esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio
## 3 Performance Analysis
### NOMA System  
In the NOMA system, it is challenging to consider a large number of users due to the occupancy of the PN spectrum band. Hence, we only focus on the superposition of signals from two users. Let us assume that the normalized signal \(x_{S}=\sqrt{a_{1}P_{S}}x_{1}+\sqrt{a_{2}P_{S}}x_{2}\). Here, \(x_{n}\) is the signal required by the nth user, \(P_{S}\) denotes the transmission power at ST, and \(a_{n}\) is the power allocation coefficient (PAC) for the nth user, indicating the proportion of the transmitted power allocated to \(x_{n}\)[32]. Following the principles of NOMA, \(a_{1}>a_{2}\) and \(a_{1}+a_{2}=1\). Therefore, the signal at \(R\) is  
\[y_{R}=h_{SR}\Big{(}\sqrt{a_{1}P_{S}}x_{1}+\sqrt{a_{2}P_{S}}x_{2}\Big{)}+n_{R}, \tag{16}\]  
where \(n_{R}\sim\mathcal{CN}(0,1)\) represents AWGN at \(R\).  
In the second slot, \(R\) amplifies \(y_{R}\) using the amplification coefficient \(G\) before propagating it to all users. The signal received by the nth user can be expressed as  
\[y_{U_{n}}=\sqrt{P_{R}}Gh_{RU_{n}}y_{R}+n_{U_{n}}, \tag{17}\]  
where \(P_{R}\) represents the transmission power at \(R\), and \(n_{U_{n}}\sim\mathcal{CN}(0,1)\) denotes AWGN at the nth user. Thus, \(G\) should be determined as  
\[G=\frac{1}{\sqrt{\Big{(}P_{S}|h_{SR}|^{2}+1\Big{)}}}. \tag{18}\]  
For each user, the desired received signals are subject to interference from the signals of other users. Consequently, successive interference cancellation (SIC) should be performed at each user to obtain its own signal. The SIC decoding order is incrementally related to the user's channel gain \(\Big{(}\big{|}h_{RU_{1}}\big{|}^{2}<\big{|}h_{RU_{2}}\big{|}^{2}\Big{)}\). Therefore, at the \(n\)th user, it will detect the \(j\)th user's signal where \(j<n\), and then subtract it from the \(n\)th user's received signal sequentially. The signal of the \(m\)th user with \(m>n\) will be treated as noise at the \(n\)th user [32].  
The generalized formula for the signal-to-interference plus noise ratio (SINR) can be computed as  
\[\gamma_{j\to n}=\frac{a_{j}P_{S}P_{R}|h_{SR}|^{2}\big{|}h_{RU_{n}} \big{|}^{2}}{P_{S}P_{R}|h_{SR}|^{2}|h_{RU_{n}}|^{2}\sum\limits_{i=j+1}^{M}a_ {i}+P_{S}|h_{SR}|^{2}+P_{R}|h_{RU_{n}}|^{2}+1}, \tag{19}\]  
where \(\gamma_{j\to n}\) represents the SINR of the \(n\)th user to decode the signal from the \(j\)th user.
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 1 Introduction
This paper proposes a simplified model for the RCS calculation of the dihedral CR considering both the panel size and the SAR local incidence angle change. Based on the CR array with varying tilted angles and sizes on the Huangtupo slope, Badong county, Hubei province, China, overlapping Sentinel data and TSX data with ascending and descending tracks, we conduct empirical research on the RCS reduction of dihedral CR arrays and evaluate the deformation monitoring accuracy. Finally, we provided design recommendations for CR size based on the experimental results.
## 3 System Model and Security Goals
### Adversary Model  
The Dolev-Yao adversary model was introduced in 1983. It is applied for the cryptanalysis of the proposed security models [46]. All possible controls that can be performed by adversaries such as duplicating the messages, modification of traffic, and wrongly updating are considered to prove the analysis. For example, an intruder attempts to listen in on a conversation and gain right of entry into personal data. Considering the adversary model in an IoT-based healthcare environment, the attacker can change the frequency rate for controlling the patient's vital signs consisting of coronary heart rate, blood stress, and blood glucose ranges. Cyberattacks can result in severe results such as patient death, economic loss, and a bad reputation for the healthcare system.
