esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
## 4 Bridge Underwater Structural Health Management System Based on BIM Technology
### Systematic Introduction
#### 4.1.2 Underwater Structure Detection Module  
The underwater part of the bridge structure has long faced water erosion and microbial erosion. Traditional cameras cannot accurately scan disease information. The underwater vehicle used in the system is equipped with a binocular camera, good environmental adaptability, and strong anti-interference ability. The binocular camera is sealed by a self-designed sealing structure to scan the disease.  
In the third part, the improved point cloud algorithm and Lite-YOLO-v4 network can process disease information accurately. In the underwater detection module, to obtain accurate disease information, the improved point cloud algorithm and Lite-YOLO-v4 network are used. Obtaining more accurate disease while realizing disease visualization must be done to facilitate managers to make judgments on diseases. For crack disease, one can clearly detect crack length, crack shape, and crack width, and determine the location of the crack; for structural defects, while obtaining the depth, length, width, volume, and other three-dimensional information of the disease, the three-dimensional image of the disease is drawn and its position is marked.  
The system uses the underwater structure detection plug-in, based on Revit software and C# programming language, to extract the disease information obtained by the camera, and then processes the extracted diseases through two algorithms, and integrates various damages into the established bridge Revit model. The integrated application of Revit and underwater structural damage monitoring can check the information and display the inspection results in the BIM model while obtaining the disease information.
## 3 Results and Discussion
### Thermal Performance of BTMS
#### 3.1.1 Effects of Air Flow Rate on Maximum Temperature  
From previous research conducted in the literature review stage of this study, the general trend observed in many published research under BTMS studies is that a higher cooling-air flow rate yields better BTMS performance in terms of the objective functions: minimization of maximum cell temperature, increases temperature uniformity amongst cells and minimizes temperature differences between cells.  
A similar trend has been observed for the battery pack model presented and tested in this study. As shown in Figure 16. The test results presented in Figure 16 just as in Figure 15 are obtained after averaging the temperature data recorded for the unique experiments after repetition.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
###### Abstract  
In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.  
weed detection; weed classification; computer vision; distance transformation; color histogram; texture features; SVM; RF; GBM; KNN Article
# Solution to Solid Wood Board Cutting Stock Problem
## 3 Algorithm Design
### Improved Ant Colony Algorithm (IACA)
#### 3.3.2 Pheromone Update  
In the process of constructing the solution, after the ant transfers from the original board node \(i\) to the standard board node \(j\), the corresponding pheromone \(\tau_{ij}\) should volatilize in a certain proportion, so as to reduce the selection probability of the nodes that have been selected before and enhance the ability to explore new layout schemes. The local pheromone is updated as follows:  
\[\tau_{ij}(t+1)=(1-\chi)\tau_{ij}(t) \tag{13}\]  
where \(\chi\) is the local pheromone volatilization coefficient.  
In order to better maintain the dynamic balance between exploration and orientation, a hybrid strategy is adopted to update the global pheromone; that is, the global optimal solution is used once every \(r\) iterations, and the iterative optimal solution is used in other iterations. The global pheromone is updated as follows:  
\[\tau_{ij}(t+1)=(1-\rho)\ \ \ \tau_{ij}(t)+\rho\ \Delta\tau_{ij}(t) \tag{14}\]  
\[\Delta\tau_{ij}(t)=\left\{\begin{array}{cc}Q/\ (1-f_{gbest})&condition\ 1\\ Q/\ (1-f_{dbest})&condition\ 2\\ 0&otherwise\end{array}\right. \tag{15}\]  
\(condition\ 1:\ t\) _is an integral multiple of \(r\) and \((i,j)\in\) the global optimal solution condition \(2:\ t\) is not an integral multiple of \(r\) and \((i,j)\in\) the iterative optimal solution_  
where \(\rho\) is the global pheromone volatilization coefficient, \(\Delta\tau_{ij}(t)\) is the pheromone increment for the \(t\)-th iteration, \(Q\) is the pheromone enhancement factor, \(f_{gbest}\) is the fitness of the global optimal solution, and \(f_{dbest}\) is the fitness of the iterative optimal solution.  
