esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
## 2 Materials and Methods
### Spatial Domain
#### 2.2.4 Selection of Optimal Index  
Scale Factor  
By extending the time-domain expression of BWT to the frequency domain, Equation (1) changes into:  
\[F\{cvt[x(t)]\}=F\{\frac{1}{\sqrt{a}}\int\limits_{-\infty}^{+\infty}x(t)\phi^{ *}(\frac{t-b}{a})dt\}=\frac{1}{\sqrt{a}}F[x(t)]\cdot F[\phi(\frac{t-b}{a})]= \sqrt{a}F(\omega)\psi(a\omega)\cdot e^{-j\omega b} \tag{11}\]  
where \(F\) denotes the Fourier transform.  
In essence, BWT is a set of multi-scale filters that can control the passband by changing the scale factor. The correlation between the scale factor and the central frequency of the passband is as follows:  
\[F(a)=\frac{F_{\mathrm{C}}}{a} \tag{12}\]  
where \(F(a)\) is the central frequency corresponding to the scale factor \(a\) and \(Fc\) is the initial center frequency of the mother wavelet.  
Theoretically, the optimal scale factor should be selected where the corresponding center frequency falls at the second harmonic of the UCA. We verified this hypothesis by traversing each scale factor. The optimal scale factor with different bubble wavelets under different transmit frequencies is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Transmit** & **Central Frequency of** & **Optimal Scale** & **Central Frequency after Bubble Wavelet** \\
**Frequency (MHz)** & **Wavelet (\(Fc\))** & **Factor (\(a\))** & **Transformation (BWT) (\(F(a)\); in MHz)** \\ \hline
3 & 0.9394 & 0.16 & 5.87 \\
4 & 0.6133 & 0.07 & 8.76 \\
5 & 0.7869 & 0.08 & 9.83 \\
6.25 & 0.8750 & 0.07 & 12.5 \\ \hline \end{tabular}
\end{table}
Table 1: The central frequency of the bubble wavelet with different transmit frequencies and their optimal scale factors.  
Eigenvalue  
As is shown in Figure 3, the maximum eigenvalue curve of the UCA region is similar to a Gaussian function. The expression of the maximum eigenvalue curve can be represented as:  
\[y=c*exp[-(\frac{x-\mu}{\sigma})^{2}] \tag{13}\]  
where \(x\) is the depth parameter; \(\mu\) is the mean value; \(\sigma\) is the variance; and \(c\) is the amplitude. The eigenvalue of the UCA region can be set as \([\mu+e^{*}\sigma,\,\mu-e^{*}\sigma]\), where \(e\) is an adjustable parameter.  
Taking the area at the width of 10 mm as an example, the eigenvalue curve and the fitted curve is shown in Figure 5, where \(c\) = 5.4e35, \(\mu\) = 32.3, \(\sigma\) = 4.2, and \(e\) is chosen as 1.  
Similarly, we fit the ESBCF curve by a Gaussian function. The ESBCF threshold can be set as \([\mu+e^{*}\sigma,\,\mu-e^{*}\sigma]\), where \(e\) is an adjustable parameter. Taking the area at the width of 10 mm as an example, the eigenvalue curve and the fitted curve is shown in Figure 6.
## 1 Introduction  
Chemical mechanical planarization (CMP) has become an essential process in the semiconductor industry, in keeping with the industry's trend toward increased miniaturization and higher integration. The CMP process is carried out by a combination of mechanical and chemical elements for local and global planarization [1; 2; 3; 4].  
In CMP, material removal occurs as a result of friction between the wafer surface, the polishing pad, and the abrasive particles in the slurry under a chemical reaction caused by the chemical components of the slurry [5]. The polishing pad is one of the most important consumables. It is attached to the bottom plate, and polishing proceeds by the relative pressure caused by contact between the pad and the wafer. For ideal polishing, the pad should maintain uniform and rough contact. This can control the overall pad asperity height, minimize pad deformation, minimize the pad wear rate, and maintain the pad's material properties [6; 7; 8].  
