esa-sceva/satcom-chunk-collection huggingface dataset:
# Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average
## 3 Proposed Action Recognition Network
### Generation of Deep Feature Maps  
In this subsection, we present a novel method to generate deep feature maps for accurate, efficient action recognition. Since action recognition requires both spatial and temporal information, we combined two types of feature maps of RGB and differential images. In addition, the generated feature maps not only include spatio-temporal information but also temporally continuous information between adjacent frames using temporal relations.  
The RGB image feature map is generated to use the spatial information of the input video clip, which is expressed as  
\[F_{\text{RGB}}=\phi(I_{n}(x,y)) \tag{2}\]  
where \(I_{n}\) represents the \(n\)-th sampled video frame, \((x,y)\) the pixel coordinate, and \(\phi(\cdot)\) the bottleneck feature map of the VGG16 backbone network.  
The differential feature map is generated using RGB frame with spatial information. Specifically, the RGB space of the sampled frames contains spatial information, and each sequence of frames contains temporal information. We generate a deep feature map using the temporal relationship between temporally adjacent frames by calculating pixel-wise difference between adjacent frames  
\[I_{n}^{\text{diff}}(x,y)=I_{n}(x,y)-I_{n-1}(x,y),\text{ for }n=1,\ldots,N-1 \tag{3}\]  
Figure 3: Architecture of the proposed action recognition network.  
Figure 4 shows the results of differential images from a set of selected UCF101 dataset [13]. A differential image does not have a still background, but moving objects. Figure 4 is a class containing human hands. When we generate a differential image from a video taken by a stationary camera, only the moving hand region mixing with the upper body of a person is detected. In the case of Figure 4a,f, some background regions remain due to the movement of the camera, but the pixel value of a person with major movements can be differentiated from background.  
The differential image feature map is generated using the differential image as  
\[F_{\text{diff}}=\phi\Big{(}I_{n}^{\text{diff}}(x,y)\Big{)} \tag{4}\]  
The differential image feature map returns a bottleneck feature map through the backbone VGG16 network in the same manner as the RGB image feature map.  
When learning actions from a video, it is important to train the spatio-temporal information of the action. Therefore, we generated a deep feature map by concatenating RGB image and differential feature maps. Using the extracted feature maps, we computed the feature vector \(V_{n}\) with multiple fully-connected layers as  
\[V_{n}=FC_{n}(\psi(F_{\text{RGB}}\circ F_{\text{diff}})), \tag{5}\]  
where \(\psi(\cdot)\) represents a feature map concatenation operator, and \(FC_{n}\) the fully-connected layer for the \(n\)-th feature map. Consequently, the generated deep feature map is a combination of two feature maps with spatio-temporal information, and enables spatio-temporal learning in the entire network.
## 3 Proposal and Verification of Improved LoTFWA
### Improved LoTFWA Based on Location Feature Mapping Rules  
The mapping rule in the traditional FWA adopts the remainder operation, which can easily map the explosive sparks, and Gaussian sparks near the origin. For a function whose optimal value is at or near the origin, this method can help the FWA quickly converge to the optimal value. However, for a function whose optimal value is far away from the origin, this method maps most sparks beyond the boundary to the position around the origin far away from the optimal solution. Instead, this increases the difficulty of optimization. In order to overcome this defect and prevent the sparks after mapping from gathering near the origin, the LoTFWA algorithm maps the sparks beyond the boundary to any point in the feasible region through the uniform random mapping rule of Equation (4). However, this method does not consider the relative position relationship between the generated spark and the upper and lower bounds of the feasible region, and does not retain the position characteristics of the generated spark. Moreover, it completely discards the information generated by the spark explosion process and randomly generates a new spark in the feasible region, which has great blindness. In order to solve this problem, this paper proposes a new mapping rule considering the position characteristics: when the spark exceeds the upper boundary of the explosion space, use Equation (5) to map the spark evenly and randomly to the region from two-thirds of the feasible region to the upper boundary; when the spark is lower than the lower boundary of the explosion space, use Equation (6) to map the spark evenly and randomly to the area from the lower boundary to two-thirds of the feasible region.  
\[\overline{X}_{i}^{k}=UB-rand*(UB-(UB+LB)2/3), \tag{5}\]  
\[\overline{X}_{i}^{k}=LB+rand*((UB+LB)2/3-LB), \tag{6}\]  
where _UB_ and _LB_ are the upper and lower boundaries of the explosion space, respectively. The mapping rules of the Improved LoTFWA (ILoTFWA) proposed in this paper effectively extract the position information generated after spark explosion and map the sparks beyond the boundary more pertinently. This not only increases the diversity of the population compared with the traditional residual mapping rule but also enhances the pertinence of the mapping position compared with the random mapping rule of the LoTFWA and retains the relative position between the generated spark and the boundary to a certain extent, which improves the search efficiency of the FWA and helps to speed up the convergence speed of the algorithm.
