esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 3 Preliminaries
### Mobility Model  
Distinct from the cell above, as shown in Figure 1, the range of node motion is restricted within the certain area, which is called super-cell. Data forward process is independent of node mobility process in the network. The area of each super-cell is \(m\), so each super-cell consists of \(\Theta(m/b(n))\) cells.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(O\) & Asymptotic upper bound \\ \(\Omega\) & Asymptotic lower bound \\ \(\Theta\) & Asymptotically tight bound \\ \(c,c1,c2,c3\) & Constants \\ \(n\) & Number of nodes \\ \(W\) & Total bandwidth available \\ \(r(n)\) & The transmission range \\ \(m\) & Area of super-cell \\ \(b(n)\) & Area of cell \\ \(\delta\) & Parameter of social model \\ \(\lambda_{i}\) & The input stream of source node \\ \(\mathcal{T}\) & Upper bound throughput of each node \\ \(\mu\) & Achieved throughput of each node \\ \(D(n)\) & Network delay \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols.  
Figure 1: Network model.  
There are some special cases: (1) \(m=1\) the range of node motion is the whole network area, each node refresh its position in each time slot, which is similar to the Independent Identically Distributed Mobility model. (2) \(m=b(n)\) the range of node motion is cell area, each node can oscillate at its initial position, which is similar to the static network.  
In the super-cell, each node randomly chooses direction in \([0,2\pi]\) and randomly chooses speed in \([0,\sqrt{m}]\). Then, we know location of nodes is independently and identically distributed in super-cell. Time is divided into slots of equal duration. At the beginning of each time slot, each node randomly selects destination cell in the super-cell, moves from its current cell to destination cell, stays in it until next time slot.
## 5 Conclusions
The overall findings of this study have significant implications for path loss assessment, optimization, and power budget analysis of digital terrestrial wireless communications operating on UHF bands in tropical urban or suburban environments. The novelty of this study over previous studies in the region is highlighted as follows:  
1. No similar studies based on in situ (real-world) data collection with the data scope of dry and wet season months for two consecutive years in the same study environments and over the experimental digital terrestrial UHF channels have been conducted. 2. The results show that empirical models have varying degrees of performance in different environments (location- and climate-dependent) and underscore the need for scientists to validate the reliability of existing empirical models in their locality before they are adapted for use. 3. The validated and optimized Hata and COST-231 models for the Ikorodu, Lagos and Akure environments, respectively, provide valuable applications in the performance assessment of existing digital terrestrial television channel links. They would also be useful in creating coverage limitation mappings for predicting television white spaces (TVWS), with the aim of maximizing spectrum and frequency reuse in the study area or similar environments. Conclusively, the significance of this study for other regions or countries includes the following:  
1. The experimental results, especially for the optimized models, can be employed for channel estimation and modeling over digital terrestrial television channels in regions or countries with geographies similar to the coastal and tropical rainforest zones of Nigeria. These results would find useful application particularly in the coastal-tropical African countries and in any similar climate. 2. This study can also be re-produced in other similar regions, with the aim of ensuring quality of service over digital terrestrial television links. Conceptualization, A.A.; Formal Analysis, A.A.; Investigation, A.A.; Methodology, A.A.; Supervision, B.T.A. ; Validation, A.A. and B.T.A. ; Writing--Original Draft, A.A. and B.T.A. ; Writing--Review and Editing, A.A. and B.T.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors acknowledge the support of Tshwane University of Technology, South Africa. The authors declare no conflicts of interest.
## 6 Advanced Considerations: Environmental Factors in Underwater VLC
### Temperature  
Temperature changes in water can have a great impact on the performance of underwater VLC systems due to the changes in density, viscosity, and refractive index. These changes will affect the light propagation, absorption, and scattering, which can be very important for communication reliability. Ref. [55] highlights that temperature affects the mean and variance of the log-normal distribution used to model optical turbulence, and any change in temperature changes the density and refractive index of water. Ref. [8], in support, has also identified that temperature variations might alter the absorption coefficients of water and thus has proposed a path loss model that needs to be validated for various scenarios.  
Vertical VLC systems are more prone to temperature effects since the temperature gradient can cause turbulence and fading effects. In this sense, Ref. [60] considers how temperature gradients may act towards stratification, leading to many difficulties in communications because of adding other fading conditions. Ref. [62] continues by discussing how in a water column, temperature gradient will induce gradients of the refraction index due to which change in light transmission results in more attenuation effects. Ref. [63] also presents how temperature affects VLC underwater systems because of the alteration in the parameters of fading; it gives rise to turbulence-induced fading with increasing average symbol error probability. The performance evaluation of [59] involves an underwater optical wireless communication system at two temperatures, 10 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, with a conclusion of changes in temperature that may actually affect the whole communication system's efficiency and reliability.
