esa-sceva/satcom-chunk-collection huggingface dataset:
# A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management
## 3 Urban Airspace Model
### CNS Risk Protection Volume Generation
#### 3.1.3 Total CNS Protection Volume in a Cooperative ADS-B Encounter  
The volume generation process is illustrated here for a scenario in which ADS-B is used to monitor an intruder aircraft. The volume in this instance bounds the GNSS error. This is then inflated to account for the effect of latency. The radius of the protection volume \(r_{PRV}\) are then computed as:  
\[r_{\text{PRV}}=\text{k}\cdot\sqrt{\sigma_{\text{GNSS}}^{2}+\left[\left(\text{v }\ \sigma_{\text{t_{SA/CA}}}\right)^{2}+\left(\text{t_{SA/CA}}\ \sigma_{\text{v}}\right)^{2}+2\sigma_{\text{v,t}}\right]} \tag{11}\]  
where \(\sigma_{\text{GNSS}}^{2}\) is the variance in the GNSS solution; v and t\({}_{\text{SA/CA}}\) are the estimated velocity and deconfiction time; \(\sigma_{\text{v}}\) and \(\sigma_{\text{t_{SA/CA}}}\) are the standard deviations in the estimated velocity and deconfiction time respectively. The term in the square parentheses [.] is the translation of the ADS-B latency from the temporal to the spatial domain. This term is obtained by applying the law of propagation of uncertainty to compute the distance covered by the vehicle over the duration of the SA/CA interval. This requires the uncertainty in the estimated velocity and in the estimated deconfiction time. This term essentially bounds the distance covered by the vehicle over the duration of the latency. k is an inflation factor specifying the number of standard deviations to expand the volume. This is chosen as k = 3 i.e., a 3\(\sigma\) or 99.7% bound. It is a commonly followed practice to employ a recursive state estimator such as the Extended Kalman Filter (EKF) as the GNSS navigation filter, to process GNSS pseudoranges and obtain a position solution. Both \(\sigma_{\text{GNSS}}\) and \(\sigma_{\text{v}}\) are inherent outputs of the GNSS EKF. \(\sigma_{\text{t_{SA/CA}}}\) can be obtained by fitting an overbounding Gaussian distribution to empirical data of system computational times.
# Non-Real-Time Wireless System for Lightning Effect Measurements
## 4 Prototype
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Proposed** & **Optical Fiber [2]** & **Scope RIGOL** & **High-Voltage** & **Rogowski Coil in** \\  & **Prototype** & **D510542 [47]** & **Line Detector [33]** & **Wind Turbine [31]** \\ \hline Ver. Resolution & 16 bit & 12 bit & 8 bit & 16 bit & 11 bit \\ Sampling & 16 MS/s & 100 MS/s & 1 GS/s & 400 KS/s & 4 MS/s \\ Wireless & YES & NO & NO & YES & YES \\ Communication & RF & Optical & Wire & GSM & Wireless \\ Dimensions & \(8\times 5\times 5\) cm & 20 \(\times\) 20 \(\times\) 20 cm & 16 \(\times\) 12 \(\times\) 31 cm & \(\sim\)20 \(\times\) 20 \(\times\) 50 cm & No data \\ Power Supply & Battery & Battery & Battery/230 V & Self-powered & 230 V \\ Working time & \textgreater{}12 h & 1–2 h & No data & No data & No data \\ Mobility & High & Medium & High (batt.) & Low & Low \\ Safety & High & High & Low & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of lightning measuring systems. Figure 3: Photography of prototype. The proposed measuring system has a built-in voltage divider, providing a measuring range similar to a typical oscilloscope (10 Vpp). Range is limited to tests (with the possibility to select the measuring range by using an appropriate divider at the device input).
## 4 Conclusions  
We present two machine learning algorithms to predict the shale gas production, both of which can reliably forecast shale gas production. The MLP neural network can forecast shale gas production by geological and fracturing reservoir parameters, and the LSTM neural network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data  
Figure 8: Relative error between the predictive value and true value of the MLP and LSTM networks.  
