esa-sceva/satcom-chunk-collection huggingface dataset:
# Random PWM Technique Based Two-State Markov Chain for Permanent Magnet Synchronous Motor Control
## 4 Experimental Results and Analysis
### Experimental Results of Conventional RPWM  
Figure 10 shows the current harmonic analysis of the conventional SVPWM. As can be seen from the figure, the harmonic energy generated by the traditional SVPWM shows a discrete distribution state throughout the entire frequency band, mainly concentrating at the integer multiples of the carrier frequency (10 kHz, 20 kHz, \(\ldots\)). These harmonic components are the main sources of EMI. The harmonic energy after three times the carrier frequency has a significant attenuation, so this paper only considers the degree of amplitude decrease in the power spectral density near 10 kHz and 20 kHz. It can also be known from the figure that with the increase in the modulation index, the distribution characteristics of the power spectral density do not have an essential change, but due to the increase in the input energy of the motor system, the harmonic energy also increases, manifested as the increase in the amplitude of the power spectral density.  
Figure 11 shows the current harmonic suppression effect of conventional RPWM. By comparing Figure 11 with Figure 10, it can be seen that the traditional RPWM disperses the originally high-amplitude harmonic energy, making the distribution of harmonic energy more uniform. However, the suppression effect is limited, and obvious noise spikes still exist. At the power spectral density of 10 kHz and 20 kHz, when the modulation index is 0.5, they, respectively, decrease from \(-\)19.01 dB/Hz and \(-\)17.52 dB/Hz to \(-\)24.42 dB/Hz and \(-\)26.78 dB/Hz; when the modulation index is 1.0, they  
By comparing Figure 11b,d, it can be observed that as the modulation index increases (i.e., the gradually increasing torque of the motor load), the sinusoidal nature of the phase current waveform significantly improves, and the current distortion rate decreases accordingly. This phenomenon indicates that the motor system can operate in a more efficient condition when the modulation index increases.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 2 The Network Architecture  
In this paper, the proposed algorithm is used in a statistic network. Nodes in the network are all motionless. By using the proposed algorithm, we can accomplish the localization task by using only one anchor. The network architecture is shown in Figure 1.  
The UANs are comprised of anchor and unknown nodes. Unknown nodes consist of reference nodes and ordinary nodes. The specific descriptions are as follows:  
1. Anchor: A node that knows its accurate position previously by GPS or manual arrangement. Other nodes can be localized by directly or indirectly communicating with it. In this paper, we only need one anchor to accomplish the localization procedure.
2. Reference Nodes: Reference nodes are selected by the anchor to help it accomplish the localization procedure and can get the positions from the anchor. In this paper, we proposed an Optimal Selection Scheme of reference nodes to increase the localization accuracy.
3. Ordinary Nodes: Ordinary nodes are sensor nodes which only receive the signals form anchors and references to get the positions.  
In the proposed DRL localization algorithm, firstly, the reference nodes are selected from the anchor and their positions are obtained. Then, other ordinary nodes receive the signals from both the anchor and the newly selected reference nodes to accomplish the localization procedure.
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 2 High-Throughput Satellite Communication System Model
### Regularized Zero-Forcing Precoding  
Zero-forcing precoding is asymptotically optimal in the case of high SNR, but it does not consider the impact of channel noise on system performance. Therefore, regularized zero-forcing precoding introduces a regularization parameter considering the influence of noise, and it has good performance in the case of low SNR. The process of the regularized zero-forcing precoding algorithm is basically the same as that of the zero-forcing precoding algorithm; that is, the minimum mean square error criterion is used to calculate the precoding matrix, and the noise and user interference are eliminated by suppressing noise amplification. The expression of the regularized zero-forcing precoding matrix is:  
\[\mathbf{W}_{RZF}=\left(\mathbf{H}^{H}\mathbf{H}+\alpha\mathbf{I}_{K}\right)^{-1 }\mathbf{H}^{H} \tag{8}\]  
It can be seen from Equation (8) that the regularized zero-forcing precoder is essentially a zero-forcing precoder regularized by a regularization factor \(\alpha\). In the case of \(\alpha\rightarrow\infty\), Equation (8) represents a matched filter. In the case of \(\alpha\to 0\), Equation (8) represents a zero-forcing precoder. For a high-throughput satellite system, \(\alpha=\frac{N_{0}\mathrm{W}}{P_{0}}\), where \(P_{0}\) is the highest power of the feed. The influence of receiver noise is considered in the calculation process of the regularized zero-forcing precoding matrix. Due to the noise factor, the interference between users cannot be eliminated. However, in the case of low SNR, the performance of regularized zero-forcing precoding is better than that of zero-forcing precoding.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 7 Results: Time-Delay Estimation Accuracy Limit for Some Representative GNSS Signals
### Performance Limits of Phase Estimation with AltBOC-Type GNSS Signals
Using AltBOC-type signals, we reduce \(\sigma_{\rho_{L1}}\) by a factor of 27; thus, the difference between code, i.e., delay, and phase observables standard deviation is reduced from a factor around 2500 (L1 C/A) to a factor around 100 (AltBOC signals). Therefore, using PPP or RTK techniques in the context of AltBOC-type signals is much less interesting than for standard signals, which supports the previous discussion on code-based AltBOC PPP or RTK.
