esa-sceva/satcom-chunk-collection huggingface dataset:
# On Noise Modeling of Capacitive Feedback Transimpedance Amplifiers in CMOS
## 5 Conclusions  
In this paper, an improved input-referred noise current model is proposed for low-noise capacitive feedback TIA. The noise model complements the previously developed gain model for the programmable-gain TIA architecture extended to support 10 k\(\Omega\), 25 k\(\Omega\), 100 k\(\Omega\), 200 k\(\Omega\), and 500 k\(\Omega\) gains using the arrays of discrete-controlled feedback capacitors and resistances in the biasing circuit. The main sources of noise present in the proposed TIA architecture were studied and their impact on the total noise current spectral density was evaluated. Analytic expressions for the respective noise components were provided along with the discussion on noise minimization strategies. A generalized mathematical model of the noise current spectral density was developed which consists of five noise terms, including those due to nonperfect biasing and flicker noise. The developed analytical model closely matches the numerical results obtained with the post-layout simulation both using 1.8 V 0.18 \(\upmu\)m CMOS and 2.5 V 0.25 \(\upmu\)m BiCMOS technologies, and provides the basis on which a detailed study on the TIA architecture and verification of the suggested  
Figure 12: Cadence-simulated noise for programmable-gain TIA configuration with cascode \(I_{\text{SS}}\) in TSMC 0.18 \(\upmu\)m CMOS. The horizontal gray dashed line indicates the target noise level of 5.0 pA/\(\sqrt{\text{Hz}}\).  
Figure 13: Cadence-simulated noise for programmable-gain TIA configuration with cascode \(I_{\text{SS}}\) in IHP 0.25 \(\upmu\)m BiCMOS. The horizontal gray dashed line indicates the target noise level of 5.0 pA/\(\sqrt{\text{Hz}}\).  
model can be performed. The obtained results confirm that the new model allows an accurate prediction of the performance for the design, with the mismatch between analytical models and computer simulation being 10%-15% in noise current spectral density at the frequency of 500 MHz and gain \(Z_{\text{T}}=10\) k\(\Omega\) for the exemplary CMOS and BiCMOS designs. Additional improvement in the noise performance was suggested by introducing a cascode in the current source of the source-follower block. Further work is planned in developing programmable-gain control for the capacitive feedback TIA with cascode, circuit manufacturing, and experimental circuit performance validation.  
All authors contributed to the present paper with the same effort in conceptualization, methodology, simulation, and writing the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and the circuit model parameters presented in this study are available on request from the corresponding author.  
The authors would like to thank editors and reviewers for many constructive suggestions and comments that helped to improve the quality of this paper.  
The authors declare no conflicts of interest.
# RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
## 3 Sentiment Analysis with RoBERTa-GRU
### RoBERTa-GRU
#### 3.3.2 Gated Recurrent Unit  
The Gated Recurrent Unit (GRU) is a type of Recurrent Neural Network (RNN) that is designed to capture long-range dependencies in the encoding from the RoBERTa model. The GRU was introduced to address the vanishing gradient problem that is common in RNNs. The GRU has two main components, namely the update gate and the reset gate. The update gate determines which information to preserve, while the reset gate controls how much past information should be forgotten.  
At time step \(t\), the input \(x_{t}\) is processed to produce the output of the update gate \(z_{t}\), reset gate \(r_{t}\), memory cell \(c_{t}\), and hidden state \(h_{t}\). The calculation of these variables can be represented as follows:  
\[\begin{split} z_{t}&=\sigma(W_{z}x_{t}+U_{z}h_{t- 1}+b_{z})\\ r_{t}&=\sigma(W_{r}x_{t}+U_{r}h_{t-1}+b_{r})\\ c_{t}&=\tanh(W_{c}x_{t}+U_{c}(r_{t}*h_{t-1})+b_{c} )\\ h_{t}&=z_{t}*h_{t-1}+(1-z_{t})*c_{t}\end{split} \tag{1}\]  
In the above equations, \(\sigma\) is the sigmoid function and \(*\) is the element-wise multiplication operation. The weights to be multiplied with the input and hidden states \(h_{t-1}\) are represented as (\(W_{z}\), \(W_{r}\), \(W_{c}\)) and (\(U_{z}\), \(U_{r}\), \(U_{c}\)), respectively, where the subscript \(z\), \(r\), and \(c\) denote the update gate, reset gate, and memory cell, respectively. The biases of the update gate, reset gate, and memory cell are denoted as (\(b_{z}\), \(b_{r}\), \(b_{c}\)).  
