esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 3 Proposed Method  
The proposed method of QoE score measurement, presented in Figure 1, can be divided into three main steps, namely, flow features and QoS metrics extraction, flow traffic classification, and QoE quantization. The features are selected to describe the flow behavior and feed into the \"Machine Learning Model\" for traffic type classification, QoS metrics (such as time delay, loss rate, and throughput rate) are used in the \"QoE Quantization Model\" to calculate the QoE score for a network flow with the traffic type identified.
## 3 Proposed Method
### Constrained Channel Module  
During the neural network training process, PN and PA nonlinearity constraints are explicitly incorporated into the channel model to construct an impairment-augmented-equivalent channel. This drives the network to learn robust constellation diagrams with nonlinear predistortion compensation capabilities.  
To depict the nonlinear characteristics of PA, a modified Rapp model is considered, which models the amplitude and phase amplitude distortion as [34; 35]  
\[G(A)=gA\left[1+\left(\frac{gA}{A_{\mathrm{sat}}}\right)^{2s}\right]^{-\frac{1} {\Delta}},\Psi(A)=aA^{q_{1}}\left[1+\left(\frac{A}{\beta}\right)^{q_{2}} \right]^{-1}, \tag{9}\]  
where \(A\) is the input signal amplitude, \(A_{\mathrm{sat}}\) is the PA saturation level, \(s\) is the smoothness parameter for the amplitude compression curve, and \(g,q_{1},q_{2},\beta\) are parameters, respectively.  
The Gaussian PN model is adopted for its analytical tractability in deriving key performance metrics like BER and CRB. Gaussian assumption also simplifies gradient computation for signal design optimization, such as constellation shaping and power allocation. The received signal under PN and AWGN is expressed as \(y_{k}=x_{k}e^{\phi_{k}}+w_{k}\), where \(x_{k}\in\mathbb{C}\) is the transmitted symbol, \(\mathbb{E}[|x_{k}|^{2}]=P_{\mathrm{avg}}\). \(\phi_{k}\sim\mathcal{N}(0,\sigma_{\phi}^{2})\) is the phase noise sample reflecting oscillator stability, and \(w_{k}\sim\mathcal{CN}(0,\sigma_{n}^{2})\) is the complex AWGN with \(\sigma_{n}^{2}=N_{0}B\), where \(B\) and \(N_{0}\) denote the channel bandwidth and noise power spectral density, respectively.  
This approach achieves adaptive constellation mapping tailored for IM-OFDM systems. By integrating channel-state information and nonlinear distortion characteristics into neural network training, the mapping scheme dynamically optimizes symbol distribution based on actual channel conditions. By incorporating PA nonlinear and PN into an equivalent channel model, the neural network is driven to train robust constellation diagrams with nonlinear predistortion compensation capabilities, resulting in spiral-like phase-dimensional distributions that suppress phase rotation interference while ensuring PAPR compliance with system transmission requirements.  
Figure 2: The framework of constellation optimization via neural network.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
AGL  Above Ground Level  
ASL  Above Sea Level  
CEPT  European Conference of Postal and Telecommunications Administrations  
CN-FDM  Crank-Nicolson Finite-Difference Method  
ECA  European Table of Frequency Allocations and Applications  
ECC  Electronic Communications Committee  
EIRP  Effective Isotropic Radiated Power  
ERP  Effective Radiated Power  
FDTD  Finite-Difference Time-Domain  
ITU  International Telecommunication Union  
ITU-R  International Telecommunication Union - Radiocommunication Sector  
LOS  Line-of-Sight  
MAE  Mean Absolute Error  
MFN  Mobile/Fixed Communications Networks  
MoM  Method of Moments  
NLOS  Non-Line-of-Sight  
PEM  Parabolic Equation Modeling  
PETOOL  Parabolic Equation Toolbox  
PWE  Parabolic Wave Equation  
RMSE  Root Mean Square Error  
RLAN  Radio Local Area Network  
RR  Radio Regulations  
SSFM  Split-Step Fourier Method  
SUI  Stanford University Interim  
TCA  Terrain Clearance Angle  
TDD  Time Division Duplexing  
WAS  Wireless Access Systems  
