esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Results
#### 3.3.2 Multi-Pitch Estimation  
The DNN and RNN were also used as comparative methods for pitch estimation. The architecture and training parameters are the same as that in onset detection, except for the final layer. Each net has 88 units in the output layer, and the output unit activation is sigmoid. In the training and evaluation, all onset time was determined accurately in advance, and the pitch estimation was carried out at each onset.  
The results of MPE are shown in Table 2. As shown in Table 2, the CNN outperformed other nets on all evaluation metrics. For example, the CNN yielded a relative improvement of 24.61% over the DNN and outperformed RNN by 15.91% on note-based F-measure. This is largely because the CNN can learn pitch-invariant features from the frames around the onset. We can also observe that the RNN outperformed the DNN on precision and F-measure, which indicates that the context information is helpful in pitch estimation. Therefore, the advantage of CNN is significant in the subtask of onset detection and MPE.  
Figure 7 shows the graphical representation of the outputs of neural networks for the music excerpt along with the corresponding ground truth piano roll. As shown in the ground truth (Figure 7d), the pitch estimation of this excerpt is challenging. The polyphony at each time instant is four in the excerpt's first 8.8 s, and the overlapping is serious. Additionally, the notes are much shorter in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall** & **Precision** & **F-Measure** \\ \hline CNN & 0.7810 & 0.8319 & 0.8056 \\ DNN & 0.6223 & 0.6727 & 0.6465 \\ RNN & 0.6020 & 0.8221 & 0.6950 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance on pitch estimation using different neural networks.  
Figure 6: Results of onset detection: (**a–c**) the output of CNN, DNN, and RNN, respectively; (**d**) the corresponding ground truth.  
the excerpt's last 1.2 s. Compared to the posteriograms of CNN and RNN, DNN estimated more pitches, where many of them were false positives. This is because DNN's topology is simple and its input is just the spectrum at onset. Utilizing the note sequence information in piano music, RNN produced a higher-precision output. However, RNN's output seemed to be a result of monophonic pitch estimation, which yielded many false negatives and corresponded to low recall. In general, the CNN's output was much closer to the ground truth than DNN and RNN. Unlike RNN's input, the context information of CNN's input is from several frames around each onset. CNN can model the attack stage of each pitch through this information, such that the MPE at onset is more accurate. There are also some octave errors which require further effort in the CNN's posteriogram. For example, the MIDI pitch of 46 (about 116.54 Hz) was estimated to be MIDI pitch 58 (about 233.08 Hz) at the eighth onset.
## 3 Recurrence Quantification Analysis
### Estimation of the Embedding Parameters
#### 3.1.2 Phase Space Dimension  
The optimal embedding dimension \(m_{0}\) for an observed signal \(y(n)\) is the minimum dimension for the state vectors \(\mathbf{x}_{k}\) to give a reliable reconstruction of \(y(n)\) phase space [35, 37]. From the literature, many approaches to estimate \(m_{0}\) have been developed [38, 42, 43]. The most used approach is based on the False Nearest Neighbors (FNNs) method [43]. According to the principle of FNNs, any two true neighboring points in the \(m-dimensional\) reconstructed phase space must remain neighbors in the \((m+1)-dimensional\) reconstructed phase space. Otherwise, they are called false neighbors. A perfect embedding means all neighboring points should be true neighbors [20, 42, 43]. Inspired by the FNN-based algorithm proposed in [42], we identify \(m_{0}\) by using the distance ratio, \(a(i,m)\), defined as follows [42]:  
\[a(k,m)=\frac{\left\|\mathbf{x}_{k}^{(m+1)}-\mathbf{x}_{l}^{(m+1)}\right\|_{ \infty}}{\left\|\mathbf{x}_{k}^{(m)}-\mathbf{x}_{l}^{(m)}\right\|_{\infty}}; \tag{5}\]  
where \(k,l=\in\{1,2,\cdots,N-m\tau\}\); \(\mathbf{x}_{k}  
ratio \(a(k,m)\) is its sensitivity to the reference state vector \(\mathbf{x}_{k}\). To overcome this drawback, we consider the average value \(\bar{a}(m,\tau)\) instead of \(a(k,m)\):  
\[\bar{a}(m,\tau)=\frac{1}{N-m\tau}\sum_{k=1}^{N-m\tau}a(k,m) \tag{6}\]  
To investigate the dependence of \(\bar{a}(m,\tau)\) on \(m\), the coefficient of proximity \(r(m,\tau)\) is defined as follows:  
\[r(m,\tau)\triangleq\frac{\bar{a}(m+1,\tau)}{\bar{a}(m,\tau)} \tag{7}\]  
When \(m\) becomes greater than a limit value \(m_{\text{lim}}\), the proximity coefficient \(r(m,\tau)\) converges to one. Hence, \(m_{0}=m_{\text{lim}}+1\) becomes the minimum embedding dimension [42]. Based on Equation (7), we can observe that \(r(m,\tau)\) depends on the time delay value \(\tau\). This assertion is corroborated by simulation results. Figure 1 illustrates, for example, the evolution of \(r(m,\tau)\) for two extreme values of \(\tau\); for \(\tau=2\), the optimal value of \(m_{0}=6\); whereas for \(\tau=100\), we obtain \(m_{0}=9\). As \(m\) depends on the choice of \(\tau\), we propose hereinafter an optimization strategy to find simultaneously the optimal values of \(m\) and \(\tau\).
