esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 2 Materials and Methods
### Functional Working  
The fifth generation (5G) of communication systems is expected to revolutionize the way we communicate and interact with each other.  
\[\left(\frac{dC*dC_{c}}{dD_{c}}\right)=\frac{1}{2}dC*dD_{c}^{2} \tag{8}\]  
To optimize the functional performance of 5G communication systems, sophisticated time series analysis can be used to analyze the collected data and observe the trends in real time.  
\[dd_{c}^{2}=\left(\frac{dC*dC_{c}}{dDc}\right)*\frac{2}{dc} \tag{9}\]  
\[dD_{c}^{2}=\left(\frac{2*dC_{d}}{dD_{c}}\right) \tag{10}\]  
Time series analysis is a powerful technical tool that has been applied in numerous industries to explore trends in data sets. Time series analysis is an invaluable tool for evaluating dynamic network conditions or changes in user demands. It can be used to provide early insight into any disruptions in user experience, allowing for a proactive and agile approach to maintaining performance excellence. Time series analysis techniques allow for the observation and detection of sudden or anomalous changes in performance metrics. This includes analyzing trends in latency, throughput, or other measures. By closely monitoring data over time, it is possible to identify bottlenecks, identify faulty components, or pinpoint areas of improvement for optimizing performance. Time series analysis also enables forecasting future performance, allowing for predictive analytics to adjust system resources accordingly. This allows organizations to anticipate load or shifts in activity and respond with changes in their deployment, allowing them to maintain optimal performance.
## 4 Conclusions  
The Flip-F-OFDM approach in this study introduces a new way to obtain a real and nondipolar signal applicable to IM/DD optical communication systems. Unlike traditional methods that require Hermitian symmetry (HS), the proposed method removes the dependence on HS yet maintains full compatibility with IM/DD systems. By laying the two parts of the complex F-OFDM signal (real and imaginary) together, the resulting real signal is then used to produce a non-bipolar signal. Moreover, this method only requires \(N/4\)-size IFFT to obtain \(N\)-size FFT, thus simplifying the system design and reducing the computational complexity. As a result, this complexity reduction leads to lower power consumption and a smaller occupied chip area for the overall system.  
In addition, the proposed NHS-Flip-F-OFDM approach enhances spectral efficiency, the efficiency of spectral control, and bit error rate performance compared to the HSF Flip-OFDM, JM-DCO-F-OFDM, ACO-OFDM, and DCO-OFDM methods. We also noted that although the proposed approach provides better efficiency in terms of hardware complexity, the proposed solution does not compromise on error rate performance. Therefore, the BER of the NHS-Flip-F-OFDM method is compatible with that of the conventional Flip-F-OFDM approach, in spite of using QAM constellations of different forms. On the other hand, the suggested technique prevents any increase in PAPR compared with the conventional Flip-F-OFDM, which provides an improvement compared with ACO-F-OFDM and DCO-F-OFDM. Further studies can investigate how techniques like pre-distortion, peak cancellation, or tone reservation in F-OFDM systems mitigate PAPR effects.  
Conceptualization, H.S.R.H. and M.I.; methodology, H.S.R.H.; software, H.S.R.H.; validation, H.S.R.H. and M.I.; formal analysis, H.S.R.H.; investigation, H.S.R.H. and M.I.; resources, H.S.R.H. and M.I.; data curation, H.S.R.H.; writing--original draft preparation,  
Figure 11: The CCDF comparison of the proposed NHS–Flip–F–OFDM, conventional Flip –F–OFDM, DCO–F–OFDM, ACO–F–OFDM, DCO–OFDM, and ACO–OFDM using a signal length \(N=512\).  
H.S.R.H.; writing--review and editing, H.S.R.H.; visualization, H.S.R.H.; supervision, M.I.; project administration, M.I.; funding acquisition, H.S.R.H. and M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data underlying the results presented in this paper are not publicly available but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 3 Design Methodology, Layout and Circuit Simulation
### Layout and Circuit Simulation
#### 3.2.1 Current and Voltage Reference  
Figure 12 shows the location of the current/voltage reference circuit inside the microphotography of the whole self-powered tag. Furthermore, a zoomed-in view of its layout is included. The silicon area consumption of the current/voltage reference is 280 \(\upmu\)m \(\times\) 220 \(\upmu\)m. The \(I_{P}\) and \(I_{C}\) are set to 50 nA, \(I_{TC}\) is set to 100 nA, and \(V_{REF}\) is set to
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Filter Module  
When the pixel data are ready to carry out the filtering operation, the control unit turns on the filter unit. This unit is the sophisticated computational unit. The architecture of the filter unit is shown in Figure 6. It has a parameter computation unit, buffers to store the pixel data, a filter decision block, internal memories, a strong filter, and a weak filter.
