esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 5 Conclusions  
The fire resistance of prestressed concrete T-beams was investigated by means of the ABAQUS finite element simulation method, and a wide range of influencing parameters were carried out. Based on the results of the finite element model calculations, an equation for calculating the fire resistance of prestressed concrete T-beams is presented. The following conclusions were obtained.  
1. The fire resistance of prestressed concrete T-beams under different fire modes shows a large variability. Under the action of high temperature, the load will make the damage of the prestressed concrete T-beam aggravated, leading to a faster decrease of the fire resistance. The fire resistance mode should be considered in the fire resistance design.
2. The ratio of prestressing bar shows an approximately linear increase in the fire resistance of prestressed concrete T-beam, and an appropriate increase in the ratio of  
Figure 15: Numerical analysis of the fire resistance calculated value and fire resistance formula simulated value comparison.  
prestressing bar can improve the fire resistance of the beam. In practical applications, an appropriate increase in the ratio of prestressing bar can be considered under the premise that the code allows and the economy is reasonable.
3. Concrete protective layer thickness, and concrete strength can significantly improve the fire resistance of prestressed concrete T-beams. For the fire resistance design of practical projects, the addition of these two parameters is effective in obtaining higher fire resistance limits.
4. For the fire mode H1, a formula for calculating the fire resistance of prestressed concrete T-beam considering several influencing parameters of load ratio, the ratio of the prestressing bar, flange plate thickness, concrete protective layer thickness, and concrete strength is proposed. The predicted results of the Equation are in good agreement with the numerical simulation results.  
Conceptualization, Z.W. and M.C.; methodology, Z.W.; software, M.C.; validation, Z.W., M.C. and Y.L.; formal analysis, M.C.; investigation, Z.W.; resources, Z.W.; data curation, M.C.; writing--original draft preparation, M.C.; writing--review and editing, Z.W.; visualization, Y.L.; supervision, Z.W.; project administration, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Innovative Projects of Civil Engineering (Changsha University of Science & Technology) 13 ZDXK08 and Open Fund of Hunan Province University Key Laboratory of Bridge Engineering (Changsha University of Science & Technology) 13 KA05.  
Not applicable.  
The relevant data are all included in the paper.  
The authors declare no conflict of interest.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-US Simulation  
#### 2.2.1 Elastodynamic Equations  
Elastodynamic equations (Equations (5) and (6)) are a group of partial differential equations to model the wave propagation:  
\[\rho(x)\frac{\partial v_{i}}{\partial t}(x,t)=\sum_{j=1}^{d}\frac{\partial T_{ ij}}{\partial x_{j}}(x,t)+f_{i}(x,t) \tag{5}\]  
Figure 4: For a meander-line-coil, the distribution of the vector potential **A** at \(z=0\). Reproduced with permission from [15], IEEE, 2016.  
Figure 5: For a meander-line-coil, the distribution of the Lorentz force density **F** at \(z=0\). Reproduced with permission from [15], IEEE, 2016.  
\[\frac{\partial\mathbf{T_{i}}}{\partial\mathbf{t}}=\sum_{j=1}^{d}\sum_{i=1}^{d}c_{ijkl}(x )\frac{\partial v_{k}}{\partial x_{l}}+\theta_{ij}(x,t) \tag{6}\]  
where \(\rho\) and \(c_{ijkl}\) are the density and the fourth stiffness tensor of the material, and \(\mathbf{f_{i}}\) and \(\theta_{ij}\) are the force source and strain tensor rate source, respectively.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 2 Materials and Methods
### Differential Interferometry  
The four co-registered SLCs, one monostatic and three bistatic ones, form six interferometric pairs with different spatial baselines with perpendicular baseline components between about 60 m and 500 m (see Table 2). All four acquisitions are quasi-simultaneous, so the interferometric time difference is 0 for all six pairs.  