## 4 Discussion
### Lessons Learned at the 3 Deployment Locations
#### 4.1.2 SkylImager at San Antonio, TX, USA  
Two critical applications of islanded microgrids are remote installations in developing countries and power systems for the military that must be entirely stand alone. While this made a military base the perfect site for testing a microgrid EMS, it also meant that obtaining base access for UTSA researchers was an issue. For safety purposes, it requires at least two people to lower the 10 m MET tower. Beyond the initial installation, access to the tower is required every month to inspect the instruments and clean the surface of the plastic dome that covers the camera enclosure. The cost of the pyranometer and weather station exceeded that of the SkylImager by a factor of 20 and prompted adding low-cost sensors to the SBC for measuring temperature, humidity, and light.  
Occasional loss of power to the SBC as the battery went through its initial testing phase was a minor issue which required tinkering so that the forecasting software was immediately brought back on line during a reboot. Initially the Mosquito MQTT broker was chosen for the UTSA software, but because of compatibility issues with the MyRio software, HiveMQ proved to be a better choice. Direct internet access to our SBC from outside the corporate network was available only through a WebEx session that required close coordination between the utility and university personnel. Lesson learned: when initially transitioning hardware/software from a research environment to a production one, it is imperative to have physical/cyber access to the equipment and network. Several Odroid's were damaged by high temperatures in the enclosure boxes and had to be replaced. MV90 meter readings every 15 min are clearly insufficient for the intra-hour forecasting problem. We are currently working with a group in Austin to add inexpensive Phasor Measurement Units (PMU) to acquire detailed frequency and phase information in conjunction with weather, irradiance, and sky images. Cloud computing [73] and 5G networks offer unique opportunities to move most of the computations off the Raspberry Pi to the cloud.
# Research on Tracking Technique Based on BPSK-CSK Signals
###### Abstract  
The code-shift keying (CSK) modulation method can achieve higher information transmission rates without changing the spread spectrum signal bandwidth. In order to optimise the spread spectrum modulation and demodulation of GNSS signals, in addition to the signal structure, binary phase-shift keying (BPSK) and CSK signals using time-division multiplexing are proposed. A tracking method based on the BPSK-CSK signals is also proposed, which generates the P-branch local codes by fast Fourier transform to obtain the code-slice spacings for the E-branch and the L-branch local codes. Then, the tracked BPSK signals and tracked CSK signals are compared and analysed. Finally, the bit error rate (BER) and tracking error of each method are compared and analysed by comparing with the tracking of conventional BPSK signals and tracking of CSK signals, in order to verify the convergence process of the I-branch integral value. The BPSK-CSK signal tracking method proposed in this paper combines the high information transmission rate of CSK-modulated signals and the low BER of BPSK signals, and the results provide a solid foundation for high-precision GNSS services.  
CSK modulation; BPSK modulation; high transmission rates; GNSS; tracking +
Footnote †: journal: Medical Informatics
## 3 Modular Modeling for BTM System
### Modeling for Induction Disturbance Source Module  
As a loop antenna, the on-board antenna gets the location information and other useful messages transmitted from balise by receiving the magnetic field signal. Meanwhile, the disturbance would be coupled to the on-board antenna too. To quantify the magnetic field disturbance, the induction disturbance source module is established by using a rectangular loop antenna with a current source \(I\), as shown in Figure 9.  
The side length of the rectangular loop antenna are \(a\) and \(b\) respectively. When the rectangular loop antenna is electrically small, the magnetic field intensity at the field point \(P\) (\(x\), \(y\), \(z\)) could be calculated by Biot-Savart law [24],  
\[B=-\frac{\mu_{0}I}{4\pi}\oint_{T_{\mathrm{loop}}}\mathrm{d}l\times\
abla\frac{ 1}{R}. \tag{5}\]  
Figure 8: Model for Susceptibility Prediction of Balise-Based Train Control Systems.  
Figure 9: Rectangular Loop Antenna with Current \(I\).  
According to Equation (5), the magnetic field disturbance is related to the size of the rectangular loop antenna, the distance \(R\) and the disturbance current \(I\). Thus, when the position and the size of every device is fixed, the level of magnetic field disturbance is determined by current \(I\). In the case of the electromagnetic environment on-site, these disturbances may be generated by the following sources which are laid close to the on-board antenna: current harmonics from the traction system, current from the rails and cables [23]. These disturbance currents could be pure sinusoidal continuous wave, transient, damped oscillation typology [9]. Using Fourier series, all of this disturbance could be described as a superposition of sinusoidal waves with different frequencies. Thus, to simplify the modeling process, pure sinusoidal continuous wave disturbance shall be applicable.  
