esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 2 Related Work
### Network Communication Protocols for IBNNs  
Due to the severe constraints of IBNNs regarding energy, transmission range, storage and computational capabilities, the general routing protocols for nanonetworks are not directly applicable in IBNNs. Therefore, in this section, we specifically discuss the existing routing protocol proposed for IBNNs. The selective flooding scheme presented in Reference [19] conserve the limited resources of IBNNs by controlling the direction of forwarded packets for reducing energy consumption and saving bandwidth. In another effort to save energy consumption, a protocol stack for IBNNs is proposed in Reference [15]. The proposed protocol stack introduces two different protocols at the network layer for prolonging the network lifetime. In the greedy scheme, the node with the maximum energy is selected for responding to the request message received from the healthcare monitoring system. The low complexity of the greedy scheme points it as the most suitable approach for conserving energy resources in IBNNs. Another energy conservation scheme presented in Reference [17] introduces nanocluster controllers for performing data aggregation. NBSs transmit data to the closed positioned nanocluster  
Figure 1: The impact of employing firefly algorithm in improving the performance of WSNs.  
controller that helps in reducing energy consumption. The work presented in Reference [57] thoroughly investigated IBNN and studied the energy harvesting options available for prolonging the life span of NBSs. In the view of the characteristics of the existing energy conservation scheme, we conclude that our proposed scheme differentiates from the state of the art schemes in the following ways:  
1. In our proposed work, we select information and energy enriched spatially correlated region for data transmission. While [PERSON] et al. [17]lects nanocontrollers for aggregating data from their respective layers, which increases the complexity of resource constraint IBNNs. Our proposed FLBDS selection is a low complexity solution for correlated region selection. Moreover, it also reduces the transmission of redundant and invaluable information.
2. In our proposed scheme, we limit the number of transmission of forwarding packets by selecting those NBSs for generating response messages, located at a minimal distance from nanorouter. This selection criterion decreases the possibility of multi-hop communication.
3. Our proposed scheme selects the optimum number of NBSs based on different fitness parameters, the selection of the optimal number of NBSs ensures that data is collected from enough number of NBSs to achieve data accuracy. Whereas the greedy scheme selects only one node for transmitting answer messages based on remaining energy. The selection based on solely differentiates energy while ignoring the distance from the nanorouter results in an increased number of forwarded packets. In addition, data transmission from only one node for energy consumption can also significantly compromise the data accuracy.
## 2 Materials and Methods
### Scenario-Building Based on Streamflow, Waste Control, and Migration Control  
Water quality mainly depends on streamflow, pollution control, and waste generation through increased anthropogenic activities. Therefore, the present study considered three scenarios based on these three strategic variables--streamflow for reoxygenation, waste control for pollution (BOD) load reduction [38], and migration control for controlling over  
Figure 1: Map of the study area.  
population. The first scenario is related to business, where policy variables are based on the current status of the river system. The river accommodates 80% of pollutants without treatment [39], the flow is minimal (5 m\({}^{3}\)/s) [40], and the city has high internal immigration and no control in migration, as it is the primary economic hub in Bangladesh [41]. Therefore, scenario 1 considered a 5 m\({}^{3}\)/s streamflow, 20% pollution control, and 0% migration control as the base line scenario. Scenario 2 is project-based, where river flow increases based on the project target (160 m\({}^{3}\)/s), and pollution control and migration control in Scenario 1 are maintained. Scenario 3 is the integrated and sustainable strategy: policy variables are set following the empirical evidence. For example, [PERSON] and [PERSON] (2019) established a similar scenario for their SD model to study water quality in the Remu River in Sorong city, Indonesia [42]. They established the sustainable scenario by combining a waste treatment plant for waste control and dredging for increased streamflow, and according to their simulation, the integrated strategy reduced BOD concentration by 89.82% and increased DO concentration by 19.07%, ensuring a sustainable river ecosystem. According to [PERSON] and [PERSON] (2015), population control, streamflow increasing through dredging, and pollution control must be synergistically considered to ensure a sustainable Buriganga river system [43]. The ideal population density for Dhaka city is below 50,000 individuals per square kilometer [44]. The present density is 70,956 individuals per square kilometer, and immigration trends suggest that the density will increase in the future [44]. Furthermore, according to [PERSON] et al. (2021), BOD concentration has to be reduced by 87.78%, which is the optimal pollution control status of an influent for maintaining sustainable water quality in urban river systems [45]. Therefore, Scenario 3 considered a river flow of 160 m\({}^{3}\)/s, an 87.78% pollution reduction in influent, and a 6% reduction in migration to maintain the ideal population density (>50,000 people/km\({}^{2}\)) for achieving sustainable water quality.
