esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work
### Achieve Better Reliability and Environmental Adaptability  
FSO communication terminals must operate in a wide range of environments, including varying temperatures, humidity levels, and vibrations. As a result, there are high expectations for the reliability and adaptability of the VBDA system. Temperature, in particular, is a crucial factor that affects the system's performance. For instance, reference [40] reported that the beam's divergence angle was 7.5 times wider than expected at \(-30\)\({}^{\circ}\)C and 4.7 times wider than expected at 60 \({}^{\circ}\)C. Therefore, designing and manufacturing a VBDA mechanism with strong environmental adaptability and temperature insensitivity is of great significance. Various measures can be used to achieve this goal, such as:  
1. Reducing the impact of temperature: There are two ways to approach this issue. 1 Selecting materials with low thermal expansion coefficients. This helps to maintain the stability and performance of the VBDA mechanism across a wide range oftemperatures. 2 Adding a temperature compensation mechanism. This compensates for any thermal-induced changes, ensuring the VBDA mechanism remains within the appropriate temperature range;
2. Real-time measurement of transmitted beam divergence angle: By continuously measuring the beam divergence angle in real time, feedback can be provided to the VBDA mechanism. This feedback can be adjusted immediately to compensate for changes caused by temperature fluctuations, ensuring accurate beam alignment and reliable communication;
3. Performing temperature tests and creating a lookup table: Conducting temperature tests on the VBDA mechanism helps to understand its performance characteristics under different temperature conditions. Based on the results, a lookup table can be created that maps temperature variations to the corresponding correction values for the beam divergence angle. This lookup table can then be used to apply real-time corrections during operation, compensating for the effects of temperature changes.  
These examples demonstrate various approaches to enhance the reliability and environmental adaptability of the VBDA mechanism in FSO communication terminals. By implementing such measures, the performance of the system can be improved, resulting in more effective and dependable communication capabilities.
## 2 Study Area and Data
### Research Data and Processing
#### 2.2.5 Geological Data  
Geological data constitute a fundamental component in identifying and mitigating geological hazards. These data integrate various influential factors, including hydrology, geotechnics, and climate interactions, serving as a crucial resource for geological hazard risk assessment and prediction [25; 26]. The geological data utilized in this study were obtained from China's 1:200,000 geological map database, covering critical nationwide geological attributes such as lithology, structural geology, and geomorphological features, thereby providing reliable foundational support for the accurate identification of geological hazards.
## 5 Results and Analysis
### Correlation Analyses  
Correlation analyses were completed for the eight participants comprising two categories of daily and biweekly time resolutions, which were computed from daily and biweekly features. We recognize that every participant's willingness to socialize and personal biases as well as physiological parameters could be different. Therefore, when the data were imputed for the days of missing data, the imputation was performed for every participant separately in order to account for personal differences and biases.  
Based on the data types from the features extracted (continuous and ordinal), [PERSON]'s correlation coefficient (\(\rho\)) was used [43] to determine the correlation between the features extracted from the sensor data and SI and FD in OAs following lower limb fractures. The [PERSON]'s correlation coefficient investigates a linear relationship between two variables [44]. The correlation values range from \(-1\) (linear negative relationship) to \(+1\) (linear positive relationship). The closer the correlation coefficient is to zero, the smaller the degree of its linear correlation [44]. A linear correlation means that when there is a relationship between the two variables and one changes, it will lead to a change in the other variable, demonstrated by their correlation value. A positive correlation means that both variables increase or decrease together. A negative correlation means that when one variable increases, the other decreases, and vice versa. The following guidelines are considered appropriate [43] for evaluating the degree of correlation for studies involving biomedical, biological, sociological, and psychological data. In this paper, the strong and moderate correlations are evaluated.  
Figure 3: Line plot of 4 domain-specific daily features, affected by fracture (top plot) and affected by all fractures (lower plot): (**a**) distance traveled from home (km); (**b**) motion-sum (total number of motions per day); (**c**) total sleep duration; and (**d**) hr-average (average heart rate during sleeping episodes). The gray line on each sub-figure indicates the trend line for the line plot.  
