esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, it is difficult to expect extremely-precise time synchronization for all nodes because FlexRay and TT-Ethernet use average values to compensate for delays for time synchronization. In contrast, time synchronization in an Ethernet network can be accomplished by using the precision time protocol (PTP), which is specified by IEEE 1588 standard [16]. In the PTP, the time is synchronized by exchanging four messages between the master and slave nodes. The time-synchronization error can be controlled to units of nanoseconds depending on the type of timestamp, thereby permitting highly-precise time synchronization [16; 17]. Hence, while the time-synchronization method for a single-protocol network, either CAN or Ethernet network is established, heterogeneous communication environments, such as a CAN-Ethernet network, require additional work to achieve time synchronization. In particular, a CAN needs some type of synchronization unless each node runs AutoSAR while FlexRay and media oriented systems transport (MOST) have their own synchronization mechanisms built as a part of their protocols. For a CAN network, [PERSON] applied the time-synchronization mechanism of IEEE 1588 [18]. Next, [PERSON] presented a method for improving the quality of service (QoS) of the network in a vehicle that applies the time trigger technique of FlexRay and Ethernet [19]; in the study, external clocks of the Ethernet nodes and gateway were first synchronized through the PTP. Then, by setting the external clock of the gateway synchronized through the PTP as the global clock, the devices in the FlexRay area were synchronized using the time-synchronization protocol supported by FlexRay. However, the study conducted by [PERSON] used two synchronization protocols, and it was limited because a gateway with an external clock was necessary. Therefore, we focused on the time synchronization of CAN-Ethernet network because CAN is almost a de facto standard with no synchronization procedure except using AutoSAR. Furthermore, Ethernet with TSN functionalities can provide high bandwidth, time synchronization, and real-time capability. This study proposes a time-synchronization method for CAN-Ethernet-heterogeneous communication networks. A method for applying the PTP mechanism to CAN communication to achieve precise time synchronization and a method for correcting the processing delay_required by the gateway is proposed. Furthermore, a laboratory-scale network environment was configured to assess the synchronization performance, and the applicability of the proposed time-synchronization method was confirmed. This paper consists of five sections. Section 2 describes the IEEE 1588-based time-synchronization method for Ethernet networks, and Section 3 describes the proposed time-synchronization method for CAN-Ethernet networks. Section 4 presents the performance evaluation environment and results of the proposed time-synchronization method, and Section 5 lists conclusions of the study and describes the direction of future research.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment
#### 5.1.5 Packet Delivery Ratio  
The packet delivery ratio is the ratio of successfully delivered packets among the transferred packets. It is expressed as  
\[Packet\ delivery\ ratio=\frac{\sum\ Number\ of\ received\ packet}{\sum\ number\ of\ sent\ packets} \tag{27}\]  
Having the maximum number of packets delivered to the UAV is highly desirable to achieve better network performances. In our proposed algorithm, we considered data collection while the UAV is hovering to minimize data loss due to the limited contact time of the UAV and CHs.
# MonoMR: Synthesizing Pseudo-2.5D Mixed Reality Content from Monocular Videos
## 4 Performance Evaluation
### Accuracy of Depth Estimation  
First, we evaluated the depth accuracy of the proposed method. We perform performance evaluations based on two capturing scenarios (small space and large space).  
In the case of a small space, the subject freely walked in a square, with a space of approximately 1.7 m in width and approximately 3 m in height, and a Kinect was used to obtain the ground truth data. Five subjects (two female) were the participants in the preparation of the ground truth set. We obtained 600 frames of full HD images and depth information for each subject. The mean absolute error between ground truth and estimated results is 24.57 cm, and the results for each subject are shown in Figure 8.  
In the case of a large space, it's not easy to acquire the accurate ground truth from a real-world scene. Therefore, we rendered some synthetic ground truth composed of 3000 frames of images and the depth information using computer graphics. The mean  
Figure 8: Mean absolute error of depth estimation for each subject in a short-range space.  
Figure 7: Display of the generated content in the real world.  
absolute error between ground truth and estimated results is 76.04 cm, and the results for each distance section are shown in Figure 9.  
At a distance of less than 20 m, depth errors were less than 1 m. As shown in Figure 9, the error increased as the model moved away from the camera because as the distance of the ground truth image increases, the number of pixels that represent the same distance decreases. Thus, the error increases as the quantization error increases.