## 2 Materials and Methods
### Methods
#### 2.2.3 IVW Fusion Method  
To fill the gap in \(\rm PM_{2.5}\) estimation by satellite, we used the inverse variance weighting method (IVW), which is based on the difference between \(\rm PM_{2.5}\) estimated by satellite (NAQPMS) and ground-based \(\rm PM_{2.5}\) data, to fuse satellite data and NAQPMS data. Since the dispersion of the variance is related to the quality of the estimation (or simulation) results, by calculating the variance of the corresponding data pair, a lower weight is assigned when the variance between the estimated (simulated) results and the ground-based data is large, and when the variance is small, a higher weight is assigned [53]. The IVW method is expressed as follows:  
\[\rm PM_{f}=\frac{\rm PM_{st-f}}{\rm Var_{stm}}+\frac{\rm PM_{model-f}}{\rm Var_{ modelm}}\over\frac{1}{\rm Var_{stm}}+\frac{1}{\rm Var_{modelm}} \tag{8}\]  
where \(\rm PM_{st-f}\) and \(\rm PM_{model-f}\) denote \(\rm PM_{2.5}\) of satellite estimation and NAQPMS simulation. \(\rm Var_{stm}\) (\(\rm Var_{modelm}\)) is the variance of the difference between the satellite estimation (NAQPMS simulation) and the ground-based \(\rm PM_{2.5}\). \(\rm PM_{f}\) is \(\rm PM_{2.5}\) concentration after fusion.  
This study fused the satellite estimated \(\rm PM_{2.5}\) with the NAQPMS simulation results to obtain spatially full-coverage \(\rm PM_{2.5}\) distribution.
# Changes in Propeller Shaft Behavior by Fluctuating Propeller Forces during Ship Turning
## 1 Introduction
The ship's operating efficiency can be improved through the application of an ultra-long stroke engine, while the shaft rotation speed can be lowered, resulting in a slight decrease in the ship's speed. Therefore, to compensate for this, the propeller's diameter can be slightly increased compared with the previous design [19]. The increase in the propeller's diameter leads to an increase in the propeller's mass, and the bearing load and pressure of the after-stern tube bearing (ASTB) are also increased. Therefore, to cope with the above-mentioned issue, the length of the ASTB of the vessel is increased in order to compensate for the increased load and pressure compared with the previous medium-range (MR) class vessel. However, the bearing load will be closer to the manufacturer's maximum allowable bearing load compared with the previous design. Considering the current trend, this situation leads to higher loads on the after-stern tube bearing. These load distribution conditions bring about a high probability of bearing damage. In these circumstances, the stability of the shaft system cannot be ensured by standardized quasi-static methods. 'Quasi-static' analysis means that the calculations are performed in a static state, but the eccentric propeller thrust values generated at the vessel's maximum design speed are reflected in the static state in the form of a bending moment. The quasi-static method has the advantage of being able to save time and reduce costs because the calculation is relatively simple, and it is easy to estimate the amount of deformation of the propulsion shaft. However, this method has fundamental limitations that make it difficult to identify potential hazards that may arise under real dynamic conditions. [PERSON] [20] found that when the shaft system is designed in such a way that only the existing quasi-static conditions are considered, the force generated by the propeller causes serious damage to the stern tube bearing and the stern tube seal ring when the ship is in a dynamic state. This has created a strong demand for securing the shaft's stability under various propeller loads continuously in dynamic ship states, such as when the ship goes straight ahead or turns [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. In order to ensure the stability of the ship's propulsion shaft, it is important to perform extensive statistical analyses to obtain reliable data to further develop rules and standards applicable to all types of ships. Therefore, in this study, the effect of eccentric propeller thrust on the propeller shaft behavior during turning, which is a typical transient operating state of a ship, was investigated using a strain gauge and a displacement sensor for a medium-range tanker, which has not been covered in past studies that rely on the conventional methods.