\(|\rho|<0.3\rightarrow\) Weak relationship  
\(0.3\leq|\rho|\leq 0.5\rightarrow\) Moderate relationship  
\(|\rho|>0.5\rightarrow\) Strong relationship
## 3 Explicit Solution to Rotation Angle for Large Deformation of Cantilever Beams by IHAM
### High-Order Deformation Equations  
Substituting Equation (66) into Equation (62) and differentiating Equation (62) \(n\) times with respect to the embedded parameter \(q\), dividing it by \(n!\), and then setting \(q=0\), we have the \(n\)th-order deformation equations as follows  
\[\mathcal{L}[\theta_{n}(\xi)-\chi_{n}\theta_{n-1}(\xi)]=\mathrm{h}R_{n}(\theta _{0},\theta_{1},\cdots,\theta_{n-1}) \tag{68}\]Equation (68) is subjected to the boundary conditions:  
\[\theta_{\pi}(0)=0,\theta_{\pi}^{\prime}(1)=0, \tag{69}\]  
where  
\[R_{n}=\left.\frac{1}{(n-1)!}\frac{\partial^{n-1}\mathcal{N}[\psi(\mathcal{Z};q), q,\varepsilon]}{\partial q^{n-1}}\right|_{q=0} \tag{70}\]  
and  
\[\chi_{n}=\left\{\begin{array}{cc}0,&n\leq 1\\ 1,&n>1\end{array}\right. \tag{71}\]  
Combining Equations (59) and (70), we have:  
\[R_{1}=\theta_{0}^{\prime\prime}(\xi)+\alpha-\frac{1}{2}\varepsilon\alpha \theta_{0}^{2}(\xi) \tag{72a}\]  
\[R_{2}=\theta_{1}^{\prime\prime}(\xi)-\alpha\varepsilon\theta_{0}(\xi)\theta_ {1}(\xi) \tag{72b}\]  
\[R_{3}=\theta_{2}^{\prime\prime}(\xi)-\frac{1}{2}\alpha\theta_{0}^{2}(\xi)+ \frac{1}{2}\alpha\varepsilon\theta_{0}^{2}(\xi)-\frac{1}{2}\alpha\varepsilon \theta_{1}{}^{2}(\xi)-\alpha\varepsilon\theta_{0}(\xi)\theta_{2}(\xi) \tag{72c}\]  
\[R_{4}=\theta_{3}^{\prime\prime}(\xi)-\alpha\theta_{0}(\xi)\theta_{1}(\xi)+ \alpha\varepsilon\theta_{0}(\xi)\theta_{1}(\xi)-\alpha\varepsilon\theta_{1}( \xi)\theta_{2}(\xi)-\alpha\varepsilon\theta_{0}(\xi)\theta_{3}(\xi) \tag{72d}\]  
\[R_{5}=\theta_{4}^{\prime\prime}(\xi)-\alpha\theta_{0}(\xi)\theta_{2}(\xi)- \frac{1}{2}\alpha\theta_{1}^{2}(\xi)+\frac{\alpha}{24}\theta_{0}^{4}(\xi)+ \alpha\varepsilon\theta_{0}(\xi)\theta_{2}(\xi) \tag{72e}\]  
The right-hand side of \(R_{n}\) can then be calculated with symbolic calculation software such as Maple, MATLAB and so on. Thereafter, the linear high-order deformation Equations (68) and (69) are solved.  
The solutions by IHAM contain the auxiliary parameter \(\varepsilon\) about nonlinear operator \(\mathcal{N}_{0}\) and the auxiliary parameter \(\mathbb{h}\) about linear operator \(\mathcal{L}\), which control the convergence region and the rate of the IHAM solution series.
