esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 5 Discussion  
Due to the accuracy of the predicting model, measuring the complexity of the rock mass conditions and the input data parameters, as well as the criteria, prediction, and estimation of the blasting vibration, becomes a more challenging and time-consuming process. Hence, conventional empirical models are insufficient, due to the limitations of the empirical formulas. To tackle this challenge, researchers over the years have used different machine learning techniques to describe complicated blasting vibration studies, by interrelating the characteristics that have been determined to cause such vibrations, as demonstrated in the works of [PERSON] et al. [43], [PERSON] et al. [44], [PERSON] et al. [45], and [PERSON] et al. [46], who conducted different research projects using soft computing intelligence for predicting the blast vibration and understanding the wave formation associated with the blasting.  
The main contribution of this research is to introduce a new prediction model, based on the Bayesian neural network (BNN), and four machine learning models (random forest, gradient boosting, decision tree, and k-nearest). Over the last few decades, researchers such as [PERSON] [21], [PERSON] et al. [47], and [PERSON] [18] have used the Levenberg-Marquardt neural network for the prediction of blast-induced ground vibration. However, due to its superior accuracy, this study utilized the Bayesian neural network (BNN) rather than the Levenberg-Marquardt neural network.  
In addition, this study introduced new input parameters; however, this study did not incorporate the geological features of the study area, due to limited resources, nor did we carry out experimental work related to rock properties.  
Finally, after assessing the results of the models' evaluation, SHAP was performed, to understand the importance of each of the models' features and to avoid the issue of the black box. When using SHAP, it is easy to understand how each parameter contributes to a model's prediction for a particular instance. Based on the comprehensive SHAP analysis, this study shows the distance has more future importance when compared to the other input parameters. However, in this study, the spacing, diameter of the hole, burden, and bench height are not included in the study, because they each have the same value, so, based on the standard deviation, the statistical findings from the spacing, burden, bench height, hole diameter shows that there is no value, in terms of deviation, that improves the model for a better prediction, hence the BNN and four machine learning models are developed without the above listed input parameters.  
Figure 13 demonstrates the importance of the input parameters for the ground vibration. Based on the SHAP analysis, this study shows the distance have the higher future importance when compared to the other input parameters.
## 4 Enhanced LMS Methods with IMC
### Novel Adaptive Filtering Algorithm Based on the IMC 2: Recursive Method
#### 4.2.1 Conceptual Explanation
The adaptive weight vector \(w_{k}\) is updated with the following equation:  
\[\mathbf{w}_{k+1}=\mathbf{w}_{k}+\mu\mathbf{x}_{k}^{*}e_{k} \tag{22}\]  
where \(\mu\) is the step size. The error signal \(e_{k}\) is obtained, and the output of the filter that has been affected by the secondary path dynamics \(y_{k}{{}^{*}}\) is defined as follows:  
\[e_{k}=d_{k}-y_{k}^{*} \tag{23}\]  
\[y_{k}^{*}=\mathbf{P}_{\mathbf{c}}\cdot y_{k} \tag{24}\]  
The traditional feedback ANC system with the filtered-X LMS algorithm has filter before the reference signal. However, in this novel system, internal model control is introduced and it is used for compensating unavoidable time delay in the feedback loop. This kind of time delay is actually from the secondary path dynamics and the estimation process. At least, the effect from the secondary path dynamics can be managed and it does not need offline identification process for the secondary path again.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
###### Abstract  
With continued scaling of CMOS technology, the critical charge required for state retention in SRAM cells has decreased, leading to increased vulnerability to radiation-induced soft errors such as single-event upsets (SEUs) and single-event multi-node upsets (SEMNUs) in space environments. To address these reliability challenges, this paper proposes a 16-transistor radiation-hardened SRAM cell, EWS-16T, designed to improve resilience against soft errors. The performance of the proposed EWS-16T cell was evaluated through the 90 nm CMOS process and compared with previously reported radiation-hardened cells, including QCCS, SCCS, SAW16T, HP16T, RHSC16T, and S8P8N. The results show that EWS-16T achieves the shortest write access time (22.11 ps) and the highest word line write trip voltage (WWTV) (279 mV) among all comparison cells. In addition, it demonstrates excellent performance in terms of critical charge tolerance (>300 fC) and low hold power consumption (53 nW). Furthermore, a comprehensive performance evaluation using the electrical quality metric (EQM) confirms that the EWS-16T cell achieves an outstanding balance among write efficiency, power consumption, and soft error resilience. These results indicate that EWS-16T is a highly promising SRAM design capable of ensuring reliable operation even in radiation-intensive space environments.  
