esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Comparison and Evaluation
#### 6.2.1 Prediction Results of Comparisons  
After five days of uninterrupted operation, 24-h real-time electrical load predictions of the power plant load in our third village are obtained; the prediction results of the initial ARIMA model and the BIC-improved ARIMA model are evaluated and reported in Table 2. To comprehensively assess the performance of each model, we employed two goodness-of-fit regression measures. The common R-squared (\(R^{2}\)) goodness-of-fit regression measure is employed to reflect how well observations are replicated by a model according to the proportion of the total variation in the outcome. \(R^{2}\) ranges from 0 to 1; the higher the score is, the better the fit [26]. The root-mean-square deviation (RMSD) is another widely used measure of the differences between values predicted by a model and observed values. Unlike \(R^{2}\), a low RMSD indicates a good fit [27].  
Both the \(R^{2}\) and RMSD results show that the improved model proposed in this study displays better prediction performance than the single models. The design of a moving window and the use of the BIC in each step to automatically select the best parameters provide an efficient method of fitting the most recent historical data and compensating for prediction errors. This approach not only improves the prediction accuracy of the proposed predictor but also provides a novel training method to reduce the training cost of the model. Figure 9 shows the 5-day real-time prediction results for each method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & \(\mathbf{R^{2}}\) & _RMSD_ \\ \hline ARIMA + BIC (this study) & 0.7378 & 174.0777 \\ ARIMA (1,1,1) & 0.7148 & 191.0377 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the results of the improved an autoregressive integrated moving average (ARIMA) model and the original ARIMA model.
## 3 Results and Analysis
### Results and Analysis of Superpave Evaluation Index of Modified Bitumen
#### 3.2.2 Dynamic Shear Rheological Test  
The results are shown in Figure 6.  
Figure 6 shows that at the same temperature, the rutting factor G*/sin\(\delta\) of the three modified bitumens is more improved than that of the virgin bitumen, which indicates that the elastic component in the bitumen increases after mixing with the modifier, improving the high-temperature rutting resistance of the bitumen. For the same bitumen, the rutting factor G*/sin\(\delta\) tended to decrease as the temperature increased. This phenomenon indicates that the ability of the bitumen to resist deformation gradually decreases; therefore, the bitumen pavement is vulnerable to rutting in a high-temperature environment. Compared with the rutting resistance factor of the virgin bitumen at 64 \({}^{\circ}\)C, Sasobit-modified bitumen increased by 82.5%, 139.4%, and 212.6%; ST-W3-modified bitumen increased by 10.3%, 16.6%, and 34.7%; and Evotherm-modified bitumen increased by 24.3%, 38.4%, and 47.7%, respectively. The three modified bitumens could reduce the deformation of the bitumen binder under high-temperature conditions, thereby improving the rutting resistance of bitumen. At the same test temperature, the rutting resistance factor G*/sin\(\delta\) of the Sasobit-modified bitumen was greater than that of the SBS-modified bitumen with the increase in admixture, indicating that the Sasobit-modified bitumen exhibited the optimum high-temperature stability among the three modified bitumens. This corresponds with the softening point test results.  
Figure 5: Results of the Brookfield rotational viscosity test.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods
### Classification Accuracy  
Estimating the purpose of the classification, the random forest classification method is also used to predict the classification accuracy, and the average probability of the expected class of trees in the forest about the possibilities of the predicted class is an input sample. The probability of a winning class is defined based on the classification certainty for a particular instance. More reliable classification and strong agreement between different trees indicate high accuracy, but disagreement between trees reduces the likelihood of predicting a reliable classification. The prediction result will be shown at the pixel- or sample-level [28].