## 1 Introduction
These factors made it difficult to generalize the scheme to other scenarios. The RSMA framework has showcased significant enhancements in spectral and energy efficiencies compared to conventional SDMA and NOMA techniques. This establishes RSMA as a promising candidate for future 6G networks, prompting a thorough examination of its performance across diverse multi-antenna systems. Moreover, the deployment of PBs holds vital significance in extending the lifespan of wireless devices, especially in IoT scenarios where battery replacement poses challenges in hard-to-access areas. However, the interference generated by PBs during the information decoding process at the user side necessitates a comprehensive investigation into an efficient beamforming design at both the BS and PB. Consequently, this study aims to develop a high-performance, low-complexity solution to jointly optimize the beamforming vectors and PS ratios in a multi-antenna system. The proposed system model integrates a multi-antenna transmitter implementing RSMA with SWIPT, along with a multi-antenna PB. The primary objective of this study is to minimize the total transmission power while fulfilling data rates and EH requirements for EH and PS users. The main contributions of this paper are as follows:  
* In the considered system model, the objective is to minimize the total transmission power of the BS and PB while meeting the minimum EH requirements for EH and PS users and ensuring a minimum data rate for PS users. * A two-step approach is adopted to address the non-convex problem presented in this study. In the first step, we optimize the common rate variables through a DL-based scheme that combines the principal component analysis (PCA) technique for dimensionality reduction with a DNN. The second step focuses on optimizing the beamforming vectors of the BS and PB, along with the PS factors. The SDR technique is used to accomplish this. * As a comparative scheme, the proposed minimization problem is addressed using a PSO-SDR approach, which is an iterative-based method that provides near-optimal solutions to the proposed problem. This scheme serves as a reference to analyze the performance of the proposed DL-based solution. * Simulation results show that the proposed DL-based method can perform similarly to traditional iterative-based schemes while significantly reducing computational complexity. Furthermore, the proposed RSMA solution is compared with NOMA and SDMA, showing that the RSMA approach achieves the lowest transmit power. Moreover, the generalization performance of the proposed DL-based method is validatedby testing its performance across several challenging scenarios not included in the DL model training. The remainder of this paper is structured as follows. Section 2 introduces the system model and formulates the problem. Section 3 outlines the proposed solution, and presents the comparative schemes. Section 4 provides the simulation results, and Section 5 reports the conclusions.
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 3 Results and Discussion  
### Spectral Characteristics Analyses  
Figure 3a depicts the spectrum curves of the 420 _Cerasus humilis_ fruit samples in the range of 895-1700 nm. The average spectra reflectance and standard deviation (SD) of various samples were obtained, and are shown in Figure 3b. Because the original hyperspectral data contain a large amount of random noise at the head and tail, the wavelength range of 945-1675 nm was selected for further research.  
As can be seen in Figure 3b, the average spectral curves of the four types of _Cerasus humilis_ fruit samples were quite different, but the trends of spectra were similar. the spectral curve shows that the four types of samples have obvious absorption peaks at around 980 and 1195 nm. Among them, the absorption peak at 980 nm was primarily caused by the absorption of water in _Cerasus humilis_ fruit [21], and the absorption peak at 1195 nm was related to the absorption of chlorophyll in the epidermis of _Cerasus humilis_ fruit [22]. Another strong absorption peak, at 1460 nm, is related to the internal water and sugar absorption of the _Cerasus humilis_ fruit [23]. As can be seen in Figure 3b, the spectral reflectance values of all the defective samples are significantly lower than those of the intact samples; this may be because the gray value of defective regions is usually lower, reducing the reflection of the incident light [24]. Compared with the two other types of defective samples, the damaged region of insect-damaged fruits is relatively small, and the reflectance value of the insect-damaged samples was slightly lower than that of the intact samples in the wavelength region below 1160 nm. This may be because the insect damage on the surface of the fruit provides more opportunities for light to interfere with the cellular material, causing more light to be absorbed and scattered. The reflectance of the cracked samples was significantly lower than that of the other three types of _Cerasus humilis_ fruit samples, which may be related to the rupture of the fruits' epidermis. The reflectance of rust spot fruit samples was lower than that of intact fruit samples; this may be caused by the difference in the cell structure between the rust spot fruit epidermis and intact fruit epidermis.
