esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Acquisition and Processing  
#### 2.2.1 Airborne Multispectral Imagery  
A two-camera airborne imaging system described by [PERSON] et al. [2] was used to acquire airborne imagery over the study area on 20 July 2016 under sunny conditions. The system consisted of two Nikon D810 digital CMOS cameras with Nikon AF Nikkor 20-mm f/1.8 G lenses (Nikon Inc., Melville, NY, USA). One camera captured red-green-blue (RGB) images, and the other camera was equipped with an 830-nm long-pass filter to obtain NIR images. Airborne images were acquired from a flying height of 3050 m above ground level with a ground speed of 225 km/h. The interval between the flight lines was spaced at 2500 m and the images were acquired at 5 s intervals to ensure at least 60% cross-track and along-track overlap. The images had a pixel array of \(8353\times 3809\) and a spatial resolution of 0.81 m. The Capture NX-D 1.2.1 software (Nikon Inc., Tokyo, Japan) was used to correct the images for vignetting and geometric distortion. Then the Pix4 DMapper software (Pix4D Inc., Lausanne, Switzerland) was used for automatic image mosaicking. The images were mosaicked using ground control points (GCPs) in Pix4 DMapper software to improve the positional accuracy. The mosaicked RGB and NIR images were combined as a four-band mosaicked image. The Quick Atmospheric Correction tool in ENVI 5.3 (Harris Corporation, Jersey City, NJ, USA) was used for image atmospheric correction [2]. According to [PERSON] et al. [6], two unsupervised classification methods and six supervised classification methods were accurate for identifying cotton root rot. Among them, the neural net technique uses standard back propagation for supervised learning [37]. Learning  
Figure 1: Location of the study area and Sentinel-2 imagery. (**a**) Study site; (**b**) Sentinel-2A color infrared (CIR) composite image; (**c**) Airborne CIR composite image.  
occurred by adjusting the weights in the node to minimize the difference between the output node activation and the output. In this study, neural network classification methods (ENVI 5.3, 2016) were applied to airborne multispectral images to identify the root rot-infested area of each cotton field, the total numbers of training pixels for the eight fields were 19,724 (0.29% of the total area) for the infested class and 30,160 (0.44% of the total area) for the noninfested class. In all the classifications, the process finished when the number of iterations reached 1000 or the training RMS reached 0.01. The classification results of each field was compared with the original imagery and field observations and the result was used as the reference of cotton root rot classification in the study area.  
In addition, the 0.81 m airborne image was resized to 10 m spatial resolution with bilinear method. The airborne image was used as the reference image for Sentinel-2A image geometric correction [2].
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 5 Fake Image Detection Method--Using Neural Networks
### Convolutional Neural Network--Image Specific
They also compared various ViT techniques with recent CNN-based architectures. They found that the ViT-based approach better detects deepfakes and fake videos. [PERSON] et al. [121] employed a vision transformer with an exception network (ViXnet) for detecting deepfakes and image forgery. ViXNet has two parts. One part looks at different parts of a face closely to find things that do not match using a special kind of attention and a vision transformer. The other part looks at the whole picture to understand where things are in space using a deep convolutional neural network. Another transformer-based technique was employed by [PERSON] et al. [122]. Dense self-attention coders and dense correction components are the two main parts of their approach. While the latter increases the hidden layer's transparency and corrects the results from various branches, the former is used to model the global context and all pairwise interactions among local patches at various scales. [PERSON] et al. [123] applied vision transformers over deepfakes and got excellent results on deepfake images shared over Kaggle. Similar results were observed by [PERSON] et al. [124] with deepfake videos. They combine patch-based positioning and vector-concatenated CNN features to interact with all positions to determine the artifact region. The sigmoid function trains the logit for the distillation token using binary cross entropy. The suggested framework is generalized to increase performance by including this distillation. The advantages of using deep learning for fake image detection are as follows:  
* Feature Vectors/Intrinsic characteristics of fake images are learned by themselves. It does not need a feature set;
* It can detect images having multiple manipulations;
* Can detect images having Pre/Post-processing after tampering is applied over the images;
* It can use pre-trained state-of-the-art DNN models, which saves time;
* Provide higher results and better accuracy;
* It can work well on unstructured images/data from various sources and formats;
* ViT's based models work well on deepfakes (GAN) images and videos. Ct \(-\) 1 = Previous Cell Memory; Ht \(-\) 1 = Previous Cell output; Xt = Input Vector  
Ct = Current Cell Memory; Ht = Current Cell output  
U, W = weights vectors for Candidate (C), Forget_gate (F), i/p gate (I), o/p gate(O)  
\[1=\text{Ft}=\sigma\left(\text{Xt}\times\text{Uf+Ht}-1\times\text{Wf}\right)\]  
\[2=\text{Ct}=\text{tanh}\left(\text{Xt}\times\text{Uc}+\text{Ht}-1\times\text{ Wc}\right)\]  
\[3=\text{It}=\sigma\left(\text{Xt}\times\text{Ui+Ht}-1\times\text{Wi}\right)\]  
\[4=\text{Ot}=\sigma\left(\text{Xt}\times\text{Uo+Ht}-1\times\text{Wo}\right)\]  
So, with the above parameters, Ct and Ht are derived as  
\[\text{Ct}=\text{Ft}\times\text{Ct}-1+\text{It}\times\text{Ct}\]  
\[\text{Ht}=\text{Ot}\times\text{tanh(Ct)}\]  
Figure 13: LSTM Cell at a time interval “T” [125].