# Does Nanosilver Have a Pronounced Toxic Effect on Humans?
## 1 Introduction  
At present, nanoparticles are found in numerous spheres of life. Their application in medicine, food and cosmetic industries, and everyday life is expanding [1]. Due to their unique electrical, optical, chemical, and antimicrobial properties, silver nanoparticles (AgNPs) are the most common type of NPs used in consumer products [2]. At the same time, according to publications in toxicology, a wealth of information has already been accumulated confirming that contact with NPs entails serious negative consequences for cells, tissues, and organs, such as the development of oxidative stress, the accumulation of DNA disorders, the induction of apoptosis and inflammation, and the disturbance of the structure and functions of tissues and organs [3; 4; 5].  
Almost every person in the modern world is constantly in contact with NPs, through products or objects containing nanoparticles, or through air, water, and soil. The environment begins to suffer greatly from pollution by various nano-sized substances as a result of emissions from enterprises and vehicle exhaust, sewage discharges, and waste. Due to the widespread use of NPs, including AgNPs, in various industries, employees in the workplace face an increased risk of health problems owing to prolonged (chronic) contact with small doses of NPs that enter the body from the air in contaminated work areas, as well as with water and/or food. Occupational pathology has already accumulated bodies of evidence on the development of diseases caused by NPs in industrial workers, whichcannot be explained by other reasons [6,7]. In women of reproductive age employed in industrial fields, contact with NPs is associated with risks for their unborn children due to the ability of NPs to penetrate the placental barrier, which leads to the transfer of NPs from mother to child [8,9]. Therefore, the study of the toxicity of AgNPs, including the modeling of chronic contact close to industrial conditions, is a critical area of research.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 6 Discussions  
### Achievements  
Inspection of weld defects from the radiographic images has always been an important and challenging task. Limitations of human evaluation persuade the researchers to develop new technologies for automatic inspections. The developments in image analysis and computer vision techniques have made the perfect conditions for proposing methods for automation of weld defect inspection. Looking into the accomplishments introduced in previous sections, developments in detection methods provide enough data for segmenting weld, extracting the features and classification in order to detect and classify nearly all defects.  
The defect segmentation methods based on traditional image processing can separate the defects from the original the images. The process can be regarded as the recognition of defect. The location of defects can be obtained from the segmented results. This is similar to the second level of Rytter classification [64], which is often used in damage detection and assessment in structural health monitoring (SHM) [65]. Detection and localization of defects have been automated to some extent. At the same time, the important parameters of defects can also be calculated from the segmented defect region which are often used as the features in the future to classify the welding defects. Thus the quality of defect segmentation would affect the performance of classification system. Most of the defect classification systems based on feature extraction, selection and classifier introduced in Section 5 achieved high accuracy. However, the features introduced are defined or designed by human, which hinder the automation of system. The innovative technologies such as deep neural networks have made it possible to learn the features automatically from the image. Deep neural networks represented remarkable performance in recent years according to their recent application in weld defect detection. If the X-ray images are from thick steel pipes, the edge of image will be blurry and the gray scale distribution of the weld region will be uneven. Deep neural networks can deal with the situation well-avoiding noise reduction, extracting and enhancing features. As the end-to-end detection system, the networks can directly determine whether the suspected defect image is a linear defect, circular defect or noise.
## 3 Methodology
### Data Collection  
This study used a large shopping mall with nine floors above the ground and four floors underground as the testing case. This building was considered mainly because it consisted of a large number of complicated pipes and conduits, as shown in Figure 2. The structural and MEP model of the building were extracted for clash detection through Autodesk Navisworks Manage 2017. Clashes detected by most BIM software can be hard clashes or soft clashes. Hard clashes exist when building elements have physical overlaps, whereas soft clashes occur when an element is not given the spatial tolerances it requires. Similar to most of the studies discussing clash detection mentioned in Section 2, this study only considers \"hard clashes\" because they have a universal definition, and therefore research results can be compared. In order to control the number of total clashes within an acceptable range for labeling work by the hired experts, this study merely selects the water supply pipes and fittings from the MEP model for clash detection against the structural model. Figure 3 shows a part of the HTML-based clash detection report. The summary table on the top records the total number of clashes in the report; the table below lists the detailed information of each conflict/clash. The detailed information includes the grid location of the conflict/clash, clash point, and distance, as well as the information of two clashing objects, including their IDs, floors, names, and types. Moreover, snapshots of two clashing objects are attached. Clicking on the thumbnails in the first column of the table allows viewing the enlarged snapshots, as shown in Figure 4. Table 1 presents the complete statistics of the clash detection report; there are a total of 415 clashes between four structural components (beams, columns, slabs, walls) and two MEP components (pipes and fittings).  
