esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Magnetic Field
#### 3.3.3 Training Functions  
Sensitivity analysis of training functions in ANNs is a fundamental step in understanding the impact of different optimization algorithms on the network's learning process and performance. The choice of a suitable training function directly influences the convergence rate, accuracy, and efficiency of the ANN model. In this paper, the four most common training functions (in the literature) have been tested: Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), Resilient Backpropagation (RB), and Variable Learning Rate Backpropagation (VLRB). Analyzing the sensitivity of these training functions provides valuable insights into their strengths, weaknesses, and suitability for the proposed model.  
Electric Field  
As can be seen in Figure 5, the LM training function has the lowest RMSE in comparison to other training functions. In contrast, its best setup has a higher response time than the others. Also, the CFNN method has more accurate results than the FFNN in all training functions. Therefore, the best model at this step is the CFNN trained with LM.  
Magnetic Field  
Similar to the electric field, according to Figure 6, the LM has the lowest RMSE and highest R-squared. Also, the CFNN model's RMSE is less than the FFNN for all training functions. Moreover, in terms of response time, there is no significant difference between all the options. So, the best option is the CFNN method with the LM training function.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Neurons** & **RMSE** & **R-Squared** & **Response Time [ms]** \\ \hline
1 & 5 & \(4.87\times 10^{-2}\) & \(9.95\times 10^{-1}\) & 3.32 \\
2 & [3 3] & \(2.46\times 10^{-2}\) & \(9.99\times 10^{-1}\) & 4.59 \\
3 & [3 7 1] & \(2.53\times 10^{-2}\) & \(9.83\times 10^{-1}\) & 7.48 \\
4 & [5 5 1 5] & \(2.77\times 10^{-2}\) & \(9.99\times 10^{-1}\) & 7.67 \\
5 & [5 5 9 5 5] & \(3.13\times 10^{-2}\) & \(9.99\times 10^{-1}\) & 6.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity analysis of Layers and Neurons for CFNN for the magnetic field.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Neurons** & **RMSE** & **R-Squared** & **Response Time [ms]** \\ \hline
1 & 13 & 0.010050 & 0.999817 & 6.543 \\
2 & [1 11] & 0.002770 & 0.999984 & 8.467 \\
3 & [1 3 7] & 0.001953 & 0.999993 & 9.151 \\
4 & [1 3 3 3] & 0.002013 & 0.999994 & 11.206 \\
5 & [2 2 2 4 2] & 0.001763 & 0.9 Figure 5: Sensitivity Analysis on the training functions for both FFNN and CFNN for triple-layer models for the electric field. (**a**) RMSE (**b**) R-squared (**c**) Response time.  
Figure 6: Sensitivity Analysis on the training for both FFNN and CFNN for triple-layer models for the magnetic field. (**a**) RMSE (**b**) R-squared (**c**) Response time.
## 5 Varying Noise Level--Word Frequency Results
### Speech Word Performance Comparison  
The performance of each of the speech word was compared using the same method described in Section 4.1 with the aid of Equation (13). The resulting comparison plot is shown in Figure 12. Note that the poorest performance was observed in the speech word recognition of SW7, 'Hover', followed by SW2 'Forward' and SW1 'Go' as indicated in the combined trendline in Figure 12. The best speech recognition performance was observed in SW4 'Right', SW8 'Land', SW10 'One', SW11 'Metre', and SW12 'Stop'.  
Figure 12: Comparing all SW trendlines.
