esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Figure 12 shows a numerically calculated dependence of the output power and energy level populations on time for 5 pulses at \(T_{tr}\) = 200 ns, with the facet reflectivity for signal wave at \(z\) = \(L\) switched between 0.04 and 0.2, L = 2 m, and a pump power of 8 W. These results were obtained by repeating the waveform from Figure 7 five times, with \(T_{p}\) = 5 ms. The results from Figure 12a show a stable train of five pulses, calculated by the numerical algorithm. Figure 5b shows the same result as Figure 5a, but with the vertical axis scaled logarithmically. Such scaling is needed to show the effect of photon population depletion, which follows the Q-switched pulse. This results from the fact that the level 2 population is drained by the Q-switched pulse. This in turn results in a very low value of signal gain, and a subsequent reduction  
Figure 10: Numerically calculated dependence of the output power on time for a 2 (m) long fiber with the facet reflectivity for signal wave at \(z\) = \(L\) switched between 0.04 and 0.2 and pump power of 8 (W): (**a**) For \(T_{tr}\) = 100 (ns); (**b**) For \(T_{tr}\) = 50 (ns); (**c**) For \(T_{tr}\) = 400 (ns); (**d**) For \(T_{tr}\) = 600 (ns). of the photon population within the laser cavity. As shown in Figure 12b,c the recovery process and a return to a steady state equilibrium is slow. In Figure 13, the results are shown of an attempt to recalculate the results shown in Figure 12 without including the effect of the spontaneous emission, i.e., by setting \(\gamma_{sp}\) in Equation (1) to zero. Only  
Figure 11: Numerically calculated dependence of the output power on time at \(T_{tr}=200\) (ns) with the facet reflectivity for signal wave at \(z=L\) switched between 0.04 and 0.2 and pump power of 8 (W): (**a**) For \(L=3\) (m); (**b**) For \(L=1.5\) (m). Figure 12: Numerically calculated dependence of the output power and energy level populations on time for 5 pulses at \(T_{tr}=200\) (ns) with the facet reflectivity for signal wave at \(z=L\) switched between 0.04 and 0.2, \(L=2\) (m) and pump power of 8 (W): (**a**) Output power on linear scale; (**b**) Output power with vertical logarithmic scale; (**c**) energy level populations. the first pulse could be reproduced accurately. The model that does not include spontaneous emission could not correctly reproduce the process of photon population build up and energy level population recovery to the steady state equilibrium. This demonstrates that the spontaneous emission process plays an essential role in these simulations. This is because after the pulse trailing edge, the photon population drops to a very low level. At low photon densities, the spontaneous emission contribution to the growth of the photon population within the cavity dominates, and is more significant than the photon population growth resulting from the optical gain. Hence, the calculated time dependence for signal power and of the level populations differ significantly from those presented in Figure 12.
# Research Progress on Carrier-Free Phase-Retrieval Receivers
## 3 Modified CF-PR Receiver Scheme  
### Improve the Performance of the CF-PR with a Reduced Number of Iterations and Better Steady Accuracy  
This section introduces the research progress on preventing the CF-PR algorithm from being strapped into the local minima, with a reduced number of iterations and better steady PR accuracy, by adding more constraints on the CF-PR algorithm.
## 3 Results and Discussion
### Analysis of Sorption Kinetics  
#### 3.5.1 Radushkevich Kinetic Model  
The Radushkevich equation in the non-linear type is:  
\[q_{t}=q_{e}*(1-e^{-k_{R}*t}) \tag{4}\]  
where \(q_{e}\) is the equilibrium sorption capacity (from the experimental data), in g/g; \(q_{t}\) is an amount adsorbed at time \(t\), in g/g; \(k_{R}\) is a Radushkevich rate constant, in min\({}^{-1}\); and \(t\) is the time, in min.  
Linearization of the equation leads to the form:  
\[\ln\biggl{(}1-\frac{q_{t}}{q_{e}}\biggr{)}=-k_{R}*t \tag{5}\]  
Figure 11: Sorption capacity of the MIPs in relation to silver.  
Figure 10: Sorption degree of the MIPs in relation to silver.  