## 4 Design of Proposed Scheme: FAPR
### Reactive Part
#### 4.3.1 Data Collection Module  
The main function of the data collection module is to gather network information from the data plane, and then process the collected information to realize standardization, which makes subsequent processing more convenient. The standardization of network information by this module is the foundation for the backup path generation module to calculate the backup path.  
In the FAPR scheme, the information collected by the data collection module is divided into three parts, namely, switch information, link information, and events sent from the switch to the controller. More specifically, the standardized switch information includes the switch's ID and the flow table entries stored in it, as shown in Figure 5. The standardized link information includes the ID of the source and destination switches of the link, the total bandwidth of the link, and the bandwidth that has been used, as shown in Figure 6. The events sent from the switch to the controller contain the packet information of flows, namely, a flow table entry, as shown in the right part of Figure 5. An event also contains the information related to the OpenFlow protocol version, parser, and timestamp involved. This module runs multiple times (for instance, when a port-status message is generated) and the information collected each time may not include all the items of the standardized information.  
Figure 5: Standardized switch information.
###### Abstract
The research shows that the image segmentation method based on deep neural networks has better performance in automatic feature extraction and segmentation accuracy [20,21]. Full Convolutional Networks (FCNs) [22], Semantic Segmentation Networks (SegNet) [23], and U-net networks [24] are commonly used image segmentation methods. U-Net network, in particular, has achieved great success in the field of medical image segmentation. It is an encoder-decoder network architecture which uses standard convolution and continuous downsampling to complete image feature extraction, as well as up-sampling operation and skip connection to complete feature image recovery and finally produce binary segmentation results. It only needs a small amount of medical data to achieve a good segmentation effect and has become a benchmark in the field of medical image segmentation. In recent years, many U-Net-based medical segmentation networks have been proposed, such as U-Net++ [25], Attention U-Net [26], ResU-Net [27], MultiResUNet [28], Unet3+ [29], and UNeXt [30]. However, these U-shaped networks still have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the down-sampling phase, the pooling operation results in the loss of feature information. In addition, some Transformer-based [31] networks have also been applied to medical image segmentation tasks [32, 33, 34]; however, the Transformer model is not suitable for breast ultrasound image segmentation due to its high demand for medical data and large computational consumption due to its attention mechanism. Therefore, basing itself on the Unet model and aiming at the shortcomings of the above U-shaped networks, this paper proposes a breast tumor segmentation network, RMAU-Net, which combines residual depthwise separable convolutions and multi-scale channel attention gates. This work has the following contributions:  
1. The feature extraction module RDw block was designed, which is simple in structure and can capture more global breast tumor feature information. 2. It proposes a multi-scale channel attention gate module to better localize irregular breast tumors by portraying low-level features in both spatial and channel dimensions. 3. It uses the Patch Merging operation for downsampling so that breast ultrasound image information will not be lost. 4. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization.
# Seismic and Geodetic Crustal Moment-Rates Comparison: New Insights on the Seismic Hazard of Egypt
## 7 Estimation of Seismicity Parameters and Seismic Moment-Rates
Then, a rough seismic moment-rate estimation has been computed for this source by adding the individual seismic moment of earthquakes that occurred in the last 50 years; a time interval that includes some of the largest earthquakes that happened in this source, in addition to the biggest one, the 22 November 1995, Mw 7.2, earthquake. To compute it, the well-known relationship between seismic moment and moment magnitude by [PERSON] [110] has been used:  
\[\log M_{0}=c\cdot m+d \tag{13}\]  
\(M_{0}\) is the seismic moment, \(m\) is the moment magnitude, and \(c\) and \(d\) are constants equal to 1.5 and 9.1, respectively, when defining \(M_{0}\) in N\(\cdot\)m.  
