esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Neural Computer Results to Clinical Data
#### 3.2.2 The FHR Score of the Experts' System Strongly Correlates with the Outcome Probability  
The FHR score is the sum of evaluation values determined by the grade of the low Apgar score in 8 quantitative FHR parameters in 5 minutes of non-interventional deliveries in the 1960s. The recent statistical study on previous monitoring studies revealed that maximal FHR scores in the first stage of labor strongly correlated with the Apgar score, i.e., neonatal depression is quantitatively estimated by the FHR score in the early stage of labor [16]. The FHR scores also correlated with fetal scalp blood pH in the 2\({}^{\text{nd}}\) stage of labor [24]. Fetal acidosis is detected by the FD index, which was determined by the FHR score, the loss of FHR baseline variability, and late FHR deceleration [25].  
The outcome probabilities obtained by the NNW analysis correlated with the FHR score; the FHR score was high when the pathological probability was large, the FHR score was moderate when the intermediate probability was positive, and the score was low when the outcome probability was normal [23]. Therefore, outcome probabilities of the NNW analysis are also capable to detect fetal hypoxic deterioration (Figure 2).  
Figure 2: The relationship between outcome probability and FHR score [23]. The X-axis is the probability of normal (1), intermediate (2) and pathological outcome (3), and the Y-axis shows the FHR score. The probability of a pathological outcome (3) is distributed in the high FHR score area, the probability of an intermediate outcome (2) is distributed in the intermediate FHR score area, and the probability of a normal outcome (1) is distributed in the low FHR score area [14]. Thus, the NNW outcome probability strongly correlates with the FHR score revealing that the NNW diagnosis based on FHR monitoring is accurate.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 2 Improved FraSegNet Algorithm
### Model Optimization
#### 2.2.3 Dynamic Learning Rate Strategy  
A dynamic learning rate strategy addresses three main issues with FraSegNet--gradient oscillation with a high learning rate, slow training with a low learning rate, and lack of dynamic learning rate adjustment. This strategy adjusts the learning rate during training, leading to faster convergence and better final performance. The learning rate changes dynamically across training stages, speeding up convergence and improving the final results. The specific optimization method is as follows:  
(1) Cosine annealing: During training, the learning rate is slowly reduced following a cosine function. At first, a higher learning rate helps with fast convergence, and later, the learning rate is gradually adjusted for better optimization.  
\[\eta_{t}=\eta_{min}+\frac{1}{2}(\eta_{max}-\eta_{min})(1+\cos{(\frac{t}{T} \pi)}) \tag{2}\]  
where  
\(\eta_{t}\) is the current learning rate;  
\(\eta_{max}\) and \(\eta_{min}\) are the maximum and minimum learning rates;  
\(t\) is the current training iteration;  
T is the total number of iterations.  
(2) Warm-up strategy: The learning rate starts small and is gradually increased at the beginning of training. This helps prevent instability from sudden parameter updates.  
The optimized FraSegNet network structure is shown in the Figure 4.  
Figure 4: Optimized FraSegNet network structure diagram.
## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### 3.1.5 Microstructure Properties
