esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 3 Duplication Suppression and Re-Queuing
### The Definition and Characteristics of the Duplication Ratio
#### 3.1.1 The Duplication Index and the Duplication Ratio
Although the duplication ratios measured in each node were somewhat different, their common features are that \(DR_{i}(\tau)\) is a monotonic increasing function of \(\tau\) and it approaches 1 as \(\tau\) increases. On the other hand, Figure 4 shows several values of \(DR_{i}(\tau)\) measured in node 8 (marked as a green circle in Figure 3a) when \(\alpha\) had different values. When \(\alpha\) was set to 0.5, \(DR_{i}(\tau)\) was almost immune to the change in \(\tau\) and was very close to 1. As \(\alpha\) increased, the value of \(DR_{i}(\tau)\) decreased for a specific \(\tau\). The reason is that as \(\alpha\) increased, the duplication index in (1) became 1 more conservatively, leading to a decrease in the duplication ratio in (2). Meanwhile, we can define \(\tau_{min}\) as the minimum value of \(\tau\) ensuring that the duplication ratio becomes close to 1, i.e.,  
\[\tau_{min}=\text{argmin}_{\tau}\big{(}DR_{i}(\tau)\geq 1-\epsilon\big{)},\ \text{for } \epsilon(>0)\approx 0.\]  
As shown in Figure 4, \(\tau_{min}\) also increased as \(\alpha\) increased. For example, when \(\epsilon=0.01\) and \(\alpha=1.0\), \(\tau_{min}\) was 9, which was somewhat smaller than \(N_{i}\) (=14), but \(\tau_{min}\) was 2 when \(\alpha=0.5\). It is worthwhile noting that even in the most conservative case (i.e., \(\alpha=1\)), \(DR_{i}(\tau)\) was very close to 1 for \(\tau_{min}(<N_{i})\)implying that it is possible to disseminate traffic in a sufficiently reliable way even though some adjacent nodes do not relay the frames as long as the duplication counter is greater than \(\tau_{min}\).
# Thermal Characteristics Study of the Bump Foil Thrust Gas Bearing
## 2 Numerical Simulation Research
### Heat Transfer Model of Bearing Components
#### 2.5.3 Temperature Boundary Condition in the Gas Film Inlet Area  
For bump foil thrust gas bearings, the pressure in the gas film area is higher than the ambient atmospheric pressure, thus leading to the leakage of air from the radial boundaries. Meanwhile, the same amount of air \(\mathcal{Q}_{suc}\) is brought into the gas film at the top foil leading edge, which will mix with recirculation air \(\mathcal{Q}_{rec}\) to achieve the conservation of the total amount of gas \(\mathcal{Q}_{in}\).  
The temperature of the inlet mixed air is calculated as follows based on the energy balance condition of the controlled gas volume:  
\[T_{in}=\frac{T_{rec}\mathcal{Q}_{rec}+T_{suc}Q_{suc}}{Q_{rec}+Q_{suc}} \tag{24}\]  
where \(T_{in}\) is the mixed gas temperature at the leading edge of the top foil (K); \(T_{rec}\) is the circulating air temperature at the rear edge of the top foil (K); \(T_{suc}\) is the temperature of the intake cooling air (K).
## 6 Methods of Microalgae Cultivation Monitoring
### Monitoring of Nutrient Content in the Culture Medium
Micronutrients such as copper (Cu), zinc (Zn), manganese (Mn), molybdenum (Mo), and others are mainly analysed using spectroscopic techniques. The most commonly used method is ICP-OES (inductively coupled plasma optical emission spectroscopy), which enables the simultaneous analysis of several elements in water or culture media. Other popular methods include atomic absorption spectroscopy (AAS), which measures the absorption of light by gaseous metal atoms, and high-performance liquid chromatography (HPLC), which is used to analyse inorganic micronutrients. All the above methods of nutrient analysis for microalgae culture media are supported by modern analytical models such as the Thermo Scientific iCAP 7400 ICP-OES (Beverly, MA, USA), PerkinElmer Optima 8300 ICP-OES (Waltham, MA, USA), Shimadzu AA-7000 AAS (Tokyo, Japan), and Hach DR 5000 UV-VIS spectrophotometer (Dusseldorf, Germany) [204]. These instruments provide high accuracy and precision in nutrient analysis and enable effective monitoring of cultivation conditions and optimisation of microalgae growth processes.
