esa-sceva/satcom-chunk-collection huggingface dataset:
# Discussions on the Processing of the Multi-Component Seismic Vector Field
## 2 Multi-Component De-Noising Methods with Respect to the Vector Seismic Field
### The Vector Wavefield Filtering Method Based on Vector Order Statistics  
Many studies have revealed that multivariate order statistic filters perform better in color image processing because they treat the color image as vector data whereas other filtering methods treat the color image as scalar data [54]. These multivariate order statistic filters can be classified into several categories according to the ordering method for multivariate data, such as the multi-channel \(\alpha\)-trimmed mean filter, multichannel Modified-Trimmed Mean (MTM) filter [55], Vector Median Filter (VMF) [56; 57], and vector direction filter [58]. A study by [PERSON] et al. [59] indicated that the VMF is more applicable to suppressing noise that has a long-tail distribution, such as negative index noise and salt-and-pepper noise; the MTM filter is more suitable for suppressing noise that has a short-tail distribution, such as Gaussian white noise.  
In recent years, [PERSON] et al. [60] applied the VMF to seismic data processing and separated blended seismic data; [PERSON] [61] used the VMF to eliminate direction anomalies of the local dip vectors of seismic events. In these two applications, however, the filter still processes every single component of the seismic data, and the vector is formed from the mathematical construction of some sampling points of adjacent traces or adjacent time, rather than a vector in physical space. [PERSON] et al. [30] and [PERSON] et al. [31] applied an extension of the vector order statistics-based filter to multi-component seismic data. First, according to the correlation between the vector signals of adjacent traces, the optimum local direction of the event was examined, and then, the vector points along the event were selected for vector order statistics-based filtering. Because every sampling point of a three-component detector records a vector, it is very intuitive, and there are practical advantages to adopting the vector order statistics-based filter to process three-component seismic data. Tests of the model data and real data showed that the noise-suppressing result using vector order statistics-based filtering is better than when using the signal-component approach [30]. The solid lines in Figure 5 show some hodograms of two-component seismic signal segments taken from the de-noised results with different methods, and the dashed lines are the hodogram curves of the uncontaminated seismic data. The first, second, and third columns are the results of low-pass filtering, single-component filtering, and vector order statistics-based filtering, respectively. From top to bottom, there are four seismic signal segments which are taken from different detectors and different events. We can see that vector order statistics-based filtering maintains the vector information of multicomponent seismic data more effectively than the single-component filtering.
## 5 Results
### Analysis of Simulation Results  
#### 5.3.1 Results from Simulation Experiments Involving Snow  
The setup of this and the following simulations is exactly the same as for rain and fog. Figure 15 depicts the detection rate under snowfall, showing that the detected number of points decays to 0 around precipitation of 28 mm/h. This is unlike the results seen in Figure 14 for rain, which show a more stable signal returned. Given that water expands when it freezes, the density decreases, and the effective cross-section becomes larger; therefore, stronger scattering events occur.  
Figure 14: Comparison of real and simulated lidar during rain. The vertical and horizontal spread and shadows stand for the standard deviation of the number of detected points and was extracted from the references.
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 6 Discussion and Future Work  
As shown in the evaluation results, multiple rounds of sensing-data exchanges among vehicles result in a small number of reports from vehicles to the edge server. On the other hand, a large number of exchange rounds incurs a longer time for the edge server to extract the object sensing information.  
Some object sensing information may need to be quickly shared among vehicles and the edge server, such as the results of obstacle detection and emergency-related information. On the other hand, there is also other sensing information that is considered to be tolerant to a small delay within a few hundreds of milliseconds. In this paper, we suppose that the object information such as communication quality, or the free space between vehicles, is not quickly changed within a few hundreds of milliseconds.  
From the evaluation results, we find that even a small number of rounds of sensing-data exchanges are effective to reduce the traffic load at the edge server. Hence, we can select a small number of exchange rounds in case the object sensing information varies quickly.  