Figure 9: Relative error between the values predicted by the MLP networks (left) and LSTM networks (right) and true values under early production time.  
change greatly. The largest relative errors of LSTM in this work on the 10 th, 100 th, and 1000 th day are 0.95%, 0.73%, and 1.85% respectively, which are far lower than the relative errors of the MLP predictions. Although these neural network models are trained based on the simulation data in this study, the research results can provide a basic theory for shale gas productivity prediction and fine design of fracturing and completion parameters. In future work, we will train a new model based on the field data.  
Conceptualization, T.W. and S.T.; methodology, W.Z.; software, T.W.; validation, H.W., Q.W. and J.S.; writing--original draft preparation, T.W.; writing--review and editing, S.T.; visualization, Q.W.; supervision, W.R.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
Financial support from the National Key Scientific Research Instrument Research Project of NSFC (No. 51827804), the Science Foundation of China University of Petroleum, Beijing (No. 2462021 XKBH013), and China Postdoctoral Science Foundation Funded Project (Project No.: 2021M703578) are acknowledged.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
This article presents an automated vision-based algorithm for the die-scale inspection of wafer images captured using scanning acoustic tomography (SAT). This algorithm can find defective and abnormal die-scale patterns, and produce a wafer map to visualize the distribution of defects and anomalies on the wafer. The main procedures include standard template extraction, die detection through template matching, pattern candidate prediction through clustering, and pattern classification through deep learning. To conduct the template matching, we first introduce a two-step method to obtain a standard template from the original SAT image. Subsequently, a majority of the die patterns are detected through template matching. Thereafter, the columns and rows arranged from the detected dies are predicted using a clustering method; thus, an initial wafer map is produced. This map is composed of detected die patterns and predicted pattern candidates. In the final phase of the proposed algorithm, we implement a deep learning-based model to determine defective and abnormal patterns in the wafer map. The experimental results verified the effectiveness and efficiency of our proposed algorithm. In conclusion, the proposed method performs well in identifying defective and abnormal die patterns, and produces a wafer map that presents important information for solving wafer fabrication issues.  
automated visual inspection; convolutional neural network; deep learning; pattern classification; semiconductor inspection; wafer map +
Footnote †: journal: Medical Imaging
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 5 Web3602--Interactive Application Applying SDM Ontology
### System Design  
Web360\({}^{2}\) retrieves the list of available content by querying the RDF store using a SPARQL query(Figure 10). When the user selects content to watch, Web360\({}^{2}\) again performs a SPARQL query on the RDF store to retrieve the video and audio media files and metadata contained in the selected content to start playing the video and audio. In addition, Web360\({}^{2}\) accepts viewer and touch operations on audio objects as input. The system renders and outputs video and audio in real time in response to the user operations, making the process intuitive and interactive. The video and audio media files are independent of each other and playback in synchronization within the system. Furthermore, 360\({}^{\circ}\) videos are streamed in HLS and projected onto a virtual sphere centered on the viewer. Based on the 3D positional information of the audio objects, audio visualization objects are drawn at each point inside the virtual sphere where the video is projected. At the same time, spatial acoustic playback is performed based on the positional information.
## 6 A Compressed Sensing Based Ultra-Wideband Cognitive Radio  
Ultra-wideband (UWB) [121, 122, 123, 124] represents a new paradigm in wireless communication. The unprecedented radio bandwidth provides advantages such as immunity from flat fading. However,  
\begin{table}
\begin{tabular}{|l|c|c|} \hline Band & Intermediate frequency (IF) & Image frequency \\ \hline
1. 0.4 – 0.9 & 0 & \\ \hline
2. 0.9 – 1.4 & 0 & \\ \hline
3. 1.4 – 2.4 & 3.9, 0 & \(5.4-6.4\) \\ \hline
4. 2.4 – 3.4 & 3.9, 0 & \(4.4-5.4\) \\ \hline
5. 3.4 – 4.4 & 3.9, 0 & \\ \hline
6. 4.4 – 5.4 & 3.9, 0 & \(2.4-3.4\) \\ \hline \end{tabular}
\end{table}
Table 3: Receiver frequency parameters (GHz).  