WiMAX  Worldwide interoperability for Microwave Access  
WP  Working Party (e.g., ITU-R WP 3K)
## 3 Results
### Simulation Results  
This section presents the simulation results on the proposed method. The model parameters are given in Table 1. The design parameters were chosen as \(\mathbf{K}_{1}=\text{diag}(10,10)\), \(\mathbf{K}_{2}=\text{diag}(10,10)\), \(\lambda=2\), \(\mathbf{\lambda}_{\theta}=\text{diag}(-0.1,0.1)\), \(\sigma_{1}=\sigma_{2}=0.1\), and \(\tau_{1}=\tau_{2}=0.1\). The initial states of the robot were taken as \(\mathbf{q}=\big{[}-0.2,0.2,0,0\big{]}^{\top}\). The friction parameters were chosen as \(\zeta_{x}=\zeta_{y}=2\ \text{N}\cdot\text{s}/\text{m}\) and \(\zeta_{\theta}=\zeta_{\varphi}=1\ \text{N}\cdot\text{s}/\text{rad}\). To consider perturbed dynamics, we assumed that we knew the nominal parameters as follows: \(m_{b}=116\) kg, \(l=0.23\ \text{m}\), \(l_{bx}=16.25\ \text{kg}\cdot\text{m}^{2}\), and \(l_{by}=15.85\ \text{kg}\cdot\text{m}^{2}\). To verify the performance of the proposed method, the reference trajectory was composed of straight lines and curves, and the proposed controller (PC) was compared with the integral backstepping hierarchical sliding mode control (IBHSMC) method [12].  
The simulation results on the trajectory tracking are shown in Figure 2. It can be seen that both methods had precise tracking performance for straight paths and slight errors for curved paths. Figure 3 shows the detailed simulation results. The trajectory  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(m_{b}\) & Body mass & 116 kg \\ \(l\) & Body length & 0.23 kg \\ \(l_{bx}\) & Moment of inertia of the body & 16.25 kg \(\cdot\text{m}^{2}\) \\  & about the roll axis & 15.85 kg \(\cdot\text{m}^{2}\) \\ \(l_{by}\) & Moment of inertia of the body & 0.1 kg \\ \(r_{w}\) & Omnidirectional wheel radius & 0.1 kg \\ \(m_{w}\) & Omnidirectional wheel mass & 0.56 kg \\ \(l_{w}\) & Moment of inertia of the & 0.26 kg \\  & omnidirectional wheel & 11.4 kg \\ \(m_{s}\) & Ball mass & 0.19 kg \\ \(r_{s}\) & Ball radius & 0.165 kg \\ \(l_{s}\) & Moment of inertia of the ball & 0.165 kg \\ \(\alpha\) & Zenith angle & 56\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the ball-balancing robot [12].
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### Message Queuing Telemetry Transport (MQTT)  
MQTT [72] is a lightweight messaging protocol designed for low-bandwidth and resource-constrained environments. It operates on a publish/subscribe messaging model, where a central broker manages communication between publishers (data producers) and subscribers (data consumers), as shown in Figure 2. Initially developed for satellite communication, MQTT has become a cornerstone of IoT communications, offering efficiency and scalability. It offers several key features that make it highly suitable for IoT-based healthcare network management [73]. One of its most significant advantages is minimal packet overhead, with packet sizes as small as 2 bytes, ensuring efficient data transmission over low-bandwidth networks. Another crucial feature is its support for different quality of service (QoS) levels, allowing for flexible message delivery reliability. QoS 0 (at most once) sends messages without requiring acknowledgment, making it ideal for non-critical data. QoS 1 (at least once) ensures message delivery with acknowledgment, preventing data loss while avoiding duplicates. QoS 2 (exactly once) guarantees that messages are received and processed only once, ensuring the highest level of reliability for critical applications. Additionally, MQTT includes a Last Will and Testament mechanism, which notifies the broker in case of an unexpected device disconnection. This feature enhances fault detection and allows for timely interventions. Another essential capability is persistent sessions and queuing, which ensures that messages are stored and delivered even when devices experience intermittent connectivity. These features collectively enhance MQTT's reliability, making it a robust and efficient protocol for managing healthcare IoT networks.  