In order to compute the seismic moment-rate for the rest of the delimited sources (EG-01, and from EG-03 to EG-09), the exact relationship by [PERSON] and [PERSON] [19] has been used, rewritten as the following:  
\[ \begin{array}{c}\overset{\bullet}{M}_{\rm seis}=\frac{b}{c-b}M_{0}(m_{\rm max })\bigg{(}\overset{\bullet}{N}(m_{\rm min})-\overset{\bullet}{N}(m^{u})\bigg{)} \frac{e^{-b(m_{\rm max}-m_{\rm min})}}{1-e^{-b(m_{\rm max}-m_{\rm min})}}\\ +\frac{M_{0}(m^{u}+\Delta m_{c}/2).\overset{\bullet}{N}(m^{u}).\big{(}1-10^{ -c\cdot\Delta m_{c}}\big{)}}{c\cdot\ln 10\cdot\Delta m_{c}}\end{array}  \tag{14}\]  
The first term in the equation corresponds to the contribution of the Gutenberg-Richter exponential magnitude recurrence relationship, while the second one belongs to the characteristic earthquake model, considered uniformly distributed in the magnitude range \(m^{u}-\Delta m_{c}/2\leq m\leq m^{u}+\Delta m_{c}/2\). This second term is only considered for the source EG-10, for which, as quoted previously, the two largest recorded earthquakes have been considered as characteristic events. In order to compute this equation, \(b\)-value is obtained from Equation (10), \(\beta=b\cdot\ln 10\), \(c\) comes from Equation (13), and \(m_{\rm min}\) and \(m_{\rm max}\) define the range in which the Gutenberg-Richter relationship is valid (Table 2), being \(m_{\rm min}\) the so-called threshold magnitude. In our assessment, \(m_{\rm min}\) has been set to zero, being then \(\dot{N}(m_{\rm min})=10^{a}\). In addition, \(M_{0}(m)\) is estimated using Equation (13), and \(\dot{N}(m)\) is the annual rate for earthquakes exceeding magnitude \(m\). Seismic moment-rate results from Equation (14) have been multiplied by the stochastic correction factor proposed by [PERSON] and [PERSON] [111], that considers the asymmetry of the moment-magnitude relation, recognizing this as an important source of uncertainty. Obtained results are tabulated in Table 2.
## 4 Results and Discussions
### Path Optimization
The convergence of agriculture and technology has provided many new entrepreneurial and investment opportunities. Hence, in the last decade, new companies have emerged to provide technology services to farmers, such as leasing agricultural drones for pesticide spraying, weed mapping, and crop status monitoring. In this regard, the system proposed in this article can be run by either experienced farm technicians or drone service provider companies.
## 2 Materials and Methods
### Preparation of Standards  
Standards for the HPLC analysis included eight phenolic acids: caffe acid, chlorogenic acid, \(p\)-coumaric acid, ferulic acid, gallic acid, protocatechuic acid, syringic acid, and vanillic acid. These standards were prepared as stock solutions at a concentration of 1 mg/mL using methanol. Serial dilutions were then made to achieve calibration concentrations ranging from 0.1 \(\upmu\)g/mL to 50 \(\upmu\)g/mL. A volume of 10 \(\upmu\)L was injected into the HPLC system for both standards and samples.
# Analysis of Grinding Flow Field under Minimum Quantity Lubrication Condition
## 3 Simulation and Analysis of Three-Dimensional Grinding Wheel Flow Field
### Simulation and Analysis of the Grinding Wheel Flow Field under MQL Conditions
#### 3.3.1 The Wedge Region under the Influence of MQL Jet  
The MQL jet enters the airflow field of the wheel and forms a disturbance to the flow field of the wheel, which in turn affects the distribution of the flow field of the wheel. The local enlargement of velocity vector of flow field and the pressure distribution in the wedge region under the influence of MQL jet are obtained by simulation. Figure 11 illustrates the local magnification of velocity distribution of flow field. Figure 12 shows the pressure distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Values** & **DPM Type** \\ \hline Wheel diameter & 100 mm & \\ Wheel linear velocity & 40 m/s & \\ Minimum gap & 0.05 mm & \\ pressure outlet boundary & 0 Bar & \\ pressure inlet boundary & 2, 4, 6 Bar & Escape \\ Workpiece wall & Fixed wall & Reflect \\ Wheel wall & Rotating wall & Reflect \\ Nozzle wall & Fixed wall & Reflect \\ Fluid Materials & Standard air & \\ Environmental temperature & 25 \({}^{\circ}\)C & \\ Flow rate of grinding fluid & 54.11, 66.41, 77.17 mL/h & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
As can be seen from Figure 11, the MQL jet has a large effect on the velocity distribution in the wedge region. Vortex flows appear at both the upper and lower positions of the nozzle outlet. Based on the flow direction of the vortex, it can be judged that the presence of the vortex will obstruct the grinding fluid droplets from going into the grinding zone and diminish the cooling and lubrication influence of the grinding fluid.  
According to Figure 12, the MQL jet enhances the pressure in the wedge zone and expands the high-pressure zone. Such a change is unfavorable for the grinding fluid to go into the grinding zone.
