esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Fluid Segmentation
#### 4.3.4 Results  
a. Qualitative evaluation  
We present a qualitative evaluation for fluid segmentation. As shown in Figure 6, we observe that UNet++ and U-Net relatively has a large number of missed detections of IRF (green region). In addition, U-Net prediction shows a large number of false detections of SRF (yellow region) and other networks perform well on the segmentation of SRF. We also observe that the prediction of MDAN-UNet-32 is of high quality and outperforms other methods. MDAN-UNet-16 prediction outperforms UNet++ and U-Net for less missed detections for IRF. Noted that there are still a lot of miss detections of PED (red region) for MDAN-UNet-16 and a lot of false detections of PED for U-Net and UNet++. The high imbalance of classes may partly explain why it is difficult for automatic segmentation methods to segment fluid correctly.  
b. Quantitative evaluation  
We present quantitative evaluation MDAN-UNet-16 and MDAN-UNet-32 in contrast with the comparative methods in terms of mean dice score and mean AVD [mm\({}^{3}\)].  
From the comparison shown in Table 6, we observe that the segmentation of IRF is better than for SRF and PED. However, [38] reported that the segmentation of PED is better than for SRF and IRF. Additionally, automatic methods' performance on Topcon scans is the lowest and [38] reports that automatic method performance on Spectralis scans is the lowest. The factor that we evaluate our method on the training set by dividing them into our own training set and test set may partly explain why there are some differences between us and [38]. We also observe that MDAN-UNet-32 achieves the best average dice score: 0.677 for images from Cirrus, 0.685 for images from Spectralis and 0.648 for images from Topcon, followed by MDAN-UNet-16 with an average dice score of 0.662, 0.679 and 0.609 for images from Cirrus, Spectralis and Topcon respectively. For the images from Cirrus, U-Net achieves a mean dice score of 0.627 for all, which is better than UNet++. Nevertheless, UNet++ shows a better performance than U-Net in terms of mean dice score for the images from Spectralis and Topcon. From the aspect of average dice score, MDAN-UNet-16 and MDAN-UNet-32 always achieve top two performances. In addition, UNet++ outperforms U-Net in Cirrus and Topcon images but gets worst performance in Spectralis images.  
Figure 6: Fluid segmentation comparison of a Test OCT B-scan from Spectralis. The green region indicates IRF, the yellow region refers to SRF and the red part refers to PED.  
We also present performance comparison of fluid segmentation in terms of mean AVD [mm\({}^{3}\)] in Table 7. We observe that MDAN-UNet-32 achieves the lowest mean AVD for images from three types of devices. MDAN-UNet-16 ranks second in terms of mean AVD for images from Spectralis and Topcon. For images from Cirrus, U-Net achieves a mean AVD of 0.161 and outperforms MDAN-UNet-16 and UNet++. Thus, in terms of average dice score and mean AVD, MDAN-UNet-32 performs best, and MDAN-UNet-16 is the second.
## 2 Data and Methods
### Methods
#### 2.2.3 Neural Network Interpretation  
We also used two simple but effective explainable artificial intelligence (XAI) methods [27], which can be used to interpret or explain some aspects of NN model behavior.  
The first was the input gradient method [28], which calculates the partial derivatives of the NN model with respect to the input variables. If the absolute value of derivative for a particular variable is large (compared to the derivatives of other variables), then the input variable has a large influence on the output value; however, since the partial derivative is calculated for a particular combination of values of the input variables, the results cannot be generalized for other combinations of input values. For example, if the NN model behaves very nonlinearly with respect to a particular input variable, the derivative might change significantly depending on the value of the variable.  
