esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Model
### Regime of Hydrodynamic Lubrication  
If the fluid film thickness is strong enough, it fully separates the contact surfaces. In addition to that, the lubricant flow is not significantly affected by the contact asperities. This is the case of the hydrodynamic lubrication regime and it occurs when the ratio between the nominal lubricant thickness \(h\) and the composite surface roughness \(\sigma\) is significantly greater than three [29]:  
\[\frac{h}{\sigma}\gg 3. \tag{6}\]  
The pressure distribution in the hydrodynamic lubrication regime can be described by the Reynolds equation [30], which is derived from the Navier-Stokes equations and the continuity equation, or from the equilibrium of the fluid element. The Reynolds equation is valid for Newtonian fluids and requires the fulfillment of several key assumptions, which can be found in [31]. The Reynolds equation in its universal form is:  
\[\begin{array}{l}\frac{\partial}{\partial x}\left(\frac{\rho h^{3}}{12\beta }\frac{\partial p}{\partial x}\right)+\frac{\partial}{\partial y}\left(\frac {\rho h^{3}}{12\beta}\frac{\partial p}{\partial y}\right)\\ =\frac{\partial}{\partial x}\left(\rho\frac{V_{1}+V_{2}}{2}h\right)+\frac{ \partial}{\partial y}\left(\rho\frac{U_{1}+U_{2}}{2}h\right)+\frac{\partial( \rho h)}{\partial t}\end{array}, \tag{7}\]  
where \(x\) and \(y\) are the coordinates in the circumferential and axial direction, \(p\) reflects the hydrodynamic pressure, \(\eta\) indicates the lubricant dynamic viscosity, \(\rho\) reflects the lubricant density, \(V_{1}\) and \(V_{2}\) represent the velocities for body No. 1 and No. 2 in \(x\) direction, and \(U_{1}\) and \(U_{2}\) are the velocities for body No. 1 and No. 2 in \(y\) direction. The hydrodynamic lubrication causes the shear stress:  
\[\tau_{x}=-\frac{\eta(V_{1}-V_{2})}{h}\pm\frac{h}{2}\frac{\partial p}{\partial x}, \tag{8}\]  
\[\tau_{y}=-\frac{\eta(U_{1}-U_{2})}{h}\pm\frac{h}{2}\frac{\partial p}{\partial y}, \tag{9}\]  
where \(\tau_{x}\) and \(\tau_{y}\) indicate the shear stress in the \(x\) and \(y\) direction, respectively.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 1 Introduction
### Background
MDT (minimization of drive tests) is a method that utilizes users' equipment (UE) to collect radio measurements and associated location information, in order to assess network performance whereas reducing the operational expenditure (OPEX) associated with traditional (manual) drive tests. It enables operators to collect feedback from regular users' devices in the field (including indoor users) without the need to run offline and trial data collection sessions  
Figure 1: Usage scenarios for 5G and beyond [3]. to observe network performance in a specific area or initiate customer care dedicated to a specific user [9]. SON targets the network's operational efficiency. MDT specifically aims at improving the end-user experience. The two features are complementary. Both address major network optimization use cases. Once applied together, they have synergized aims to achieve better network performance and satisfied subscribers. Automated approaches exist, however, 5G merging and maintenance appear much more complex due to the 5G traffic variation characteristic of different users, flexible network deployments, and varying implementations and capabilities. For instance, 5G enables using multiple input-multiple output (MIMO) antennas, many base stations including macro sites operating in cooperation with smaller stations, employing a variety of radio technologies depending on service needs, use cases, and/or the spectrum available. Yet, 5G networks may support network slicing. Primarily, network slices may correspond to the 5G usage scenarios, i.e., _eMBB_, _URLLC_, and _mMTC_, and enable an end-to-end solution, in which multiple independent and dedicated virtual subnetworks (network instances) may be created within the substantially same infrastructure to run services that have different requirements on latency, reliability, throughput, and mobility. Overall, the coexistence of an extremely large number of connection options and the need to process very high volumes of data become a bottleneck for the effectiveness and efficiency of known autonomous solutions for network optimization. Although AI (artificial intelligence) and ML(machine learning) methods demonstrate a promising ability to resolve difficult classification problems in big data collection sets, difficulty in integrating AI/ML with the existing tools and infrastructure, and the compatibility of AI/ML across different devices, are exemplary challenges. For that reason, 3 GPP has conducted studies on AI/ML usage in collecting data in mobile networks in [10]. The standardization support for AI/ML is focused on building the functional architecture of using AI/ML (i.e., data acquisition and exposure), and the high-level requirements of operating an ML-based RAN (i.e., the ML algorithms are assumed to be up to implementation). The studies recommend a framework, which guides further development of technical solutions and practical implementations.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
###### Abstract  
Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time.  