In the calculation of the orbital and topographic phases, and also later on in the interpretation of the differential interferometric phases, the bistatic nature of the pairs is considered. The two-way slant range of the pair HT1-A-HT1-B, for example, corresponds  
\begin{table}
\begin{tabular}{c c} \hline
**Interferometric Pair** & **Perpendicular Baseline Component [m]** \\ \hline HT1-A–HT1-B & 275 m \\ HT1-A–HT1-C & \(-230\) m \\ HT1-A–HT1-D & \(-64\) m \\ HT1-B–HT1-C & \(-506\) m \\ HT1-B–HT1-D & \(-339\) m \\ HT1-C–HT1-D & \(166\) m \\ \hline \end{tabular}
\end{table}
Table 2: Perpendicular component of interferometric baseline of investigated HT1 multi-static acquisition.  
to the sum of the one-way slant range of HT1-A plus the one-way slant range of HT1-B. The resulting differential interferograms have high coherence. They all show small overall phase trends that may be related to the exact geometry of the constellation, orbit movements, the geometric and phase models used in the bistatic SAR data focusing and the geometric model used in the simulation of the topographic and orbital phases. Forests and water areas were masked in the estimation of this overall low-frequency phase term. The forest areas were not considered, as the phase clearly deviates from the low-frequency residual orbital phase. Over the water, the phase is noisy and therefore not reliable. The remaining phase was filtered, unwrapped and spatially interpolated. This low-frequency residual orbital phase was then subtracted from the differential interferogram to obtain the \"detrended\" differential interferogram. For clarity, the intermediate results of this detrending process are shown for one of the pairs in Figure 2. The six detrended differential interferograms are shown in Figure 3.  
Figure 3: Detrended differential interferograms after subtracting the overall low-frequency phase. One color cycle corresponds to one phase cycle.  
Figure 2: Differential interferogram detrending steps. From left to right the images show the initial bistatic differential interferogram (including a substantial low-frequency residual orbital phase); the masked differential interferogram; the filtered, unwrapped and interpolated masked interferogram (corresponding to our estimate of the low-frequency residual orbital phase); and the detrended differential interferogram.
## 2 Material and Methods
### Semirigid Connection Frame Theory with Lateral Displacement
#### 2.2.1 Theoretical Calculated Length Factor for Frames with Laterally Shifted Semirigid Connections  
[PERSON] [28] derived the Formula (1) for the calculated length factor of the semirigid frame column using the three-story frame model with lateral shift, and the three-story frame model with lateral shift is shown in Figure 4.  
Figure 4: Three-layer frame model with lateral displacement.  
Figure 3: Bending moment–rotation angle curve of nodes under different torsional moments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Number** & **Moment of Twisting (N-m)** & **Fitting Formula** & **The Initial Stiffness Value of the** **Joint (kN-m/Rad)** \\ \hline KJ1-1 & 20 & M \(=-28.44\theta^{2}+10.14\theta+0.10\) & 10.14 \\ KJ1-2 & 30 & M \(=-72.09\theta^{2}+16.61\theta+0.06\) & 16.61 \\ KJ1-3 & 40 & M \(=-164.08\theta^{2}+24.35\theta+0.09\) & 24.35 \\ KJ1-4 & 50 & M \(=-264.90\theta^{2}+31.67\theta+0.06\) & 31.67 \\ KJ1-5 & 60 & M \(=-226.11\theta^{2}+42.37\theta+0.02\) & 42.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quadratic function fitting right angle fastener connection moment–rotation function formula.  