## 4 Enabling Technologies for FSO Communication
### Aperture Averaging
#### 4.5.1 Overview  
A straightforward method to mitigate the fading effect is to employ a relatively large lens at the Rx to smooth out intensity fluctuations. This technique, commonly referred to as _aperture averaging_, serves as an inherent form of receive diversity. It involves adjusting the size of the detector or receive aperture to capture as much of the traversing optical field as possible. In FSO systems operating under strong and saturated conditions, aperture averaging is essential to address (1) signal scintillation or irradiance flux variations [232; 233]; (2) address AoA fluctuations and boresight angle effects; and (3) to optimize the average signal power at the detector plane. Additionally, aperture averaging is a fundamental scheme that ensures spatial diversity [46]. In this context, aperture averaging can be considered a basic form of spatial diversity when the Rx lens aperture exceeds the fading correlation length [47; 233; 234].  
Aperture averaging calculations are crucial in Rx design. The Rx needs to be large enough to gather adequate power and minimize scintillation effects at a specific distance, yet small enough to remain practical. Typically, the Rx does not require a significantly large diameter due to the nonlinear decrease in scintillation with increasing diameter. It is important to note that while aperture averaging becomes increasingly effective at reducing intensity variance as turbulence intensifies, this improvement is limited to a certain extent [233; 235].
###### Abstract  
Modern condition monitoring and industrial fault prediction have advanced to include intelligent techniques, aiming to improve reliability, productivity, and safety. The integration of ultrasonic signal processing with various machine learning (ML) models can significantly enhance the efficiency of industrial fault diagnosis. In this paper, ultrasonic data are analyzed and applied to ensemble ML algorithms. Four methods for reducing dimensionality are employed to illustrate differences among acoustic faults. Different features in the time domain are extracted, and predictive ensemble models including a gradient boosting classifier (GB), stacking classifier (Stacking), voting classifier (Voting), Adaboost, Logit boost (Logit), and bagging classifier (Bagging) are implemented. To assess the model's performance on new data during experiments, k-fold cross-validation (CV) was employed. Based on the designed workflow, GB demonstrated the highest performance, with less variation over 5 cross-folds. Finally, the real-time capability of the model was evaluated by deployment on an ARM Cortex-M4F microcontroller (MCU).  
classification; ensemble learning; signal processing; industrial fault diagnosis; ultrasonic sensor; feature extraction; boosting Article
## 3 Numerical Simulations
### Verification of Numerical Results
#### 3.1.2 Pitch RAO of a Spherical Float Constrained to a Hinge Point  
Figure 5 depicts a spherical float constrained to a hinge point with a massless rigid arm. The diameter and draft of the float were 6 m and 5 m, respectively. The mass of the float was 57,962 kg. The water depth was 20 m, and the density of the water was 1025 kg/m\({}^{3}\). The float was modeled with 7200 quadrilateral elements. The float was constrained in motion except for the rotational motion in the \(y\)-axis by a hinge joint. A linear rotational damper was applied to the hinge point to describe the PTO energy extraction (damping) system. The moment acting on the hinge point (\(M_{PTO}\)) can be expressed as  
\[M_{PTO}(t)=B_{PTO}\cdot\hat{\theta}(t) \tag{23}\]  
Figure 4: Comparison of heave RAOs of a spherical float (Lin: Linear analysis; B-N: Body-nonlinear analysis) [41].  
Figure 3: Model description of a spherical float: (**a**) Numerical model; (**b**) Panel distribution.  
where \(\dot{\theta}\) is the angular velocity of the float, and \(B_{PTO}\) is the rotational PTO damping coefficient of a rotational damper. \(B_{PTO}\) = 3.52 \(\times\) 10\({}^{6}\) N\(\cdot\)m/(rad/s) was applied for comparison. Frequency-domain hydrodynamic parameters were also obtained using WAMIT. The diffraction and radiation forces were applied as linear forces among the external forces.  
For this example, a time history of the incident random wave was generated using the white noise spectrum of frequency range from 0.1 rad/s to 2.6 rad/s with 126 regular component waves. A simulation result of 3600 s after a 100 s ramping period was used for the subsequent analysis.  