Figure 1: Process of research methodology.  
Figure 3: Illustration of the clash detection report produced by Autodesk Navisworks Manage 2017.  
Figure 2: Case study building: exterior and internal pipeline configuration of the building object.
## 3 The Proposed Routing Protocol--CRD
### Underlying Technology--Necessary Algorithms
#### 3.3.1 The Proposed Connected Dominating Set Selection Algorithm--CDS Selection  
We consider a MCR-MANET model as an un-directed graph \(G=(V,E)\), where \(V\) represents a set of mobile nodes and \(E\) is a set of edges. An edge between node pairs \(\{v,u\}\) indicates that both nodes \(v\) and \(u\) are in their wireless transmitter ranges. We assume that the transmission range of nodes are the same. It means that, if there is an edge \(e=\{v,u\}\) in \(E\), it indicates \(u\) is within \(v\)'s range and \(v\) is within \(u\)'s range. According to paper [10], let \(G\) be a connected graph. A subset \(D\) of the vertex set \(V(G)\) of a graph \(G\) is said to be a dominating set if every vertex not in \(D\) is adjacent to at least one vertex in \(D\). A dominating set \(D\) is said to be a connected dominating set if the subgraph \(<D>\) induced by \(D\) is connected in \(G\). In graph theory, finding a CDS which has the smallest size is NP-hard [41].  
A node \(v\) is an intermediate node if node \(v\) has two neighbors \(u\) and \(w\) which can not connect together [11]. The proposed CDS selection algorithm can be explained as follows. First, we consider all nodes as unmarked nodes. Second, each node \(v\) uses common channel to send a information packet to all neighbors. If a node \(u\) receives a information packet from node \(v\), it records \(v\) into the list of node \(u\)'s neighbors, called \(N(u)\). If a node \(u\) receives a information packet from PU, it records the information of PUs affected region. Third, each node \(v\) uses common channel to send a \(N(v)\)-information packet to all neighbors. If a node \(u\) receives a \(N(v)\)-information packet, it records the information of \(N(v)\)  
Figure 4: The packet structures used for routing process.  
Figure 3: The packet structures used for clustering process.  
Finally, if a node \(v\) has two unconnected neighbors, node \(v\) becomes an intermediate node. The set of intermediate nodes is a CDS.  
In Figure 5, because each of nodes 1, 4, 5, 8, 10, 11, 12 and 14 has two neighbors which can not connect together, these nodes become intermediate nodes. The CDS is \(\{1,4,5,8,10,11,12,14\}\).
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 4 Hikari-2021 Generation Methodology
### Network Configuration for Generating Dataset  
Figure 2 shows our network configuration, where attackers are on a separate network from the victims. The format of the data we captured is pcap. The important point in this configuration is as follows:  
1. The attacker network with two machines is deployed with CentOS 7 and CentOS 8. There are no specific criteria of the attackers' machines as long as they can run Bash and Python scripts. The Python version is 3.8.8 from Miniconda 3.
2. In the victim network, three machines are deployed with one Debian 8 machine running Joomla 3.4.3, and two Debian 9 machines running Drupal 8.0 and WordPress 5.0. There are no specific criteria for the OS version for the victim network, and the three different Content Management Systems (CMS) such as Drupal, WordPress, and Joomla use default themes and plugins. These three open-source CMSs were chosen based on their popularity. These machines are used for collecting the background traffic.
