esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results and Discussion
The fundamental current at 60 Hz was about 16.35 A. Using the IP current controller, the third harmonic component was reduced from 1.810 A to about 0.403 A, the fifth harmonic was reduced from 0.95 to 0.1855 A, and the seventh was reduced from 0.47 to 0.1133 A. The THD in the supply current under full load condition was reduced from 19.49% to about 5.23%. With different loading conditions, the simulation results of the designed PFC boost converter with different current control techniques was examined. Table 2 describes the PFC boost converter power factor and THD performance with loading conditions of 25%, 50%, 75%, and 100% of the full load condition (2500 W). From the results, under the full load condition, the designed PFC boost converter with the proposed current controlling technique solves the problem of the zero-current crossing distortion, reducing the THD to about 5.23% with input power factor about 99.93%. Under the light load condition of 25% loading condition, the THD value was about 34.8% with the PI controller and 30.20% with the IP controller. When the PFC converter operated at reduced load, discontinuous conduction mode (DCM) will appear during part of the line cycle or even the entire line which causes high distortion in the supply input current [34]. So, based on the THD and  
Figure 31: Frequency spectrum of the supply current at full load with the proposed IP current controller. Figure 30: Waveforms of the supply current-voltage with the proposed IP current controller. power factor values, the optimum range of the designed converter loading condition is \(75\%\) to \(100\%\) of the full loading condition.
# Training from Zero: Forecasting of Radio Frequency Machine Learning Data Quantity
### RF Characteristics  
In the idealized world where the transceivers are in a physically stationary environment, RF signals can be thought of as processing signals at a complex baseband (BB) with a channel between transmitter and receiver. The transmitter's waveform is then represented as  
\[s_{BB}(t)=s_{\text{re}}(t)+js_{\text{im}}(t), \tag{7}\]  
where the channel introduces a static set of unknowns: gain (\(\alpha_{0}\)); delay (\(\tau_{0}\)); and phase shift (\(\theta_{0}\)), and a time-varying additive noise \(\
u(t)\) to the receiver observation, in addition to the transmitter's modulated baseband signal, however, with the received signal being modeled after perfect synchronization, eliminating the static unknowns with a perfect low pass filter results in the received signal given as  
\[r_{BB}(t)=s_{BB}(t)+\
u_{BB}(t). \tag{8}\]  
The ratio of power in the signal to that of the noise, or the Signal-to-Noise Ratio (SNR), often expressed in dB (\(10\log_{10}(\int|s_{BB}(t)|^{2}dt/\int|v_{BB}(t)|^{2}dt)\)), is then the primary limiting factor explaining the performance of the system, with \(\
u_{BB}(t)\) most commonly assumed to be a circularly symmetric complex Gaussian process.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Training** & & \\ \hline Symbol & Source & Description \\ \hline \(\Omega_{\text{C}}\) & Capture & \begin{tabular}{l} Consists of only capture \\ examples \\ \end{tabular} \\ \hline \(\Omega_{S}\) & Synthetic generation using KDE & \
\begin{tabular}{l} Consists of simulated \\ examples using the KDE of \\ the capture dataset \\ \end{tabular} \\ \hline \(\Omega_{A}\) & Augmentation using KDE &
\begin{tabular}{l} Consists of augmented \\ examples from the capture \\ dataset using the KDE \\ \end{tabular} \\ \hline
**Evaluation** & & \\ \hline \(\Omega_{TC}\) & Capture &
\begin{tabular}{l} Consists of only capture \\ examples \(\Omega_{TC}\cap\Omega_{C}=\emptyset\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of datasets used within this work.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Set** & **Waveforms** \\ \hline \(\Phi_{3}\) & BPSK, QPSK, Noise \\ \hline \(\Phi_{5}\) & \(\Phi_{3}\), QAM16, QAM64 \\ \hline \(\Phi_{10}\) & \(\Phi_{10}\), AM-DSB, BFSK, FM-NB, BGFSK, GMSK \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three waveform sets used in the work.