The sigmoid function ensures that the output values of the update gate and reset gate are within the range of \([0,1]\). The memory cell utilizes the reset gate to decide whether to keep the past information. When the value of the reset gate is close to zero, most of the past information is discarded.  
The hidden state combines the information from the past hidden state and the current memory cell weighted by the update gate. If the value of the update gate is close to zero, then most of the past information is ignored, and the hidden state is updated with the current memory cell content. Finally, the output of the GRU layer is passed to the flatten layer.
## 3 Results
### Dynamic Analysis  
#### 3.3.1 Deflections  
In this subsection, the deflection results over time from the dynamic analysis are presented. Figure 15 shows the relationship between the corrugation amplitude and the maximum deflection of the element over a given period of time for the considered thicknesses of a steel sheet.  
The analysis of the graphs yields several significant conclusions. Firstly, thinner sheets exhibit a greater relative gain in stiffness for a given corrugation (the same result might be observed in static analysis). This indicates that corrugation is particularly effective in enhancing the structural performance of thinner plates, resulting in more pronounced deflection reductions compared to thicker plates.  
Additionally, the graphs reveal the superposition of two waves--one associated with the external forcing and the other with the natural frequency of the plates. The forcing wave remains consistent over time, while the natural wave exhibits variability. This superposition indicates a complex dynamic interaction, where the constant frequency of the external force interacts with the variable natural frequency of the corrugated plates, influencing the overall deflection behaviour.  
Figure 14: The relationship between the corrugation amplitude and value of a chosen natural frequency for various numbers of periodic cells in each direction and for various thicknesses of the steel sheet: (**a**) first natural frequency; (**b**) second natural frequency; (**c**) third natural frequency; (**d**) fourth natural frequency; (**e**) fifth natural frequency; and (**f**) sixth natural frequency.  
Moreover, for certain values of corrugation, the relative differences in deflections begin to diminish. This suggests that there is an optimal range of corrugation, beyond which, further increases in corrugation amplitude yield diminishing returns in terms of deflection reduction.
## 6 Conclusions  
Most of the routing protocols for ad hoc networks suffer from low throughput and overhead, especially in large network scenarios where the number of nodes per area is high. BATMAN, a layer-2 routing protocol for ad hoc networks, defines transmission quality as a metric that comprises link quality, path quality, asymmetric link penalty, and hop penalty. The analytical analysis presented in this paper points out that the hop penalty used in BATMAN suffers from \(TQ\) metric decrement as the number of hops is increased. Therefore, to balance the heavy impact imposed by the hop penalty, a split hop penalty is proposed to improve the network's performance, especially delays, throughput, and jitter. The improvement is achieved by splitting the hop penalty into two branches: one where wireless links are utilized and the same radio is used for the reception and transmission of the data packets, and into a wired medium where transmitter and receiver are separated  
Figure 16: Comparison of packet loss vs. number of nodes.  
Figure 17: Comparison of average jitter.  
internally. The results presented show an average improvement of 11% in end-to-end delay, increased average throughput by 52%, and a 45% improvement in jitter as compared to the original BATMAN. The packet loss results differ from the original hop penalty, but the overall average is similar, as the new method offers less packet loss when the number of nodes is low, while it increases with the increasing number of nodes. During the experimental tests, we found several duplicate packets of acknowledgment from ICMP received at the transmitter's end, suggesting that BATMAN is not a loop-free routing protocol and should be investigated in the future. The future work resides in improving the \(TQ\) metric of BATMAN as the current method involves receiving echoes of sent packets, which takes more time in determining the link quality.  