Figure 25: Receiver front-end architecture.  
extremely high sampling rate analog to digital conversion (A/D) becomes a major challenge in UWB communication systems. According to Nyquist sampling theorem, the sampling rate should be at least twice the bandwidth of the signal, and oversampling is required for better quality. For example, a 5 GHz UWB signal needs over 10 Gsps A/D if oversampling is considered, which is not feasible even for the state-of-art hardware.  
Compressed Sensing (CS) [21, 125] gives an opportunity to overcome this challenge. The sampling rate can be reduced to less than one tenth of the Nyquist rate, as long as the transmitted signal is sparse in some aspect. CS has been used to UWB communications [126, 127]. A novel CS based UWB communication system is proposed. The channel itself is considered as part of compressed sensing. The hardware complexity of the receiver is moved to the transmitter side. The A/D sampling rate for a 5 GHz UWB signal, covering the 3-8 GHz frequency band, is reduced to as low as 125 Msps [128].  
Cognitive Radio (CR) is another challenge in the UWB system. The ultra wide spectrum a UWB system occupied will interfere or be interfered by other narrowband or wideband systems sharing the same spectrum. A simple method is suggested and verified in the CS based UWB communication system.
## 2 Related Work
### Optimal Resource Scheduling on General NOMA
#### 2.1.2 Power Allocation Strategies  
Power allocation is another cornerstone of NOMA optimization. Various algorithms have been proposed to efficiently allocate power among users while balancing interference and system capacity. [PERSON] et al. [9] analyzed power allocation schemes and their computational complexity, which limits their implementation in real-time systems. [PERSON] et al. [16] focused on balancing complexity and effectiveness in dynamic environments, such as vehicular networks. Furthermore, [PERSON] et al. [14] proposed a lookup table to simplify the pairing and power allocation process while maintaining optimal performance. [PERSON] et al. [17] proposed an energy minimization for NOMA networks with the use case of federated learning. The authors first proposed a joint optimization for time and power allocation. In addition, to reduce the computation complexity, the bisection algorithm has been applied. However, the limited evaluation scenario, i.e., one single-antenna base station, casts doubts on the scalability.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 2 Related Work on Point Cloud Attribute Compression
### Problem Formulation
Subsequently, utilizing the eigenvector matrix of \(\mathbf{Q}_{c,c}\), the residuals of \(\mathbf{x}_{\mathbf{c}}\) can be decorrelated into the frequency domain, resulting in a more compact representation. Therefore, the fundamental task for achieving optimal transform in point cloud attribute compression lies in constructing an optimal graph to obtain the optimal transform matrix. To construct a graph that models the spatial relationship among point cloud attributes, we consider a graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{W})\) comprising the point set \(\mathcal{V}\) and the edge set \(\mathcal{E}\) connecting adjacent points, along with a weighted adjacency matrix \(\mathbf{W}\). The adjacency matrix \(\mathbf{W}\) is a symmetric non-negative weight matrix describing the weights assigned to the edges connecting two points. Then, the graph Laplacian matrix \(\mathcal{L}\) can be obtained as \(\mathcal{L}=\mathbf{D}-\mathbf{W}\), where \(\mathbf{D}\) is a diagonal matrix formulated as \(D_{i,i}=\sum_{j=1}^{n}W_{ij}\). The matrix \(\mathbf{D}\) represents the degree of connection between each point and other points on the graph. With the constructed graph \(\mathcal{G}\) and the associated graph Laplacian matrix \(\mathcal{L}\), the graph transform matrix \(\mathbf{\Phi}\) can be obtained via the eigenvalue decomposition of the graph Laplacian matrix \(\mathcal{L}\), expressed as \(\mathcal{L}=\mathbf{\Phi}\Lambda\mathbf{\Phi}^{-1}\), where \(\Lambda\) is a diagonal matrix containing the eigenvalues of the graph Laplacian matrix \(\mathcal{L}\). Finally, we utilize the obtained optimal graph transform matrix \(\mathbf{\Phi}\) in point cloud attribute compression. The transformed point cloud attributes \(\hat{x}\) are obtained through the following process:  
\[\hat{x}=\mathbf{\Phi}^{T}x. \tag{4}\]
## 3 The Layers Architecture  
The architecture of the system has been conceived as a hierarchical layers structure for the full control of \"unities\" made up of sensors, actuators and local control cards. Each layer performs some control activities and transmits information to the other layers. The architecture foresees three layers called apparata, local control and remote control.  