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 3 Results  
In this section, we will discuss the effect of the laser pulse on the electron trajectories via the various parameters of an incident, tightly focused laser pulse in the presence of an applied magnetic field.  
Our numerical simulation parameters are peak laser amplitude a\({}_{0}=5\) and beam waist radius b\({}_{0}=4\lambda_{0}\). \(\lambda_{0}\) denotes the initial acting wavelength of the laser pulse, where \(\lambda_{0}=1\) um. The unit of the magnetic field B\({}_{0}\) is KT. We calculate the electron motion trajectories under different pulse lengths L and different magnetic field strengths B\({}_{0}\) by writing MATLAB R2020a programs based on this parameter.
###### Abstract  
This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. As legacy DCS platforms, such as those installed at the Tae-An Thermoelectric Power Plant, face limitations in applying advanced logic mechanisms, a simulation-based test bench was developed to validate the algorithm in anticipation of future DCS upgrades. The algorithm operates by partitioning function code executions into segment groups, enabling fine-grained, real-time CPU and memory utilization monitoring. Simulation studies, including a modeled denitrification process, demonstrated the system's effectiveness in maintaining load balance, reducing power consumption to 17 mW under a 2 Gbps data throughput, and mitigating overload levels by approximately 31.7%, thereby outperforming conventional control mechanisms. The segmentation strategy, combined with summation logic, further supports scalable deployment across both legacy and next-generation DCS infrastructures. By enabling proactive overload mitigation and intelligent energy utilization, the proposed solution contributes to the advancement of self-regulating power control systems. Its applicability extends to energy management, production scheduling, and digital signal processing--domains where real-time optimization and operational reliability are essential.  
distributed control system; overload detection algorithm; optimal utilization; energy conversion system Article
# 2-D Seismic Response Analysis of a Slope in the Tyrrhenian Area (Italy)
## 3 Site Response Analysis
### 2-D Analysis  
In addition to 1-D results, a 2-D analysis of the slope was performed using QUAKE/W code. The importance of the comparison for the same case study between 1-D and 2-D analyses is well known and it plays an important role not only for a sloping ground area but, in general, for geometric irregularities. An example is reported by [PERSON] et al. [40] by analyzing the stability of an earth dam affected by a seismic action. Authors compared two methods for calculating the peak dynamic shear stress that occurs in embankments during an earthquake based on 1-D analyses performed using the SHAKE code [41] and on 2-D analyses carried out using the FLUSH code [42].  
Furthermore, an accurate evaluation of the PGA also plays a very important role in slope stability in seismic conditions. In fact, the influence of the PGA on the identification of the critical slip surface under seismic loading, and, consequently, on the safety factor of the slope, is well known. In addition, the knowledge of the response time history and of the PGA can be very helpful in the study of permanent displacements, as recently reported by [PERSON] at al. [43; 44].  
The following figures show in red, blue and green, respectively, the EERA, QUAKE/W 1-D and QUAKE/W 2-D result in terms of acceleration time history and spectral acceleration. Figure 16 shows the surface maximum acceleration along the 2-D modelization through the QUAKE/W code.  
Figure 16: Surface acceleration profile along the 2-D section analyzed through QUAKE/W code for the [PERSON] et al. [26] input.  
Figures 17-19 show the 1-D and 2-D analysis for the [PERSON] et al. [26] input, and the results have been summarized in Table 7.  
Figure 17: Comparison between 1-D and 2-D analyses performed by EERA and QUAKE/W for point 1 in terms of acceleration time history and spectral acceleration, for the [PERSON] et al. [26] input.  