The proposed method has a low level of computing complexity at both vehicles and the edge server. First, in the local extraction of the minimum factor of sensing results, a vehicle mainly needs to find and update the minimum sensing value in a collection of sensing results obtained in the exchange sensing information among nearby vehicles. The maximum number of sensing results collected at a vehicle is not larger than the number vehicles in the road region, in which a BS/AP collects sensing information. Second, for an edge server to enable the global extraction of the minimum factor of the sensing results, the edge server only needs to find the minimum sensing value in a collection of sensing reports obtained in the exchange sensing information among nearby vehicles. The maximum number of sensing reports to the edge server is also not larger than the number of vehicles in the road region, to which a BS/AP collects sensing information.  
In this paper, the study topic focuses more on the cooperation mechanism of information sharing and extraction among vehicles and the edge server. We suppose that the sensing data at each vehicle are reliable. As for future work, we will examine the reliability issue of the information sharing and extraction from vehicles.
# Application of Additive Manufacturing Technology for Chair Parts Connections
## 2 Materials and Methods  
### Materials  
The wooden components of the experimental samples consisted of one leg part and two stretcher parts were made from larch wood (_Larix decidua_ Mill.), the material used to manufacture a set of chairs in the frame of Faculty of Furniture Design and Wood Engineering at Transylvania University of Brasov. The objective of the research is to replace the classical mortise-tenon joint between the leg and stretcher with the 3D-printed connector designed for this purpose. The classical mortise-tenon joint and the dimensions of the sample used in the experiment as a reference one are presented in Figure 1a, and the chair assembled parts using the designed connector are presented in Figure 1b.  
As seen in Figure 1a, the sizes of the piece of the leg in the assembly was 50 mm \(\times\) 35 mm \(\times\) 35 mm, and the sizes of the stretcher including the tenon were 222 mm \(\times\) 35 mm \(\times\) 22 mm, and without tenon were 210 mm \(\times\) 35 mm \(\times\) 22 mm. The larch wood had a moisture content of 9.2%. A tight fit was used between the wooden parts and connector. Mortise and tenon were glued with commercial polyvinyl acetate Novobond D2.  
The material selected to be used for 3D printing with SLS technology is DuraForm PA Plastic powder manufactured by 3D Systems, 2 nd Floor, West and South Wings, The Maylands Building, 200 Maylands Avenue, Hemel Hempstead, Hertfordshire. HP2 7 TG, United Kingdom. The characteristics of this material, according to the technical sheet of the material, are presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Specific Gravity, g/cm\({}^{3}\) & 1.00 \\ Moisture Absorption—24 h & 0.07\% \\ Tensile Strength Ultimate, MPa & 43 \\ Tensile Modulus, MPa & 1586 \\ Elongation at break, \% & 14 \\ Charpy Impact _Unmodel_, J/m\({}^{2}\) & 336 \\ Charpy Impact _Noched_, J/m\({}^{2}\) & 32 \\ Flexural Strength, Ultimate MPa & 48 \\ Flexural Modulus, MPa & 1387 \\ Shore Hardness & 73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of DuraForm PA Plastic.  
Figure 1: Dimensions of samples prepared for the experimental research: (**a**) Reference sample with stretchers and legs mortise–tenon jointed; (**b**) Assembled leg and stretchers with the 3D-printed connector (up) and connector’s dimensions (the yellow arrows represent the limit of introducing the wooden part into the connector).
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 3 Establishment of Safety Potential Field Model in Bridge Area
### Superposition Field Model  
By superimposing Equations (1), (5), (7) and (12), the form of each safety field after superposition, expressed by Equation (14), can be obtained. Using the ship sailing direction as the \(x\)-axis and the vertical ship sailing direction as the \(y\)-axis, the coordinate system can be established. Figure 6 shows the superimposition field model of the water area in the bridge region, where red represents navigation marks, black squares represent docks, solid and dashed lines represent gridding of the water area, and simple ship icons represent up and down ships. By comprehensively considering the interactions of the potential energy field \(E_{Si}\), the kinetic energy field \(E_{Di}\), the boundary field \(E_{R}\) and the behavioural field \(E_{Hi}\), the superimposed fields provide a comprehensive safety assessment for ships navigating in the bridge area.  
\[E_{T}=E_{Si}(x,y)+E_{R}(x,y)+E_{Di}(x,y)+E_{Hi}(x,y) \tag{14}\]  
Each field strength describes the potential navigational risk posed by the corresponding type of traffic factor in an actual navigation scenario.
