esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Saber  
Intrinsically, atomic oxygen causes the occurrence of 557.7 nm airglow; hence, atomic oxygen concentration that can be obtained from SABER data is the basis to estimate airglow intensity from satellite data. The technique described in [12; 20] allows obtaining height profiles of atomic oxygen concentration from height profiles of hydroxyl, temperature, ozone and atmospheric density. After that, one can obtain a height profile of the 557.7 nm volume emission rate (VER) based on data presented in [21], which describes the method of atomic oxygen concentration calculation from the known 557.7 nm intensity. For further comparison with data from ground-based instruments, the resultant height profile of volume emission rate must be converted to Rayleighs (\(10^{-6}photons/\text{cm}^{2}/\text{sec}\)). To do this, the below expression can be used:  
\[R=10^{-6}\times\sum_{i}^{N}I_{i}\Delta h\times 10^{5}=I_{5}\Delta h\times 10^{-1} \tag{5}\]  
where \(I_{i}\) is the volume emission rate (\(photons/\text{cm}^{3}/\text{sec}\)) for each level (\(i\)), \(N\) is the number of levels in height profile, and \(h\) is the layer thickness in kilometers. Such transformations can be found in [22], for example. In our study, the layer thickness \(h\) is 85-100 km.  
In addition, using height profiles of temperature and I 557.7 nm at 85-100 km, we calculated effective temperature from SABER data. The effective temperature was calculated with the intensity profile shifted two kilometers up relative to the temperature profile, because according to previous results, seasonal variations in effective temperature show the best match with seasonal variations in temperature recorded using FPI [16]. The same paper [16] describes in detail the technique of synthesizing intensity and deriving effective temperature.  
If only integral parameters are compared, it reduces the research potential of the SABER dataset, because in this case, information on vertical dynamics of the parameters in question will not be used. Therefore, in addition to integral values, it is useful to extract information about extreme values from height profiles. In this study, we shall also use data on heights of temperature minimum, maximum atomic oxygen concentration and 557.7 nm airglow intensity maximum.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 4 A Numerical Experiment
### Experimental Results  
#### 4.2.1 Feature Selection for Defining HI  
A correlation matrix of features was computed as in Figure 9. Then, we could apply a filtering operation to select features with strong correlations as follows. We could observe that WF and WFE had a strong correlation coefficient of 0.86 and WF had a strong correlation coefficient of 0.85 with kurtosis. Therefore, we decided to select WFE and kurtosis as representative ones. Furthermore, since RMS, peak-to-peak (P2P) and kurtosis had high correlation coefficients larger than 0.75 and they had similar correlation coefficients with others, we could only consider kurtosis by filtering RMS and peak-to-peak (P2P) out. As a result, we finally selected kurtosis, skewness, crest factor, and waveform entropy as candidates for the HI.  
Then, we defined feature vectors containing filtered features and trained the benchmark LSTM using them, where the benchmark LSTM used hyperparameter settings including the number of timesteps = 4, the number of LSTM layers = 3, the number of neurons = 30. The limit of the maximum length of the feature vector was set to 3 for efficient training. We performed 20 repetitions and recorded both the mean and standard deviation of training and validation accuracy calculated from the trained LSTM, which are summarized in Table 4.  
Figure 9: Correlation matrix of features for HI.  
We could derive some insights from the experiment as follows. Although WFE showed the best performance among features when they were used alone, a different result appeared when feature vectors containing it were used to train LSTM. Specifically, we could observe that the feature vector with the largest validation accuracy did not include WFE, while the vector contained CF with the lowest validation accuracy among features. A similar tendency appeared in the result of the feature vector with length 3, where feature vectors including kurtosis dominated those including WFE in terms of validation accuracy. Feature vectors with a longer length showed better accuracy in most cases. However, feature vectors with many features brought about additional computation. As a result, we chose the feature vector including kurtosis, skewness, and CF as the HI, which showed the highest validation accuracy.