## 2 Related Works
### Digital Signal Compression and EMG  
The application of digital signal compression has been practiced in our prior researches [21; 22; 23]. An appropriate analysis of digital signal redundancy enables the development of a powerful compression algorithm, and an effective compression ratio.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Study** & **Notes** \\ \hline [PERSON] [14] & Batch processing algorithm based on analog-compressed sensing (CS) for the receiver side of an ultra-low-power wearable and wireless surface EMG (sEMG) sensor. \\ \hline [PERSON] [15] & Compression of high definition (HD) EMG signals recorded by two-dimensional electrode matrices at different muscle-contraction forces. Also includes methodological aspects of compressing HD EMG signals of the non-pimate (upper trapezius) and pinnate (medial gastrocnemius) muscles using image compression techniques. No real-time supportability. \\ \hline [PERSON] [16] & Algorithm based on an embedded zero-tree wavelets (EZW) scheme. Does not support real-time. \\ \hline [PERSON] [17] & Compression algorithm based on wavelet transform, neural network bit allocation procedure and arithmetic entropy coding. Does not support real-time. \\ \hline [PERSON] [18] & Batch processing algorithm based on a recurrent pattern algorithm. \\ \hline [PERSON] [7] & Algorithm based on discrete wavelet transform for spectral decomposition and de-correlation. Does not support real-time. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected prior studies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sub-Category** & **Gestational Period** \\ \hline Extremely pre-term & Less than 28 weeks \\ Very pre-term & 28 to 32 weeks \\ Moderate to late pre-term & 32 to less than 37 weeks \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of Pre-term Birth Based on the Gestational Period.  
In this study, compression of the EMG signal of maternal patients was attempted. The EMG signal is generally produced by skeletal muscles and can be used to detect medical abnormalities. Figure 2 shows an example EMG signal of one of the signals used in this study.  
There are limitations in efficiently compressing EMG signals because they contain a lower redundancy due to their irregularity (Figure 2). For example, ECG signals contain a very high redundancy due to their distinctive periodical cycles (PQRST interval). Efficient lossless ECG compression in a real-time medical information system environment has been proposed [8], but in this study, a different approach was needed. EMG data had to be analyzed more thoroughly due to its extreme irregularity (low redundancy) compared to ECG (high redundancy). A different compression algorithm was applied, and the main modification was analyzing and developing a different dictionary code word optimized for EMG. The specific features of the algorithm proposed in this research are described in Section 3.2.
###### Abstract  
Automatic modulation recognition (AMR) is used in various domains--from general-purpose communication to many military applications--thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.  
modulation recognition; K-nearest neighbor; genetic algorithm; higher-order cumulants IEEEexample:BSTcontrol  
1 Faculty of Engineering & Computer Sciences, National University of Modern Languages, Islamabad 44000, Pakistan1  
Department of Engineering and Applied Sciences, National University of Modern Languages, Islamabad 44000, Pakistan1  
Department of Engineering and Information Technology, University of Bergamo, 24129 Bergamo, Italy2  
School of Engineering & Applied Sciences, ISRA University, Islamabad Campus, Islamabad 44000, Pakistan1  
Department of Management, Information and Production Engineering, University of Bergamo, 24129 Bergamo, Italy2  
School of Computing and Engineering, University of West London, London W5 5 RF, UK5  
College of Computing and Information Technology, University of Doha for Science and Technology, Doha 24449, Qatar6  
Department of Computer Science, Comsats University, Islamabad 45550, Pakistan1  
Faculty of Computing and Informatics, University Malaysia Sabah, Kota Kinabalu 88400, Malaysia8
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Structure
#### 4.2.2 Multilayer FSSs
Although multilayer construct helps to achieve the optimum design, getting higher BW at lower operating frequency is a major obstacle for the FSS designers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**FSS Ref.**} & **Conductive Layers** & \multirow{2}{*}{\(\mathbf{f_{\ell}}\) (GHz)} & **Array Element Size** & \multirow{2}{*}{\(\mathbf{Total}\) Thickness} & \multirow{2}{*}{**FBW \%**} & \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Patch and Aperture Elements**} \\ \hline
[93] & 3 & 10 & 0.15\(\lambda\) & 0.033\(\lambda\) & 20 & 2 & Inter-digital capacitance patch with wire grid \\ \hline
[94] & 5 & 16.5 & 0.104\(\lambda\) & 0.22\(\lambda\) & 10 & 2 & Capacitive patch and inductive wire grid \\ \hline
[96] & 3 & 3.8 & 0.076\(\lambda\) & 0.038\(\lambda\) & 10 & 2 &
\begin{tabular}{c} Patches of outer square ring with four inner stepped-impedance transmission lines and wire grid \\ \end{tabular} \\ \hline
[12] & 2 & 2.4 & 0.104 & 0.013 & 29.2 & – &
\begin{tabular}{c} Meandered lines (printed \\ patterned wires) and 4 symmetric square metal patches \\ \end{tabular} \\ \hline
[99] & 3 & 10.4 & 0.225\(\lambda\) & 0.035\(\lambda\) & 23 & 3 &
\begin{tabular}{c} Capacitive patches \& hybrid \\ resonator (wire grid \& balanced \\ spiral resonators) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different multilayer FSSs. Figure 10: (**a**) Pattern of two compact resonating layers of FSS (**b**) Elements of double screen Koch FSS (**c**) Structural diagram of FSS based on complementary Minkowski Fractal elements.