## 2 Beidou-Binocular Cooperative Navigation Model
### Intelligent Collaborative Fusion and Path Planning Module
#### 2.3.2 Global-Local Path Planning Coordination  
Path planning is one of the core tasks of the navigation system, with the goal of finding the optimal path from the robot's current position to the target position. The BBCN model combines the global path planning capabilities of Beidou with the local obstacle avoidance capabilities of binocular vision to propose a collaborative path planning method.  
1. Global Path Planning: Global path planning is based on the global position information from the Beidou navigation module, and the A* algorithm is used to generate an initial path from the starting point to the target point. The path cost function is \[f(n)=g(n)+h(n)\] (17) where \(g(n)\) is the path cost from the starting point to the current node \(n\); and \(h(n)\) is the heuristic cost from the current node to the target node, typically using Euclidean distance.
2. Local Path Planning: Local path planning is based on the depth map and obstacle recognition results from binocular vision, generating real-time obstacle avoidance paths. The local path represents the environment using a grid map, where cells occupied by obstacles are marked as impassable; the A* algorithm is used to calculate the local path from the robot to the target point. The cost function for the local path includes a dynamic obstacle avoidance weight: \[f_{\text{local}}(n)=g_{\text{local}}(n)+h_{\text{local}}(n)+w_{o}\cdot O(n)\] (18) where \(O(n)\) represents the obstacle risk at node \(n\), and \(w_{o}\) is the obstacle weight coefficient.
3. Global-Local Collaborative Optimization: The global and local paths are combined in the coordination module, and the final path is obtained through a weighted optimization formula: \[f_{\text{total}}(n)=\alpha\cdot f_{\text{global}}(n)+\beta\cdot f_{\text{ local}}(n)\] (19) where \(\alpha\) and \(\beta\) are the weight coefficients for the global and local paths.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 3 Methods and Results
### Literature Overview
#### 3.4.2 Classification in Terms of Country  
According to the information screened from these 28 research papers, the research on logistics space mainly focuses on developed countries, such as the United States (\(n=7\)), France (\(n=5\)), Japan (\(n=2\)), The Netherlands (\(n=2\)), Sweden (\(n=2\)), Flanders (\(n=1\)), Germany (\(n=1\)), Canada (\(n=1\)), Switzerland (\(n=1\)), Australia (\(n=1\)), Spain (\(n=1\)). The remaining three researches are also in developed areas of developing countries, such as China (\(n=5\)), India (\(n=1\)). Figure 3 shows the countries to which 28 literatures researching logistics space belongs. These countries are located in Europe, North America, Asia, and Oceania. They have long coastlines, developed ports, and advanced logistics facilities. The close relationship between economic development and logistics development is obvious. On the one hand, with full utilization of the economic strength, developed areas provides a solid base to develop logistics, bolstering logistic infrastructure development and industrial scales, improving logistic service quality, and keeping the rapid growth of the whole industry. On the other hand, logistics industry, as a new \"engine\" driving the fast development of national economy, can reduce the market transaction cost and integrate different resources of logistics industry, which then provides guarantee for the improvement of regional economic capacity [15].  
Table 4 shows the basic situation of some of the study regions in the 28 papers, including population, area, population density, and the change in the number of logistics facilities. We have found that research on logistics space is mainly concentrated in densely populated regions, and that these regions have perfect transportation infrastructure, proximity to ports and airports, and dense multimodal transport networks, which provide a good environment for the development of logistics. For example: Los Angeles has the largest container seaport complex in the United States [10]. Tokyo Metropolis has developed highways, and the seaport and airport gather a lot of traditional industries [5]. High population density represents high logistics demand. In the conventional mode, population is an important driving force for location of logistics activities [62]. In addition to Zurich and Randstad, there has been a significant increase in the number of logistics facilities in other regions. It was particularly noticed that there has been an inverse increase in the number of logistics facilities in the Tokyo area because small facilities near the city center have been replaced by large logistics facilities in the suburbs [5].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Years** & **Number of Articles** & **\%** \\ \hline
2016–2018 & 13 & 46 \\
2013–2015 & 8 & 29 \\
2010–2012 & 5 & 18 \\
2007–2009 & 2 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of papers through the years.