capable: 2025 15 6988 +
Footnote †: journal: Applied sciences  
Article
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 5 Discussion
### Supplementary Experiment  
We want to explore the influence of the position and number of SGA modules on the network. We designed some ablation experiments. In this part, we compare the performance of SGA conducted at different convolution stages (Table 4). The ranks 1, 10, 20, and mAP tend to increase when SGA is conducted on more layers. The best result appears when we perform SGA on Stage 2 and 3. This is probably because searching more stages expands the search space, which allows us to explore more varied selections of features. However, blindly extending the search scope will pose great challenges to the discovery of an optimal feature set. The underlying reason is that STE (straight-through estimator) will generate more and more gradient estimation errors when backpropagating through too many layers [51].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Settings** & \multicolumn{4}{c}{**All-Search**} & \multicolumn{4}{c}{**Indoor Search**} \\ \hline
**Methods** & **r = 1** & **r = 10** & **r = 20** & **mAP** & **r = 1** & **r = 10** & **r = 20** & **mAP** \\ \hline B & 45.88 & 82.96 & 90.38 & 46.36 & 49.73 & 90.90 & 96.97 & 59.21 \\ \hline B+S & 48.51 & 84.41 & 91.69 & 46.12 & 54.30 & 90.62 & 97.10 & 61.10 \\ \hline B+G & 49.65 & 84.12 & 92.72 & 49.01 & 54.57 & 91.44 & 96.24 & 62.21 \\ \hline B+S+G & 51.64 & 86.80 & 94.66 & 49.99 & 57.29 & 92.89 & 96.38 & 64.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SYSU ablation experiment results.  
Based on the results in Table 4, we can draw the following conclusions.  
1. Compared with the baseline (M1), the SGA module could improve the performance of the network. At r = 1, the results of M2-M10 are better than the baseline.
2. The network of two SGA modules gives better results. For long-range dependency, two SGA modules can better preserve this relationship, which could mine the discriminative features.
3. Experiments prove that the extracted features of the network proposed in this paper are more discriminative. The rank 1 accuracy reaches 51.64%. The framework in this paper is optimal.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 2 Motivation
These aspects represent the categories in which the literature is summarized, and for each of them, general solutions and relevant challenges are outlined. Furthermore, a general review of machine learning (ML)-based algorithms for CR networks is made in [8]. It is focused on various ML methods for spectrum sensing, dynamic spectrum allocation, and spectrum prediction, and the relevant research challenges are outlined. _In summary, these surveys offer limited discussion of the design and implementation of graph-based RA methods in contemporary wireless networks, thus providing ample motivation for the analysis made in this article._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Survey Paper** & **Topics Addressed** & **Principal Advantages** & **Limitations, Relevant to** \\  & & & **This Work** \\ \hline [PERSON] et al. (2021) [5] & DRL algorithms for RA and UA in Het-Nets & \begin{tabular}{c} Categorization of Het-Net \\ scenarios and of the DRL \\ methods used; advantages \\ of DRL \\ \end{tabular} & \begin{tabular}{c} Graph-based algorithms are \\ not discussed \\ \end{tabular} \\ \hline [PERSON] et al. (2021) [11] & \begin{tabular}{c} GNN-based solutions for \\ wireless communications and \\ image processing \\ \end{tabular} & Key challenges are outlined & Limited discussion of RA \\ \hline [PERSON] (2021) [12] & \begin{tabular}{c} GNN-based solutions for \\ telecommunication networks \\ \end{tabular} & \begin{tabular}{c} Comprehensive survey in \\ wired, wireless, \\ and software-defined \\ networks \\ \end{tabular} & Limited discussion of RA \\ \hline [PERSON] et al. (2021) [4] & RA methods in Het-Nets & \begin{tabular}{c} Categorization of RA \\ algorithms according to their \\ relevant tasks and \\ performance parameters; new \\ models for RA are proposed \\ \end{tabular} & \begin{tabular}{c} Limited discussion of \\ graph-based RA methods \\ \end{tabular} \\ \hline [PERSON] et al. (2018) [6] & Aspects of D2D & \begin{tabular}{c} Categorization of methods for \\ implementing the main \\ implementation aspects \\ of D2D \\ \end{tabular} & \begin{tabular}{c} Limited discussion of \\ graph-based RA \\ \end{tabular} \\ \hline [PERSON] et al. (2021) [8] & \
\begin{tabular}{c} ML-based algorithms for \\ spectrum sensing and RA in \\ CR networks \\ \end{tabular} &
\begin{tabular}{c} Categorization of ML \\ methods according to the \\ design for spectrum sensing, \\ prediction, and allocation \\ \end{tabular} & Limited discussion of \\ \hline
**This paper** & \
\begin{tabular}{c} Graph-based RA algorithms \\ for RA in homogeneous and \\ Het-Nets, D2D, CR and ISTNs \\ \end{tabular} &
\begin{tabular}{c} Comprehensive survey of the \\ design and performance of \\ graph-based RA algorithms; \\ discussion of challenges for \\ graph-based design for \\ inter-connectivity and \\ coexistence algorithms in \\ ISTNs through GRIST \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of relevant surveys on graph methods applied in wireless communication networks.