In network management, MQTT facilitates real-time monitoring by allowing devices to publish telemetry data, enabling network managers to track device status, health, and performance continuously. The protocol also ensures reliable command execution, as messages sent by network managers to devices are delivered based on QoS levels, providing appropriate reliability for various applications. Furthermore, MQTT's Last Will and Testament feature allows the network to detect disconnected devices and alert administrators for prompt remedial action. Its scalability is another major advantage, efficiently handling communication in large-scale IoT networks with thousands of connected devices while maintaining minimal overhead. These features make MQTT an essential protocol for managing healthcare networks, ensuring reliable and efficient communication across medical devices and systems. However, it lacks robust security features, making it vulnerable to cyber threats [72].
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 6 Performance Study of the UWOC Link  
Considering real characteristics of practical components, a set of numerical results is provided in this section to study the performance of the different O-OFDM techniques.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 2 Establish Predictive Methods to Calculate Flow Properties of Polydisperse Particles
### Polydisperse Smooth-Surface Glass Bead Composite Material
#### 2.1.3 Introduced Method: G-JCAL/JCAPL to Calculate Flow Properties
The absorption coefficient predictions were compared with both the predictions of the PSD model method and the experimental results as shown in \[\mathrm{k}_{0}=\frac{d^{2}\varphi^{3m}}{4 am^{2}} \tag{1}\]2 The predictions (broken black lines) agreed well with both the PSD model prediction (solid pink line) [35] and the experiment (solid blue line) [35] throughout the specified frequency range. Similarly, the same flow properties were used to make the transmission loss predictions in MATELYS AlphaCell and compared with the independently measured transmission loss versus frequency as shown in \[\mathrm{k}_{0}=\frac{d^{2}\varphi^{3m}}{4 am^{2}} \tag{1}\]3 The predictions (broken black line) agreed well with the PSD model predictions (solid pink line) [35] and the experiment (solid blue line) [35] up to a 1200 Hz frequency but deviated from the experiment beyond 1200 Hz for a 44 mm thick specimen. Overall, the agreement is reasonable. This validates that the flow properties calculated from the 25 mm thick specimen absorption coefficient response alone can be used to predict both the absorption coefficient and transmission loss response for any thickness for a specific particle-size material system. The flow properties are unique. Based on these assessments, we conclude that when the ratio of minimum specimen dimension to mean particle diameter is about 10 or more, then the flow properties are unique, i.e., independent of specimen geometry for a specified particle-size material system (for a polydisperse solid bead material system). Further, the multivariable-fit inverse method using the G-JCAL/JCAPL method is robust enough and consistent in calculating the flow properties of the model materials. Both the PSD model-based inverse method and the current introduced G-JCAL/JCAPL-based multivariable-fit inverse method will be applied to the polydisperse particle material systems of a lightweight \(\upmu\)PHB composite material to establish the associated pore parameters and the flow properties, respectively.
## 4 Residual Currents  
Here we consider some nonperturbative effects in graphene which are not sufficiently studied in the standard QED or possess some specific features. We restrict ourselves here to the case of a linearly polarized electric field directed along the axis \(x_{1}\).  
Let us begin by investigating the residual currents that persist in graphene after the passage of a strong electric field pulse. In the nondissipative model considered here the conduction current discontinues its evolution and remains constant while the polarization current performs damped oscillations. The character of these oscillations and their damping depends on the form of the electric field pulse, as it follows from Equations (37) and (39).  
Thus, some oscillating and damped component will be present in the total residual current. In order to calculate it, we will use the formulas for the polarization currents (47) in the low energy model and their analogues in the tight binding model (below in Section 7) with the corresponding polarization function \(u^{\rm out}(t)\) for \(t>t_{\rm off}\),  
\[u^{\rm out}(t)=Re\,\Phi(t>t_{\rm out}), \tag{55}\]where the order parameter \(\Phi(t>t_{\rm out})\) in the out-state is defined by Equations (37)-(39).  
According to Equation (37) the frequency of the order parameter is defined by the doubled quasienergy (38). However, while these oscillations are smoothed out upon integration over the momentum space in the polarization current (47), their influence remains quite appreciable, see Figure 1.  