Figure 18: Comparison between 1-D and 2-D analyses performed by EERA and QUAKE/W for point 2 in terms of acceleration time history and spectral acceleration, for the [PERSON] et al. [26] input.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**PGA\({}_{\text{EERA}}\)**} & \multicolumn{2}{c}{**PGA\({}_{\text{QUARE/W 1-D}}\)**} & \multicolumn{2}{c}{**PGA\({}_{\text{QUARE/W 2-D}}\)**} \\  & **[g]** & **[g]** & **[g]** \\ \hline Point 1 & 0.208 & 0.156 & 0.242 \\ Point 2 & 0.208 & 0.156 & 0.400 \\ Point 3 & 0.184 & 0.152 & 0.138 \\ \hline \hline \end{tabular}
\end{table}
Table 7: PGA by 1-D and 2-D analyses for the [PERSON] et al. [26] input at point 1, 2 and 3.  
Figure 19: Comparison between 1-D and 2-D analyses performed by EERA and QUAKE/W for point 3 in terms of acceleration time history and spectral acceleration, for the [PERSON] et al. [26] input.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **PGA** & **PGA** & **PGA** & **PGA** & **PGA** & **PGA** & **PGA** & **PGA** \\  & **eera** & QUAKE/W 1-D & QUAKE/W 2-D & EERA & QUAKE/W 1-D & QUAKE/W 2-D & EERA & QUAKE/W 1-D & QUAKE/W 2-D \\  & **[g]** & **[g]** & **[g]**
## 4 Discussion
Furthermore, our findings revealed that different concentrations of exogenous ABA significantly elevated the activities of protective enzymes (POD, SOD, CAT) and the levels of malondialdehydedehyde (MDA) and proline in _S. vicitiglia_ seedlings. This suggests that exogenous ABA contributes to improved osmoregulation and enzymatic activity under drought stress, thereby enhancing the seedlings' capacity for ROS scavenging and peroxide detoxification,and safeguarding the integrity of cytoplasmic membrane structures and functions [38]. This study's insights are congruent with research conducted by [PERSON] et al. (2017) and [PERSON] et al. (2019), highlighting the beneficial role of exogenous ABA in enhancing plant physiological responses to drought stress [39; 40]. The evidence points to exogenous ABA not only as a mitigator of drought stress effects on seed germination but also as a vital enhancer of drought resilience in plants, promoting a broader understanding of its utility in agricultural practices and ecological restoration efforts, especially in drought-prone environments like karst regions. ABA plays a pivotal role in regulating plant growth and development, markedly enhancing stress resistance, particularly under conditions of drought. Through the application of a membership function comprehensive analysis, this study has determined that a concentration of 50 mg\(\cdot\)L\({}^{-1}\) ABA is highly effective in promoting both the germination and subsequent growth of _S. vicitiglia_ seedlings, as well as in boosting plant enzyme activities. These findings underscore the potential benefits of ABA application in the cultivation of _S. vicitiglia_, suggesting its viability as a strategy for enhancing plant resilience to drought. However, the application's effectiveness can be influenced by various environmental factors, including soil type, the topographical landscape, and the composition of soil microbiota [41; 42; 43]. These elements may introduce certain constraints to the practical application of ABA, indicating the need for a thorough consideration of the specific cultivation conditions of _S. vicitiglia_. This research contributes to the development of novel drought-resistant strategies and lays a theoretical foundation for the thriving cultivation of _S. vicitiglia_ in the challenging conditions of karst drought-affected soils.
## 2 Materials and Methods  
### Image-Based Fire Heat Release Rate Prediction Methods  
A real-time image-based HRR prediction method is proposed in this study, using deep learning and image processing techniques, which can estimate HRR values by identifying flame characteristics. The proposed method involves (1) detecting and segmenting flame regions from fire images or videos, (2) extracting fire diameter and flame height information, and (3) predicting the HRR in real time based on the established correlation between flame size and HRR. As the core component of the proposed method, the YOLO-YCbCr model is proposed for flame segmentation, which integrates YCbCr color-space image processing with deep learning-based object detection (YOLOv8) to enable lightweight training and efficient flame segmentation. For comparison purposes, the YOLOv8 segmentation (YOLO-seg) model was selected as a representative deep learning segmentation model, which typically offers fast inference and high prediction accuracy [33].