The voltage of induction disturbance \(S_{2}\) could be obtained as [25]  
\[V_{2}=I\cdot R_{s}, \tag{6}\]  
where \(R_{s}\) is the port impedance of the rectangular loop antenna.
## 7 Future Work
### Matching among Various Network Nodes  
In this paper, only the matching problem between HAP drones and LEO satellites is considered. However, in the space-air-ground integrated network, in addition to HAP drones and LEO satellites, there are also a variety of network nodes, such as ground users, gateway stations, and geostationary earth orbit satellites. In the future, it is necessary to investigate the matching relationships among different nodes to improve the topology of the space-air-ground integrated network. For example, the matching problem between ground users and HAP drones should be addressed by comprehensively considering multiple factors such as the location, movement speed, and service requirements of ground users and the payloads of HAP drones.  
Figure 6: Total transmitted traffic value under different beam numbers.
## 5 Extending Simheuristics with Fuzzy Logic  
This section introduces an extension of the simheuristic framework, which is called the fuzzy simheuristic framework [42]. As depicted in Figure 3, this framework combines heuristic-based and simulation components with fuzzy elements to solve combinatorial optimization problems with both stochastic and fuzzy uncertainties. The approach begins by replacing both stochastic and fuzzy elements with their expected or estimated values to obtain a deterministic version of the original problem. The deterministic problem is solved using a metaheuristic algorithm that generates high-quality solutions while discarding low-quality or infeasible solutions. The framework in Figure 3 assesses the performance of these high-quality solutions under uncertainty by conducting a relatively small number of simulation runs with different values assigned to the random variables or fuzzy elements in each run, according to their probability distribution or fuzzy rules. Descriptive statistics are computed for all solutions to obtain detailed information about them. To handle fuzzy elements, the proposed approach employs type-2 fuzzy sets (T2 FS) [43], which are fuzzy sets whose membership degrees are themselves type-1 fuzzy sets (T1 FS). By incorporating T2 FS into the analysis, the approach can more effectively handle complex and uncertain models, improving the results' accuracy and robustness. The evaluation of solutions not only allows for the assessment of solutions under uncertainty but also provides guidance to the metaheuristic during the generation of new solutions. The best found solutions are stored in a pool of 'elite' solutions, which is limited in size. The generation of new solutions finishes when the metaheuristic satisfies a stopping criterion, which usually refers to a computational time budget. Next, the elite solutions are subjected to a more thorough examination under uncertainty by obtaining more observations of their stochastic and fuzzy behavior (e.g., by performing more runs during the simulation procedure and the fuzzy analysis procedure). Based on these observations, the solutions are ranked based on central statistics (e.g., average) and dispersion statistics (e.g., standard deviation or quantiles). The solutions with the best performance are then recommended to the decision maker. Notice that in a scenario under uncertainty, the selection of the 'best' solution may consider more criteria than just the expected value, such as the variance (risk) or the reliability of each solution (i.e., the probability that the solution can be implemented in practice without any failures that force us to apply a repairing operation, which is usually costly).  
Figure 3: Schema of the fuzzy simheuristic approach.
## 2 Proposed Scheme and RF Transmission Algorithm
### RF Transmission Algorithm  
The procedure for RF transmission is outlined in Algorithm 2. Implementing a long-term evolution (LTE) System Toolbox, the algorithm replicates an LTE signal that satisfies the requirements of a realistic signal transmission environment. The process starts by setting the configuration parameters for the transmission based on the needs of the transmitting signal. These parameters are the type of configuration, sampling rate, bandwidth and filter. Using these parameters, the proposed algorithm generates the reference measurement channel (RMC) waveforms. The algorithm also implements a root-raised cosine type filter (\(ECG_{TX}\)) before transmitting the signal to reduce inter-symbol interference, improving the qualityand reliability of the signal. The AD9361 transceiver is initiated by setting up the input (\(inputTX\)) and output (\(outputRX\)) channels of the card once the signal is prepared. Finally, the proposed algorithm transmits the encrypted signal on the 2.45 GHz frequency and receives it on the RX1 channel (\(ECG_{RX}\)). As an overview, the transmission methodology is described as follows:  