# An Analysis of the Factors Influencing Dual Separation Zones on a Plate
## 3 Simulation Method and Numerical Validation
### Simulation Method  
This study employed a two-dimensional steady-state calculation approach, utilizing the SST k-\(\upomega\) model to close the viscous governing equations. The merging and re-separating process of dual separation zones was simulated through the overset grid method. The height of the first layer is 0.001 mm, the vertical growth rate is set to 1.15, and the y+ values along the wall are within 0.1. The calculation domain is 800 mm long and 100 mm high with about 180,000 grid cells in total. Other grid details are illustrated in Figure 2. Since the incoming flow is supersonic, the momentum is sufficiently large to ignore the influence of gravity. Within a single cycle of merging and re-separating processes, only the foreground grid (highlighted in red in Figure 2b) undergoes translational adjustment to modulate the distance between dual separation zones, while keeping all other flow field characteristics invariant.  
To obtain the flow field parameters at different stages of mergence and re-separation, the translation of the shock generator is divided into N movement steps with equal length. After each single movement step, the current steady-state flow field is saved. The leading tip T of the SG is referred to as the shock-generating point. The initial position of the shock-generating point at the initial moment is denoted as T\({}_{0}\). Based on the distance between the current shock-generating point T\({}_{n}\) and T\({}_{0}\), the current flow field can be defined as the nth step flow field. The formula for calculating the shock travel distance d\({}_{S}\) is as follows:  
\[\text{d}_{S}=\text{X}_{n}-\text{X}_{0}=\text{n}\,*\,\Delta\text{d},\,1\leq \text{n}\leq\text{N}\]  
X\({}_{n}\) represents the x-coordinate of T in the flow field at the nth step, while a larger d\({}_{S}\) indicates that the shock generator is positioned closer to the ramp.  
The front and top of the computational domain are set as pressure far-field boundaries. The shock generator and flat plate are configured as no-slip adiabatic solid walls. A pressure outlet with back pressure set to 0 is specified. Detailed flow parameters can be found in Table 1.  
Figure 2: Sketches: (**a**) computational domain with boundary conditions; (**b**) numerical grid distribution and details in boundaries.
## 3 Results and Discussion
### Method Validation
#### 3.4.5 Precision  
The study's intra-day and intermediate (inter-day) precision were calculated based on %RSD values, which ranged from 0.92% to 4.11%, and thus were found to be within acceptable limits as per ICH guidelines (Tables 6 and 7), indicating a high degree of precision of the developed method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{
\begin{tabular}{c} **Theoretical Amount** \\ **(ng/band)** \\ \end{tabular} } & \multicolumn{3}{c}{**Run 1**} & \multicolumn{3}{c}{**Run 2**} & \multicolumn{3}{c}{**Run 3**} \\  & **Measured Amount** & **Measured Amount** & **Measured Amount** & **Measured Amount** & **Measured Amount** & **Mean (ng/band)** & **SD** & **\%RSD** \\  & **(ng/band)** & **(ng/band)** & **(ng/band)** & **(ng/band)** & **(ng/band)** & & \\ \hline Vitamin D\({}_{2}\) & & & & & & & \\ \hline
150 & 156.80 & 153.60 & 150.70 & 153.70 & 3.05 & 1.99 \\ \hline
200 & 203.30 & 195.53 & 205.47 & 201.43 & 5.22 & 2.59 \\ \hline
250 & 245.43 & 248.43 & 252.53 & 248.80 & 3.56 & 1.43 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision study of vitamins D\({}_{2}\), D\({}_{3}\), E, and K\({}_{1}\) (Intra-Day).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{
\begin{tabular}{c} **Theoretical** \\ **Amount** \\ **(ng/band)** \\ \end{tabular} } & \multicolumn{3}{c}{**Run 1**} & \multicolumn{3}{c}{**Run 2**} & \multicolumn{3}{c}{**Run 3**} \\ \cline{2-9}  & **Amount** & **Recovered** & **\% Recovery** & **\% Mean (ng/band)** & **Accovered** & **\% Recovery** & **\% Mean (ng/band)** & **Recovered** & **\% Recovery** & **\% Mean Recovery** \\ \hline Vitamin D\({}_{2}\) & & & & & & & & \\ \hline
150 & 146.52 & 97.68 & & 157.30 & 104.87 & & 155.70 & 103.80 & \\ \hline
200 &
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
