esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 3 Data
### Training Data Augmentation  
Large networks are trained using large amounts of training data to avoid overfitting [36]. Our dataset of 930 samples was inadequate for this purpose; therefore, we generated synthetic data from our initial dataset for training purposes. We added random noise to waveforms to change their signal-to-noise ratio (SNR), as shown in Figure 5. We varied the SNR [39] from 1 to 5 as determined by the following:  
\[SNR=\frac{P_{signal}}{P_{noise}}=\bigg{(}\frac{A_{signal}}{A_{noise}}\bigg{)} ^{2}, \tag{5}\]  
where \(P\) is average power and \(A\) is the root mean square amplitude. The resulting augmented dataset used for training contained 4650 synthetic samples (5 \(\times\) 930).
## 2 Theoretical Model  
The IGOT model is an expansion of the GOT model, developed by [PERSON] et al. [11], that extends the normalized bistatic radar cross section (BRCS) model to account for attenuation due to short-wave diffraction. The GOT model [11] was developed from first principles for GNSS-R measurements of a bare surface with heterogeneous roughness in the strong diffuse regime. Thus, scattering is modeled as noncoherent. The GOT model uses the geometrical optics limit of the Kirchhoff approximation in deriving the normalized BRCS.  
This paper follows the same notations as the original model, [PERSON] et al. [11], for consistency. The normalized BRCS \(\sigma_{0}\) of the original model is given as [11] (Equation (46))  
\[\sigma_{0}(\vec{\rho})=\frac{\pi|\mathcal{R}|^{2}q^{4}}{q_{\pm}^{2}}p_{\text{ rg}}\bigg{(}-\frac{\vec{q}_{\pm}}{q_{z}}-\
abla_{\perp}\zeta_{\text{dem}}\bigg{)} \tag{1}\]  
where \(\mathcal{R}\) is the polarization-dependent Fresnel reflection coefficient, and \(p_{\text{rg}}\) is the probability density function (pdf) of \(\
abla_{\perp}\zeta_{\text{res}}\) at \(\vec{\rho}\). The 2-D scalar field \(\zeta\) denotes the surface height relative to the \(x-y\) plane, and the subscripts _dem_ and _res_ denote the DEM and the residual, respectively. The vector \(\hat{z}\) is the outward normal of a planar or a locally planar surface, which can be an earth ellipsoid or a geoid model. The symbol \(\
abla_{\perp}\) denotes the \(\hat{x}\) and \(\hat{y}\) components of the gradient, which are the components that are perpendicular to the \(\hat{z}\) vector. Thus, \(\
abla_{\perp}\zeta_{\text{dem}}\) is a vector containing the \(\hat{x}\) and \(\hat{y}\) components of the DEM gradient. The quantity \(q\) is the norm of \(\vec{q}\), and \(q_{z}\) is the \(\hat{z}\) component of \(\vec{q}\). This \(\vec{q}\) vector is the scattering vector, which is defined as [11] (Equation (19))  
\[\vec{q}=k\big{(}\hat{R}_{\text{rs}}-\hat{R}_{\text{st}}\big{)}=-\
abla_{\text {s}}k\big{(}R_{\text{rs}}+R_{\text{st}}\big{)} \tag{2}\]  
where \(k\) is the wavenumber and \(\
abla_{\text{s}}\) denotes the gradient operator with respect to the scattering point. The quantities \(\hat{R}_{\text{rs}}\) and \(R_{\text{rs}}\) are the unit vector and the magnitude, respectively, of the vector at the scattering point pointing toward the receiver. Furthermore, \(\hat{R}_{\text{st}}\) and \(R_{\text{st}}\) are the unit vector and the magnitude, respectively, of the vector from the transmitter pointing toward the scattering point.
# MLMD--A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm
## 2 Theoretical Background
### Malware Detection  
Currently, these are the most widespread malware detection methods:  
* Signature-based detection--the most commonly used malware detection method [9]. Signatures are unique sequences of bytes obtained by malware analysis. These signatures are used to identify a particular piece of malware [8]. Security programs contain a database of signatures of known malware. Whenever a new file is checked, this file is analysed and compared with the database [9]. If the analysed file contains a signature listed in the database, it is highly probable that the file is malicious.
* Behaviour-based detection--during the detection, the behaviour of the program and its activities are examined. Attempts to perform abnormal or unauthorised action could indicate the program is malicious or at least suspicious. Examples of abnormal actions include modifying other files, adding new users to the system, and stopping system security software, to name a few [9].
