esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Transition Phase for Compound-Wing UAVs  
The flight process of compound-wing UAVs can be divided into vertical takeoff and landing state, cruising state, and transition state. For compound-wing and other VTOL aircraft, the transition phase is a stage of changing the flight mode, such as the compound-wing aircraft switching from fixed-wing mode to rotor mode when decelerating to stall speed, which can achieve subsequent short-distance/vertical landing, and the tilt-rotor aircraft placing the tilt-rotor horizontally after accelerating to stall speed, etc. [13].  
Due to the reduction in flight speed of electric vertical takeoff and landing aircraft in the deceleration transition phase, the aerodynamic force gradually weakens, resulting in lift loss, dynamic stability reduction, and aerodynamic control surface efficiency decline. In addition, the transition phase of the compound-wing aircraft is a complex process [14] involving significant nonlinear characteristics and complex aerodynamic characteristics, and is difficult to control, making it a critical and dangerous stage throughout the entire flight process. Therefore, more stringent requirements are put forward for the control law, which become the key and challenging aspect in the design of UAV control law.  
[PERSON] et al. [15] pointed out in their study that autonomous transition control of V/STOL aircraft is an important challenge, different from general flight control problems. Transition control is a nonlinear, nonaffine control problem, and attitude and position movements are affected during the transition process, so the flight path must be controlled well. In addition, the transition phase is a multi-input problem with special inputs.  
[PERSON] et al. demonstrated in their research on the transition phase of fixed-wing VTOL UAVs that the transition flight phase should be carefully planned, not only for stable operation, but also for safe transition between flight modes in urban airspace. They also proposed a design of the flight control system to compensate for the aerodynamic effects during the transition phase [16].  
Therefore, when designing the transition stage of the compound-wing unmanned aircraft, safety must be given a higher priority. For this, we consider trading time and energy for higher safety, and use simulations to verify the rationality and necessity of this approach.
# Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration
## 2 Background  
### Literature Review  
Recent advancements in federated learning have demonstrated significant potential in heart disease prediction by leveraging distributed data while preserving data privacy [5; 6]. [PERSON] et al. [7] introduce a federated learning approach that integrates IoT capabilities with Electronic Health Records (EHRs) to predict heart diseases. Employing a soft-margin L1-regularized Support Vector Machine classifier and a cluster primal-dual splitting algorithm enables the methodology to handle high-dimensional data effectively, ensuring enhanced prediction accuracy and data privacy.  
Asynchronous updates in federated learning models improve the efficiency and accuracy of predictive models for cardiovascular diseases. The authors of [8] proposed an asynchronous federated learning model using DNNs, incorporating an asynchronous learning technique to update parameters and a temporally weighted aggregation technique. This method significantly reduces communication costs and improves the convergence rates of the models, demonstrating its effectiveness over traditional synchronous federated learning approaches.  
[PERSON] et al.'s [9] research emphasizes federated learning's potential to facilitate multi-institutional medical research without compromising patient data privacy, and its design allows for the collaborative training of algorithms without direct data exchange, aligning with strict data privacy regulations like GDPR and HIPAA. Enhancements in federated learning efficiency and accuracy promote its adoption across various medical fields, enabling global research collaborations utilizing large, diverse datasets.  
The study by [PERSON] et al. [PERSON] et al. (2019) examines the application of federated learning to manage heart disease data across Internet of Medical Things (IoMT) devices, focusing on privacy without compromising data utility. The paper details how federated learning allows for local data processing, enhancing data security in distributed healthcare environments. This approach addresses key data privacy and utility challenges within the rapidly evolving smart healthcare landscape.  
In [PERSON] et al. (2019), the authors introduce split learning as a privacy-preserving technique for healthcare applications, particularly in heart disease prediction. The paper demonstrates how deep learning models can be trained on segmented data, ensuring that sensitive patient information remains localized and secure. This method facilitates collaborative research while maintaining strict data privacy, marking a significant contribution to secure medical data processing.
