of MDevD, the higher the match between the candidate model and the conditioned data. The comparison of the test results between this paper's method and the MDevD method was conducted 100 times, as shown in Figure 7. The results show that the recognition rates of this paper's method and the MDevD method are very similar, indicating the reliability of the proposed method. In the process of 100 tests, the MDevD method takes a lot of time, with the total time consumption reaching 13.7 h, while the method proposed in this paper takes 53 s, indicating that the algorithm in this paper marks a great improvement in computational efficiency, and has a high degree of practicality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Recognition Rate** & **50 m Channel** & **100 m Channel** & **150 m Channel** & **200 m Channel** \\ \hline cd\_W1 & 96\% & 4\% & 0\% & 0\% \\ cd\_W2 & 4\% & 90\% & 4\% & 2\% \\ cd\_W3 & 0\% & 12\% & 80\% & 8\% \\ cd\_W4 & 0\% & 0\% & 14\% & 86\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition rates for four different channel width models.  
Figure 6: MDevD attribute values for four different river candidate models.
## 3 Reliability-Based Design Optimization  
RBDO methods are used to perform design optimization while accounting for uncertainties in the design matrix through simulations and probability analyses. The RBDO for structures incorporates structural reliability analysis in the structural optimization process to achieve the optimal design under the given probabilistic constraints pertaining to the uncertainties in structural integrity-related parameters, such as the material property, magnitude and direction of forces, and modeling. The RBDO formulated such that theoptimal structure satisfies each failure mode with predetermined probabilities is referred to as component-RBDO (CRBDO). A CRBDO problem can be defined as:  
\[\begin{array}{ll}\underset{\mathbf{d},\mu_{x}}{\text{min}}&f_{obj}(\mathbf{d},\mu_{x})\\ \text{s.t.}&P_{fi}=P[g_{i}(\mathbf{d},\mathbf{x})\leq 0]\leq P_{fi^{\prime}}^{ \text{t}},\;i=1,\ldots,n_{c}\\ &\mathbf{d}^{\text{i}}\leq\mathbf{d}\leq\mathbf{d}^{\text{u}}\end{array} \tag{12}\]  
where \(f_{obj}\) is the objective function, \(\mathbf{d}\) is a vector of deterministic design variables, \(\mu_{x}\) is the vector of the means of random variable \(\mathbf{x}\),\(P[g_{i}(\mathbf{d},\mathbf{x})\leq 0]\) is the failure probability of the \(i\)-th limit-state function \(g_{i}(\cdot)\), \(P_{fi}^{\text{t}}\) represents the target failure probability, \(n_{c}\) is the number of probabilistic constraints, and \(\mathbf{d}^{\text{i}}\) and \(\mathbf{d}^{\text{u}}\) are the lower and upper bounds of the design variables, respectively. Gradient-based optimization algorithms such as sequential linear programming, sequential quadratic programming [29], convex linearization [30], optimality criteria [31], and method of moving asymptotes (MMA) [32] are often used to solve Equation (12). Many strategies are available to implement the reliability analysis in the RBDO. For instance, in the double-loop approach [15; 33], each step of the iterations for the design optimization involves another subloop of iterations for the reliability analysis. Single-loop (SL) approaches [34; 35; 36] approximate the equivalent deterministic constraint by using the Karush-Kuhn-Tucker (KKT) optimality condition to convert the double-loop problem into a single-loop problem. Decoupling approaches [37; 38] decouple the outer-loop optimization from the reliability analysis by constructing an equivalent deterministic optimization problem. A comprehensive review of the structural reliability analysis and RBDO has been presented by [PERSON] et al. [27].
## 3 Results
### Extreme Learning Machine Model results
#### 3.3.2 UVE-ELM Established by Sensitive Variables  
The sensitive variables screened by UVE are used as independent variables to construct the UVE-ELM model. The optimal number of conditioning neurons is 14, and the model runs at 0.02 s; the results of this model are shown in Figure 7.  
The model's training set achieves a goodness-of-fit R\({}^{2}\) of 0.943 and an RMSE of 0.544%, and the validation set has an R\({}^{2}\) of 0.946 and an RMSE of 0.581%. The UVE-ELM model built with sensitive variables improved the R\({}^{2}\) by 0.02, and the prediction error was reduced substantially compared with the ELM model built with the full set of temperature and humidity variables. This indicates that the UVE-ELM model can better achieve the prediction of outgoing corn moisture content with stronger prediction ability and smaller error. Also, the training set and validation set R\({}^{2}\) are close to each other, indicating that the model will not be overfitted; this result also shows that the sensitive variables screened by UVE are strongly representative, reducing the redundancy and covariance among data, and are more conducive to constructing the prediction model. The ELM method, compared with the multivariate-linear-fitting method, has obvious advantages in the prediction of outgoing moisture content in the drying process of corn.