Pad-related research has long been conducted by many investigators in the field of CMP because the pad has a key influence on polishing patterns and efficiency. Predicting and managing the profile of the pad surface is an important consideration for improving polishing efficiency. [PERSON] and [PERSON] [9] reported the correlation between the degeneration of the pad surface and the material removal rate during processing. [PERSON] et al. [10] proposed a mathematical model to predict the pad surface shape resulting from diamond disc conditioning. Researchers have also explored the effect of pad temperature changes on pad performance in terms of the pad's material properties. [PERSON] et al. [11]investigated the performance of SiO in CMP as a function of the pad's water soaking time, its conditioning, and its surface temperature. [PERSON] et al. [12] examined the phenomenon of pad surface hardening at various process temperatures and developed an effective method to minimize changes in the mechanical properties of the pad surface. Although such studies have solved many pad-related challenges, research on the shape of the pad asperity itself is still lacking. Most of the asperity-based material removal rate models assume a simple cylindrical shape. It is true that the shapes of the asperities are quite irregular, and there are aspects that are difficult to consider. However, typical changes that affect the result, such as the angle of the asperities, need to be considered.  
In this study, therefore, we investigate how the pad temperature changes that occur during CMP cause changes to the pad surface. Unlike previous works that focused only on changes in the pad surface roughness, this study considers the changes in the pad asperities themselves. This approach is believed to be useful for identifying phenomena that have been difficult to assess using the surface roughness factor alone.
## 2 Laser Beacon: General Overview  
The primary aim of integrating and testing the proposed laser beacon within the NEMO tower is to facilitate the development of an inter-string calibration system for neutrino telescopes. In addition to this main objective, a concise list of supplementary goals for the integration of the laser beacon into the NEMO tower includes:  
* Conducting a practical assessment of the container's ability to withstand high pressure.
* Establishing effective communication between an external container and the laser beacon system.
* Assessing the reliability of the electronics used in the laser beacon.
* Evaluating the laser's light emission characteristics, specifically by verifying the distance reached by the emitted light and its temporal attributes, both of which are essential for accurate time calibration purposes in neutrino telescopes.  
The central components of the laser beacon encompass several crucial elements:  
* The titanium container serves as the structural housing for the laser system.
* The internal aluminum mechanics provide the necessary framework for integrating the various components of the laser beacon.
* A connector enables underwater connectivity, facilitating both the power supply to the laser beacon and external communication.
* The glass rod, coupled with an attached diffuser, effectively scatters the laser light in the desired pattern, preventing the adverse effects of biofuling.
* The electronics component is responsible for supplying power to the laser beacon's elements. Additionally, it manages operations remotely using the I2C protocol. This control extends to functions such as switching the laser on/off, adjusting the laser emission frequency, and overseeing the filtering level of the electronic attenuator.
* At the heart of the system is the laser itself, specifically a Q-switched Nd-YAG laser, which serves as the primary component responsible for emission.
* An **electronic attenuator** is integrated to modulate the intensity of the laser beam emitted by the Q-switched Nd-YAG laser.  
Figure 1: Laser beacon installed in the NEMO tower. **(Left)** The laser beacon is open and supported by the rod opening, which is protected by a PCB cap painted in red. It serves as a warning for its removal before deployment. **(Right)** The laser beacon installed at the base of the tower before deployment.
## 6 Simulation
### Drag Force on Fix Cylinder  
The accuracy of hydrodynamic force on particle is critical for IMB-DEM-LBM particle flow simulation. A fixed cylinder is located in a channel with fixed boundary velocity (see Figure 10). Different inlet velocity generates steady laminar flow with different Reynolds  
Figure 8: Poiseuille flow.  
Figure 9: Comparison of analytical solution and proposed solution at \(x=30\).  
number. The cylinder drag coefficient \(C_{d}\) is expressed analytically by a function of Reynolds number, which is shown in Equation (28) [27,28].  
\[C_{\text{D}}=\left\{\begin{array}{cc}\frac{24}{R\epsilon}\Big{(}1+0.15 Re^{0.68 7}\Big{)}&1<Re\ <800\\ \frac{24}{R\epsilon}&0<Re\ <1\end{array}\right. \tag{28}\]  
The inlet velocity is simulated with Zou-He boundary condition. The drag force on cylinder is \(F_{simulation}\), and the fluid velocity at cylinder is \(U_{simulation}\). The drag coefficient is shown in Equation (29).  