\[\left[36K_{1}^{\prime}K_{2}^{\prime}-\left(\frac{\pi}{\mu}\right)^{2}\right]\!\sin \!\frac{\pi}{\mu}+6\!\left(K_{1}^{\prime}+K_{2}^{\prime}\right)\frac{\pi}{\mu} \!\!\cdot\!\cos\!\frac{\pi}{\mu}=0 \tag{1}\]  
\[K_{1}^{\prime}=\frac{\sum_{A}\alpha_{u}\frac{EI_{b}}{L_{b}}}{\sum_{A}\frac{EI_{ c}}{L_{c}}} \tag{2}\]  
\[K_{2}^{\prime}=\frac{\sum_{B}\alpha_{u}\frac{EI_{b}}{L_{b}}}{\sum_{B}\frac{EI_{ c}}{L_{c}}} \tag{3}\]  
In the formula:  
\(K_{1}^{\prime}\)--Upper end constraint coefficient of frame column with lateral displacement considering semirigid joints  
\(K_{2}^{\prime}\)--Constraint coefficient of lower end of frame column with lateral displacement considering semirigid joints  
\(EI_{b}\)--Bending stiffness of beam section  
\(L_{b}\)--Beam length  
\(EI_{c}\)--Bending stiffness of column section  
\(L_{c}\)--Column length  
\(\alpha_{u}\)--Stiffness correction coefficient of frame beam with lateral displacement  
Through the Formula (1), it can be seen that the effective length coefficient \(\mu\) of the frame column is related to the constraint coefficients \(K_{1}^{\prime}\) and \(K_{2}^{\prime}\), and the constraint coefficient is obtained by modifying the stiffness at both ends of the beam. Therefore, the accuracy of the beam end stiffness correction is the key to considering the semirigid characteristics of the joint. For the convenience of calculation, the effective length coefficient \(\mu\) Formula (4) of the lateral frame column in China's 'Steel Structure Design Standard' (GB50017-2017) [29] can be used to calculate:  
\[\mu=\sqrt{\frac{7.5K_{1}^{\prime}K_{2}^{\prime}+4\left(K_{1}^{\prime}+K_{2}^{ \prime}\right)+1.52}{7.5K_{1}^{\prime}K_{2}^{\prime}+K_{1}^{\prime}+K_{2}^{ \prime}}} \tag{4}\]
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 5 Mechanisms of Persister Cell Formation
### Hunker Theory of Persistence  
For antibiotics to reach their target, they must first penetrate the cell wall and then bind to the target. Cells that slow down their essential processes (such as growth, metabolism, or antibiotic activation) will be killed more slowly, leading to a state of persistence. This \"hunker down\" theory of persistence aligns with the finding that persisters are not necessarily slow-growing, as seen in the specialized class of persisters ([PERSON], 2014). According to [PERSON] et al. (2014), not all slow-growing cells are persisters, and not all persisters are slow-growing. Furthermore, while low growth may predispose a cell to enter a state of persistence, it is neither sufficient nor a necessary condition.
## 4 Detection Performance and Discussion
### Interference Suppression  
In this section, the MHOC method for interference suppression is illustrated using the data for a human subject 600 cm from the antenna. Figure 18a shows the results after suppressing the harmonics using the MHOC method, and the corresponding area of interest in the range-frequency matrix is given in Figure 18b. The SNR obtained using (32) with the data for a human subject 600 cm from the antenna versus the number of times the frequency accumulator is employed is shown in Table 3. These results indicate that the SNR is significantly improved when the frequency accumulator is employed four times compared with only once or twice, and there is little improvement when it is used six times. Thus, the frequency accumulation method is employed four times in this paper.  
Figure 18 indicates that there still exist harmonics in the respiration frequency band after the frequency accumulator is employed. The results after suppressing the harmonics using the proposed method are shown in Figure 9d, and the corresponding normalized amplitude is shown in Figure 10. The normalized amplitude-frequency using the data with a distance between the antenna and human subject of 9 m and 11 m is shown in Figure 19. These results illustrate the effectiveness of the proposed method in interference and harmonic suppression. The respiration frequency can easily be determined to be approximately 0.3 Hz.  
Figure 17: Results after performing the DWFT with indoor data, (**a**) range estimation at 700 cm, (**b**) range estimation at 1000 cm, (**c**) frequency estimation at 700 cm, and (**d**) frequency estimation at 1000 cm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **1 FA** & **2 FA** & **4 FA** & **6 FA** \\ \hline SNR (dB) & 0.51 & 1.27 & 6.14 & 6.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SNR with the frequency accumulation method.