Conceptualization: S.T.A.J. and F.Q.; Methodology: S.T.A.J. and I.A.; visualization: S.A., J.Y. and F.Q.; writing--original draft preparation: S.T.A.J. and I.A.; writing--review and editing: S.A. and Z.H.A.; funding acquisition: F.Q. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by Universiti Kebangsaan Malaysia Fundamental Research Grant Scheme Code # FRGS/1/2019/ICT01/UKM/02/1 and Geran Galakan Penyelidik Muda (GGPM) with Scheme Code # GGPM-2021-040. The research project is partially supported by the Ministry of Science and Technology (MoST), Pakistan.  
Not applicable.  
The authors would also like to thank the respected Editor and Reviewer for their support.  
The authors declare no conflict of interest.
## 7 Conclusions  
This paper proposes a power-aware ant colony optimization (ACO)-based autonomous secure routing protocol for FSNs in mobile surveillance systems. The decision of the next hop to the destination is based on a probability value that is calculated based on three parameters. The remaining battery power is given the maximum weight in P-BIOSARP, and a lower weight is assigned to the link quality and the packet delay. As a result, the optimal choice of the neighboring flying sensor node is mainly related to the remaining battery power, but the link quality and delay are also taken under consideration with less priority. P-BIOSARP transfers data packets securely by encrypting important header information to preserve privacy. FSN scenarios were configured with variations in the number of mobile nodes and malicious nodes. The experimental analysis supports that P-BIOSARP consumes 17% less energy with a better delivery ratio and with less data packet overhead compared to the E-BIOSARP, BIOSARP, and SRTLD routing protocols. Moreover, the results show that P-BIOSARP performance rises with the increase in time, nodes, and the packet rate.  
In the future, P-BIOSARP will be tested on real flying sensor nodes by varying the parameter weights. Furthermore, additional testing will be performed based on the distribution function.  
**Author Contributions:** Conceptualization, I.A. and K.S.; methodology, K.S.; software, K.S.; validation, I.A. and K.S.; formal analysis, K.S.; investigation, K.S.; resources, I.A. and K.S.; writing--original draft preparation, K.S.; writing--review and editing, I.A. and K.S.; visualization, I.A. and K.S.; supervision, I.A. and K.S.; project administration, I.A. and K.S.; funding acquisition, I.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was funded by Institutional Fund Projects under grant no. IFPIP-1254-611-1442. Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This research work was funded by Institutional Fund Projects under grant no. (IFPIP-1254-611-1442). Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Q System Classification and Tunnel Support Design  
The tunneling quality index, also known as the \(Q\) system for the classification of rock mass, is the tunneling-data-based empirical classification system used for the tunnel support design [12]. The system groups the ground for the design purpose into nine rock-mass classes. The \(Q\) value is calculated using Equation (12) and ranges from a minimum value of 0.001 to a maximum value of 1000:  
\[Q=\left(\frac{RQD}{J_{n}}\right)\times\left(\frac{J_{n}}{J_{a}}\right)\times \left(\frac{J_{w}}{SRF}\right) \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{2-10}
The main modifications in the structure of Equation (12) since 1974 were made by including a normalized factor owing to the important part of \(\sigma_{c}\) in the properties of rock mass, and the modified system was represented using \(Q_{c}\) (Equation (13)) [26]:  
\[Q_{c}=\left(\frac{RQD}{J_{n}}\right)\times\left(\frac{J_{r}}{J_{a}}\right)\times \left(\frac{J_{w}}{SRF}\right)\times\left(\frac{\sigma_{c}}{100}\right) \tag{13}\]  
Since 1974, the tunnel support technology has improved considerably, and with these modifications, the support diagram for the support design of a tunnel has also been revised. In the present position, the support diagram of the \(Q\) system shows that the spacing of the rock bolt depends on the \(Q\) or \(Q_{c}\) value only; however, the thickness of the shotcrete depends on the \(D_{e}\) value in addition to the \(Q\) or \(Q_{c}\) values [27]. In the support chart of the \(Q\) system, \(D_{e}\) is the tunnel span divided by the ESR.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 4 Validation and Evaluation
### Performance Evaluation  
To show and validate the accuracy of the proposed ML models, three statistical indices, namely mean absolute error (MAE), root-mean-squared error (RMSE), and the coefficient of determination \(R^{2}\), were used and expressed in Equations (14)-(16), respectively. The average deviation of the performance of an individual model or an ensemble model from a benchmark model in terms of the three statistical measures (MAE, RMSE, and \(R^{2}\)) was calculated using Equation (17).  
