esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: This study aimed to investigate the association between body composition (BC) specific parameters such as fat mass (FM) and lean body mass (LBM) and bone quality in obese and non-obese young Slovak adults (18-30 years) using bioelectrical impedance analysis and quantitative ultrasound while considering the factors of sex and obesity status; Methods: BC was evaluated using bioelectrical impedance analysis (InBody 770) and bone quality by the speed of sound (SOS) in radius using QUS (Sunlight MiniOmnni) in 774 young Slovak young adults aged 18-30 years, categorized by sex and obesity status (body fat %, PBF, \(\geq\)28% for men and \(\geq\)20% for women); Results: In non-obese individuals, radial SOS correlated positively with FM parameters. Significant positive correlations with LBM, including skeletal muscle mass (SMM), were observed in non-obese men but not in women. Conversely, SOS correlated negatively with FM parameters in obese individuals, significantly only in women. The correlation of SOS with LBM in obese individuals was insignificant in both sexes. Age, visceral FM, FM in the arm, and vitamin D intake were identified as the main predictors of radial SOS in young adults, except in obese men. Conclusions: Findings indicate FM benefits bone tissue in non-obese individuals, while excessive adiposity deteriorates bone quality in obese individuals, necessitating tailored evaluations and interventions based on sex, obesity status, and specific predictors.  
bone tissue; quantitative ultrasonography; fat mass; lean body mass; young adults 2024147319. [[https://doi.org/10.3390/app14167319](https://doi.org/10.3390/app14167319)]([https://doi.org/10.3390/app14167319](https://doi.org/10.3390/app14167319))
## 4 Conclusions  
In this research, we conducted the first ever assessment of the microwave shielding efficiency properties of polyaniline-based NiO/ZnNb\({}_{2}\)O\({}_{6}\) composites. NiO/ZnNb\({}_{2}\)O\({}_{6}\) composites hold the potential to serve as cost-effective and straightforward solutions for microwave shielding applications. Among the compositions tested, the epoxy-based combination of NiO-ZnNb\({}_{2}\)O\({}_{6}\) (75-25% by weight) with Aniline at a 1/1 ratio demonstrated the most remarkable microwave shielding efficiency. Notably, this composite material achieved an impressive low of \(-41.16\) dB at a frequency of 6.24 GHz, all while maintaining robust shielding effectiveness across a broad frequency band, registering values below \(-10\) dB from 0 to 0.36 GHz, 3.16 to 4.43 GHz, 4.74 to 6.52 GHz, and 7.31 to 8 GHz. These findings are particularly significant as they contribute to the advancement of innovative composite materials that have the ability to shield electromagnetic fields and are useful in a variety of applications.  
Furthermore, there is potential for investigating the microwave shielding efficiency, reflection loss, and absorption efficiency of NiO/PANI/ZnNb\({}_{2}\)O\({}_{6}\) composites when augmented with various additives, particularly at higher radar frequencies or in tandem with technological advancements in elevated frequency bands. Such composites can serve as valuable models for combating electromagnetic pollution, developing armor and shielding materials, and facilitating the comprehensive evaluation of diverse composite material attributes.  
Methodology, M.E., E.I.S. and J.-E.F.M.I.; Validation, E.I.S.; Formal analysis, M.E., E.I.S. and J.-E.F.M.I.; Investigation, M.E., E.I.S. and J.-E.F.M.I.; Data curation, M.E. and J.-E.F.M.I.; Writing--review and editing, E.I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This work is dedicated to the cherished memory of [PERSON], [PERSON], and [PERSON]. We extend our sincere gratitude to Adana Alparslan Turkes Science and Technology University, Adiyaman University, and the University of Miskolc for their invaluable support in facilitating this research.  
