esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Concluding Remarks  
In this study, the computational fluid dynamics (CFD) simulations were first conducted to investigate the critical flutter wind speeds and post-flutter limit-cycle oscillations (LCOs) of the bridge decks with various leading-edge angles. Based on the obtained CFD data, a reduced-order model for the nonlinear, unsteady aeroelastic forces on the bridge decks was developed by training a deep long short-term memory (LSTM) network. At each time step, the LSTM-based nonlinear aeroelastic forces were used to calculate the bridge deck motion (displacements, velocities and accelerations) through the Newmark-\(\beta\) algorithm and were then fed into the LSTM network as the inputs of the next time step. A hybrid loss function involving the prediction errors of both aeroelastic forces simulated by the LSTM network and bridge deck responses calculated by the Newmark-\(\beta\) algorithm was designed to improve the training efficiency and stability. The LSTM-based predictions of the critical flutter wind speeds and post-flutter LCO amplitudes presented an excellent agreement with the CFD simulations. The ability of the LSTM model to efficiently predict the post-flutter responses of the bridge decks with various leading-edge angles can be effectively used to facilitate the aerodynamic optimization in wind-resistant bridge design. In future studies, more design variables that can comprehensively represent the geometry changes of bridge decks will be input into the LSTM network. The developed LSTM model can be readily extended to the three-dimensional full-bridge configurations.  
Conceptualization, T.W.; Methodology, X.A. and T.W.; Software, X.A.; Validation, X.A.; Formal analysis, X.A.; Writing--original draft, X.A.; Writing--review & editing, S.L. and T.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Innovation-Driven Project of Central South University (No. 2020 CX009) and the Institute of Bridge Engineering at University at Buffalo.  
Not applicable.  
All data are available from the corresponding author by request.  
The authors declare no conflict of interest.  
Figure 15: The time history comparison between CFD simulations and LSTM predictions for \(U=11.8\) m/s and \(\alpha_{d}=134^{\circ}\). (**a**) Lift force. (**b**) Torsional moment. (**c**) Vertical displacements. (**d**) Torsional angle.
## 3 Stability Analysis and Discussion
### 2-DOF Model of VHCVSS
#### 3.2.2 Simulation and Analysis
In comparison, variable helix characteristics of a VHC aggravate the degree of regeneration disturbance, and thus relatively weaken the stability influence ability of VSS in the combination process.
## 2 Materials and Methods
### Methods  
This research conducted uniaxial compression tests at six real-time temperatures (25 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C). In order to ensure the accuracy of the experiments and minimize the experimental errors caused by variations in the composition of the granite structures, three samples were tested simultaneously at each temperature. The experimental apparatus, as illustrated in Figure 2, primarily consisted of three systems: loading, temperature control, and data acquisition, operated by a microcomputer-controlled electronic universal testing machine. The loading system included a reaction force frame, high-temperature clumps, a high-temperature furnace, and displacement sensors (LVDTs). The heating rate was set to 5 \({}^{\circ}\)C/min. Upon reaching the target temperature, to mitigate the thermal gradient effect on the granite and ensure the uniform heating of the samples, the samples were held at a constant temperature for 2 h before testing commenced. The tests were conducted under displacement-controlled loading at a rate of 0.1 mm/min, with a fixed force attenuation amplitude of 60%. The samples were loaded continuously until failure, with the axial force and displacement changes recorded throughout the entire process. The final test results are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline T/(\({}^{\circ}\)C) & \(\sigma_{s}\)/(MPa) & \(\varepsilon_{s}\)/(10\({}^{-3}\)) & E/(GPa) \\ \hline
25 \({}^{\circ}\)C & 149.42 & 6.53 & 31.87 \\
200 \({}^{\circ}\)C & 171.64 & 6.67 & 33.39 \\
300 \({}^{\circ}\)C & 164.89 & 7.12 & 30.07 \\
400 \({}^{\circ}\)C & 134.09 & 7.91 & 23.20 \\
500 \({}^{\circ}\)C & 123.01 & 9.05 & 18.42 \\
600 \({}^{\circ}\)C & 78.81 & 11.63 & 8.98 \\ \hline \end{tabular}
\end{table}
Table 1: Uniaxial compression test results at real-time temperatures.  