This is why we also used a second method, which calculates the span of possible output values. The span represents the difference between the maximal and minimal output value as the value of a particular (normalized) input variable gradually increases from 0 to 1 (we used a step of 0.05), while the values of other variables are held constant. Thus the method always yields positive values. If the span is small (compared to the spans linked to other variables) then the influence of this particular variable is small. Since the whole range of possible input values between 0 and 1 is analyzed, the results are  
Figure 3: An example of training of NN in Setup X (the setup is described in Table 3) for the same-day forecasts of \(T_{\text{max}}\); (**a**) shows the reduction of the loss function during training while (**b**) shows the comparison of observed and forecast values of \(T_{\text{max}}\) in the test set. Red line indicates, where the observed and predicted \(T_{\text{max}}\) would be equal.  
somewhat more general compared to the input gradient method (although the values of other variables are still held constant).  
The problem for both methods is that the results are only valid for specific combinations of input values. This issue can be partially mitigated if the methods are applied to a large set of input cases with different combinations of input values. Here we calculated the results for all the cases in the test set and averaged the results. We also averaged the results over all 50 realizations of training for a specific NN setup--thus the results represent a more general behavior of the setup and are not limited to a particular realization.
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailed Flapping Wing Micro-Air Vehicles  
In this section, we intend to present the details of system identification techniques for tailed micro air vehicles (i.e., bird like micro air vehicles). In [126; 127], a flight test of a 0.45 kg ornithopter was flown in a straight and level mean flight. A visual tracking strategy was employed to provide measurements of the states, which benefited with low-noise, minimally-invasive measurements. The spatial distributions of kinematics over a wing stroke were investigated while using a multi-body model of the flight dynamics. The models of lift, thrust, and the coefficients of pitching moment were extracted via stepwise regression accompanied by least mean square estimation. Both the frequency and time domain system identification strategies were invoked, which resulted in quite close estimates. The identified models can be used for feedback control purposes. A grey box approach was proposed for the system identification of the DelFly II in [93; 94; 128], which by definition belongs to the top-down approach. The purpose of their work was to bridge the existing gap between the theoretical and experimental strategies. Another core objective was the development of benchmark aerodynamic models for the DelFly II based on aircraft system identification techniques. In addition, the extension of these techniques to the flapping wing system for autonomous flight was the key objective. The identification was mainly done via the least mean square estimation and extended Kalman filter (EKF), which are highlighted in the subsequent study.
## 2 Performance of IWP with Steel Anchor Beam
### FE Analysis and Results
#### 2.3.3 Results and Discussion  
Figure 17 shows the Mises stress distribution on the end plate and surface plate under a 2.5P load. At 2.5P, the maximum Mises stress on the end plate is 429.9 MPa. The overall stress level of the steel plate is within a reasonable range, with larger Mises stress in the area contacting the steel bracket, while the Mises stress in other areas does not exceed 50 MPa.  
Figure 16: Comparison between segment loading test and FEM results: (**a**) Mises stress on plate D; (**b**) calculated stress at measurement points p-10 and p-15; (**c**) slip at WY-3.  
Figure 18 illustrates the Mises stress distribution on the perforated plates under a 2.5P load. For plate A, the maximum Mises stress is 202.9 MPa, and the overall stress level is low, with most areas experiencing stress below 30 MPa. Plate B shows a maximum Mises stress of 425.4 MPa, but the overall stress remains low, with most of the plate under 70 MPa. On plate C, the maximum Mises stress reaches 430 MPa, with two regions of higher stress: one at the upper connection to the bracket support plate, experiencing concentrated tensile stress, and another at the lower connection, experiencing concentrated compressive stress. Plate D exhibits a maximum Mises stress of 417.3 MPa, and like the other plates, the overall stress level is low, with most areas under 80 MPa. In summary, while certain regions on the plates exhibit higher stress concentrations, the overall stress levels across all plates remain relatively low.  
Figure 17: Mises stress distribution of steel plates in FE analysis under 2.5 times design load: (**a**) end plate; (**b**) surface plate.  
Figure 18: Mises stress distribution on perforated plates in FEM analysis under 2.5 times design load: (**a**) plate A; (**b**) plate B; (**c**) plate C; (**d**) plate D.  