###### Abstract  
Recent years has seen a tremendous increase in processing requirements of present-day embedded system applications. Embedded systems consist of multiple processing elements (PEs) connected to each other using different types of interfaces. Many complicated tasks are accomplished by embedded systems in varied settings, which may introduce errors during inter-processor communication. Testing such systems is tremendously difficult and challenging from testing non-real time systems. A major part of testing real time embedded systems involves ensuring accuracy and timing in synchronous inter-process communication More specifically, the synchronization and inter-processor communication of real-time applications makes testing a challenging task and due to the demand for higher data rate increases, day-by-day, making testing of such systems even more complex. This paper presents a novel frame work that uses multiple instances of simulators with physical high-speed serial interfaces to emulate any real time embedded system communication. The framework presents a testing technique that detects all faults related to synchronization of high-speed synchronous serial interfaces in a systematic manner. The novelty of our approach is to simulate communication across multiple processors in a simulation environment for detecting and localizing bugs. We verify this framework using a case study consisting of an embedded software defined radio (SDR) system. The test results show the applicability of our approach in fixing bugs that relates to synchronization issues that otherwise are very hard to find and fix in very complicated systems, such as SDR.  
Keywords:real time embedded system; processing elements (PEs); simulators; inter-processor communication; synchronization; high speed synchronous serial interfaces; testing; software defined radio (SDR) +
Footnote †: journal: Applied Sciences
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 3 Background
### SE Module  
As is shown in Figure 2, The SE (squeeze-and-excitation) module is an attention mechanism [42; 43; 44; 45] that can learn to use global information to selectively emphasize useful channel information and suppress useless channel information. It learns the correlation between channels and can perform dynamic channel feature recalibration to adjust the network and improve the network expression ability. The use of the SE module is very flexible and can be added to an existing network without disturbing the original main structure of the network. The SE module mainly includes three parts: squeezing, excitation and scaling. W and H indicate the width and height of the feature map. C represents the number of channels, and the size of the input feature map is W \(\times\) H \(\times\) C.  
The first step is the squeezing operation, which is implemented through a global average pooling. After the compression operation, the feature map is compressed into a \(1\times 1\times\) C vector. The next step is the excitation operation, which consists of two fully connected layers, where \(\varepsilon\) is a scaling parameter. The purpose of this parameter is to reduce  
Figure 2: Structure of SE module.  
the number of channels and reduce the amount of calculation. The first fully connected layer has C \(\times\)\(\varepsilon\) neurons, the input is \(1\times 1\times\) C, and the output is \(1\times 1\times\) C \(\times\)\(\varepsilon\). The second fully connected layer has C neurons, the input is \(1\times 1\times\) C \(\times\)\(\varepsilon\), and the output is \(1\times 1\times\) C.  
The last step is the scale operation. After the \(1\times 1\times\) C vector is obtained, the original feature map can be scaled. The weight of each channel calculated by the SE module is respectively multiplied with the two-dimensional matrix of the corresponding channel of the original feature map, and the result obtained is output.
## 3 Material and Methods
### Screening Strategy  
This step aims to assess the relevance and quality of the selected studies. Different from a systematic review, a literature analysis only needs to screen the complete text when necessary. In this paper, the relevance of each article was assessed by looking at its title and abstract. Articles found in this stage that did not meet the following two screening criteria were excluded from the analysis [1]:  
1. The use of NLP should play a significant or major part in the study (its research objective, questions, etc.). Studies with an unrelated or secondary focus on the usage of NLP were excluded from this phase. For example, those articles may use other methods in AI but merely mention NLP technologies and do not use them in their research.
2. NLP use in government (or the public sector) should be the main focus of the study, and the study's objective should directly serve or benefit governments. For example, if NLP was not applied in the context of a government, or the authors only used the open data source from the government to conduct research in other fields, these articles were excluded.  
The screening of 1203 titles and abstracts was carried out by the authors and an expert in the NLP area independently. When there were different views, papers were included rather than excluded to ensure that all potential records could be examined. The screening criteria resulted in the exclusion of 114 publications. Finally, 1032 papers were included for literature analysis (Figure 1), and the abstracts of these papers were the data used to conduct the research. The full list of the literature before and after screening is provided in the Supplementary Materials.  
