esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Applied Model Evaluation Metrics  
To assess the performance of the models, we utilised a comprehensive set of evaluation metrics. Accuracy was calculated to measure the proportion of correct predictions, alongside its 95% Confidence Interval (CI) to estimate the reliability of the accuracy value. The No Information Rate (NIR) and corresponding _p_-value [Acc > NIR] were considered to evaluate whether the model significantly outperformed a random guess. Kappa statistics were employed to account for agreement occurring by chance. Additionally, the [PERSON]'s Test _p_-value was used to statistically assess the differences in predicted versus actual classifications. For each class, detailed per-class metrics were extracted, including Sensitivity (True Positive Rate), Specificity (True Negative Rate), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Prevalence, Detection Rate, and Detection Prevalence. Balanced Accuracy was calculated to equally weigh Sensitivity and Specificity in cases of class imbalance. Finally, the F1 Score was reported as a harmonic mean of the Precision and Recall, providing a robust indicator of classification performance especially in imbalanced datasets.
## 3 Numerical Verification
### Damage Identification Based on Continuous Wavelet Transform  
Previous studies have shown that when a structure is damaged, its response exhibits local singularities. Time-frequency domain methods, represented by continuous wavelet transform [27; 28; 29; 30; 31], do not rely on the bridge response under healthy conditions, thus having broad practical value. By applying wavelet transform to the local singularities of structural responses at appropriate scale factors, variations in wavelet coefficients can be observed, enabling effective identification of bridge damage. The central frequency of the Mexican hat wavelet is 0.25 Hz, and the sampling frequency in the numerical simulation is 1000 Hz. According to Equation (6), the wavelet scale corresponding to the structural fundamental frequency is Scale = 84. Figure 3a shows the wavelet coefficient plot obtained by applying continuous wavelet transform to the acceleration response of the contact point in Case 3. The vertical axis in the figure represents the continuous wavelet transform wavelet coefficients, and the horizontal axis is the relative position of the bridge. At the wavelet scale corresponding to the fundamental frequency, the vibration mode of the bridge structure can be effectively reflected. Still, the damage location cannot be determined across the entire time domain. The wavelet coefficients of the contact point acceleration response at three times the fundamental scale were further extracted to locate the damage. Figure 3b shows the WC map for a scale factor of three times the fundamental scale (Scale = 253) in Case 3, and for comparison, a WC map of the undamaged bridge is also shown. As shown in Figure 3b, although the acceleration values at the damaged location show a certain upward trend, this trend is not very pronounced as the overall trend of WC is significantly disrupted by its fluctuations, especially those with high-frequency content.
# Improvement of Mesh Atomizer Performance by Electrolytic Polishing
## 5 Experimental Analysis of Atomization Amount
### Atomization Amount Measurement
As the electrolysis time continues, the atomization sheet enters the CD passivation zone, and a new passivation film is formed on the anode surface. The passivation film is continuously dissolved and formed at the same time. At  
Figure 10: Atomization amount under different conical angles. the beginning, the formation rate of the passivation film is greater than the dissolution rate, and the thickness of the passivation film keeps increasing. At this point, the metal dissolution is still going on, but the rate of metal dissolution is very slow due to the obstruction of the passivation film [20]. Owing to the existence of a passivation film and measurement error, the atomization amount measured by the atomizers with an electrolytic polishing time of 1 min is slightly lower than that generated by the untreated atomization sheet. When the formation speed and the dissolution rate of the passivation film reach an equilibrium, the atomization sheet enters the DE overpassivation zone, and the thickness of the passivation film stops growing. Metal dissolution occurs at a relatively steady rate. Therefore, to achieve a good polishing effect, the electrolytic time should be controlled within a reasonable range of the overpassivation zone. According to the experiment, when the electrolytic polishing time is 5 min, it is the time node with the maximum atomization amount, which reaches 5.202 mL/min. When \(t>5\), \(Z\) cannot be treated as a constant because \(Z\) will affect the conical angle \(\theta\) along with the change in t as the electrolytic polishing time increases. The conical and nozzle diffuser loss coefficients are then gradually affected, thus altering \(Z\). Consequently, the atomization amount is reduced.
## 1 **Corrosion in Reinforced Concrete Panels: Wireless Monitoring and Wavelet-Based Analysis**  
**[PERSON] 1,2,*, [PERSON] 3,*, [PERSON] 4, [PERSON] 5 and [PERSON] 1,2**  
1 Key Lab of Structures Dynamic Behavior and Control, Harbin Institute of Technology,  
Ministry of Education, Harbin 150090, China; E-Mail: [EMAIL_ADDRESS]  
2 School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China  
3 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China  
4 Centre for Composite Material and Structure, Harbin Institute of Technology, Harbin 150090,  
China; E-Mail: [EMAIL_ADDRESS]  
5 Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China; E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (G.Q); [EMAIL_ADDRESS] (G.S.); Tel./Fax: +86-451-8628-2209 (G.Q.).  