## 2 Construction of GM-BP Combined Prediction Model
### GM-BP Combined Prediction Model  
Following the initial prediction by the GM(1,1) model, a BP neural network is employed to train the regression, utilising the initial prediction value as its input. Ultimately, a new prediction value is derived, thereby enhancing the accuracy of the model's prediction. The flowchart of this is shown in Figure 2. The primary modelling process of the GM-BP combined prediction model is as follows:  
1. A GM(1,1) grey model is fitted to the temperature at the time of measurement, the cantilever gravity, the distance from the tension section to the support, the height of the beam cross-section, the theoretical standing pre-camber, and the actual standing pre-camber to obtain the initial predicted values of these various factors.
2. In order to enhance the network's generalisation capacity and the precision of its calculations, while also mitigating the influence of the disparate scales of various factors, this paper employs Equation (12) to normalise the prediction outcomes of the GM(1,1) model. \[x_{i}^{\prime}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}\] (12) In this equation, \(x_{i}^{\prime}\) represents the normalised data, while \(x_{\text{max}}\) and \(x_{\text{min}}\) correspond to the maximum and minimum values in each data set, respectively.
3. The prediction data of the GM(1,1) model are used as the input value of the BP neural network, and the actual standoff pre-camber data are used as the output value to construct the BP neural network model and to adjust the training parameters of the  
Figure 1: Flowchart of GM(1,1) model calculation.  
BP neural network, including the maximum number of training times, the learning speed, and the minimum error of training.
4. The data to be predicted are input into the trained neural network to obtain a normalised predicted value, and finally the normalised predicted value is back-normalised to obtain the predicted elevation.
## 2 Effect of Plasma on Seed Germination
Accumulation of the GA\({}_{3}\) germination hormone and amylolytic mRNA in spinach seeds treated with DBD plasma has also been reported [28]. [PERSON] et al. [43] detected high concentrations of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) in cold plasma (Ar/Air, at low pressure) treated seeds and concluded that H\({}_{2}\)O\({}_{2}\) is a signaling molecule that stimulates seed germination. Hydrogen peroxide serves to maintain a low ABA/GA ratio, which promotes the activation of amylase, the mobilization of food reserves, and the low production of antioxidant enzymes. Nitric oxide (NO) is another reactive species that plays a regulatory role in seed germination. NO regulates the production of ABA, a vital phytohormone that initiates germination and breaks dormancy. In plants, NO is produced from the reduction of nitrate (NO\({}_{3}\)-) to nitrite (NO\({}_{2}\)-) by nitrate reductase. Hence, NO\({}_{3}\)- in plasma-activated water is primarily responsible for enhanced seed germination [57]. A model of the plasma effects on seed germination and the possible molecular and biochemical events is presented in Figure 1.
## 8 FE (Finite Element) Steady-State Vibration Analysis  
Frequency domain steady-state analysis has been proposed to calculate only the permanent vibration amplitude from the basic equation of motion as follows.  
\[[\mathrm{M}][\mathrm{u^{\prime\prime}(t)}]+[\mathrm{C}][\mathrm{u^{\prime}(t )}]+[\mathrm{K}][\mathrm{u(t)}]=\{\mathrm{p(t)}\} \tag{3}\]  
Converting to complex form, then the following is true.  
\[[\mathrm{K}+\mathrm{i}w\mathrm{C}-\mathrm{w}^{2}\mathrm{M}]\;a=p \tag{4}\]  
in which \(K=[\mathrm{K}+\mathrm{i}w\mathrm{C}-\mathrm{w}^{2}\mathrm{M}]\) is a complex impedance matrix, \(\mathrm{K}-\mathrm{w}^{2}\mathrm{M}\) represents stiffness and the inertia effect, and \(\mathrm{i}w\mathrm{C}\) is the imaginary part considering the damping effect. Thus, the equation of motion can be expressed with a function of frequency as follows.  