# Hybrid Fault Diagnosis of Bearings: Adaptive Fuzzy Orthonormal-ARX Robust Feedback Observer
###### Abstract  
Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX adaptive fuzzy logic-structure feedback observer, is developed. A fuzzy orthonormal-ARX algorithm is presented for non-stationary signal modeling. Next, a robust, stable, reliable, and accurate observer is developed for signal estimation. Therefore, firstly, a classical feedback observer is implemented. To address the robustness drawback found in feedback observers, a multi-structure technique is developed. Furthermore, to generate signal estimation performance and reliability, the fuzzy logic technique is applied to the structure feedback observer. Also, to improve the stability, reliability, and robustness of the fuzzy orthonormal-ARX fuzzy logic-structure feedback observer, an adaptive algorithm is developed. After generating the residual signals, a support vector machine (SVM) is developed for the detection and classification of the bearing fault conditions. The effectiveness of the proposed procedure is validated using two different datasets for single-type fault diagnosis based on the Case Western Reverse University (CWRU) vibration dataset and multi-type fault diagnosis of bearing using the Smart Health Safety Environment (SHSE) Lab acoustic emission dataset. The proposed algorithm increases the classification accuracy from 86% in the SVM-based fuzzy orthonormal-ARX feedback observer to 97.5% in single-type fault and from 80% to 98.3% in the multi-type faults.  
hybrid-based fault diagnosis; bearing fault diagnosis; fuzzy orthonormal-ARX technique; adaptive fuzzy logic-structure feedback observer; support vector machine fault classification
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 4 Experimental Results and Discussion  
In this experiment, a regional temperature downscaling model is constructed based on a convolutional neural network, with the input being a 41 \(\times\) 41 matrix of ERA5 reanalysis data temperature field at T moments, and the output is the observed temperature of Macau stations at the corresponding moments. Ground-based observations from the Macau Meteorological Bureau were used as the true value comparison, and the evaluation metrics included \(RMSE\), \(MSE\), \(MAE\), \(MRE\), and Correlation.
# Structural Health Monitoring of Large Structures Using Acoustic Emission-Case Histories
###### Abstract  
Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [1], and their uses have been expanded to ever larger structures [2; 3], especially since the structural health monitoring (SHM) of large structures has become an urgent task for engineering communities globally. The needs for advanced methods of AE monitoring are felt most keenly by those dealing with aging infrastructure. Many publications have appeared covering various aspects of AE techniques, but documentation of actual applications of AE techniques has been limited mostly to reports of successful results without technical details that allow objective evaluation of the results, except for some exceptions in the literature where special applications were detailed [4; 5; 6]. In this Special Issue of the Acoustics section of Applied Sciences, we sought contributions, like the exceptions cited here, which describe case histories of AE being applied to large structures. That is, papers that have achieved the goals of SHM and do so by giving adequate technical information supporting the success stories. Gathered here are 14 such articles that cover structures from aerospace and geological structures, bridges, buildings, factories, nuclear facilities, etc.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem
### Pre-Processing Stage  
The pre-processing stage is described in Algorithm 3. This part of the solution is performed once, before the beginning of the mission, and the preliminary probability map, the value of the lowest flight altitude, and a list of obstacle vertices at this altitude are received as inputs.  
To determine the smallest area the drone could cover in a single time instant during flight, its footprint size is calculated at the lowest flight altitude; see Figure 0(a) and Equation (1). As the orientation of the drone is unknown at the early stages of planning, the circle inscribed in the rectangle image is considered its footprint.  
The set of points of interest \(POI\) is extracted using the probability map and obstacle locations. It contains all cell coordinates in the map boundaries with a probability larger than zero and within the free space \(\tau_{f}\). \(POI\) is then clustered with a bandwidth of \(r_{f}\)--thedrone footprint size. The collection of all clusters' centers creates the set of candidate waypoints \(\mathcal{V}\) that are used as a baseline for the planning stage.  
In addition to the set \(\mathcal{V}\), a visibility-graph \(\mathcal{VG}\) is built as part of the pre-processing stage corresponding to the obstacles of the lowest altitude. Based on the assumption that obstacles at all altitudes are contained within obstacles at the lowest altitude, the visibility graph represents safe flight routes at all altitudes.  
Note: To enable the drone to safely travel the free space while avoiding obstacles, the obstacle locations given as input are first edited such that an offset is added to each of the obstacles. This allows the drone to be treated as a point vehicle while planning the trajectory.