## 3 Results and Discussion
### Mechanical Properties of CCNF/CS Nanocomposite Films  
The CCNF prepared under pH 1 was used as filler in the CS film, which was plasticized by glycerol. The mechanical properties of the CS/CCNF composite film with CCNF content of 0.5, 1, 2, 3, or 4 wt% are presented in Figure 7. When CCNF was added to starch, the tensile strength and elongation at break of CS/CCNF composite films were significantly increased at first, then reached a turning point, and then were gradually decreased with the increase of CCNF content. The turning point for strength was at 0.5% of the CCNF, and for breaking elongation at 1%. Generally, cellulose nanofiber is an excellent polymer filler for reinforcing [41]. In a previous study [42] of our research group on starch/nanocellulose composite film, the tensile strength increased with the increase of the filler content up to 20%. The difference between the data of this study and previous studies may be explained by the huge difference in crystallinity: the crystallinity of CCNF obtained in this study is only \(\sim\)2% (Table 2), whereas common cellulose nanofibers have high crystallinity, such as 80-90% for bacterial cellulose nanofiber and 90% for nanocrystalline cellulose [11]. This means that the CCNF with lower crystallinity may not be applicable to polymer reinforcement.
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
## 1 Introduction
### Useful 2D Geometric Definitions
To illustrate the use of the convex hull we must consider the existence of three receivers centered at coordinates \(C_{1}\
eq C_{2}\
eq C_{3}\) (and these coordinates cannot all be collinear) that collect a signal from a point with distance \(r_{1},r_{2}\) and \(r_{3}\) (radial distance), respectively. \(P_{kj}\) and \(Q_{kj}\), \(k\
eq j\), represents the polar-points that lie to the circle centered in \(C_{j}\) that is obtained by the external point \(C_{k}\) (center of another circle), as Figure 2a shows. Thus, the smallest convex polygon that contains all obtained polar-points is the convex hull for these points and this minimal polygon defines our ROI. This region in red-color lines is used to illustrate the ROI for the proposed geometric models presented below (always representing a convex hull to define a ROI). **(d-9)**: The location estimation of the emitter, \(E_{xy}\big{(}E_{x},E_{y}\big{)}\), is based on a set \(S\) that contains \(n\) points, \((x,y)\), which are collected in a defined ROI. This location is given by the centroid point among all points in \(S\), by  
\[D_{\overline{PQ}}=\sqrt{(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}}.\]0  
Section 2 presents the models (methods) proposed in this work. All examples illustrated in Section 2 are generated in a simulator system (MatLab code) that implements the respective proposed models. This simulator is available for download (www.coding2 change.org/articles/GeometricModels.zip). The simulated cases presented in this work use systems composed by three, four, or five receivers, but the simulator is able to operate with any number (>1) of receivers. The Figure 2b presenting a Cartesian system to locate points in meters. In all figures bellow that involves example of location problems the description of the axes (label) were omitted. The axes \(x\) and \(y\) represents the Cartesian system to locate a point in the plan. The coordinate values are free of linear unit (meters, feet, or geographic coordinates).