## 3 Brachytherapy Combined with Teleradiotherapy in Advanced, Non-Operative Stages
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \(n\) & **Stage** & **BT** & **EBERT** & **CHT** & **LC** & **OS** & **Toxicity** \\ \hline [PERSON] 2002 [33] & 40 & III–IV & 10 & 40 & - & - & 1 year: 30\% & \\  & & & & & & 2 years: 17.5\% & \\ \hline [PERSON] 2005 [34] & 70 & T1–3, No-1 & 20 & 50 & PF & 2 years 75\% & 5 years: 28\% & \\ \hline [PERSON] 2007 [35] & 23 & II–IV & 21 & 44.2 & - & - & - & 5 years: 10\% & \\ \hline [PERSON] 2011 [38] & 62 & T1–4, No-1 & 12 & 60 & - & 1 year: 71\% & 1 year: 75\% & \\  & & & & & 2 years: 50\% & 2 years: 34\% & 5 years: 11\% & and strictures (16\%) \\ \hline [PERSON] 2002 [39] & 77 & III–IV & 10–24 & 40–65 & - & \begin{tabular}{c} 2 years: \\ \textless{}5 cm: 83\%, \\ 2’s cm: 25\% \\ \end{tabular} & \begin{tabular}{c} 2 years: 35.6\% \\ 5 years: 10.6\% \\ \end{tabular} & - \\ \hline [PERSON] 2015 [42] & 75 & III–IV & 16 & 20–30 & - & - & 1 year: 27\% & 27\% (tenosis, fistula, bleeding) \\ \hline [PERSON] 2015 [43] & 59 & I-IV & 10–15 & 27–30 & - & - & 1 year: 51\% & \begin{tabular}{c} Senosis: \% \\ 3 years: 7\% \\ \end{tabular} \\ \hline [PERSON] 2020 [44] & 41 & III–IV & 15 & 30 & - & - & 1 year: 68\% & \\  & & & & & & 2 years: 50\% & \\ \hline [PERSON] 1997 [40] & 53 & II–III & 10 & 60 & \begin{tabular}{c} PC, \\ Mitomycin \\ \end{tabular} & \begin{tabular}{c} LC \\ 1 year: 7.6\% \\ 3 years: 30\% \\ \end{tabular} & \begin{tabular}{c} 3 years: 27\% \\ 3 years: 27\% \\ 3 years: 30\% \\ \end{tabular} & \begin{tabular}{c} Senosis: 13\% \\ Severe late toxicity: 11\% \\ \end{tabular} \\ \hline [PERSON] 1996 [36] & 51 & III–IV & 10 & 40 & - & - & 1 year: 20\% & \begin{tabular}{c} 25 stenosis: 1\% \\ preumonitis \\ \end{tabular} \\ \hline [PERSON] 2023 [41] & 71 & I-IV & 10 & 59.4 & \begin{tabular}{c} Most only SFU, \\ some PF \\ \end{tabular} & \
\begin{tabular}{c} mLC 30.5 m \\ \end{tabular} &
\begin{tabular}{c} mC 32.7 m \\ \end{tabular} & Esophageal stenosis 22.5\% \\ \hline Mingyue Ye 2022 [37] & 64 & I-IV & 10 & 50 & - & 1 year: 88\% & \\  & & & & & 2 years: 72\% & 3 years: 38\% & 2 pt: dysphagia, 3 pt: bone marrow suppression \\ \hline \multicolumn{6}{c}{Legend: BT–bachytherapy, EBERT–external beam radiotherapy, CHT–chemotherapy, LC–bachyened control, OS–overall survival.} \\ \end{tabular}
\end{table} 
Table 2: Results of brachytherapy combined with teleral radiotherapy in advanced, non-operative stages.