Figure 1: Flowchart detailing the data collection, assessment, and inclusion processes.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Results
#### 5.2.3 Performance Comparison in a WSN with Temporal Burst Errors
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Error Period**} & \multicolumn{4}{c}{**600 s**} & \multicolumn{4}{c}{**1200 s**} & \multicolumn{4}{c}{**1800 s**} \\ \hline \multirow{2}{*}{**Error Rate \%**} & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{**FAR**} & \multicolumn{2}{c}{**Packet Drop Rate \%**} & \multicolumn{2}{c}{**FAR**} & \multicolumn{2}{c}{**Packet Drop Rate \%**} & \multicolumn{2}{c}{**FAR**} & \multicolumn{2}{c}{**Packet Drop Rate \%**} \\ \cline{3-14}  & & & **Beta** & **Hybrid** & **GRP** & **Beta** & **Hybrid** & **GRP** & **Beta** & **Hybrid** & **Beta** & **Hybrid** & **GRP** & **Beta** & **Hybrid** \\ \hline
4 & 1 & 0 & 0.09 & 35.96 & 5.03 & 1.30 & 0 & 0.09 & 35.96 & 5.03 & 1.30 & 0 & 0.09 & 35.96 & 5.03 & 1.30 \\ \hline
12 & 3 & 0 & 0.09 & 36.07 & 5.29 & 1.46 & 0 & 0.09 & 36.03 & 5.45 & 1.64 & 0 & 0.09 & 36.17 & 5.53 & 1.66 \\ \hline
28 & 7 & 0 & 0.09 & 36.20 & 5.58 & 1.65 & 0 & 0.09 & 36.27 & 5.73 & 1.98 & 0 & 0.09 & 36.36 & 5.82 & 2.14 \\ \hline
56 & 14 & 0 & 0.09 & 36.34 & 5.82 & 1.84 & 0 & 0.09 & 36.61 & 6.33 & 2.65 & 0 & 0.09 & 36.76 & 6.56 & 3.04 \\ \hline
72 & 18 & 0 & 0.09 & 36.40 & 5.92 & 2.25 & 0 & 0.09 & 36.75 & 6.62 & 3.02 & 0.02 & 0.09 & 36.93 & 6.72 & 3.45 \\ \hline
100 & 25 & 0 & 0.11 & 36.56 & 6.40 & 2.50 & 0 & 0.11 & 37.02 & 7.22 & 3.39 & 0.02 & 0.11 & 37.38 & 7.59 & 3.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: False alarm rate and packet drop rate in a WSN with temporal burst errors.
## 3 Research Methodology
### Evaluation of Models  
In this phase, we evaluate the performance of all models built. For this purpose, we use the four metrics given by Formulas (1)-(4) namely, accuracy, precision, recall and F1-score, due to their wide use in the literature. These measures are calculated according to the confusion matrix, which summaries the number of correct and incorrect predictions made by a given classifier, as shown below.  
\[\text{Accuracy}=\frac{TN+TP}{TN+FP+TP+FN} \tag{1}\]  
Figure 3: Distribution of the tweets from _Main_dataset_.  
\[\text{Precision}=\frac{TP}{FP+TP} \tag{2}\] \[\text{Recall}=\frac{TP}{TP+FN}\] (3) \[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{4}\]  
\begin{tabular}{l c c} \hline \hline N: Negative, & **N** & **P** \\ \hline P: Positive, & **N** & TN & FP \\ T: True, F: False & **P** & FN & TP \\ \hline \hline \end{tabular}  
* **True Positives:** when current and predicted values are positive with respect to a given class (i.e., both the current label and the label output by the model match the class label);
* **True Negatives:** when current and predicted values are negative with respect to a given class (i.e., both the current label and the label output by the model does not match the class label);
* **False Positives:** when the current value is negative while the predicted value is positive with respect to a given class;
* **False Negatives:** when the current value is positive while the predicted value is negative with respect to a given class.
