esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In order to address the current issues with the WYPE technique and further validate its effectiveness in identifying damage in box beams and orthotropic plates, this study examines a finite element model of a steel box beam. Expanding upon previous experiments, the vibration acceleration responses of the steel box beam under sinusoidal excitations at frequencies of 10 Hz, first- and second natural frequencies are analyzed in greater detail. Moreover, the robustness of this theory is assessed under various white noise interference conditions, providing further evidence of its practical applicability. The main conclusions of this study are outlined below:  
Figure 19: Acceleration spectra and the proportion of energy in each frequency band. Spectra: (**a**) Condition 2 with SNR of 20 dB, (**b**) Condition 2 with SNR of 10 dB, (**c**) Condition 3 with SNR of 20 dB, and (**d**) Condition 3 with SNR of 10 dB. The proportion of energy in each frequency band: (**e**) Condition 2 with SNR of 20 dB, (**f**) Condition 2 with SNR of 10 dB, (**g**) Condition 3 with SNR of 20 dB, and (**h**) Condition 3 with SNR of 10 dB.  
Figure 20: PED value under different white noise degree (2 nd-order frequency). (**a**) No noise. (**b**) 10 dB white noise. (**c**) 30 dB white noise.  
The newly proposed theory proves its effectiveness in identifying the location of damage and estimating its severity under different sinusoidal excitation frequencies. This shows that the theory performs consistently well across various frequencies, not just at specific ones. Consequently, the WPPE damage identification theory shows great potential for practical applications in handling vibrations at different frequencies.  
The WPPE technique demonstrates impressive noise resistance capabilities. The test results revealed that with background noise levels reaching 10 dB, or even almost masking the vibration response, WPPE can still accurately assess the location and severity of damage in steel box beams or orthotropic plates, highlighting its remarkable robustness.  
Conceptualization, Y.Z. ([PERSON]) and J.K.S.; methodology, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); software, Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]), Y.Z. ([PERSON]) and J.K.S.; formal analysis, Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]); resources, Y.Z. ([PERSON]); data curation, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, J.K.S. and Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); supervision, J.K.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 3 Theoretical Calculation of Ultimate Bearing Capacity
### The Formula of Ultimate Bearing Capacity  
The calculation formula of the ultimate bearing capacity of concrete-filled circular CFRP-steel middle long columns \(N_{u,cf}\) is as follows, where \(N_{u}\) is the ultimate bearing capacity formula of concrete-filled circular CFRP-steel stub columns.  
\[A=\frac{1}{2m_{c}}+\sqrt{(1-\frac{1}{2m_{c}})^{2}+\alpha_{1}(m_{c}-1)} \tag{7}\]  
\[B=0.5\sqrt{4-3{k_{s}}^{2}} \tag{8}\]  
\[C=0.5k_{f} \tag{9}\]  
\[m_{c}\approx 0.18f_{c}+6 \tag{10}\]  
\[\varphi=1-0.1337\sqrt{L/D-5.7},\ 5.7\leq L/D\leq 8 \tag{11}\]  
\[N_{u}=Af_{c}A_{c}+Bf_{sy}A_{s}+Cf_{cf}A_{cf} \tag{12}\]  
\[N_{u,cf}=\varphi\cdot N_{u} \tag{13}\]  
\(m_{c}\) is a parameter of concrete materials. \(\varphi\) is obtained by the statistics and regression of the experimental data, which can be used only in the case of \(5.7\leq L/D\leq 8\).