_Received: 9 November 2013; in revised form: 23 January 2014 / Accepted: 8 February 2014 / Published: 19 February 2014_  
To realize the efficient data capture and accurate analysis of pitting corrosion of the reinforced concrete (RC) structures, we first design and implement a wireless sensor and network (WSN) to monitor the pitting corrosion of RC panels, and then, we propose a wavelet-based algorithm to analyze the corrosion state with the corrosion data collected by the wireless platform. We design a novel pitting corrosion-detecting more and a communication protocol such that the monitoring platform can sample the electrochemical emission signals of corrosion process with a configured period, and send these signals to a central computer for the analysis. The proposed algorithm, based on the wavelet domain analysis, returns the energy distribution of the electrochemical emission data, from which close observation and understanding can be further achieved. We also conducted test-bed experiments based on RC panels. The results verify the feasibility and efficiency of the proposed WSN system and algorithms.  
reinforced concrete structures; corrosion monitoring; wireless sensor and networks; electrochemical emission; wavelet
# The Effects of 3D and 2D Imaging on Brain Wave Activity in Laparoscopic Training
###### Abstract  
The current study tested the effects of a state-of-the-art stereoscopic three-dimensional (3D) display and a traditional two-dimensional (2D) display on performance and mental workload during simulated laparoscopic tasks with different levels of depth perception over a longer duration than in previous publications. Two different simulated laparoscopic tasks with depth perception, peg transfer, and circle-tracing were performed by 12 participants using 2D and 3D vision systems. The task performances (mean completion time and mean error frequency) and mental workload measures (gamma and alpha brain wave activity, blink frequency and NASA-TLX ratings) were recorded as dependent variables. The physiological mental workload measures were collected via a MUSE EEG headband. The 3D vision system had advantages in mean movement time and mean error frequency in the depth-perception peg transfer task. The mean completion time of the non-depth perception circle-tracing task was significantly lower for 2D than for 3D. For the peg transfer task, EEG alpha wave activity was significantly higher for 3D than for 2D. The EEG gamma wave activity for 2D was significantly higher than that for 3D in both tasks. A significantly higher blink frequency was found for both the peg transfer task and the 3D system. The overall NASA-TLX score of the 2D system was significantly higher. The findings of this research suggest that a 3D vision system could decrease stress, state of attentiveness, and mental workload compared with those of a 2D system, and it might reduce the completion time and increase the precision of depth-perception laparoscopic operations.  
3D laparoscopy; depth-perception tasks; mental workload; EEG; NASA-TLX +
Footnote †: journal: Applied  
Article
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 7 Discussion
### Comparative Analysis of Energy Consumption in LWSNs and Traditional WSNs  
Energy consumption in LWSNs follows unique patterns due to the linear topology, which often involves fewer transmission paths and restricted routing options compared to traditional WSNs. In traditional WSNs, sensor nodes are often arranged in a multi-dimensional array, allowing for multiple routing paths, which can distribute the energy load more evenly among nodes. This also permits them to efficiently utilize cluster-based aggregation to reduce redundant data transmission and conserve energy. However, in LWSNs, nodes are deployed linearly, with each node having only two immediate neighbors along the linear path, leading to specific energy demands and challenges, especially near the sink node, where the energy depletion rates are typically higher. Furthermore, data aggregation is less effective due to fewer opportunities for clustering along the linear path.  
This study identifies that LWSNs consume more energy for multi-hop communication along linear paths, as data are relayed through the intermediate nodes with limited alternative routes. To mitigate these challenges, energy-efficient routing protocols specifically designed for linear topologies are recommended. These protocols should emphasize balancing energy consumption among nodes and managing transmission power to extend the lifespan of the nodes closest to the sink. Insights drawn from this comparison indicate that adopting directional antenna configurations, using mobile sinks (e.g., UAVs for data collection), and optimizing relay node placement can improve energy efficiency and reduce communication overheads, ensuring a more stable operational lifespan for LWSNs.
## 3 Conclusions and Future Directions  
MNPs offer a promising strategy for targeted delivery and improved efficacy of CE-CI. Preclinical studies have shown that this approach can enhance cell attachment and restore corneal clarity. Combining MNPs with biocompatible materials such as hyaluronic acid gels could further optimise cell viability and controlled release.  