Figure 19 shows the principal tensile stress distribution in the concrete under a 2.5P load. Under this load, the maximum principal tensile stress reaches 4.3 MPa, occurring primarily in the Y-direction in the local concrete region beneath the upper part of the end plate. The area near the lower edge of the bracket web experiences significant local compressive stress, while the principal compressive stress in other regions is relatively small. Overall, under 2.5P load, the principal compressive stress in most areas of the concrete does not exceed 20 MPa.
## 2 Context and Summary of Numerical Simulations
[39]) studied the effect of particle volume fraction and collisions on particle segregation and clustering in homogeneous isotropic turbulence in the same configuration and numerical set up as those employed in the present study. It was found that for particle volume fractions lower than \(0.1\%\), the effect of increasing collision frequency with \(\alpha_{p}\) was marginal as all evaluated clustering measures (segregation parameter, correlation dimension, radial distribution, Voronoi diagrams and Minkowski functionals) were only slightly modified; however, for higher volume fractions, of about \(1\%\), such measures were noticeably altered due to the re-dispersing effect of inter-particle collisions.
# Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem
## 3 Binary Cockroach Swarm Optimization  
Binarization is a method of reducing the number of possible states to feasible solutions [26]. The binarization of continuous space transforms the values of the space into a limited number of possible states [26]. Several binarization techniques are described in the literature, and these include: the sigmoid function, random-key, smallest position value, modified position equation, great value priority and nearest integer [26]. The sigmoid function is a very popular method of transforming continuous space into a binary space. The transformation process converts each element of each dimension to a binary digit [26]. In this paper, the sigmoid function technique of binarization was adopted. We investigated the optimization of a pseudo-Boolean function \(f:\{0,1\}^{D}\rightarrow\mathbb{R}^{D}\) in cockroach swarm optimization.  
Binary Cockroach Swarm Optimization (BCSO) was designed to accommodate some problems that require a binary decision. The cockroach's probability of decision making, such as, yes or no, true or false, is considered. In the BCSO algorithm, the cockroach position is defined in terms of the probability that a bit will be in the state of either zeros or ones. The positions \(x_{i}\) and personal best \(p_{i}\) are integers of zeros or ones, \(x_{i}\in\{0,1\}^{D}\), \(p_{i}\in\{0,1\}^{D}\), where \(D\) is the cockroach population dimension size. The idea of using a sigmoid function in this paper came from the studies on binary versions of some evolutionary algorithms, including [12; 17; 18]. A logistic transformation was used to actualise the change in cockroach position as described in [12] using a sigmoid function \(s(x_{i})\).  
\[s(x_{i})=\frac{1}{1+\exp^{-x_{i}}}\]  
The resulting change in position is defined by:  
\[x_{i}=\left\{\begin{array}{ll}1&\mbox{if }\mbox{rand}()\;\;\;\leq\;\;\frac{1}{1+ \exp^{-x_{i}}}\\ 0&\mbox{otherwise}\end{array}\right. \tag{1}\]  
\(i=1...N\), where \(N\) is the swarm size, \(rand()\) is a randomly-generated number between zero and one and \(x_{i}\) is the current position of the cockroach.  
The BCSO computational steps are given as:  
1. Initialize a cockroach swarm with uniform distributed random integers of zeros or ones as in \(x_{i}\in\{0,1\}^{D}\), \(f:\{0,1\}^{D}\rightarrow\mathbb{R}^{D}\); and set all parameters with values.
2. Perform cockroach operations.  
Use the sigmoid function to transform the position probabilistically using Equation (1). This means the probability that the current position \(x_{i}\) will take on a one or zero value.