\[C_{simulation}=\frac{8F_{simulation}}{\rho U_{simulation}^{2}\pi d^{2}} \tag{29}\]  
The simulation domain cell number is \(60\times 150\). The dimensionless variable are listed as follows: (a) collision time \(\tau=1\), (b) dimensionless viscosity \(\
u_{dimensionless}=0.167\), (c) cell size \(\Delta x=1\), and (d) timestep \(\Delta t=1\). The boundary inlet velocity are 0.01, 0.03, 0.05, 0.1, 0.15, 0.2, and the corresponding Reynolds number are 9, 22.5, 45, 90, 135, 180, respectively. The steady simulation result of velocity vector with inlet velocity 0.2 is shown in Figure 11. The simulation force on cylinder is obtained, and then drag coefficient is calculated, as well as the \(error=(simulation-analysis)/analysis\), as shown in Table 1. A comparison between the analytical solution and the proposed solution is shown in Figure 12. As shown, the simulation result agrees with the analytical result. In addition, the simulation has a better performance at high Reynolds number. The simulation error at low Reynolds number is caused by velocity fluctuation near cylinder boundary.  
Figure 11: Velocity vector with Uinlet = 0.2 and Re = 180.  
Figure 10: Distribution velocity of D2Q9.  
Error at low Reynolds number is introduced by numerical noise. In low Reynolds number flow, the interaction drag force is not dominant but it helps match the particle and fluid velocity. The particle velocity oscillation is inevitable in the simulation, which is different from the actual physical progress. Using a large drag force helps in decreasing oscillation. This shows the limitation of the coupling method presented in this study; however, this error can be neglected because low Reynolds number condition is rare for particle flow in coastal engineering.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
###### Abstract  
The use of airships to launch space probes has been an effective means of conducting typhoon surveys in recent years, but the carrying area and weight of airships are very limited, so it is necessary to reduce the weight and volume of the release device as much as possible. This paper reports the first use of a probe with cylindrical and omnidirectional characteristics, as well as a four-arm spiral receiving antenna, which can also act as the downward release device of the inner wall and improve the space utilization rate. Through further analysis and improvement, it was determined that the four-armed helical antenna can be printed on the medium using a flexible material, which not only reduces the weight of the device but also avoids direct contact between the antenna and the sounding device and improves the stability of the antenna. The designed antenna was modeled and simulated using Ansys HFSS 2021 simulation software.The simulation results show that the antenna presented in this study achieves good performance at the center frequency of 403 MHz, the input voltage VSWR and return loss of the antenna are ideal, and the antenna has a good directional map.  
four-arm helical antenna; sounders; typhoon detection; HFSS +
Footnote †: journal: Applied  
1
Footnote 1: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China;  
[EMAIL_ADDRESS] (T.H.); [EMAIL_ADDRESS] (Y.L.)
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Environmental Factors and Dynamic Obstacles  
#### 5.3.1 Challenges  
Quadcopters are affected by adverse weather conditions, such as rain, wind, and clouds, which can reduce their reliability in certain climates. Additionally, dense vegetation and dynamic obstacles can impact the safety of quadcopters and increase the risk of crashes [160; 161; 162].
## 1 Introduction
In this work, methods to reduce the complexity of NOMS and the NSNS decoding algorithm are proposed, which involve using TT decomposition or TR decomposition, and apply it to the decoding of Hamming code, BCH codes and LDPC code. The novelty of this paper is summarized as follows:  
* As a continuation of the work in paper [20], we apply the TT decomposition to the NOMS and NSMS decoding algorithms, called the TT-NOMS and TT-NSMS algorithms, which can reduce the complexity of the algorithm. * We apply the TR decomposition to the NOMS and NSMS decoding algorithms, and achieve the same compression effect as TT, but TR decomposition has more flexible choices in rank. * In addition, we apply the TT-NOMS, TT-NSMS, TR-NSMS, and TR-NOMS algorithms to the decoding of Hamming code, BCH codes and LDPC code, which reduces the complexity of these decoding algorithms, while the performance is almost not degraded. The remainder of this paper is organized as follows: OMS and SMS, NOMS and NSMS, TT decomposition and TR decomposition are described briefly in Section 2. In Section 3, we propose the TT-NOMS and TT-NSMS decoding algorithms. TR-NOMS and TR-NSMS decoding algorithms are proposed in Section 4. Simulations results and the complexity comparison are shown in Section 5. Section 6 shows the performance evaluation with NOMS, NSMS, TT-NOMS, TT-MSMS, TR-NOMS and TR-NSMS algorithm. Section 7 concludes the paper.