## 6 Case Study on Metro M
### Calculation Results and Analysis  
#### 6.3.1 Optimal Solution in Two-Layer Model  
An optimal train operation plan for Metro Line M during morning peak hours can be obtained by solving the model with a genetic algorithm. The solution process of the algorithm is to use the genetic algorithm to solve the optimal solution of the upper model (the optimal solution in 50 generations), and then use the enumeration method to solve the optimal solution of the lower model. This is a complete solution process. We repeat this process 30 times to save the 30 optimal solutions. Selected solutions that meet the objectives in the resulting analysis are listed in the table. The best four from the 30 optimal outcomes are selected: the minimum passenger waiting time, the minimum running kilometers of trains, the optimal objective function of the upper model, and the optimal objective function of the lower model. Table 7 summarizes four results. Table 8 summarizes a traditional train plan of full-length and short-turn routes and a single train plan. Then, from five evaluation indexes, it analyzes the train plan of full-length and short-turn routes of virtual coupling trains, traditional train plan of full-length and short-turn routes, and single train plan.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inter-Station** & **Sectional Passenger Volume in** & **Sectional Passenger Volume in** \\
**Segment No.** & **the Up Direction (Person/h)** & **the Down Direction (Person/h)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Minimum Passenger** & **Minimum Running** & **Optimal** & **Optimal** \\  & **Waiting Time** & **Kilometers of Trains** & **Upper Model** & **Lower Model** \\ \hline \hline \multirow{6}{*}{Decision variables} & Departure frequency of the & \multirow{2}{*}{12} & \multirow{2}{*}{10} & \multirow{2}{*}{12} & \multirow{2}{*}{15} \\  & full-length train (pairs/h) & & & \\ \cline{1-1}  & Departure frequency of the & \multirow{2}{*}{12} & \multirow{2}{*}{10} & \multirow{2}{*}{12} & \multirow{2}{*}{5} \\  & short-turn train (pairs/h) & & & \\ \cline{1-1}  & Starting station of & & & \\ \cline{1-1}  & short-turn route (stations) & & & \\ \cline{1-1}  & Terminal station of & & & \\ \cline{1-1}  & short-turn route (stations) & & & \\ \cline{1-1}  & Number of marshalled & & & \\ \cline{1-1}  & vehicles of the full-length & \multirow{2}{*}{2} & \multirow{2}{*}{2} & \multirow{2}{*}{2} \\  & train (vehicles) & & & \\ \cline{1-1}  & Number of marshalled & & & \\ \cline{1-1}  & vehicles of the short-turn & \multirow{2}{*}{4} & \multirow{2}{*}{4} & \multirow{2}{*}{4} \\ \cline{1-1}  & trains (vehicles) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Train Plan of Full-length and Short-turn Routes of Virtual Coupling Trains.
## 2 Proposed Method
### BEGAN Baseline Model  
Conventional GANs have a generator and a discriminator; the generator creates fake images, whereas the discriminator receives both real and fake images as input and attempts to distinguish them. The goal of a GAN is to match the probability distribution of the fake samples generated by the generator to that of the real samples. Therefore, the output of the discriminator is essentially a probability score, and this is fed into the loss function. However, BEGAN has a discriminator with an auto-encoder structure, meaning that the output of the discriminator is an image of the same size as the input.  
Auto-encoder-based GAN models can be optimized by reducing the Wasserstein distance between the reconstruction loss distributions of the real and fake images rather than their sample distributions directly [12; 19]. The discriminator attempts to reconstruct only real images, but the generator attempts to produce an image that can be accurately reconstructed by the discriminator. Therefore, the reconstruction performance of the discriminator is crucial for the generator to be able to produce high-quality output. If the decoder within the discriminator produces poor-quality images when reconstructing the input, the generator could easily fool the discriminator with those poor-quality images.  