c pacs: **Keywords:** cognitive radio; cooperative spectrum sensing; deep learning; convolutional neural networks; long short-term memory; multilayer perceptron +
Footnote †: journal:
## Abbreviations  
* 4-PAM Four-Level Pulse Amplitude Modulation
* 4G Fourth-Generation
* 5G Fifth-Generation
* 6G Sixth-Generation
* 8A Average Bit Error Rate
* 9 Amplify-And-Forward
* 10 Adaptive Modulation
* 11 Access Point
* 12 Avalanche Photodiode
* 13 Avalanche Photodiode Adaptive Gain Control
* 14 Automatic Repeat Request
* 15 Average Spectral Efficiency
* 16 Average Symbol Error Rate
* 17 Arbitrary Waveform Generator
* 18 Additive White Gaussian Noise
* 19 Buffer-Aided
* 20 Bit Error Rate
* 21 Beamforming
* 22 Bit-Interleaved Coded Modulation
* 23 Bit Interleaved Coded Modulation with Iterative Decoding  
\begin{tabular}{l l} BPF & Bandpass Filter \\ BPPM & Binary Pulse Position Modulation \\ BS & Base Station \\ C-RAN & Cloud Radio Access Network \\ CATV & Cable Television \\ CDF & Cumulative Distribution Function \\ CMCA & Centralized Maximal Cells Algorithm \\ CR & Cognitive Radio \\ CSI & Channel State Information \\ CU & Central Unit \\ DBPSK & Differential Binary Phase Shift Keying \\ DF & Decode-And-Forward \\ DFB LDs & Distributed Feedback Laser Diodes \\ EDFA & Erbium-Doped Fiber Amplifier \\ ESR & Ergodic Sum Rate \\ EXIT & Extrinsic Information Transfer \\ FEC & Forward Error Correction \\ FiWi & Fiber-Wireless \\ FPGA & Field Programmable Gate Array \\ FSO & Free Space Optical \\ GS & Ground Station \\ HAP & Hybrid Access Point \\ HAPS & High Altitude Platform Station \\ HARQ & Hybrid Automatic-Repeat Request \\ HD & Heterodyne Detection \\ HSM & Hybrid Spatial Modulation \\ IM/DD & Intensity Modulation with Direct Detection \\ IMD & Intermodulation Distortion \\ IoT & Internet Of Things \\ LD & Laser Diode \\ LDPC & Low-Density Parity-Check \\ LED & Light Emitting Diodes \\ LEO & Low Earth Orbit \\ LiFi & Light Fidelity \\ LOS & Line-Of-Sight \\ LTE & Long-Term Evolution \\ MGF & Moment Generating Function \\ MIMO & Multiple-Input Multiple-Output \\ MISO & Multiple-Input Single-Output \\ MMW & Millimeter-Wave \\ MPPM & Multiple Pulse Position Modulation \\ MRC & Maximum Ratio Combination \\ MRT & Maximum Ratio Transmission \\ MSIM & Multiple Subcarrier Intensity Modulation \\ MSM-RS & Mixed SIMO Relaying System \\ MSS-RS & Mixed SISO Relaying System \\ MU & Mobile User \\ NFP & Networked Flying Platform \\ NIR & Near Infrared \\ NLOS & Non-Line of Sight \\ NOMA & Non-Orthogonal Multiple Access \\ Non-BA & Non-Buffer-Aided \\ NR & New Radio \\ NRZ-OOK & Non-Return to Zero On-Off Keying \\ OAM & Orbital Angular Momentum \\ OAM & Optical Amplifier \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OOK & On-Off Keying \\ \end{tabular}
## 2 Related Articles
The results indicate that the algorithm effectively improves EEG signal quality while preserving essential bioelectrical patterns, making it a promising tool for clinical applications. Hybrid methods combine the advantages of different approaches to enhance artifact removal [17]. Because EEG signals can be contaminated by multiple artifact types (e.g., eye movements, muscle activity, and cardiac activity), a single technique may be insufficient. Hybrid methods allow for more effective artifact removal by merging various techniques, such as blind source separation, wavelet analysis (DWT/CWT), clustering, or machine learning algorithms. SSA-CCA (singular spectrum analysis--canonical correlation analysis), described in [18], effectively removes muscle artifacts from EEG signals. It decomposes the EEG signal into components (RCs) using SSA, then applies CCA to isolate muscle artifacts by leveraging their low autocorrelation (setting the corresponding rows of the source matrix to zero). Summing the remaining artifact-free components yields a cleaned EEG signal. Interest in machine learning-based methods, particularly convolutional neural networks (CNNs), is growing [19]. CNNs offer high flexibility in modeling nonlinear relationships and excel at removing complex artifacts. They can automatically extract and adapt features for diverse datasets, making them more versatile than classical methods. However, their limitations include high computational demands and the need for large, diverse training datasets--a challenge for rare or highly specific artifacts. Additionally, comprehensive comparative analyses of both classical and advanced approaches across large, varied datasets are lacking, underscoring the need for further research. Most existing studies focus on artifact removal using only EEG signals. Incorporating information about interference sources--such as EMG recordings--and integrating them with CNN-LSTM based neural networks may represent a major breakthrough in this field.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems
### MIMO Systems  
In this section, we present the optimized algorithms, which use different techniques for the channel estimation in MIMO systems.  