## 3 Scattering Amplitudes and Scattering Lengths
### Magnetic Moment of Nanoparticle Parallel to the Velocity of Incident Electron
\begin{array}{c} 0\\ 1\end{array} \right) \tag{22}\]  
where  
\[f^{\parallel}_{\uparrow}(\varphi) = \frac{6\gamma\pi}{a}\big{[}\alpha(I_{1}\sin^{2}\frac{\varphi}{2} +I_{2}\cos^{2}\frac{\varphi}{2})+\frac{\beta}{2}\big{(}I_{2}-I_{1}\big{)}\sin \varphi\big{]} \tag{23}\] \[f^{\parallel}_{\downarrow}(\varphi) = \frac{6\gamma\pi}{a}\big{[}-\beta(I_{1}\sin^{2}\frac{\varphi}{2} +I_{2}\cos^{2}\frac{\varphi}{2})+\frac{\alpha}{2}\big{(}I_{2}-I_{1}\big{)} \sin\varphi\big{]} \tag{24}\]  
The quantity \(f^{\parallel}_{\uparrow}(\varphi)\) and \(f^{\parallel}_{\downarrow}(\varphi)\) are the scattering amplitudes of electrons having spin up and down after scattering, respectively. The denotation \(\parallel\) stands for the initial electron velocity, which is parallel to the magnetic moment of the nanomagnet. It would be convenient to present Equations (23) and (24) as  
\[f^{\parallel}_{\uparrow}(ka,\varphi) = \sqrt{L_{0}}\bigg{[}\alpha\sqrt{\widetilde{L}_{1}}+\beta\sqrt{ \widetilde{L}_{3}}\bigg{]} \tag{25}\] \[f^{\parallel}_{\downarrow}(ka,\varphi) = \sqrt{L_{0}}\bigg{[}-\beta\sqrt{\widetilde{L}_{1}}+\alpha\sqrt{ \widetilde{L}_{3}}\bigg{]} \tag{26}\]  
Here we introduced the quantity with dimension of length  
\[L_{0}=32\pi^{3}a^{5}\
u^{2}\bigg{(}\frac{\mu_{p}^{2}n_{a}m}{\hbar^{2}}\bigg{)}^ {2} \tag{27}\]  
and the dimensionless parameters  
\[\tilde{L}_{1} = \frac{1}{ka}\big{[}I_{1}\sin^{2}\frac{\varphi}{2}+I_{2}\cos^{2} \frac{\varphi}{2}\big{]}^{2} \tag{28}\] \[\tilde{L}_{3} = \frac{1}{4 ka}\big{[}I_{2}-I_{1}\big{]}^{2}\sin^{2}\varphi \tag{29}\]  
We also express the magnetic moment of nanomagnet as \(\mu=\frac{4\pi}{3}\
u\mu_{B}a^{3}n_{a}\) (\(n_{a}\) is a density number of atoms in nanomagnets, \(\
u\) is a number of Bohr magnetons per atom of the nanomagnet). The differential scattering lengths for the specified processes are given by  
\[|f^{\parallel}_{\uparrow}(ka,\varphi)|^{2} = L_{0}\bigg{[}\alpha\sqrt{\widetilde{L}_{1}}+\beta\sqrt{ \widetilde{L}_{3}}\bigg{]}^{2} \tag{30}\] \[|f^{\parallel}_{\downarrow}(ka,\varphi)|^{2} = L_{0}\bigg{[}-\beta\sqrt{\widetilde{L}_{1}}+\alpha\sqrt{ \widetilde{L}_{3}}\bigg{]}^{2} \tag{31}\]  
In [10], we considered the scattering amplitudes of the polarized beam of electrons. These results can be recovered from Equations (30) and (31) setting \(\alpha=1\), \(\beta=0\) and \(\alpha=0\), \(\beta=1\).
