esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Array Antenna
### 4 Antenna Array and Measurements
The difference in the gain of the main lobes and sidelobes varies from 13.4 dB to 14.5 dB in the \(\phi=0^{\circ}\) plane, whereas this difference is larger in \(\phi=90^{\circ}\) plane that is from 13.8 dB to 15.8 dB. The measured X-pol levels are below \(-15\) dBic in the broadside direction. Table 3 compares the presented work with the previous literature. The designs in [24,25] are very compact with the C-to-C distance of 0.48\(\lambda_{0}\) and 0.45\(\lambda_{0}\) between the elements. Both the designs achieve low measured peak gains of 12.5 dBic. In [24], the IBW and 3 dB ARBW of the array are very narrow but in [25] these are wider as compared to the proposed design. Similarly, the design in [26] bears wide IBW and ARBW of 14.7% and 12.4%, respectively. The E-to-E distance among the elements is 0.74\(\lambda_{0}\) which bears a high gain of 18 dBic. A design with a similar E-to-E distance has been given in [27], which results in a narrow IBW of 6.08% and ARBW of 4.4%. Moreover, the manuscript lacks information about the maximum gain. The proposed design is compact that has the C-to-C distance of 0.535\(\lambda_{0}\) with a gain of 15.5 dBic. The proposed design achieves good performance in terms of gain as [24,25]. Similarly, the performance of the design in [26] is better at the cost of larger size. Finally, the proposed design has higher IBW and ARBW as compared to [27] despite the small size. Furthermore, the proposed design has been analyzed in terms of LHCP gain and AR within a certain degree of angular width, which is the requirement of the satellite application. The LHCP gain is maintained above 12 dBic and AR below 1.5 dB in the angular width that ranges from \(-10^{\circ}\) to \(+10^{\circ}\) in the whole bandwidth. Regarding the radiation pattern, these are stable in both the planes. Figure 8: Comparison of simulated and measured results: (**a**) Reflection coefficients. (**b**) AR and LHCP gain. Figure 7: A 4 \(\times\) 4 CP array antenna: (**a**) Optimized design. (**b**) Photo of prototype. Figure 9: LHCP gains at five frequency samples in the angular range of \(\pm\)10\({}^{\circ}\): (**a**) Simulated LHCP gain in the \(\phi\) = 0\({}^{\circ}\). (**b**) Simulated LHCP gain in \(\phi\) = 90\({}^{\circ}\). (**c**) Measured LHCP gain in the \(\phi\) = 0\({}^{\circ}\). (**d**) Measured LHCP gain in \(\phi\) = 90\({}^{\circ}\). Figure 11: Simulated and measured radiation patterns of 4 \(\times\) 4 array at five frequency samples in \(\phi\) = 0\({}^{\circ}\) plane: (**a**) 8.05 GHz (**b**) 8.10 GHz. (**c**) 8.15 GHz. (**d**) 8.20 GHz. (**e**) 8.25 GHz. Figure 10: AR at five frequency samples in the angular range of \(\pm\)10\({}^{\circ}\): (**a**) Simulated AR in the \(\phi\) = 0\({}^{\circ}\). (**b**) Simulated AR in \(\phi\) = 90\({}^{\circ}\). (**c**) Measured AR in the \(\phi\) = 0\({}^{\circ}\). (**d**) Measured AR in \(\phi\) = 90\({}^{\circ}\).
# Virtual Signal Processing-Based Integrated Multi-User Detection
###### Abstract  
The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called _Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD)_. In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user's signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals' dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system's bit-error rate (BER) performance.  