###### Abstract  
In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference.  
heterogeneous satellite networks; interference coordination; LEO satellite networks; resource allocation 2025251005  
2521005  
2
## 3 Methodology
### Channel-Spatial Domain Attention Module  
In 2018, ref. [16] proposed to use the CBAM module, which is composed of the channel attention module (CAM) and the spatial attention module (SAM), to optimize adaptive features from the channel dimension and spatial dimension. The CAM can learn more important feature content, and the SAM can learn more important feature positions. However, the CAM simply adds the eigenvectors obtained after the pooling layer without considering the correlation between the imbalanced data and vector [17]. To this end, we improve the CBAM module with the weight function to increase the representation ability of these samples.  
As shown in Figure 2, the feature graph obtained after the convolutional layer is used as the input. We compress the spatial dimensions by global max pooling (GMP) and global average pooling (GAP) and obtain two \(1\times 1\times\text{C}\) feature vectors called MaxPool and AvgPool. We input the feature vectors into the same multi-layer perception (MLP) to obtain the GMP-based weight vector \(\text{W}_{1}\) and GAP-based weight vector \(\text{W}_{2}\). We then use the activation function to operate on M, which is obtained by adding the two weight vectors and multiplying the weight function, to obtain the eigen weight vector \(\text{M}_{\text{C}}(\text{F})\). In addition, we multiply \(\text{M}_{\text{C}}(\text{F})\) with the input feature map to assign each category the corresponding weight.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category Distribution** & **ID** & **ID\({}^{\prime}\)** \\ \hline
[1,1,1,1,10] & 10 & 0.8000 \\
[2,4,6,8,10] & 5 & 0.2831 \\
[1,10,10,10,10,10] & 10 & 0.2000 \\
[10,10,10,10,10,10] & 1 & 0 \\
[1,10,100,1000,1000] & 10,000 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Imbalance of different category distributions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category Distribution** & **ID\({}^{\prime}\)** & **f(\(\boldsymbol{\lambda}\)\(\boldsymbol{\text{N}_{\text{i}}}\)\(\boldsymbol{\text{N}_{\text{max}}}\))** (\(\boldsymbol{\lambda}\)=4) \\ \hline
[1,1,1,1,10] & 0.8000 & [3.1623,3.16233,3.16233,3.1623,1] \\
[2,4,6,8,10] & 0.2831 & [2.2361,1.5811,1.2910,1.1180,1] \\
[1,10,10,10,10,10] & 0.2000 & [3.1623,1,1,1,1] \\
[10,10,10,10,10] & 0 & [1,1,1,1,1] \\
[1,10,10,100,1000,1000] & 2 & [10.5,6.234,3.1623,1.7783,1] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Imbalance of different category distributions.  
Figure 2: The framework of the proposed method.  
The \(\mathrm{M_{C}(F)}\) calculation formula is shown in Equation (4), where \(\sigma\) represents the sigmoid function.  
\[\mathrm{M_{C}(F)}=\sigma[(W_{1}+W_{2})\mathrm{f}(\lambda,\mathrm{N_{i}},\mathrm{ N_{max}})] \tag{4}\]
###### Abstract  
The influence of copper addition (0.5-2 mol%) on the crystal structure, densification microstructure, and electrochemical properties of Ce\({}_{0.8}\)Gd\({}_{0.2}\)O\({}_{1.9}\) synthesized in a one-step sol-gel combustion synthesis route has been studied. It has been found that Cu is very active as sintering aids, with a significative reduction of GDC firing temperature. A reduction of 500 \({}^{\circ}\)C with a small amount of copper (0.5 mol%) was observed achieving dense bodies with considerable ionic conductivities. Rietveld refined was used to investigate the crystal structure while relative density and microstructural examination were performed in the sintering temperature range of 1000-1200 \({}^{\circ}\)C after dilatometer analysis. High dense bodies were fabricated at the lowest sintering temperature, which promotes the formation of Ce\({}_{0.8(1-x)}\)Gd\({}_{0.2(1-x)}\)Cu\({}_{x}\)O\({}_{[1.9(1-x)+x]}\) solid solution and the absence of secondary phase Cu-rich or the segregation or copper at the grain boundary. As compared to the pure GDC an improvement of total conductivity was achieved with a maximum for the highest copper content of 2.23\(\cdot\)10\({}^{-3}\)-9.19\(\cdot\)10\({}^{-2}\) S cm\({}^{-1}\) in the temperature range of 200-800 \({}^{\circ}\)C.  