Using the sorption data, the curve of \(\ln(1-\mathrm{q_{t}}/\mathrm{q_{e}})=\mathrm{f(t)}\) is plotted (Figures 12 and 13), from which the following parameter is obtained:  
\[\mathrm{k_{R}}=-\mathrm{Slope}(\mathrm{S}) \tag{6}\]  
\[\mathrm{k_{R}}(\mathrm{MIP}(\mathrm{DEGDMA}))=-(-0.0179)=0.0179\,\mathrm{min}^{-1}\]  
\[\mathrm{k_{R}}(\mathrm{MIP}(\mathrm{DVB}))=-(-0.0113)=0.0113\,\mathrm{min}^{-1}\]  
In the Radushkevich model, a higher \(\mathrm{k_{R}}\) indicates a faster sorption rate toward equilibrium. This suggests that MIP(DEGDMA) reaches equilibrium more quickly than MIP(DVB) under the same conditions.  
The Radushkevich model is derived based on assumptions about the energy distribution in heterogeneous adsorption systems. A higher \(\mathrm{k_{R}}\) implies that the adsorption process is more favorable or that the sorbent has a higher affinity for the adsorbate, leading to  
Figure 12: Radushkevich kinetic model’s curve for MIP(DEGDMA).  
Figure 13: Radushkevich kinetic model’s curve for MIP(DVB).  
quicker saturation. MIP(DEGDMA) is likely more efficient in terms of kinetics, as it adsorbs the solute faster than MIP(DVB).  
The difference in \(\mathrm{k_{R}}\) values could arise from variations in:  
* The cavity structure and surface area: MIP(DEGDMA) may have a more accessible cavity network or a higher surface area, facilitating faster diffusion and adsorption.
* The chemical affinity: MIP(DEGDMA) may have stronger interactions (e.g., electrostatic, van der Waals, or chemical bonding) with the adsorbate.
* The particle size: Smaller particles (if applicable) could lead to faster kinetics due to shorter diffusion paths.  
Thus, according to the Radushkevich model, MIP(DEGDMA) saturates faster during silver sorption in comparison with MIP(DVB), but the latter sorbs a higher concentration of silver. These results correlate with the results of sorption experiments.
## 4 Experiments
### Chime-4  
In this subsection, we evaluate the proposed LA CTC/Attention and SA CTC/Attention for multichannel end-to-end speech recognition task on the CHIME-4 dataset. The multichannel ASR algorithm allows the mask-based neural beamformer and ASR components to be jointly optimized to improve the end-to-end ASR objective and leads to an end-to-end framework that works well in the presence of strong background noise. The overall Multichannel Hybrid Attention CTC/Attention ASR framework is shown in Figure 3. A unified architecture [24] was proposed to jointly optimize the neural beamformer and the ASR components. The neural beamformer consists of two parts: ideal ratio mask (IRM) estimator and the Minimum Variance Distortionless Response (MVDR) beamformer. Joint training means that the gradients derived from the ASR loss will back-propagate through all the way from the acoustic model to the complex-valued beamforming and the mask estimation network. Therefore, the improvement of acoustic modeling ability will strengthen the capability of a neural beamforming network. In this paper, we reproduce the multichannel ASR architecture as  
Figure 2: The train and valid losses of the baseline, LA CTC/Attention, and SA CTC/Attention architectures for SWBD. described in ESPnet firstly. Then, we apply the time-restricted LA CTC/Attention and time-restricted SA CTC/Attention separately to improve the back-end acoustic modeling ability. According to the experiments and analyses before, we set the _attentionwindow_, self-attention dim, and self-attention head quantity just as the corresponding parameters in Section 4.2. The detailed results are shown in Table 4.  