## 5 Results and Discussion
### Static Analysis Test Comparison
#### 5.1.1 Modal Analysis Test Comparison  
The main goal of the research is to prove that the finite element model of the machine is accurate by comparing the results of the modal analysis, using ANSYS Workbench and experimental modal analysis. Both approaches successfully extracted the results. The 3D CAD model utilized for the study in ANSYS Workbench was the original molded component used in modal testing. The boundary condition was also implemented in the ANSYS Workbench to match the experimental testing configuration. To connect the results of two techniques of modal analysis, the extracted natural frequencies of the machine are compared which shown in Figure 25. numerical validation of model analysis. There are certain frequencies that neither ANSYS Workbench nor the experimental approach can stimulate. Therefore, only those frequencies that were stimulated by both methods are selected for comparison. The graph depicts the comparison of the natural frequencies. The first-order frequency of the modal analysis was 28.7 Hz, and its main vibration shape was the X-direction displacement of the tool. The first-order frequency of the modal experiment was 27.3 Hz, and its main vibration was the X-direction displacement.  
The results of the first-order modal analysis and modal experiment, as well as the mode shape, had a frequency error of 4.8%. The second-order frequency of the modal analysis was 34 Hz, and its main mode shape was the displacement of the tool magazine in the Z-direction. The results of the first-order modal analysis and the modal experiment, as well as the mode shape, had a frequency error of 1.1%. The third-order frequency of the modal analysis was 48.5 Hz, and its main vibration shape was the Z-direction displacement of the tool magazine and the Y-direction displacement of the worktable. The third-order frequency of the modal experiment was 48.3 Hz, and its main vibration shape was the displacement of the tool magazine. The displacement in the Z-direction and that of the worktable in the X-direction, as well as the third-order modal analysis and modal experiment results, had a frequency error of 0.4%, with a slightly different mode shape.  
Figure 25: Numerical validation of model analysis.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Modelling
#### 3.2.1 Parametric Modelling  
For queue modelling, correlation is a feature that needs to be identified to see if it exists, as queueing models assume no correlation. We have applied extensive studies on these EDI messages around correlation. Please refer to our paper for further reading [11].  
When modelling EDI messages, we found many challenges. From these challenges, the classification model was born. The different elements of the classification model helped us answer a challenge we faced. For example, we could not tell what happened to a message as it was being translated. We did not know if a message was dependent on another message. We were challenged with modelling the data with a continuous distribution. We faced many challenges around the removal of correlation and trying to break the head and tail of the data at a suitable point to help parametric fitting.  
The classification model helped us iterate through the different techniques to help us address the challenges we faced. Our next set of sections will help bring the reader through our techniques based on these challenges.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Quantum Deep Learning Techniques with Copyright Detection
#### 2.2.2 Quantum Approximate Optimization Algorithm (QAOA) in Machine Learning Contexts  
The Quantum Approximate Optimization Algorithm (QAOA) [44] is a hybrid quantum-classical approach that has shown promise in addressing combinatorial optimization problems relevant to machine learning [45]. In the context of machine learning, QAOA can be applied to tasks such as feature selection, hyperparameter optimization, and training quantum neural networks, potentially offering advantages over classical methods for certain problem classes. The algorithm prepares a quantum state that encodes the optimization problem, applies parameterized quantum gates, and uses classical optimization to fine-tune these parameters. While QAOA shows potential for enhancing machine learning tasks on near-term quantum devices, challenges remain in scaling to larger problems and demonstrating clear advantages over classical algorithms in practical applications.
# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 6 Selection of Representative Objects  
Let us consider a model database \(M\) representing a set of models \(M_{k}\) of objects belonging to the same object class. Each model \(M_{k}\) is represented by a 3D mesh. As previously explained, every mesh is segmented into convex and concave segments, and each of these segments is represented by a CTI descriptor. In order to facilitate the explanations of the proposed approach, a model \(M_{k}\) will be represented by a sequence of point sets \(M_{k}=(C_{1k},\ldots,C_{n_{M},k},P_{k})\), where \(C_{ik}\) is the \(i^{\text{th}}\) model segment and \(P_{k}\) is a set of 3D points lying on the surface of the \(k^{\text{th}}\) model. For the computational efficiency, these points can be obtained by sub-sampling the mesh vertices. The similarity of the \(k^{\text{th}}\) and the \(l^{\text{th}}\)object is assessed by measuring the distances between the points of the \(k^{\text{th}}\) model to the CTIs of the \(l^{\text{th}}\) model and opposite. The object similarity measure is computed by:  
\[y_{kl}^{O}=y_{lk}^{O}=z_{kl}^{O}\cdot z_{lk}^{O}, \tag{13}\]  
where:  
\[z_{kl}^{O}=\frac{1}{|P_{k}|}\sum\limits_{p\in P_{k}}\exp\left(-\frac{1}{2} \frac{\delta_{pM}^{2}(p,M_{l})}{\sigma_{p}^{2}}\right) \tag{14}\]  
\[\delta_{pM}(p,M_{l})=\min\limits_{C_{jl}\in M_{l}}(\delta_{pC}(p,C_{jl})) \tag{15}\]  
\[\delta_{pC}(p,C_{jl})=|\max\limits_{m=1,\ldots,l_{d}}(a_{m}^{T}p-d_{jlm})| \tag{16}\]  
Parameter \(\sigma_{p}\) in (14) is an experimentally determined constant. If \(C_{jl}\) is a concave segment, then the \(min\) operation is used in (16) instead of \(max\). Note that \(y_{kl}^{O}\in[0,1]\).  