In the \"apparata layer\" we consider all the hardware and software designed for the autonomous and automatic control of the single apparatus or unit. This layer communicates with the higher one and _vice versa_ using a ZigBee mesh wireless tlc network particularly suitable for closed devices and low bit rates [3]. The local control unit collects the data received from the single units to check their activity and functionality. It is realized with a RaspBerry-Pi control card, which collects and locally elaborates data from the single apparatus verifying that there are no problems. If a problem(s) occurs, the card sends an error message on the internet exploiting the upper layer. The latter is realized with a WiMAX trans-receiver, which is connected, _via_ radio link, to the World Wide Web. Figure 1 shows the architecture of the system.
## 2 Experimental Study on Slurry Diffusion
### Testing Equipment  
The experimental setup comprises grouting equipment, pull-out equipment and data acquisition systems.  
The grouting equipment includes an air compressor, a pressure barrel, and a slurry pipe, as illustrated in Figure 2a. The air compressor is capable of delivering a maximum pressure of 0.6 MPa, while the pressure barrel, with a capacity of 8 L for cement slurry, can withstand a maximum pressure of 0.8 MPa. The slurry pipe, measuring 580 mm in length, is connected to an 8 mm Teflon pipe that extends from the pressure barrel.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Sample** & \multicolumn{8}{c}{**Percentage of Particle Mass Less Than a Certain Particle Size/\%**} & **Cu** & **Cr** \\
**Number** & **100 mm** & **60 mm** & **40 mm** & **20 mm** & **10 mm** & **5 mm** & **2 mm** & **1 mm** & **0.5 mm** & **0.25 mm** & **0.075 mm** \\ \hline
1 & 100 & 100 & 80.2 & 58 & 42.3 & 36.6 & 31.2 & 29.1 & 22.7 & 5.4 & 3 & 68.9 & 0.3 \\
2 & 100 & 90.75 & 76.25 & 53.67 & 40.32 & 34.64 & 30.07 & 29.56 & 25.87 & 3.6 & 3.2 & 79.6 & 0.4 \\
3 & 100 & 100 & 84.19 & 55 & 38.38 & 34.12 & 27.64 & 27.32 & 24.17 & 6.07 & 3.37 & 77.0 & 1.3 \\
4 & 100 & 86.75 & 77.13 & 52.94 & 43.31 & 36.41 & 32.24 & 29.8 & 24.57 & 4.78 & 2.88 & 81.8 & 0.1 \\
5 & 100 & 85.75 & 81.93 & 55.62 & 39.63 & 33.84 & 28.79 & 27.75 & 23.65 & 5.37 & 2.02 & 74.5 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size gradation test results.  
Figure 1: Undisturbed particle size gradation curve.  
Figure 2: Indoor test equipment: (**a**) grouting equipment; (**b**) drawing equipment.  
The pull-out equipment consists of a jack and a manual pump. The jack, model RCH-60100, can apply a load of up to 600 kN and has a maximum stroke of 100 mm, as depicted in Figure 2b.  
The data acquisition system is equipped with a pressure sensor, two dial indicators, two cable displacement sensors, an XL2101B5G static strain gauge, and its accompanying data acquisition and analysis software. The pressure sensor has a capacity of 50 T.
# Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
## 4 Conclusions  
This paper presented the method to adopt the posteriori residuals based robust extended Kalman filter to re-determine the weight of BDS GEO and IGSO in GPS + BDS PPP/INS tight integration. The corresponding mathematical models were described and validated by a set of land-borne vehicle tests. Results indicate that (1) The enhancements from BDS on improving PPP performance was significant; however, such enhancements from GEO and IGSO were different. Such appearance was mainly caused by the lower quality and the worse spatial distribution of GEO satellites. (2) The position improvements from IGSO were more visible than those from GEO. However, such improvements were different in different data processing modes. On average, there were about 22.7%, 23.9%, and 7.0% in PPP, EKF based PPP/INS tight integration, and robust extended Kalman filter based PPP/INS tight integration, respectively. When the robust method was utilized, the difference of the influences from GEO and IGSO was narrowed significantly. (3) INS can help to provide high-accuracy velocity and attitude; however, whether using the multi-GNSS or adopting the robust estimator, there are little enhancements on the accuracy of velocity and attitude determination.  
