esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
## 2 Rectenna Design
### Design of the Rectifier  
In the rectenna design, rectifying network is more important because it determines the RF-signal-to-DC transformation efficiency. A voltage doubler circuit is used as a rectifier circuit in the proposed design to obtain a good compromise among the circuit complexity and the rectifier efficiency at low input power. The designed rectifier circuit (RC) with an impedance matching network (IMN) is depicted in Figure 4. The RC with the IMN is simulated using Keysight ADS software, and co-simulator harmonic balance is used to explore the characteristics of the RC. A simple L-section IMN is connected among the RF source and the rectifier to improve the conversion efficiency by transferring maximum power from the RF source output to the rectifier input and also by reducing the components required for the circuit and thereby the miniaturization of the device. The IMN consists of a parallel-series combination of 1.4 pF capacitance and an 11.4 nH inductor is utilized to match the RF source impedance of \(50.1+3.65\) i with the \(21.3-150.5\) i rectifier impedance at 2.45 GHz. The L-section IMN is optimized using the smith chart matching technique in the ADS at 2.45 GHz.  
Figure 3: Comparison of the S11 plots acquired from the CMCP antenna and the equivalent circuit.  
Figure 2: The analogous circuit of the proposed CMCP antenna.  
The RC is a voltage doubler network consisting of a bypass capacitor C\({}_{1}\) = 100 pF, a pair of Schottky diodes used for rectifying purposes, a shunt capacitor C\({}_{2}\) = 100 pF, and an optimal load resistance of RL = 10 K\(\Omega\). In rectifying the circuit, choosing the proper diode is a critical step because it changes the received AC signal into a usable DC signal. Higher rectifying efficiency can be obtained by using a smaller built-in voltage diode. In the proposed circuit model, HSMS2852 Schottky diode is considered because of its low forward bias voltage of 150 mV and a breakdown voltage of 3.8 V. By varying the input RF power from \(-\)30 dBm to 20 dBm, the DC voltage at the output and the RF-to-DC-transformation-efficiency plots are analyzed. When RC is straightforwardly connected to the RF source, the efficiency of the doubler network is very low than with the RC connected through IMN to the RF source. This is due to improper power transfer between the RF source output and the rectifier input. The proposed rectifier is fabricated on FR4 substrate with 1.6 mm thickness to analyze its performance, as depicted in Figure 5.  
Figure 4: The proposed simple voltage doubler rectifier circuit with L-section IMN.  
Figure 5: Photo of the fabricated rectifier.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 3 Results
### The Workflow for Flight Logs Using NFT-Based Technology  
The workflow for flight logs using an NFT-based system presents a technologically advanced approach to recording, verifying, and utilizing flight data (Figure 8) to create a secure, transparent, and immutable record of each flight, from pre-flight preparations to post-flight analysis.  
The process begins with the pre-flight stage, where a unique NFT is created for the upcoming flight. This digital token serves as the primary record for all subsequent flight-related data and activities.  
Next, the flight initiation stage occurs. Here, the NFT is updated with initial flight details, including departure information, flight plan, and any pre-flight checks. Concurrently, crew records are accessed and integrated into the flight log, updating the NFT with information about the flight crew, their qualifications, and duty times.  
As the flight progresses, the in-flight data collection phase begins. During this crucial stage, the NFT is continuously updated with real-time flight data. This may include navigation information, aircraft performance metrics, weather conditions, and any significant events or decisions made during the flight.  
Upon landing, the post-flight stage commences. The NFT is updated with arrival information, any notable occurrences during the flight, and preliminary post-flight checks.  
Figure 8: Flowchart of the workflow for flight logs.  
This stage also involves updating crew records with completed flight hours and any relevant notes.  
Following the post-flight updates, the data verification process begins. This stage is critical for ensuring the accuracy and integrity of the flight log. The NFT data is cross-checked against other systems and verified for compliance with regulatory requirements. Any discrepancies or issues are identified and addressed at this point.  