* Heuristic detection--this means looking for certain features indicating malicious behaviour by using rules and algorithms, either by looking for commands and instructions typical of malware or by monitoring its behaviour and activities during execution, or even--frequently--by a combination of the two [10]. Each activity is then rated in terms of risk, and--if a set threshold is surpassed--preventive action is taken [11].  
The most significant disadvantage of signature-recognition-based detection is that it can only detect known malware registered in the signature database [8]. This disadvantage can be observed especially in the case of polymorphic and metamorphic malware. A partial solution to this issue is to use behaviour-based detection and heuristic detection, as these methods are capable of detecting new malware with yet unknown signatures. However, their disadvantage is a higher false positive rate, that is, they tend to mislabel benign programs as malicious ones [8].
# LUMDE: Light-Weight Unsupervised Monocular Depth Estimation via Knowledge Distillation
## 4 Experiments
### Inference Efficiency  
We tested our inference efficiency on both a CPU and GPU, as shown in Figure 4a,b. It can be seen that even on a CPU, our model can reach 36 ms (27.8 FPS) compared with 228 ms (4.4 FPS) for the teacher net without pre- & post-processing. If we consider the pre-& post-processing, our compact network can also achieve 52 ms (19.2 FPS) on an Nvidia GeForce MX330 GPU. Pre- & post-processing refers to necessary procedures for inference such as image resizing, read & write, and coloring.  
Figure 4: (**a**) Inference efficiency on an i5 core CPU; (**b**) Inference efficiency on an Nvidia GeForce MX330 GPU. In the inference stage, only the DepthNet is used to test inference efficiency. Pre- & post-processing refer to the necessary procedures for inference such as image resizing, read & write, and coloring. Image size is 640 \(\times\) 320.  
Figure 3: Qualitative comparison of depth ground truth, depth from teacher network, baseline, and LUMDE about outdoor traffic scenes, with RGB images shown in the first column.
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 2 Literature Review
In real traffic scenarios, due to factors such as road characteristics and PIO types, although two nodes are adjacent, there may be differences in traffic state in the same time period, as shown in Figure 1.  
Figure 1: Various trends in daily traffic volume for adjacent network nodes. Nodes 1, 2, and 3 are adjacent to node 0, but each node has different traffic trends and peak periods. The results indicate heterogeneity in traffic-state changes among adjacent traffic nodes during the same time period. Therefore, a fixed-graph structure is not sufficient to capture complex dynamic traffic-flow changes. In this study, a spatial self-attention mechanism was used to capture the dynamic changes in traffic flow in the spatial dimension. Subsequently, two different attention forms were used to capture the spatial-temporal dynamic features, namely a time self-attention mechanism, which considers the entire sequence for temporal dependency relationships, and a spatial attention mechanism, for road-network-traffic-node spatial characteristics.
## 2 Methods
### Identification and Information Extraction of Rock Mass Structural Planes
#### 2.1.2 Extraction of Rock Mass Structural Planes Information  
After region growing, the points within the same structural planes are grouped together to form a point set with different point sets assigned different colors to distinguish each structural plane. Based on the least squares principle, plane fitting is performed for each point set, and the normal vector of the plane is calculated. Using principles of analytical geometry, the normal vector is converted into the structural plane's orientation information, specifically dip direction and dip angle.  
Let the normal vector of the structural planes be \(\overset{\rightarrow}{N}=\left(a,b,c\right)^{T}\) with \(c>0\), and the formulas for calculating the dip direction \(\alpha\) and dip angle \(\beta\) of the structural planes can be applied, as verified and used by [PERSON] [30]. In the geographic coordinate system, assuming the positive direction of the \(Y\)-axis is north, the positive direction of the \(X\)-axis is east, and the positive direction of the \(Z\)-axis is upward, the formulas for calculating the dip direction \(\alpha\) and dip angle \(\beta\) are as follows:  
\[\begin{array}{c}\beta=\arccos(c)\\ if\ a\geq 0,b\geq 0,\alpha=\arcsin(a/\sin\beta)\\ if\ a<0,b<0,\alpha=360^{\circ}-\arcsin(-a/\sin\beta)\\ if\ a\geq 0,b\geq 0,\alpha=180^{\circ}-\arcsin(a/\sin\beta)\\ if\ a\geq 0,b\geq 0,\alpha=180^{\circ}+\arcsin(-a/\sin\beta)\\ \end{array} \tag{1}\]
# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 2 Materials and Methods
### Model Structure  
The structure of our proposed Parallel-Convolution-based U-Net (PSNet) is shown in Figure 2. It is noted that our PSNet has two parts. Part one is the encoder structure, and part two is the decoder structure.  