However, several challenges must be addressed before widespread clinical use is feasible. The variation of methods across studies, including the origin of CECs, animal models, magnet types, injury models, culture mediums, and negative controls, hinders the generalisability of study results and limits their application to clinical practice. Further research is needed to develop standardised protocols for the large-scale, high-yield culture of functional CECs with magnetic properties. Additionally, alternative injectable biomaterials should be explored to improve cell survival and targeting efficiency. Current studies mainly focus on short-term outcomes, making long-term follow-up essential to evaluate the durability of the treatment and potential delayed side effects. It is also crucial to investigate potential immune responses to MNPs, particularly those utilising animal immunoglobulin material. Future data considering all significant parameters will inform the development of protocols in compliance with GMP guidelines.  
Despite these challenges, ongoing phase I human trials and promising preclinical data indicate that MNPs have significant potential to improve the treatment of corneal endothelial dysfunction.  
Author Contributions:[PERSON], [PERSON], M.C., M.W., [PERSON] and [PERSON] have made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
###### Abstract
This study uses data preprocessing, training and validation to analyze the problem, and selects two algorithms to predict this nonlinear vibration phenomenon to analyze its accuracy. The two algorithms are: Deep Neural Networks (DNN) and Long Short-Term Memory model (LSTM). Among them, DNN is a supervised learning method, which is suitable for processing labeled data. LSTM is a branch of Recurrent Neural Networks (RNN) that solves the performance of time recurrent neural networks in long sequences and also solves the problems of gradient disappearance and gradient explosion, and is suitable for dealing with problems highly related to time series. Finally, use the Long Short-Term Memory model to add a small number of neuron hidden layers (LSTM-NN) to find the best learning model. This study aims to explore suitable learning models for predicting convergence results of nonlinear vibration equations in specific cases. To accomplish this, two different types of the Duffing equation will be examined as examples. The goal is to identify a learning model that can effectively predict the convergence outcomes of these nonlinear vibration equations. The selection of the Long Short-Term Memory (LSTM) network structure in this proposed method is based on the unique characteristics of the problem we are addressing, which involve sequences and dependencies over time. LSTM is well-suited for dealing with sequential data, making it an ideal choice for time series analysis. In this study, we are working with time-dependent data from the Duffing equation, where each data point's value depends on previous data points. LSTM was selected due to its proficiency in handling sequential data with long-range dependencies, which is crucial for modeling the intricate convergence patterns in the Duffing equation. Unlike traditional RNNs, LSTM mitigates the vanishing gradient problem, ensuring accurate predictions in time series analysis. LSTM's memory cells further enhance its ability to capture complex temporal relationships, making it the optimal choice for our problem. We employ machine learning techniques, particularly LSTM and LSTM-NN models, to predict the convergence results of the Duffing equation. While Duffing equation studies exist, applying deep learning methods to this classical nonlinear differential equation is novel. We address two distinct scenarios: undamped free vibration and forced vibration with damping. These represent fundamental yet complex situations in nonlinear dynamics, and applying machine learning to predict their convergence outcomes is an innovative approach. Analytical comparison: We thoroughly explore the LSTM and LSTM-NN models, evaluating their performance in predicting both binary and multiple solutions. This in-depth analysis provides a novel perspective on the suitability of these models for specific types of data. Our research holds potential applications in various real-world systems, such as aircraft wings and automobile components, where nonlinear vibrations can occur. The practical implications of our findings contribute to the novelty of our work.
# The Chromosome Number and rDNA Loci Evolution in _Onobrychis_ (Fabaceae)
## 2 Results
### Number and Localisation of rDNA Loci  
The number and localisation of the rDNA loci were determined using FISH with 25S and 5S rDNA probes. The rDNA number and localisation are reported for 17 diploid and seven tetraploid _Onobrychis_ species. Except for _O. vicitifolia_, only one accession per species  
Figure 1: Chromosome number and distribution of rDNA loci in diploid and polyploid _Onobrychis_ species. Fluorescence in situ hybridisation was performed with 5S rDNA probe (green fluorescence) and 35S rDNA probe (red fluorescence). **(A)**_O. sternohiza_, **(B)**_O. tumulis_, **(C)**_O. michauxii_, **(D)**_O. gauhoe_, **(E)**_O. allissima_, **(F)**_O. grandis_, **(G)**_O. caput-galli_, **(H)**_O. pplemaica_, **(I)**_O. hynapyra_, **(J)**_O. gracilis_, **(K)**_O. supina_, **(L)**_O. megataplros_, **(M)**_O. alb albu subsp. laconica_, **(N)**_O. situtensii_, **(O)**_O. vassilczenkoi_, **(P)**_O. chorossanica_, **(Q)**_O. kachetica_, **(R)**_O. persica_, **(S)**_O. radiata_, **(T)**_O. vaginalis_, **(U)**_O. iberica_, **(V)**_O. cristagalli_, **(W)**_O. vicitifolia_1 (PI170583), **(X)**_O. vicitifolia_ (PI200872), **(Y)**_O. arenaria_, **(Z1)**_O. tmuscanucasica_, **(Z2**_O. cyri_, **(Z3)**_O. biebersteinii_, **(Z4)**_O. inermis_, **(Z5)**_O. subacaulis_. All bars: 5 μm.  