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Proportion of** & & & & & \\  & **CH/CNC or** & & & & & \\  & **CH/CD** & & & & & \\ \hline CH & 100/0 & 23.94 \(\pm\) 3.75 \({}^{\rm aA}\) & 5.20 \(\pm\) 0.25 \({}^{\rm aA}\) & 598.55 \({}^{\rm aA}\) & 4.60 \(\pm\) 0.50 \({}^{\rm aA}\) & 25.98 \(\pm\) 1.24 \({}^{\rm aA}\) & 1.22 \(\pm\) 0.19 \({}^{\rm aA}\) \\  & 75/25 & 21.02 \(\pm\) 0.42 \({}^{\rm aA}\) & 4.82 \(\pm\) 0.01 \({}^{\rm aA}\) & 525.42 \(\pm\) 10.39 \({}^{\rm aA}\) & 4.36 \(\pm\) 0.09 \({}^{\rm aA}\) & 24.08 \(\pm\) 0.04 \({}^{\rm aA}\) & 1.07 \(\pm\) 0.02 \({}^{\rm aA}\) \\ CH/CNC & 50/50 & 21.71 \(\pm\) 0.14 \({}^{\rm aA}\) & 3.99 \(\pm\) 0.03 \({}^{\rm aA}\) & 542.72 \(\pm\) 3.52 \({}^{\rm aA}\) & 5.44 \(\pm\) 0.00 \({}^{\rm aA}\) & 19.95 \(\pm\) 0.14 \({}^{\rm aA}\) & 1.11 \(\pm\) 0.01 \({}^{\rm aA}\) \\  & 25/75 & 16.31 \(\pm\) 4.10 \({}^{\rm aA}\) & 3.31 \(\pm\) 0.85 \({}^{\rm aA}\) & 271.82 \(\pm\) 68.63 \({}^{\rm aA}\) & 4.93 \(\pm\) 0.03 \({}^{\rm aA}\) & 16.55 \(\pm\) 4.24 \({}^{\rm aA}\) & 0.83 \(\pm\) 0.21 \({}^{\rm aA}\) \\  & 75/25 & 10.33 \(\pm\) 1.53 \({}^{\rm aA}\) & 3.60 \(\pm\) 0.37 \({}^{\rm aA}\) & 305.72 \(\pm\) 105.43 \({}^{\rm aA}\) & 2.91 \(\pm\) 0.73 \({}^{\rm aA}\) & 17.98 \(\pm\) 1.87 \({}^{\rm aA}\) & 0.53 \(\pm\) 0.08 \({}^{\rm aA}\) \\ CH/CD & 50/50 & 20.90 \(\pm\) 2.74 \({}^{\rm aA}\) & 5.62 \(\pm\) 0.30 \({}^{\rm aA}\) & 696.57 \(\pm\) 91.22 \({}^{\rm aA}\) & 3.71 \(\pm\) 0.29 \({}^{\rm aA}\) & 28.08 \(\pm\) 1.52 \({}^{\rm aA}\) & 1.06 \(\pm\) 0.14 \({}^{\rm aA}\) \\  & 25/75 & 16.57 \(\pm\) 0.21 \({}^{\rm aA}\) & 4.14 \(\pm\) 0.22 \({}^{\rm aA}\) & 414.23 \(\pm\) 5.22 \({}^{\rm aA}\) & 4.01 \(\pm\) 0.16 \({}^{\rm aA}\) & 20.68 \(\pm\) 1.10 \({}^{\rm aA}\) & 0.84 \(\pm\) 0.01 \({}^{\rm aA}\) \\ \hline \hline \end{tabular} Values are given as mean \(\pm\) standard deviation. Different letters in the same column indicate significant differences (\(p<0.05\)) when analyzed by the Duncan’s Test. Small letters indicate differences based on the films’ materials while capital letters show differences based on their proportions.  
\end{table} 
Table 4: Mechanical properties of CH, CH/CNC and CH/CD final formed edible films.
## 3 Results
### Method Validation
#### 3.2.2 Trueness and Precision
Preliminary tests at higher concentrations using simulant D1 showed a lack of linearity for the studied NCAs, most probably due to solubility issues. This phenomenon can be tentatively explained by the solubility difficulties and particularities of the target NCAs. The 500 ng mL\({}^{-1}\) limit was selected to ensure good linearity for the validated method. These solubility difficulties have already been reported by other researchers, which were partially solved by switching to more aggressive organic solvents, such as THF [2,4]. Within this framework, the results for both trueness and precision are certainly within acceptable thresholds [19,21], being between 85-103% and RSDs lower than 12.5%. These results are comparable to the limited number of relevant analytical and validated methods, like [PERSON] et al. showing recoveries from 95.8% up to 104.2% in real samples for all studied NCAs [4]. Concerning precision, RSDs were lower than 8%, though these results were obtained with a GC-MS method after derivatization of the samples.
## 5 Experimental Results and Analysis
### Results for the Disruptions by Event-Driven RH Scheduling Strategy
It could also obtain a better response to different disruption conditions (e.g., machine breakdown, operator change, dynamic deck environment) and reduce the computation time for recovering the schedules in the event of disruptions illustrated by numerical experiments. The Gantt chart of the scheduling activities by RH recovery scheduling strategy can provide a convenient guidance to the carrier aircraft support operations, and make the rescheduling more suitable for the dynamic operational environment of the carrier flight deck.