multi-user detection (MUD); signal processing; bit-error rate (BER) 202525254761
## Appendix A  
The AWG integrated machine, in the calculation model, is represented by a network of interlinked nodes, dynamically assembled, instead of a whole, monolithic system. Each node is the mathematical counterpart of each of the main real-life components of an integrated machine (compressors, pumps, heat-exchangers and so on). Each node is designed as a \"black box\" to the other parts of the system (using the strong paradigm of \"object-oriented\" approach) so that, at any time, it is possible to expand the model by adding new node types. The model calculates how the different fluids (air, coolant and water) traverse the network of nodes, delegating to each component the actual calculation of how energy is exchanged between fluids. Fluids are bonded to follow the mass conservation (mass entering a component must equal mass exiting plus mass \"storage\"), and the same holds true for energy fluxes entering/exiting each component. Thus, for each node \"\(j\)\", the following four equations must hold true:  
\[\sum_{z}\varphi_{zj}\,\Delta\tau=\Delta en_{j} \tag{1}\]  
\[\sum_{z}a_{zj}\,\Delta\tau=0 \tag{2}\]  
\[\sum_{z}q_{zj}\,\Delta\tau=0 \tag{3}\]  
\[\sum_{z}r_{zj}\,\Delta\tau=\Delta R_{j} \tag{4}\]  
where, in Equation (1), the sum of the energy fluxes, \(\varphi_{j}\), integrated by the time step, \(\Delta\tau\), are equal to the energy variation of the node, \(\Delta en\). Equation (2) describes the fact that the dry air mass flows, \(a_{j}\), are not accumulated in any node, as well as for the water mass flows, \(q_{j}\), as described in (3). The last Equation (4) describes the coolant mass flows, \(R_{j}\), exchanges in each node. Additionally, in this case, it is possible that, in some nodes, such as the heat exchangers, there could be a coolant mass variation, \(\Delta R\).  
Such a calculation is iterated at each time step, \(\Delta\) (the time step is user-adjustable), from the starting conditions. At each node, the model stores energy, (1), and/or coolant mass, (4), the simulation is able not only to calculate the steady-state performance of the machine but also the transient behaviour of the system when it is started/stopped or when the external conditions dynamically change.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 6 Conclusions  
Providing reliable data transmission over WMSNs is a challenging open issue due to the inherent features of these kinds of networks. The proposal is motivated by the unreliability of data transmission over WMSNs. In this paper, to resolve high packet loss rate in WMSNs, an intelligent video surveillance platform for wireless multimedia sensor networks (IVSP) is proposed. We classify the traffic into two classes and correspondingly maintain priority queues in each sensor nodes. To predict the congestion more accurately, we detect congestion by combining buffer length and its change rate together. We adopt a procedure in which every node has unique IP address and can establish a route from itself to sink through multi-hop communication. Our rate control mechanism analyses the occupancy at each node and sends feedback information to the sender node. The sender node gets the feedback information, analyses it, and makes any required adjustments to its transmitted streams. We propose the different retransmission mechanisms for different packets. The packets with lower priority can be retransmitted when the link is not busy to avoid congestion. We analyze the performance of proposed IVSP and show that IVSP outperforms ECODA and NoIVSP in terms of packet delivery ratio.  
This work was supported in part by National key R&D plan (Grant number 2016 YFC0105002 and 61674162), Shenzhen Key Lab for RF Integrated Circuits, Shenzhen Shared Technology Service Center for Internet of Things, Guangdong government funds (Grant numbers 2013S046 and 2015B010104005), Shenzhen government funds (Grant numbers JCYJ20160331192843950), and Shenzhen Peacock Plan.  
[PERSON] and [PERSON] contributed to the main idea of this paper; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON] provided the funding.  
The authors declare no conflict of interest.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 3 Co-Analysis of Rain Rate and C-Band SAR Backscattering
### Timeliness of the Data  
As explained in Section 2, we selected the rain data with the closest time to each Sentinel-1 case. The timeliness of the two datasets is very important and may be decisive for efficient training when preparing the reference dataset since the rain cells can have an apparent shift up to several minutes. In particular, the rain splash and ring waves are triggered immediately by raindrops but the wave damping is expected to be maximum after the rain event. Figures 3 and 4 illustrate this point. In Figure 3, two different rain events are considered. The image in VV polarization is plotted along with the base reflectivity corresponding to different times before (a1-a2 and b1-b2) and after (a4-a5 and b4-b5) the acquisition time (a3 and b3). For a given transect across these two images (red line), the NRCS values for co- and cross-polarization are plotted together with the base reflectivity measured at different times around the SAR data acquisition. For each case, we observe a high negative correlation between the rain rate and the NRCS value at about 30 min (see Figure 4 before SAR acquisition time). Please note that for these two cases, we only observed the decrease in the co-polarized channel so that only VV-polarized images are presented in Figure 3. Here, the decrease cannot be attributed to attenuation of the radar signal by rain in the atmosphere, since the dark area is in front of the bright area with respect to the antenna look angle. This indicates that the rain occurring 30 min before the SAR data acquisition has generated turbulence. In addition, the maximum rain rate is observed at the SAR acquisition time, which is coincident with the enhanced NRCS in the SAR images. This observation suggests that NRCS enhancement is related to surface scattering at rain-generated splash products [18] or backscattering in the atmosphere [21]. The information of hydrometeor classification provided by NEXRAD network could help to classify this effect, which shall be discussed next.  