\[K(w)\;a(w)=p(w) \tag{5}\]  
The solution from Equation (5) for multi-degrees-of-freedom (MDOF) can be easily calculated through the finite element analysis solver. For the given vendor's dynamic unbalanced forces (F) at operating speed, one can generate: em factor = F/(2\(\pi\)t)\({}^{2}\). The steady-state function value can be given by \(\omega^{2}=(2\pi\)t)\({}^{2}\). Taking advantage of the efficient steady-state solver in SAP2000, any nodal amplitude of interest can be generated, along with the frequency range. This gives a full-spectrum view of dynamic amplitudes over the frequency range and elevates the reliability of a vibrational assessment. For instance, several nodal amplitudes of the compressor foundation are plotted in Figure 12 for a hysteretic damping of 1% and 2%. The frequency-domain analysis results show that the dynamic amplitudes reflect a significant and meaningful engineering mode and modal responses in the sense of resonance checking. The hysteretic damping effect on the amplitude calculation over the range is clearly shown. Therefore, the importance of the selection of a reasonable damping value should be emphasized. From these observations, the steady-state vibration analysis technique enables foundation engineers to utilize the exact soil-pile stiffness and damping at certain frequencies for the corresponding amplitude calculations over such ranges.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Range 0–70 Hz**} & **Vertical Stiffness** & **Vertical** & **Horizontal** & **Horizontal** \\  & **(N/m)** & **Damping** & **Stiffness** & **Damping** \\  & **(N sec/m)** & **(N/m)** & **(N sec/m)** \\ \hline Frequency & \(1.734\times 10^{8}\) & \(2.203\times 10^{6}\) & \(9.89\times 10^{7}\) & \(1.392\times 10^{6}\) \\ Independent & \(1.82\times 10^{9}\)\(\sim\) & \(6.05\times 10^{6}\)\(\sim\) & \(3.398\times 10^{8}\)\(\sim\) & \(1.3\times 10^{6}\)\(\sim\) \\ Dependent & \(2.2\times 10^{9}\) & \(2.45\times 10^{6}\) & \(3.44\times 10^{8}\) & \(7.6\times 10^{5}\) \\ \hline Delta & 10–13 Times & 3–1 Times & 3.4–3.5 Times & 0.9–0.5 Times \\ (0–70 Hz) & Increase & Increase & Increase & Reduction \\ \hline \hline \end{tabular}
\end{table}
Table 6: Individual pile stiffness and damping comparison.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 8 Polar-2
### Detector Design  
Compared to its predecessor, the POLAR-2 detector will be a factor of four larger in size, thereby employing a total of 6400 plastic scintillator bars. These scintillators are read out in groups of 64, using segmented SiPM arrays connected to their own front-end electronics, as indicated in Figure 13. As the GRB photons enter the detector, they can undergo Compton scattering in the detector array, followed by photo-absorption in a second scintillator. Their azimuthal scattering angle can be constrained using the relative position of the two scintillators in which the photon interacted. As the photons will scatter preferentially perpendicular to their initial polarization, this measurement allows determining the polarization of the incoming photon flux.  
Figure 13: An exploded view of one of the 100 POLAR-2 detector modules that make up the instrument. The detector measures the polarization of the incoming photons when these undergo Compton scattering in the segmented scintillator array. The scintillators are read out by a temperature-controlled Silicon photomultiplier (SiPM) array connected to its own front-end electronics. Taken with permission from [59].  
While the increase in detector area provides an increase in effective area bu a factor of four compared to POLAR, further improvements to its design allow for additional improvements in its sensitivity. In particular, the switch from the PMTs as used in POLAR to SiPMs results in a significant increase in sensitivity at lower energies.
## 5 Verification of the Prediction Model for Height of WFFZ
### Feature Importance  
Analysis of feature importance can better interpret the value of existing features and infer which features may be most favorable for predicting. To grasp the influence of input features on output, the importance of input features was analyzed by the algorithm Random Forest [25]. The number of trees in the random forest was set to 100, the deepest depth was 4, and the feature importance calculated by the algorithm Random Forest on the original data set are shown in Figure 12. The horizontal axis is on behalf of the importance score of the input features, and the vertical axis represents the input features. The results manifest that mining height (importance score: 0.416) has the greatest influence on the height of the WFFZ prediction, followed by mining method (importance score: 0.251), working face length (importance score: 0.152), the ratio of the thickness of hard rock layers and soft rock layers in the bedrock (importance score: 0.146), and buried depth (importance score: 0.035).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & **Height of WFFZ** & **Proportionality** & **Absolute Error** & **Relative** \\  & & **(m)** & **Coefficient** & **(m)** & **Error (\%)** \\ \hline  & SSA-ELMAN & 124.9 & 23.51 & 1.1 & 0.87 \\  & ELMAN & 168.9 & 31.87 & 42.9 & 34.05 \\  & BP & 69.2 & 10.72 & 56.8 & 45.08 \\ Empirical & \(H=4.82M+60.13\ln\frac{s}{100}\) & 166.8 & 31.47 & 40.8 & 32.38 \\  & \(+3.43M\ln\frac{b}{100}+16.17\) & & & & \\ Empirical & \(H=\frac{100M}{1.6M+3.6}\pm 5.6\) & 49.47 & 9.33 & 76.53 & 60.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the prediction results of the different models.