## 3 Compressive Sensing for ECG Signal  
Knowing that many signals satisfy the sparsity property, the applications of CS can be listed as follows: speech and audio signal processing [95; 96], underwater signal processing, power system monitoring [97; 98], acoustic and linear frequency modulated signal processing [99], image reconstruction [100; 101; 102; 103], radar and communications [104; 105; 106], pattern recognition [107; 108; 109; 110; 111], video processing [112; 113], micro and nano electronics and VLSI [114; 115; 116] and biomedical applications [117; 118; 119; 120; 121; 122].  
As this paper relates to ECG signal compression, this section gives some basic ideas of the heart and its related function. The heart is divided into four chambers: the left and right atrium and the left and right ventricles, [123; 124] and some of the most commonly occurring heart diseases are:  
* Coronary artery disease (Blood vessel disease)
* Arrhythmias, problems related to rhythm of Heart
* Congenital heart disease (defects at birth)  
Cardiovascular disease (CVD) refers to a group of illnesses that affect the heart and blood vessels [125; 126; 127]. The ECG records the electrical activity of the heart [128; 129; 130]. The history of the ECG dates back to 1781, and the machine traced the electrical exertion of the human heart for the first time [131; 132; 133; 134; 135].  
It is common to use online public databases such as NYU Langone's Electronic Medical Record, MIT-BIH database, PhysioNet, Physikalisch-Technische Bundesanstalt (PTB), The American Heart Association (AHA) database etc. to evaluate CS algorithms for ECG signals [136]. Arrhythmia is one of the most frequent cardiological diseases [137; 138; 139; 140; 141]. Arrhythmias can be classified in many different ways but in general it can be categorized in two principal ways:  
* Origination of arrhythmia in the heart
* Whether the arrhythmia increases or decreases the heart rate [142; 143; 144].  
The domain conversion approach is a type of compression where the time domain signal is translated to a frequency or other domain, and data compression is applied following the conversion. Wavelet transforms, Fourier transforms, and discrete cosine transforms are some of the transform examples. The majority of lossy compression algorithms are based on domain conversion [145; 146; 147].  
In general, the ECG signal is not sparse. The sparse representation approach will be used since the frequency domain ECG signal has appropriate sparse qualities. Figure 6 represents original ECG and its sparse representation [148].  
Figure 6: Original and Sparse representation of the ECG signal.
## 4 Simulation Results and Discussion
### Simulation Results in the Case with Constructive Constraint  
In this subsection, we consider the BSN deployment scenario where BSNs move inside a realistic BSN deployment area with constructive constraints in hospital. Figure 9 shows the floor map of the UK Good Samaritan Hospital Emergency Department area. The standard operational process of emergency is as follows: (1) the patient first registers at the registration counter; (2) the patient then waits for triage at the waiting area; (3) after the triage, the patient goes to the health information center to get preliminary consultations and is later checked in the pathology lab by doctors. When BSNs are deployed in the hospital Emergency Department, BSNs will be operational during processes (2) and (3). One possible route of the BSN user is shown in Figure 9. In the simulation, a BSN user follows certain predefined routes, leaving only the movement direction to be predicted.  
Figure 10 shows the comparison of handover rate in the BSN deployment scenario with constructive constraints. It can be seem from Figure 10 that the handover rate of the proposed scheme is lower than  
Figure 8: Comparison of the number of outage in the BSN deployment case without constructive constraints.  
that of the case without constructive constraints (see Figure 7). This is because when the route of the BSN users is known, the only predicted parameter is movement direction, resulting in more accurate position prediction. Figure 11 shows the number of outages in the case with constructive constraints. For our proposed scheme, the number of outages in Figure 11 is lower than that of the case without constructive constraints (see Figure 8). Similarly, this is because with more accurate position prediction, handover can be performed promptly to avoid outage.  