3.1 Three-Dimensional Multiple Measurement Vector (3D-MMV) and the Look Ahead Orthogonal Match Pursuit (3D-MLAOMP) Algorithm  
In [80], the scenario focuses on a multi-BS and multi-UE in RIS-assisted cell-free systems and investigates the multi-BS cooperation and multi-UE joint estimation. For a cascaded channel estimation, a 3D-MMV framework has been used to jointly estimate the cascaded AoDs for all users in which the BS and RIS and multi-UE channels contribute to a common part (BS-RIS) (characteristic 1). They additionally use tensor contraction to present a 3D-MLAOMP algorithm. Moreover, when UE and RIS and multi-BS channels communicate on a common part (RIS-UE) (characteristic 2), it is not implemented in the cascaded channel estimation. For more details, see Algorithm 1 in [80].
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 5 Performance Evaluation
### API Evaluation Results  
The results presented are based on the metrics and the use cases presented. The variation in the number of users will allow us to evaluate the behavior of the service in scenarios with competition for the same resources. The tests presented here are experimental. In real scenarios, the numbers may vary.  
Figure 5 shows the results for one user. The response times are similar for four and eight threads as expected. It can be seen that the longer the execution time the smaller the standard deviation, reaching 0 in 30 s, thus presenting a smaller confidence interval. The obtained response time meets the requirements in [41] for Smart Home scenarios and even games, which require latencies of 10 ms or less.  
Figure 5: Performance with 1 user.  
The results for 50 users indicate the solution's viability for Smart Farming scenarios, with the transmission of data from sensors and location tags, for example, [39, 40]. With 100 users, the results indicate scenarios that accept considerably high response times, such as Smart Home, location tags, and medical remote consultation [40, 41].  
The results show that the solution performed as expected. The times displayed in the graphs indicate the solution's viability in various use cases presented in the literature and this work. However, as applications and the network evolve, new use cases may arise, and new demands may be required. Therefore, in the long term, it is necessary to review the viability of this solution within the scenarios and needs that will arise.
## 4 Results and Discussions  
### Real-Time Monitoring System  
Data visualization was developed by utilizing JavaScript framework as a monitoring system to present sensor data in real-time. The manager could monitor the status of assembly line process as well as receive the early warning once the abnormal event (fault) is detected in real-time through the proposed system. The IoT-based sensor devices sent the sensor data to Apache Kafka, then Apache Storm will process the data as well as sent the sensor data and its fault prediction results directly to the monitoring system in real-time, and finally the sensor data and its prediction result are stored into MongoDB. As can be seen in Figure 6, the real-time monitoring system can be easily accessed via a web-browser on a personal computer. The proposed system presents the sensor data such as  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Classified as “Yes”** & **Classified as “No”** \\ \hline Actual “Yes” & TP & FN \\ Actual “No” & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix of a classifier.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Performance Metric** & **Formula** \\ \hline Precision & TP\(/(\text{TP}+\text{FP})\) \\ Recall/Sensitivity & TP\(/(\text{TP}+\text{FN})\) \\ Accuracy & \(\left(\text{TP}+\text{TN}\right)/\left(\text{TP}+\text{TN}+\text{FP}+\text{ FN}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance metrics for the classification model.  