Figure 1: Granite sample and polarized light microscopy image: (a) granite sample; (b) polarized microscope image of granite.
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 6 Conclusions  
In the last decades, remarkable achievements in the field of phosphate based fiber lasers and amplifiers have been obtained thanks to the progress in the diode technology, advanced fiber fabrication technology and novel pumping techniques. In this review, we reported on current state-of-the-art CW, pulsed, and single-frequency fiber lasers and amplifiers based on phosphate glass optical fibers. Phosphate glasses main properties were also briefly discussed to give to the readers a general overview of this outstanding gain medium.  
The reported results show that phosphate glass fiber lasers and amplifiers can be highly efficient in a short fiber laser arrangement and can thus represent a promising alternative for other glass lasers  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Reference**} & \(\mathbf{\lambda_{signal}}\) & **Pulse Energy** & **Pulse Duration** & **Average Power** & **Beam Quality** & **Peak Power** \\  & **(nm)** & **(m)** & **(ns)** & **(W)** & **(M\({}^{2}\))** & **(kW)** \\ \hline
[21] & 1538 & 0.054 & 153 & 1.08 & 1.2 & 0.332 \\
[91] & 1530 & 0.126 & 100 & - & 1.2–1.4 & 1.2 \\
[92] & 1550 & 0.384 & 3 & - & - & 128 \\
[93] & 1550 & 0.200 & 20 & 1 & - & \textgreater{}10 \\ \hline \end{tabular}
\end{table}
Table 4: Main results obtained in the 1.5 \(\upmu\)m region with high energy, high peak power pulsed single-frequency all-fiber amplifiers based on highly Yb\({}^{3+}\)/Er\({}^{3+}\) co-doped phosphate glasses.  
at medium output power performance. Thus, in future, phosphate based fiber lasers and amplifiers are expected to be extensively employed in different applications fields.  
This article has been written by [PERSON] and revised by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.
###### Abstract  
The construction of urban rail transit (URT) guides and promotes urban development. Different URT line construction schedule, including construction sequence (priority order of line construction) and construction timing (when to build), will have different effects on urban traffic and development. Therefore, the planning of construction schedule is an important part of URT network planning. At present, the determination of construction schedule is mainly based on qualitative analysis methods (i.e., experience, comparisons with other cities, and expert opinion) in engineering practice. In this study, based on an analysis of the main factors affecting the construction sequence and the construction timing data of existing URT lines, a quantitative double-level model of a construction schedule is proposed. The model consists of construction sequence and construction timing sub-models. The construction sequence sub-model employs an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Rough Set method; the construction timing sub-model takes the results of the construction sequence model and the factors associated with urban development characteristics into account and presents an improved Logistic-\(\beta\) method. The model is verified using the Chengdu rail transit network as the case study. The results of the study show that the double-level calculation model could provide quantitative theoretical support for the construction schedule planning of URT network.  
urban rail transit; construction schedule; construction sequence; construction timing; TOPSIS; Logistic-\(\beta\)
## 2 Related Work  
Several prior studies have been conducted on minimizing and preventing the occurrence of epidemics by predicting trends in illnesses based on web data. A representative example is a study by [PERSON] et al. [17] in which the authors attempted to monitor influenza trends based on the online social network platform Twitter; another is a study by [PERSON] et al. [18; 19] in which the authors attempted to predict the trend of the flu using Twitter data. However, considering the characteristics of web data, there are limitations to this type of data, as advertisements may be included, and some data may be duplicated. To supplement these limitations, [PERSON] et al. utilized actual chicken pox occurrence data provided by the Korea Disease Control and Prevention Agency, as well as data from Naver News and Twitter, to predict the occurrence of chicken pox [20].  