Set \(R\) of the representative objects is selected by a greedy procedure that maximizes the similarity between \(R\) and all objects \(M_{k}\in M\). The similarity between an object model \(M_{k}\in M\) and set \(R\) is defined as the similarity between this model and the most similar model \(M_{r}\in R\). This similarity can be measured by a value \(y_{k}^{R}\) computed by:  
\[y_{k}^{R}=\max\limits_{M_{r}\in R}y_{kr}^{O}.\]  
The similarity between \(M\) and \(R\) can be measured by value \(y^{MR}\) representing the total sum of values \(y_{k}^{R}\), i.e.,  
\[y^{MR}=\sum\limits_{k=1}^{|M|}y_{k}^{R}.\]  
In the approach proposed in this paper, the set of representative objects \(R\) is selected by an iterative procedure, where in each iteration, a model that maximizes \(y_{MR}\) is selected until a predefined number of representative objects \(n_{R}\) is selected.
## 2 Proposed Position Tracking System Based on Composite Sensors
### EKT-Based MEMS IMU and UWB Integrated Positioning Algorithm
Here, \(\hat{x}_{k|k-1}\) and \(P_{k|k-1}\) represent the predicted state estimate and the predicted covariance estimate, respectively, as follows:  
\[\hat{x}_{k|k-1}=f\Big{(}\hat{x}_{k-1|k-1},u_{k}\Big{)} \tag{36}\]  
\[P_{k|k-1}=F_{k}P_{k-1|k-1}F_{k}^{T}+Q_{k} \tag{37}\]\(F_{k}\) and \(H_{k}\) are the state transition and state variable matrices defined to be Jacobians. \(Q_{k}\) and \(R_{k}\) are the covariance matrix of the system and the measurement noise, respectively. The final estimated value \(\hat{x}_{k|k}\) is predicted by multiplying an appropriate weight on the basis of \(K_{k}\) with the predicted value \(\hat{x}_{k|k-1}\) and the measured value \(z_{k}\), followed by summing the two values, while \(K_{k}\) is inversely proportional to the measured noise component, \(R\). Specifically, as the measured noise component becomes greater, \(K_{k}\) decreases, and the rate at which \(z_{k}\) is reflected in \(\hat{x}_{k}\) is decreased. It can be seen that the predicted error covariance, \(P_{k|k-1}\), is proportional to the system noise component, \(Q_{k}\), and that \(K_{k}\) is proportional to \(P_{k|k-1}\). Therefore, \(K_{t}\) is proportional to \(Q_{k}\). In other words, an increase in \(Q_{k}\) signifies that the reliability of \(\hat{x}^{\prime}_{t}\) is decreased, in which the components of the measured value are considerably reflected when predicting \(\hat{x}_{k|k-1}\)[6].