## 2 Material and Methods
### Landslide Prediction Models  
#### 2.4.1 Anfis  
The ANFIS is derived from the ANN and fuzzy logic [107]. The ANN model does not obtain the output data from making decision. Although it has automatic learning capability and the fuzzy logic is inverse of ANN model [108], the ANFIS algorithm has the advantages of both the ANN and fuzzy logic models on producing input and output data in one framework [109], and it is known as a commonly used data-driven model for solving nonlinear issues [110] in the fields of data processing and fuzzy control [111].  
This algorithm is based on the Takagi and Sugeno's type by using two \"If-Then\" rules as follows [112]:  
Rule 1: If x\({}_{1}\) and x\({}_{2}\) are A\({}_{1}\) and B\({}_{1}\), respectively, then f\({}_{1}\) = p\({}_{1}\)x\({}_{1}\) + q\({}_{1}\)x\({}_{2}\) + r\({}_{1}\).  
Rule 2: If x\({}_{2}\) and x\({}_{2}\) are A\({}_{2}\) and B\({}_{2}\), respectively, then f\({}_{2}\) = p\({}_{2}\)x\({}_{2}\) + q\({}_{2}\)x\({}_{2}\) + r\({}_{2}\).  
where A\({}_{1}\), A\({}_{2}\), B\({}_{1}\), and B\({}_{2}\), are the fuzzy sets, p\({}_{i}\), q\({}_{i}\) and r\({}_{i}\) are the parameters, x\({}_{1}\) and x\({}_{2}\) are input data and \(f_{1}\) and \(f_{2}\) are output data [113]. The ANFIS system includes six layers and the detailed descriptions on these steps can be referred in [112].
## 3 Results and Discussion
### Numerical Simulation of Stress-Relief Groove Utility Analysis
#### 3.1.4 Influence of Pressure Relief Tanks on Roadway Support  
Under stress conditions, displacement and stress effects are minimal with or without support. Support significantly reduces the shear failure plastic zone area. Figure 15 shows that the plastic zone volume with support is considerably smaller than without support. For a 2 m \(\times\) 2 m stress-relief groove at the rib corner, the plastic zone volume with support is 2.66 m\({}^{3}\).
## 4 Simulations and Results
### Security Features of Proposed System
#### 4.6.1 Data Integrity  
In our proposed system, data generated by vehicles is encrypted using symmetric key encryption. Data is stored in a distributed file system, i.e., IPFS, which returns the hash of the data stored in it. This hash is stored in blockchain, which ensures data integrity because it is not possible to tamper the data in blockchain.
## 2 Models and Methods
### Aerodynamic Model  
Generally, the aerodynamic forces acting on an aircraft can be calculated by the following formula:  
\[\left\{\begin{array}{l}L=\frac{1}{2}\rho v^{2}\cdot S_{W}C_{L}\\ D=\frac{1}{2}\rho v^{2}\cdot S_{W}C_{D}\end{array}\right. \tag{9}\]  
where \(L\) is the lift of aircraft, the \(D\) drag force of aircraft, \(C_{L}\) and \(C_{D}\) are the lift and drag coefficients, \(\rho\) is the atmospheric density. In this paper, the \(\rho\) value on altitude use the relevant section of the 1976 Standard Atmosphere, as described in reference [25; 26].  
As we known, the HALE SPA usually has the characteristics of large aspect ratio and its wing are larger than the fuselage. Therefore, \(C_{L}\) and \(C_{D}\) of this type aircraft are closely related to airfoil profile, angle of attack and Reynolds number. In order to simplified calculation, the \(C_{L}\) and \(C_{D}\) of SPA can have the following formulas:  
\[\left\{\begin{array}{l}C_{L}=0.9C_{L0}\\ C_{D}=C_{D0}+C_{Dp}+C_{Di}\\ C_{Di}=\frac{C_{L}^{2}}{e\pi AR}\\ e=1.78(1-0.045 AR^{0.68})-0.64\end{array}\right. \tag{10}\]  
where \(C_{L0}\) is the lift coefficient of airfoil, \(C_{D0}\), \(C_{Dp}\) and \(C_{Di}\) are the coefficients of the airfoil zero-lift drag, the parasitic drag and the lift-induced drag, \(e\) is the Oswald's coefficient, \(AR\) is the aspect ratio of SPA.  
