esa-sceva/satcom-chunk-collection huggingface dataset:
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 3 Discussion
### Future Research  
Since adaptations are shown to improve the Neurokit2 pipeline, it is very possible that other methods can also be adapted to process infant ECGs. Additionally, the Neurokit2 package is being continually updated and the interaction of newer methods [61] with infant ECGs should be considered. Some exploratory analyses of the HeartPy and Pan-Tompkins methods were carried out and were not initially encouraging (though they were not in-depth enough to draw concrete conclusions). Additionally, while the computational inefficiency of the EMD approach was a concern for longform ECGs, many studies have focused on shorter infant ECG signals where the processing time was less of a factor. Given the strong performance of the approach on Dataset A, it is very possible that small alterations in the EMD methodology (such as the criteria for IMF rejection) can prove to be a positive avenue for future research.  
The datasets captured here lay the groundwork for a future infant-specific approach for R-peaks, especially for machine-learning approaches. Additionally, they could also be of great use in future infant-specific analysis of the remaining ECG morphology.  
Infants have fast heart rates, and their movements and activities can add substantial noise to ECGs. Our method was developed particularly to address these issues for infant ECG recordings. However, our pipeline can be adapted for other applications in which researchers may need to deal with noisy data. For example, the pipeline was adapted by using a different high-pass filter in preprocessing (e.g., 0.5 Hz vs. 15 Hz) to allow the whole pipeline to work with adults [13]. It could be informative to acquire ECG recordings from a range of ages (e.g., 3-18 year olds) to further test our pipeline to account for different heart rate speeds [16] and investigate developmental trajectory through ECG recordings. Finally, further testing is needed to investigate the performance of the HR SQI and peak-correction algorithms on atypical heart rates (e.g., arrhythmias).  
Finally, sensor orientation and position, particularly for small wearable sensors, can have a great impact on different infant ECG complexes. A general analysis creating a consistent set of guidelines for wearable infant ECGs can be of great use to the scientific community, as can further validations of different aspects of this pipeline on the data collected at specific sensor orientations and positions.
## 4 Results
### Preliminary Analysis
\begin{table}
\begin{tabular}{l l c c} \hline \hline \multicolumn{1}{c}{**Command ID**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Absolute** \\ **Frequency** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Relative** \\ **Frequency** \\ \end{tabular} } \\ \hline
**ID\_EDIT\_MOVE** & Move selected objects or their copies & 369 & 16.0\% \\
**ID\_FINISH\_SEKTH** & Finish sketch & 339 & 30.6\% \\
**ID\_OBJECTS\_WALL** & Create a wall & 237 & 40.9\% \\
**ID\_FILE\_SAVE\_TO\_MASTER\_SHORTCUT** & Save the active project to the central model again & 182 & 48.8\% \\
**ID\_OBJECTS\_CURVE\_LINE** & Create a line & 169 & 56.1\% \\
**ID\_FILE\_SAVE\_TO\_MASTER** & Save the active project to the central model again & 125 & 61.5\% \\
**ID\_EDIT\_MOVE\_COPY** & Move copies of selected objects & 113 & 66.4\% \\
**ID\_EDIT\_C\_CURVE\_RECT** & Create a rectangle & 56 & 68.8\% \\
**ID\_SPLIT** & Divide walls and lines & 45 & 70.7\% \\
**ID\_REVIT\_FILE\_SAVE** & Save the active project & 44 & 72.7\% \\
**ID\_EDIT\_MATCH\_TYPE** & Copy the object type to other objects & 40 & 74.4\% \\
**ID\_OBJECTS\_DOOR** & Create a door & 39 & 76.1\% \\
**ID\_OBJECTS\_CW\_GRID** & Create a grid line in a curtain wall & 38 & 77.7\% \\
**ID\_OBJECTS\_MULLION** & Create a million & 33 & 79.1\% \\
**ID\_LOAD\_INT\_PROJECTS** & Load document into open projects & 33 & 80.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contributory commands most frequently used by the architectural designer. (in descending order by quantity of executions) with which at least 80% of the contributory command executions is reached. By adopting a simplified assumption whereby each contributory command makes the same contribution to the final product, it can be said that the Pareto principle applies to all the specialists except for the structural designer, who showed a more balanced number of executions of a variety of commands: the 80% threshold was reached with 31.3% of the contributory commands used. Although the architectural and plumbing designers attained 80% with 22% of their contributory commands, this was considered a fair approximation to the Pareto principle. The identification of each designer's preferred commands can be useful for detecting bad modeling practices or which actions should be given focus in further personalized training.