1. Declare the specific parameters for LTE signal configuration.
2. Generate the RMC waveform following the established parameters.
3. Implement a root-raised cosine filter and signal strength adjustment.
4. Define transmission and reception parameters for the AD9361.
5. Transmit the generated signal through the set transmitter.
6. Use of the configured receiver to capture the transmitted signal.  
```
1:Modulated signal \(mS\)
2:Received signal \(ECG_{RX}\)
3:procedureGenerate_Transmit_LTE_Signal
4:LTE Parameters Configuration
5: Set LTE configuration parameters
6: Generate RMC signal with specified parameters state
7:Transmission
8:\(ECG_{TX}\leftarrow\) filter_design(\(mS\), rolloff, span)
9:inputTX\(\leftarrow\) {real(\(ECG_{TX}\)), imag(\(ECG_{TX}\))}
10:AD9361_TX(inputTX)\(\triangleright\) Setup AD9361 for signal transmission.
11:AD9361_RX(outputRX)\(\triangleright\) Setup AD9361 for signal reception.
12:Signal transmission
13:\(ECG_{RX}\leftarrow\) AD9361_RX(outputRX)\(\triangleright\) Reception on channel RX1
14:return\(ECG_{RX}\)
15:endprocedure
```  
**Algorithm 2** Generation and Transmission of LTE Signal
# Postbuckling and Free Vibration of Multilayer Imperfect Nanobeams under a Pre-Stress Load
## 2 Problem Formulation
### Constitutive Equations
Therefore, the equation of motion reduces to  
\[ \begin{array}{ll}m\;\ddot{w}-\mu\;m\;\ddot{w}^{\prime\prime}&+\Big{[}D_{11}+ \mu\Big{(}\frac{A_{11}}{2L}\int_{0}^{L}\Big{(}{w^{\prime}}^{2}-{w_{0}^{\prime}} ^{2}\Big{)}dx-\overline{N}\Big{)}\Big{]}w^{i\
u}\\ &-\Big{[}\frac{A_{11}}{2L}\int_{0}^{L}\Big{(}{w^{\prime}}^{2}-{w_{0}^{\prime}} ^{2}\Big{)}dx-\overline{N}\Big{]}w^{\prime\prime}=D_{11}w_{0}^{i\
u}\end{array}  \tag{15}\]  
In order to assess the significance of the size, layup, and initial curvature on the resulting static and dynamic response, we introduce the following nondimensional parameters:  
\[x=\frac{\hat{x}}{L}\;,\;w=\frac{\hat{w}}{r},\;r=\sqrt{I/A}\;,\;t=\hat{t}\sqrt{ \frac{D_{11}}{mL^{2}}}\]  
The () identifies dimensional quantities, which is understood to apply to all previous equations. As a result, the nondimensional equation of motion can be simplified and expressed as follows:  
\[ \begin{array}{ll}\Big{(}1-\overline{\mu}\;\frac{d^{2}}{dx^{2}}\Big{)}\ddot{w }&+\Bigg{[}1-\overline{\mu}\;\left(\overline{P}-\frac{1}{2}\alpha\int\limits \limits_{0}^{1}\Big{(}{w^{\prime}}^{2}-{w_{0}^{\prime}}^{2}\Big{)}dx\right) \Bigg{]}\;w^{i\
u}\\ &+\Bigg{(}\overline{P}-\frac{1}{2}\alpha\int\limits_{0}^{1}\Big{(}{w^{\prime}} ^{2}-{w_{0}^{\prime}}^{2}\Big{)}dx\Bigg{)}w^{\prime\prime}=w_{0}^{i\
u}\end{array}  \tag{16}\]  
where the nondimensional size parameter, \(\overline{\mu}\), pre-stress load, \(\overline{P}\), and lamination coefficient, \(\alpha\), are defined as  
\[\overline{\mu}=\frac{\mu}{L^{2}},\;\overline{P}=\frac{\overline{N}\;D_{11}}{L ^{2}},\;\alpha=\frac{A_{11}\;r^{2}}{D_{11}}\]  
Investigating Equation (16), one can notice that the size parameter contributes to the inertia and the stiffness of the beam. Due to the nature of the buckling shape, the second derivative with respect to the spatial coordinate, \(x\), yields a minus sign, which causes the effect of the size parameter to increase the inertia. On the other hand, the size parameter reduces the stiffness, which means the small-size effect is of a softening type. Therefore, one concludes that as the size becomes smaller, the beam will end up having less buckling load and less natural frequencies. This will be clearly shown by the numerical results presenting lower buckling loads and lower natural frequencies in Sections 4 and 5, where the significance of the size parameter, \(\overline{\mu}\), the initial curvature, \(a\), and lamination coefficient, \(\alpha\), on the buckling and free vibration of multilayer nanobeams is presented.