Once verified, the process moves to log finalization. Here, the flight log NFT is sealed, creating a finalized, immutable record of the entire flight. This finalized log is then linked to the broader aircraft record, contributing to the comprehensive history of the aircraft's operations.  
The completed flight log NFT becomes a valuable resource for several subsequent processes:  
1. The flight log provides crucial data for planning and scheduling aircraft maintenance, ensuring that all required services are performed at the appropriate intervals.
2. The detailed flight data captured in the NFT enables the in-depth analysis of aircraft and crew performance, helping to identify trends, efficiencies, and areas for improvement.
3. In the event of any irregularities or incidents, the immutable and comprehensive nature of the NFT-based flight log provides investigators with reliable, tamper-proof data for analysis and incident investigation.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### Local Transfer Mechanism of Edge Model  
#### 3.2.1 Local Classification Head Feature Transfer  
In order to improve the detection capability of specific categories while ensuring the integrity and stability of the backbone structure of the edge detection model, this paper designs a migration mechanism based on local classification head replacement. Specifically, for a certain category \(c\in C\), only train the weight vector \(Wc\) and the bias term \(b_{c}\); the rest of the network is frozen. The training goal is to minimize the classification loss and bounding box regression loss of the model on the current category. The optimization objective function is  
\[L_{c}=L_{cls}+\beta L_{reg} \tag{8}\]  
where \(L_{cls}=CE(f_{\theta c}(x),y_{c}^{cloud})\)[23] represents the cross-over loss between the classification head's prediction of the category and the cloud-side fusion label, \(L_{reg}\)[24] represents the smooth LI loss of the bounding box position prediction, which is used to constrain the position offset, and \(\beta\) is the loss weight coefficient.  
The training process uses mini-batch gradient descent SGD (Stochastic Gradient Descent) [25] optimization, and training is continued only when the classification loss decreases significantly. If the loss of multiple consecutive batches is lower than the threshold of 0.001, the system automatically triggers early termination to avoid overfitting. After training is completed, the optimized parameters are extracted and saved as category patches, and the patches are stored as pth files: \(patch_{c}=(W_{c}^{new},b_{c}^{new})\). The patch will be integrated into the basic model on the edge side when it is later determined that it needs to be migrated, improving the detection performance of this category.
## 4 Conclusions  
The secondary breakup characteristics and mechanism of single electrified Al/n-decane nanofluid fuel droplet in the electrostatic field were investigated in detail. The charge transportation, droplet deformation, Taylor cone formation, and secondary breakup were characterized by analyzing quantities of images and key data, some conclusions can be made as follows:  
1. The charge distribution is non-uniform at the droplet surface, resulting in a gradient of the electric field within the droplet and locally deforming the droplet to produce the Taylor cone. The Charge-to-Mass ratio ranges from 10\({}^{-7}\)-10\({}^{-5}\) C/kg. Overall, one cone was formed, occasionally two cones at Al nanoparticle concentration of 8.0 mg/mL.
2. The C/E values keep constant of \(\sim\)0.20 at most of Al nanoparticle concentrations, testifying that the reasonability of equilibrium dominated by the electrical force and the surface tension. On breakup, the surface charges are saturated but much less than the Rayleigh limit. At 6.0 mg/mL, the surface charge density reaches a maximum of 24.0%. The average diameters of secondary breakup sub droplets almost reached a minimum, suggesting that the large surface charge density achieves better breakup performance of single droplets.
3. Nanoparticle concentration has a significant effect on the charging of droplets, and the secondary breakup performance. At dilute concentrations below 3.0 mg/mL, the jet possibly does not produce, while at dense concentrations of 4.0-10.0 mg/mL, the jet filaments are formed and break up into uniform fine sub droplets. The minimum average diameter of secondary breakup droplets is 55.4 \(\mu\)m at 6.0 mg/mL.  
Conceptualization, S.L. and X.H.; methodology, H.L. and H.D.; experiments, H.L. and Y.L.; data treatment and software, Y.L. and H.D.; mechanism and analysis, S.L. and X.H.; writing--original draft preparation, review and editing, S.L. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Zhejiang Province (LY20E060004), the National Natural Science Foundation of China (51976050) and the Fundamental Research Funds for the Provincial Universities of Zhejiang (GK209907299001-004).  