In order to avoid stagnation, the pheromone is limited between \([\tau_{min},\tau_{max}]\), and the upper and lower bounds of the pheromone are determined by the calculation method in the literature [41]. In addition, the initial pheromone is set at \(\tau_{max}(0)\) to improve the global search ability of ants in the initial period.
## 4 Results and Analysis
### Comparison and Discussion  
#### 4.2.1 Random Waypoint Results  
The graphs below illustrate the impact of the number of nodes on Delay, Jitter, Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Throughput, and Lost Packets. It can be observed that MCW_OLSR shows improvements compared to OLSR, DSDV, and SR_OLSR, particularly in networks with a larger number of nodes. These results demonstrate the effectiveness of MCW_OLSR in dense network scenarios.  
In Figure 3, the time taken for data packets to travel from source to destination is examined. The results indicate that MCW_OLSR offers a lower delay compared to OLSR, DSDV, and SR_OLSR. This improvement can be attributed to the enhanced stability, security, and longevity of the nodes, resulting in more efficient and stable connections with direct neighbors. This method ensures that paths remain valid and secure with optimal linking.  
The jitter is decreased due to the multicriteria-weighted MPR scheme. In this context, MCW_OLSR exhibits lower jitter compared to OLSR, DSDV, and SR_OLSR, as shown in Figure 4. This improvement suggests that the technique provides a distinct advantage in broadcast and communication, especially in scenarios with a higher number of mobile nodes.  
Compared to OLSR, DSDV, and SR_OLSR, the MCW_OLSR protocol demonstrates a lower value of the PLR, as presented in Figure 5. This is due to the improved link stability and reliability between mobile nodes, which supports the successful transmission of packets.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Transport Layer & User Datagram Protocol (UDP) \\ \hline Traffic Model & CBR \\ \hline Packet Size & 1024 bytes \\ \hline Rate & 0.4 \\ \hline Mobility Models & Random Waypoint and Manhattan Grid \\ \hline Pause Time & 1 s \\ \hline Maximum Node Speed & 25 m/s \\ \hline Simulation Time & 100 s \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 3: Mean Delay comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 6 compares the packets successfully broadcasted by the protocols. Even in a dense network, MCW_OLSR achieves a better distribution of packets compared to OLSR, DSDV, and SR_OLSR. The graph confirms that MCW_OLSR performs well in terms of PDR.  
Figure 4: Mean Jitter comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 5: PLR comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 6: PDR comparison between MCW_OLSR, OLSR, DSDV, and SR_OLSR.  
Figure 7 shows the comparison of lost packets. The number of lost packets in MCW_OLSR is negligible compared to OLSR, DSDV, and SR_OLSR. The improved link quality, link duration, and overall stability help avoid frequent broken links between mobile nodes.  
Figure 8 presents the impact of the number of nodes on throughput. The MCW_OLSR protocol, integrated into the selection of MPRs, offers improved throughput compared to OLSR, DSDV, and SR_OLSR. Hence, the multicriteria-weighted MPR technique supports successful packet transmission.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Ad-Hoc
#### 3.2.1 Brain Cancer  
A capsule network trained to recognize explainable radionomic features was proposed by the authors of [132]. The explainability of capsule networks is studied. The authors' results show that the radiomics features extracted by Capsule networks can not only distinguish between the tumor types, but also show considerable correlation with hand-crafted features, which are more acceptable and reliable from a physician's point of view.
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 4 Design of Platform Application Modes and Case Study
### Scientific Analysis  
As a platform for data import and visualization, Sino-InSpace also opens interfaces to export calculation and query results. The SSimMainApp component (Figure 15) provides most of the output interfaces, mainly divided into three types: system information (time, viewpoint, mouse position, etc.), environment data (position, distribution, temperature, intensity, etc.), and entity object data (attribute, structure, position, and attitude). The latter two types are meaningful for scientific analysis. The analysis of space objects collision warning was carried out as shown in Figure 18. The orbit and position data were obtained to estimate the most likely approach and even collision events outside the platform [(74)]. In contrast, the result could be validated through the reappearance of collision scenario. The distances in three orthogonal directions (radial, in-track, and cross-track) between two risk targets were plotted as curves. The closest time was clear with the help of visual analysis.  