## 1 Introduction
### Mixed V2G and V2V Communication  
Besides research on V2G- or V2V-based conflict detection, there was some research that combined V2G and V2V conflict detection. The Federal Aviation Administration (FAA) and its industry partners formed a team to integrate the detect and avoid (DAA) systems featuring the Airborne Collision Avoidance System (ACAS) Xu logic. The system is embedded in onboard UAS vehicle platforms and a ground-based cloud environment in a UTM service supplier (USS) platform. It used ADS-B as V2V and V2G communication with the addition of radar in V2G conflict detection. The test involved a Bushmaster UAV equipped with GE Aviation's low SWaP avionics computer and a manned intruder Piper Cherokee aircraft [25]. The last publication on combined V2G and V2V came from a NASA test flight to demonstrate and evaluate a set of technologies and an over-arching air-ground system concept aimed at enabling the safety of UTM operations. The research UAV was tracked continuously via the DSRC communication channel during nominal planned flight paths while autonomously operating over moderately populated land. In some flights, off-nominal risks were simulated, such as V2V encounters [26].  
Based on other literature found, analyzing the effect of data latency and flight parameters for UAS conflict detection is a research area that has not been fully explored yet. In this research, a flight test using UTM conflict detection based on the network's remote identification (Remote ID) communication is conducted. The present study endeavored to analyze the flight test outcomes of the UTM monitoring system, focusing on communication latency, flight parameters, and their consequential impacts on system performance. Statistical and machine learning methodologies were employed for this analysis. The distinctive contributions of our research lie in the utilization of network Remote ID for the development of a UTM monitoring system, as well as the introduction of a novel conflict detection algorithm based on an inverted teardrop shape. This manuscript contains the UTM monitoring description, including the conflict detection algorithm in Section 2, the flight test setup to validate the conflict detection in Section 3, the flight test results and analysis in Section 4, and the conclusion in Section 5.
## Abbreviations  
### Acronyms  
NHPP Nonhomogeneous Poisson Process  
LSE Least Squared Estimation  
MLE Maximum Likelihood Estimation  
MVF Mean Value Function  
OSS Open-Source Software  
SIF Stochastically Introduced Fault  
PM Proposed Model  
CSS Closed-Source Software  
RM Reliability Model  
FDR Fault Detection Rate  
IF Inflection Factor  
NF New Fault  
FD Fault Detection  
ID Imperfect Debugging  
**Notations**  
\(\psi(t)\) Expected cumulative number of the detected faults by time t  
\(\phi(t)\) Fault content function  
\(b(t)\) Fault detection rate function  
\(\mu(t)\) Intensity function of software fault introduction  
\(\gamma(t)\) Standardized white noise of Gaussian  
\(\eta(t)\) One-dimensional Wiener process with a Gaussian distribution  
\(\Lambda(t)\) Number of faults observed by time \(t\)  
\(\sigma\) Magnitude of the irregular fluctuation  
\(a\) Expected total number of initially detected faults  
\(b\) Fault detection rate  
\(d\) Shape parameter  
\(\beta\) Inflection factor  
\(\theta\) Rate parameter of the intensity of fault introduction
## 8 Conclusions  
The study presents a significant improvement in the field of Wireless Sensor Networks (WSNs) through the development of the K-BCO algorithm for clustering. By integrating BCO with WSA, the proposed clustering algorithm effectively addresses the challenge of energy consumption during data transmission in WSNs. The combination of the BCO with WSA enhanced the performance, robustness, and adaptability of the resulting hybrid approach. This combination led to more efficient solutions in dynamic and resource  
Figure 8: Execution time of the proposed K-BCO comparison with H-LEACH, DBCP and ABC-ACO.  
constrained environments like Wireless Sensor Networks. The results from extensive simulations demonstrate that the K-BCO not only improves the reliability and efficiency of cluster formation but also optimizes energy consumption, thereby prolonging the network's lifetime. The heterogeneity of sensors in the study is a critical factor that influences the design and implementation of the K-BCO algorithm by addressing the challenges posed by diverse sensor capabilities, energy levels, data generation rates, dynamic topologies, and application requirements.  
The findings indicate that the proposed approach leads to a notable enhancement in the performance metrics of WSNs, including reduced error rates, energy cost or energy consumption and improved packet delivery ratios. Thus, although the K-BCO performed highly in terms of packet delivery, it consumed less energy, leading to a prolonged lifespan for the entire network. This study underscores the potential of leveraging collective behaviors observed in nature to solve complex problems in network optimization.  