###### Abstract  
The state of Kuwait is facing a substantial challenge in responding to the spread of the novel coronavirus 2019 (COVID-19). The government's decision to repatriate stranded citizens back to Kuwait from various COVID-19 epicenters has generated a great concern. It has heightened the need for prediction models to estimate the epidemic size. Mathematical modeling plays a pivotal role in predicting the spread of infectious diseases to enable policymakers to implement various health and safety measures to contain the spread. This research presents a forecast of the COVID-19 epidemic size in Kuwait based on the confirmed data. Deterministic and stochastic modeling approaches were used to estimate the size of COVID-19 spread in Kuwait and determine its ending phase. In addition, various simulation scenarios were conducted to demonstrate the effectiveness of nonpharmaceutical intervention measures, particularly with time-varying infection rates and individual contact numbers. Results indicate that, with data until 19 April 2020 and before the repatriation plan, the estimated reproduction number in Kuwait is 2.2. It also confirms the efficiency of the containment measures of the state of Kuwait to control the spread even after the repatriation plan. The results show that a high contact rate among the population implies that the epidemic peak value is yet to be reached and that more strict intervention measures must be incorporated  
COVID-19; forecasting; stochastic SIR; mathematical modeling; compartmental models +
Footnote †: journal: Applied Statistics and Biostatistics
# Multi-Objective BiLevel Optimization by Bayesian Optimization
## 4 Experiments
### Noise-Free Experiments
#### 4.1.1 Parameters  
We use the Python programming language to implement the problems. BoTorch library [40] is used for the upper-level optimization problem. qEHVI and qParEgo acquisition functions for multi-objective optimization problems can be found in the library. _SobolQM-CNormalSampler_ is used for random sampling for starting the optimization. The main reason for choosing this sampling method is its popularity in the multi-objective Bayesianoptimization literature. \(2(d+1)\) random upper-level decision points are selected, where \(d\) represents the dimensions of the problem for constructing the GP model as recommended in [14]. _MaternKernel_ is used as it is the default selection. During acquisition optimization, we selected the number of starting points \(num\_restarts=10\) and \(raw\_samples=512\) as suggested in [14] for both the qEHVI and qParEGO acquisition functions. The multi-start optimization of the acquisition function is performed using _LBFGS-B_ with exact gradients computed via auto-differentiation. Other parameters are set as default for Bayesian optimization to observe the performance. Lower-level optimization problems are solved using a genetic algorithm. For the implementation of the problems the PyMOO library [41] is selected. We use NSGA-II proposed in [31] with the following parameters. We selected \(pop\_size=50\) to represent the population size, and the number of generations is set to 50. The main purpose of this selection is to make a fair comparison with other evolutionary methods, i.e., [25]. The _real_random_ sampling method is used for sampling. After we obtained the Pareto-optimal solution set, we used a uniform random selection from the Pareto-optimal solution set. The algorithm is executed 10 times for each test function with different batch sizes.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 1 Introduction
However, this model only considered the selection of one old node, while in reality, multiple old nodes and their neighbors may be selected. Therefore, it holds significant importance to delve into and explore the regulations of information dissemination based on this network model. In this paper, we study the hyperdegree distribution and analyzed the relationship with the clustering coefficient and focus on the structural parameters of the hypernetwork, including the number of nodes, clustering coefficient, new nodes, old nodes, new hyperedges, as well as the propagation parameters such as spreading rate, recovering rate, and spreading seed, which all impact information propagation. We compare our model with the BA hypernetwork and find that fundamentally, the BA hypernetwork is an aggregation-phenomenon hypernetwork with a clustering coefficient of zero. The structure of the paper is as follows. Section 2 introduces the concept of hypernetworks, the evolution process of the agglomeration-phenomenon hypernetwork, and the concepts of the SIS model. In Section 3, the information dissemination model is introduced, and the hyperdegree distribution derivation and theoretical analysis are performed. The last two sections of the paper elaborate on the simulation results and present our conclusions.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Powering and Navigation
#### 2.3.8 Catalytic Micromotors and Nanomotors  
Many microorganisms harvest chemical energy in the form of nutrients from their surroundings. Similarly, catalytic micromotors and nanomotors propel themselves by harnessing and transforming chemical free energy (H\({}_{2}\)O\({}_{2}\)) into mechanical energy [112]. This motion is governed by chemical reactivity, such as the nature of the reaction, the location of the catalyst, and the shape of the particle [113].