The authors declare no conflict of interest.
## 3 Data and Methods
### Methods  
#### 3.2.1 1-D Surface Deformation Derived from SBAS Technique  
The SBAS technique is applied to derive deformation rate and time series in a one-dimensional (1-D) line-of-sight (LOS) direction from a set of highly coherent interferograms generated with small temporal and spatial baselines [20; 31]. The SBAS technique is applied to each ascending and descending track individually to derive the deformation results based on GAMMA software [32].  
Multilook average processing of ten pixels in the range and two pixels in azimuth directions for Sentinel-1 data, as well as one pixel in the range and five pixels in azimuth directions for RADARSAT-2 data, is adopted to reduce the phase noise and to obtain large scale deformation results. All Sentinel-1 interferograms are generated by setting the temporal and spatial baseline thresholds as 100 days and 200 m, respectively. Then, the differential interferograms are calculated, filtered, unwrapped and geocoded and resampled to the common grid. Differential interferograms are filtered using the adaptive filtering method based on the local fringe spectrum [33] and unwrapped by the general minimum cost flow (MCF) approach [32]. A 2-D quadratic model is used to remove the baseline residual error and to mitigate the long-wavelength artifacts of atmospheric disturbance. Then, deformation rates at highly coherent pixels in the LOS direction are derived by the stacking technique [32].
## 2 System Model and Problem Formulation
### Problem Formulation  
In the scenario studied in this paper, each visible satellite will receive data, which needs to be downloaded to the ES, denoted by \(b_{i}\). Given the download data rate of each visible satellite, a set of satellites and an ES, our challenge is to design a strategy that allows visible satellites to utilize ISLs to transfer data from satellites with high data loads to those with light loads, so that the bandwidth resources of feeder links can be fully used and the LEO system's download throughput is maximized. Let \(B_{i}\) denotes the actual capacity of the feeder link of satellite \(v_{i}\), and it also denotes the maximum data rate that can be downloaded to the ES through this feeder link. The objective of our optimization is to minimize the packet loss rate due to feeder link congestion, which can be given by:  
\[\min\Sigma_{i}|b_{i}-B_{i}|\,, \tag{4}\]  
where  
\[\|b_{i}-B_{i}|=\left\{\begin{array}{cl}b_{i}-B_{i}&when\ b_{i}-B_{i}>0\\ 0&when\ b_{i}-B_{i}<0.\end{array}\right. \tag{5}\]  
For simplicity, the following assumptions are made:  
1. The size of data packets delivered across all satellites is the same. Under this assumption, the amount of data can be expressed in terms of the number of packets;
2. Each satellite is equipped with four laser terminals to establish ISLs with four adjacent satellites in the same orbit and different orbits, and a wireless signal terminal to establish a feeder link with ESs. These links work in simplex mode.