In this study, the Zephyr 7 is the research object. Hence, the airfoil coefficient of aerodynamic characteristic refers to FX 63-137, 13.7% smoothed, Re = 20000 and the parasitic resistance coefficient \(C_{Dp}\) is 0.005. The detailed data is shown in the Table 1.
## 2 Related Works  
### 3D U-Net and Variants  
After U-Net [7] was proposed in 2015 and made extensive research and application in medical image segmentation, lots of U-Net-based variant networks have also proposed, the most representative of which is 3D U-Net [18]. The structure of 3D U-Net is shown in Figure 1.  
This model extends the previous U-Net (2D) and consists of the encoder-decoder architecture. The encoder part analyzes the input image and performs feature extraction and analysis. The corresponding decoder generates a segmented mask. This model supervises the extraction of a mask by minimizing a cost function. What distinguishes 3D U-Net from 2D U-Net is that, after 3D U-Net gets the input of volume data, 3D convolution block, 3D maxpooling, and 3D deconvolution block are used in turn to extract and restore its features. Besides, 3D U-Net adds batch normalization to avoid bottlenecks and to speed up convergence.  
Due to its out-performance and simple architecture, 3D U-Net is gradually studied and applied for medical image segmentation tasks [19; 20; 21; 22]. At the same time, there are also many different variants based on 3D U-Net. nnU-Net [23] proposed a self-adapting framework based on 2D and 3D vanilla U-Nets. [PERSON] et al. [24] proposed another 3D U-Net to accurately extract liver vessel from CT images. [PERSON] et al. [25] introduced the multi-scale supervision into the decoder and designed postprocessing method to reduce the obviously wrong pixels. [PERSON] et al. [26] redesigned skip connections between encoder and decoders to collect features with different scales. Then, they proposed UNet++ for semantic segmentation and instance segmentation.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 3 System Architecture
### Pilot-Based Channel Estimation  
A typical OFDM transceiver in a UWA channel is presented in the first subsection. A detailed description of the least squares channel estimator is given in the second subsection.
# A Wireless Covert Channel Based on Dirty Constellation with Phase Drift
## 2 Concept of Dirty Constellation  
The main idea for creating covert channels based on the dirty modulation results from the fact that the received signals do not have an ideal constellation. Instead, we see a radio channel and parameter imperfection caused by both the transmitting and receiving devices' influence on blurring (spreading) the received-signal constellations. This effect translates to phase and amplitude distortions and ultimately to an increase in BER.  
To increase the bandwidth efficiency, the \(N\)-ary PSK or QAM modulations are commonly used in telecommunications together with OFDM access. A time-frequency structure (waveform) of the OFDM signal provides a lot of space and possibilities for creating the covert channel, e.g., [26]. The suggested solutions include the use of the OFDM symbol waveform. When analyzing the QAM signal, the covert channel is provided based on the errors, which are the differences between the theoretical and the real points of the constellation. This results from environmental noise and hardware impairments. The theoretical, finite \(M\) number of constellation points corresponding to the \(N\)-ary PSK or QAM, in practice, has the form of the finite number of constellation point sets concentrated around the theoretical values without any distortions. Therefore, the channel will remain \"hidden\" as long as it is perceived as a noisy version of the carrier signal (i.e., the PSK or QAM signal) by the third uninformed party. It is important that after applying the covert information, the carrier remains distorted as little as possible so that the reception of the primary signal should be error-free.  
It is the idea of using the dirty constellation proposed in [30]. The bits of covert information are mapped into additional constellation points placed around the base (i.e., carrier) constellation points, which is illustrated in Figure 1. According to the assumption, these additional constellation points are perceived as noise/error by the uninformed user. The value of the covert information symbol is defined in relation to the carrier constellation point.  