## 3 Results and Discussion
### Groundwater Flow Direction Map  
To evaluate groundwater flow direction, the generated and assessed results in ArcGIS were exported and analysed in Surfer 16. The flow regime of the groundwater, its geomorphology-induced surface drainage system, as observed by some authors, e.g., [49], is compatible with the observed/predicted groundwater flow regime and the geomorphology-induced surface drainage system of the study area. Furthermore, groundwater flows pri  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Percentage** & **Minimum** & **Maximum** & **Mean (m)** & **Standard** \\  & **(\%)** & **(m)** & **(m)** & **Mean (m)** & **Deviation,** \\  & & & & & **SDV (m)** \\ \hline Baseline (0) & 0 & 253.925 & 376.227 & 315.076 & 0 \\ \hline
1 & 15 & 253.359 & 375.657 & 314.508 & 0.57 \\ \hline
2 & 30 & 252.766 & 375.096 & 313.931 & 1.131 \\ \hline
3 & 60 & 252.766 & 375.096 & 313.931 & 2.267 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pumping scenario.  
Figure 21: Scenario 3.  
marily northward from the southern highlands, where the Congo River is located, as can be seen in Figure 22 below.  
Additionally, from the hydrological concept, water flows from higher potential energy (elevations) to lower potential energy (elevations) and in reference to Figure 22 (the map illustrates where most of the groundwater is situated), which is highlighted by the blue part. Thus, this can also serve as a guiding tool or advice for the community and policy makers regarding where to drill and conserve the land. However, it does not imply that there is no groundwater potential in other parts. For instance, for the area highlighted in red, deeper drilling would be required compared to the blue parts.
## 2 Principle of SM Using a Novel Active Transmitter Detection Scheme with SSD
### The Novel Active Transmitter Detection Scheme
#### 2.3.1 Signal Pre-Distortion Technique  
The signal pre-distortion technique is applied after CAP modulation at the transmitter side, which can be described in Equations (1) and (2) as:  
\[\begin{cases}S_{1}=S_{1}\times\rho\\ S_{2}=\frac{S_{2}}{\rho}\times\tau\end{cases}\text{when both }(\max(|S_{1}|)<\max(|S_{2}|)) \text{ and }(\mathrm{h}_{1}>\mathrm{h}_{2})\text{ are met, with }\rho=\sqrt{\frac{\max(|S_{2}|)}{\max(|S_{1}|)}} \tag{1}\]  
\[\begin{cases}S_{1}=\frac{S_{1}}{\rho}\times\tau\\ S_{2}=S_{2}\times\rho\end{cases}\text{when both }(\max(|S_{1}|)>\max(|S_{2}|)) \text{ and }(\mathrm{h}_{1}<\mathrm{h}_{2})\text{ are met, with }\rho=\sqrt{\frac{\max(|S_{1}|)}{\max(|S_{2}|)}} \tag{2}\]  
where \(S_{1}\) and \(S_{2}\) are two consecutive CAP signal symbols that form an exclusive pair, with several samples included in each CAP symbol due to CAP pulse shaping. Additionally, \(\tau\) is the suppress ratio with \(\tau\in(0,\,1)\), and \(\mathrm{h}_{1}\) and \(\mathrm{h}_{2}\) are the total channel gains of two spatial channels, i.e., the channel gains from optical modulation to the optical detection, which pass through the electrical-optical-electrical conversion. Here, it is noted that except for the pre-knowledge of the allocation of the maximum channel gain across the spatial channels, there is no other explicit CSI that needs to be pre-known, which simplifies the implementation of the pre-distortion technique.  
