esa-sceva/satcom-chunk-collection huggingface dataset:
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 3 Experiment  
### Data Analysis and Preprocessing  
The dataset used in this study is sourced from publicly available automatic identification system (AIS) ship trajectory data collected by the United States Coast Guard, USA. The data is accessible at [[https://marinecadastre.gov/ais/](https://marinecadastre.gov/ais/)]([https://marinecadastre.gov/ais/](https://marinecadastre.gov/ais/)) (accessed on 3 April 2024). For this experiment, we selected 86,431,580 AIS records from 1 June to 10 June 2022, as our research subjects. Due to the large volume of data, the storage and preprocessing processes were implemented within a MySQL database.  
During the denoising process based on our data preprocessing methods, we set the reasonable speed range to between 0 and 40 knots (\(V_{max}=40\)), allowed for all possible headings, and considered global latitude and longitude ranges. According to these filtering criteria, a total of 565,710 trajectory points with missing values, outliers, or duplicates were identified and removed. After field selection and category filtering, the data distribution across different vessel categories was summarized (see Table 1). The results indicate that tugboats accounted for more than thirty thousand records, whereas cruise ships numbered only 5020.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of** & **Fishing** & **Towing** & **Sailing** & **Passenger** & **Tanker** \\ \hline Number of & 4,423,130 & 28,025,380 & 3,609,600 & 5,796,500 & 2,518,370 \\ track points & 10,460 & 33,880 & 13,150 & 9960 & 5020 \\ Number of & & & & & \\ ships & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of data volume statistics for various types of ships.  
To address the issue of imbalanced trajectory data, we randomly downsampled each vessel category to 5000 instances to improve the balance during model training.  
During the extraction of trajectory sequences, the speed threshold for anchoring points \(V_{T}\) was set to 0.3 knots, the time interval threshold for trajectory segmentation \(T_{seg}\) was set to 10 min, and the trajectory sequence length threshold \(P_{num}\) was set to 15. Based on these parameters, a preliminary extraction yielded 551,324 trajectory sequences.  
Subsequently, we performed undersampling on the majority classes (fishing, sailing, passenger, and tanker) based on the distribution of trajectory sequences across categories (see Figure 6). A portion of the trajectory sequences from the majority classes was randomly deleted to ensure that each vessel category had an equal number of sequences. Ultimately, the dataset was processed to contain 247,750 trajectory sequences, with each vessel category comprising 49,550 sequences.  
Statistically, the maximum length of the trajectory sequences \(L_{max}\) was found to be 60. Finally, the trajectory sequence dataset underwent min-max normalization, and was split into training and testing sets in an 8:2 ratio.
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 6 Comparison  
Table 2 compares the characteristics of the proposed handset antenna design and some reported 5G smartphone MIMO antenna designs in the literature with the recent states of the art [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. To the best of our knowledge, most of the reported sub-6 GHz 5G antennas are single-band operating, and only a few works have reported on the use of the dual-band or multi-band techniques for 5G smartphone applications. The main contribution of our work is that we propose a planar 8 \(\times\) 8 MIMO diversity antenna array with a triple-band function covering the LTE 41, 42/43, and 47 bands. As clearly shown, compared with the recently introduced MIMO handset antenna systems with planar and uniplanar structures, our proposed handset antenna provides better characteristics in terms of impedance matching and bandwidth and low-profile radiators.  
Figure 12: Calculated (**a**) ECC and (**b**) TARC results of the adjacent elements.  
Figure 13: Two-dimensional radiation patterns at (**a**) first (2.6 GHz), (**b**) second (3.6 GHz), and (**c**) third (5.8 GHz) resonances.  
The proposed design achieves improvements not only around tri-band impedance bandwidth but also offers polarization diversity function at different edges of the mainboard. This is mainly due to orthogonal placements of the adjacent PIFA resonators at different corners of the PCB. In addition, unlike the reported handset antennas in the literature, the presented antenna is set on the single (back) layer of the platform which makes the design easy to integrate with the handset circuit. Furthermore, due to the small clearance of the proposed handset antenna, its characteristics in terms of data and talk modes are not changed significantly; that is, the proposed eight-element antenna array has the advantage of the comprehensive performance, meaning that it can be applied well in future 5G mobile terminals.