## 1 Introduction
### Challenges and Contributions
#### 1.2.3 Contributions  
We develop a novel cross-layer architecture to address current deficiencies in the mobile IEEE 802.11\(ay\) framework that enables simultaneous modeling and optimization of multipath schedulers and beamforming mechanisms. We handle the aforementioned challenges in developing a new _architecture and experience-driven protocol_ with the following three key solutions:  
1. _Intelligent multipath scheduler._ We investigate developing learning-based (intelligent) multipath scheduling algorithms that distribute information and coded packets to links with the least amount of delay at the receiver.
2. _Collaborative beamforming and scheduling design._ Considering the challenges in designing intelligent FEC-aware scheduling and low latency error-correcting codes at the PHY/MAC layer, the influence of the data size on the ideal multipath scheduler, and the necessity for collaborative bonding, beamforming, and scheduling, we employ multi-armed bandit with federated learning (FL) over the links, to explore and exploit optimal characteristics for the proposed integrated cross-layer design.
3. _Multipath network support from the TRA layer._ In addition to the above innovations, we develop a coupled low-delay multipath scheduling protocol (LD-MPSP) for 802.11\(ay\) networks. We design tuneable packet cloning to adapt to the size of the flows and resolve the impeding challenges over erroneous 802.11\(ay\) links.
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 5 Discussion
### Analysis of the Results and Comparison to Common Processing Techniques
For SNR2, the maximum values are produced for a variance of 0.05 in the case of experimental defects, while for synthetic defects, variances greater than 0.05 maximize defects located at a depth less than or equal to 3 mm and a variance of 0.01 maximizes the deeper defects. SNR3 produces results similar to SNR2 in the case of synthetic defects, while for experimental defects, very similar results are obtained for all the analyzed variances, with slightly higher values for variances less than 0.5. For SNR4 and SNR5, synthetic defects located at depths less than or equal to 3 mm produce maximum values for variances greater than 0.05, while defects located at greater depths maximize SNR for variances less than 0.05. Regarding the experimental defects, the SNR values are maximized for variances less than 0.5 in both cases. SNR6 produces responses similar to SNR4 and SNR5 for the case of synthetic defects, while for experimental defects it produces very similar results for all the variance values analyzed, with slightly higher levels for variances of 0.05 and 0.1. Finally, for SNR7, synthetic defects located at depths greater than 3 mm produce maximum values for variances greater than 0.5, while defects at depths less than or equal to 3 mm maximize the SNR for variances less than 1. Regarding the experimental defects, in all cases the maximum values of SNR occur for variances greater than 0.01. Figure 14 shows the mean SNR values obtained with the defects analyzed for each of the SNR metric definitions. The y-axis values are represented in a logarithmic scale to be able to correctly visualize the data for all cases. Important differences are observed among the values that each of the definitions produces. In general, defects located at shallower depths and larger defects have produced higher SNR values for all metrics except for SNR1, where synthetic defects at different depths have very similar values, even in some cases, defects located at greater depths have higher SNR values. It is observed in Figure 14 that the SNR5 metric produces very high values, of the order of \(10^{6}\), for the most superficial synthetic defects, compared to the rest of the synthetic and experimental defects of the order of \(10^{2}\). The values produced by SNR3 and SNR6 for the different defects are much more balanced than the rest of the metrics, due to the  
Figure 14: Average SNR values obtained with the different SNR metric definitions for the analyzed defects. application of logarithms in their calculation. For the rest of the metrics, the differences between the SNR values for different defects is much more significant, clearly highlighting the values for the most superficial synthetic defects.
## 3 Numerical Result of Bandgap Characteristics
### Effect of the Initial Compressive Stress on the Bandgap Characteristics  
The comparison of bandgap results in Table 3 shows that the finite element results in this paper are consistent with those in reference [11]. In order to study the influence of initial stress on the bandgap of CCPM, we calculated the bandgap characteristics by considering the initial stress of the structure under different compression deformations. Figure 8 plots the bandgap distributions of different compression displacement. The bandgaps with axial compressive displacements of 0 mm, 5 mm, 10 mm, 15 mm, 20 mm, 25 mm, and 30 mm are given. Figure 8a,b are two bandgap diagrams with and without considering the initial compressive stress. From Figure 8b, we can see that the first bandgap (colored in yellow) begins to contract and move upwards, and the second additional bandgap (colored in red) begins to open and gradually widens as the compression displacement increases. Compared with Figure 8b, as the compression displacement increases, the initial stress begins to change. When considering initial stress [see Figure 8a], the first bandgap (colored in yellow) also begins to contract but moves downwards. The second additional bandgap (colored in red) begins to open. In addition, the third brand new additional bandgap (colored in blue) emerges. Numerical results show that after the compression displacement of 20 mm (the average initial axial stress of the CCPM reaches 20,325 Pa), the third additional bandgap (colored in blue) appears and begins to widen. After the compression displacement of 25 mm (the average initial axial stress of the CCPM reaches 23,273 Pa), the first initial bandgap closes. The comparison of Figure 8a,b demonstrates how the initial stress can be used to effectively modify the bandgaps in the structure.  