The carrier constellation is marked with blue circles, while the constellation related to the covert subsymbols (i.e., covert message bits) is marked with red ones. The probability of detection by adversaries is limited by reducing the predistortion radius, \(r\). In addition, \(r\) can be changed (e.g., randomly) within a preset range. [PERSON] et al. for masking purposes used the fact that the QAM signal is transmitted by using the OFDM [30]. Therefore, each unused OFDM subcarrier may be a masking element providing more random (noiselike) in relation to the general character of the covert transmission. Additionally, [PERSON] et al. propose inserting angle rotation, \(\theta\), for successive OFDM harmonics, to increase the number of the received QAM constellation states for the subcarrier set. However, it does not change the number of constellation points if only one of the OFDM subcarriers is analyzed.
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 2 Related Work
[52] to detect abnormal segments in monitoring data. A novel hyperspectral anomaly detection method with a kernel isolation forest was proposed in [53]. The method was based on an assumption that anomalies, rather than the background, could be more susceptible to isolation in the kernel space. An improved computational framework that allows one to effectively seek the most separable attributes and spots corresponding to optimized split points was presented in [54]. [PERSON] et al. [55] introduced the so-called functional isolation forest, which generalized iForest to the infinite-dimensional context, i.e., the model dealt with functional random variables that took their values in a space of functions. [PERSON] et al. [56] proposed the deep isolation forest, which was based on an isolation method with an arbitrary (linear/nonlinear) partitioning of data implemented by using neural networks. The above works are only a some of the many extensions and modifications of iForest that have been developed due to the excellent properties of the method. However, to the best of our knowledge, there are no works considering approaches to incorporating an attention mechanism into iForest.
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
###### Abstract
in [21] proposed a series of RGB channels selection criteria to make the color generation process objective and generalize the application procedure to any processing algorithm. The current study advances this second line of research for colorization of thermographic images. As with previous cases, this work is based on the RGB color space to include information in the available channels, but on the contrary, an innovative approach is applied to produce the RGB sequence, which is subsequently processed by quaternion analysis to enhance the detection ability. The results obtained by applying different quaternion algebra techniques are evaluated by several metrics demonstrating they outperform the current state of the art.
## 3 The Proposed Control Strategies
### Case 1: Zero Circulating Currents  
One of the main aims of controlling MMCs is suppressing the circulating currents as much as possible. In this subsection, zero circulating currents are assumed. Consequently, using (1), (2) and (5), (6), the upper and lower arm currents will be equivalent with (7):  
\[i_{u(l)k}=0.5\sum\limits_{j=1,5,7}^{\infty}I_{mj}\cos\Bigl{(}\omega_{j}t+n \frac{2\pi}{3}+\beta_{j}\Bigr{)}\pm\frac{i_{dc}}{3}. \tag{7}\]  
According to (7), the achieved arm currents consist of harmonic components due to the output MMC currents. As a result, by substituting the relationships (5)-(7) into the dynamic Equations (3) and (4), the proposed control laws regarding to this case are:  
\[\begin{array}{l}v_{u(l)k}=\frac{v_{dc}}{2}-L_{t}\frac{i_{dc}}{3}-R_{t}\frac{ i_{dc}}{3}\\ \pm(0.5L_{t}+L)\sum\limits_{j=1,5,7}^{\infty}I_{mj}\omega_{j}\sin\Bigl{(}\omega_ {j}t+n\frac{2\pi}{3}+\beta_{j}\Bigr{)}\\ \mp(0.5R_{t}+R)\sum\limits_{j=1,5,7}^{\infty}I_{mj}\cos\Bigl{(}\omega_{j}t+n \frac{2\pi}{3}+\beta_{j}\Bigr{)}\\ \mp\sum\limits_{j=1,5,7}^{\infty}v_{mj}\cos\Bigl{(}\omega_{j}t+n\frac{2\pi}{3} \Bigr{)}\\.\end{array} \tag{8}\]  
Both proposed control laws in (8) will be affected by the magnitude and phase of various harmonic components of the output voltages and currents. Also, the dc-link voltage can shift the proposed control laws. The dc-link current can also contribute to the aforementioned shift. The proposed control strategy under zero circulating currents is depicted in Figure 2. According to this figure, three kinds of proportional-integral (PI) controller are utilized for regulating the magnitudes and phases of \(\{\beta_{j},I_{mj},v_{mj}\}\) in order to reach the desired values.