## 5 Numerical Results
### Comparison for Results from the 10-Unit System with Different Cost Function Characteristics
#### 5.1.2 Case 1: 10-Unit System with MFs and VPEs  
This system had ten generating units with the sum of several non-convex functions. The load demand of the test system was 2700 MW. The obtained results including the best cost, mean cost, worst costs, and standard deviation for 100 trial runs are shown in Table 2. In addition, \(N_{p}\), \(G_{max}\), \(N_{fes}\), and \(ic\) (%) are also reported for comparison in detail. The best cost value of the IALO method was the lowest among all compared methods, including PSO, FOA, IFOA, CSA, RCSA, and ALO. The highest improvement was 6.124% as compared to the FOA method and the lowest improvement was 0.004% as compared to the RCSA method. Clearly, FOA was not a powerful method for the system and it was modified and changed into IFOA with much better results with an \(ic\) of 0.007% even though the IFOA used only half of the number of fitness evaluations. CSA [27] used the highest number of fitness evaluations, at 10,000, among all compared methods, but it could not reach the best solution and, therefore, the proposed method reached improvement over that method with 0.005%. The proposed method reached the best solution, but it used the highest number of fitness evaluations, excluding the comparison with CSA. The results show that it is hard to improve the results for the system with a small number of fitness evaluations, similar to the other compared methods. However, the running time of the proposed method was small compared to those of the other methods. Consequently, the proposed method is a potential method for 10-unit system taking multi-fuel options and vale point loading effects into account.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 3 Data Sources
### Sensor Maintenance Plan  
To ensure long-term reliability and data accuracy, a structured maintenance plan is implemented for key components of the monitoring system, focusing on operational cycles and technical specifications:  
The meteorological instrument (YL-QX86) undergoes daily self-checks to verify parameter consistency (e.g., temperature, humidity) and flag anomalies. Monthly, probes and connector cleaning mitigate debris interference. Quarterly, calibration against reference stations ensures measurement accuracy (e.g., temperature within \(\pm 0.5\)\({}^{\circ}\)C).  
For GNSS deformation monitoring, the ComNav A300 receiver weekly antenna inspections maintain a \(15^{\circ}\) clear sky view and secure mounting. Monthly signal analysis tracks satellite count (\(\geq\)6) and PDOP (\(\leq\)6) to identify signal degradation. Annual firmware updates optimize positioning algorithms, maintaining horizontal/vertical accuracy of \(\pm\)(8 + 1 \(\times\) 10\({}^{-6}\)\(\times\) D) mm and \(\pm\)(15 + 1 \(\times\) 10\({}^{-6}\)\(\times\) D) mm, respectively.  
Visual cameras (Sony 3351) require biweekly lens cleaning to preserve image clarity. Monthly test chart calibration corrects color distortion. Quarterly power checks ensure stable low-power operation (\(\leq\)15W during use) to avoid interruptions.  
The DJI Mavic 3E UAV undergoes pre-flight checks for propeller integrity, gimbal stability, and signal transmission (4K, 30 fps, latency \(\leq\) 200 ms) and post-flight data backups to secure aerial imagery. Monthly battery maintenance replaces units with a capacity of \(<\)80%, and firmware updates ensure compatibility with the \"well-shaped\" tilt photography route.  
The DJI Robo Master S1 also undergoes pre-flight camera/gimbal tests and post-flight data storage for remote analysis. Monthly inspections check gimbal smoothness, image quality, and battery (capacity \(<\) 80%). Periodic software (V1.1.1) updates enhance edge detection for deformation analysis.  
A centralized log tracks all maintenance (dates, tasks, component status) activities for traceability. This plan ensures compliance with specifications, minimizes downtime, and enhances system reliability. By synchronizing maintenance with technical requirements, the plan addresses environmental challenges (e.g., dust, signal interference) and equipment wear, safeguarding accuracy and operational integrity in critical infrastructure monitoring.
## 4 Results  
### Movement Time and the Applicability of Steering Law by the Tracking Direction  
The Shapiro-Wilk normality test was conducted on the log-transformed _MT_ data and the normality was not rejected (\(W=0.96\), \(p=0.1481\)). Hence, a paired two-way analysis of variance was performed, with the two independent variables \(\theta\) and _ID_ as the main effects. The results showed that the head-movement-based line drawing manipulation was significantly affected by the tracking direction \(\theta\) (F\({}_{23,184}=14.87\), \(p<0.001\), \(\eta_{p}{}^{2}=0.158\)) and by the task difficulty _ID_ (F\({}_{3,24}=409.2\), \(p<0.001\), \(\eta_{p}{}^{2}=0.704\)). There was no significant difference (F\({}_{69,2280}=0.75\), \(p=0.938\)) in the interaction.  