## 2 Operational Retrievals of ET from EO
### Operational Capability of EO Technology in ET Estimation
#### 2.2.1 Spinning Enhanced Visible and Infrared Imager (SEVIRI)
ET retrievals from this operational product is found to be generally 25% if ET is greater than 0.4 mm h\({}^{-1}\) and 0.1 mm h\({}^{-1}\) in any other case [18; 33; 34; 35]--see example of validation studies shown in Figure 2 below which comes from a recently published work of one of the co-authors of this manuscript, [35]). Yet, to our knowledge, very few studies have been concerned with establishing the accuracy of the SEVIRI ET instantaneous operational product, particularly at a continental scale [35; 36; 37]. Figure 1: SEVIRI ET product on 6 August 2011 at different hours on the same day for Spain. The spatiotemporal variability of ET throughout the day can be observed.
## 2 Materials and Methods
### Sinogram Inpainting Generative Adversarial Network (SI-GAN)
The loss function plays a key role in the quality of the result of the model and is a basic element in machine learning. In the SI-GAN network, the loss function has three parts: generation adversarial loss, hole loss, and multi-scale L1 loss. The expression of the generation adversarial loss function is:  
\[\mathcal{L}_{GAN}(G,D)=\mathbb{E}_{y\sim P_{data}(y)}[\log D(y)]+\mathbb{E}_{x \sim P_{data}(x)}[\log(1-D(G(x)))] \tag{3}\]  
where \(G\) is the generator network that strives to learn the mapping of \(G(x)\to y\) and to generate through \(x\) as realistic images as possible that conform to \(y\) distribution, \(x\) represents the missing projection data, and \(y\) denotes the real non-missing projection data. The purpose of discriminator \(D\) is to determine as much as possible whether the input image is real or generated by the generator, and \(P_{data}\) represents the data distribution. In order to make the generator generate data that are as real as possible in the missing area of the image, we introduced a hole loss to constrain the optimization results:  
\[\mathcal{L}_{hole}(G)=\mathbb{E}_{x\sim P_{data}(x)}[\left\|(y\odot m)-(G(x) \odot m)\right\|_{1}] \tag{4}\]  
where \(m\) is the mask manually set according to the missing sinogram of the input. The value of the missing portion of the projection data (for example, in \([91^{\circ},180^{\circ}]\)) is set as 1, and the value of the rest of the projection data (for example, in \([0^{\circ},90^{\circ}]\) and \([181^{\circ},192^{\circ}]\)) is set as 0, \(\odot\) represents a dot multiplication of matrices, and \(\left\|\ \right\|_{1}\) represents the \(L_{1}\) norm. The multi-scale \(L_{1}\) loss limits the output results of each layer of the decoder, so that the output results of each layer are as similar as possible to the original image of the same scale. The loss constraints of different scales can further improve the model performance:  
\[\mathcal{L}_{ms}=\sum_{p=1}^{5}\left\|y^{p}-f(\phi^{p})\right\|_{1} \tag{5}\]  
where \(\phi^{p}\) is the output of the p-th layer of the decoder, and the function \(f\) encodes the input image into a grayscale image with the same size and a channel number of 1. \(y^{p}\) represents the original image with the same size as \(\phi^{p}\). Finally, we defined the complete loss function as follows:  
\[\mathcal{L}_{SI}=\lambda_{1}\mathcal{L}_{GAN}(G,D)+\lambda_{2}\mathcal{L}_{hole }(G)+\lambda_{3}\mathcal{L}_{ms} \tag{6}\]  
In this article, the values of the hyperparameters \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) were, respectively, 0.05, 6, and 0.5.
## 2 Materials and Methods
### Optical Emission Spectroscopy  
Optical emission spectroscopy is a general term for techniques used to obtain optical emission spectra, i.e., the light emission of an excited sample. In plasma analysis, it is necessary to determine the wavelength of a particular spectral line, which enables the identification of a particular species in the plasma system.  
Optical emission spectroscopy has very good analytical performance, especially for quality control and material science analyses. The application of optical emission spectroscopy to plasma systems generated by high-voltage electrical discharges is fast and simple and allows high spectral reproducibility and the achievement of consistent results under different conditions with relative standard deviations below 30%, which is in line with common guidelines, e.g., of the Association of Official Agricultural Chemists. The estimation of uncertainty in optical emission spectroscopy is influenced by the overlap of spectral lines, the operating conditions to reduce background noise, and the characteristics and method of using optical fibres for signal transmission. The use of spectrometers with high optical resolution is recommended, while ensuring thermal stability, low humidity and minimal mechanical vibrations.  