We select the supergaussian model of the electric field  
\[E(t)=-\dot{A}(t)=E_{a}\exp[-(t-t_{\rm max})^{4}/(2\tau^{4})], \tag{56}\]  
where \(t_{\rm max}\) determines the position of the maximum amplitude \(E_{a}\) of the field. This choice of the pulse waveform allows to realize abrupt fronts of switching on and off, see the upper panel of Figure 1, and to clearly identify the presence of a alternating polarization current, see the lower panel of Figure 1. This picture demonstrates also dominance of the polarization current.  
Another feature of the polarization current becomes apparent in presence of a constant electric field  
\[E(t)=E_{a}={\rm const},\ \ A(t)=-E_{a}t. \tag{57}\]  
Here the oscillations of the polarization function (Figure 2) transform to damped oscillations of the polarization current (Figure 3). This damping is caused by the monotonic growth of the quasienergy (8) with time at \(t\geq 0\) and, as a consequence, by the decrease of the oscillation amplitude of the polarization current. This mechanism can be traced visually in the Markovian approximation (33).  
Figure 1: **Upper panel:** Supergaussian electric field (56) where \(t_{max}=1000\ t_{0}\ (2.46\times 10^{-13}\ {\rm s})\) and \(E_{a}=0.00001\ E_{0}\ (1.088\times 10^{3}\ {\rm V/cm})\). **Lower panel:** The density of the polarization current.
## 1 Introduction
The objectives of the study were to identify the range of detected displacements in relative and absolute positioning modes and compare the selected low-cost antennas for their future use in monitoring a real natural or man-made object. Additionally, observations made with low-cost devices were compared with those obtained using geodetic GNSS devices. The structure of the paper is organized as follows: A review of different tests using low-cost GNSS equipment with a special focus on deformation monitoring is presented (Section 1). The study area, the equipment used, the established geodetic network, the methods used to detect displacements, and the statistical hypothesis are described in Section 2. Then, the results are presented (Section 3) and discussed (Section 4). Finally, the derived conclusions are provided (Section 5).
## 3 Results
### Input Parameters for Estimating Bloom Indicators
#### 3.1.2 N\({}_{2}\)-Fixing Cyanobacterial Biomass  
The N\({}_{2}\)-fixing cyanobacteria are most prevalent during the summer in the Baltic Sea. As the name suggests, they fix nitrogen from the atmosphere and make nitrogen bioavailable which can have cascading effects on food webs [59; 60]. The equation used here to estimate the biomass from the satellite data is based on a relationship between the MCI values against the in situ-measured N\({}_{2}\)-fixing cyanobacteria biomass (mg m\({}^{-3}\)):  
\[\text{Biomass}=346.33\times\text{MCI}^{2}+1043.2\times\text{MCI}+1089.1 \tag{4}\]  
The relationship between the in situ biomass and the MCI was relatively poor (R\({}^{2}\) = 0.39, \(p\) < 0.05). An important aspect is that the MCI method for estimating cyanobacteria biomass is only useful at higher concentrations (>300 mg m\({}^{-3}\)). The MCI uses the red and NIR wavelengths that are also influenced by turbidity which makes it difficult to estimate low chl-a. The poor relationship here is partly explained by the very low concentrations of chl-a and higher turbidity in the coastal areas compared to inland lakes, where the MCI has previously been successfully used [61].  
Monitoring the seasonality of cyanobacteria blooms is of key importance. Using satellites gives a better overview than only relying on in situ sampling for monitoring seasonality. The average in situ biomass was highest in the Hara and Kolga by (731.4 mg m\({}^{-3}\)) and Muuga-Tallinna-Kakumae Bay (615.9 mg m\({}^{-3}\)) and lowest in the Gulf of Riga (NW) (1.0 mg m\({}^{-3}\)) and Moonsund Sea (1.3 mg m\({}^{-3}\)). It is important to note that in situ measurements are point measurements and satellite data are an average of a polygon (black dots and line in Figure 7) or a 3 \(\times\) 3 pixel (red stars in Figure 7). As can be seen from Figure 8 and especially from Figure 7, the in situ measurements are sparse both in time and space, only having measurements throughout the entire time series for four coastal areas. Additionally, the concurrence of satellite-derived biomass with data from in situ sampling points in Haapsalu Bay (Figure 7A) shows that satellite data can provide good coincidence with the in situ-measured values within one vegetation period. Many of the peaks of N\({}_{2}\)-fixing cyanobacteria biomass would be missed when relying exclusively on in situ sampling. However, the in situ dataset considers all concentrations, whereas satellite data do not consider concentrations below 300 mg m\({}^{-3}\). In some of the coastal water bodies, in situ and satellite-derived biomass are relatively similar, mostly in the areas belonging to the Gulf of Finland. Most similar were the results in Soela Strait. On longer timescales (2016-2022), the largest differences between the two methods can be observed in the Haapsalu, Matsalu, and Parnu bays (Figure 8). Although the averaged results for the years are similar, significant seasonal differences can be observed, for example, in Muuga-Tallinna-Kakumae Bay, when comparing cyanobacteria biomass (Figures 7B and 8).