###### Abstract
In this paper, therefore, we propose three additional low complexity demodulation methods to reduce computational complexity. The existing Euclidean distance (ED) method demodulates the signal by comparing the degree of similarity between test patterns obtained from training sequences and channel state information (CSI) received by multiple antennas [12]. This scheme uses all sub-carriers in the symbol and, thus, has enhanced BER performance, but the computational complexity is high. The SET (subcarrier energy-based threshold) method, which is one of the proposed demodulation methods, uses only sub-carriers with high reliability for calculation of Euclidean distance. The TCST (tag's channel state-based threshold) method selects only sub-carriers with good discrimination among CSI of preamble for calculating Euclidean distance. The SED (similar Euclidean distance) method measures the degree of similarity using absolute values of magnitude difference, which include only an addition operation. The contributions of this paper are as follows. 1. We proposed a MIMO (multiple-input multiple-output) Wi-Fi backscatter system using multi-antennas tag sensor. Since proposed system controls the impedance of multi-antennas independently and reflection gain does not decrease, degree of difference in the CSI pattern between Wi-Fi symbols was maintained. As a result, BER performance did not deteriorate while increasing data rate. 2. Instead of increasing data rate as needed, another backscatter method for improving reliability was presented by turning all switches on/off in multi-antennas tag sensor. 3. Data rate and reliability improved by using multiple antennas, but computational complexity also increased. So, we proposed dedicating three demodulation methods that reduce computational complexity while maintaining BER performance. Section 2 briefly describes an existing Wi-Fi backscatter communication system. Section 3 describes the proposed multiple-input and multiple-output (MIMO) Wi-Fi backscatter communication method. Section 4 describes the demodulation method applied for the proposed MIMO Wi-Fi backscatter system and improved demodulation methods to reduce computational complexity. In Section 5, we compare the performance and computational complexity of the existing system with the proposed tag sensor with multi-antenna in the Wi-Fi backscatter system. The contributions of this paper are as follows.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Performance Evaluation and Trade-Off Analysis
#### 4.3.3 Outage Probability  
The probability of failure quantifies the likelihood that the quality of the received signal is insufficient to meet a predefined quality of service requirement. Specifically, it is defined as the probability that the instantaneous SINR falls below a given threshold \(\gamma\). Mathematically, it is expressed as  
\[P_{\text{out}}=\mathbb{P}[\text{SINR}<\gamma] \tag{21}\]  
where \(\gamma\) is the minimum SINR required to sustain reliable communication.  
To reduce the risk of outages, the proposed system employs predictive coordination mechanisms and proactive RIS reconfiguration strategies. These techniques adaptively track channel variations and mobility dynamics, thus minimizing the probability of outages and maintaining a high level of communication reliability even in rapidly changing high-mobility environments.
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 2 LDPC Code in the IEEE802.16e Standard
### High-Efficiency and Low-Complexity Coding Algorithm (HE-LC)
The implementation flow of the high-efficiency and low-complexity coding algorithm is presented in Table 1.  
In the coding algorithm process, the maximum computational complexity lies in the first step \(f_{1}=Au^{T}\), where the matrix multiplication is solved by performing a cyclic shift operation on the \(u^{T}\) matrix. This results in a linear relationship between the computational complexity and the code length. Using the same method, \(f_{2}\) and \(f_{5}\) can be obtained. For an identity matrix \(E_{n\times n}\) and any column vector \(A_{n\times 1}\), if we cycle up \(A_{n\times 1}\) bit of column vector \(q\) to obtain \(B_{n\times 1}\), and cycle right \(E_{n\times n}\) bit of column vector \(q\) to obtain \(E^{\prime}_{n\times n}\), then \(E^{\prime}_{n\times n}\), \(A_{n\times 1}=B_{n\times 1}\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Step** & **Computational Formula** \\ \hline Step I & Calculate: \(f_{1}=Au^{T}\),\(f_{2}=Cu^{T}\) \\ Step II & Calculate: \(f_{3}=T^{-1}f_{1}\),\(f_{4}=Ef_{3}\) \\ Step III & Calculate: \(p_{2}=f_{4}+f_{2}\) \\ Step IV & Calculate: \(f_{5}=Bp_{2}^{T}\) \\ Step V & Calculate: \(f_{6}=f_{1}+f_{5}\) \\ Step VI & Calculate: \(p_{1}=T^{-1}f_{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Implementation flow of the high-efficiency and low-complexity coding algorithm. Figure 2: Simulation diagram of the \(\varphi\) matrix generation. For the calculation of step 2 \(f_{3}=T^{-1}f_{1}\) and step 6 \(p_{2}=T^{-1}f_{6}\), if the inverse matrix method is used to solve, it will have a high computational complexity, and it is highly likely to damage the sparsity of the matrix, which is not conducive to hardware implementation. For