## 3 Rice Industry By-Products
### Properties of Rice Husk and Rice Husk Ash
Concerning the composition of husk, the presence of cellulose, hemicellulose, lignin, and silica components is evident, since the bands at 780 and 2920 cm\({}^{-1}\), 1030 cm\({}^{-1}\) and 3340 cm\({}^{-1}\), and at wavenumbers lower than 1200 cm\({}^{-1}\) were attributed in literature to C-H deformation vibration and stretching in cellulose, C-O stretching vibration in cellulose/hemicellulose and aryl-OH group in lignin, and free and hydrogen-bonded OH stretching [64; 65; 66; 67; 68]. The existence of different types of polar groups on the rice husk surface gives an appreciable amount of cation exchange capacity, outlining the possible effectiveness in physisorption mode [69]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Chemical Component**} & \multicolumn{4}{c}{**Composition (mg/g)**} \\ \cline{2-5}  & [56] & [56] & [57] & [37] \\ \hline Cellulose & 334.7 & 292 & 344 & 322.4 \\ Hemicellulose & 210.3 & 201 & 293 & 213.4 \\ Lignin & 267 & 307 & 192 & 214.4 \\ Extractives & & & 18.2 \\ Water & & & 81.1 \\ Mineral ash & & & 150.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chemical constituents of rice husk (the composition of the mineral ash obtained after pyrolysis is reported in Table 3) (modified from [37; 56; 57]). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Composition (mg/g)**} \\ \cline{2-7}  & [58] & [59] & [54] & [60] & [61] & [37] \\ \hline SiO\({}_{2}\) & 924 & 946.4 & 884.7 & 810.9 & 920 & 945 \\ Al\({}_{2}\)O\({}_{3}\) & & & & 0.5 & 2.9 & \\ Fe\({}_{2}\)O\({}_{3}\) & 4 & 2.3 & 4 & 1.4 & 1 & \(<\)5 \\ CaO & 7 & 18.9 & 18 & 10.7 & 12.8 & 2.5 \\ MgO & 3 & 9.6 & 7.1 & 7.5 & 3.7 & 2.3 \\ Na\({}_{2}\)O & 0.7 & 3.9 & 2.6 & & 0.5 & 7.8 \\ K\({}_{2}\)O & 25.4 & 5.8 & 25 & 13.9 & 21.9 & 11.8 \\ SO\({}_{3}\) & & & & 14.5 & 9.4 & 6 \\ loss on ignition, LOI & 23.1 & & & 87.3 & 34.3 & \\ Zn (ppm) & & 18.2 & 32.28 & & & \\ Mn (ppm) & & 52.24 & 56.44 & & & \\ Cu (ppm) & & 32.17 & 16.98 & & & \\ Cd (ppm) & & 0.48 & 0.49 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chemical composition of rice husk ash (modified from [37; 54; 58; 59; 60; 61]). According to the works of literature, rice husk adsorbent has been used in different ways for the removal of various types of pollutants such as inorganic anions, heavy metals, phenol, and other organic compounds, pesticides, and dyes from the water and wastewater. Many investigations have been conducted through different treatment methods, such as untreated rice husk as adsorbents, chemical and biological modification of husk, biochar, activated carbons, and ashes. The use of these different treatments for rice husk shows that many works have reported acceptable results from their research with adsorption capacity more than 10 mg/g. However, comparison among different treatment methods is relatively difficult due to different experimental conditions.
## 3 NARX Model on the Rotor-Bearing System
### NARX Model under Different Frequency Ranges
#### 3.3.3. Under the Over-Critical Speed Condition  
When the input signal contains the frequency of \(\omega_{over\ critical}\in[350,450]\) rad/s, the NARX model can be identified as:  
\[\begin{array}{l}y(k)=\sum\limits_{i=1}^{10}\beta_{i}(k)\widetilde{\theta_{i} }\\ =-2.7421\times 10^{-5}u(k-6)\\ +1.3522y(k-1)+2.3854\times 10^{-5}u(k-6)+\cdot\cdot\cdot 0.0190y(k-8)\end{array} \tag{28}\]  
Figure 10: Output comparison in time and frequency domain at a critical speed.  
Figure 9: Output comparison in time and frequency domain at a low speed.  
where the harmonic input with \(\omega\) = 500 rad/s is used to test the model in both the time and the frequency domain, presenting in Figure 11. And the RMES is 6.4411 \(\times\) 10\({}^{-6}\) m.  
Figure 11 indicates that the NARX model identified in the low frequency range provides a more accurate prediction result than that of (25). Also, the RMSE is better than 1.4032 \(\times\) 10\({}^{-5}\) m, as shown in Table 2.  
As the above three sections indicate, both the graphs and the values of RMSE show a better accuracy than that of Section 3.2. That is, from both a qualitative and quantitative point of view, the method of narrowing the frequency range of input excitations can optimize the NARX model and make the model perform better.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Solving Phase I (Search): Maze Exploration  
The first phase of the Search and Rescue operation starts with the search agents rapidly exploring the maze, partially guided by the strength of the received beacon signal, as well as being influenced by the movement of fellow agents.