Figure 2: Three case studies with SAR data acquired on 13 June 2017 23:20 UTC (**Top**), 1 September 2017 23:53 UTC (**Middle**) and 31 August 2017 00:10 UTC (**Bottom**) with (from **Left** to **Right**) Sentinel-1 VV and VH-polarized NRCS images, co-located base reflectivity from NEXRAD at the same time. The wind vectors in the third column refer to ECMWF data. The last column presents the statistics of NRCS with respect to base reflectivity for each case. All these case studies have approximately the same range of incidence angle from about 35\({}^{\circ}\) to 41\({}^{\circ}\).
## 4 Signal Processing Algorithm for Classification of 17 Motions
### Training of Artificial Neural Network Classifier  
A feature vector of a given motion consists of 36 elements as denoted in (1). Since there are 17 motions, a data set can produce 17 feature vectors (one feature vector for each motion). In the training stage, feature matrix generate from \(m\) data sets are used to train the classifier as defined in (4). Thus, the teaching signal \(\mathbf{T}\) is given by  
\[\mathbf{T}=\left[ \begin{array}{cccc}\mathrm{I}_{17}&\mathrm{I}_{17}&\cdots& \mathrm{I}_{17}\end{array} \right]^{\mathrm{T}}\in\Re^{17m\times 17}. \tag{6}\]  
In this study, the artificial neural network (ANN) is chosen as the classifier to discriminate the 17 motions. Currently, several kinds of ANN have been proposed. In this paper, a multilayered perceptron, which is a feed-forward ANN is chosen. The multilayer perceptron is a multilayered ANN which consists of one or more hidden layers. This type of ANN is known as a supervised network because it requires a desired output (teaching signal) in order to learn. A set of learning data that consists of the feature vector and the corresponding teaching signals is used to have the perceptron acquire appropriate connection weights and threshold values of the middle layer and the output layer by machine learning. The multilayered perceptron and many other ANN learn by using an algorithm called backpropagation. With back propagation method, the input data (feature vector) is repeatedly presented to the ANN. With each presentation, the output of the ANN is compared to the desired output (teaching signal) and an error is computed. This error is then feedback (back-propagated) to the ANN and used to adjust weights so that the error decreases with each iteration and the ANN model gets closer and closer to producing the desired output [37; 38]. When performing real-time classification, these connection weights and threshold values are fixed and determined beforehand. To keep the computational cost low, an ANN with only one hidden layer is structured in this study. Table 2 shows the number of neurons in the input, hidden and output layer along with the learning coefficient of each classification scenarios (see Section 5.1.2). Based on our experience [35] and also suggested by [39], overfitting may occur in ANN if the training iteration (epoch) is too long. To prevent this, the training process is terminated when the error reaches 0.001 or when the number of training epoch reaches 2000. It is also worth noting that the number of neurons in the hidden layer for each classification scenarios are determined based on our experience and trial and error. More specifically, the number of neurons that produces the lowest training error is selected. In the case that the training error reaches 0.001 for two or more neuron arrangements, the number of neurons that requires the least training epoch will be chosen.
# High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6
## 2 Decoding Process and Parameters Calculation of the Decoder
### Co-Processing Parameter Calculation
#### 2.2.2 Compute the Number and Length of the Codewords  
The number of codewords \(N_{\_}{CW}\) to be transmitted and the length of the codewords \(L_{\_}{LDPC}\) to be used can be calculated from Table 1. Where R repeats the code rate.