To support the Chinese Mars Exploration Project 2020, China's Ministry of Science and Technology funded research on planetary surface precise landing guidance and control (Grant No. 2012 CB720000-G) during the twelfth five-year plan. The landing area selection is one of the most important scientific objectives. Based on the strategy of the multiresolution pyramid model (Figure 3), the altitude could be obtained in real time. Then, terrain analysis tools (Figure 19) containing point, distance, area measurement, and profile-map were developed, which supported mouse selection and manual input. It played a role in the terrain and geomorphology analysis of the Mars surface.  
Figure 17: Van Allen radiation belt provided by the AE8/AP8 model.
# Concept Drift Adaptation with Incremental-Decremental SVM
## 1 Introduction
An SVM is also used in hybrid approaches in regression problems, such as predicting the lead time needed for shipyards to arrange production plans [15], or modelling pan evaporation [16], phenomena that could also present drift, given their temporal nature. Some SVM models were especially adapted for concept drift handling [17; 18; 19]. However, adapting the window to the concept drift requires classifier retraining on the new window, which is computationally inefficient. The SVM models in [17; 18; 19] do not consider the presence of concept drift and assume stationary conditions for the input data. Defining an online SVM classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We believe that, considering the drift, the accuracy obtained by the SVM model has the potential to improve. The ability of the incremental-decremental SVM model to naturally adapt to concept drift, combined with the power of the Hoeffding test (Hoeffding inequality theorem [20]) used for determining the obsolescence of past samples, gave us the raw inspiration for our work. Our contribution is the introduction of the Adaptive Incremental-Decremental SVM (AIDSVM) algorithm, a generalization of the incremental-decremental SVM, capable of dynamically detecting the concept drift and adjusting the width of the shifting window accordingly. To our knowledge, our model is the first incremental SVM classifier using dynamic adaptation of the shifting window. According to our experiments, it has the same or better accuracy compared with common drift-aware classifiers on some of the most common synthetic and real-world datasets. Experimental evidence shows that AIDSVM-detected drift positions are close to the theoretical drift points. We provide the source code of our algorithm on Github as a Python implementation ([[https://github.com/hash2100/aidsvm](https://github.com/hash2100/aidsvm)]([https://github.com/hash2100/aidsvm](https://github.com/hash2100/aidsvm)), accessed on 2 October 2021). The rest of the article is structured as follows. We present related work and the state of the art in concept drift with adaptive windows in Section 2. To make the paper self-contained, Section 3 summarizes notations and previous results we use in our approach: the ADWIN principle of detecting concept drift based on the Hoeffding bound and the incremental-decremental SVM. Section 4 introduces the AIDSVM algorithm. Experimental results on synthetic and real datasets are described in Section 5, where we compare our method with current approaches in concept drift. Section 6 summarizes the main achievements of our work and discusses further possible extensions.
## 4 Parameter-Identification-Based Configuration Design
### Effectiveness in Stowing Efficiency
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{8}{c}{**Antenna**} \\ \cline{2-10}  & **Dornier FIRST**[31] & **Dornier MEA** & **RadioAstron** & **MilliMetron** & **NPSSDA**[32] & **Uniaxial Model**[36] & **CFR Model**[40] & **Sunflower** & **Improved Sunflower** \\ \hline Aperture & & & & & & & & & & \\ diameter (m) & 8.0 & 4.7 & 10.0 & 10.0 & 1.2 & 1.2 & 1.0 & 4.42 & 1.5 \\ Focal length (m) & [2.4] & [1.41] & 4.30 & 3.00 & 0.48 & 0.48 & 0.37 & 1.77 & 1.25 \\ Panel number & 24 & 24 & 27 & 24 & 24 & 20 & 30 & 18 & 18 \\ RSE & 2.78 & 2.78 & 3.33 & 3.33 & 3.16 & 2.82 & 3.76 & 2.29 & 2.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Structural parameters of existing RRSAs. In summary, adopting a deviation-angle panel is more efficient than pose adjustment for improving the RSE. If both approaches are combined, the RSE will be improved to the maximum extent. All of these facts demonstrate that the newly proposed segmentation scheme of the RSSA possesses significant superiority in improving the radial stowing efficiency.