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 5 Foude Similarity Test
### Hydrodynamic Characteristics Test
(2) The model is mainly composed of an outer polyethylene tube and internal counter-weight steel rings, with the shape and mass distribution being similar to that of a real tunnel tube. By maintaining the spacing between the steel rings and leaving a gap between the steel rings and the polyethylene tube, the impact of the steel rings on the overall stiffness of the structure can be effectively reduced. This is the first study to provide a detailed description of the design methodology, construction process, and similarity assessment for scaled SFT models, thereby offering a high level of reproducibility. (3) Following the fabrication of the model, its dynamic characteristics were tested. A series of experiments were conducted, including model mass tests, static load tests, free decay tests, and white noise wave tests in a water tank. The results of these experiments showed good consistency with the outcomes of numerical simulations, with relative errors all being less than 10%. This demonstrates that the model is capable of simultaneously satisfying both the Froude and the Cauchy similarity principle. (4) The hydrodynamic characteristics of the model were evaluated through white noise wave tests. Based on the frequency distribution observed in the model, the constraints imposed by the mooring cables resulted in a coupled interaction between the horizontal motion, vertical motion, and rotation within the SFT employing an inclined mooring configuration. The natural frequency of the horizontal acceleration of the model was the same as that of its rotational natural frequency, and there was energy distributed at the horizontal acceleration natural frequency within the vertical acceleration spectrum. This coupling effect can be explained by the motion pattern of a simplified four-bar linkage. In future work, the serviceability and fatigue behaviors should be considered according to the hydrodynamic analysis. Conceptualization, Z.L. ; methodology, Z.L. and H.R. ; software, H.R. and G.Z. ; validation, H.R. and Z.L. ; formal analysis, T.G. and Z.L. ; investigation, H.R. and Z.L. ; resources, T.G. and X.X. ; data curation, H.R. and G.Z. ; writing--original draft preparation, H.R. and Z.L. ; writing--review and editing, Z.L., G.Z. and X.X. ; visualization, T.G. and X.X. ; supervision, T.G. and Z.L. ; project administration, T.G. and Z.L. ; funding acquisition, T.G. and Z.L. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Natural Science Foundation of China (52108272), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX22_0045), Natural Science Foundation of Jiangsu Province (BK20210257) and National Key R&D Program of China (2023 YFB3711500). Not applicable. All data used are available from the corresponding author upon reasonable request. The authors declare no conflicts of interest.
# An Ultra-Wideband Vivaldi Antenna System for Long-Distance Electromagnetic Detection
###### Abstract  
Enlarging or reducing the antenna beam width of antennas can improve the positioning capability of detection systems. A miniaturized and easily fabricated ultra-wideband (UWB) antenna system for long-distance electromagnetic detection is proposed in this article. Two ultra-wideband Vivaldi antennae were designed. One was the transmitting antenna with a beam width of 90\({}^{\circ}\) or above, the other was a narrow beam antenna array with beam width less than 10\({}^{\circ}\), as a receiving antenna. Both proposed antennae feature broadside gain diagrams with stable radiation patterns and wideband impedance matching in the frequency range between 25 GHz and 4 GHz. After detecting their frequency and time-domain behaviors, the detection system can achieve measurements covering a radius of 30 m.  
Vivaldi antennas; wideband antennas; electromagnetic detection +
Footnote †: journal: Applied sciences
## 2 Method
### A Logit form Signal Control Policy  
Broadly speaking, a signal control policy can be defined as any method used to determine intersection signal settings as a function of vehicular flows approaching the intersection. Assuming a given layout of the control plan (number, type, and sequence of signal phases), signal settings typically consist of timing parameters such as cycle length, green splits, and possibly offsets in the case of coordinated systems. Consistent with the assumptions outlined in Section 2.2, in what follows we limit ourselves to the consideration of green splits, i.e., the phase green times expressed as fractions of the signal cycle length.  
In the present study, green splits are determined according to the following expression, which has the form of the well-known Logit model  
\[G_{i}=\frac{\exp(\gamma P_{i})}{\sum_{j=1}^{N}\exp\bigl{(}\gamma P_{j}\bigr{)} }\qquad\quad i=1,\ldots,N \tag{40}\]  
where  
\(i\) denotes the generic phase of the signal cycle;  
\(N\) represents the total number of signal phases;  
\(G_{i}\) is the green split assigned to phase \(i\);  
\(P_{i}\) is the traffic pressure in phase \(i\);  
\(\gamma\)\((>0)\) is a control parameter.  
We note that an expression similar to (40) for the determination of green splits has been adopted by [PERSON] et al. [37] within a different modeling framework. In general, traffic pressure is a measure of the request for capacity from vehicles moving in a given phase and can be defined in several specific forms [32]. For the purpose of the analysis presented in this study, we define pressure simply as the ratio of traffic flow to saturation flow, a quantity commonly termed flow ratio in the traffic engineering literature. The value of parameter \(\gamma\), which is fully controlled by the traffic manager, determines the sensitivity of the computed green splits to the phase traffic pressures. In the limit, as \(\gamma\to 0\), the cycle is evenly allocated among signal phases, regardless of traffic pressures. At the other extreme, as \(\gamma\rightarrow+\infty\), the green split of the most pressurized phase tends to unity. In spite of the evident formal analogy with the effect of the dispersion parameter in the Logit route choice model, it should be noted that the latter relates to drivers' behavior, while \(\gamma\) is strictly a control parameter.