# Applications of Medical Physics
## 2 Contributions
A radiation detector with a relatively longer history is the GEMPix, a detector developed at CERN a few years ago. It couples two technologies, the Gas Electron Multiplier as an amplifier for electric charges and four highly pixelated Timepix ASICs as readout. [PERSON] et al. [17] propose a comprehensive review about the medical applications of GEMPix. The authors describe examples of the use of this device for relative dose measurements in X-ray radiation therapy and hadron therapy. Some preliminary measurements performed to check the capabilities of GEMPix in proton tomography are also illustrated, as well as the most recent application of the detector in the field of microdosimetry. A discussion about on-going and future developments concludes the review. In the frame of internal dosimetry, the study by [PERSON] et al. [18] aims at the validation of a platform, named MCID, based on patient-specific images and direct Monte Carlo (MC) simulations for internal dosimetry in the radioembolization of liver tumors with \({}^{90}\)Y-labeled microspheres. The validated platform allows the fast implementation of a personalized MC dosimetry, based on patient imaging data. Additionally, the authors investigate the impact of tissue inhomogeneities on the dosimetric evaluation for the radioembolization treatment and the potential improvement of a MC approach in this therapy. The same treatment strategy for liver lesions, based on percutaneous ablation using \({}^{90}\)Y microspheres embedded into a sealant matrix, is considered by [PERSON] et al. [19]. In this manuscript, the absorbed dose per unit administered activity is assessed in small lesions by MC calculations considering a simplified geometry. Furthermore, an analytical formula derived from MC calculations that incorporates the absorbed fractions for \({}^{90}\)Y is proposed. The authors conclude that in a scenario of the intra-tumoral injection of microspheres, the proposed equation can be usefully employed in the treatment planning of spherical lesions of small size (down to 0.5 cm diameter) providing dose estimates in close agreement with MC calculations. Lastly, under the main topic of dosimetry, the Special Issue includes the study by [PERSON] et al. [20] which contribute to the development of a practical protocol for fingernail electron spin resonance (ESR) dosimetry useful for the routine monitoring of the extremity exposures of medical workers handling radiation sources. The authors investigate the effects of chemical treatment using dithiothreitol (DTT) on the changes of the ESR spectra in irradiated and unirradiated fingernails after a long storage time. The results show that the treatment of fingernail samples with DTT lead to a significant reduction of the background signal. They also point out that freezer storage of the samples is more effective than vacuum storage. Furthermore, after the DTT treatment, the fingernail samples provided good signal stability for both background and radiation-induced signals with less intra-individual variations.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Red Noise
#### 4.3.2 Logarithmic Signal  
As an example of a signal that is not low-rank, consider a series with the signal (14). In this case, we use the version of the criteria with the stationarity check (Algorithm 1, CHECKSTAT = TRUE).  