## 3 Materials and Methods
### RFI Mitigation Technique
#### 3.3.2. Inverse Short-Time Fourier Transform (ISTFT)  
In order to reconstruct the original signal, the inverse short-time fast Fourier transform (ISTFT) [59] of the signal is evaluated. Similar to the STFT, the inverse discrete Fourier transform (IDFT) [60] is achieved by using the inverse fast Fourier transform (IFFT), as shown in Equation (12). The window function, as a weight for the reconstruction, as shown in Equation (13), where \(v(m)\) is the window function; \(w(m)v(m)\) is the overlap-add (OLA) function.  
\[\widetilde{s}(m)=\frac{1}{N}\sum_{k=1}^{N}S(k)e^{+j2\pi\frac{nk}{N}} \tag{12}\]  
\[\widetilde{s}(n)=\frac{h}{w(m)v(m)}\widetilde{s}(m)v(m) \tag{13}\]
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 2 Background
### Evaluation Protocol  
The evaluation protocol describes how to measure the performance of the TKGC method. Typically, for a test quadruple \((s,p,o,t)\), it generates corresponding corrupted quadruples for the subtasks entity predict, relation predict, and time prediction, respectively. For entity predict, the corrupted quadruples are generated by replacing either the subject entity (for subject entity predict) or the object entity (for object entity predict) with all possible entities such that the resulting corrupted quadruples do not belong to the TKG, i.e., \(C_{s}=\{(s^{{}^{\prime}},r,o,t):s^{{}^{\prime}}\in E,(s^{{}^{\prime}},r,o,t) \
otin G\}\) or \(C_{o}=\{(s,r,o^{{}^{\prime}},t):o^{{}^{\prime}}\in E,(s,r,o^{{}^{\prime}},t) \
otin G\}\). The corrupted quadruples of relation prediction and time prediction are generated in a similar way: \(C_{r}=\{(s,r^{{}^{\prime}},o,t):r^{{}^{\prime}}\in R,(s,r^{{}^{\prime}},o,t) \
otin G\}\) and \(C_{t}=\{(s,r,o,t^{{}^{\prime}}):t\in T,(s,r,o,t^{{}^{\prime}})\
otin G\}\), respectively. Then, the corrupted quadruples and test quadruple on each subtask are sorted according to the scoring function to obtain the rank of the test quadruple.  
Commonly used evaluation metrics include _Mean Rank (MR)_, _Mean Reciprocal Ranking (MRR)_, and _Hits@N. MR_ is the average rank for all test quadruples, and _MRR_ is the inverse of _MR. Hits@N_ is the percentage of test quadrupplets with ranks lower than or equal to N.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Multiple-Attributes Route Selection Metric (MARS)  
The MARS metric estimates the device selection criteria's values \(DS_{RB(i,j)}(t),DS_{mob(i,j)}(t)\), and \(DS_{QL(i,j)}(t)\) continuously and independently, where the selecting of the optimal route requires a minimum threshold value of devices' lifetime. It collectively combines all the devices' selection criteria values and constructs one function, shown in Equation (8). MARS's weight will be calculated using the Attribute Hierarchy Process (AHP) technique [49] according to the user's preferences. The AHP is a well-known technique utilized to calculate the optimal weight as a multi-attribute decision-making technique that aids in setting priorities and executing the optimal decision. The MARS decision is set as follows:  
\[MARS=\left\{W_{RB}\times DS_{RB(i,j)}(t)+W_{mob}\times DS_{mob(i,j)}(t)+W_{QL} \times DS_{QL(i,j)}(t)\right\} \tag{8}\]  
where \(W\) is the weight obtained by AHP among the device's selection criteria according to the user's preferences. The devices with higher MARS metric values are shortlisted for selecting an optimal path for source-destination pairs. The computation steps of the MBMQA scheme is illustrated in Algorithm 1. In order to arrange the destination, the _Multipath Dijkstra_ algorithm has been utilized for the purpose of the route discovery process and computing the multi-path between the source and destination pair [50]. The MQA routing scheme, described with a flow chart, is presented in Figure 2.