The linear-analysis results are first presented in Figure 6 to compare the pitch RAO of the float with the previous study [42], which generally shows good agreement. Figure 6b presents the pitch RAOs obtained by authors through the body-nonlinear analysis under regular and irregular wave conditions. Both results agree well except for some minor differences in the low-frequency region. When comparing Figure 6a,b, the magnitude of the pitch RAO became smaller when the body-nonlinear analysis was performed. This trend was different from the case of Figure 4 (only leave-allowed sphere) without a rotating arm. When the floating body rotates around the hinge point, the floating body moves up and down but also in the horizontal direction, i.e., the instantaneous x-position of the body also changes. This caused the increased difference between the linear and body-nonlinear results of Figure 6b compared to Figure 4. This example illustrates that the body-nonlinear analysis can estimate the performance of the hinged-buoy system more accurately than the linear analysis.
## 3 Results and Discussion
### Water and Sediment Composition
#### 3.3.1 Water Composition and Quality
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & **pH** & **EC** & **DBO5** & **NH4\({}^{+}\)** & **NO2\({}^{-}\)** & **NO3\({}^{-}\)** & **PO4\({}^{3-}\)** \\ \hline \multicolumn{8}{c}{Water} \\  & & dS cm\({}^{-1}\) & & & mg L\({}^{-1}\) & & \\ \hline Louro (n = 15) & \(8.3\pm 0.8\) & \(23.3\pm 10\) & \(29\pm 27\) & \(0.30\pm 0.22\) & \(0.08\pm 0.04\) & \(2.19\pm 1.24\) & \(0.10\pm 0.06\) \\ Vixán (n = 18) & \(8.2\pm 0.9\) & \(10.1\pm 0.7\) & \(10\pm 9.4\) & \(0.09\pm 0.04\) & \(25.7\pm 37.4\) & \(2.34\pm 1.37\) & \(0.18\pm 0.23\) \\ Xurno (n = 18) & \(6.6\pm 0.3\) & \(0.46\pm 0.2\) & \(6.2\pm 6.1\) & \(0.08\pm 0.10\) & \(0.03\pm 0.02\) & \(1.96\pm 2.23\) & \(0.10\pm 0.20\) \\ Bodeira (n = 12) & \(7.2\pm 0.2\) & \(0.41\pm 0.1\) & \(9.1\pm 11\) & \(0.08\pm 0.12\) & \(0.08\pm 0.07\) & \(23.5\pm 42.9\) & \(0.13\pm 0.13\) \\ \hline \multicolumn{8}{c}{Sediment} \\  & pH & EC & Eh & TOC & TS & Fe oxyhyd. & Fe pyrite \\  & & dS cm\({}^{-1}\) & mV & & \% & \multicolumn{2}{c}{\(\mu\)mol g\({}^{-1}\)} \\ \hline Louro (cm) & (n = 2) & (n = 2) & (n = 2) & (n = 4) & (n = 4) & (n = 4) \\
0–15 & 6.8 & 24.1 & 124 & \(8.6\pm 1.8\) & \(2.0\pm 0.2\) & \(37.0\pm 15\) & \(367\pm 126\) \\
40–60 & 6.9 & 20.1 & 110 & \(4.2\pm 0.7\) & \(1.2\pm 0.1\) & \(11.9\pm 5.8\) & \(197\pm 13\) \\ \hline Vixán (cm) & (n = 1) & (n = 1) & (n = 3) & (n = 4) & (n = 4) & (n = 4) \\
0–15 & 7.8 & 14 & \(-78\pm 9\) & \(8.7\pm 2.6\) & \(0.91\pm 0.4\) & \(22.5\pm 1.9\) & \(131\pm 55\) \\
40–60 & 7.3 & & \(-150\pm 25\) & \(1.6\pm 0.3\) & \(0.20\pm 0.3\) & \(23.0\pm 7.0\) & \(73.9\pm 3.9\) \\
90–110 & na & 10.8 & na & \(4.2\pm 0.8\) & \(0.92\pm 0.1\) & na & na \\ \hline Xurno (cm) & (n = 1) & (n = 1) & (n = 1) & (n = 4) & (n = 4) & (n = 4) \\
0–15 & 6.9 & \(-60\) & 0.56 & \(11.5\pm 0.6\) & \(0.26\pm 0.16\) & \(187\pm 39\) & \(0.24\pm 0.17\) \\
40–60 & 6.4 & \(-45\) & na & \(0.22\pm 0.11\) & \(0.02\pm 0.02\) & \(2.85\pm 0.01\) & \(0.05\pm 0.01\) \\
90–110 & 6.5 & \(-12\) & na & \(1.76\pm 0.06\) & \(0.61\pm 0.04\) & \(1.04\pm 1.52\) & \(1.38\pm 1.32\) \\ \hline Bodeira (cm) & (n = 1) & (n = 1) & (n = 1) & (n = 4) & (n = 4) & (n = 4) \\
0–15 & 6.1 & 70 & 0.76 & \(24.6\pm 4.6\) & \(0.72\pm 0.32\) & \(89.1\pm 18\) & \(0.04\pm 0.03\) \\