# Design, Manufacturing, and Flight Testing of an Experimental Flying Wing UAV
## 3 Weight Estimation for an Electric UAV
### Battery Weight Estimation with Endurance Requirement  
The electric-powered UAV's endurance mainly depends on its power consumption and battery. Its endurance is optimal for minimum power consumption. We use the minimum power consumption to estimate the battery weight. The battery mass \(M_{ battery}\) is calculated as:  
\[M_{ battery}=\frac{E_{ battery}}{\rho_{ battery}} \tag{10}\]  
The energy density of the battery (\(\rho_{ battery}\)) selected in this design is 243 Wh/kg. According to the conservation of energy, the aircraft's power consumption should be equal to the variation of battery energy during flight.  
\[\frac{dE_{B}}{dt}=-\frac{(\frac{P_{RMin}}{\eta_{motor}\eta_{ propeller}}+P_{others})}{\eta_{discharge}} \tag{11}\]  
Assuming \(P_{others}=0.1\)-\(P_{RMin}\), the amount of aircraft power required between the start and end of flight includes take off and climb.  
Therefore, the relation between battery energy and minimum power required \(P_{RMin}\) is:  
\[-E_{B_{0}}f_{DoD}=-\frac{1.1P_{R_{Min}}}{\eta_{motor}\eta_{ propeller}\eta_{discharge}}T_{ cruise} \tag{12}\]  
where the depth of discharge \((f_{DoD})\) indicates the percentage of the battery that has been discharged relative to the overall capacity of the battery. From the definition of the energy density of the battery, the battery weight in consideration of endurance can be estimated by dividing the battery power density to obtain as follows:  
\[W_{ battery}=\frac{1.1P_{R_{Min}}}{\eta_{motor}\eta_{ propeller}\eta_{discharge}f_{DoD}\rho_{ battery}}T_{ cruise} \tag{13}\]  
\[P_{R_{MIN}}=\sqrt{\frac{2W_{Total}^{3}}{\rho_{\infty}S_{ref}}}\left\{\frac{C_{ L}^{3/2}}{C_{D}}\right\}_{MAX}=\sqrt{\frac{2W_{Total}^{3}}{\rho_{\infty}S_{ref}}} \left\{\frac{C_{L}^{3/2}}{C_{D_{0}}+KC_{L}{}^{2}}\right\}_{MAX} \tag{14}\]  
where the minimum power required for a propeller-driven aircraft (\(P_{RMin}\)) occurs when \((C_{L}{}^{3/2}/C_{D})\) is maximum [5].  
Therefore, the weight of battery in terms of wing loading, zero-lift drag coefficient, induced drag factor and endurance required is given.  
\[W_{ Battery}=T_{Endurance} \tag{15}\] \[(\frac{1.1\sqrt{\frac{2W_{Total}^{3}}{\rho_{\infty}S}}\{i\frac{1}{4} \left(\frac{3}{KC_{D_{0}}^{3/2}}\right)\}}{\eta_{motor}\eta_{pre}f_{discharge}\rho_{ battery}})\]
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 4 System Architecture Design and Verification
### Hardware Architecture  
In the second stage of the design we have developed the hardware architecture as shown in Figure 4. This was modeled using _System Verilog_ and implemented using 32 nm standard CMOS technology. The functionality of the design has been verified at each stage of the implementation by comparing it with the golden model as explained in the previous section. The functionality of each block of the system is explained below:  
Figure 3: BER performance of 4-QAM over 2 \(\times\) 2 MIMO system using LR-aided K-best and compared to the corresponding K-best.  
1. _The QR block_ decomposes the input H matrix into an orthonormal matrix Q and an upper triangular matrix R. The precision of the QR decomposition has a considerable effect on the performance of the symbol detection. Low-quality QR decomposition directly causes a bit error rate, especially in systems with large numbers of antennas and/or large signal constellation size. There are three common-used algorithms for the QR decomposition including Gram-Schmidt, householder reflections and given rotations. Among these, the given rotation method offer the best performance-hardware cost tradeoff. In this work, given rotations algorithm is chosen for QR decomposition.
2. _The LLL block_ implements the Lenstra-Lenstra-Lovasz algorithm to compute the T matrix from Q and R matrixes. The CORDIC algorithm is used to implement all square root functions as it replaces multiplications with add and shift operations, which greatly simplifies the hardware.
3. _The MULT_1 block_ multiplies the T matrix with the H matrix to produce the orthogonal \(\hat{\text{H}}\) matrix.