## 5 Conclusions  
In this paper the research work on remote sensors and data transmission undertaken by LA SALLE and the Ebro Observatory in the antarctic spanish station Juan Carlos I is revised. Three different sensors, shuch as (i) a geomagnetic sensor, (ii) a vertical ionosonde and (iii) an oblique ionosonde, are described and conclusions about research outcomes are given. These three sensors have been generating data to study the earth magnetic field in this remote region as well as the behavior of the ionosphere from a physical and from a radio communications points of view.  
Moreover a proposal to implement the physical layer of the skywave link between the Antarctica and Spain is studied. The main goal of this radio link is to transmit the remote data generated by the above sensors in almost real time. Research work has been focused in two possible modulation candidates:(i) OFDM and (ii) DS-SS. Both are able to cope with the channel impairments (low SNR and high level of interference at the receiver site, multipath and time variant channel,...) and link restrictions (long distance, low transmission power, non-directive antennas,...). Both modulations can achieve a data rate of 150 bits-per-second when the channel is available, which is enough for the amount of data to be transmitted from the remote sensors.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 2 Theory and Method
### Network Architecture
#### 2.2.4 Decoder  
The decoder reconstructs the waveform from the encoded representation of the direct wave or the reflected wave. The decoder can be reformulated as another matrix multiplication,  
\[\hat{\mathbf{x}}_{i}=\mathbf{d}_{i}\mathbf{V}, \tag{11}\]  
where \(\hat{\mathbf{x}}_{i}\in\mathbf{R}^{1\times L},i=1,2\) is the reconstructed segment of the direct wave and the reflected wave; and the rows of matrix \(\mathbf{V}\in\mathbf{R}^{N\times L}\) are the decoder basis functions, where each basis function has length L. The overlapping reconstructed segments are added together to  
Figure 4: Conceptual illustration of the ICD generation process. The first element of the ICD matrix is obtained by the Hadamard product of the shadow matrix in \(\mathbf{y}\) with the convolution kernel \(\mathbf{K}\) and then calculating the dot product with the window function \(\mathbf{h}\). B represents the number of 2-D convolution kernels (or the number of filters). The image is adapted from Figure 4 in the study of [PERSON] et al. [24], to maintain consistency with Equation (9).  
produce the final waveform \(\widehat{\mathbf{v}}_{i}\in\mathbb{R}^{1\times T},i=1,2\) (see Figure 3c) [29]. In MC-ConvTasNet, the decoder is implemented by a 1-D transposed convolution layer. The number of convolution kernels is 512, the convolution kernel size is 16 and the step size is 8.
# Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks +
## 3 Methodology  
RED optimizes queue by maintaining an average queue length _aql_ at all times bounded by two threshold levels \(min_{th}\) and \(max_{th}\) as shown in Figure 1. Both threshold levels are configurable parameters with \(max_{th}\) being close to the maximum queue length of the node.  