## 6 Cellular Basis of Laser-driven Radiation Therapy
### DNA Damage Sequence and Biological Effects  
The time scale of sequential events for an isolated cell along a single particle (damage) track that lead to biological effects after irradiation [93] (Figure 19) spans about twenty-one orders of magnitude. Because a cell comprises mostly water (approximately 70%), we focus on water radiolysis.  
The radiation effect to DNA is also considered, as it is the main target for cellular radiobiological effects [90] even though it constitutes only \(\sim\)0.05% of the cell weight. After photons or particles pass through a cell, ionizations and excitations occur followed by dielectric relaxations. Following this initial physical stage, which occurs on the picosecond timescale, ions and ejected electrons will proceed to generate various radicals. In addition to direct ionization of DNA and DNA radicals, attacks by water-derived radicals mainly contribute to DNA damage within the picosecond to millisecond time frame. Subsequently, due to the induced DNA damage, biological events, such as DNA repair and cell cycle retardation can occur. If the damage is unrepaired and inhibits replication or cell division, cells will eventually die. Furthermore, a cell has a suicide pathway called apoptosis, in which a cell kills itself when it recognizes that the extent of damage exceeds its capacity of repair. If replication occurs with some damage unrepaired alterations of the DNA sequence (mutations) can occur. These biological events usually take place within minutes to hours. Induced mutations could become the origin of cancer. If the mutation is fixed in the cells from which egg or sperm cells are derived (germ line cells), it will lead to inherited mutation which is observed several decades after cells have been exposed to radiation.  
Figure 19: Time scales of major events that lead to biological effects of ionizing radiation. Above the scale bar are shown the corresponding research fields that investigate the events. Reproduced from [93]. Copyright 2004, Taylor & Francis Group, LLC.
## 4 Test Results
### Residual Stress  
Figure 12 is a comparative analysis graph of the residual stress due to the influence of waterproof reinforcement in a test body mixed with OPC and fiber. In the case of the OPC specimens, the residual stress showed excellent results in the specimens reinforced with Typar and Preprufe. Compared to PE, Typar was found to have improved residual stress values of about 260% and Preprufe of about 250%, even after concrete cracking. In particular, Typar and Preprufe reinforcements were found to have excellent residual stress in all test specimens containing fibers. As a result of the comparison between the steel fiber-incorporated test specimen (SF20) and OPC, the PE reinforcement was 6.7 times, Typar 7.9 times, Bittuhene 6.2 times, and Preprufe 6.5 times. As a result of the comparison between the test sample (MF1.0) incorporating organic fiber and OPC, the PE reinforcement material was 2.3 times, Typar 5 times, Bittuhene 3.1 times, and Preprufe 4.4 times. The comparison results of the organic fiber mixing rate (MF2.8) and OPC showed that the PE reinforcement was 3.6 times, Typar was 8 times, Bittuhene was 6.2 times, and Preprufe was 8.5 times. In summary, the improvement of residual stress due to fiber mixing showed excellent results in all waterproof reinforcements in the test body (SF20) incorporating steel fibers. In particular, the best results were obtained in the steel fiber reinforced concrete specimens reinforced with Typar. In the case of the organic fiber, MF2.8 showed excellent results and showed a tendency almost equal to the SF20. The best crack resistance in the organic fiber reinforced concrete combined with Typar and Preprufe was confirmed.