Figure 13 depicts the mean estimated ranks and the optimal ranks for the smallest root of average MSEs over 1000 realizations. In this case, the superiority of TRMAT is clearly evident at relatively low noise levels, as illustrated in Figure 14. At high noise levels, however, both methods perform poorly, largely due to a significant underestimation of ranks.  
Figure 14: Example ’log’: Dependence of relative RMSE on red noise level, TRMAT and MDL.  
Figure 13: Example ’log’: Dependence of rank estimation on red noise level, TRMAT and MDL.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## Materials  
### Study Area  
Prydz Bay is located in the Indian Ocean sector of the Southern Ocean between 66\({}^{\circ}\)E and 79\({}^{\circ}\)E (Figure 1). In late November, the outer edge of pack ice can reach approximately 63-64\({}^{\circ}\)S, some 600 km transit distance to Zhongshan Station. Large inter-annual variability of the sea ice conditions exists in the region due to complex bathymetry and water-mass distribution as well as the ocean-sea ice interaction [15]. The average thickness of drift ice and land-fast sea ice are typically 0.7 m and 1.4 m,  
Figure 1: RV _Xuelong_ navigation region in Prydz Bay outside Zhongshan Station. Cruise tracks for the 28\({}^{\text{th}}\) (2011, red), 29\({}^{\text{th}}\) (2012, blue), and 30\({}^{\text{th}}\) (2013, magenta) CHINARE are shown. The inset shows the locations of all four Chinese Antarctic research stations. CHINARE: Chinese National Antarctic Research Expedition.  
respectively. On average, transecting in the drift ice zone takes 2-4 days, followed by 1-3 days of breaking through the land-fast ice to the anchorage location off Zhongshan Station.
## 3 Results
### Analysis of the Impact of Spatial Differentiation on Vegetation GPP  
#### 3.4.1 Impact of Individual Factors on Vegetation GPP  
The spatial correlation patterns between GPP and various driving factors solely characterize their association strength, without establishing quantitative causal relationships regarding GPP's influence on these factors. Geographical detectors enable the examination of both individual factor contributions and multifactor interaction effects on dependent variables, while quantitatively assessing these influences to enhance explanatory power [37]. Compared with conventional statistical approaches, geographical detectors demonstrate superior capability in objectively analyzing spatial data, quantitatively assessing influencing factors, and revealing their coupling relationships with geographical phenomena. The application of geographical detector models to vegetation GPP studies enables quantitative evaluation of both individual factor contributions and their synergistic effects [42; 43], as illustrated in Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Driving Factor** & \begin{tabular}{c} **Positive** \\ **Correlation (\%)** \\ \end{tabular} & \begin{tabular}{c} **Negative Positive** \\ **Correlation (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Significant (\%)** \\ **(\% \(<\) 0.05)** \\ \end{tabular} &
\begin{tabular}{c} **Slope** \\ **Slope** \\ \end{tabular} \\ \hline Precipitation & 70.1\% & 29.9\% & 8.0\% & \(-\)4.420–15.170 \\ Temperature & 83.7 & 16.3 & 40.6\% & \(-\)0.004–0.094 \\ Radiation & 17.1\% & 82.9\% & 15\% & \(-\)15.260–4.770 \\ Vapor Pressure Deficit & 84.3\% & 15.7\% & 21\% & \(-\)0.992–3.502 \\ Evapotranspiration & 85.0\% & 15.0\% & 38\% & \(-\)9.180–12.440 \\ Leaf Area Index & 82.6\% & 17.4\% & 34.9\% & \(-\)0.009–0.017 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of driver change rates and significance analysis. Figure 11: _Cont._The spatial correlation patterns between various influencing factors and GPP merely reflect regional correlation strength, without enabling quantitative determination of their actual impacts on GPP. The geographical detector model's differentiation and factor detection results (Table 3) reveal that all influencing factors exhibit statistically significant impacts (\(p<0.001\)) on the spatial distribution of GPP across the Qinghai-Tibet Plateau, confirming the robust explanatory power of the selected factors [44]. This study employs the geographical detector method to analyze six natural factors, temperature, precipitation, radiation, vapor pressure deficit (VPD), evapotranspiration (ET) and leaf area index (LAI), influencing vegetation GPP spatial patterns simulated by the  
Figure 11: Detection of GPP impact factor q-value: (**a**) 2001; (**b**) 2006; (**c**) 2011; (**d**) 2016; (**e**) 2022; (**f**) 2001–2022.