40–60 & na & na & na & \(5.30\pm 1.40\) & \(0.09\pm 0.04\) & \(63.4\pm 15\) & \(2.54\pm 1.73\) \\
90–110 & 7 & \(-120\) & na & \(1.61\pm 0.62\) & \(0.45\pm 0.02\) & \(188\pm 16\) & \(2.99\pm 1.78\) \\ \hline \end{tabular}
\end{table} 
Table 2: Water and sediments physicochemical properties and composition, na: not analyzed. n: number of samples.
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 4 The Proposed SC-FDM and OFDM  
In general, SC-FDM behaves like a single-carrier system with a short symbol duration compared to OFDM. To achieve this, SC-FDM introduces an N-point discrete Fourier transform (DFT) block right after the serial to parallel converter in the OFDM structure. The DFT block converts parallel sequences of symbols in the time domain to different frequency points. The major disadvantage of the OFDM is its high PAPR. This is a consequence of the fact that the transferred signal is the  
Figure 4: Block diagram of a UAV transmitter.  
Figure 3: Unmanned aerial vehicle (UAV) communication system components.  
amount of all the modulated subcarriers and some of them are in a phase with high amplitudes that cannot be avoided [30]. Due to this reason, the power structure of the transmitter is characterized by relatively low average- and high-power peaks. SC-FDM allows a symbol to be transmitted in parts over multiple subcarriers, but in OFDM, we have one to one mapping between symbol and subcarrier. For example, in OFDM one symbol occupies one subcarrier of 15 kHz, but in SC-FDM, the same symbol is distributed among multiple subcarriers of 15 kHz, as described in Figure 5[31].
# Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles
## 4 Results and Analysis  
### Comparison with Two Types of Channels  
Figure 6 shows that the comparison of the polarization curves between the PEMFC with a single straight channel and the PEMFC with a channel with four porous baffles. It was found that the performance of the channel with four porous baffles was not superior to that of the single straight channel at low current density. However, the average current density of the PEMFC with the porous flow channel was higher than that of the PEMFC with the single straight channel at a high current density. At 0.4 V, the average current density of PEMFCs increases from 1.105 A/cm\({}^{2}\) to 1.224 A/cm\({}^{2}\), i.e., an increase of 12.8%.  
Figure 5: Model validation: comparison between simulation results of the straight channel model and experimental results obtained from [PERSON] [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline Porous baffle length (\(L_{ch}\)) & 0.4 & mm \\ Porous baffle width (\(W_{ch}\)) & 1 & mm \\ Porous baffle height (\(H_{ch}\)) & 1 & mm \\ Number of porous baffles & 4 & - \\ Porous (\(\varepsilon_{b}\)) & 0.8 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The geometric parameters.  
The comparison of power density curves between the PEMFC with a straight channel and the PEMFC with a channel with four porous baffles is shown in Figure 7. With the increase in the current density, the influence of the flow channel with the porous baffle on the performance of the PEMFC gradually increases and the maximum power density of the PEMFC with the four porous baffles increases from 0.5139 (W/cm\({}^{2}\)) to 0.5789 (W/cm\({}^{2}\)), i.e., an increase of 12.7%.  