## 5 Conclusions  
In this study, an efficient data preprocessing technique and ML application were developed to analyze occupational accident data in the construction industry, where it is difficult to derive features owing to a large number of variables and elements in the accident data. The following conclusions were drawn:  
* For construction accident data involving many variables and wide categories, it is possible to identify the most influential variable among many variables by using clustering, chi-square test, and other procedures.
* Because the types or categories of the major variables are numerous, it is difficult to identify meaningful relationships. Therefore, standardization and element grouping can be performed, and the accuracy can be analyzed according to the categories of the variables; through this, an optimal grouping using the fewest elements can be found.  
Figure 8: Classification of severity with principal component analysis (PCA) using three major variables.  
* The correlations between factors can be analyzed by examining the correlations between and contributions of variables, using ML analysis on the optimal variable type and category.
* Through PCA and clustering, the distribution and combinations of variables that contribute to the prediction of each variable can be understood, and we anticipate that effective accident prevention measures can be established by utilizing these results.
* The severity level in the classified list of personal damage was predicted and analyzed, so this study can have some limitations. The more quantitative data such as the days of convalescence for each accident can yield more reliable results.
* There are differences in variables and elements to be filled out because construction accident data are all different in forms managed by countries and companies. Therefore, to apply the analysis method proposed in this study, the data standardization is necessary.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] contributed device/analysis tools; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research Assistance Program (2020) of the Incheon National University and by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No.2018R1D1A1A02085377).  
The authors declare no conflict of interest.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 2 Related Work
### Machine Learning Algorithms  
In this section, machine learning algorithms used for pseudo-labeling are briefly explained.  
* XGBoost  
XGBoost [34] is a type of ensemble as well as a boosting algorithm that combines multiple decision trees. It is a scalable version of Gradient Boosting introduced by [PERSON] et al. [35]. It adjusts the weights of the next observation based on the previous one. The algorithm generally shows strong predictive performance and it has earned much popularity at the Kaggle competitions by achieving high marks in numerous machine learning tasks.
* Random Forest  
Random Forest [36] is a bagging algorithm that uses an ensemble of decision trees. It effectively decreases the variance in the prediction by generating various combinations of training data. Its major advantage lies in the diversity of decision trees as variables are randomly selected. It is frequently used for both classification and regression tasks in machine learning.
* Extra Trees Extra trees [37] is a variant of random forest which is a popular bagging algorithm based on the max-voting scheme. extra trees are made up of several decision trees in which there are multiple root nodes, internal nodes, and leaf nodes. For each decision tree, a different bootstrapped data set is used. It should also be noted that for bootstrapping, all samples are eventually used because they are selected without replacement. At each step in building a tree and forming a node, a random subset of variables is considered resulting in a wide variety of trees. At the same time, a random value is selected for each variable under consideration leading to more diversified trees and fewer splitters to evaluate. The diversity and smaller tree depths are what make extra trees more effective than Random Forest as well as individual decision trees. Once various decision trees are established, a test sample would run down each decision tree to deliver outputs. The most frequent output will be provided as the final label to the test sample as the result of the max-voting scheme.
* Multilayer Perceptron  
Multilayer perceptron [38] is a feed-forward neural network composed of multiple layers of perceptron. It is usually differentiated from DNN by the number of hidden layers. It utilizes a non-linear activation function to learn the non-linearity of input features.
## 2 Materials and Methods
### Classification Methods
#### 2.5.3 Artificial Neural Networks (ANNs)  
Artificial Neural Networks (ANNs) are computational systems made up of simple processing units (artificial neurons) that mimic the functions of the human brain [42; 45; 46]. ANNs consist of inputs, weights, summation functions, activation functions, and outputs. The goal of a neural network is to minimize the error between the outputs and the actual values by adjusting the weights optimally. To achieve this, the weights are continuously updated until the error falls below a threshold.
## 4 Data Processing and Imaging Results  
### Data Processing of the HTEM System  
First, the background field data were interpolated two times (before and after the flight of the survey line) to obtain the background field correction value of the system during the flight of the survey line, and the background field correction of the HTEM data was completed. Second, the clipping and wavelet filtering methods were used to eliminate natural background electricity noise and motion noise generated by helicopter and coil movement, respectively. We used the attitude information measured by the navigator to calibrate the data [24; 30]. Finally, the data that were collected within 1 s were stacked to extract traces with an equal logarithmic interval (total of 21 gates). The data of L5 and L7 are shown in Figure 5.  
Figure 4: GAFEM system on survey. (**a**) Airborne magnetic recording system. (**b**) Ground high-power transmitting system. (**c**) Ground electromagnetic reference station.  
Figure 5: Field measured data by the HTEM system. (**a**) L5 line. (**b**) L7 line.