Numerous factors are involved in health, and the World Health Organization noted that climate change, such as air pollution, is a significant health threat that leads to various illnesses and premature deaths [21]. Subsequently, many studies have been conducted to predict the possibility of illnesses according to the meteorological and atmospheric environments. [PERSON] et al. analyzed precipitation, lowest and highest temperature, relative humidity, and daily temperature range with Naive Bayes to provide the degree of risk for the common cold [22]. [PERSON] et al. proposed an influenza-like disease monitoring system through statistical analysis [23], and [PERSON] et al. studied the effect of air pollution on juvenile rheumat using statistical analysis [24]. [PERSON] et al. utilized the common cold, eye disease, and middle ear inflammation as autoregressive integrated moving average models to propose a model that calculates the expected number of patients for each illness by utilizing drug prescription data [5]. [PERSON] et al. developed a cold index by predicting the number of patients suffering from the common cold [25]. There are also studies that have confirmed the correlation between air pollution and cardiovascular disease [26; 27; 28]. However, these studies conducted analyses with data from individuals of all ages and did not consider differences among the age groups.  
Currently, the KMA provides the health meteorological index. This is a collection of data that has indexed the level of possibility of occurrence of illnesses such as food poisoning, asthma, cerebral apoplexy, the common cold, and pollen allergies. However, this service fails to reflect various climatic environments, as it excludes atmospheric environment information and only considers weather conditions, such as lowest temperature and air pressure, in its analysis. Additional limitations of this service are that it does not provide optimized illness information for each age group and that it only provides information on the illnesses most commonly suffered from by people in general.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 4 Methodology
### Theoretical Analysis
#### 4.1.2 Link Budget Analysis  
To evaluate the maximum link coverage performance of the test modules, the end-user configuration with a transmit power of 20 dBm is implemented using the SX1278 transceiver. For the payload, the LoRa gateway with the SX1301 module is used. This experiment uses a frequency of 433 MHz with 8 channels capacity and a carrier spacing of 0.25 MHz. Each carrier is ADR-enabled and has the ability to automatically adjust the SF as per the received signal strength. Half-wave dipole antennas are used for both ends with a gain of 2.5 dBi. The gateway module has a noise figure of 6 dB and transmits a power of 27 dBm. Receive sensitivity can be obtained using Equation (15) [24],  
\[S=-174+10\log_{10}BW+NF+SNR\ [\mathrm{dB}] \tag{15}\]  
where \(BW\) = Bandwidth in kHz,  
\(NF\) = Noise Figure of the receiver in dB,  
\(SNR\) = Signal-to-Noise Ratio as per spreading factor,  
\(S\) = sensitivity in dB.  
To calculate the path loss, a link performance in Equation (16) is used,  
\[P_{RX}=P_{TX}+G_{SYSTEM}-L_{SYSTEM}-L_{CHANNEL}-M\ [\mathrm{dB}] \tag{16}\]  
where \(P_{TX}\) = Transmit Power,  
\(P_{RX}\) = Receive power,  
\(G_{SYSTEM}\) = System Gains,  
\(L_{SYSTEM}\) = Losses,  
\(L_{CHANNEL}\) = Channel Propagation Losses,  
\(M\) = Fading Margin.  
The Free Space Path Loss (FSPL) is then calculated using Equation (17),  
\[FSPL=20\log_{10}d+20\log_{10}f+20\log_{10}4\frac{\pi}{c}\ [\mathrm{dB}] \tag{17}\]  
where \(d\) = Antenna distance between receiver and sender  
\(f\) = Frequency  
\(c\) = Speed of light  
Due to the slant range, different angles will have an additional fade margin. Table 3 shows the path loss comparison concerning the slant range using Equations (11) and (17).  
\begin{table}
\begin{tabular}{c c c} \hline
**Elevation (deg)** & **Slant Range (km)** & **FSPL (dB)** \\ \hline
0\({}^{\circ}\) & 2705.3 & 148.8 \\
5\({}^{\circ}\) & 2205.9 & 147.0 \\
10\({}^{\circ}\) & 1815.7 & 145.4 \\ \hline \end{tabular}
\end{table}
Table 3: Path loss calculation with the look angle and distance measured using Equations (11) and (17).