## 1 Introduction
From the analysis conducted in this paper and the obtained results, we can see that the proposed system ensures a high-level of performance and can be easily developed and implemented by the local authorities to scale up the remote patient monitoring capabilities, ultimately saving lives. The main contributions and originality of this paper are the following:  
1. The design of an in-depth analysis of IoT challenges regarding the SARS-CoV2 pandemic;
2. The performance evaluation of the communications protocol that can be integrated in the patient monitoring architecture distributed over a large geographical area;
3. The design, development, and implementation of a novel integrated system for COVID-19 illness management;
4. The patient monitoring IoT multi-sensor platform integration in a wearable device that is easy to wear and fully reconfigurable;
5. The multi-sensor IoT wearable device can monitor the health condition related parameters like pulse, blood oxygen saturation and body temperature, as well as the patient's current location increasing the monitoring capabilities of the local authorities and saving lives.
## 8 Results and Discussion
The proposed PSS-SCA technique provides better damping of angle deviation and location of eigenvalues compared with the other optimisation methods. In terms of the location of eigenvalues, the proposed PSS-SCA algorithm provides more shift towards the  
Figure 11: Rotor angle deviation for loading condition 3 of the SMIB with PSS. Figure 12: Eigenvalue’s location on the complex s-plane for loading condition 4 of the SMIB with PSS. Figure 10: Eigenvalue’s location on the complex s-plane for loading condition 3 of the SMIB with PSS. The PSS parameters are optimised by using different optimisation techniques, and the eigenvalues related to the electromechanical modes obtained, damping ratio as objective function and damping ratio of loading condition 5 for SMIB are provided in Table 11. The region of eigenvalue locations in the s-plane and rotor angle deviation are shown in Figures 14 and 15, respectively. The proposed PSS-SCA technique provides better damping of angle deviation and location of eigenvalues compared with the other optimisation methods. In terms of the location of eigenvalues, the proposed PSS-SCA algorithm provides more shift towards the real axis of the s-plane to limit the abrupt variation in amplitude of angle deviation and increase the system stability by moving eigenvalues more to the left of an imaginary axis line. The PSS-SCA system required 38 iterations to converge the maximum objective function having the exact optimisation parameter values \(r_{1}\)= 1.24, \(r_{2}\) = 0.0609, \(r_{3}\) = 1 and \(r_{4}\) = 0.9532 for loading condition 5 compared with the other methods of optimisation (Table 11). The PSS-MFO and PSS-EP systems take 99 and 13 iterations to converge on the results and have exact optimisation parameter values \(d\) = 0.00025, \(t\) = \(-\)0.6495 and \(\beta\) = 0.1, respectively. The PSS-EP system has better convergence with the minimum objective function value. The proposed technique offered better objective function with respect to both other procedures and has satisfactory computational efficiency compared with the PSS-EP system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Loading Condition 5**} \\ \hline
**Methods** & **PSS-SCA** & **PSS-MFO** & **PSS-EP** & **PSS-U** \\ \hline \multirow{3}{*}{PSS optimised parameters} & \(T_{1}\) = 0.02396 & \(T_{1}\) = 0.03471 & \(T_{1}\) = 0.02888 & \(T_{1}\) = 0.1 \\  & \(T_{2}\) = 0.04134 & \(T_{2}\) = 0.04984 & \(T_{2}\) = 0.04339 & \(T_{2}\) = 0.04 \\  & \(K_{\text{stb}}\) = 140.61181 & \(K_{\text{stb}}\) = 147.61060 & \(K_{\text{stb}}\) = 154.93417 & \(K_{\text{stb}}\) = 9 \\ \hline Objective function & 0.8299 & 0.7839 & 0.6384 & 0.0641 \\ \hline Number of iterations & 38 & 99 & 13 & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of the PSS optimised parameters, eigenvalues, damping factor and damping ratio for loading condition 5 of the SMIB. Figure 14: Eigenvalue’s location on the complex s-plane for loading condition 5 of the SMIB with PSS. Figure 13: Rotor angle deviation for loading condition 4 of the SMIB with PSS.