Figure 1: Experimental setup for high-voltage electrical discharge (HVED) with optical emission spectrometer (OES).  
Figure 2: Part of high-voltage electrical discharge (HVED) setup: glass reactor with electrodes.  
Plasma diagnostics were carried out using the Ocean FX extended range optical emission spectrometer OCEAN-FX-XR1-ES (manufacturer Ocean Optics, Orlando, FL, USA) (Figure 4). The emission of a specific volume in the plasma chamber was transmitted to the spectrometer via an optical fibre (600 \(\upmu\)m FIBRE, solarisation resistant, 2 m, S/N). The optical fibre was mounted near a glass reactor above the water surface. The collimating lens 74-UV Collimating Lens UV/VIS 200-2000 nm (manufacturer Ocean Optics, Orlando, FL, USA) can be screwed onto the end of a fibre to collimate the light. The spectrometer is connected to a computer for data processing. The OCEAN FX-XR1-ES is a versatile extended range spectrometer (wavelength 200-1025 nm) that features fast acquisition (up to 4500 scans per second; varies depending on operating system and computer performance), onboard averaging (up to 5000 spectra), onboard processing for improved signal-to-noise ratio (SNR 290:1 full signal), and a reduced data transfer time. The integration time is 10 \(\upmu\)s-10 s, the optical resolution is 1.69 nm FWHM (typical) and the thermal stability is 0.11 pixels/\({}^{\circ}\)C. It is used for high-speed processes and the measurement of fast events, including flicker in illumination. The spectrometer can be easily connected to other devices via Wi-Fi, RS-232, Gigabit Ethernet and USB. A version of the spectrometer with increased sensitivity (-ES) has a converging lens for the detector, which increases the efficiency of light collection. The spectra were recorded with an integration time of 250 ms and 30 scans for averaging.
## 8 Conclusions  
This article proposed a robust \(PI^{\alpha}D^{\beta}\) controller based on sine-cosine algorithm (SCA) for high-order continuous interval plant (HOCIP) using a model order reduction technique. For controller design, HOCIP is initially diminished to reduced-order continuous interval plant (ROCIP) using the modified Routh-Pade approximation technique. The denominator polynomial of ROCIP is derived using modified Routh approximation which always guarantees the stability and the numerator polynomial is obtained by matching TMs and MPs that improve the transient and steady-state response of the ROCIP. Then, SCA algorithm is used to determine the five unknown parameters of \(PI^{\alpha}D^{\beta}\) controller for ROCIP. The values of controller parameter are determined by minimizing two non-linear equations. For the design of \(PI^{\alpha}D^{\beta}\) controller, gain crossover frequency (\(\omega_{gc}\)) and phase margin (\(\phi_{pm}\)) are considered as performance criteria. The simulation results demonstrate that the desired frequency domain specifications for both ROCIP and HOCIP are approximately fulfilled with the proposed SCA-based FOPID controller. Thus, it is established that proposed technique is an excellent alternative approach for the design of an FOPID controller using  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & \(\phi_{pc}\) **(deg)** & \(\omega_{gc}\) **(rad/sec)** & \(K_{a}\) & \(K_{b}\) & \(K_{c}\) & \(\alpha\) & \(\beta\) \\ \hline SCA & 87.32 & 54.7 & 324.565 & 658.081 & 204.214 & 0.616 & 0.992 \\ PSO & 75.83 & 43.2 & 245.496 & 335.474 & 443.675 & 0.457 & 0.845 \\ NMS & 58.57 & 28.3 & 832.765 & 147.164 & 379.852 & 0.329 & 0.693 \\ LJ & 31.54 & 33.7 & 473.359 & 259.876 & 156.952 & 478 & 0.573 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Controller parameters and specifications for different algorithms.  
Figure 15: Bode response of controlled HOCIP with the FOPID controller.  
reduced-order modeling. The future line of work for the proposed technique lies in the design of a controller for multi-input-multi-output (MIMO) continuous and discrete interval systems.  
The paper was a collaborative effort among the authors. Conceptualization, methodology and simulation, J.K.B, N.P., T.V.; validation and formal analysis, J.K.B.; visualization and supervision, V.P.S. All authors have read and agreed to the published version of the manuscript.  
The work reported herewith has been financially supported by SERB grant (Ref.: ECR/2017/000212).  
SERB grant (ECR/2017/000212).  
The authors declare no conflict of interest.