# Auto-Encoder Learning-Based UAV Communications for Livestock Management
###### Abstract  
The advancement in computing and telecommunication has broadened the applications of drones beyond military surveillance to other fields, such as agriculture. Livestock farming using unmanned aerial vehicle (UAV) systems requires surveillance and monitoring of animals on relatively large farmland. A reliable communication system between UAVs and the ground control station (GCS) is necessary to achieve this. This paper describes learning-based communication strategies and techniques that enable interaction and data exchange between UAVs and a GCS. We propose a deep auto-encoder UAV design framework for end-to-end communications. Simulation results show that the auto-encoder learns joint transmitter (UAV) and receiver (GCS) mapping functions for various communication strategies, such as QPSK, 8 PSK, 16 PSK and 16 QAM, without prior knowledge.  
unmanned aerial vehicle; convolutional auto-encoder; livestock farming; deep neural networks +
Footnote †: journal: 25 September 2022  
The UAVs are controlled either remotely or manually by a pilot at a ground station, guided using a pre-programmed flight procedure. Wireless communication is one of the critical technologies for UAV wireless communication and is classified into a command and control link and a data link [12]. The command and control link provides essential information about the environment, operating conditions and control instructions for a UAV's safe operation. Therefore, it requires high reliability and low latency. Compared to the command and control link, the data link often maintains the target-related information and thus supports higher data rates
# Bias and Variance Analysis of Contemporary Symbolic Regression Methods
## 4 Results
### Relation Between Parsimony, Bias, and Variance  
While there is a clear relationship between bias, variance and test error of algorithms, the relation between model size as a notion of the inverse of parsimony, bias, and variance is not clear. Effects like bloat, over-parameterization [7] as well as different sets of used mathematical functions distort clear connections between those properties. The relation between the median values of model size and bias/variance over all problems is outlined in Figures 7 and 8. FFX is excluded from both figures as its huge model size values would distort the axis scale.  
Figure 7 shows that algorithms with larger models tend to lower bias, which is expected. An outlier is again AIFeynman with low bias only at low noise levels, which results in an overall low median bias as shown in Table 4. In contrary to the clear connections in bias, Figure 8 does not show a clear connection between model size and variance over all algorithms. The relation between variance and size is distorted by the different achieved accuracy levels of each algorithm. Operon provides both the largest models and the smallest variance. Given that Operon is one of the most accurate algorithms, its small variance is expected, as the error can be decomposed to bias and variance. GP-
# Enhanced Nondestructive Testing Using Pulsed Eddy Current and Electromagnetic Ultrasonic Techniques
## 4 Composite Detection Probe Optimization
Figure 8 illustrates the voltage signal in the detection coil, generated by the ultrasonic wave reflected from the defect and the plate's bottom. Notably, the ultrasonic echo signal is weak. To strengthen the echo signal and increase the energy conversion efficiency of the composite detection probe, this study simulated and analyzed the effects of the coil distance, coil turns, coil lift distance, permanent magnet magnetic field strength, and excitation frequency on the probe's energy conversion efficiency. The results are presented in Figure 9.  
Figure 8: Ultrasonic detection signal. Figure 9 demonstrates that reducing the coil distance and lift distance enhances the energy conversion efficiency of the composite detection probe. Additionally, increasing the external magnetic field strength increases the energy conversion efficiency. However, within a certain range, as the number of coil turns increases, the energy conversion efficiency decreases. Furthermore, the energy conversion efficiency varies with different excitation signal frequencies; it generally decreases as the excitation frequency increases. Thus, in optimizing the probe, it is advisable to select the smallest possible coils and coil thickness to minimize the lift distance. Moreover, maximizing the external magnetic field strength and selecting an appropriate number of turns is recommended. This study's simulation results led to the final selection of a PCB double-spiral coil with a plate thickness of 0.6 mm, 18 turns, a coil spacing of about 0.1 mm, and a line width of about 0.2 mm, as shown in Figure 10.  
Figure 10: PCB double-helix coil. Figure 9: Effects of various factors on transducer efficiency of the composite probe: (**a**) effect of coil spacing; (**b**) effect of coil turns; (**c**) effect of coil lift distance; (**d**) effect of external magnetic field strength; (**e**) effect of excitation frequency. To enhance the external magnetic field intensity, an N52-grade NdFeB permanent magnet measuring 30 mm \(\times\) 30 mm \(\times\) 30 mm was selected as the magnetic field source. The remanent magnetic intensity of this magnet ranges from 1.38 T to 1.48 T.