# XCO\({}_{2}\) Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
## 2 Materials and Methods
### Spatial Extreme Random Trees
To validate the performance of the spatial extreme random trees method in reconstructing XCO\({}_{2}\), we selected mainstream downscaling methods such as Kriging interpolation and extreme random trees for comparison and analyzed the results of resolution reconstruction using various methods. We employed the 10-fold cross-validation method to evaluate the performance of the spatial extreme random trees. The original dataset was randomly partitioned into ten equally sized subsets, nine of which were allocated for training the model, while the remaining segment was designated for validating the model's performance. The best model parameters were determined through ten-fold cross-validation. The performance of the established models was evaluated using three evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and coefficient of determination (R\({}^{2}\)). The calculation of these statistical metrics can be achieved as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}} \tag{3}\]  
\[\text{MAE}=\frac{1}{n}{\sum_{i=1}^{n}}{|y_{i}-\hat{y_{i}}|} \tag{4}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_ {i}-\overline{y_{i}})^{2}} \tag{5}\]  
In this context, \(n\) stands for the sample size; \(y_{i}\) and \(\hat{y_{i}}\) denote the observed XCO\({}_{2}\) value and the predicted XCO\({}_{2}\) value for sample \(i\), respectively; and \(\overline{y_{i}}\) represents the average of the observed XCO\({}_{2}\) values.
## 2 Methodology  
### Geometric and Mathematical Models  
The structure of the multi-blade centrifugal fan utilized in this study is depicted in Figure 1a. Notably, the impeller, shown in Figure 1b, constitutes the primary component of the fan. Crucially, it is the blades on the impeller that dictate the fan's performance, which is the focal point of this research. The blade with a single-arc structure is shown in Figure 1c.  
The multi-blade centrifugal fan model depicted in Figure 1 was utilized for fluid simulation by CFD to obtain the fluid parameters for flow-field analysis and airfoil design. The detailed structural parameters of the multi-blade centrifugal fan are presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Inner diameter of impeller \(D_{1}\) (mm) & 108 \\ \hline Outer diameter of impeller \(D_{2}\) (mm) & 133 \\ \hline Installation angle of blade inlet \(\beta_{1}\) (\({}^{\circ}\)) & 81 \\ \hline Installation angle of blade outlet \(\beta_{2}\) (\({}^{\circ}\)) & 173 \\ \hline Number of blades \(Z\) & 38 \\ \hline Volume width \(B\) (mm) & 215 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the multi-blade centrifugal fan.  
Figure 1: Schematic diagram of fan structure: (**a**) fan, (**b**) impeller, and (**c**) single-arc blade.  
The dynamic parameters of the fluid in the fan can be described by the fundamental equations of fluid mechanics. In the internal flow field of the multi-blade centrifugal fan, the airflow moves at a low speed, and the temperature change can be neglected. Therefore, the airflow can be considered as incompressible adiabatic flow. At the lowest flow point, the inlet velocity of the fan is 0.28 m/s. At this moment, the calculated Reynolds number is 5840, which is greater than the critical value of 4000 for turbulence. Therefore, the flow model is regarded as a turbulent model, and the Realizable k-\(\epsilon\) model is used in the calculation, which can accurately calculate the turbulence [7; 9]. During the two-dimensional simulation process of airfoils, the widely used SST k-\(\omega\) turbulence model is adopted.
# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 4 Intelligent Integration Model of Wi-Fi/BLE/QR/Mems Sensors
### Accuracy Estimation of Inertial Odometry  
The biggest problem with inertial odometry is the cumulative errors that occur when using for a long time period, especially under magnetic-interference contained indoor environments. In this work, to estimate the performance of the designed inertial odometry, an indoor scenario with a long corridor was used as the experimental scenario, as described in Figure 4. The tester began with point A, through to points B, C, D, E and F, and then returned to point A. For the long-term positioning accuracy evaluation, this walking route was repeated 12 times, and the total estimation period lasted more than 25 min. The collected magnetic environment in this scene is shown in Figure 5.  
As can be seen in Figure 5, the selected experimental site contained serious magnetic interference that needed to be detected and eliminated. A comparison of the performance between the inertial odometry originated heading, the gyroscope originated heading, and the magnetic heading is shown in Figure 6.  
Figure 4: Experimental site of first scene.  
Figure 5: Description of indoor magnetic field.  