## 1 Introduction  
The exploitation of hydrocarbon resources from subsurface reservoirs has been a cornerstone of modern industrial civilization. As global energy demands continue to rise, the development and optimization of unconventional resources have taken on an increasingly critical role in meeting these demands. Among these unconventional resources, naturally, fractured tight reservoirs have gained significant attention due to their vast hydrocarbon potential. These reservoirs, characterized by low permeability and complex fracture networks, present unique challenges and opportunities for exploration and production. Therefore, the significance of tight reservoirs in the energy industry cannot be overstated. These reservoirs, which encompass a wide range of geological formations, including shale, sandstone, and carbonate rocks, hold vast quantities of hydrocarbons. The exploitation of these resources has transformed the energy landscape, particularly in regions such as North America, where the shale revolution has led to a surge in oil and gas production [1]. One key aspect that has garnered attention in recent years is the effect of externally applied stress on the multiphase flow characteristics within the naturally fractured tight reservoirs.  
Tight reservoirs, commonly referred to as unconventional reservoirs, have become the focal point of the energy industry's quest to meet the world's ever-growing energy demands [2]. These reservoirs are characterized by low permeability, which hinders the flow of hydrocarbons through the rock matrix [3]. Consequently, unconventional reservoirsrequire advanced drilling and stimulation techniques to make hydrocarbon extraction economically viable [4]. Naturally fractured tight reservoirs, in particular, are a subset of these unconventional reservoirs and possess dual challenges, i.e., their inherent low permeability and the presence of fractures that significantly affect fluid flow [5; 6; 7]. The flow of fluids in naturally fractured tight reservoirs is a multifaceted phenomenon, often involving the coexistence of different phases, including oil, gas, and water [8; 9]. Multiphase flow in such reservoirs is inherently complex, with interactions between the phases and rock matrix playing a decisive role in determining production performance [10]. The presence of fractures, which serve as preferential pathways for fluid flow, further complicates the picture. While the influence of fractures on multiphase flow in tight reservoirs is well documented, the impact of externally applied stressors has received comparatively less attention. External stresses, including tectonic forces, reservoir compaction, and hydraulic fracturing operations, can alter the subsurface stress field [11; 12].
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
###### Abstract  
Satellite-ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications. Nevertheless, current SemCom methods still struggle to adapt to varying channel conditions. To address this, we propose a SemCom transmission model based on a Channel Code-Book (CCB) for adaptive image transmission in diverse channel environments. Our model reconstructs and restores the original image by documenting fading and noise states under various channel conditions and dynamically adjusting the denoiser's model parameters. Extensive experimental results demonstrate that our CCB model outperforms three representative baseline models, including Deep JSCC, ASCN, and WITT in various environments and task conditions, achieving an advantage of more than 10 dB under high signal-to-noise ratio conditions.  
s _Sensors_  
202525269
## 4 Progressive Collapse Simulation Approaches for RC Structures
### Validation of the Modelling Techniques  
To validate the previously described modelling techniques, a one-third-scale three-storey four-bay RC frame tested by [PERSON] et al. [63] is employed. The configuration of the frame is shown in Figure 6. The middle column at the ground floor was replaced by a hydraulic jack and a concentrated load was imposed on the top of the column at the third floor. Subsequently, the hydraulic jack in the first storey was gradually unloaded to simulate the progressive loss of the middle column, in which the vertical displacement was recorded. More details can be found in the article from [PERSON] et al. [63]. Regarding the material mechanical properties, parameters obtained from the test are adopted [63].  
The difference between the imposed load P and the axial load N in the middle column, i.e., P-N, against the displacement is shown in Figure 7a, i.e., the pushdown curve. A good agreement is found between the test and the simulated pushdown curve, in which both the beam mechanism and the catenary action (CA) are well captured. The ultimate load-bearing capacities are 106.1 kN and 107.3 kN for the test and simulated results, respectively. In addition, the horizontal displacements at the beam-column joints in the first floor ('3-1', '3-2', '3-3', and '3-4' in Figure 6) are plotted and compared to the test results (positive values correspond with a movement towards the central removed column), as in Figure 7b. It can be seen that a good performance is obtained for the previously mentioned numerical modelling approach.  
Subsequently, the predicted dynamic capacity curve using the EBM is calculated and plotted in Figure 7a (curve 'EBM'). The ultimate dynamic capacity is 87.2 kN. To validate the EBM, a series of NTTA, i.e., the incremental dynamic analysis (IDA) technique, are carried out to determine the dynamic capacity curve by progressively increasing the imposed concentrate loads from a small value to the ultimate load-bearing capacity. The result is plotted in Figure 7a (curve 'IDA'). Two situations are considered to study the influence of damping effects (Rayleigh damping is adopted, in which the involved stiffness matrix is updated in each step), i.e., the damping ratios of DR = 0% and DR = 5%. The EBM curve is almost identical to the IDA curve in the case of DR = 0% (IDA-0%), in which the resistance is 87.0 kN. In the case of DR = 5% (IDA-5%), the IDA curve is almost identical to the EBM curve in the small displacement stages, while some differences are found at the larger displacement levels. The ultimate capacity is 93.0 kN for that case. Overall, a good performance for the EBM can be observed.  
Figure 6: Layout of the RC frame (dimensions in mm).