binary encoding, the forward permutation method can be used to solve this type of operation. In the implementation process, the calculation of \(f_{3}=T^{-1}f_{1}\) and \(p_{2}=T^{-1}f_{6}\) can be simply achieved using XOR gates. At the same time, the calculation of \(f_{6}=f_{1}+f_{5}\) and \(p_{1}=f_{4}+f_{2}\) can also be achieved using XOR gates, and, finally, the complete codeword can be obtained by combining the information sequence and verification sequence in an integer order. The computational complexity analysis of check bits \(p_{1}\) and \(p_{2}\) in efficient and low-complexity encoding algorithms is shown in Tables 2 and 3. From the observation and analysis of Tables 2 and 3, it can be seen that the computational complexity of the improved, efficient, and low-complexity encoding algorithm exhibits a linear relationship with the size of the code-length value.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 2 Principle and Methods
### Dynamic Bayesian Network for Fatigue Crack Growth  
Figure 4 shows the structure of a Bayesian network for one time step. The nodes in the figure are all random nodes, indicating that the variable is random given the value of the parent node; therefore, the arrow pointing to it represents the conditional probability distribution (CPD). The variable names and value ranges of the node symbols in Figure 4 are shown in Table 1. Diamond nodes are function nodes, which means that the variable is a deterministic formula calculation result given the value of the parent node; therefore, the arrow pointing to it represents a deterministic function. In addition, elliptical nodes represent continuous variables. Rectangular nodes represent observed variables (such as load and crack length), where \(\Theta\) refers to all uncertain material fatigue parameter variables considered in the crack propagation formula.  
Figure 4: A single-step Bayesian network for crack propagation.  
For ease of calculation, all uncertain factors above, except for the initial prior values of fracture toughness, tensile strength, and damage initiation displacement, are assumed to be normal distribution continuous nodes. Usually, load \(P\) and crack length \(a\) are observable nodes.
## 3 Materials and Methods
### Sensor Node Monitoring  
The sensor node shown in Figure 2b was programmed using the Arduino IDE, enabling programming and uploading files to the ESP32 Wi-Fi module. Web browsers on smart gadgets, also referred to as web clients, are the responsibility of an asynchronous web server. The communication between the web client and the web server, the hosting server and the domain name is established by the Hypertext Transfer Protocol (HTTP). Web clients can connect to the web server through a router and the internet. The required activities are carried out using a standard client-server architecture and building a client that requests a PHP script to publish sensor readings in a MySQL database. The webpage displays sensor readings in a plot that can be accessed anywhere. With the assistance of this method, sensor data can be transmitted from websites to web clients. These data may then be utilised to evaluate, investigate, and take necessary action about ambient temperature and humidity variables. The following technologies to construct an optimised communication model are utilised:  
* Arduino IDE, to programme the ESP32;
* Domain name and hosting server;
* PHP script for inserting data into the MySQL database and displaying it on a web page;
* MySQL database for storing readings;
* PHP script for plotting data from the database into charts.  
The program's design includes a HyperText Markup Language (HTML) structure that specifies how the webpage should be displayed. The ESP32 must connect to the appropriate sensors to monitor the environment to complement successfully uploading the programming code. This idea aims to have a domain name and a hosting account that allows us to store sensor readings from the ESP32. It can visualise the readings from anywhere by accessing its own server domain. To build the charts, the Highcharts library was used. It shows two charts: temperature and humidity over time. The charts display a maximum of 70 data points, and a new reading is added every 30 s. The capability of data points can be changed at any time in the code. The web client may access the data through a web browser thanks to the design of an Internet of Things network for temperature and humidity monitoring and its implementation using a web server. Figure 6 depicts the high-level overview of hosting PHP applications and MySQL database.  
Figure 5: Sensor node architecture.  
The proposed IoT-based temperature and humidity monitoring framework comprises sensor nodes with an ESP32 Wi-Fi module and a DHT11 temperature and humidity sensor with a power supply. In this research, the ESP32 microcontroller with a dual-core processor, and Wi-Fi connectivity was used. The sensor nodes connect with a base station either directly or via Wi-Fi-enabled nodes near the base station. The base station provides connectivity to the internet. Sensor data can be obtained via connecting to the internet. Client-server architecture is used to perform the essential task.