# Online Signature Verification Systems on a Low-Cost FPGA
## 4 Dtw  
The DTW result is used to find the optimal time alignment between signals \(s\) and \(t\) by computing a \(G\) matrix, which is composed of \(g(i,j)\) points. The optimal warping path is the set of points with minimal \(g(i,j)\), pairing each \(s(j)\) sample with another \(t(i)\) sample. Figure 7 shows the bidimensional DTW implemented in the signature algorithm, which aligns 256-width \(x\)-\(y\) samples from the acquired signature \(\left\{s_{x},s_{y}\right\}\) to the enrolled template \(\left\{t_{x},t_{y}\right\}\). The DTW computes the result matrix \(G\) by pairing shape samples \(\left\{s_{x}(j),s_{y}(j)\right\}\) and \(\left\{t_{x}(i),t_{y}(i)\right\}\) on a permitted region \(R\). Typically, DTW regions can be the Sakoe-Chiba band or the Itakura parallelogram [15; 24], which is the one chosen in the work presented. The algorithm starts declaring the initial condition and continues computing each new \(g(i,j)\) on \(R\), until the last \(g(255,255)\) is calculated.  
The initial condition is declared at the point \(i=j=-1\). Alignment outside the region is not permitted by declaring \(g(i,j)=\infty\) on the points that do not belong to \(R\).  
\[g(-1,-1)=0 \tag{2}\]  
\[g(i,j)=\infty;(i,j)\
otin R \tag{3}\]  
Each \(g(i,j)\) on \(R\) is selected as the minimum value of three possible paths, depending on both previously computed points of the \(G\) matrix and three distances.  
\[g(i,j)=min\left\{\begin{array}{l}g(i-1,j-2  
The \(d(i,j)\) denotes the Euclidean distance between the bidimensional samples, which involves a square root operation.  
\[d(i,j)=\sqrt{\left(t_{x}(i)-s_{x}(j)\right)^{2}+\left(t_{y}(i)-s_{y}(j)\right)^{ 2}} \tag{5}\]  
In order to enhance the execution time of the DTW algorithm, all three approaches minimize the number of square root operations by an auxiliary matrix \(D\), which is previously calculated. The distances required to calculate \(g(i,j)\) are retrieved from the \(D\) matrix, avoiding repeating the distance computation on the same point. Furthermore, the runtime of the DTW algorithm can be optimized by parallelizing the computations of the matrices \(D\) and \(G\), which are adopted in the last two approaches. Finally, a high throughput square root calculation on the previous optimizations can greatly reduce the DTW execution time, as in the custom circuit approach.
# Smart Healthcare Network Management: A Comprehensive Review
## 3 Smart Healthcare Remote Monitoring Contexts
### Considered Contexts (Typical)  
These contexts encompass, but are not limited to, the workplace and outdoor environments. Patients can continue their daily activities, including going to work, while being monitored for specific health conditions. Workplace monitoring can be facilitated through a dedicated gateway or by utilizing the patient's smartphone. When patients  
Figure 1: Typical and atypical location contexts.  
travel outside their homes but remain within the same country, connectivity to the backend system can be maintained through their mobile device or a specialized device provided by the service provider.
## 4 Experimental Results
### Statistical Results
The results confirmed that the proposed GFSDP algorithm was more accurate than the conventional algorithms for cuffless BP and CI estimations. As previously mentioned, GP algorithms predict distributions rather than single-response values. Therefore, CIs were provided along with the predicted responses to represent the uncertainty. However, even with the GP algorithm, few samples for each patient resulted in wide CIs [8].