Figure 6 shows that the magnetic heading can be seriously affected by indoor artificial interference and caused large fluctuations. The gyroscope heading showed cumulative error, with the heading error increasing over time. The odometry-provided heading maintains high accuracy and stability after long-term application and the estimated error was less than 2\({}^{\circ}\).  
The positioning performance of the proposed inertial odometry approach was compared with the improved step-length-based approach (ISL) proposed in [40]; to be fair, the same inertial odometry-originated heading was adopted in this case, and the final localization performance comparison is shown in Figure 7.  
Figure 7 shows that the inertial odometry approach demonstrated better localization performance compared with the ISL approach; the cumulative positioning error maintained a slower divergence. The evaluated localization errors for the inertial odometry approach and ISL approach are compared in Figure 8.  
Figure 6: Comparison of heading estimation.  
Figure 7: Positioning comparison of ISL and inertial odometry.  
Figure 8 shows that the estimated positioning error of the proposed inertial odometry method in this work provides better localization accuracy than the ISL approach, with an overall estimated positioning error during a time period of 25 min lower than 5.46 m in 75%, compared with the ISL approach with a positioning error of 9.08 m in 75%.
# Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)
###### Abstract  
This study reports on a low-cost unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) system called LasUAV, from hardware selection and integration to the generation of three-dimensional point clouds, and an assessment of its performance. Measurement uncertainties were estimated in angular static, angular dynamic, and real flight conditions. The results of these experiments indicate that the point cloud elevation accuracy in the case of angular static acquisition was 3.8 cm, and increased to 3.9 cm in angular dynamic acquisition. In-flight data were acquired over a target surveyed by nine single passages in different flight directions and platform orientations. In this case, the uncertainty of elevation ranged between 5.1 cm and 9.8 cm for each single passage. The combined elevation uncertainty in the case of multiple passages (i.e., the combination of one to nine passages from the set of nine passages) ranged between 5 cm (one passage) and 16 cm (nine passages). The study demonstrates that the positioning device, i.e., the Global Navigation Satellite System real-time kinematic (GNSS RTK) receiver, is the sensor that mostly influences the system performance, followed by the attitude measurement device and the laser sensor. Consequently, strong efforts and greater economic investment should be devoted to GNSS RTK receivers in low-cost custom integrated systems.  
LiDAR; system designing; system testing; Global Navigation Satellite System; real-time kinematics technology; forest monitoring 20181001094; doi:10.3390/rs10071094
## 3 Experimental Setup and Result Analysis
### Implementation Details  
This subsection focuses on developing a subtype classification model for breast cancer histopathology images and conducting a series of experiments to validate the proposed method's effectiveness. All the experiments were performed on a machine with the following setup: initial learning rate set to 0.0001, batch size of 32, and 50 training iteration rounds. The Adam optimizer was employed to update network weights, with convergence controlled by the cross-entropy loss function. Softmax was used as the output function to achieve the classification of eight pathology images. The experimental environment was based on the Ubuntu 18.04.5 operating system, equipped with an NVIDIA GeForce RTX 3080 Ti GPU and 45 GB of RAM (NVIDIA, Santa Clara, CA, USA). This setup provided ample processing power and storage space. The development was carried out using Python 3.8.10 and PyCharm, with all deep learning tasks executed under the Pytorch 1.8.0 framework. GPU acceleration was facilitated by CUDA 11.7, ensuring efficient model training and inference performance.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 4 Twin System Modeling and LSTM-DQN-APF Algorithm
### Virtual and Real Communications  
The method of achieving interactive communication between the virtual and physical parts of the constructed twin system is as follows: As shown in Figure 5, the flight control system of the physical UAV is connected to the onboard computer via a serial port. The flight control system communicates with the local ground station computer through the UDP protocol, allowing the ground station to obtain real-time flight data and send control commands for remote monitoring and operation. The onboard computer communicates with the flight control system using the MAVLink protocol, and after processing and analyzing the data using the modular ROS framework, the data are sent to the local computer. The local computer then transmits the processed data to the virtual platform via TCP/UDP data pass-through, controlling the virtual UAV to achieve virtual-physical synchronization.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery
### Adaptive Decomposition Methods
#### 3.1.6 Other Adaptive Methods  
[PERSON] et al. [90] utilized two adaptive filters least mean squares (LMS) and fast block LMS (FBLMS)) to identify early bearing defects in gearboxes. [PERSON] et al. [91] proposed a reweighted SVD strategy to denoise the signals and enhance weak features for fault diagnosis of rotating machinery. [PERSON] et al. [92] used a least mean squares algorithm to reduce the noise, and then the meshing frequency sidebands was found to successfully identify gear faults. [PERSON] et al. [93] used linear multi-scale segmentation to divide the signal into segments with nearly linear frequency, then the multi-order fractional Fourier transform filter was used to filter each segment. Finally, the filtered signal was demodulated for gear failure diagnosis at an early stage. [PERSON] et al. [94] used a machine learning algorithm to generate a baseline for identification of the normal operation conditions. Then the ICD method was used for gear fault type recognition in wind turbines. The abovementioned methods are summarized in Table 6.