## 1 Introduction  
Developing lightweight joints with fuel efficiency and light weight in the automobile industry, shipbuilding and transportation [1; 2] has been widely researched, and dissimilar aluminum-steel materials are regarded among the lightweight joints. The different metals can undergo galvanic corrosion in the corrosion environment. In addition, the corrosiveness of the dissimilar metal weld joint at high temperature should be considered due to the influence of the high working temperature, such as in the aerospace and energy industries. Although some scholars [3; 4; 5; 6] have studied the galvanic corrosion behavior of dissimilar aluminum-steel joints, the effect of high-temperature oxidation (HTO) treatment on the corrosion resistance of the aluminum-steel joints has not yet been reported.  
In this work, laser-MIG (metal inert gas) hybrid welding-brazing [7; 8] was chosen to weld aluminum and steel, which is one of the processes that can inhibit the growth of intermetallic compounds to obtain aluminum and steel joints. Then, the laser weld-brazed samples were cut into small samples and put into the heat treatment furnace for high-temperature oxidation treatment. The microstructure, chemical and corrosion behavior of the 304/6061 dissimilar metal welded joint with HTO treatment were investigated. The effect of high-temperature oxidation treatment on the microstructure, morphology and chemical constituents of the HTO film was examined by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The corrosion behavior was assessed by potentiodynamic polarization and electrochemical noise (EN). The current focus of work is on the adverse effects of HTO treatment on metal corrosion resistance, rather than enhancing corrosion resistance [9], which has received less attention in other studies. The authors believe that this study will contribute to providing a reference for the corrosion prevention scheme for 304/6061 joints in the future.
## 3 Experimental Results and Discussion
### Heat Map Comparison  
As the Res-Attention module is integrated in RiceDRA-Net, Grad-CAM was adopted to extract the heat map of the disease pictures after the Res-Attention module to visualize the map extracted by the convolutional neural network features. This experiment extracted the feature maps output from the last layer of the improved Res-Attention module in  
Figure 15: Average PR curves of six models using CBG-Dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **FLOPs/GB** & **Parameters/MB** \\ \hline AlexNet & 0.71 & 61.10 \\ Vgg-19 & 19.63 & 143.67 \\ MobileNet & 0.33 & 3.50 \\ ResNet-101 & 7.87 & 44.55 \\ DenseNet-121 & 2.90 & 7.98 \\ RiceDRA-Net & 2.90 & 7.56 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of Computational Complexity.  
RiceDRA-Net to better show the parts of the picture samples that were focused on in the model, and RiceDRA-Net was tested on the CBG-Dataset. The heat maps are shown in Figure 16.  
We used three disease images, bacterial blight, blast and brown spot, for our experiments. We also simultaneously extracted the last layer of convolutional layers from four classical models, AlexNet, Vgg-19, MobileNet, ResNet-101, and DenseNet-121. In order to compare these four models for rice disease, a heat map was compared with our proposed model. It can be seen from Figure 16 that the five different classical models used in the comparison were able to identify the exact disease location for a specific disease. However, this was not very accurate for several other diseases. For example, ResNet-101 was more accurate in locating bacterial blight, but not blast or brown spot. Furthermore, DenseNet-121 was accurate for bacterial blight and brown spot, but not for blast. However, RiceDRA-Net was very accurate at rice disease location and was able to locate several diseases in rice leaves. This model was able to locate the location of the disease in the rice leaf, which achieved a better recognition effect. This also demonstrates that adding the Res-Attention module to the dense residual network can better achieve accurate disease identification.