The authors declare no conflicts of interest.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 4 Depth Distribution Algorithm Based on Linear Block Codes
### The Algorithm of Code Starting Bit Recognition  
After the recognition of code length \(n_{0}\), we take out \(n_{0}\) the value of \(\varepsilon_{n_{0},d}\), then the maximum of \(\varepsilon_{n_{0},d}\) is the starting bit of the code word. In practical application, the requirement of the code word starting bit recognition is higher than the code length recognition. There may be a situation in which the code length is correct but the code word starting point cannot be recognized. Therefore, a method of solving \(\varepsilon_{n_{0},d}\) multiple times and obtaining the average value is adopted. The specific steps are as follows:  
Step 1: Select consecutive points \(P\) near the maximum value in sequence \(\varepsilon_{n_{0},d}\);  
Step 2: For each point selected in Step 1, the row vectors of corresponding analysis matrix \(X(n_{0},d)\) are randomly exchanged and \(\varepsilon_{n_{0},d}\) is recalculated, which is carried out a total of \(Q\) times to obtain its mean value \(\varepsilon^{\prime}_{n_{0},d}\);  
Step 3: The shift value corresponding to the maximum value in the new \(\varepsilon^{\prime}_{n_{0},d}\) sequence is selected, which is the starting bit of the code words.  
The specific algorithm flow chart is shown in Figure 3.
# Change in Water Quality of the Vistula River During the Emergency Discharge of Untreated Wastewater
###### Abstract  
This paper presents an environmental analysis of the impact of an emergency discharge of untreated municipal sewage from the sewage system in Warsaw. This study was based on monitoring water quality in the Vistula River at stations located before and after the discharge site. Statistical analysis, including correlation analysis and Friedmann's test, was performed for the data covering eight measurement stations and sixteen chemical parameters. The results confirm the negative impact on the water quality in the river, especially in the mixing zone. In the mixing zone, a statistically significant increase in pollution was found for TN, N-NO3\({}^{-}\), N-NH4\({}^{+}\), TP, P-PO4\({}^{3+}\), SS, EC, COD, and TOC. The results of the analysis show the self-cleaning capacity of the river ecosystem and illustrate the response to a sudden, excessive influx of pollution. The direct impact of the untreated sewage discharge was found on a section of the river 1200 m after the place of the failure. The good ecological status and drinking water standards are not met in the mixing zone. The supply of water to the people requires the use of highly effective physical and chemical treatment.  
sewage; water pollution; people's health; ecological status +
Footnote †: journal: applied sciences
## 2 Materials and Methods
### Ultrasound Measurement  
Ultrasound imaging device Aplio i700 (Canon Medical Systems, Tochigi, Japan) was used to measure skin and muscle thickness, and to quantify the modulus of elasticity of a lumbar erector spinae and multifidus muscle. A convex i8 CX1 probe with a 5.5 MHz central frequency was used for collecting SWE images. The convex probe was used because of the curved shape of the multifidus muscle [23]. Measurement sites were defined as follows: (a) 2.5 cm laterally between L3 and L4 spinous processes for erector spinae 1 indicated superficial erector spinae muscles, (b) 7 cm laterally from the L3 spinous process of erector spinae 2 indicated deep erector spinae muscles, and (c) 2 cm laterally to the L4 spinous process for the multifidus (Figure 1) [24,25]. Measurement postures for data collection in the prone posture were performed on the flat and the Wilson table (Figure 2). Before ultrasound image collection, each spinous process of the lumbar spine was palpated and marked to ensure that ultrasound images were captured at the correct position. At each posture, each subject was asked to relax. Then, five SWE images of the lumbar erector spinae and multifidus muscles were collected.  
Each shear elastic modulus of the aforementioned muscles was evaluated by measuring shear-wave propagation speed in the tissue, which was generated using SWE at each measurement posture. The circular regions of interest (ROIs) were set in the color-coded boxes on the SWE mode (Figure 3). Three circular ROIs with a diameter of 10 mm were set in the color-coded box, with one located at the top-center of the box and the other two located at the inferior to the first-assigned circular ROI (Figure 3) [21,24]. Shear elastic modulus at the set ROI was automatically calculated from the ultrasound imaging device. The mean shear-elastic-modulus values of the three circular ROIs were calculated for each muscle per posture for five images. ROIs were determined by the same examiner.  
Figure 1: Location of ultrasound probe to measure skin thickness, muscle thickness, and shear-wave elastography (SWE). (**a**) Erector spinae 1; (**b**) erector spinae 2; and (**c**) multifidus.  