## 4 Experimental Results
### The Reconstruction of CSNGAN-ASPP  
The simulation performance of CSNGAN-ASPP is validated using the three different types of TIs and hyperparameter value ranges mentioned above. In addition to the intuitive visual comparison, the simulation results are qualitatively and quantitatively assessed through facies proportion statistics, multidimensional scaling (MDS) diagram variogram function, and connectivity function. Python 3.10.9 and the deep learning framework Py  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **2D** & **3D** \\ \hline _epoch_ & 40 & 70 \\ _batch_size_ & 32 & 16 \\ \(lr_{G}\) & \(1\times 10^{-4}\) & \(1\times 10^{-5}\) \\ \(lr_{D}\) & \(2\times 10^{-4}\) & \(3\times 10^{-5}\) \\ \(\lambda_{con}\) & 100 & 1000 \\ _Optimizer_ & Adam & Adam \\ Beta 1 & 0.5 & 0.5 \\ Beta 2 & 0.999 & 0.999 \\ Dist & L1-norm & L1-norm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The hyperparameter settings for 2D and 3D networks.  
Torch 1.10.1 were used as the basic compilation environment for the experiments. Training and testing were performed on a workstation equipped with an Intel Comet Lake 3.3 GHz CPU, 24 GB of RAM, and an NVIDIA 3090 Ti GPU.
## 2 Results
### Functional Characterization of GmOCS  
To better characterize _GmOCS_, it was firstly subcloned into pET32a to prepare recombinant proteins. Soluble GmOCS protein was successfully induced by IPTG (Figure S4). Afterward, enzymatic assays were performed with four kinds of reported substrates: GPP, NPP, (_E_,_E_)-FPP, and (_Z_,_Z_)-FPP, respectively. Compared to the prokaryotic proteins from bacteria expressing empty vector, GmOCS catalyzed GPP mainly into (_E_)-\(\beta\)-ocimene (94.27%), (_Z_)-\(\beta\)-ocimene (3.47%), and linalool (2.26%) (Figure 4A, Table S2). However, no obvious products were observed when GmOCS was incubated with NPP, (_E_,_E_)-FPP, or (_Z_,_Z_)-FPP.  
To further validate the functions of _GmOCS_, we constructed a transient expression vector harboring _GmOCS_ driven by _35S_ promoter and transformed it into _A. tunnefaciens_ GV3101 which was sequentially infiltrated into _N. benthamiana_ leaves transiently overexpressing _GmOCS_ mainly released (_E_)-\(\beta\)-ocimene (Figure 4B), validating the result concluded from in vitro enzymatic assays. Herein, GmOCS could tentatively be attributed as a monoterpene synthase or (_E_)-\(\beta\)-ocimene synthase exclusively.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
### Paper Structure  
This paper is organized as follows: Section 2 describes the design of the two types of communicating parties--the ground station and the satellite, including their required operations to obtain FSO communication. Section 3 presents the optimization tracking problem of scheduling a ground station to the related satellites in its site. Section 4 presents results from different perspectives--link-budget analyses, lab experiments and field experiments from our pico-satellite (SATLLA-2B). Finally, Section 5 concludes the FSO network model and discusses deployment and possible future work.
## 5 Results and Discussion
### Comparison and Discussion  
#### 5.3.1 Comparison between Suspenders  
According to the optimization results, the optimized values of five optimization variables are shown in Table 6. The table shows that the proportional coefficients of uniformly distributed load valuing \(x_{4}\) of the optimized models are all less than one, in line with the optimization requirements of decreasing uniformly distributed load. The proportional coefficients of concentrated force valuing \(x_{5}\) are all greater than 3.5, and the \(x_{5}\) of the HMM is close to 4, verifying that the function of the concentrated force should be emphasized when establishing traffic load model of suspenders in order to reflect the actual load effect level more reasonably. The optimization results of the adjustment coefficients of effect \(x_{1}\) and \(x_{2}\) basically reflect a similar situation, which reduces the uniformly distributed load's contribution to the total effect, while increasing the proportion of concentrated force in the total effect. The optimization results of the proportional coefficient of influence line area \(x_{3}\) are all 0.9, and the corresponding loading lengths of the uniformly distributed load of each representative suspender are 224 m for the HMM, 1244 m for the HSL, and 1463 m for the HSS, respectively. These results verify the feasibility of optimizing the loading length of the uniformly distributed load for the load effect calculation of suspenders on the basis of keeping the rationality of the load effect analysis. Although the final optimized values of \(x_{3}\) for the different suspenders are the same, it is actually the result of balancing several optimization objectives. The length of the sensitive effect zone (\(SL\)) does have an influence on the proposed optimized model, and this can be confirmed from the different value ranges of the optimal solutions of optimization objective 2 for the three representative suspenders, as shown in Figure 8. The optimization effect is most obvious for the HMM, which belongs to the Inverted icicle type, verifying the correctness of the geometric feature analysis of the influence line in Section 4.2.2.