## 2 Materials and Methods
### Dataset
#### 2.2.1 WRF Model  
The WRF model (version 4.2 applied in this work), a new-generation mesoscale forecasting model and assimilation system, plays a crucial role in wind energy assessment by simulating and predicting atmospheric circulation and wind field distribution. WRF can simulate the variations in wind fields in specific regions and periods by incorporating data such as topography, land use, and sea surface temperature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **HY-2C** & **CFOSAT** \\ \hline
10 Oct & 10\_10\_10:28:44 & 10\_10\_10:19:50 \\
16 Oct & 10\_16\_08:45:15 & 10\_16\_09:56:42 \\
18 Oct & 10\_18\_23:13:03 & 10\_18\_23:07:33 \\
24 Oct & 10\_24\_21:29:38 & 10\_24\_22:14:48 \\
25 Oct & 10\_25\_21:47:04 & 10\_25\_22:00:09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The time interval between the passage of the two satellites, HY-2C and CFOSAT, over the Yellow Sea was less than 3 h of transit time.
## 3 Wake-Up Radio Communication Protocols
### Passive Wake-Up Signal Packet  
Here, we describe a typical passive wake-up signal data frame and its composition. Although various technologies have different packet structure, we attempt to illustrate a generic structure which meets the standards and compliance requirements of diverse applications. Efforts [98] have been made in recent times to develop a standard packet structure for medical applications. The packet of a generic wake-up signal is shown in Figure 7.
# Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm
## 3 Workspace Analysis
### Workspace Computation
A comparison of the examples shown in Figure 3a,b, which were both based on the 5R MASR model, makes it clear that for the same number of points (20,000), the cloud generated with the beta distribution (b) has a much more homogenous spread, but the upper-outer boundary has been left out and the bottom and inner boundaries are vague. To be able to more accurately describe the workspace boundary, for various numbers of links, without having to increase the number of generated cloud points, a more generalized approach must be considered. Figure 3: Workspace computation of the 5R manipulator, with a uniform link length of 200 mm and a relative joint angle range \(\theta_{j}\in[-60^{\circ},60^{\circ}]\), using 20,000 Monte Carlo points with uniform and beta distribution functions. (a) Generating a workspace cloud with uniformly distributed points (green); (b) Using a beta distribution with \((\alpha,\beta)=0.43\); (c,d) combining the two regions produced with \((\alpha,\beta)=0.1\) (blue) and \((\alpha,\beta)=0.9\) (green) for 20,000 and 1,000,000 points, respectively. By analyzing the alteration of the workspace's cloud as a function of change in the (\(\alpha,\beta\)) parameters, we found that for all NR models for the MASK (i.e., where the highest was \(N=5\)), low parameter values (\(\alpha,\beta\to 0\)) accurately depicted the bottom and inner boundaries of the cloud, and high values (\(\alpha,\beta\to\)1) described the upper-outer boundary. This shows that no single parameter value can be used to accurately find all of the workspace boundaries. Instead, we suggest using two pairs of parameters where the work volume is the union of each cloud:  
\[U_{Beta}=Beta(\alpha_{1},\beta_{1},\frac{1}{2}N_{p})\cup Beta\left(\alpha_{2},\beta_{2},\frac{1}{2}N_{p}\right) \tag{5}\]  
where \(U_{Beta}\) is the union beta function, (\(\alpha_{1}\),\(\beta_{1}\)) and (\(\alpha_{2}\),\(\beta_{2}\)) are, respectively, the low and high parameter values, and \(N_{p}\) is the total number of cloud points generated, i.e., each sub-region is created with half of the total number of cloud points. For example, instead of creating a uni-parameter value cloud, as shown in Figure 3b, half of the points (10,000) can be generated with \(\alpha_{1}=\beta_{1}=0.1\) and the other half with \(\alpha_{2}=\beta_{2}=0.9\). The union of the low value region (blue in Figure 3c) with the high value region (green in Figure 3c) creates a cloud with much clearer boundaries, without changing the total number of generated points. The accuracy of the boundaries in this new approach is illustrated in Figure 3c and when increasing \(N_{p}\) dramatically to one million points (Figure 3d). The comparison shows that although the inner cloud was completely covered with cloud points, the shape and size of the boundaries remain practically unchanged.
# Demand Response Requirements from the Cultural, Social, and Behavioral Perspectives
## 6 Discussion, Solutions, and Outlook
### Outlook  
Results obtained in this paper point to numerous pathways for future research works that will help to improve the potential of future DR programs.  
Appropriate cost-benefit analysis frameworks should be developed to overcome barriers related to the lack of the understanding of DR. The DR provider's decision making should be accurately represented in such a framework. It is also possible to conduct thorough research on all prospective DR markets. Although there has been some progress in this direction, there is still a long way to go.  