Figure 8: The bandgap of the CCPM at different compressive displacements: (**a**) with initial stress; (**b**) without initial stress.  
Figure 7: Comparison between the bandgap diagram and transmittance spectra: (**a**) without initial stress, (**b**) with initial stress, (**c**) transmission spectra (cites from paper [11]).  
Figure 9 shows the specific bandgap distribution of CCPM under different compression displacements. It can be seen from Figure 9a that the width of the first initial bandgap gradually decreases as the amount of compression increases. Its beginning and ending frequencies are shifted towards low frequencies considering initial stress. The first initial bandgap closes after the compression displacement reaches 30 mm (the average initial axial stress of the structure reaches 25,648 Pa). As shown in Figure 9b, the second additional bandgap appears as the amount of compression increases. The bandgap width is proportional to the compression displacement without initial stress. In the case of considering initial stress, its bandgap width shows a nonlinear trend in Figure 9b. Specifically, the width of the bandgap increases from 0 Hz to a maximum of 13 Hz and then decreases again. Moreover, the beginning and ending frequencies of the second additional bandgap shift down.
## 5 Conclusions  
RP-MPSK modulation is proposed for Industry 4.0 and M2M communication that offers a data rate up to 2 Mbps. Simulation results have shown that RP-MPSK is a robust and flexible modulation technique that can deal with various design goals for LPWANs. High reliability due to superior error performance, greater power efficiency due to increased sensitivity, and efficient use of bandwidth are the main advantages of this modulation. To simplify the complex RPW channel, Q-RPW model is proposed that also improves the quality of reception with quaternion combining. Q-RPW model can also reduce the complexity of stochastic analysis to obtain a few important performance metrics such as BER over fading channels. Channel estimation technique for RPW communication system can also be simplified with Q-RPW. Future works include stochastic analysis of RPW with RP-MPSK modulation, a rigorous comparison of LoRa and RPW physical layers, MAC layer implementation, and development of RPW transceiver prototype employing RP-MSPK.  
Conceptualization, Z.A., S.J.H. and F.Z.R., K.T.; methodology, Z.A.; software, Z.A. and F.Z.R.; validation, S.J.H., F.Z.R., S.A.R.A.-H. and K.T.; formal analysis, Z.A.; investigation, Z.A.; resources, S.J.H., A.S. and K.T.; data curation, Z.A.; writing--original draft preparation, Z.A.; writing--review and editing, S.J.H., A.S. and K.T.; visualization, Z.A.; supervision, S.J.H., F.Z.R., S.A.R.A.-H., A.S.; project administration, S.J.H., S.A.R.A.-H. and F.Z.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 4 Modified Two-Zone Temperature Field Model
### The Governing Equations of the Two-Zone Model  
The two-zone model (Figure 28) is one of the most widely used fire zone models [50]. The model assumes that the building is naturally ventilated, regardless of the cooling effect of the roof and walls and the presence of personnel. It divides the space into two zones: the upper hot smoke layer and the lower cold air layer. The plume above the fire source entrains the lower cold air into the upper smoke layer, the smoke is discharged outdoors through the smoke exhaust port, and the outdoor air enters the indoor lower air through the air supply port. The material and energy exchange between the two zones is only carried out through the flue gas plume. The mass and energy conservation analysis of the two zones is carried out respectively, and the respective control equations are listed and solved. The interface position of the flue gas layer and the change of the flue gas temperature with time can be obtained.  
Figure 28: The two-zone model.  
Total volume of space:  
\[V=V_{s}+V_{a} \tag{34}\]  
In this way, in eleven differential equations, there are eleven variables to be solved (\(m_{s}\), \(m_{a}\), \(E_{s}\), \(E_{a}\), \(V_{s}\), \(V_{a}\), \(T_{s}\), \(T_{0}\), \(\rho_{s}\), \(P\)), given the initial conditions, boundary conditions, and the value or expression of each coefficient in the equation, in theory, the change of these eleven unknowns with time can be obtained. With seven additional equations, as long as the expression of four variables can be obtained from the eleven variables to be solved, the other seven variables can be deduced.