From this research, it can be concluded that the robust extended Kalman filter can be applied to BDS related data processing to remove adverse influences from GEO. However, such weaknesses would be reduced along with large progress in improving the signal quality and orbit accuracy of BDS GEO satellites in the future. Particularly, under the BDS-3 case after December 27, 2018, more MEO satellites and better special signal quality will be provided, which will weaken the influence of GEO in data processing and provide better solutions.  
[PERSON] and [PERSON] provided the idea for this contribution. [PERSON] implemented the software with the guidance from [PERSON], [PERSON], and [PERSON] [PERSON] and [PERSON] wrote the main manuscript. [PERSON], [PERSON], and [PERSON] helped with the writing. All authors reviewed the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) for Young Scientists (Grant No. 41804027), the Fundamental Research Funds for the Central Universities (Grant No. 2652018026), and the National Key Research and Development Program of China (Grant No. 2016 YFB0501804).  
Many thanks to the Navigation Group in the GNSS Research Center, Wuhan University, China for providing the inertial measurements and GPS/BDS observations, and many thanks to the German Research Centre for Geosciences (GFZ) for providing satellites' precise orbit-clock products.  
The authors declare no conflicts of interest.
# Modeling and Control Simulation of Power Converters in Automotive Applications
## 2 Electothermal Modeling and Control Algorithm
### AC/DC Converter Model and Control
The sine and cosine trigonometric functions are crucial in the feedback control block. Figure 7 depicts the control logic governing the voltage on the DC bus and the current in the totem pole. Employing PWM modulation concepts, the objective is to simulate the reverse engineering of an inverter designed for conventional DC-AC conversion but operating with active devices. The external control loop, supported by a PID system, stabilizes voltage errors by calculating the magnitude of the current at the totem pole. With the modulus and the cosine function, adapted to the AC voltage, the reference value for the internal current control is obtained. In the internal control, the proportional-resonant (PR) controller is preferred over the traditional PID to handle sinusoidal systems. Figure 5: Schematic illustration of the reason for using a simple LPF for AC source frequency detection. Figure 6: The phase-locked-loop shown in a block diagram. Figure 7: An illustration (in block diagram form) of the voltage/current feedback controller. It combines proportional and resonant, offering good performance and simplicity of implementation, especially in active rectifiers, where standard PI/PID controllers may have limitations. \[V_{g}^{ref}=\left[\left(P+\frac{I}{s}+D\frac{Ns}{s+N}\right)\left(V_{o}^{ref}-V _{o}\right)\frac{s}{s^{2}+\partial_{g}^{2}}-I_{g}\right]\left(K_{p}+\frac{K_{ rs}s}{s^{2}+\omega_{r}{}^{2}}\right)+V_{g} \tag{2}\]  
Equation (2) offers a mathematical representation of the active rectifier controller, linked to the schematic illustration above. The \(\omega\) resonant pulse plays a vital role by providing optimized gain precisely tuned to the resonant frequency. This crucial feature allows the proportional-resonant (PR) controller to effectively reduce steady-state recurring errors when following or canceling sinusoidal signals; this is consistent with the general principles of the internal model. Functionally, the PR controller's output is carefully designed to counteract any disturbances encountered and meet precise DC voltage amplitude requirements. It is inserted as an additive or subtracted component with respect to the input AC voltage. A detailed analysis of the modulation block reveals a precise division into two parts, each of which serves the modulation needs of the circuit. The first part deals with pulse width modulation (PWM) methods, precisely regulating the branch composed of GaN-type MOSFETs. On the other hand, the second part generates a \"high\" signal that is precisely timed to coincide with the positive half-wave of the cosine signal, carefully calculated in the previous blocks.