The relative costs and advantages of each of DR programs are currently unknown. In different circumstances, an analysis for each DR program will provide power utilities with information on which programs are the most cost-effective in reducing peak demand. Thus, future studies could be conducted to help to determine when and where DR programs should be focused.  
The environment and climate changes were introduced in this paper as the motives for customers to participate in DR programs. As a result, power companies should consider whether or not to add marketing on the benefits of smart grid initiatives and DR technologies. Determining the impact of marketing concerning the benefits of DR technologies for the environment and society could be a future market research project. Moreover, developing the mathematical models and conducting quantitative studies in various cultural, social, and behavioral fields, could be one of the future research directions.  
Finally, as clarified in this paper, there are various cultural, social, and behavioral aspects, affecting the success level of DR that should be identified based on the characteristics of the system under study. Such studies are completely dependent on the specifications of the system. Indeed, people in different countries may have various habits, beliefs, and values, and DR programs should be designed considering their behaviors, besides the technical aspects. Therefore, different research studies should be conducted to specify the effective factors (including different aspects, such as cultural, social, technical, economic, etc.), determine the barriers, find the solutions, and also propose the most appropriate ways to enable DR.
## 3 Materials and Methods
### RFI Mitigation Technique
#### 3.3.1. Short-Time Fourier Transform (STFT)  
Let \(s(n)\) be a real-world signal with N samples and \(0\leq n\leq N\). The STFT of each segment \(k\) is the discrete Fourier transform (DFT) of s(n), as shown in Equation (7).  
\[S(k)=\sum_{m=1}^{N}s(m)e^{-j2\pi\frac{nk}{N}} \tag{7}\]  
The DFT is implemented by fast Fourier transform (FFT) is similar to that proposed by [PERSON] and [PERSON] [56, 57, 58]. The signal frames \(s(m)\) at regular intervals can be expressed as shown in Equation (8).  
\[s(m)=s_{h}(m)w(m) \tag{8}\]  
\(w(m)\) is the window function, \(1\leq m\leq M\). \(M\) is the analysis window length, \(M\leq N\). To improve the performance of the FFT algorithm, the signal \(s(m)\) is padded with zeros such that \(2^{(2^{N})}\geq M\). Here, \(s_{h}(m)\) is the signal frame expressed concerning the hop size. Hop size is the time advance from frame \(m\) to \(m+1\). \(s_{h}(m)\) is expressed as shown in Equation (9).  
\[s_{h}(m)=s(m+lh) \tag{9}\]  
\(l\) is the frame index, and \(0\leq l\leq L-1\). \(L\) is the total number of frames: \(L\leq N\).  
Given a sampling frequency \(f_{s}\), the linear frequency vector \((f)\) can be expressed as shown in Equation (10).  
\[f=\frac{nf_{s}}{N} \tag{10}\]  
Similarly, the time vector \((t)\) can be calculated using Equation (11).  
\[t=\frac{lh}{f_{s}} \tag{11}\]  
Therefore, given a signal \(s(n)\) with a window function \(w(m)\), a hop size \(h\), with \(N\) total samples, and that was sampled at \(f_{s}\) frequency, the STFT \(S(k)\), the frequency vector \(f\), and the time vector \(t\) can be calculated by Equation (7), Equation (10), and Equation (11), respectively.
# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 2 Related Works
First, the boundary box generated by YOLOv4 is used to automatically crop the image to generate the region of interest, and then, the weighted average of the current and previous instantaneous mIOU values is taken to improve the application of the mIOU method in traffic congestion detection. In addition to the aforementioned machine vision-based traffic congestion detection methods, there are other studies [29; 30] that approach traffic congestion detection from different perspectives. [PERSON] et al. [29] characterized all streets in Valencia based on the driving times of vehicles under varying degrees of congestion, using a real traffic model obtained from previous work. They conducted simulation experimentsusing SUMO to obtain the driving times of vehicles on different road segments under different traffic saturation levels. They used a regression strategy to adjust the curves and obtain an expression that describes the driving times. [PERSON] et al. [30] utilized the Traffic Telco Big Data (Traffic-TBD) structure to provide micro-level road traffic modeling and prediction. All of the above methods are targeted at urban road traffic congestion, but due to the differences between highway and urban road conditions, traffic congestion detection methods based on urban roads cannot be directly applied to highways, so further research is needed. Considering the above issues, [PERSON] et al. [8] utilized the traffic information provided by the Vehicular Ad Hoc Networks to propose a mobile model based on the Markov chain to solve the traffic congestion detection problem in multi-lane highways. [PERSON] et al. [31] considered that images taken by cameras on highways have richer scenes and used two classic convolutional neural networks, AlexNet and GoogLeNet, to classify highway congestion states. Due to the fact that cameras on highways are usually located at higher positions and focus on a larger field of view, the background area of the generated images is larger and vehicle detection is more difficult. In addition, expressways have stronger closure than urban roads, and once congestion occurs, it lasts longer, requiring an efficient method to detect traffic congestion in real time. Unlike the above articles, this paper proposes a new method for detecting traffic congestion on expressways based on an improved CrowdDet algorithm.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Semantic Aggregation of Evidence Based on a Capsule Network
#### 4.3.2 Evidence Aggregation Based on a Dynamic Routing Mechanism  
In the fake news detection task, high-level capsules are regarded as the representations of news (source information) authenticity, namely, category capsules. Specifically, there are two types of category capsules, namely, fake or true, in our capsule network. Each category capsule is assembled from the underlying evidence capsules using a weighted summation over all corresponding vectors. It can be described as follows:  
\[\mathbf{v}_{j}=squash(\sum_{i=0}^{m}O_{|ji|}\mathbf{W}_{ji}\mathbf{e}_{i}),j\in(0,1), \tag{15}\]  
where \(v_{j}\in R^{~{}d_{v}}\) is a category capsule. \(O_{|ji|}\) is the probability that evidence \(e_{i}\) supports that source information belongs to category \(j\), which can be calculated by a dynamic routing mechanism on original logits \(b_{ji}\). The specifics of this process are provided in Algorithm 1. \(\mathbf{W}_{ji}\in R^{~{}d_{v}\times np}\) is a learned parameter matrix. To enable the module of the category capsule to determine the probability that information belongs to this category and increase nonlinear characteristics, a squash operation is applied to compress the module length of the capsule to [0, 1].  
\[\mathbf{v}_{j}=squash\big{(}\mathbf{v}_{j}\big{)}=\frac{\|\mathbf{v}_{j}\|^{2}}{1+\|\mathbf{v }_{j}\|^{2}}\frac{\mathbf{v}_{j}}{\|\mathbf{v}_{j}\|} \tag{16}\]  
```
Input:\(\mathbf{W}_{ji},\mathbf{e}_{i}\) Output:\(\mathbf{v}_{j}\)
1: Init the coupling parameter \(b_{ji}==0\)
2:for each iteration do
3: Update \(O_{|ji|}=softmax(b_{ji})\)
4: Update all the class capsules based on Equation (15)
5: Update \(b_{ji}=W_{ji}\mathbf{e}_{i}\cdot\mathbf{v}_{j}\)
6:endfor
7:return\(\mathbf{v}_{j}\)
```  
**Algorithm 1** Dynamic Routing Mechanism
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 3 Preliminaries and Problem Definition