Figure 10: Comparison of handover rate in the BSN deployment case with constructive constraints.  
Figure 9: Floor map of Emergency Department in UK Good Samaritan Hospital.
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 3 DR-Net and CTAM  
### DR-Net  
The video images obtained by the onboard video monitoring system contain less frontal information of crew members, which is limited by the deployment location of the cameras. Crew members work in complex environments and their limbs are often obscured by equipment and cargo, which leads to difficulties for the camera in capturing complete body features. Severe weather reduces the clarity of monitoring images and further increases the difficulty of feature recognition. These factors result in fewer crew features obtained through surveillance videos, making feature extraction difficult and detection accuracy low. The lack of information can be compensated for by increasing the number of layers of the feature extraction network, but it can easily lead to the problem of overfitting and poor generalization ability.  
In this paper, a new feature extraction network, Dense Residual Network (DR-Net), is constructed by combining the dense block and residual block to improve the feature extraction capability of crew transgression behavior.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 4 The Model of THz Signal Attenuation in Plasma  
To explain the different antenna radiation performances, a simple model was established. Since plasma parameters along the wave transmission path are strongly inhomogeneous, the propagation constant for the concerned signals in the plasma sheath is expressed as [2]:  
\[\gamma_{p}(l)=\alpha_{p}(l)-j\beta_{p}(l) \tag{6}\]  
The transmitted signal is obtained by:  
\[Trans=\frac{\left|E_{t}\right|^{2}}{\left|E_{0}\right|^{2}}=\frac{1}{\left| \exp[\int_{0}^{L}2\gamma_{p}(l)dl]\right|} \tag{7}\]  
where \(L\) is the total length of the transmission path, \(E_{t}\) is the electric field for the transmitted wave, and \(E_{0}\) is the electric field for the incident wave. With Equations (6) and (7), the transmitted signal intensity in different plasma sheaths can be obtained with the scattering matrix method (SMM).  
Figure 5 shows the signal attenuation in plasma sheaths. The phenomenon is known as the'skin depth'. The signal attenuation relates to electron density and electron collision frequency according to the above equations. However, the order of magnitude of maximum electron density and maximum electron collision frequency of the two plasma sheaths are almost the  
Figure 4: The radiation pattern of the antenna in plasma for different vehicle shapes and in vacuum.  
same. In such cases, the plasma thickness becomes the determinant of signal attenuation. The thickness of the sharp-coned vehicle plasma sheath is smaller. Therefore, the performance of the sharp coned vehicle antenna is better. It should be noticed that the model is two-dimensional. Therefore, the signal attenuation is not the same as the result in Figure 4.
## 3 Results
### FRM4 DOAS Validation and Performance Assessment
As can be seen from Figure 13, MAX-DOAS AOD retrievals follow the temporal evolution of the sun-photometer AOD well. During the first days, agreement with the partial AOD is clearly better than with the full AOD as expected. When evaluating individual MAX-DOAS retrievals, there is a clear tendency for MMF and MAPA 1.0 retrievals to underestimate sun-photometer AODs, whereas the other two MAPA retrievals are closer to the validation data. This could be interpreted as support for the need for an O\({}_{4}\) scaling factor as, for example, discussed in [51]. As expected, AOD values vary widely during cloudy days, and no validation data are available, as sun-photometer measurements are only possible during clear-sky periods. While the scope of this validation exercise is limited to a few days and one location, it shows the overall good performance of the FRM4 DOAS system for aerosols, NO\({}_{2}\) and HCHO retrievals. Data from instruments with different characteristics could be ingested, and results are consistent with long-path and CIMEL validation data. The deviations observed as well as the problems with some oscillating profiles, challenges when NO\({}_{2}\) is constrained to a very shallow layer and limited sensitivity above 2 km are typical for MAX-DOAS retrievals and not specific to FRM4 DOAS. The large number of retrievals flagged as being invalid is a serious problem and has therefore been addressed in more recent versions of the processor (see also Section 2.3.7).