Figure 3: Example for setting regions of interest to measure shear elastic modulus in different postures on flat and Wilson table. In this case, erector spinae 1 located at L3–4 is shown: (**a**) flat table; (**b**) Wilson table.  
Figure 2: Two different prone postures on (**a**) flat table and (**b**) Wilson table.  
Skin and muscle thicknesses were measured from the collected shear-wave ultrasound images using ImageJ 1.52a (National Institutes of Health, Bethesda, U.S.). Skin thickness was measured as the distance (cm) between the surface of the epidermis and the subcutaneous tissue. Muscle thickness was measured as the distance between superficial and deep muscle facias. Both skin and muscle thicknesses were measured by drawing a straight line through the center of the image [26] (Figure 4). Skin and muscle thicknesses were measured using one shear-wave ultrasound image per muscle and condition.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 7 Comparison of the Proposed Antenna with Other Antennas  
The obtained parameter values for the proposed microstrip antenna can be compared in terms of impedance matching and appropriate bandwidth, with other published results for the purposes of comparative evaluation. For example, the frequency response of the measured S\({}_{11}\) parameter of the proposed antenna may not be the lowest as compared with the S\({}_{11}\) values obtained for the antennas presented in [18; 20; 32], but it is relatively low.  
Figure 13 shows a comparison of the reflection factor as a function of frequency for the proposed antenna, and the antennas developed in other works. The corresponding frequency responses using the VSWR parameter are compared with each other, as shown in Figure 14.  
Figure 14: The voltage standing wave ratio (VSWR) as a function of frequency for the proposed antenna, and antennas from other works.  
Figure 13: The reflection coefficient as a function of frequency for the proposed antenna and antennas from other works.  
The minimum reflection loss value obtained in this study is about \(-22.51\) dB (VSWR \(\approx\) 1.16), while in [20] it is about \(-26.00\) dB (VSWR \(\approx\) 1.10), in [18] it is about \(-21.00\) dB (VSWR \(\approx\) 1.20), and in [32] it is about \(-18.00\) dB (VSWR \(\approx\) 1.28). Moreover, for the antenna proposed in this work, the operating band of the antenna (BW) is the largest, amounting to S\({}_{11}\leq-10\) dB (VSWR \(\leq\) 2) 5.57 GHz, and is centered exactly at 28 GHz, while in [20] the operating band is 2.63 GHz and is also centered exactly at 28 GHz, in [18] the operating band is 2.85 GHz and is also centered at about 28.1 GHz (shift of about 100 MHz), and in [32] the operating band is 1.07 GHz and is also centered exactly at 28 GHz.  
A comparative summary of the proposed antenna with the antennas described in [18,20,32] is presented in Table 3. It shows that the proposed microstrip antenna in this work has the best impedance matching bandwidth performance in all cases, especially for stringent matching conditions (VSWR \(\leq\)2, VSWR \(\leq\)1.5, and VSWR \(\leq\)1.25).
## Abbreviations  
ADAM Adaptive Moment Estimation  
AR-BCI Augmented Reality based Brain-Computer Interface  
BCI Brain-Computer Interface  
CCA Canonical Correlation Analysis  
CNN Convolutional Neural Network  
DFT Discrete Fourier Transform  
DNN Deep Neural Network  
EEG Electroencephalography  
EMG Electromyography  
ERD/ERS Event-Related Desynchronization/Event-Related Synchronization  
FBCCA Filter Bank Canonical Correlation Analysis  
FBCNN Filter Bank Convolutional Neural Network  
FB-EEGNet Filter Bank EEGNet  
GAN Generative Adversarial Network  
ITR Information Transfer Rate  
K-NN K-Nearest Neighbors  
LDA Linear Discriminant Analysis  
LDA-SFS Linear Discriminant Analysis with Sequential Feature Selection  
LDA-T Linear Discriminant Analysis with \(t\)-test  
MPL Multi-layer Perceptron  
P300 P300 Wave  
QDA Quadratic Discriminant Analysis  
QDA-SFS Quadratic Discriminant Analysis with Sequential Feature Selection  
QDA-T Quadratic Discriminant Analysis with \(t\)-test  
SDG Stochastic Gradient Descent  
SSVEP Steady State Visually Evoked Potentials  
SVM Support Vector Machine  
MLP Multilayer perceptron  
sMP simplified Matching Pursuit
## 5 Results and Discussion
### Overall Performance Evaluation
#### 5.3.2 Overall Network Lifetime  
Network lifetime is a critical performance metric, often measured in terms of the number of rounds until the first node dies (FND), half the nodes die (HND), or the last node dies (LND). Figure 21 illustrates the survival rate of nodes over the network's operational period. The proposed method significantly extends the network lifetime on both the FND and LND scales, as shown in Figure 22.  