Figure 6: The comparison of the polarization curves between the PEMFC with a straight channel and the PEMFC with a straight channel and four porous baffles.  
Figure 7: Comparison of power density curves between the PEMFC with a straight channel and the PEMFC with straight channel and four porous baffles.  
Figure 8 shows the two-dimensional cloud diagram of the GDL cathode oxygen concentration distribution at 0.4 V in the single channel model and the channel model with four porous baffles. Obviously, the oxygen concentration in the channel is the highest at the entrance and decreases with the progress of the chemical reaction. Additionally, the oxygen concentration in the contact area between the GDL and the runner is much higher than that under the rib. Compared with the straight channel, the porous blockage in the channel led to higher oxygen concentration and significantly improved the local distribution of oxygen.
## 3 Results and Discussion
### The Electronic Spectra  
Electronic spectral studies for all these complexes were carried out in DMSO at \(10^{-3}\) M concentration. The spectrum of the Schiff base free ligand has observed bands at 264.28, 321.93, and 357.31 nm assigned to \(\pi\)-\(\pi^{\ast}\) and n-\(\pi^{\ast}\) transition, respectively [12]. The metal complexes showed a shift towards lower frequencies of the absorption bands and confirmed the coordination of the ligand to the metallic ions [28]. The electronic spectra of the complexes are shown in Figure S3. Since they are dark-colored complexes, the electronic spectra of d-d transitions are inferred with the broad peak of the high excitation coefficient of ligand contribution.  
Therefore, they are an expression that is forbidden as the _Laporte rule_ of electronic absorptions; this is because the extinction coefficient for these complexes is low compared with the ligand's contribution in the electronic spectra, and it is not easy to see the peaks of these complexes next to the ligand peak.
## 3 Rational for Optimization in WSNs
### Domains of Optimizations in Wireless Sensor Networks
#### 3.2.1 Energy Efficiency _vs._ QoS  
Wireless sensor networks are primarily characterized by their small amount and non-replenishable energy supply. Advancements in wireless sensor networks have led to a number of new protocols explicitly designed for sensor networks where energy awareness is the main consideration. Some of the research works have been done focusing on routing protocols since they might differ depending on the applications. Routing protocols aim to provide uniform energy dissipation during transmission to the sink node. This energy is mainly used for transmitting and receiving sensor readings, which are energy hungry operations. If all the sensors want to communicate with the BS directly, then it could result in the premature death of the whole network, so without a proper communication reduction strategy the whole system might be in jeopardy. Hence, the need for energy efficient infrastructure is becoming very important since it impacts the network's operational lifetime.  
Almost all of the routing protocols can be classified as data-centric, hierarchical or location-based. Few of these protocols are aware of QoS. Along with the routing function, they include routing approaches that are based on general network-flow modeling and protocols that strive to meet some QoS requirements, but keeping in mind the resource constraints, the network QoS may suffer from lack of computing and communication resources [31]. As an example, if a number of nodes want to transmit \(l\) bit of message over the same WSN, they have to compete for the limited bandwidth that the network provides. As a result, some data transmissions may experience long delays, resulting in poor level of QoS, especially in real time applications. Also due to the limited memory size of the nodes, some data packets may be dropped or lost before they even reach the destination/sink.  
Data redundancy is another important QoS parameter related to the issues of energy constraints. WSNs are characterized by sensor data high redundancy. However, while the redundancy in the data does help loosen the reliability or robustness (QoS) requirements of data delivery, it unnecessarily spends much energy. Data compression can be a good solution in providing energy efficiency by removing the data redundancy, but this energy efficiency can come at the cost of reduced reliability and increased delays and distortion.  
Thus, in a sense QoS is also related to the issues of energy efficiency. In fact, energy efficiency itself is a QoS parameter. Somehow these two conflicting but incorporated areas need to be dealt with utmost intelligence and in this case biological intelligence can play a vital role. In order to achieve a prolonged network lifetime with a proper balance of power and suitable QoS support, energy loads must be evenly allotted among all the nodes. As a result, the energy of a single sensor or a small set of sensors will not be drained much earlier than others. QoS management must take this factor into account.