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 8 Analysis and Discussion  
The extensive experiments, performed in previous sections, demonstrate the remarkable characteristics of the proposed SOA algorithm. The experiments included not only standard benchmark functions but also the complex set of optimization functions from CEC2017. In both sets of functions, they have been solved in 30 dimensions. Therefore, a total of 50 optimization problems were employed to comparatively evaluate the performance of SOA with other popular metaheuristic approaches, such as ABC, CMAES, CSA, DE, MFO and PSO. From the experiments, important information has been obtained by observing the end-results, in terms of the mean and standard deviations found over a certain number of runs or convergence graphs, but also in-depth search behavioral evidence in the form of exploration and exploitation measurements were also used.  
The generation of efficient search patterns for the correct exploration of a fitness landscape could be complicated, particularly in the presence of ruggedness and multiple local optima. A search pattern is a set of movements produced by a rule or model that is used to examine promising solutions from the search space. Exploration and exploitation correspond to the most important characteristics of a search strategy. The combination of both mechanisms in a search pattern is crucial for attaining success when solving a particular optimization problem.  
In our approach, the temporal response of second-order system is used to generate the trajectory from the position of \(\mathbf{x}_{i}^{k}=\left\{x_{i,1}^{k},\ldots,x_{i,d}^{k}\right\}\) to the location of \(\mathbf{b}=\left\{b_{1},\ldots,b_{d}\right\}\). Three different search patterns have been considered based on the second-order system responses. The proposed search patterns can explore areas of considerable size by using a high rate of velocity and the same time, refining the solution of the best individual \(\mathbf{b}\) by the exploitation of its location. This behavior represents the most important property of the proposed search patterns. According to the results provided by the experiments, the search patterns produce more complex trajectories that allow a better examination of the search space.  
Similar to other metaheuristic methods, SOA tries to improve its solutions based on its interaction with the objective function or on a 'trial and error' scheme through defined stochastic processes. Different from other popular metaheuristic methods such as DE, ABC, GA or CMAES, our proposed approach uses search patterns represented by trajectories to explore and exploit the search space. Since SOA employs search patterns, it presents more similarities with algorithms such as CSA, MFO and GWO. However, the search patterns used in their search strategy are very different. While CSA, MFO and GWO consider only spiral patterns, our proposed method uses complex trajectories produced by the response of second-order systems.
# FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning
## 2 Related Work
### Class Imbalance  
In the majority of machine learning, the training dataset is considered well-balanced (i.e., each class contains a similar number of samples) [26]. However, class distribution is usually imbalanced (i.e., some classes contain considerably more samples than other classes) in real-world scenarios, including fraud detection [27], medical diagnosis [28], software failure prediction [29], etc. The class imbalance problem has a detrimental impact on machine learning, such as the convergence and generalization ability of the model [30]. A more extensive introduction of class imbalance is provided in [31].  
As [32] suggests, the mainstream solution to class imbalance can be summarized into two approaches: data-level methods and algorithmic-level methods. Data-level methods aim to eliminate class imbalance by modifying the training dataset, such as oversampling [33] (randomly selects samples from minority class and duplicates them) and undersampling [34] (randomly selects samples from majority class and discards them). However, these sampling methods may degrade the final performance, such as causing overfitting [35]. Algorithmic-level methods, instead, aim to address the class imbalance problem by modifying the learning algorithms. Threshold moving is one of the most famous algorithmic-level methods. The main idea behind threshold moving is to adjust the output (e.g., weights) of the model continuously to accommodate the imbalanced distributions of samples [36]. In the machine learning area, both algorithmic-level and data-level methods are commonly used. For example, ref. [37] proposes a novel hybrid sampling method to address class imbalance based on generative adversarial network.  