copper; gadolinium doped ceria; sintering aid; sol-gel combustion synthesis; SOFC
## 3 Challenges for Designing MAC Protocols in UWSNs
### Synchronization  
The difference between UWSNs and a TWSNs is considered to lie in the propagation delay and node movement. Therefore, to differentiate between nodes a time synchronization stamp algorithm will be required at MAC protocol design level for each sensor node in a UWSNs [67; 68]. Due to the lack of accurate synchronization, the activity cycle method cannot ensure effective processing of sensory networks by dealing with the uncertainty of time between sensory nodes. This is due to the distribution delay factor is very high and changes from time to time [9]. The network underwater does not need to conduct global time synchronizations on a regular basis when employing time stamp synchronization, which cuts down the time it takes to synchronize clocks across sensor nodes [68].
## 5 Conclusions and Future Work  
This study presents a CAS algorithm for LRV to avoid the collision by using only a multilayer laser-scanning sensor mounted on a test vehicle called \"Silkworm\". The proposed system has three modules. The first one is a sensor module that is based on a state-of-the-art laser scanning technology. The second module, called \"recognition and tracking module\" has three submodules to identify and track the obstacles in the scanned area. After identifying and tracking the obstacles, the third module calculates the collision probability. The whole system was applied to the Silkworm, which was manufactured by Durmazlar Machine Inc., Bursa, Turkey. Initial tests have been carried out at the Durmazlar Machine Inc. test facility. In normal traffic conditions, the objects that the system will encounter mostly would be pedestrians and other vehicles, so the test objects were restricted to pedestrians and other LRVs. Three primary test scenarios have been implemented. The first case is a LRV on the same line, the second one is a pedestrian on the route of the test vehicle, and the last one consists of both a LRV and multiple pedestrians on the route of the test vehicle.  
It has been observed that designed recognition and tracking module is robust against rapidly moving and quick maneuvering objects and is able to follow a pedestrian running at over 6 m/s. The collision avoidance rate of the system is high due to its three phase warning system: visual warning for tram driver, acoustic warning for pedestrians, and finally automatic braking. Throughout the ten scenarios tested (Table 1), the laser scanning sensor has successfully detected all of the objects. The classification algorithm failed in two situations.  
1. The pedestrian suddenly starts to move and keeps moving slowly (Table 1 Scenario 6)
2. Two pedestrians get close to each other (Table 1 Scenario 10)  
The qualification processes have not yet been completed, driving tests in the city have not begun. Therefore, the designed system could not be tested in various weather conditions with various scenarios. Therefore, our future work will consist of implementing adverse weather conditions and varying scenarios during the qualification phase. Furthermore, to improve the efficiency of detection and classification algorithms, the next research step will include developing a new method of laser/camera fusion or laser/radar fusion. On the other hand, the system that uses only a laser scanning sensor was used for the first time in real-time tests for trams.  
CAS that warn of collision and automatically brake in an emergency are increasingly spreading in rail transportation systems and they provide the basis for automated trams. All kinds of vehicles are expected to be smarter in the near future, so it is the first step for unmanned railway vehicles.  
[PERSON], [PERSON] and [PERSON] conceived and lead the research. [PERSON] performed and analyzed the experiments. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest with Durmazlar Machine Inc.  
Figure 13: The scenarios for multiple pedestrian and static LRV.
# Several Cases for the Validation of Turbulence Models Implementation
## 1 Introduction  
Computational fluid dynamics (CFD) has become an integral part of the design systems in aerospace and other engineering fields. The increase in computational power in the last two decades allows us to tackle complex, engineering relevant problems. Using today's supercomputers, it has become customary to solve transport phenomena problems with tens or hundreds of millions of grid points and generate results overnight.  
As the hardware improves, the models used for predicting transport phenomena should also improve. One standing challenge for flow simulation is the turbulence modeling. As direct numerical simulation is still too computationally expensive for engineering relevant flows, the governing mass, momentum, and energy equations must be closed using a turbulence model.  
The widely used \(\kappa-\epsilon\) turbulence model developed by [PERSON] and [PERSON] [1] cannot properly capture the turbulent boundary layer up to flow separation. The first turbulence model that accurately predicted separated airfoil flows was the Johnson-King model [2]. Being an algebraic model, the Johnson-King model was not easily extensible to three-dimensional flow solvers. The \(\kappa-\omega\) turbulence model proposed by [PERSON] [3] improved the prediction for adverse pressure-gradient flows. In addition, the model has a simple formulation in the viscous sublayer. The \(\kappa-\omega\) model, however, depends strongly on the freestream values of the specific turbulent dissipation rate, \(\omega_{f}\). This issues was addressed by combining the \(\kappa-\omega\) and \(\kappa-\epsilon\) models, so that the former models the inner region of the boundary layer and the latter models outer part. The resulting model, called the Shear Stress Transport turbulence model [4], was developed to respond to the need for accurate prediction of flows with strong adverse pressure gradients and separation. A turbulence model that is less computationally expensive than the Shear Stress Transport turbulence model is the Spalart-Allmaras model [5]. This is a one-equation model that models an eddy viscosity-like variable, \(\hat{v}\).  