* Mean absolute error (MAE). \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-y_{i}^{{}^{\prime}}|\] (14)  
Figure 8: Schematic diagram of the stacking model [72].  
* Root-mean-squared error (RMSE) \[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-y_{i}^{{}^{\prime}})^{2}}\] (15)
* Coefficient of determination (\(R^{2}\)) \[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-y_{i}^{{}^{\prime}})^{2}}{\sum_{i=1}^{n}(y_{ i}-\overline{y})^{2}}\] (16)
* Deviation (\(Dev\)) \[Dev(\%)=\frac{P_{i}-P_{j}}{P_{j}}*100\] (17)  
where \(y_{i}\) is the target output, \(y_{i}^{{}^{\prime}}\) is the predicted output, \(n\) is the number of samples, and \(\overline{y}\) is the mean of the target output. \(Dev\) indicates the statistical performance improvement compared with a benchmark model. \(P_{i}\) is the statistical performance (MAE, RMSE, or \(R^{2}\)) of an individual or ensemble method. \(P_{j}\) is the corresponding performance of a benchmark model, LR, or an individual method used in the ensemble method as the base learner.  
It should be noted that the MAE and RMSE are commonly used indicators in ML to measure the error. Small values of the MAE and RMSE indicate less error, meaning better predictive models have been achieved. In this study [75], the MAE statistic is a measure of errors between the predicted values (the estimated value of crack width of ECC  
Figure 9: Ten-fold cross-validation approach.  
after self-healing) with the target values (the observed value of the crack width of ECC after self-healing in empirical data). The RMSE statistic computes the square root of the average residual error between the predicted values and the target values. \(R^{2}\) measures the strength of association between the predicted values and the target values, based on the proportion of the total variation of the outcomes. A greater value close to \(1\) represents a better prediction performance that commendably replicates the observed crack width of ECC after self-healing. The deviation statistic indicates the improvement of the prediction performance of an individual or an ensemble model from a benchmark model, which can be the LR model or the individual model used as base learners in the corresponding ensemble model.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
###### Abstract  
One major problem that affecting grape production is that of infestations by fungal pathogens, among which _Plasmopara viticola_ is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as 'favorable night condition' and 'wind speed' as critical for sporaniga spreading. The comparison of the model with the commercial Metos(r) warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms.  