Figure 4 shows the relationship between _ID_ and _MT_ at \(\theta=0\) degrees. There was high linearity between the two, with a coefficient of determination R\({}^{2}\) of 0.9991. Figure 5 summarizes the _MT_ for all 24 tracking directions evaluated in the experiment. Figure 6 summarizes the coefficient of determination R\({}^{2}\) of the regression line for each tracking direction. Figure 6 shows that R\({}^{2}>0.97\) in all directions, which is highly linear and compatible with the steering model, and indicates that the steering law is applicable to drawing operations by head movement regardless of tracking direction. Figure 7 summarizes the _IPs_ for the 24 tracking directions. According to Figure 7, the _IP_ is higher in the horizontal and vertical directions compared with diagonal directions. The minimum value of IP (2.31 s\({}^{-1}\) at \(\theta=135^{\circ}\)) was 46.7% of the maximum value (4.94 s\({}^{-1}\) at \(\theta=0^{\circ}\)).  
Figure 4: Relationship between _ID_ and _MT_ at \(\theta=0\) degrees.  
Figure 5: Relationship between tracking direction and \(MT\).  
Figure 6: Relationship between tracking direction and R\({}^{2}\) of the regression line.
## 4 Experiment and Analysis
### Observation Residuals of RDSS Data  
The POD experiments by RDSS data are only made by fixing the RDSS time delay value based on SLR data calibration. Only the satellite orbit and dynamic parameters are solved. The residual sequence of the RDSS data from 1 to 3 July is shown in Figure 4. The root mean square (RMS) value of the RDSS residuals for the GEO satellite is 1.74 m. The residual magnitude is decided by the observation accuracy of the data type. Because the observation accuracy of RDSS data is 1 to 2 m, the residual magnitude is normal.
# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications in the IoT in Terms of Security
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & **Work** & **Domain** & **Technology** & **Research Purpose** & **Research Problem** & **Dataset** & **Year** \\ \hline
1 & [PERSON] et al. [94] & Wireless communication networks, dynamic spectrum access & FURL & Optimizing spectrum allocation efficiency & Spectrum sharing optimization problem & Simulated data & 2022 \\ \hline
2 & [PERSON] et al. [95] & 5G networks & FL, blockchain, and SG & Improving SC networks, integration of blockchain and FL & Security, privacy, and scalability challenges of SG networks & Simulated data & 2020 \\ \hline
3 & [PERSON] et al. [88] & Dynamic spectrum & FRI & Improved & Incombert interference & Simulated environment & 2021 \\ \hline
4 & [PERSON] [96] & Fog computing and IoT & Federated computing & Service provisioning & Criticality management & Simulated data & 2021 \\ \hline
5 & [PERSON] et al. [97] & Cloud-enabled IoT & FURL & Privacy-preserving offloading & Privacy preservation & Own dataset & 2022 \\ \hline
6 & [PERSON] et al. [96] & IoT and data sharing & FURL & Secure data sharing & Secure and efficient data sharing & Simulated data & 2021 \\ \hline
7 & [PERSON] et al. [99] & Edge computing and IoT & DURL, FL and edge computing & Resource optimization & Privacy-preserving & Real-world dataset & 2022 \\ \hline
8 & [PERSON] et al. [100] & Federated learning & FL, and federated RL framework & Multi-task & Privacy-preserving & \(\mathrm{N/A}\) & 2021 \\ \hline
9 & [PERSON] et al. [20] & Healthcare decision system & FL, edge computing, and RL & Privacy-preserving, and clinical decision & Resource constraints, privacy, and clinical decision-making & \(\mathrm{\frac{1}{[107]}}\) & 2021 \\ \hline
10 & [PERSON] et al. [101] & Networking and IoT & FL, DBL, and SDN & Traffic monitoring & Traffic monitoring & \(\mathrm{N/A}\) & 2021 \\ \hline
11 & [PERSON] et al. [86] & SDN-based IoT networks & FRL, IoT, and SDN & IoT network optimization, automation & IoT control complexity, network performance, and network management efficiency & Simulated environment & 2020 \\ \hline
12 & [PERSON] et al. [102] & Wireless IoT networks & FRL, IoT, and SG & Improve IoT communication & Routing, privacy, and QoS & Simulated data & 2021 \\ \hline
13 & [PERSON] et al. [22] & AI, multi-agent systems & MARL, FL, and cybernetics & Secure multi-agent communication and privacy-preserving incentives & FMNIST dataset & 2021 \\ \hline
14 & [PERSON] et al. [103] & Network function virtualization (NIV) and service function chain (SFC) & NIV, STC, and FRL & NIV service orchestration enhancement & STC inelability and scalability problem & Simulated data & 2021 \\ \hline
15 & [PERSON] et al. [100] & Networking and resource management & FL, DL, and multi-access edge computing & Optimizing network resources and edge computing. & Resource allocation optimization & Simulated environment & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of FRL-based solutions for secure IoT operations.