4. _The shift and scale_ block implements Equations (5) and (6) in order to extend the matrix for the received signal \(\mathbf{Y}\) for the benefits of the following MIMO detection stage.
5. The functionality of the _MLD block_ is to detect \(\mathbf{z}\) as described in Section 3, as \(\mathbf{z}=\mathbf{T}^{-1}\)\(\mathbf{x}\). To achieve this it needs to perform the search process described in Equation (7) using a breadth-first K-Best algorithm  
_The MULT_2 block_ multiplies the output of the MLD block by the matrix T to recover the original constellation point x.
## 2 Opaque Passive Antenna Design  
The reconfigurable wideband Vivaldi antenna proposed in this study was fabricated on a fused quartz substrate measuring 12.7 mm \(\times\) 12.7 mm \(\times\) 0.2 mm. The substrate's dielectric properties were characterized by a relative permittivity \(\epsilon_{\mathrm{r}}\) of 3.75 and a loss tangent (tan \(\delta\)) of 4 \(\times\) 10\({}^{-4}\). The antenna structure consisted of two distinct parts, referred to as \"petals\", forming a radiating element measuring 7.25 mm \(\times\) 8.94 mm. A specially designed ground plane was printed onto the front side of the substrate (Figure 1a). To energize the radiating element, a transition was implemented between a slot and a microstrip line. The microstrip line had a length of 3.01 mm and terminated with a quarter circle with a radius of 0.6 mm, which was printed onto the rear of the fused quartz substrate (as depicted in Figure 1b).  
To achieve beam tilt in three specific orientations, namely, state 1, state 2, and state 3, different configurations utilizing a metal strip were employed as depicted in Figure 1a.  
State 1 is attained by establishing a connection between petal 1 and petal 2 with the ground plane.  
In state 2, petal 2 is connected to both the ground plane and petal 1.  
State 3 involves connecting petal 1 to both the ground plane and petal 2.
## 2 Study Area  
The study area is located in Mao County, Sichuan Province, Southwest China (Figure 1), extending from Songpinggou to Feihong, with a total area of 611.8 km\({}^{2}\). The geographic coordinates of the study area are between 103\({}^{\circ}\)32\({}^{\prime}\)-103\({}^{\circ}\)47\({}^{\prime}\) E and 31\({}^{\circ}\)47\({}^{\prime}\)-32\({}^{\circ}\)9\({}^{\prime}\) N. The area is located on the upper Minjiang River, in the transition zone from the Qinghai-Tibet Plateau to the Sichuan Basin. The geomorphology is dominated by alpine valleys, with Minshan Mountain in the north, Longmen Mountain in the south, and Qionglai Mountain in the west. The terrain is steep, sloping from northwest to southeast, with a relative elevation difference of 1000-2500 m in the northwest and 500-1500 m in the southeast.  
The region is characterized by a complex geological structure and strong neotectonic movements and seismic activities. The peak seismic acceleration is 0.20 g, making the area prone to geological hazards such as landslides and debris flows. The Minjiang fault, Songpinggou fault, and Shidaguan fault are the main fault structures in the study area, causing lithological fractures and inversions. The main exposed strata in the research area are Triassic, Permian, Devonian, and Silurian. The lithology of the research area consists of the upper Triassic series (T3x, T3 zh) metamorphic sandstone and sericite slate, the middle Triassic series (T2z) metamorphic sandstone and slate, the lower Triassic series (T1b) phyllite and slate, Permian siliceous intercalated with carbonatite, Devonian gray-black metamorphic sandstone and phyllite, and Silurian green sericite slate and quartz sandstone.  
The area has a plateau monsoon climate, and vertical differences in the climate and regional differences are pronounced due to large elevation differences, resulting in a complex local climate. The average annual temperature is 11 \({}^{\circ}\)C, and the minimum and  
Figure 1: Location, topography, and geology map of the study area.  
maximum temperatures are \(-11.6\)\({}^{\circ}\)C and \(32.2\)\({}^{\circ}\)C, respectively. The rainy season lasts from May to October, with a maximum daily rainfall of 75.2 mm, average annual rainfall of about 480 mm, maximum annual rainfall of 560 mm, and minimum annual rainfall of 335.5 mm. The area is prone to rainstorms, floods, and debris flows [47].