## 3 Integrated Driving HIL Simulator for Evaluation of Cooperative Eco-Driving System
### Cohda Wireless MK5  
In the proposed study, the Cohda Wireless MK5 device was used to transmit and receive real V2X communication messages in the CHEV, and to develop V2X applications. Two models of MK5 were used--on board unit (OBU) for vehicles and roadside unit (RSU) for infrastructures, each generating and transmitting V2V communication messages and vehicle-to-infrastructure (V2I) communication messages.  
The MK5 device used in the proposed test bed has two roles. The first role is a packet converter for packet emulation. Packet emulation is a means of converting virtual network packets generated by a network simulator into real network packets and vice versa, such that real network devices can receive virtual V2X communication messages through a real V2X communication channel, which is often used for lab testing. The packet converter converts a virtual V2X message into a real V2X message whenever the node mapped to the HV receives the V2X message from a surrounding virtual node in QualNet and transmits the message through a real V2X communication channel. By contrast, it also converts a real V2X message into a virtual V2X message whenever it receives the real V2X message from the device under test (DUT) and transfers the message to the virtual node of the HV in QualNet. The second role is the DUT for developing and operating cooperative eco-driving applications. DUT is one of the ECUs composing the CHEVs, enabling HIL testing of V2X communication protocols and V2X applications. DUT receives the V2X messages generated by the surrounding virtual nodes during the simulation, and then operates a cooperative eco-driving application and transfers the results of the application to driving HIL simulator. Figure 3 shows the V2X network emulation architecture using the V2X device and network simulator.
# Feasibility of Magneto-Encephulography Scan under Color-Tailored Illumination
## 3 Results
### Lamp Control  
LUME-20R lamp is designed to be controlled via Bluetooth communication, as displayed (Figure 2a). However, in this application, it is impossible to communicate with the lamp via a wireless signal because the lamp is installed inside the MEG-shielded room. The only available method of communication with the lamp is via wired mode. For that reason, the USB communication method has been selected from USB and serial RS232 (Figure 2).  
There were few factors involved in the selection of USB as the communication mode. Firstly, USB is the most common communication method these days, and most of the laptops do not have serial communication ports. Secondly, the distance is too long to control via serial communication with higher baud rate. A LUME-20R lamp micro-controller is configured to UART's baud rate as 115,200 bit per second. RS232 is able to communicate up to 1.2 m, when it is operating at 115,200 bit per second. However, USB 2.0 can communicate up to 5 m. The distance from the lamp to the outside controller is roughly 3 m. Therefore, for these two reasons, USB is the best method of communication available. LUME-20R lamp was connected via UART to USB converter to avoid noise associated with Bluetooth, as shown in Figure 2b.  
The software was developed using the Python software language. Python is a high level language, which has object oriented capabilities, and it supports other different styles of coding, too. Furthermore, it runs on any platform, such as Windows, Linux, Mac. It requires a very small amount of memory because Python is a scripting language [12]. In addition to that, the Python language interpreter is free, and many libraries are freely available. This project has used Python packages, such as ColorPy, Colorsys, and Tkinter. ColorPy is a free Python package, which converts physical attributes of light to RGB color space by using standard color mixing formulas presented in Appendix A[13]. The Colorsys package gives bidirectional conversion color values, such as hue, saturation, brightness, and HSV to RGB colors, according to formulae presented in Appendix A. Hue \(H\) has a range of 0\({}^{\circ}\) to 360\({}^{\circ}\), saturation \(S\) is from 0 to 1, and brightness value is \(V\); chroma \(C\) is given by \(C=V\times S\). Appendix B shows a practical example illustrating that the xy-chromaticity is independent from the luminance (brightness).
# Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors
## 2 Methods
### The Numerical Human Body Model
The electric constant was equal to \(\varepsilon\approx 54\) and the conductivity \(\sigma\approx 1.5\) S/m. These values are close to the average electric parameters of human tissues (\(\varepsilon\) = 52, \(\sigma\)= 1.8 S/m [20]). Values of \(\varepsilon\) = 54 and \(\sigma\) = 1.5 S/m were also used in the simulations presented later in the paper. A cylindrical human body model was used for further investigation to achieve independence of rotation angle phi and distance \(d\) of the antenna that was moved across the chest. For an irregular body shape that would be difficult (there is also a difficulty in defining and calculating distance \(d\)). For the simplified cylindrical phantom it is straightforward. The motivation for the following work is the fact that the values of \(S_{21}\) and \(S_{31}\) strongly depend on the chest node antenna position, not just the antenna distance. This is evident in the example of simulation results obtained for two different locations of the chest antenna, which is presented in Figure 4.  