The purpose of this paper is to provide several cases that can be used to validate the implementation of the Shear Stress Transport and the Spalart-Allmaras turbulence models. The results generated using an in-house CFD code and two NASA codes, using a couple of turbulence models, are compared with experimental data.  
The next section presents the physical model and provides details on the governing equations. This is followed by a brief summary of the numerical method used to solve the governing equations. The results section presents four validation cases, ranging from a flat plate to a cascade of turbine airfoils.
## Materials and Methods
### UAV Data Acquisition and Processing  
All imagery data were captured using a DJI Matrice 100 quadcopter platform equipped with a MicaSense Red Edge camera. The MicaSense Red Edge has five sensors (blue: 465-485 nm, green: 550-570 nm, red: 663-673 nm, red edge: 712-722 nm, and near-infrared: 820-860 nm), and an external irradiance sensor with GPS and inertial measurement unit (IMU) placed on top of the UAV to capture sensor angle, sun angle, location, and irradiance for each image during flight. Physical radiometric targets were imaged prior to flight for radiometric calibration. This camera can capture imagery at 8 cm of pixel GSD (per band) at 120 m (\(\sim\)400 ft) above ground level (AGL) flying height. Visually contrasting white and black ground control points (0.25 m\({}^{2}\)) were surveyed using a Trimble GEO 7X, and differentially corrected to \(<\)5 cm of horizontal accuracy. Mission planning was conducted using Pix4 Dcapture software and flown in a grid pattern. We flew over the treatment plots on two days with clear conditions. On Day 1 (25 July 2018) we varied the altitude of flight from 30 m to 120 m during one-hour pre and post solar noon (four flights). On Day 2 (26 July 2018) we flew at a constant altitude (60 m) every hour from 8 am to 3 pm (eight flights). Overall, we captured the experiment 12 times (Table 1).  
The images were imported into Pix4 Dmapper 4.3.31 [53] for processing. Camera correction and calibration were applied to remove geometric distortions from images. A stitched orthomosaic image was generated with a GSD resolution ranging from 2.2 cm (30 m altitude) to 8.7 cm (120 m altitude). Each orthomosaic image was radiometrically calibrated with the image of the standard white reflectance panel. Individual crowns from the experiment were delineated manually using the highest resolution image (flown at 30 m altitude with a GSD of 2.2 cm). In this image, treatment canopies are easily and precisely identified. The outlines of each plot (e.g., Figure 1) were used in subsequent analyses.
## 3 Experimentation and Analysis  
### Laboratory Testing  
In order to demonstrate the decomposition methodology described above, and to investigate how impact response signals vary in the presence of underside corrosion, a series of laboratory-based experiments were undertaken. A National Instrument (NI) device was used to collect the signals. Different depths of artificial defects were created in two rail sections, based on a railway maintenance standard [26], while a third rail section was kept as a healthy rail sample. Details of the artificial defects are shown in Figure 3 and described in Table 1.  
The impact response acoustic signal generated by striking the healthy rail sample in the location indicated in Figure 3 is shown in the time and frequency domains in Figure 4.  
As described above, the IVMD-SVD algorithm was used to decompose the experimental signals. Initially K-values of 3 to 12 were used in the analysis.  
To demonstrate the procedure for the selection of the optimal K-value, a curve obtained from the relationship between IMFs generated using IVMD-SVD and the original signal was used, and the curve is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Depth** & **Area** & **Position** \\ \hline
0.1 mm & 6 mm \(\times\) 6 mm & 50 mm from the rail end \\
0.75 mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Artificial corrosion geometry.  
Figure 4: Acoustic impact response signal.  
Figure 3: Geometry of artificial corrosion.  
The curve in Figure 5 shows a general downtrend. If the K-value was large, the sub-signals from the higher numbered decomposed modes were generally less relevant to the original signal. Conversely, if the K-value was too small, useful information might be excluded. The preferred K-value occurred at the first point of significant inflection in the curve. This was identified by observing the sign and the difference in values of the second derivative of the curve. The difference in values should be at least 0.05, as identified in [16].  
Using the example in Figure 5, the values of the second derivative at K-value 5, and K-value 6 were 0.0403 and \(-\)0.0121, respectively. The product of these two values was negative. The absolute difference was larger than 0.05. Hence, the optimal K-value was chosen to be K = 6.