# Secure PBFT Consensus-Based Lightweight Blockchain for Healthcare Application
## 4 Experimental Results
### Communication between Nodes  
The number of communications between the nodes to achieve consensus affects the overall performance of the system, which is interrelated to the number of resources consumed for the deployment of the algorithm. However, communication times are experimentally computed considering different nodes and compared with the traditional PBFT algorithm. Figure 10 illustrates the communication times of PBFT and the proposed algorithm; the general observation is that the proposed model induces less communication overhead. The main reason for minimal communication between the nodes in the proposed algorithm is due to the segregation of nodes into two lists. Furthermore, only a trusted consensus node will participate in the transaction validation process. However, eventually if we vary the nodes from 2, 4, 6,..., 18, the number of communications will also increase in both algorithms. Finally, the proposed model consumes less resources with minimal communication among the nodes.  
Figure 8: Transaction Latency.  
To summarize, the experimental results of the proposed PBFT consensus algorithm has shown a 25% increase in transaction confirmation time, and the steady increase in transaction latency as the number of nodes increases in the network, with minimal communication overhead compared to the traditional PBFT consensus algorithm. The comparative analysis of the proposed algorithm with the existing popular consensus algorithms considering the parameters such as nodes participating in the consensus process, node reliability assessment, network type, node election fairness, and communication overhead, is shown in Table 3.  
Figure 10: Communication between nodes.  
Figure 9: Faulty node rate.
## 2 Related Work
### Efficiency Evaluation  
In order to evaluate the efficiency of some well-known distributed spanning tree algorithms, we implemented the DFS algorithm proposed in [24] and the DBF [12] and SHM [9] algorithms, which are based on the BFS method. The algorithms were simulated assuming an IEEE802.15.4-based [25] WSN. The simulator considers the typical radio parameters to determine the transmission and interference ranges and implements the CSMA/CA MAC strategy. Further details about the simulation framework are presented in Section 4.1. Two scenarios are considered involving network topologies formed by 50 and 100 nodes. The metrics are the number of messages required for the tree construction and the runtime of the algorithms, whose results are shown in Figures 1 and 2.  
Among the evaluated algorithms, DBF presents the shortest runtime. However, the performance with respect to the number of messages considerably degrades with the increasing number of nodes. In a network formed by 50 nodes, DBF requires approximately 25 messages per node for the tree construction. In a network formed by 100 nodes, the number of messages increases by approximately 80%. WSNs are generally formed by a large number of nodes, which makes DBF an unsuitable solution. The advantage of DBF is simplicity: it demands few computational resources and does not require network topology knowledge.  
In order for the sink node to know whether the algorithm terminated, SHM requires a complex synchronization mechanism. For this reason, the algorithm presents low efficiency with respect to the number of messages and runtime. Compared to DBF, the runtime of SHM increases approximately 130%. Furthermore, sensors have to know which nodes are one-hop away from them. The algorithm is inflexible with respect to the metric used to compute the cost of the links. However, the main drawback of SHM is the fact that it is not prepared for message losses or node failures during the SPT construction. A parent node can remain indefinitely waiting to receive a message from one of its children to continue the execution of the algorithm. The advantage of SHM is a resilient tree, since sensor nodes have alternative  
Figure 1: Average number of messages per node.  
Figure 2: Runtime.  
parents. If the parent node fails or dies, children nodes have alternative routes to send the collected data to the sink. The alternative routes can also be used as mechanisms for load balancing.  
The DFS algorithm is a scalable solution with respect to the number of messages. Comparing the scenarios with 50 and 100 nodes, the number of messages increases by approximately 13%. However, the performance with respect to the runtime considerably degrades when the number of nodes increases. Comparing the scenarios with 50 and 100 nodes, the runtime increases by approximately 125%. The reason for the poor performance with respect to runtime is the fact that DFS is a sequential algorithm [21]. For the same reason, DFS is not prepared for message losses or node failures and requires sensors to know which nodes are one hop away from them. Table 1 summarizes the main features of the evaluated algorithms.