Future work should focus on further refining the algorithm and exploring its applicability in various real-world scenarios, including dynamic environments and heterogeneous sensor networks. Again, future work should focus on addressing the execution time of the proposed K-BCO. Overall, the proposed K-BCO framework offers promising implications for the deployment and operation of WSNs, paving the way for more efficient and sustainable wireless communication systems.  
Conceptualization, P.M., I.E.A. and G.A.-S.; methodology, P.M., I.E.A. and G.A.-S.; software and simulation, P.M. and I.E.A.; validation, W.L.B.-A., P.A. and E.F.; formal analysis, P.M. and E.F.; investigation, P.M. and W.L.B.-A.; writing--original draft preparation, P.M., I.E.A. and G.A.-S.; writing--review and editing, P.M., G.A.-S., P.A. and W.L.B.-A.; visualization, P.M. and I.E.A.; supervision, G.A.-S., R.C.M. and W.L.B.-A. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China (Grant Number: 2023 YFE0110200) and in part by the National Research Foundation of South Africa (Grant Number: 151178).  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The is no conflicts of interest.
## 4 Experiment and Analysis
### Ablation Experiments  
In order to verify the accuracy and anti-interference ability gain of the proposed method on standard and corrupted datasets, two ablation experiments are designed in this paper.
## 3 Numerical Solution  
We present an explicit numerical scheme for the AC equation in the 2D space \(\Omega=(a,b)\times(c,d)\). Performing the numerical computation with the explicit method is efficient because the evolution depends on the a sign of curvature at each grid point. The 3D case is defined analogously. Let \(N_{x}\) and \(N_{y}\) be positive integers, \(h=(b-a)/N_{x}=(d-c)/N_{y}\) be the uniform mesh size, and \(\Omega_{h}=\{(x_{i},y_{j}):x_{i}=a+(i-0.5)h,y_{j}=c+(j-0.5)h,\ 1\leq i\leq N_{x},\ 1\leq j \leq N_{y}\}\) be the discrete domain. Let \(\phi_{ij}^{n}\) be an approximation of \(\phi(x_{i},y_{j},n\Delta t)\), where \(\Delta t\) is the time step. The explicit Euler scheme for \(1\leq i\leq N_{x},1\leq j\leq N_{y}\) is given as  
\[\frac{\phi_{ij}^{n+1}-\phi_{ij}^{n}}{\Delta t}=\begin{cases}-\mu_{ij}^{n}& \text{if }-\mu_{ij}^{n}>0,\\ 0&\text{otherwise},\end{cases} \tag{6}\]  
where \(\mu_{ij}^{n}=((\phi_{ij}^{n})^{3}-\phi_{ij}^{n})/\epsilon^{2}-\Delta_{h}\phi_ {ij}^{n}\) and \(\Delta_{h}\phi_{ij}=(\phi_{i-1,j}+\phi_{i+1,j}+\phi_{i,j-1}+\phi_{i,j+1}-4\phi _{ij})/h^{2}\).  
Because the governing equation is a second-order partial differential equation (PDE), solving the AC equation by the explicit Euler method gives not only accuracy by using a small enough time step size, but also simplicity. For more details about the reason for using the explicit scheme for the second-order PDE, please refer to the article of [PERSON] and [PERSON] [27]. The advantages of using the scheme are consistent with our purpose for simulation of the tissue growth.  
We use two kinds of boundary conditions in this study. One is the Neumann boundary condition,  
\[\phi_{0j} = \phi_{1j},\ \phi_{N_{x}+1,j}=\phi_{N_{x}j},\ 1\leq j\leq N_{y},\]\[\phi_{i0} = \phi_{i1},\;\phi_{i,N_{y}+1}=\phi_{iN_{y}},\;1\leq i\leq N_{x},\]  
and the other is the periodic boundary condition,  
\[\phi_{0j} = \phi_{N_{x}i},\;\phi_{N_{x}+1,j}=\phi_{1j},\;1\leq j\leq N_{y},\] \[\phi_{i0} = \phi_{iN_{y}},\;\phi_{i,N_{y}+1}=\phi_{i1},\;1\leq i\leq N_{x}.\]