Firstly, we show the decoding results without RNNLM. Compared with the baseline, LA CTC/Attention generally performs worse than the baseline system. However, the SA CTC/Attention achieves 2.5% and 3.4% relative CER reduction for et05 simu and et05 real. Moreover, it also achieves 3.7% and 2.7% relative WER reduction for et05 simu and et05 real. Meanwhile, the CER and WER of SA CTC/Attention in development datasets generally remain steady. In order to improve the overall performances, RNNLM is employed during decoding. With RNNLM, the LA CTC/Attention acquires 3.0% and 4.1% relative WER reduction for et05 simu and et05 real. However, it performs poorly in the simulate development dataset. For SA CTC/Attention, however, it acquires consistent WER and CER degradation for all evaluation and development sets. In particular, it achieves 11.2% and 7.7% relative WER reduction for et05 simu and et05 real. At the same time, it also achieves 10.1% and 9.8% CER reduction for et05 simu and et05 real. For the development sets, although the WER keeps constant for dt05 simu, it obtains 8.9% relative CER reduction and 11.0% relative WER decrease for dt05 real. As the neural beamformer and ASR components are jointly optimized, enhancing the acoustic modeling ability contributes to the capability of neural beamforming network. To evaluate the modeling ability of the neural beamforming, we compute the objective measure PESQ (Perceptual
## 4 Methodology
### Data Collection  
This quantitative study investigates the perceptions of learners and faculty regarding the help provided by the feedback process, which aligns with and integrates the Matrix of Feedback for Learning [15] in the asynchronous online component of a blended course, and the relationship between student perceptions and scores. As shown in Figure 3, after securing approval from the Institutional Review Board (IRB), the study proceeded with data collection through structured surveys (researcher-constructed) administered to both students and faculty. Additionally, learner responses to Q. 21 from the anonymous institutional course evaluation (institutionally constructed), which pertains the clarity and helpfulness of feedback provided by the instructor in the course, were used to corroborate the results of the learner survey. Learner scores from the online components of courses, accounting for 10% of the final grade, were also examined to quantitatively assess the relationship between student perceptions and scores. The analysis of the collected data informs a discussion on the feedback process and its wider implications. Figure 3: Flowchart depicting the research design. Created by the authors. A 36-item survey was administered, consisting of 3 demographic questions and 33 self-constructed and validated 5-point Likert-scale questions (1 = Strongly Agree, 2 = Agree, 3 = Neither Agree nor Disagree, 4 = Disagree, 5 = Strongly Disagree) [57]. It encompasses feedback process elements from [PERSON] et al. 's [12]) study and results from [PERSON]'s [36] content analysis on assessment and feedback design within EAP-blended courses (see Table 1). In January 2022, the survey was piloted among 51 students from the fall 2021 EAP 1002 course, with 37 (73%) responding. Additionally, 10 students provided feedback via informal interviews on different aspects of the survey. Following the student interviews, multiple-answer questions asking about the types of feedback that helped most were removed due to redundancy. Items overlapped with other survey items. The yes\(-\)no question, \"_Before taking the EAP 1002 blended course, I had experience with receiving, giving, and using feedback for learning_\", was also removed. In fact, pilot interviews showed that the EAP 1002 novice learners, unfamiliar with \"feedback for learning\" from prior schooling, interpreted it inconsistently, leading to unreliable responses. [PERSON]'s \(\alpha\) measure of internal consistency and Confirmatory Factor Analysis were conducted to investigate the survey validity.
## 4 Verification Experiments  
### Experimental Setup  
The feasibility and measurement accuracy of the proposed positioning method was validated by arranging a single laser transmitter station in an outdoor space with dimensions of about 30 m \(\times\) 30 m. The structural parameters (internal parameters) of the transmitter station used in the experiments are listed in Table 1. The multipoint receiver array employed during verification testing is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Plane NO.** & \multicolumn{4}{c}{**1**} & \multicolumn{4}{c}{**2**} \\ \hline Parameter & \(a_{1}\) & \(b_{1}\) & \(c_{1}\) & \(d_{1}\) & \(a_{2}\) & \(b_{2}\) & \(c_{3}\) & \(d_{2}\) \\ \hline Value & 0 & \(-0.432757\) & \(0.901510\) & 0 & \(-0.757457\) & \(0.00227\) & \(0.652880\) & \(0.166872\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the laser planes of the transmitter station employed for verification testing, where \(a_{j}\), \(b_{j}\), \(c_{j}\), and \(d_{j}\) are the parameters of Equation (1).  