# Noises in Double-Differenced GNSS Observations
## 2 Materials and Methods
### Autocorrelation Function  
The time correlation of the observation time series was determined by the ACF, which measures the correlation between the \(x(t)\) and \(x(t+\tau)\) of the \(x(t)\) stochastic process expressed by [1]:  
\[\sigma^{2}(\tau)=\frac{1}{T}\sum_{t=1}^{T-\tau}[x(t)-\overline{x}][x(t+\tau)- \overline{x}] \tag{3}\]  
where \(\sigma^{2}(\tau)\) expresses the time series \(x(t)\) of the sample covariance with the same series shifted at time lag \(\tau\), \(\overline{x}\) is the mean of \(x\), and T denotes the effective sample size for time series x. The time correlation coefficient \(\rho_{t}(\tau)\) for lag \(\tau\) can be determined as:  
\[\rho_{t}(\tau)=\frac{\sigma^{2}(\tau)}{\sigma_{0}^{2}} \tag{4}\]  
where \(\sigma_{0}^{2}\) is a simple variance of \(x(t)\) for \(\tau=0\).  
If the considered time series is affected by purely random noise, then the time correlation coefficient takes the values  
\[\rho_{t}(\tau)=\left\{\begin{array}{ll}1&\tau=0\\ 0&\tau\
eq 0\end{array}\right. \tag{5}\]  
Otherwise, the time series can be described as an autoregressive (AR) model.
The original approach to make DNA-AuNP conjugates is to bind thiolated DNA to AuNPs with the formation of gold-thiolate bonds [33; 34]. More recently, another strategy has been reported to adsorb non-thiolated DNA strands onto AuNPs via polyadenine bases that interact strongly with the gold surface [35]. Since this method covers AuNP surfaces quickly and effectively with unmodified ssDNA and provides an acceptable loading capacity, it was adapted in our study for synthesis of DNA-functionalized AuNPs. Here, we present the construction and analysis of DNA hydrogels, some with embedded percolating networks using DNA-wrapped CNTs and DNA-attached AuNPs as crosslinkers. We started at the molecular level by designing DNA sequences and moved up to macroscale hydrogels realized by bottom-up fabrication. We applied the crosslinker plus spacer design, where oligonucleotides were designed to form molecular networks by sequence-directed hybridization with sticky ends on crosslinker units (i.e., DNA tiles or DNA/nanomaterial conjugates) and coupling components (i.e., spacers) to construct 3D networks (Figure 1). We were able to improve the mechanical strength of the formed hydrogels using a variety of strategies including adjusting the spacer length and mixing different types of crosslinkers. We also showed that the 3D structures of nanomaterials can be programmed efficiently via nucleic acid sequence, and that it is possible to direct the formation of percolating networks with DNA self-assembly. In addition, using inspiration from biological neural networks that display extraordinary signal dynamics and processing abilities, we aimed to mimic some aspects of the morphology of natural neural networks using DNA self-assembly to fabricate nanoelectronic devices with measurable function. Non-linear electrical properties of nanocomposites that integrate DNA-modifiedCNTs are reported. Our eventual goal is to harness molecular recognition to precisely control the configuration and connection of nanomaterials to self-assemble into controllable nanostructures and, thus, to engineer, fabricate, and characterize DNA-based hydrogels for desired applications. Future DNA hydrogel composites may find impactful application as building blocks in artificial computer hardware, with architectures inspired by natural neural systems for memory and information-processing applications.