was analysed in this study. The number and chromosomal localisation of 5S rDNA and 35S rDNA loci are demonstrated in Figure 1 and schematically summarised in Table 1.  
Among the diploids, high interspecific polymorphisms of rDNA loci number and localisation were observed, and nine patterns of rDNA loci chromosomal organisation were distinguished (Figure 1F-Z5; Table 1). The number of 35S rDNA loci ranged from one to three (Figure 1), whereas the number of 5S rDNA loci was from one to two per haploid chromosome set in diploids (Figure 1); however, most often (10 species) one locus of 35S rDNA and one locus of 5S rDNA was observed (Table 1; Figure 1F-I,N-QS,U). The locus of 35S rDNA in the interstitial chromosomal position was most often observed (14 diploid species; Figure 1F,H-QS-U). The subterminal position of 35S rDNA loci was observed in three diploid species (Figure 1G,R,V). In karyotypes of eight diploids, the locus of 5S rDNA was observed exclusively in the subterminal position (Figure 1H,N-QS-U). The interstitial localisation of the 5S rDNA locus/loci was found in seven species (Figure 1F,G,I-L,V), whereas two species had both interstitial and subterminal loci of 5S rDNA (Figure 1M,R). Most analysed diploids had 5S and 35S rDNA loci in different chromosome pairs (Figure 1F-L,N-QS-U). In karyotypes of three species (_O. megataphros_, _O. alba_ subsp. _laconica_ and _O. crista-galli_), one or two chromosomes bearing both 35S and 5S rDNA loci were observed (Figure 1L,M,V).
## 4 Establishment and Grading of Evaluation Indicators
### Selection and Correlation Analysis of Evaluation Factors
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Factor** & **Slope** & **Aspect** & **SM** & **TR** & **EGRG** & **CLT** & **VC** & **LUT** & **SS** & **SH** \\ \hline Slope & 1.00 & 0.01 & 0.03 & 0.48 & 0.11 & \(-\)0.04 & 0.39 & \(-\)0.09 & 0.05 & 0.21 \\ Aspect & 0.01 & 1.00 & 0.00 & 0.02 & 0.06 & \(-\)0.01 & 0.00 & \(-\)0.02 & 0.08 & 0.09 \\ SM & 0.03 & 0.00 & 1.00 & 0.03 & 0.00 & \(-\)0.01 & 0.00 & \(-\)0.03 & 0.10 & 0.15 \\ TR & 0.48 & 0.02 & 0.03 & 1.00 & 0.11 & \(-\)0.05 & 0.43 & \(-\)0.10 & 0.04 & 0.23 \\ EGRG & 0.11 & 0.06 & 0.00 & 0.11 & 1.00 & 0.08 & 0.22 & \(-\)0.01 & 0.46 & 0.09 \\ CLT & \(-\)0.04 & \(-\)0.01 & \(-\)0.01 & \(-\)0.05 & 0.08 & 1.00 & \(-\)0.07 & 0.08 & \(-\)0.06 & \(-\)0.13 \\ VC & 0.39 & 0.00 & 0.00 & 0.43 & 0.22 & \(-\)0.07 & 1.00 & \(-\)0.25 & 0.16 & 0.18 \\ LUT & \(-\)0.09 & \(-\)0.02 & \(-\)0.03 & \(-\)0.10 & \(-\)0.01 & 0.08 & \(-\)0.25 & 1.00 & \(-\)0.10 & \(-\)0.11 \\ SS & 0.05 & 0.08 & 0.10 & 0.04 & 0.46 & \(-\)0.06 & 0.16 & \(-\)0.10 & 1.00 & \(-\)0.02 \\ SH & 0.21 & 0.09 & 0.15 & 0.23 & 0.09 & \(-\)0.13 & 0.18 & \(-\)0.11 & \(-\)0.02 & 1.00 \\ \hline \hline \end{tabular}  
* The abbreviations in the table are as follows: SM represents Slope Morphology, TR represents Topographic Relief, EGRG represents Engineering Geological Rock Group, CLT represents Cover Layer Thickness, VC represents Vegetation Coverage, LUT represents Land Use Type, SS represents Slope Structure, and SH represents Slope Height.  