# A UAV Wind Field Perception System Inspired by Biological Perception
## 1 Introduction
The wind parameter measurement resolution is around 300 m. Due to the inability to detect the airflow angle, the recorded wind speed is a scalar, necessitating unique flight procedures to provide a measurement of the horizontal wind with subpar horizontal precision. However, due to their flight characteristics, such drones have a limited battery life and range. As mentioned above, ultrasonic wind measuring equipment has high measurement accuracy, but it can only measure wind speed in the horizontal direction. The flow field around the rotor can easily disturb the installation on the multi-rotor UAV, necessitating the installation of a longer support rod, which could compromise the UAV's stability and safety. The usage of a fixed-wing UAV platform must therefore be taken into consideration for the task of continuously measuring the wind  
Figure 1: (**A**) Albatross’s trajectory in the wind field [4], (**B**) Albatross’s unique nostril structure. field over a wide area. The above research status shows that the technology of using UAVs to measure wind fields has attracted more and more attention from scholars at home and abroad. Among the current measurement schemes, only the German M2 AV meteorological detection drone equipped with a miniature five-hole airspeed meter has realized the detection of three-dimensional wind field information. Its measurement accuracy is high, and the frequency and resolution of wind field detection are higher than other methods. There are many restrictions in the actual usage process of the micro five-hole airspeed meter because it has high requirements for the processing accuracy of the sensor and also has to adjust the measurement data under various airflow situations. The horizontal airspeed zeroing method and the two-dimensional wind field calculation method currently used have problems, such as low measurement frequency, incomplete acquisition of wind field information, and low measurement resolution. Therefore, this paper studies the three-dimensional wind field measurement technology based on the wind vane sensor and pitot tube airspeed meter. By evaluating and comparing existing wind field measurement methods and UAV-based wind measurement schemes, a real-time wind field perception scheme based on UAV platforms in this study was proposed, system integration research was carried out, and wind tunnel experiments were conducted to test the wind field. A system integration study was carried out, and a wind tunnel experiment was conducted to evaluate the performance of the wind measurement system. The main research work of this paper is as follows: (1) analysis of wind field measurement scheme based on UAV, (2) modeling and prediction of wind field environment, (3) real-time measurement system for three-dimensional wind field information., (4) the main sensors of the measurement system are evaluated and calibrated based on wind tunnel experiments, (5) flight experiment of the three-dimensional wind field measurement system.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 4 Numerical Analysis
### Simulation Parameters  
The simulation area is within radius of 500 m where the BS is deployed in the center of the cell and CUs and D2D pairs are randomly distributed in the simulation cell. In the simulations, the CUs, D2D pairs and BS are set to the maximal transmission power of 21 dBm, 21 dBm, and 27 dBm, respectively. Slow fading with a standard deviation of 8 dB and fast fading subjecting an exponential distribution with unit mean are considered as the channel gain coefficients. The noise power spectral density is assumed to be \(-144\) dB, and the number of CUs and D2D pairs are 10. To the Monte Carlo experiment, the results in each graph are obtained from 2000 channel implementations. Simulation parameters are elaborated in Table 1.  
To validate the effectiveness of the proposed JUAD resource allocation scheme, we compare our scheme with only reuse uplink resources (OU) and only reuse downlink (OD) resources schemes.  
* OU: A two-step resource management scheme is proposed to optimize transmission power of D2D pairs and the spectrum efficiency of the network in [31]. Firstly, the interaction between BS and D2D pairs is modeled as a two-level Stackelberg game to get the best transmission power for each D2D. Secondly, the uplink resource allocation algorithm based on the Hungarian algorithm is proposed to assign spectrum to each D2D pair.
* OD: A social-aware jamming allocation for D2D multicast secure communication is proposed in reference [32]. Firstly, a novel D2D cluster and jammer formation scheme is designed, which takes account of the physical domain and the social domain. Secondly, a joint optimal power control and jamming allocation is obtained, and the Hungarian algorithm is introduced to provide the final solution.