NB-IoT; energy harvesting; grapevine downy mildew; decision support; early warning
## 1 Introduction
As a result, repelling and strong configuration mixing occurs between the upper levels of the 3D and 3F lines [12], resulting in anomalous behavior in relevant atomic parameters, including transition rates and collision strengths, along the isoelectronic sequences near Z = 54 [14; 58; 59; 60]. [PERSON] et al. [15] demonstrated that the strong configuration mixing between the upper levels of the 3D and 3F lines at Z = 54 can lead to a strong enhancement of the 3F/3D line-intensity ratio. This enhancement is a result of the redistribution of collision strengths and level populations. Their findings indicate that conducting precise measurements of the 3F/3D line-intensity ratio in the Ne-like Xe\({}^{44+}\) can offer a valuable tool for exploring the effects of level crossings and configuration interaction and for testing the reliability of theoretical models for simulating the spectra of the Ne-like ions near the level crossing point. It is worth noting that a subsequent study conducted by [PERSON] et al. [16] has revealed a substantial discrepancy between the experimental values of the 3F/3D line-intensity ratio and those obtained from HULLAC synthetic calculations across various electron-beam energies. These notable discrepancies actually highlight the limitations of the existing theoretical models and the inaccuracies in atomic parameters for plasma diagnostics. Therefore, there is a pressing need for further advancements in both the experimental measurements and spectral simulations of the Ne-like Xe\({}^{44+}\) to enhance their reliability and accuracy. In this work, we have conducted precise measurements of the 3D and 3F lines in Ne-like Xe ion, along with accompanying satellites and the two-electron-one-photon-transition (TEOPT) lines from neighboring charged states, at the Shanghai Electron Beam Ion Trap (Shanghai-EBIT) using a high-resolution crystal spectrometer. Specifically, we have conducted a comprehensive analysis of the line-intensity ratio of 3F/3D in the experimental spectra. The emission intensities of the spectral lines have been simulated using the collisional-radiative model (CRM) within the Flexible Atomic Code (FAC), providing the dependence of line-intensity ratios on electron-beam energies. Our results have achieved a significant improvement in the agreement between measurements and simulations of the 3F/3D line-intensity ratios across various ranges of the electron-beam energy, significantly reducing the discrepancy reported in previous studies. In particular, at the energies above 6 keV, the present experimental and simulated results exhibited a very high agreement,whereas small discrepancies still persist in the low-energy region. To address the remaining discrepancies, we delved into the population equilibrium of the involved levels within the present CRM. Based on these analyses, we have proposed potential approaches to further minimize these discrepancies, ultimately establishing the 3F/3D line-intensity ratio as a reliable tool for plasma diagnostics.
# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
###### Abstract  
To address the limitations of traditional manual measurements of phenotypes, a study focused on the rapid acquisition of phenotypic parameters in wheat grains was conducted. This research introduced an index for bud-point determination, which offers valuable insights into breeding selection, optimizing sowing and growth direction for grains, and assessing seed vitality. A grain collection device utilizing a linear array camera was designed and constructed, accompanied by custom software to simplify device operation. Image processing was performed using Halcon image, enabling the development and implementation of a system for extracting key phenotypic parameters. The preprocessing method involves isolating wheat grain regions, combined with S channel extraction, global thresholding, and opening and closing operations to achieve robust segmentation. Based on this preprocessing method, a phenotypic analysis was performed by evaluating the geometric characteristics of wheat grains. The process for extracting phenotypic characteristics and determining the bud point was subjected to experimental analysis. The results demonstrated high correlation and accuracy. The errors estimating the comprehensive grain length of five wheat varieties using the extraction algorithm developed in this study, the determination coefficient and root mean square error indices, were 0.986 and 0.0887, respectively, compared with manual measurements. Similarly, for grain width, the determination coefficient and RMSE were 0.9505 and 0.0541; for grain weight, these values were 0.7635 and 3.8329, respectively. Furthermore, the algorithm achieved a bud point direction determination accuracy rate of 97.5%. The mean error in the positioning accuracy of germination points was within 0.10 mm, and the mean angular error was maintained within \(\pm\)3 degrees. The phenotype analysis of wheat using the image processing techniques presented in this study shows strong consistency with manual detection methods, providing valuable quantitative data to assist in breeding selection and accelerate genetic improvement practices.  
w +
Footnote †: journal: applied sciences  
Article
## 4 Performance Analysis and Evaluation
The result shows that at the minimum network energy consumption, the optimal number of clusters is seven.