# A Method to Reduce Non-Nominal Troposphere Error
## 3 Non-Nominal troposphere Model and Error Bounding Method
When the signal to the GBAS ground facility leaves the weather wall (path 1), it experiences different conditions T\({}_{0}\), P\({}_{0}\), RH\({}_{0}\), then the signal to the user that continues in the weather wall (path 2) and experiences conditions T\({}_{\text{w}}\), P\({}_{\text{w}}\), RH\({}_{\text{w}}\), which may introduce a larger differential residual troposphere error. The calculation process of non-nominal troposphere error is as follows:  
1. Collect the troposphere meteorological parameters (temperature, pressure and relative humidity) of the ground station for one year. 2. Calculate the maximum hourly changes in temperature, pressure and relative humidity measurements and then use these values to establish the weather wall parameters. 3. Calculate the troposphere delay differences between the GBAS ground station and the aircraft using the weather wall model and the Modified Hopfield Model (MHM). 4. The bound of the troposphere delay differences is the final non-nominal troposphere error (3, 5). Figure 8: Weather wall model. Figure 7: Wedge model. Divide (0\({}^{\circ}\), 90\({}^{\circ}\)) into 90 elevation ranges at 1\({}^{\circ}\) intervals. 6. Assume that all satellites in each elevation range have the same maximal bias \(\mu_{\max,i}\) using the absolute bias method [13]. Based on a meteorological data analysis of 18 ground stations in China, the troposphere weather parameters are as follows:  
* Nominal weather conditions: T\({}_{\rm{W}}\) = 38 \({}^{\circ}\)C, P\({}_{\rm{W}}\) = 1024 hPa, and RH\({}_{\rm{W}}\) = 70%. The temperature lapse rate is set to \(-\)6.5 K/km. * Weather wall conditions: T\({}_{0}\) = 25 \({}^{\circ}\)C, P\({}_{0}\) = 984 hPa, and RH\({}_{0}\) = 100%. The temperature lapse rate is set to \(-\)6.5 K/km. Taking the Linzhi airport as an example, when the height between the aircraft and the ground station is 30 m, the non-nominal troposphere error can be obtained, as shown in Figure 9.  
The black line in Figure 9 represents the largest non-nominal troposphere error at different elevation ranges, while the green line represents the fitted non-nominal troposphere error curve, which is a function of satellite elevation. According to the fitting results, the non-nominal troposphere error is:  
\[\mu_{\max,i}=1.31\times\exp(-\frac{ele_{i}}{16.21})+0.15 \tag{1}\]  
where \(ele_{i}\) is the elevation angle of the \(i\)th satellite. From the above analysis, it can be concluded that the non-nominal troposphere error decreases as the satellite elevation angle increases.
## 3 Results and Discussions
The proposed algorithm also has strong flexibility to adapt complicated speech environment for EL device users, because the over-subtraction factor of each frequency band can be adjusted, so that the proposed algorithm is able to fit other different or complex speech environment. This makes it possible to obtain better speech quality via speech enhancement under some rigorous speech environment. As a single channel subtractive-type speech enhanced methods, the proposed algorithm in this paper can be applied into the enhancement of EL speech in a practical electronic situation. For example, an EL speech enhanced system embedded with this algorithm can be developed. With the help of digital signal processing (DSP) technology, the speech enhancement function can be realized with a microprocessor and implanted into an EL-telephone, EL-microphone, or other electronic media. Different enhancement algorithms can be selected through the switch based on different noisy conditions. Along with the development of efficient enhancement methods, the quality of EL speech will be extensively improved for better perception.