As shown in Figure 2, it can be seen that the PSNet proposed by us is a fully convolutional neural network. Firstly, it is a symmetric U-shaped structure, with the encoder on the left side and the decoder on the right side. Carefully examining the PSNet, there are several Parallel Convolution Modules (PCMs) and several Self-Gated Attention Blocks (SGABs) in the encoder. The main purpose of the PCM is to avoid feature information loss caused by down-sampling and the main purpose of the SGAB is to control the flowing of features, which is highlighting features related to cracks and decreasing the weight of features unrelated to cracks. Additionally, it is noted that the SGAB could choose features in two dimensions, the channel dimension as well as the spatial dimension. Moreover, traditional convolution layers are used in the encoder to connect the PCM and the SGAB while extracting abstract high-level features. As for the decoder, we could see that several convolution layers and deconvolution layers are used to decode features generated from the encoder for the purpose of generating the label map.
## 5 Experimental Verification
### Experimental Results
#### 5.3.3 Comparison of Errors at Different Iteration Times  
Figure 8 shows the trajectory tracking error plots for iterations 2, 4, 6, 8, and 10. The ILC-ASMC can significantly improve the tracking accuracy in just a few iterations in the case of a sinusoidal signal reference. The absolute value of the maximum position tracking error tends to 0, achieving accurate tracking. The proposed ILC-ASMC algorithm can converge the initial state error to close to zero in a short time, ensuring that the FTS system can track the desired trajectory in a short time. The designed ILC is sufficiently robust to periodic disturbances. The controller can not only achieve a fast dynamic response while tracking the design trajectory, but also improve the anti-disturbance performance of the FTS system.  
Figure 7: SMC, ASMC, and ILC-ASMC tracking results of sinusoidal trajectory at 40 Hz.
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
## 2 Literature Review
discussed a digital audio watermarking algorithm that utilizes blockchain technology to protect the copyright of musical works. The authors proposed a method that combines the benefits of blockchain technology (e.g., decentralization and tamper proofing) with the robustness of digital watermarking. The proposed method can effectively protect the copyright of digital music and verify the authenticity of musical works [12]. Based on the characteristics of blockchain, [PERSON] and [PERSON] illustrated that the technology brings opportunities for digital copyright, such as reducing costs, solving registration and proof challenges, and responding to transaction needs [13]. [PERSON] and others proposed a strategy to improve existing text similarity detection methods. This strategy integrates the term frequency-inverse document frequency index (TF-IDF), linguistic attributes, word length, and word position to form a comprehensive weighted text similarity detection approach. Through this approach, the accuracy of copyright clearance algorithms hasbeen significantly enhanced. Moreover, they analyzed the challenges faced by traditional digital copyright protection systems, such as the erosion of the value basis of works, rampant infringement, obvious shortcomings in copyright registration, difficulties in rights protection, the public's insufficient awareness of copyright, etc. [14]. The theoretical foundations and algorithmic implementations of SimHash were described in detail by [PERSON] et al. SimHash is a locally sensitive hashing algorithm that quickly detects similar data in high-dimensional space. In the article, the authors detailed the theoretical foundations of SimHash, including the design of its hash function, the similarity calculation method, and how to use SimHash for fast similarity detection [15]. [PERSON], M.S. and [PERSON], C.K. proposed a novel method utilizing SimHash to detect near-miss clones in large-scale software systems. Their significant contribution lies in the development of a new clone detection approach that uses SimHash to gauge code similarity, further employing indexing strategies and data clustering algorithms to accelerate clone search. This approach has demonstrated high efficiency and accuracy when managing large-scale codebases [16]. In summary, the literature underscores the potential of blockchain technology as a transformative tool for digital rights management. While the initial focus of blockchain was primarily in the realm of cryptocurrencies, its application has since expanded to various sectors, including the protection of digital recipes. Additionally, the advancements in text similarity detection algorithms have paved the way for more accurate copyright infringement detection. Our study draws inspiration from these foundational works, aiming to address the existing gaps and contribute to the ongoing discourse in this domain.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.6 Consumer Sequence Type  
A _consumer sequence type_ is a scheme by which different packet names are generated. We assume that all requested packets have the same prefix and only the suffixes of the requests differ, e.g., example/packet0, example/packet1, etc. In the following, only the suffixes denote the packet names for the different sequence types. In particular, these sequence types are _incremental_, _repetitive_, _simple random_, and _linear random_. They have been taken from [26].  