Figure 4: Scattering matrix parameter _vs._ frequency (**a**) and position of the chest node (**b**). Figure 3: Human body model: (**a**) NMR Hershey; (**b**) simplified 6 cylinder model: H = 1800 mm, A = 500 mm, L = 790 mm, T = 836 mm, \(\phi_{1}\) = 110 mm, \(\phi_{2}\) = 160 mm, \(\phi_{3}\) = 315 mm. The antennas in this case were tuned to 2.4 GHz. The position 1 of the chest node corresponds to the initial location of the pocket in which the node was placed. In this case \(S_{21}=-79\) dB and \(S_{31}=-98\) dB. The position 2 of antenna No. 1 corresponds to the closest possible location of this antenna to the antenna No. 3 within the feasibility region (a possible location of the pocket). Even though the distance is smaller, the attenuation of the radio wave is higher due to propagation effects in the proximity of the body. For this case \(S_{21}=-81\) dB and \(S_{31}=-110\) dB. This is motivates us to carry out a thorough study of antenna placement with optimization algorithm and it makes the distance based approach inapplicable. The path loss also depends significantly on the surrounding multipath environment, but for clarity we neglect this issue in the paper and concentrate on a free-space case. A discussion of an indoor multipath propagation scenario can be found in [21].
## 2 Materials and Methods
### Analytical Techniques
#### 2.3.3 X-ray Photoelectron Spectroscopy (XPS)  
X-ray photoelectron spectroscopy measurements were performed in UHV (Pressure \(\sim\)5 \(\times\) 10\({}^{-10}\) mbar) system equipped with a hemispherical electron analyzer (SPECS, Phoibos 100-1D-DLD) and a non-monochromatized dual-anode Mg/Al X-ray gun. Survey and narrow scans of C 1s, O 1s, N 1s, S 2p, Na 1s, and Cl 2p spectra were recorded by using the X-ray source MgK\(\alpha\) at 1253.6 eV photon energy. Two analyzer pass energies were used, 10 eV and 20 eV, giving a full width at half maximum (FWHM) of 0.85 eV and 1.0 eV for the Ag3d\({}_{5/2}\) line, respectively. Spectra were accumulated and fitted using commercial software (SpecsLab Prodigy; Specs GmbH, Berlin, Germany). The XP core level peaks are deconvolved with a mixed Gaussian-Lorentzian function after a Shirley background subtraction. Novacron Ruby S-3B and Remazol Brilliant Blue R dyes have been characterized after being drop cast. Each dye was dissolved in triple distilled water and dried on a glass substrate, which had a coating of Indium-Tin Oxide (ITO) on its surface. The coating offered electrical conductivity to the substrate, reducing the chances of electrical charging occurring when measuring the samples.
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 2 Measurement of the Ocean Surface Current
### Measurement Method with Single Antenna SAR Data  
According to the above analysis, the Doppler centroids in different azimuth angles reflect the current information in different directions. However, single antenna full-aperture SAR data usually only provide the Doppler frequency in one direction. Therefore, the method in this paper firstly carries out sub-aperture processing to acquire sub-apertures image data representing different look directions, and then carries out Doppler centroid estimation, respectively. Furthermore, the equations are established according to the estimated Doppler centroids. Finally, the current vectors are estimated by the least squares method. This method can be divided into three steps: sub-aperture processing, Doppler centroid estimation and vector estimation of current. The flow chart is shown in Figure 2.  
In the first step, the input is Gaofen-3 single look complex (SLC) data. After implementing azimuth FFT of the input SLC, the sub-aperture data is extracted by aperture division in the Doppler frequency domain, then IFFT is performed to acquire L sub-aperture SLC in time-domain as the output of the first step. Besides, the azimuth angle \(\varphi_{k}\) corresponding to each sub-aperture is also the output and used as the input for the third step. The L sub-aperture SLC is inputted in step 2 to estimate the Doppler frequency of each sub-aperture, respectively. In step 2, each input sub-aperture SLC data is divided into \(M*N\) blocks, and the Doppler centroid frequency of each block is estimated. The Doppler frequency at the same spatial position block \((i,j)\) with different sub-apertures form a vector \(\mathbf{Y}_{i,j}\) as the output. Therefore, azimuth angle \(\varphi_{k}\) and vector \(\mathbf{Y}_{i,j}\) are the inputs of the third part. The coefficient matrix \(\mathbf{W}\) is generated by combining the Gaofen-3 system parameters, that is, wavelength, incident angle, flight speed, and the azimuth angle of L sub-apertures. Then, the vector equation of the unknown vector \(\mathbf{X}\) is established with vectors \(\mathbf{Y}_{i,j}\) and \(\mathbf{W}\). The unknown vector \(\mathbf{X}\) includes current vector information, so the current vector at each position in space can be calculated by solving the equation with the least squares method. The output of step 3 is the sea surface current field of the entire data. The specific processing process and the calculation of variables are explained respectively in the following subsection.