## 4 Results
### Calculating FT for Dataset 1 (Accelerated Life Test Dataset)
#### 4.1.2 Model 2: Proposing the FT by Considering the Failure Modes in Copulas  
In this model, PC\({}_{1}\) was obtained using a similar approach to that described for the previous model. However, in the current model, the data classified by the failure modes were used to extract the joint probability distribution separately. Thus, having separated joint distributions resulted in a specific FT for each failure mode.  
The experimental data used in this study were classified into two groups according to the observed failure mode (inner race failure or rolling element failure) at the end of each experiment (Table 3). Following the analysis of the correlation between vibration features with PC\({}_{1}\), the peak of the time signal had the highest correlation for both failure modes (Table 6). Therefore, using the statistical copula models (Table 2), Gumbel and Gaussian copulas were chosen as the best to create the joint distribution functions for inner race and rolling element failure modes, respectively. The criteria of the model fitness evaluation and corresponding parameter values are listed inTable 7.  
Finally, the conditional probability distribution function of the peak given PC\({}_{1}\) was obtained using Equation (6). According to the definition of the FT proposed in this paper, the high fluctuation of vibration occurred for the rolling element degradation and inner race degradation when PC\({}_{1}\) was equal to 15.1 and 35, respectively. Furthermore, the conditional distributions of the peak for the mentioned values of PC\({}_{1}\) were extracted from the corresponding joint probability distributions. These distributions, which were used as the FT distributions, are depicted in Figure 8. Additionally, the mode of each distribution can be used as the FT value for the degradation of each component (3.1 g for the rolling element and 8.7 g for the inner race).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Inner Race Failure Mode**} & \multicolumn{2}{c}{**Rolling Element Failure Mode**} \\ \hline
**PCC** & **Feature** & **PCC** & **Feature** \\ \hline
0.9349 & Peak amplitude & 0.9462 & Peak amplitude \\ \hline
0.9349 & Peak-to-peak amplitude & 0.9462 & Peak-to-peak amplitude \\ \hline
0.8906 & Shape factor & 0.9214 & Margin factor \\ \hline
0.8883 & Clearance factor & 0.92 & Shape factor \\ \hline
0.8721 & Crest factor & 0.915 & Impact factor \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation of time signal features with PC\({}_{1}\) for two classes of experimental data.  
Figure 7: Estimated FT distribution of model 1.
# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 4 Experiment Result Analysis
### Data Selection  
#### 4.2.1 Source Domain Data Set  
The source data was collected from the CWRU data set in this project due to three accelerometers, including fan-end bearing accelerometers, base-plate accelerometers, and drive-end bearing accelerometers, which can be used for collecting vibration signals of different bearings. Electrical discharge machining (EDM) can automatically create seeded faults in the bearing. The bearing fault types contained inner and outer races with three distinct inches (0.07 mm, 0.014 mm, and 0.021 mm) collected from 48 k drive end bearing fault data. The fault diameters of the inner race used in this experiment were 0.07 mm, 0.014 mm, and 0.021 mm, and their motor speed was 1730 rpm. The outer race's fault diameters and motor speed were identical to the inner race. The position relative to the Load Zone of the outer race was centered at 6:00. Additionally, the motor load for both inner and outer races was 3 hp. Table 1 shows the detail of the data:  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & **Fault Diameter** & **Motor Load** & **Motor Speed** & \multirow{2}{*}{**Data Size**} & **Sampling Frequency** & **Outer Race** & **Class** \\  & **(mm)** & **(HP)** & **(rpm)** & & **(Hz)** & **Position** & **Label** \\ \hline Inner Race & 0.007 & 3 & 1730 & 480,000 & 48 k & * & 0 \\ Inner Race & 0.014 & 3 & 1730 & 480,000 & 48 k & * & 0 \\ Inner Race & 0.021 & 3 & 1730 & 480,000 & 48 k & * & 0 \\ Outer Race & 0.007 & 3 & 1730 & 480,000 & 48 k & Centered at 6:00 & 1 \\ Outer Race & 0.014 & 3 & 1730 & 480,000 & 48 k & Centered at 6:00 & 1 \\ Outer Race & 0.021 & 3 & 1730 & 480,000 & 48 k & Centered at 6:00 & 1 \\ \hline \hline \end{tabular}  
* denotes null.  
\end{table}
Table 1: The details of the source data.