Our proposed FocalMatch combines FixMatch (the state-of-the-art semi-supervised learning framework) and focal loss (an algorithmic-level method) to address the class imbalance problem that occurs when generating pseudo labels in the unsupervised learning part. It surpasses the traditional cross-entropy loss function used by the model instead of modifying the original dataset, which is more in line with the application of semi-supervised learning and ensures data privacy. FocalMatch adjusts the loss weights of different samples based on how close their predictions are to their pseudo labels. It decreases the loss weights of easy samples so that the overall unsupervised loss will not be overwhelmed by easy samples. A detailed ablation study to investigate the effectiveness of FocalMatch is discussed in Section 6. Comparing FocalMatch to other state-of-the-art models (including \(\Pi\) model [38], Mean Teacher [39], MixMatch [40], ReMixMatch [24], UDA [25], and FixMatch [15]), our experiments reveal that FocalMatch significantly reduces the difference in the total number of pseudo labels generated for each class and has a more gradual learning curve. FocalMatch surpasses most state-of-the-art semi-supervised learning algorithms on several benchmarks, particularly when the amount of labeled data is severely constrained.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design
### Solar Power Generation
The body-to-sun unit vector \(\hat{r}_{s/b}\) is represented in a spherical coordinate system with respect to the body frame, and as such, the results are presented against azimuth and elevation angles of the sun. Figure 8b shows the exposed area of panel 1 ignoring shadows cast by panel 2, while Figure 8c shows the projected shadow cast by panel 2 on panel 1, and finally, Figure 8d shows the total exposed area of panel 1. Our method is able to calculate the total exposed solar panel area for the above CubeSat with two panels, which is used for more complicated designs evolved through the optimization process. Figure 8: Solar panel exposed area (**a**) CubeSat model with two panels for simulation; (**b**) exposed area of panel 1 without panel 2; (**c**) projected shadow cast by panel 2 on panel 1; (**d**) total exposed area of panel 1. All areas are in mm\({}^{2}\).
# Few-Shot Wideband Tympanometry Classification in
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \(l^{GP}\) & \(l^{CE}_{r}\) & **AUC/_SynDatal_** & **AUC/_SynDatalN_** & **AUC/_SynDatalM_** \\ \hline ✗ & ✗ & \(93.74\pm 1.09\) & \(94.11\pm 0.92\) & \(94.09\pm 0.88\) \\ ✓ & ✗ & \(96.28\pm 0.94\) & \(97.30\pm 1.27\) & \(97.19\pm 1.34\) \\ ✗ & ✓ & \(96.22\pm 0.75\) & \(96.76\pm 1.01\) & \(95.86\pm 1.38\) \\ ✓ & ✓ & \(97.61\pm 1.13\) & \(97.75\pm 1.21\) & \(97.88\pm 1.10\) \\ \hline \end{tabular} Values are expressed as mean \(\pm\) standard deviation (\(r=10\) experimental replicates) of percentages \(\lambda=0.5\) in GPGDA. ✓: enabling the update flow affected by the loss; ✗: disabling the corresponding flow. \(p<0.001\) for the effect of update flow conditions and not significant for the effect of source datasets on AUC; two-way ANOVA. \(p<0.05\) for the second-row condition versus the fourth-row condition, not significant for the second-row condition versus the third-row condition, and \(p<0.001\) for the other cases; [PERSON]’s multiple comparison test, adjusted \(p\)-values for multiple comparisons.  
\end{table} 
Table 3: AUC performance of the GPGDA algorithm when the update gradient flows from \(l^{CP}\) and \(l^{CE}_{r}\) are enabled or disabled. Experiment on the algorithm run in a weakly supervised manner. Since Gaussian processes naturally output uncertainty without the information on labels, the proposed GPGDA algorithm can potentially exploit both unlabeled and labeled data. Considering that the WBT dataset size was 135 and a five-fold cross-validation was adopted, the size of the training set in our previous experiments was equivalent to 108. To assess the effectiveness of the algorithm on unlabeled data, 2/3 of the training data were regarded as labeled, and the rest was considered as unlabeled (Table 4). It should be noted that unlabeled data were not employed for data augmentation. The use of unlabeled data resulted in a performance improvement of about one percent, which suggested that the algorithm can work in a weakly supervised manner.