## 3 Results and Discussion
### Challenges in Using Sentinel-2 Data for Crop Biomass Mapping
Sentinel-1 C-band SAR may be used for soil moisture mapping but only when the canopy is sparse [81]. The SMAP L-band radiometer has superior sensitivity to surface soil moisture even at moderate canopy water content, but its spatial resolution is low (40 km) [82]. Although some studies have integrated SAR data at high resolution and with SMAP radiometer data to produce soil moisture at a 1 km resolution [83], it is still impractical to do this at the 20 m resolution. The last challenge for crop biomass mapping using models is the limitation of computation resources. For example, it takes half a day to simulate crop GPP at a 20 m resolution using a 54-core computer (2.5 Ghz) for our study area. This means that, for these process-based deterministic models, the computation of GPP mapping for a county at 20 m resolution is comparable to the GPP simulation of a global ecosystem at the 0.1\({}^{\circ}\) resolution. If a data assimilation approach, such as the ensemble Kalman filter (EnKF) is used to assimilate any crop biophysical parameter into a crop growth model, the computation will be increased by an order of 1 to 2, suggesting that it is almost impossible to make use of EnKF for data assimilation at the current cost of computation. These challenges, although not limited to Sentinel-2 data applications, suggest that although BEPS (hourly version) has an advanced model structure to simulate canopy-scale photosynthesis [52], this requires significant computation effort. In contrast, some crop growth models are executed at a daily step and/or LUE-based photosynthesis model [15; 20; 21], but with the strength of crop traits modeling and handling of soil nutrition and water management. However, such an approach necessitates availability of these crop traits at large scales. Operational crop biomass (and yield) mapping may require a compromise between large scale and high spatial, and simplicity of the model structure. Mapping biomass at field scales (segmentation from crop identification map) might be a good alternative approach to address current big-data challenges. Similar to this work, two recent studies have used Sentinel-2-derived LAI for biomass and yield mapping. [PERSON] et al. [84] reported that the R\({}^{2}\) between pasture biomass and LAI ranges from 0.16-0.73 to 0.22-0.76, for the NDVI-derived LAI and radiative transfer model-based LAI, respectively. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [30] demonstrated that the R\({}^{2}\) values between LAI and yield are 0.68, 0.62, 0.8 and 0.48 for cotton, maize, millet and sorghum, respectively. Here, we show that the R\({}^{2}\) values between GPP and AGB range from 0.65 to 0.96 for six annual crops which dominate Manitoba's agro-ecosystem. Despite these aforementioned challenges, our study demonstrates the advantage of using LAI time-series and integrating both biotic and abiotic factors for biomass mapping at very high spatial resolutions.
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
###### Abstract  
A significant research gap exists in investigating large-scale microalgae cultures exposed to outdoor conditions, with the addition of phytohormones using non-sterile growth media. Implementing these conditions is crucial for verifying the industrial viability of this strategy. This study aimed to evaluate the effect of indole-3-acetic acid (IAA) supplementation on _Spirulina_ sp. LEB 18 cultures conducted indoors and outdoors in raceway bioreactors. The outdoor experiments were performed under uncontrolled environmental conditions. The indoor cultures were maintained within a thermostat-controlled chamber at a consistent temperature and lighting intensity. The outdoor experiments supplemented with IAA achieved a biomass concentration of 5.43 g L\({}^{-1}\) and productivity of 173.9 mg L\({}^{-1}\) d\({}^{-1}\). These values increased 122.5% and 130.9% in biomass concentration and productivity, respectively, compared to the indoor experiments with the same supplementation. Moreover, roughly half of the biomass generated from outdoor cultivation with IAA consisted of carbohydrates (45%). Compared to indoor cultivation, this approach reduced production costs for biomass (55%) and lowered production costs for carbohydrates, proteins, and lipids by 86%, 44%, and 50%, respectively. The successful application of phytohormones in microalgae cultures, particularly under larger scale, nonsterile, and outdoor conditions, represents a significant advancement toward industrial implementation.  
Arthospira; auxin; carbohydrate; cyanobacteria; microalgae; phytohormone
## 1 Introduction
To optimize network coverage, the competitiveness of the metaheuristic algorithm [2] lies in its search accuracy, stability, and convergence speed. Existing coverage algorithms [3, 4, 5, 6] have a larger number of coverage holes and also higher redundancy in the monitoring area. We propose an algorithm that provides a more uniform distribution of sensor nodes with minimal redundancy and enhances network area coverage and connectivity.