4. Calculate the fitness value and update the global best position.
5. Repeat the loop until the end criteria is reached.
## 2 Methods
### Model Architecture and Metrics  
In the previous section, CNN basic architectures were introduced. These kinds of networks are well suited to capture the context of an image but with a loss of spatial information due to the down-sampling effect. This drawback, due to the alternation of convolution and max pooling layers, makes CNN basic architecture not well suited for semantic segmentation tasks. For that reason, we use the U-Net encoder-decoder model first introduced by [PERSON] et al. [31], as shown in Figure 2. The U-Net architecture is composed of two paths: the encoder and decoder. The encoder is a contracting path with 4 successive convolution blocks associated with max pooling steps. The convolution blocksextract raw features from DGW images (\"what is a diamond grits?\"), and max pooling operations grab the most dominant ones. This implies a spatial reduction in the (XY) image axis by increasing the depth of the feature channels. A direct consequence is a loss of information about the position of the object (diamond) in the image. To address this issue, [PERSON] et al. introduced skip connections, an image concatenation mechanism that allows high-resolution spatial features to be cropped from the encoder and concatenated with the corresponding up-convolution of the expansion path. Indeed, the decoder is composed of an expanding path of 4 transposed convolution blocks and respective skip connections in order to learn spatial information (i.e., \"where is the diamond?\"). Furthermore, a fully connected layer is connected at the end of the decoder for classification purposes. In addition, we use the pre-trained weight of ResNet34 [29] as a feature extraction for the contracting path. This transfer learning technique allows us to save a large amount of training time, computational resources and leverage the U-Net architecture to perform semantic segmentation on a small DGW dataset of 153 images.  
Evaluating the performance of semantic segmentation tasks is essential in order to assess the prediction accuracy of the developed model. One way to access such performance prediction is to use the confusion matrix as a starting point for the definition of segmentation metrics [42; 43]. Such a matrix is a tabular summary of correct and incorrect predictions.  
From the confusion matrix, it is possible to extract the respective coefficients--True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN)--and use them to define two fundamental metrics used in our work: the Intersection Over Union (IoU) and the F1-score. These metrics are defined as follows:  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{1}\]  
\[\text{F}_{1}=\frac{\text{2 TP}}{\text{2 TP}+\text{FP}+\text{FN}} \tag{2}\]  
Figure 2: U-Net architecture for DGW image semantic segmentation.  
Although the IoU metrics are popular to assess the performance of the semantic segmentation model, they tend to penalize over and under segmentation prediction. Therefore, prediction performance tends to be underestimated with this index. That is why it is convenient to also use the F1-score defined in Equation (2).
# Instruction2 vec: Efficient Preprocessor of Assembly Code to Detect Software Weakness with CNN +
## 3 Related Work  
### Vulnerability Detection Using Machine Learning  
In 2011, [PERSON] published a study on vulnerability detection using machine learning in \"Vulnerability Extrapolation: Assisted Discovery of Vulnerabilities Using Machine Learning\" [15]. In the paper, [PERSON] identified vulnerabilities using a feature based on API usage pattern vectors. However, the approach has the limitations that it is white-box based and only API functions are included in the feature. [PERSON] introduced a new approach in \"Toward large-scale vulnerability discovery using machine learning\" [16] in 2016. The method extractsd static and dynamic features from a black box to overcome the limitation of the previous approach. Furthermore, when extracting features, _Word2 vec_ and a bag of words are used to vectorize API functions and argument values. Even though this method is improved by extracting dynamic features, it still has significant limitations because only the features based on API functions and argument values are used.  
Several papers about deep-learning-based vulnerability detection were published in 2018. [PERSON] published a paper about vulnerability detection based on deep learning. In \"VulDeePecker: A Deep Learning-Based System for Vulnerability Detection\" [17], VulDeePecker generated code gadgets, transformed them into vectors, and then detects vulnerabilities using Bidirectional LSTM(BLSTM). It was found that deep learning provided higher accuracy compared to pattern-based and code-similarity-based vulnerability detection systems. [PERSON] proposed a detection technique using CNNs in \"Automated Vulnerability Detection in Source Code Using Deep Representation Learning\" [18]. They embedded source code using their lexer and employed convolutional feature extraction. They utilized the random forest classifier to classify vulnerabilities. However, the above-mentioned methods commonly use general source code, not assembly code, and they are provided in API functions. Thus, there are limitations in expressing the structure of a program in detail.  