## 2 Materials and Methods  
### Universal GNSS Channel Design Decisions  
For a universal channel [32; 33] to successfully address any signal particularity identified above, some design decisions had to be made to achieve the lowest possible design complexity. The following paragraphs detail different channel architecture aspects (_cf._ Figure 1), _i.e.,_:  
1. If to Baseband Down-conversion and Carrier (including FDMA) Wipe-off module
2. Sub-carriers and Spreading Codes Wipe-off module
3. Spreading Codes (including Time Multiplexing) Generation module
4. Correlation module
5. Data & Pilot components merging
6. Discriminator and Filters  
An overall resource and power assessment is then presented.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 2 Materials and Methods
### Unit Cell Design and Parameter Specifications
The capacitance per unit length of the paralleled strip lines is calculated using  
\[C\approx\varepsilon_{e}\epsilon_{0}F(k) \tag{10}\]  
where '\(\varepsilon_{e}\)'is the permittivity of the free space, '\(\epsilon_{0}\)' and \(F(k)\) are calculated as  
\[\varepsilon_{e}=1+(\varepsilon_{r}-1)F(k)/2F(k1) \tag{11}\]  
\[F(k)=\left\{ \begin{array}{l}\frac{1}{\pi}ln\left(2\frac{1+\sqrt{k^{2}}}{1- \sqrt{k^{2}}}\right),0<k\leq\frac{1}{\sqrt{2}}\\ \pi ln\left(2\frac{1+\sqrt{k^{2}}}{1-\sqrt{k^{2}}}\right),\,\frac{1}{\sqrt{2} }<k\leq 0\end{array} \right. \tag{12}\]  
where \(k=\frac{a}{b}\), \(k^{\prime}=\sqrt{1-k^{2}}\), \(k1=\sin h\left(\frac{\pi a}{2h}\right)/\sin h\left(\frac{\pi b}{2h}\right)\). To analyse the structural characteristics of the proposed unit cell, the reflection (\(S_{11}\)) and transmission (\(S_{21}\)) coefficients are obtained to calculate the permittivity and permeability [33] and are expressed as follows  
\[V_{1}=S_{11}+S_{21} \tag{13}\]  
\[V_{2}=-(S_{11}-S_{21}) \tag{14}\]  
\[\varepsilon_{r}\approx\frac{2}{j\mathrm{k}_{0}d}\times\frac{(1-v_{1})}{(1+v_ {1})}\]  
Effective permittivity  
\[\varepsilon_{r}\approx\frac{c}{j\pi fd}\times\{(1-s_{11}-s_{21})/(1+s_{11}+s _{21})\} \tag{15}\]  
\[\mu_{r}\approx\frac{2}{j\mathrm{k}_{0}d}\times\frac{(1-v_{2})}{(1+v_{2})}\]  
Effective permeability  
\[\mu_{r}\approx\frac{c}{j\pi fd}\times\{(1-s_{21}+s_{11})/(1+s_{21}-s_{11})\} \tag{16}\]  
where \(k_{0}=\omega/c\), \(d\) = slab thickness, \(c\) = speed of light. The unit cell structure was designed on an FR4 substrate, and results were observed using CST software. The electromagnetic characteristics can be well explained by reflection (\(S_{11}\)) and transmission (\(S_{21}\)) coefficients. The simulated results of effective permittivity (\(\varepsilon_{real}\)) and permeability (\(\mu_{real}\)) are observed in Figure 9. However, Figure 9a shows the permeability of the metamaterial having negative values at 0.915 and 2.4 GHz frequencies and Figure 9b shows negative permittivity values at 0.915 and 2.4 GHz frequency bands.
## 3 Results and Discussion
### Results
#### 3.4.4 Routing Table Size  
As explained above, to obtain the advantages offered by the multi-path mechanism, it was necessary to increase the number of entries that each node must store in the routing table, as well as to add some fields to those entries. Both actions translated into an increase in the amount of memory required in each node to store its routing table. To quantify this increase, the routing table size was also measured during the simulations. Figure 10 shows the results, both for the basic HWMP and for the modified protocol. For every network size, the minimum and maximum values are depicted (that is, the nodes with the smallest and largest table). Besides, the boxes represent the 25 th, 50 th, and 75 th percentiles values.  
When the basic HWMP was used, the routing table size was independent of the network size, since the nodes must only store the route to the data concentrator. However, with the multi-path modifications, the different available paths were stored to be assigned to the different traffic types. Thus, when the network size was increased, more paths became available, and so, the amount of memory needed for the routing table also grew. As shown in the figure, in the worst case taken into account in the simulations (36 nodes), the amount of memory needed (for the node with the largest table) was around 2.3 megabytes.  
This fact could represent a scalability problem if the number of nodes in the network could grow indefinitely, but this is not the case with smart grid NANs, where one node represents one home. In any case, the amount of memory needed could reach the order of tens or hundreds of Mbytes, which with current memory technologies does not represent any problem.