\end{table} 
Table 1: Pearson phase relation table of geological disaster-susceptibility evaluation factors. uation factors are all smaller than 0.5, indicating that the evaluation factors are mutually independent and can all be used for disaster-susceptibility assessment [36].
# Calculation of the Relaxation Modulus in the Andrade Model by Using the Laplace Transform
## 4 Asymptotic Behaviour via Tauberian Theorem
### Asymptotic Behaviour for \(T\to+\infty\)  
We know that the Laplace transform of the relaxation modulus in the Andrade model is (7)  
\[\tilde{G}_{\alpha}(s)=\frac{\mu\,\tau}{s\,\tau+\Gamma(\alpha+1)(s\,\tau)^{1- \alpha}+1},\quad\alpha\in(0,1). \tag{61}\]  
Since  
\[\frac{1}{1-x}=1+x+x^{2}+\ldots,\quad|x|<1, \tag{62}\]  
we have that  
\[\frac{1}{1+x}\approx 1-x,\quad x\to 0^{+}, \tag{63}\]thus, taking \(x=s\,\tau+\Gamma(\alpha+1)(s\,\tau)^{1-\alpha}\), we get  
\[\tilde{G}_{\alpha}(s)\approx\mu\,\tau\Big{[}1-s\,\tau-\Gamma(\alpha+1)(s\,\tau) ^{1-\alpha}\Big{]},\quad s\to 0^{+}. \tag{64}\]  
Since \(\alpha\in(0,1)\),  
\[\tilde{G}_{\alpha}(s)\approx\mu\,\tau\Big{[}1-\Gamma(\alpha+1)\,(s\,\tau)^{1- \alpha}\Big{]},\quad s\to 0^{+}. \tag{65}\]  
According to (58), the asymptotic behaviour of \(G_{\alpha}(t)\) as \(t\to+\infty\) is calculated as  
\[G_{\alpha}(t) \approx \mu\,\tau\mathcal{L}^{-1}\Big{[}1-\Gamma(\alpha+1)\,(s\,\tau)^{1 -\alpha};t\Big{]}\] \[= \mu\,\tau\,\delta(t)-\mu\,\frac{\Gamma(\alpha+1)}{\Gamma(\alpha- 1)}\bigg{(}\frac{t}{\tau}\bigg{)}^{\alpha-2}.\]  
Applying the factorial property of the Gamma function, \(\Gamma(z+1)=z\,\Gamma(z)\), and taking into account that \(\delta(t)=0\) as \(t\to+\infty\), we conclude that  
\[G_{\alpha}(t)\approx\mu\,\alpha(1-\alpha)\bigg{(}\frac{t}{\tau}\bigg{)}^{ \alpha-2},\quad t\to+\infty. \tag{67}\]  
Taking more terms in the expansion of \(\tilde{G}_{\alpha}(s)\) as \(s\to 0^{+}\), we can calculate more terms of \(G_{\alpha}(t)\) as \(t\to+\infty\) by using (58). Thereby, we obtain  
\[G_{\alpha}(t)\approx\mu\,\alpha\Bigg{[}(1-\alpha)\bigg{(}\frac{t}{\tau}\bigg{)} ^{\alpha-2}-2\,\frac{\Gamma^{2}(1+\alpha)}{\Gamma(2\alpha-1)}\bigg{(}\frac{t} {\tau}\bigg{)}^{2\alpha-3}+\ldots\Bigg{]},\quad t\to+\infty. \tag{68}\]  
For the particular case \(\alpha=\frac{1}{3}\) in (67), we have  
\[G_{1/3}(t)\approx\frac{2}{9}\,\mu\bigg{(}\frac{t}{\tau}\bigg{)}^{-5/3},\quad t \to+\infty. \tag{69}\]  
As a consistency test, we can obtain (69) from the the expression given in (50) for \(G_{m/n}(t)\) with \(m=1\) and \(n=3\), and the asymptotic formula ([17] [Eqn. 18.1(22)]),  
\[\mathrm{E}_{\alpha,\beta}(z)\approx-\sum_{k=1}^{N}\frac{z^{-k}}{\Gamma(\beta- \alpha k)}+O\Big{(}|z|^{-N}\Big{)},\quad z\to\infty,\quad|\mathrm{arg}(-z)|< \Big{(}1-\frac{\alpha}{2}\Big{)}\,\pi. \tag{70}\]