In this paper, we propose a method to detect software weaknesses by extracting features from assembly code and using deep learning to overcome the above-mentioned limitations. We can expect reasonably higher accuracy because assembly code describes the structure of a program in more detail compared to API functions.
## 1 Introduction
### Penetration Depth of THz: Bio-Medical Applications
The THz-skin freeze method of skin imaging and other applications based on the disparity between the THz dielectric properties of liquid and frozen water can provide the motivation for development of better THz technology. Noteworthy, a realistic numerical model which takes into account material properties and geometry of structures and their composition could immensely help in prediction of light (vis-to-THz) penetration to different depths under the skin for conditions at surgery under freeze conditions. For the THzspectral range, such efforts have been started [23]. In this study, we used rendered pork lard as an analog for the \"dry\" portion of skin. It was experimentally determined that THz absorption of different fats and oils is very similar [24]. More complicated and realistic skin models will evolve and will be useful for definition of numerical phantoms of the actual skin.
# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
## 2 Methods
### Improvement of the DGAIL Algorithm
#### 2.1.1 Generator G Is DQN  
The original GAIL method was designed for a continuous state space and continuous action space, and the generator G adopted TRPO [15] or PPO [16] methods based on stochastic policies. For the method based on a random strategy, the action strategy taken by the agent when the state is s obeys a normal distribution, whose mean is \(\xi\), and whose variance is \(\sigma\). The action strategy function can be expressed as:  
\[\pi_{\Theta}(\mathrm{a},\mathrm{s})=\frac{1}{\sqrt{2\pi\delta}}\mathrm{exp} \left(-\frac{(\mathrm{a}-\xi)^{2}}{2\delta^{2}}\right) \tag{1}\]  
where s is the state space of the agent, a is the action space of the agent, \(\pi_{\Theta}(\mathrm{a},\mathrm{s})\) is the probability density of the action policy function when the agent adopts the action, and the parameters of normal distribution are \(\xi\) (mean value) and \(\sigma\) (variance).  
For the method based on the random action policy, the gradient can be expressed as:  
\[\
abla_{\Theta}\mathrm{J}(\pi_{\Theta})=\mathrm{E}_{(\mathrm{s},\mathrm{a}) \sim\pi_{\Theta}}[\
abla_{\Theta}\mathrm{log}\pi_{\Theta}(\mathrm{a}|\mathrm{ s})\mathrm{Q}^{\pi_{\Theta}}(\mathrm{s},\mathrm{a})] \tag{2}\]  
where s is the state space of the agent, a is the action space of the agent, \(\pi_{\Theta}(\mathrm{a},\mathrm{s})\) is the probability density of the action policy function when the agent adopts the action, and in the states, \(\mathrm{Q}^{\pi_{\Theta}}(\mathrm{s},\mathrm{a})\) refers to the expected reward value when the action policy function is \(\pi_{\Theta}(\mathrm{a},\mathrm{s})\).  
Equations (1) and (2) show that when the agent faces the same state s, the output of the action policy function obeys a normal distribution, and the output value a is different each time. When updating the gradient, the random action strategy fully samples the environment and then calculates the real expected value according to the state distribution and action distribution, which increases the time cost of sampling and reduces the utilization of samples.  
If the action space of the agent is discrete, TRPO needs to discretize the output action policy function and then use the softmax function to select the maximum Q value, while the DQN method is based on the deterministic policy acting in the same states. The choice of a is deterministic, and the maximum Q value can be directly and accurately output, which saves the invalid exploration time in the training process. Therefore, in the discrete space scenario set in this paper, the DQN algorithm samples less of the environment during the training process, and the utilization rate of the samples is high, especially for complex discrete systems such as the intelligent vehicle decision model. The DQN algorithm has a low sampling rate. The learning efficiency will be greatly improved.