_Incremental_ is of the form \([0,1,\ldots,n-1]^{p}\), where \(p\) is the number of repetitions of the sequence and \(n-1\) is the maximum sequence number. The type can be used to model packets requested in a regular pattern.  
_Repetitive_ has the form \([0]^{p},[1]^{p},\ldots,[n-1]^{p}]^{k}\), where \(p\) is the number of repetitions per element and \(k\) is the number of repetitions of the entire sequence. This type can be used to simulate a network with many repeated requests.  
_Simple random_ selects a uniformly random element from \([0,1,\ldots,n-1]\) and can be used to model packet names with no discernible pattern.  
_Linear random_ also selects a random element from \([0,1,\ldots,n-1]\), but the distribution is not uniform: Instead, assuming \(s=\sum_{i=1}^{n}i\), the probability \(P(i)\) for suffix \(i\in\{0,1,\ldots,n-1\}\) is \(P(i)=\frac{n-i}{s}\). It can easily be seen that \(\sum_{i=0}^{n-1}P(i)=1\). Certain packets are assumed to be more relevant to a consumer than others here.
# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
###### Abstract  
Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector's ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods.  
Keywords:underwater target detection; sonar image; deep learning +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Conclusions  
1. In this paper, the mathematical model for predicting the SMD of the circular-hole rotating core micro-fog nozzle was constructed and included the axial distance, outlet diameter, and water supply pressure. The droplet size decreased with the increase of the axial distance and water supply pressure. As the outlet diameter increased, the droplet size decreased first and then increased.
2. Through the analysis of the range method, it was concluded that among the three factors, the axial distance had the greatest influence on the droplet size, followed by the outlet diameter; the water supply pressure had the least influence on it.
3. According to Figure 5, the smaller the droplet size of the nozzle, the higher the ability to remove dust. In order to obtain the droplet size with the best dust removal effect, the optimal level of the factors was an outlet diameter of 1.1 mm, a water supply pressure of 1 MPa, and an axial distance of 70 cm.
4. The predicted value of the mathematical model for predicting the SMD of the circular-hole rotating core micro-fog nozzle was in basic agreement with the experimental value, and the average relative error was 16.11%, which was less than 20%.  
Conceptualization, Y.C. and C.C.; methodology, P.W.; software, C.C.; validation, Y.L., R.L. and Y.Z.; formal analysis, C.C.; writing--original draft preparation, C.C.; writing--review and editing, Y.C.; supervision, M.L.; project administration, S.L. All authors have read and agreed to the published version of the manuscript.  
Financial support for this work was provided by the Natural Science Foundation of Hunan (2020)5172), the Scientific Research Project of the Hunan Province Office of Education  
Figure 7: Comparison of experimental and predicted values of the SMD in the literature.  
(No. 19B197), and the Science and Technology Innovation Project of the Hunan Provincial Department of Communications (201943).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Acknowledgments:** Financial support for this work was provided by the Natural Science Foundation of Hunan (2020 JJ5172), the Scientific Research Project of the Hunan Province Office of Education (No. 19B197), and the Science and Technology Innovation Project of the Hunan Provincial Department of Communications (201943), and they are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Comparison between Horizontal and Vertical Interchannel Decorrelation
###### Abstract  
The perceptual effects of interchannel decorrelation on perceived image spread have been investigated subjectively in both horizontal and vertical stereophonic reproductions, looking specifically at the frequency dependency of decorrelation. Fourteen and thirteen subjects graded the horizontal and vertical image spreads of a pink noise sample, respectively. The pink noise signal had been decorrelated by a complementary comb-filter decorrelation algorithm, varying the frequency-band, time-delay and decorrelation factor for each sample. Results generally indicated that interchannel decorrelation had a significant effect on auditory image spread both horizontally and vertically, with spread increasing as correlation decreases. However, it was found that the effect of vertical decorrelation was less effective than that of horizontal decorrelation. The results also suggest that the decorrelation effect was frequency-dependent: changes in horizontal image spread were more apparent in the high frequency band, whereas those in vertical image spread were in the low band. Furthermore, objective analysis suggests that the perception of vertical image spread for the low and middle frequency bands could be associated with a floor reflection; whereas for the high band, the results appear to be related to spectral notches in the ear input signals.  
decorrelation; horizontal image spread; vertical image spread; 3D audio +
Footnote †: journal: Applied Physics Letters  
**Acticle**  
**A Comparison between Horizontal and Vertical Interchannel Decorrelation**  
**[PERSON] and [PERSON]**  
1
Footnote 1: email: 22 September 2017; Accepted: 17 November 2017; Published: 22 November 2017