# Uniform Pooling for Graph Networks
## 5 Conclusions  
We proposed a novel pooling method UGPool with a new point-of-view in selecting pooled nodes, which assigns a score to each node, and uniformly pool the neighboring nodes in score-space instead of pooling top nodes as in existing papers. UGPool method has permutation invariance to graph nodes and uses a consistent number of parameters regardless of the input graph size. By uniformly preserving the representative node features, our UGPool method outperforms other node-based pooling methods with an average accuracy gain of 1.08% and achieves performance comparable to edge-based EdgePool with an average reduction in runtime of 64%. The efficiency advantage of UGPool is more prominent in processing large-scale graphs with dense edges. Moreover, UGPool can be integrated with multiple GCN architectures and effectively improve the classification accuracy by 1.63% on average on multiple benchmark datasets.  
Our work provides a new perspective for the pooling method, which will promote uniformly coarsening graph-structured data, learning hierarchical graph embedding, and classifying the whole graph. For example, the proposed UGPool can be applied to coarsen and learn the hierarchical structure of graph data such as protein graphs, social network graphs, citation graphs, and brain network graphs. UGPool can also be applied to improve classification tasks of the above graph data by being integrated with different graph convolution models. In the future, it will be interesting to automatically generate the graph edges, instead of manually calculating it as currently done. Moreover, the pooling of dynamic graphs will be also the future direction. In that case, the graph structure and graph nodes are dynamically changing, which requires the pooling method to have dynamic scalability.  
Conceptualization, J.Q., L.L., and H.S.; methodology, J.Q. and L.L.; software, J.Q.; formal analysis, J.Q.; writing--original draft preparation, J.Q.; writing--review and editing, L.L., H.S., and D.H.; funding acquisition, H.S., and D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers 61773391 and 61722313), the National Key Research and Development Program (2018 YFB1305101), and the Science and Technology Innovation Program of Hunan Province (2018 RS3080).  
The authors declare no conflict of interest.
## 2 Study Area and Datasets
### Data Source and Processing  
The ES identifications were based on quantitative remote sensing data, together with additional land-use data, productivity data, observations of the wildlife spatial distribution, and multiple socioeconomic components. To represent these factors and make sure that the analysis covers TRHR, we used different resolution data (Table 1). Considering data availability and the size of the study area, all spatial datasets are represented in a uniform coordinate system. To match all types of data, we resampled all raster data to a resolution of 1 km. The GlobeLand30 dataset [50] was recombined into six classes for subsequent ESV assessments. Based on the Google Earth Engine (GEE) platform, remote sensing data were processed annually for calculating the RSEI. Based on the Google Earth Engine (GEE) platform, remote sensing data were processed annually for calculating the RSEI. Specifically, low-clouded images from March to July were selected and retained all pixels that are categorized as \"good\" (QA = 0) or \"marginal\" (QA = 1) quality in the QA layer. Principal component analysis, water body masking, and declouding were among the other processes applied to the images [2]. The Yearly Net Primary Productivity data have been preprocessed with atmospheric correction, and the MODIS Reprojection Tool (MRT) software provided by NASA was used for data conversion, image stitching, and batch cropping using Python. The NPP data for the study area at five-year intervals from 2000 to 2020 were finally obtained. Precipitation data [51] and vegetation fraction coverage data [52] were processed on the timescale from monthly to annually and transferred from.NC format to.tiff format.  
Figure 1: Land use types and the location of the study area.
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
###### Abstract  
The goal of full-reference image quality assessment (FR-IQA) is to predict the perceptual quality of an image as perceived by human observers using its pristine (distortion free) reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulting feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is explained. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with a small amount of data to reach high prediction performance. Our best proposal--called ActMapFeat--is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.  
full-reference image quality assessment; deep learning; convolutional neural networks +
Footnote †: journal:
# A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph
## 1 Introduction  
Graphs can describe many applications in the real world, such as social networks, communication networks, dependency among software packages, and debt networks, and detecting cycles in a graph is a fundamental algorithmic problem. However, the existing solutions cannot handle large-scale digraphs using single commodity personal computers (PC), because they often lead to out-of-memory exceptions. This paper presents a multi-threading parallel and iterative algorithm of detecting and removing cycles, and this algorithm can take full advantage of multicore PCs. The main contributions of this paper include:  
* It defines a new problem of detecting and removing cycles in a vertex- and arc-weighted digraph according to vertices' priority.