## 7 Conclusions  
In this paper, a CEM method has been employed to identify the Arctic sea ice from observation datasets derived from the following satellites: Sentinel-2, HY-1C, ZY3-02, and HJ-2A. Additionally, an EAEND model has been proposed to optimize the filter coefficient and facilitate the CEM method's identification of the Arctic sea ice. Meanwhile, the proposed EAEND model with an integration term makes up for the shortcomings of the CEM method in terms of the noise immunity, improving the stability and accuracy of the CEM method for identifying Arctic sea ice. Moreover, a Taylor numerical difference formula is utilized in the construction of the EAEND model, allowing it to reach a steady-state error of \(O(\sigma^{3})\) order. Comparative identification experiments have confirmed the feasibility, accuracy, and robustness of the CEM method with the EAEND model in identifying Arctic sea ice.  
Conceptualization, Y.X. and D.F.; methodology, Y.X. and H.H.; software, D.W. and H.H.H.; validation, D.F.; formal analysis, Y.X., D.W. and H.H.; investigation, D.F.; resources, D.W.; data curation, Y.X.; writing--original draft preparation, Y.X. and D.F.; writing--review and editing, Y.X. and H.H.; visualization, Y.X. and D.W.; supervision, D.W., D.F. and H.H.; project administration, D.F.; funding acquisition, D.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key Research and Development Program of China under Grant 2022 YFC3103101, Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory under Contract GML2021 GD0809, National Natural Science Foundation of China under Contract 42206187, Key projects of the Guangdong Education Department under Grant 2023 ZDZX4009.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 1 Introduction
Our design advocates for the dual use of both energy signal and artificial noise in facilitating efficient wireless energy harvest and providing secure communication. The beamforming design is formulated as a non-convex optimization problem. For obtaining a tractable solution, we reformulated the considered optimization problem by applying a S-procedure and variable substitutions with a tractable solution. The resulting reexpressed non-convex optimization problem can be solved via a semi-definite programming based beamformer design algorithm. Furthermore, the obtained solution is proved to be the optimal solution. The main contributions of this work are summarized as follows:  
Firstly, to explore the network performance, an optimization problem is formulated to maximize the minimum harvested energy power among all the energy vehicular receivers, which is subjected to the required SINR constraints at an information vehicular receiver and energy vehicular receivers, the collected energy power constraints at the information vehicular receiver, and the transmit power constraint at the base station. Secondly, since the considered problem is non-convex and challenging because there are infinite inequality constraints taking into account the nonlinear EH model and coupling among optimized variables. In order to solve the problem, a beamforming design algorithm is proposed based on the S-procedure and semidefinite relaxation (SDR). Moreover, It is proved that the optimal solution can be guaranteed by applying our approach. Thirdly, our simulation results demonstrate that the proposed secure beamforming design scheme under the nonlinear EH model with channel uncertainties can provide performance gains compared with that under the linear EH model with channel uncertainties. Moveover, it is shown that a trade-off is found between the SINR of the information vehicular receiver and the energy harvested by energy vehicular receivers. This rest of this paper is organized as follows. The system model and problem formulation is described in Section 2. Section 3 presents a solution for modelling max-min harvested energy power among all the energy vehicular receivers with imperfect CSI and a nonlinear EH model. Simulation results are provided in Section 4 to verify the effectiveness of the proposed robust secure beamforming design algorithm. Finally, we conclude the paper in Section 5.
## 5 Numerical Results
In addition, the variation in the CBTR with respect to the crack angle is more sensitive than that of the GPLRC plate shown in Figure 8b. Thus, it can be seen that the thermal buckling stiffness of the CNTRC plate is significantly affected by the CNT alignment and the crack angle, differing from the GPLRC plate. It can also be seen from Figure 12a that the CBTR value is the highest when the CNT alignment is normal to the crack line and lowest when both alignments become parallel. Note that the CNT alignment is measured with respect to the \(x\)-axis, differing from the crack angle measured from the \(y\)-axis, as depicted in Figure 4.  
Figure 12b compares the postbuckling paths between the GPLRC and CNTRC plates when the CNTs are aligned along the crack line. First, it can be seen that the thermal buckling stiffness of the GPLRC plate is higher than that of the CNTRC plate when two volume fractions \(V_{GPI}^{*}\) and \(V_{cnt}^{*}\) are the same. Second, the thermal postbuckling path of GPLRC is shown to be much steeper than that of CNTRC. It is confirmed that GPLRC is superior to CNTRC in terms of thermal buckling and postbuckling according to the comparison made when the buckling stiffness of CNTRC was the highest. Thus, the results justify that FG-GPLRC plates exhibit excellent thermal buckling strength, making them an ideal candidate for engineering structures, especially in high-temperature operating environments.