Figure 5: The result of DBSCAN-based outlier detection.  
temperature, humidity, accelerometer, and gyroscope data in real-time. The device ID (IoT-based sensor device) and recorded time was collected and presented for every record. In addition, the hybrid prediction model was used to predict the fault and present the result into real-time monitoring system. The proposed system has been implemented and tested in one of automotive manufacturing in Korea from 1 August 2017 to 31 March 2018. Four IoT-based sensor devices were installed in the manufacturing assembly line and transmitted the sensor data to the remote server every 5 s. During this testing period, around 19 million records (with approximate size is 3 gigabytes) has been collected. Our proposed real-time monitoring system consists of three parts: the IoT-based sensor, the big data processing platform and hybrid prediction model. The performance evaluation are presented for each part in Section 4.2, Section 4.3 and Section 4.4, respectively.
## 4 Discussion
### Relationship Between Structural Parameter and Density  
Soil density is a class of indicators that reflect the relative mass and volume content of the soil's three-phase material. When there are only solid and gas phases in the soil, the density of the soil is called the dry density, which reflects the mass of the soil particle skeleton per unit volume of soil. The dry density of the soil includes the effect of the material composition of the soil particles. In addition to dry density, void ratio and porosity also reflect the compactness of the soil. The pore ratio is the size of the pore volume gap per unit volume of soil particles, and its reciprocal is the volume of soil particles per unit volume of pore space. Together they give a fuller picture of the tightness of the soil's skeleton. The lower the dry density of the soil, i.e., the higher the pore ratio, the more unstable the particle arrangement and association. The changes in soil structure after disturbance and immersion are more pronounced, with the soil showing stronger structure and greater structural parameter.  
To reveal the effect of soil density on its structural properties, the relationship between the structural parameter and the ratio of dry density and pore ratio was analyzed by reflecting the dry density of soil per unit volume of soil particle mass and the volume of soil particles corresponding to unit pore volume to synthesize the degree of soil compaction, as shown in Figure 6. It shows that the structural parameter of loess with different moisture contents generally show a decreasing trend with an increasing ratio of dry density to pore space ratio. The lower the dry density of the soil, the higher the pore ratio, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interfering** & **Confining Pressure** & **Moisture Content** & **Dry Density** \\
**Factor** & & & \\ \hline coefficient of & & & \\ variation & 0.288 & 0.579 & 0.268 \\ weight value & 0.254 & 0.51 & 0.236 \\ ranking of evaluation & 2 & 1 & 3 \\ indicators & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: The Weight Values of Different Influencing Factors.  
greater the changes in the arrangement and association characteristics of the soil skeletal particles subjected to the effects of immersion and disturbance, and the greater the structural parameter of the soil. The greater the dry density of the soil, the smaller the pore ratio, the less the changes in the arrangement and association characteristics of the skeletal particles subjected to the effects of immersion and disturbance, and the smaller the structural parameter of the soil.  
Figure 6 also shows that the structural parameters are more discrete with respect to the pattern of change in the dry density and pore ratio ratios. Given the same soil source and moisture content, the structural parameters of loess are affected not only by dry density and void ratio, which reflect soil compactness, but also by other physical properties of loess.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 4 Performance Analysis
### Reinforcement-Based Routing  
From earlier results we observed that clustering-K mean performs the best in EE, therefore, we apply RL on the best performing scheme. Figure 11 shows the EE between the best performing scheme and the best performing scheme.  
Figure 8: Energy efficiency.  
Figure 10: Packet delivery ratio.  
Figure 9: End-to-end delay.  
ing scheme clustering-K mean and clustering-K mean-RL. The results show an improvement of up to 15% at 10 s when compared with the underlying scheme. In addition, we have also compared RL-based distance and energy only variant of Equation (15). Its worth mentioning that RL-based distance only variant performed much better compared to both conventional Dijkstra and Dijkstra with UAV. Figure 12 shows PDR between the clustering-K mean and its RL variant. RL-based K mean variant shows at least a 4% improvement.  
Figure 13 shows a comparison of the total number of control packets sent by all the schemes including clustering and Dijkstra-based schemes. Both Dijkstra-based schemes transmit almost the same number of control packets. It is interesting to observe that the control overhead of Dijkstra-based schemes is approximately 90% higher than the clustering-energy-GW. Figure 14 shows the same comparison between different clustering schemes. The control overhead of clustering-energy-GW is 32% higher when compared to clustering-energy, clustering-K mean-GW, and clustering-K mean-RL.  
Figure 15 shows the number of CH changes in different clustering schemes. The energy-based schemes show the highest number of CH changes when compared with the K mean clustering schemes because of their distributed nature. Clustering-energy-GW shows 53% more CH changes when compared with clustering-K mean-GW.  
Figure 11: Energy efficiency of the reinforcement learning scheme. RL: Reinforcement Learning.  
Figure 12: Packet delivery ratio of the reinforcement learning scheme.