Specifically, the proposed method increases the number of rounds before the first node dies (FND) to 2702, compared to just 568 rounds for RLEACH, 588 rounds for CRPFCM, and 892 rounds for EERPMS (Figure 22a). Similarly, the proposed method extends the network lifetime on the LND scale to 2830 rounds, compared to 1019 rounds for RLEACH, 1614 rounds for CRPFCM, and 2077 rounds for EERPMS (Figure 22b).  
This significant improvement in network lifetime can be attributed to the proposed method's ability to maintain energy balance across the network, particularly by evenly distributing energy-intensive tasks such as data aggregation and relaying across nodes with higher residual energy.
# Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia
## 6 Simulations
### Robustness to Perturbations and Noise  
During the maintenance phase, it is essential that the controller is capable of rejecting disturbances that occur during surgery.  
The second simulation illustrated the robustness of the control scheme to perturbations and noisy measurements, specifically, those perturbations that affect the value of the BIS index in a patient. These perturbations occur because of, for example, intubation of the patient, painful stimuli, or blood loss. Figure 16 shows the artificial disturbance signal. Figure 17 shows the BIS response of patient 1 with the four control schemes. Note that the controllers are capable of compensating for these perturbations and noise, although the under-shoots in the responses are accentuated for the value of the adaptive gain and the lack of negative control. In Figure 18, the tracking error is shown, and Figure 19 shows the evolution of the controller's parameters.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Controller** & **1 st Order FOMRAC** & **1 st Order FOCRMAC** & **2 nd Order FOCRMAC** & **3 rd Order FOCRMAC** \\ \hline PE(\%) [min, max] & 2.0702 [\(-3.99,6.38\)] & 0.0803 [\(-1.74,0.40\)] & 0.1826 [\(0.09,0.26\)] & 0.1456 [\(0.05,0.25\)] \\ MDPE(\%) [min, max] & 2.2505 [\(-1.94,7.39\)] & 0.2282 [\(-0.33,0.12\)] & 0.0986 [\(0.05,0.21\)] & 0.0759 [\(0.04,0.15\)] \\ MDAPE(\%) [min, max] & 2.8269 [\(0.82,7.34\)] & 0.3478 [\(0.03,3.24\)] & 0.0986 [\(0.05,0.21\)] & 0.0759 [\(0.04,0.15\)] \\ Wobble(\%) [min, max] & 0.5764 [\(-4\times 10^{-16},4.92\)] & 0.3196 [\(-6\times 10^{-16},3.24\)] & 0.0003 [\(-6\times 10^{-17},0.005\)] & 0.0028 [\(-6\times 10^{-18},0.03\)] \\ Global score [min, max] & 3.4033 [\(0.82,12.26\)] & 0.6674 [\(0.03,6.48\)] & 0.0986 [\(0.05,0.22\)] & 0.0787 [\(0.04,0.16\)] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance characteristics for the fractional-order adaptive controllers applied to 30 virtual patients. The values are reported as mean values and the minimum and maximum values within the 30 patients are presented.  
Figure 16: Artificial disturbance signal.  
Figure 17: BIS response under disturbances and noisy measurements.  
Figure 18: Tracking error of the adaptive schemes under disturbances and noisy measurements.