The receiver positions (i.e., control points) and probe position of the receiver array must be calibrated in advance. Considering that a millimeter precision level is sufficient in engineering applications, the receiver array can be calibrated using the positional parameters obtained from the wMPS. However, the probe coordinates cannot be measured by directly installing the receiver. Therefore, the distance constraint method was used to measure the probe coordinates. This can be conducted with a high degree of precision by keeping the probe in contact with a fixed target point and changing the posture of the receiver array multiple times to obtain the coordinates of the control points in different postures. Accordingly, the multiple different coordinates can be used in the least square method under over-determined conditions to obtain the spatial 3D coordinates of the probe and control points based on the fact that the distances between the control points and the probe are constant. The obtained positions of the control points in the _o-xyz_ frame are listed in Table 2 based on the probe point being defined as the origin, where the direction from the probe to P\({}_{0}\) is defined as the \(z\) axis, a line perpendicular to the \(z\) axis and passing through P\({}_{1}\) is defined as the \(x\) axis, and the direction of the \(y\) axis is defined according to the right-hand rule.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Baseline & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Single-Task Restoration & & \(\surd\) & & & \\ Sub-Network & & \(\surd\) & & & \\ Multitask Restoration & & & & & \\ Sub-Network & & & & & \\ Image Restoration Feature & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Difference Feature & & & & & \(\surd\) \\ Multi-Scale Feature Fusion & & & & & \(\surd\) \\ Module & & & & & \\ \hline LIVE & 0.950 & 0.958 & 0.961 & 0.965 & **0.969** \\ CSIQ & 0.894 & 0.907 & 0.914 & 0.920 & **0.928** \\ LIVEC & 0.820 & 0.833 & 0.842 & 0.847 & **0.852** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The SROCC results of the ablation experiments. The top results are shown in bold font. Figure 6: The bar chart of the ablative experiments, where MTRN represents the multitask restoration network, DF represents the difference feature, and MSFFM represents the multi-scale feature fusion module. In summary, the multitask image restoration sub-network, image restoration features, image difference feature, and multi-scale feature fusion module proposed in this paper can effectively improve the model's performance, as evidenced by the results of the above experiments.
###### Abstract  
_Fusarium graminearum_, a devastating fungal pathogen, causes great economic losses to crop yields worldwide. The present study investigated the potential of _Streptomyces pratensis_ S10 to alleviate _F. graminearum_ stress in wheat seedlings based on plant growth-promoting and resistance-inducing assays. The bioassays revealed that S10 exhibited multiple plant growth-promoting properties, including the production of siderophores, 1-aminocyclopropane-1-carboxylic acid deaminase (ACC), and indole-3-acetic acid (IAA), phosphate solubilization, and nitrogen fixation. Meanwhile, the pot experiment demonstrated that S10 improved wheat plant development, substantially enhancing wheat height, weight, root activity, and chlorophyll content. Consistently, genome mining identified abundant genes associated with plant growth promotion. S10 induced resistance against _F. graminearum_ in wheat seedlings. The disease incidence and disease index reduced by nearly 52% and 65% in S10 pretreated wheat seedlings, respectively, compared with those infected with _F. graminearum_ only in the non-contact inoculation assay. Moreover, S10 enhanced callose deposition and reactive oxygen species (ROS) accumulation and induced the activities of CAT, SOD, POD, PAL, and PPO. Furthermore, the quantitative real-time PCR (qRT-PCR) results indicated that S10 pretreatment increased the expression of SA- (_PR1.1, PR2, PR5_, and _PAL1_) and JA/ET-related genes (_PR3_, _PR4a_, _PR9_, and _PDF1.2_) in wheat seedlings upon _F. graminearum_ infection. In summary, _S. pratensis_ S10 could be an integrated biological agent and biofertilizer in wheat seedling blight management and plant productivity enhancement.  
_Streptomyces pratensis_ S10; plant growth promotion; genome mining; callose deposition; defense-related enzymes
###### Abstract  
This paper proposes integrating low earth orbit satellites (LEO-Sats) and multiple aerials to provide rescue services in post-disaster areas. Aerials are distributed to provide wireless connectivity to survivors and rescue workers, while LEO-Sat exhibits backhaul linkages to aerials to connect them with the closest surviving ground base station (GBS). In this context, the aerials' deployment should maximize the total system rate while guaranteeing fairness among the served post-disaster regions within aerials' limited battery budget and LEO-Sat's limited bandwidth resources. Therefore, a combinatorial bandit model with arms fairness and budget constraints (CB-FBC) is proposed to address the aerials' deployment while maintaining fairness in covering post-disaster regions within the aerials' limited battery resources. Additionally, the aerials' transmit communication powers and LEO-Sat's bandwidth resources are optimized according to traffic requests of LEO-axial linkages using a genetic algorithm (GA). By means of numerical analysis, the proposed GA shows superior performance over other naive benchmarks.  