## 3 Results and Discussion  
### Curing Behavior of Neat NR with CV and MF  
Represented in Figure 2 are the curing curves of NR-CV and NR-CVMF\({}_{3}\) at 170 \({}^{\circ}\)C for 1 h. Their cure characteristics are displayed in Table 2. NR-CV attains a maximum torque in 6.13 min and then exhibits a sharp declination in the rheometric torque with time due to reversion. The reversion was calculated and reported in percentage using Equation (4) and the values are depicted in Table 3.  
\[\text{Reversion }(\%)=\frac{S^{\prime}_{max}-S^{\prime}_{60}}{S^{\prime}_{max}} \times 100 \tag{4}\]  
where \(S^{\prime}_{max}\) is the maximum torque and \(S^{\prime}_{60}\) is the torque at 60 min.  
Figure 2: Curing curves of mixes 1 and 2 at 170 \({}^{\circ}\)C for 1 h.  
The nature of the cure curve of NR-CVMF\({}_{3}\) was almost similar to NR-CV during the initial stage of curing. However, NR-CVMF\({}_{3}\) exhibits around 16% higher curing torque from the point where the reversion started in NR-CV. As a result, the state of cure in terms of \(\Delta\) torque in NR-CVMF\({}_{3}\) was improved by 19% compared to NR-CV.  
From Table 3, it is clear that NR-CV exhibits around 26% reversion at the end of the given curing time. The intensity of reversion in NR-CV could be significantly reduced to 7.5% after incorporating 3 phr MF (NR-CVMF\({}_{3}\)). It has been reported that the curing process of diene rubbers with the CV system generates polysulfide crosslinks in the cured network. These polysulfide crosslinks are unstable at elevated temperatures and rearrange to produce a certain amount of conjugated dienes on the rubber backbone, particularly at the point of reversion [14]. Therefore, the possibility of Diels-Alder reaction between the in situ formed conjugated diene at the point of reversion and the maleimide moieties of MF is responsible for the enhanced \(\Delta\) torque during the curing of NR-CVMF\({}_{3}\)[20; 21; 22]. The chemical stability of the bismaleimide-based bonds generated in the vulcanized network of NR-CVMF\({}_{3}\) can be considered as its enhanced reversion resistance at elevated temperatures.
## 2 Study Area and Datasets Used  
### Study Area  
The Bhima River is one of the main tributaries of the Krishna River. The Upper Bhima sub-basin covers 17.6% of the area of the Krishna basin, which is 46,066 km\({}^{2}\). The majority of the sub-basin, accounting for approximately 98.4% of its area, is situated in Maharashtra, while the remaining 1.6% is located in the state of Karnataka. The study area extends between latitude 17.18 N to 19.24 N and longitude 73.20 E to 76.15 E. The elevation within the watershed ranges from 160 m in the eastern region to 1472 m in the Western Ghat Mountains region of the sub-basin. The location map and Digital Elevation Model (DEM), providing information on the topography of the study area, are shown in Figure 1.  
The Upper Bhima sub-basin has a very diverse climate, spatially and temporally. The eastern part of the Western Ghats receives more than 4000 mm of annual precipitation and less than 500 mm on the Deccan Plateau plains. The annual average rainfall is 872 mm/year for the whole sub-basin [24]. A total of 80-90% of the yearly precipitation occurs between June to September, i.e., in the monsoon season. From 2002 to 2013, the minimum temperature observed was 5 \({}^{\circ}\)C and the maximum temperature was 46 \({}^{\circ}\)C.