* It presents a multi-threading parallel and iterative algorithm to solve the problem. The algorithm avoids the out-of-memory exception by simplification and iteration, and it leverages the advantage of multicore computers by multithreading parallelism.
* It performs thorough experiments to show the performance of the proposed algorithm.  
The organization of the rest of paper is as follows. Section 2 presents the related work. Section 3 introduces the problem. Section 4 presents the detail of proposed algorithm and time complexity analysis. Section 5 illustrates the performance comparison among five different versions of the proposed algorithms, using randomly generated graphs and real world digraphs. The final section contains conclusions and discussions.
# Slipping Trend Prediction Based on Improved Informer
## 2 Preliminary  
In Figure 1, \(r\) is the wheel rolling radius; \(V_{w}\) is the wheel rotation angular velocity; \(V_{t}\) is the train's speed; \(W\) is the locomotive load, also known as the positive pressure exerted on the track by the wheelset; and \(F_{h}\) is the tangential traction force acting on the wheel-rail contact surface, but is also the adhesion force.  
\"Adhesion\" refers to the phenomenon that the wheel rolling has no relative slipping, and the wheel-rail contact remains relatively static during the locomotive's operation [19]. In Figure 1, when the train is running, the wheel rolls under the action of torque T, and the wheel-rail contact surface bears the positive pressure exerted by the wheel on the rail. Because the wheel and rail are elastic bodies, under the action of axle load, the wheel-rail contact surface produces elastic deformation, forming an elliptical contact area. The contact area is divided into a slipping zone and an adhesion zone. The relative velocity in the adhesion zone is zero, and the creep occurs in the slipping zone, which makes the actual running distance of the train less than the pure rolling distance. When the wheel rolls,tangential adhesion between the wheel and rail, namely, traction around the wheel, is generated, and is the only power causing the train to move forward. In order to measure the adhesion force between the wheel and rail under different creep rates, the adhesion coefficient \(\mu\) is defined as the ratio of the wheel-rail adhesion force to the vertical load on the wheel [20].  
\[\mu=\frac{F_{\mu}}{W\cdot g} \tag{1}\]  
The adhesion force makes the medium of the wheel-rail contact surface move relatively, and the train velocity is less than the circumferential velocity of the wheel rolling, which is called creep [21]. With an increase in applied torque, the area of the slipping zone increases. When a certain threshold is reached, the slipping zone will cover the entire contact surface, creep will rapidly develop into macro-slipping, and the wheel will slip [22].  
In order to measure the slipping degree, the slipping velocity \(V_{s}\) and the slipping rate \(\lambda\) are defined as follows:  
\[V_{s}=V_{t}-V_{w} \tag{2}\]  
\[\lambda=\frac{V_{s}}{V_{t}} \tag{3}\]  
Adhesion is a very complex phenomenon that is affected by many conditions. Under different conditions, the adhesion characteristics are different. Figure 2 shows the adhesion characteristic curve, showing the relationship between the slipping rate and adhesion coefficient under different rail surface conditions. At the point where the adhesion coefficient is the largest, the adhesion limit is reached. The adhesion limit divides the curve into two regions. The left side is the creep zone, and the right side is the slipping zone. In the creep zone, the wheel-rail contact characteristics are normal. If the creep rate exceeds the adhesion limit and enters the slipping zone, the wheel becomes prone to slipping.  
Figure 1: Schematic diagram of deformation of contact area.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 5 Experiments and Results  
### Numerical Simulation  
To illustrate the performance of the guidance laws based on the proposed algorithm, a series of tests in numerical simulation are conducted, and extensive comparison and analysis are performed. In the first step, the experiments on tracking stationary targets are carried out, then for tracking moving targets, target state estimations are conducted. At last, cooperative tracking simulation verifies the  
Figure 9: Cooperative target tracking using three unmanned aerial vehicles (UAVs).  
faster convergence of the proposed controller. In numerical simulations, base configurations are listed in Table 1.
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 2 Literature Review
### Handoff Techniques
#### 2.1.1 IP Passing Scheme  
IP addresses the switch between the vehicles when V-1 moves from the space of Base Station-1 (BS-1) towards BS-2 and V-2 moves from the area of BS-2 towards BS-1. The IP addresses are switched when these two vehicles traverse each other in inverse ways. A vehicle resources another vehicle behind it so as to play out a handoff. DHCP is capable of controlling the allocating of IP delivers to ensure extraordinary distinguishing proof after handoff is performed.  
Following 3 steps are necessary for checking the passed IP address.  
1. The IP address is produced by an authentic DHCP.
2. No manufacturing is done previously or amid the going of an IP address from vehicle to vehicle.
3. The IP address is checked at the receiving vehicle, on the off chance that its manufactured IP will be disposed of, and asked for from DHCP straight forwardly, rather than tolerating a passed IP from a vehicle.