RED works by randomly dropping or marking the arriving packets with a probability if the _aql_ is between two threshold levels, while no packet dropping or marking takes place if _aql_ is below \(min_{th}\). Similarly, if \(max_{th}\) is defined as less than the maximum queue capacity and _aql_ is greater than \(max_{th}\), then all the arriving packets are dropped by the queue. Dropping enough packets will ensure that _aql_ will stay between the two threshold levels. Parameter _aql_ is determined by the equation below:  
\[aql=\frac{q_{avg}-min_{th}}{max_{th}-min_{th}} \tag{1}\]  
While the dropping probability is determined by the equation below:  
\[P_{d}=P_{max}\times aql \tag{2}\]  
where, \(P_{max}\) is also a configurable parameter with default value of 0.1, which means that each arriving packet, when _aql_ is between \(min_{th}\) and \(max_{th}\), will have a dropping probability of 10% of _aql_. RED was made to work with TCP connections so that dropped packets are retransmitted with no loss of information but it also causes the TCP sending window to  
Figure 1: RED queue parameters.  
shrink. Although these issues were later addressed by the research community, RED also suffers from performance drops as RED behavior still allows queue overflows [13].  
AgRED modifies the linear behavior of RED such that dropping probability allows packet dropping aggressively when the _aql_ is near _minth_. Dropping probability of AgRED \(P_{AgRED}\) uses a sigmoid function that is most commonly used in deep learning and defined below:  
\[P_{AgRED}=\frac{1}{1+P_{d}} \tag{3}\]  
where \(P_{d}\) from Equation (2) is modified to  
\[P_{d}=\left\{e^{-MAX(P_{AgRED})}\times\frac{e^{-q_{avg}}-e^{-min_{th}}}{e^{- max_{th}}-e^{-min_{th}}}\right\} \tag{4}\]
## 4 Discussion  
Figures 8-10 present the difference between the electrical power measured at battery terminals and mechanical power measured at the coupling between DCM and spoke-type PMSM. It is obvious that the difference between those curves is mainly the expression of power losses on the kinematic chain: power convertor losses, PMSM losses, and toothed belt transmission losses. Measurement error tolerances can also play a small role in this difference. Considering that the SEVCON power converter has a rather high efficiency (Appendix A, Figure A2) at a transferred power of 2-3 kW (Figures 7-9) of about 98%, the remaining power losses are attributed to spoke-type PMSM and belt transmission. In Figure 9, the graph is zoomed from Figure 8, where the PMSM is at high speed. In this case, the drivetrain efficiency is between 75 and 80%. This means that the PMSM and the transmission efficiency are around 77-82%. In Figure 10 a zoom of Figure 8 was made, but for a low speed operation. Here, the drivetrain efficiency is around 90%, more consistent with the simulation results. As a partial conclusion, at high speeds, the mechanical (bearings) and transmission losses are increasing significantly. However, the PMSM iron losses and Joule losses are also important, at high speeds, due to high fundamental frequency and flux-weakening implementation. Another visible aspect that should be explained is the slight delay of the battery power curve when compared with the mechanical power curve. This is due to higher battery current and voltage filter constants compared with the filter constants on the speed and torque measured values.  
The implementation of the propulsion system on the prototype vehicle was made to validate the developed structure. Acceleration and regenerative braking pedals were used to control the SEVCON controller aside from a mechanic brake pedal. The prototype vehicle was not fitted with all the elements of a standard car (doors, windows, lights, front and rear renders, etc.). Nevertheless, only maximum speed and autonomy could be affected in this case. Thus, one can conclude that the propulsion system was completely validated.
## 2 Architecture of the IMU/GNSS Integrated Navigation System  
### IMU/GNSS Integration Architecture  
Figure 1 illustrates an overview of the EKF-based tightly coupled IMU/GNSS integration architecture. The inputs are the GNSS pseudo-range, \(\rho\), pseudo-range rate, \(\dot{\rho}\), GNSS receiver clock offset, \(\delta\rho_{c}\), and GNSS receiver drift, \(\delta\dot{\rho}_{c}\), from the GNSS ranging processor and the IMU position, \(P_{IMU}\), velocity, \(V_{IMU}\), and attitude, \(A_{IMU}\), navigation parameters from the inertial navigation processor. The outputs of the integrated navigation system are the corrected IMU position, \(\hat{P}\), velocity, \(\hat{V}\), and attitude, \(\hat{A}\). The integration is completed in the range domain. In this study, the error-state inertial navigation equations based EKF (i.e., the error-state EKF) was used for the IMU/GNSS integration architecture, and the outputs of the IMU/GNSS integration Kalman filter were the UAV navigation parameter error estimation, \(\delta\hat{X}\)[21].