Figure 1(a) shows the signal flow for implementing pre-distortion. The pre-knowledge of the maximum channel gain allocation is obtained through initial channel training and then fed back to the transmitter side to enable pre-distortion. If \(\mathrm{h}_{1}>\mathrm{h}_{2}\) and \(\max(|S_{1}|)<\max(|S_{2}|)\) are both met, pre-distortion is conducted using Equation (1); if \(\mathrm{h}_{1}<\mathrm{h}_{2}\) and \(\max(|S_{1}|)>\max(|S_{2}|)\) are both met, pre-distortion is conducted using Equation (2). Otherwise, the CAP symbol pair remains intact. Figure 1(b) shows an example of the generation of a pre-distorted CAP waveform. The blue, yellow, red and green dashed boxes in the CAP waveform indicate the range of implementing signal pre-distortion, where each dashed box contains one CAP symbol pair (oversampling factor = 4 for each symbol).  
The pre-distortion technique is proposed based on the observation of CAP signal characteristics at the receiver. Due to the many signal levels possessed by CAP, the high signal levels in CAP with lower channel gain may be even larger than the low signal levels in CAP with higher channel gain, leading to a false active transmitter detection. The pre-distortion technique tackles this issue by limiting the high signal levels in CAP with lower channel gain while enhancing the low signal levels in CAP with higher channel gain. Although some nonlinearity is introduced in this technique, it still significantly improves the performance of resolving the active transmitter at the receiver.
# Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
## 3 Hydroacoustic Integrated System Architecture  
The underwater integrated system is illustrated in Figure 6. During operation, the transmitter sends communication signals to the other users. This signal can also be used to detect targets via the signal echoes. In the waveform design, it is better to accommodate existing sonar systems, so a detection waveform is selected. This facilitates the use of CPM.  
Figure 5: Schematic diagram for calculating the ambiguity function of a shared waveform.  
A block diagram of the integrated hydroacoustic system is shown in Figure 7. The transceiver comprises two processing modules, a communication module and a detection module. The communication module's first channel encodes the transmit data, then applies CPM modulation and combines it with an LFM waveform, and sends it out with transducer array after amplification. At the receiving end, the signal is received by the transducer array, filtered and amplified, and then split for separate processing. Specifically, the communication receiving end demodulates the data using Viterbi decoding, and then channel decodes it to the destination.
## 5 Experiments and Discussion  
### Numerical Simulation for Dynamic Model of Disturbance Propagation in a Crowd  
To reveal the characteristics of internal disturbance propagation in a crowd and verify the anisotropy of the proposed dynamic model, numerical examples were implemented. Using the numerical analysis method of crowd flow described in reference [34] and the MATLAB R2022a simulation tool, a simulation scenario with internal disturbance propagation in a crowd was simulated.  
The simulation scenario was a 30 m \(\times\) 30 m square area with a 10 m-wide exit at the top and a 5 m \(\times\) 5 m square obstacle at the bottom left of the scene. In the lower half of the square space, pedestrians were waiting to be evacuated. The maximum crowd equilibrium state was \(\rho_{0}=7\) p/m\({}^{2}\), \(\gamma=1\). The pressure between the crowd in the equilibrium state was \(P_{e}=\rho_{0}\). The initial disturbance pressure intensity \(P_{\xi}(20,5,0)=10.00\) was generated by a sudden disturbance at position P(20,5).  
The simulation stopped when the pressure of observation point P reached the maximum value of 7, indicating with a yellow color, at the step = 150. The specific characteristics of the propagation of the disturbance pressure are shown in Figure 3. The contour color indicates the intensity of the disturbance pressure and point P in Figure 3 indicates the burst point of the disturbance pressure in the crowd. We can see from Figure 3 that the sudden disturbance pressure of the crowd fluctuates and spreads with the crowd movements. Moreover, the pressure gradient of the crowd near the obstacle is large.  
In reality, the pressure propagation of a crowd flow is anisotropic. Using the same numerical analysis method, a crowd flow based on the Hughes model was constructed. The crowd density distribution in the scenario when the simulation step was equal to 150 is shown in Figure 4, where the contour color indicates the crowd density. Comparing Figures 3 and 4, the simulation results of the proposed dynamic model for disturbance propagation in the crowd indicate that the upstream crowd is greatly affected by the disturbance of the downstream population, reflecting the anisotropy of the pressure propagation.