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
## 4 Discussion
Research by [PERSON] [61] also indicates that DEM errors, resulting from information loss during the transformation of 3D spatial data, correlate with the density of effective measurement points and terrain features. [PERSON] et al. [62] found through experimentation that IDW is most sensitive to point cloud density, while TIN is the most effective in mitigating interpolation errors as point density changes, and OK constructs the optimal model. Local interpolation errors vary spatially, increasing more rapidly in areas with higher surface roughness. Thus, interpolation fusion is proposed to address these challenges, improving interpolation accuracy and facilitating experiment completion. Notably, this experiment does not rely heavily on computational resources, and the results confirm that the method enhances accuracy in a simple and efficient manner. However, this study has limitations, as it only evaluates four interpolation methods. Future research will need to determine if further improvements are possible with other methods. Looking ahead, we aim to enhance interpolation accuracy through deep learning techniques and explore whether locally weighted fusion can further refine results and extend the method's applicability.
## Appendix A  
Post hoc analyses were performed to compare potential differences in user experience survey results based on cognitive function, as measured by the Montreal Cognitive Assessment (MoCA) score, and motor function, as assessed by the Fugl-Meyer Assessment--Upper Extremity (FMA-UE) score.  
For cognition, participants were stratified into two groups: normal cognition (MoCA score \(\geq\) 26; \(n\) = 19) and cognitive impairment (MoCA score \(<\) 26; \(n\) = 11). Independent \(t\)-test compared differences in SUS, QUEST, and IMI category (value/usefulness, perceived choice, perceived competence, effort/importance, interest/enjoyment, and pressure). The results did not reach significance in any case (\(p\) > 0.05), and therefore, we fail to reject the null hypothesis, indicating no significant difference between the groups based on cognition.  
For motor function, participants were stratified into two groups: mild impairment (FMA-UE score \(>\) 42; \(n\) = 19) and moderate to severe impairment (FMA-UE score \(\leq\) 42; \(n\) = 11). An independent \(t\)-test compared differences in SUS, QUEST, and IMI category scores as described above. Again, the results did not reach significance in any case (\(p\) > 0.05), and therefore, we fail to reject the null hypothesis, indicating no significant difference between groups based on motor function.  
Although our post hoc analyses did not reveal any significant differences in user experience based on cognition or motor function, we acknowledge that our study was not originally designed to adequately compare these groups. Further research with larger sample sizes is warranted to provide more robust evidence that user experience is comparable across cognitive and motor function levels.
## 3 Experimental Results and Analysis
### Light Intensity Distribution
The probability density distribution statistics of the return signal intensity signals of each group of data were determined, and the normalized logarithmic light intensity data were divided into different intervals for the statistics to obtain the normalized data frequency; thus, further obtaining the probability density distribution histogram and its fitted results, as shown in Figure 4.  
Figure 4: Histogram of probability density distribution of normalized logarithmic light intensity and its fitted results. (**a**) Histogram of probability density distribution of normalized logarithmic light intensity and its fitted results at 20:20 on 4 September 2022. (**b**) Histogram of probability density distribution of normalized logarithmic light intensity and its fitted results at 20:10 on 5 September 2022. (**c**) Histogram of probability density distribution of normalized logarithmic light intensity and its fitted results at 20:10 on 6 September 2022. Figure 3: Normalized logarithmic intensity of Mie-scattering lidar return signal. (**a**) Normalized logarithmic intensity of return signal at 20:20 on 4 September 2022. (**b**) Normalized logarithmic intensity of return signal at 20:10 on 5 September 2022. (**c**) Normalized logarithmic intensity of return signal at 20:10 on 6 September 2022. It can be seen from Figure 4 that the log-normal distribution can well describe the actual shape of the probability density distribution of the logarithmic light intensity of the received return power in the actual detection of atmospheric turbulence by Mie-scattering lidar; that is, it verifies that, under the weak fluctuations in Mie-scattering lidar, the return logarithmic light intensity basically follows the log-normal distribution.