## 2 Results and Discussion
### Antitumor Activity  
The cytotoxicity of the synthesized pyridines **4a,b,e** and **6a,b,e** was evaluated against the human liver carcinoma cell line (HepG2-1) using doxorubicin as a reference drug (IC\({}_{50}\) value of doxorubicin = 0.08 \(\pm\) 0.07 nM) and MTT assay. The data generated were used to plot a dose response curve of which the concentration of the tested compounds required to kill 50% of cell population (IC\({}_{50}\)) was determined. Cytotoxic activity was expressed as the mean IC\({}_{50}\) of three independent experiments. The results are depicted in Table 1 and Figure 1.  
The results revealed that the descending order of the antitumor activity of the tested compounds against HEPG2-1 cell line is as follow: **4b** > **6b** > **4a** > **6a** > **4e** > **6e**.  
The pyridine derivatives **4b** and **6b** (IC\({}_{50}\) = 1.9 \(\pm\) 0.16 and 2.4 \(\pm\) 0.29 nM, respectively) have promising antitumor activity against HEPG2-1. On the other hand, pyridine derivatives **4e** and **6e** have poor inhibitory activity (IC\({}_{50}\) > 17 nM) compared with doxorubicin which used as reference drug.
\(C\) and impact widths \(\delta\) of RBF are initialized randomly. In addition, it is necessary to solve the Moore-Penrose generalized inverse and the minimum norm least-squares solution to determine the output weights between hidden layers [12].  
However, because traditional ELM-RBF [11] generates input layer weights (ILW) and hidden layer bias (HLB) randomly, this kind of method is often unstable and prone to falling into local optimal solutions [13; 14]. Considering that the genetic algorithm (GA) [15] has many advantages, such as excellent global search capability and extensibility, which make it easily combine with other algorithms [16], we present a synergistic adaptive genetic algorithm (SAGA) to optimize ELM-RBF. In other words, SAGA is used to improve the prediction accuracy of ELM-RBF by adjusting ILW and HLB. As a result, an improved multi-label learning method, ELM-RBF-SAGA, is proposed.  
In a word, the main work of this paper is summarized as below.  
* To avoid falling into a local optimal solution, we present two adaptive optimization measures in SAGA. One is about adjusting the range of fitness value, which is used to maintain population diversity and provide adequate power for evolution. The other is mainly reflected in calculating the crossover and mutation probability, which are the two crucial factors in the optimization process.
* In order to promote the performance of ELM-RBF, we utilize SAGA presented in this paper to optimize ILW and HLB, and then propose a modified extreme learning model for multi-label classification, ELM-RBF-SAGA.
* Sufficient experiments have been carried out on several public datasets to verify model's performance. Experimental results demonstrate that SAGA is very effective in optimizing ELM-RBF. In addition, ELM-RBF-SAGA has obvious advantages over comparing methods and is very suitable for multi-label classification.  
The rest of this article is arranged as below: In Section 2, related works about ELM and its improved model, the genetic algorithm, are described briefly. The proposed ELM-RBF-SAGA is introduced in Section 3. Experiments and conclusions are shown in Section 4 and Section 5, respectively.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 2 EDM Interelectrode Gap Model Based on Plate Capacitor
### EDM Equivalent Circuit  
According to the EDM interelectrode structure characteristics, the EDM can be considered equivalent to the plate capacitance discharge model shown in Figure 2, and the pulse power source is a constant voltage source. In Figure 2, \(S_{\text{w}}\) is a pulse power switch, \(R_{\text{P}}\) is the power internal resistance, \(R_{\text{E}}\) is the equivalent resistance of the electrode, \(R_{\text{W}}\) is the equivalent resistance of the workpiece, \(C_{g}\) is the capacitance value of the interelectrode capacitance, \(S\) is the effective area of the planar capacitance (the positive opposite area of the electrode and the workpiece), and \(d\) is the interelectrode gap.  
For the EDM discharge test, the interelectrode voltage and current waveforms are shown in Figure 3. Under the action of the constant voltage pulse power supply, the discharge current can be formed after the interelectrode dielectric is broken down. When the interelectrode dielectric is not broken down, there is no discharge current in the discharge circuit, as shown in Figure 3a. Enlarge the red rectangle region in Figure 3a to get Figure 3b.  
Figure 1: Electric discharge machining (EDM) test equipment.  
Figure 2: EDM discharge circuit model.