### Problem Definition  
Given a cross-domain spatiotemporal relational network \(G=\{V,E,T\}\), the network abnormal event prediction problem based on basic graph representation is to map complex network data (complex network data includes host nodes, in time series behavioral data composed of various events above, etc.) into a mixed-curvature space \(\mathbb{P}\) by learning a graph-embedding method \(f:V\rightarrow\mathbb{P}\) to obtain the low-dimensional vector representation of the event \(\mathcal{E}\) in the complex network data in the space \(\mathbb{P}\), and retain the node attributes and the relationship \(\mathcal{R}_{\ell}\) between nodes, and finally get the event sequence event vector sequence \(\mathcal{E}_{t}^{d}\), where \(d\) is the domain where the event occurs, and \(t\) is the timestamp of the relationship between the events. At the same time, the attention-based LSTM event prediction model is trained based on the event vector sequence \(\mathcal{E}_{t}^{d}\) to obtain a weighted representation of the event relationship, where \(\mathcal{E}_{t}^{d}=\left[\left(t_{c-h},e_{x}^{d}\right)\), \(\left(t_{c-h+1},e_{x}^{d}\right)\), \(\left(t_{c-h+2},e_{x}^{d}\right),\ldots,\left(t_{c},e_{x}^{d}\right)\right]\), \(d\in\mathcal{D}\) means a different domain, and \(e_{x}^{d}\in E^{d}\) indicates the \(x\) event or behavior that occurs under the domain \(d\) and predicts whether an abnormal event or behavior will occur between the future time range \(t_{c+1}\) to \(t_{c+h^{\prime}}\). The target indicator function is as follows:  
\[\mathbb{I}=p\Big{(}\Big{[}\Big{(}t_{c-h},e_{x}^{d}\Big{)},\Big{(} t_{c-h+1},e_{x}^{d}\Big{)},\Big{(}t_{c-h+2},e_{x}^{d}\Big{)},\ldots,\Big{(}t_{c},e_ {x}^{d}\Big{)}\Big{]}\Big{)} \tag{4}\] \[=\left\{\begin{array}{l}1,\ t_{c+1}\ to\ t_{c+h^{\prime}}\ abnormal event or behavior will occur\\ 0,\ otherwise\end{array}\right.\]  
specific:  
\[\begin{array}{l}p\Big{(}\Big{[}\Big{(}t_{c-h},e_{x}^{d}\Big{)},\Big{(}t_{c -h+1},e_{x}^{d}\Big{)},\Big{(}t_{c-h+2},e_{x}^{d}\Big{)},\ldots,\Big{(}t_{c}, e_{x}^{d}\Big{)}\Big{]}\Big{)}\\ =\mathbb{I}\Big{(}f\Big{(}\Big{[}\Big{(}t_{c-h},e_{x}^{d}\Big{)},\Big{(}t_{c-h+ 1},e_{x}^{d}\Big{)},\Big{(}t_{c-h+2},e_{x}^{d}\Big{)},\ldots,\Big{(}t_{c},e_{x }^{d}\Big{)}\Big{]}\Big{)},\text{threshold}\Big{)}\end{array} \tag{5}\]  
where \(f(*)\in[0,1]\) is the output of the model CDSTAEP, threshold is the threshold line of the model, \(\mathbb{I}(a,b)\) is the indicator function, when \(a\geq b\) is true, \(\mathbb{I}(a,b)=0\); otherwise \(\mathbb{I}(a,b)=0\).
# Different Formulations to Solve the Giesekus Model for Flow between Two Parallel Plates
## 3 Different Formulations to Obtain the Fully Developed Laminar Flow with the Giesekus Model  
This section presents different formulations to find the fully developed viscoelastic fluid flow between parallel plates using the Giesekus model. The first formulation is based on Schleiniger and Weinacht [10], the second one is a new formulation that solves the Giesekus equation based on the independent variable change (\(y\) for the tensor \(T_{xy}\)). With this change in the system, it is possible to obtain a restriction condition for the pressure gradient, showing that this model has some restrictions on the previous formulation. The third formulation is a high-order simulation (HOS) code using vorticity-velocity formulation and a log-conformation formulation adopted for the extra-stress tensor calculation to overcome the high Weissenberg number problem (HWNP).
## 1 Introduction
(2018) used a variable universe adaptive fuzzy controller with speed compensation to control the established system model considering the eddy current effect. [PERSON] et al. (2018) achieved the dynamic adjustment and parameter optimization of the existing nominal controller and improved the robustness of the suspension system. The current fluctuation caused by eddy current can be reduced by optimizing the control algorithm. However, this method has some limitations because the suspension force provided by the electromagnet has an upper limit. Facing the seriously attenuated suspension force, it is more effective to put forward improvement methods from the perspective of the structure of the suspension system. [PERSON] (2018) proposed a scheme, to add a permanent magnet on the suspension electromagnet at the front of the maglev train. The principle is to establish the magnetic field in advance through the permanent magnet. [PERSON] et al. (2018) proposed a new suspension module composed of five suspension magnets. The principle is similar to the reference ([PERSON] et al., 2018). [PERSON] et al. (2018) proposed two new schemes, which are changing the structure of the air spring and suspension magnet, respectively. After calculating the effectiveness of these two schemes, the improved suspension magnet scheme is used in the maglev train version 2.0 with a design speed of 160 km/h. Hardly any scholars put forward solutions to this problem from the perspective of rail structure. In fact, this is also a feasible idea. The rail of the Shanghai maglev line is made of 0.5 mm silicon steel sheets and effectively prevents the generation of induced eddy current ([PERSON] et al., 2018). For the EMS medium-and-low-speed maglev train, such a rail structure can be used for reference. The following contents are as follows. In Section 1, the formula of the suspension force considering the eddy current effect is derived. By observing the parameters of the formula, the factors affecting the eddy current effect are obtained. In Section 2, the experimental verification is carried out on the Tangshan maglev experimental line. In Section 3, three kinds of rail structures are proposed, and the effectiveness of these new structures is verified by the three-dimensional transient field simulation. Section 4 is the summary and prospect.