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
###### Abstract  
Drilling fluids are critical in oil and gas well drilling, particularly deep shale gas drilling. In recent years, applying nanoparticles as additives in drilling fluids has received widespread attention to address the various challenges associated with deep shale gas drilling. This study focused on the performance of three nanoparticle-enhanced oil-based drilling fluids (OBDFs), carbon nanotubes (CNTs), silicon dioxide (SiO\({}_{2}\)), and aluminum's oxide (Al\({}_{2}\)O\({}_{3}\)) in terms of improving thermal capacity and cooling efficiency. The potential of the nanoparticles to improve the thermal management capability of the drilling fluids was evaluated by measuring specific heat capacity and thermal conductivity. The results showed that CNTs exhibited the most significant improvement, with thermal conductivity increasing by 7.97% and specific heat capacity by 19.38%. The rheological properties and high temperature and high pressure (ITHP) filtration performance of the nanoparticle-enhanced OBDFs were evaluated, demonstrating that CNTs and SiO\({}_{2}\) significantly improved the rheological stability of the drilling fluids and reduced the filtration loss under high temperature conditions. When 3% CNTs were added, the HTHP filtration loss was reduced by 42.86%, exhibiting excellent sealing properties. The cooling effect of different nanoparticles was evaluated by calculating their effects on the bottomhole temperature. The results showed that CNTs performed the best in lowering the bottomhole temperature by 4.53 \({}^{\circ}\)C, followed by SiO\({}_{2}\) by 1.47 \({}^{\circ}\)C and Al\({}_{2}\)O\({}_{3}\) by only 0.88 \({}^{\circ}\)C. The results showed that CNTs were the most effective in lowering the bottomhole temperature. These results indicated that nanoparticles as additives to drilling fluids could significantly increase the thermal capacity and cooling efficiency of OBDFs, making them effective additives for high-temperature deep shale gas drilling applications.  
oil-based drilling fluids; nanoparticles; specific heat capacity; the rheology of drilling fluids; wellbore cooling +
Footnote †: journal: applied sciences
## 3 Numerical Experiments Setup
### Benchmark Problems
#### 3.2.3 Shifted-Rotated Rastrigin Function  
To address real-world optimization problems, where the design space of the objective function is usually multi-modal, the Rastrigin function is selected as benchmark. The variable range is defined as \(\mathbf{x}\in[-0.1,0.2]^{D}\), and the function is shifted to change the position of the minimum and rotated to change the properties of the function itself within the variable space:  
\[f_{1}(\mathbf{z})=\sum_{i=1}^{D}\Big{(}z_{i}^{2}+1-\cos(10\pi z_{i})\Big{)} \tag{18}\]  
with  
\[\mathbf{z}=R(\theta)(\mathbf{x}-\mathbf{x}^{\star})\ \ \ \text{with}\ \ \ R(\theta)=\begin{bmatrix}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{bmatrix} \tag{19}\]  
where \(R\) is the rotation matrix in two dimensions which can be extended to \(D\) dimensions by using the Aguilera-Perez algorithm [42] and the rotation angle is set to \(\theta=0.2\).  
The fidelity levels can be defined following the work of [PERSON] and [PERSON] [43] as  
\[f_{i}(\mathbf{z},\phi_{i})=f_{1}(\mathbf{z})+e_{r}(\mathbf{z},\phi_{i})\ \ \ \text{for}\ \ \ i=1,2,3 \tag{20}\]  
with \(\phi_{1}=10,\!000\) (high-fidelity), \(\phi_{2}=5000\) (medium-fidelity), and \(\phi_{3}=2500\) (low-fidelity). The resolution error is given by  
\[e_{r}(\mathbf{z},\phi)=\sum_{i=1}^{D}a(\phi)\cos^{2}(w(\phi)z_{i}+b(\phi)+\pi) \tag{21}\]  
with \(a(\phi)=\Theta(\phi)\), \(w(\phi)=10\pi\Theta(\phi)\), \(b(\phi)=0.5\pi\Theta(\phi)\), and \(\Theta(\phi)=1-0.0001\phi\). These fidelity levels are displayed in Figure 4.  
Figure 3: Rosenbrock function (from left to right, high-, medium-, and low-fidelity).
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 6 Simulation Results
### \"Less Cost\" vs. \"Optimized Tree\" Approaches  
Using the simulation values presented in Table 1, the \"Less cost\" and \"optimized tree\" approaches are implemented, as explained in Section 4. While the \"less cost\" approach focuses on keeping minimum change in the network structure, the \"optimized tree\" approach allows each node to check in its neighbors for a better CQI. Simulations are conducted to estimate how costly keeping an optimized topology could be in terms of link substitutions. Figure 16 illustrates the gap between the two approaches, which becomes more considerable once the number of users reaches 20. Starting at 30 users, the gap begins to stabilize around 2 to 3 points on the CQI scale. The CQI value represents the average value of all established direct communication links in the network. To the same graph, the total incremental number of updates (divided by 2000 for better visibility) as well as the periodic number of updates per second were added. We notice that their value increases exponentially while the number of added users is in a linear progression. With 70 UEs, it takes up to 25 operations per second to keep the network optimized. During 2 h of simulation, more than 32,000 substitution operations were performed.  
Figure 15: Progression of the three group connectivity levels according to the number of UEs and the corresponding time duration in percentage.