genetic algorithm; LEO satellite; multi-armed bandit; combinatorial bandits; aerials 2023124964103390/electronics12244964  
1
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 5 Conclusions  
This study investigates four typical Zernike system aberrations and laser propagation characteristics in marine atmospheric turbulence through numerical simulation models, yielding the following key conclusions: \(Y\)-axis coma (\(Z_{7}\)) exhibits the most significant impact on the scintillation index, with the fluctuation range of its relative change rate surpassing other aberrations (\(Z_{4}\), \(Z_{11}\), \(Z_{25}\)), underscoring the critical need for coma correction in practical system design. Although beam wandering remains negligible under weak turbulence and short-range transmission, the synergistic interaction between turbulence strength and aberration weight coefficients significantly exacerbates beam expansion. The intensity distribution retains log-normal characteristics, consistently displaying persistent negative skewness (S < 0) and positive kurtosis (K > 0) in on-axis logarithmic intensity, a pattern aligned with terrestrial environments, thereby highlighting the universality of distortion mechanisms. Furthermore, as turbulence strength intensifies, its effects markedly elevate the required signal-to-noise ratio to achieve a bit error rate of \(10^{-9}\). Notably, when turbulence strength exceeds a threshold, the relative impact weights of different aberrations on system performance may shift. Collectively, these findings provide a theoretical basis for the design and optimization of laser transmission systems in marine atmospheric turbulence and lay the foundation for future research.  
Conceptualization, T.Z.; methodology, C.C.; software, Z.W.; writing--original draft preparation, J.P.; writing--review and editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (No. 2022 YFC2807702, No. 2022 YFC3104202), the National Natural Science Foundation of China (No. 12024620, No. 62401306), the Shandong Province Key Research and Development Program(No. 2022 CXPT020, No. 2022 JMRH0102, No. 2024 TSGC0164), the Natural Science Foundation of Shandong Province (No. ZR2023 QF173, No. ZR2023 LLZ002, No. ZR2022 MD068, No. ZR2021 QF015), the Natural Science Foundation of Qingdao (No. 24-4-zrij-124 jch), the Youth Innovation Technology Project of Higher Education Institutions in Shandong Province (No. 2023 KJ326), and the Fund Project of Qilu University of Technology (Shandong Academy of Sciences) (No. 2023 PX072, No. 2023 RCKY038).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Multi-Objective Reliability Optimization Method
### Monte Carlo Simulation  
Monte Carlo simulation (MCS) is a very important numerical analysis method guided by probability and statistics theory. The MCS method can be used to propagate the uncertainty in design variables to predicted responses. In MCS, random design variables are first generated according to statistical distribution. Their responses are usually predicted using surrogate models because the number of simulations is very large. Then, the probability distribution of responses can be obtained through statistical analysis. The process of MCS calculation using simple random sampling method is shown in Figure 1.  
Based on MCS, the reliability of the design can be defined as:  
\[R=1-P_{f}=1-\frac{N_{F}}{N} \tag{6}\]  
where \(P_{f}\) is the failure probability; \(N_{F}\) is the number of samples that does not satisfy the constraints; and \(N\) is the total samples.
# Study on the Residual Strength of Nonlinear Fatigue-Damaged Pipeline Structures
###### Abstract  
The heat alternation and bending during the service process of the pipeline structure are the key issues affecting its residual strength. The nonuniform plastic deformation of its cross-section causes nonlinear damage to the structure, which makes it difficult to calculate the residual strength of the overall structure. This study proposed a novel variable-strength material damage model with damage accumulation, which achieved the coupling analysis of structural damage and material fatigue residual strength, making the structural strength of nonlinear damage cross-section computable. Based on this, a theoretical ultimate load model for nonlinear damage cross-section and a finite element analysis model for dynamic material strength with damage accumulation were established. The analysis was conducted on the repeated banding damage to coiled tubing. The results showed that the residual yield strength of nonlinear damaged coiled tubing showed a trend of first rapid decrease and then slow decrease with damage accumulation, and the tensile displacement showed an increasing trend with damage accumulation. The ultimate internal pressure strength of nonlinear damaged structures showed a similar downward trend to the yield strength. The nonlinear damage coupling analysis model proposed in this study has significant practical engineering application value.  
residual strength; low-cycle fatigue; fatigue damage; nonlinear damage; pipeline