[PERSON] et al. [12] introduced the hyperparameter \(\gamma\in[0,1]\) to maintain the balance between generator and discriminator loss, defined as  
\[\gamma=\frac{\mathbb{E}\left[\mathcal{L}(\mathrm{G}(\mathrm{z}))\right]}{ \mathbb{E}\left[\mathcal{L}(x)\right]}, \tag{1}\]  
where \(\mathcal{L}(\cdot)\) denotes the \(L_{1}\) or \(L_{2}\) reconstruction error from the auto-encoder; i.e., the discriminator. \(\mathbb{E}(\cdot)\) denotes expectation operator. \(G(z)\) denotes a fake image from the generator and \(x\) denotes a real image. This ratio (\(\gamma\)) enables users to control the balance between the visual quality and diversity of the output images. If \(\gamma\) is low, the model focuses more on reducing the reconstruction loss of the real images; i.e., the auto-encoding ability of the discriminator increases. This leads to higher visual quality and lower diversity. However, BEGAN has limitations in terms of visual quality and diversity due to the inherent structure of the generator, the lack of reconstruction ability in the discriminator, and unstable training.
## 2 Related Work
### Learning-Based Methods  
Learning-based methods have also been gaining attention in the field of image matching. The major reason is that these represent a data-driven approach and can learn features automatically. Specifically, several non-linear data mapping functions can be stacked to form a deep network function. This deep neural network approximates arbitrarily complex functions and exploits the abstract representation based on a large number of training samples [23]. The Contextdesc network [24] adopts a CNN and introduces context awareness to augment local features, which aims to solve cross-modality image matching problems. D2-Net [25] uses a single CNN as a dense feature descriptor and feature detector, and presents a novel approach for local feature extraction. R2D2 [26] combines the detector and descriptor to improve the repeatability and reliability of keypoints. [PERSON] et al. [27] proposed the ASLFeat, which jointly learns the local feature detector and descriptor for accurate shape and localization of keypoints.  
Considering the ability of deep learning for feature extraction, learning-based methods have been gradually applied to remote sensing image matching and have shown superiority. A CNN feature-based multitemporal remote sensing image registration method has been proposed [28]. It uses CNN to learn multi-level features of images to achieve multi-temporal image matching. To compensate for the disadvantage that SIFT only uses local low-level image information, [PERSON] et al. [29] used the CNN and SIFT fusion features to achieve multi-sensor image matching. However, these methods only use a pre-trained CNN model by fine tuning, and do not carry out their own training on remote sensing data. Similarly, [PERSON] et al. introduce a two-stage framework [30], which casts the matching task by combining deep features and handcrafted features, and uses a random sample consensus (RANSAC) [31] method to refine feature matching and transformation estimation. However, the computational complexity of this method is increased. In recent years, Siamese-type architecture network structures that learn similarity functions between image patches have been receiving attention in the field of image matching [32; 33]. To solve the matching problem of remote sensing images with a complex background, a Siamese convolutional neural network (SCNN) [34] was constructed. However, this matching method is not suitable for the remote sensing images with weak textures. [PERSON] et al. [19] use a network self-learning approach for image patch similarity functions learning, and proposed a deep learning framework for remote sensing image registration. However, the image patches were obtained by SIFT and did not consider high-level feature representation.  
In addition, [PERSON] and [PERSON] [35] evaluated the image patch matching performance of several Siamese-type network structures such as a two-channel networkstructure, a Siamese network structure and a pseudo-Siamese network structure. The results demonstrated that two-channel network structure using image patch feature matching had significant advantages.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 3 State of the Art
### Summary and Discussion  
There are not many destination-less forwarding protocols for OppNets and none of them have explored data popularity. Most of the existing work in data forwarding in OppNets focuses on contact prediction between users and is strictly destination-oriented [8; 9; 12; 15; 16; 17; 18; 19; 20; 21; 22]. Data popularity itself has been successfully leveraged for data caching, but only with globally available historical data. Our work shows the great potential of using popularity in OppNets, while mastering the challenge of predicting it without a global view.