## 2 Tuning of PDA Controllers for IPDT Plant
### Quadruple Real Dominant Pole Controller Tuning  
From the characteristic quasi-polynomial  
\[A(s)=se^{T_{d}s}+K_{s}K_{a}s^{2}+K_{s}K_{d}s+K_{s}K_{p} \tag{6}\]  
the condition of quadruple real dominant pole \(s_{o}\) can be formulated as follows  
\[\left\{A(s);\ \frac{d}{ds}A(s);\ \frac{d^{2}}{ds^{2}}A(s);\ \frac{d^{3}}{ds^{3}}A(s) \right\}_{s=s_{o}}=\mathbf{0} \tag{7}\]  
From  
\[\frac{d^{3}}{ds^{3}}A(s)=[sT_{d}^{3}+3T_{d}^{2}]e^{T_{d}s}=0 \tag{8}\]  
one gets optimal values of the dominant pole and the dominant closed-loop time constants as  
\[s_{o}=-3/T_{d};\ \ T_{o}=-1/s_{o}=T_{d}/3 \tag{9}\]  
From the remaining conditions (7) then follow the \"optimal\" controller parameters  
\[K_{po}=\frac{27}{2K_{s}T_{d}e^{3}}=\frac{0.6721}{K_{s}T_{d}};\ K_{do}=\frac{5}{ K_{s}e^{3}}=\frac{0.2489}{K_{s}};\ K_{ao}=\frac{T_{d}}{2K_{s}e^{3}}=\frac{0.0249T_{d}} {K_{s}} \tag{10}\]  
If we introduce the controller time constants using  
\[T_{D}=K_{d}/K_{p}=0.3704T_{d};\ T_{A}^{2}=K_{a}/K_{p}=0.0370T_{d}^{2} \tag{11}\]  
we can further introduce a pre-filter \(F_{p}(s)\), the task of which is to cancel the zeros from the \(F_{c}(s)\) (5)  
\[F_{p}(s)=\frac{b_{2}s^{2}+b_{1}s+1}{T_{A}^{2}s^{2}+T_{D}s+1} \tag{12}\]  
and possibly also some of the closed-loop poles \(s_{o}\) (time constants \(T_{o}\)). In the simplest but the most robust case, one can choose  
\[b_{2}=0;\ b_{1}=0; \tag{13}\]  
Setpoint step responses can be accelerated by selecting the \(F_{p}\) numerator so that it cancels one closed-loop pole \(s_{o}=-1/T_{o}\)  
\[b_{2}=0;\ b_{1}=T_{o}; \tag{14}\]  
Two closed-loop dominant time constants \(T_{o}\) can be cancelled by the numerator tuning  
\[b_{2}=T_{o}^{2};\ b_{2}=2T_{o}; \tag{15}\]
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
## 4 Experiment Results and Analysis
### Comparison with Other Regression Algorithms  
We studied the predictive cellular flow data of kELM and other regression algorithms and compared the prediction results of other regression algorithms with those of kELM, as shown in Table 6.  
In Table 6, the parameters of MFO-ELM (Gaussian) and MFO-\(\
u\)5 VR are [C ag] and [c, g, v], while the parameters for ELM and BP are the number of hidden layer nodes. \"Time\" is the parameter optimization time.  
From Table 6, it can be seen that MFO-\(\
u\)5 VR had the smallest MAPE in the test set at 11.082%, while the MFO-kELM had the smallest MAPE in the training set at 9.411%. However, kELM was more efficient, and its optimization time was 149.49 s, which was much less than the optimization time of 11,405.70 s for \(\
u\)5 VR. The worst performance for cellular flow prediction was ELM, which had the largest MAPE in both the test and training sets. Differences between the results predicted by each regression algorithm and actual value of cellular traffic is shown in Figure 9. The standard deviation of kELM and vSVR was 0, while that of BP and ELM was not 0, i.e., the training results of kELM and \(\
u\)5 VR were more stable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**MAPE**} & \multicolumn{2}{c}{**Standard Deviation of MAPE**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Time (s)**} \\ \cline{2-2} \cline{5-6}  & **Train** & **Test** & & & \\ \hline MFO-ELM (Gaussian) & 9.411\% & 11.150\% & 0 & 0 & [622.078, 1.770] & 149.49 \\ MFO-\(\
u\)5 VR & 10.26\% & 11.082\% & 0 & 0 & [1371.092, 0.024, 0.891] & 11,405.70 \\ ELM & 13.448\% & 13.367\% & 0.374\% & 0.608\% & 100 & — \\ BP neural network & 13.066\% & 12.128\% & 0.881\% & 0.340\% & 30 & — \\ \hline \hline \end{tabular}
\end{table}
Table 6: kELM compared with other regression algorithms.  
Figure 9: The predicted values of each regression algorithm minus the actual values. (a) kELM, (b) \(\
u\)5 VR, (c) ELM, (d) BP.  
In addition, from Table 6, we can also see that the introduction of kernel functions in ELM to map features to high-dimensional space not only significantly improved the prediction accuracy of ELM but also eliminated the uncertainty brought by the random initial weight to model prediction performance.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 4 Slot Bonding Implementation
### Platform  
The slot bonding proof-of-concept is implemented in the latest version ([[https://github.com/contiki-ng/contiki-ng](https://github.com/contiki-ng/contiki-ng)]([https://github.com/contiki-ng/contiki-ng](https://github.com/contiki-ng/contiki-ng)), commit 5 fdfc98, accessed on 12 May 2021) of the Contiki-NG firmware [22]. Contiki-NG is an open-source, cross-platform operating system for Internet of Things (IoT) devices with support for the 6 TiSCH stack. The implementation is developed on the Zolertia hardware platform, more specifically the RE-Mote (revision B) platform featuring the Texas Instruments CC1200 sub-GHz low-power transceiver ([[http://www.ti.com/product/CC1200/datasheet/](http://www.ti.com/product/CC1200/datasheet/)]([http://www.ti.com/product/CC1200/datasheet/](http://www.ti.com/product/CC1200/datasheet/)), accessed on 9 May 2021). As the Contiki-NG firmware is ported to many different platforms, the slot bonding implementation can also be used on different hardware.
# A Cyclic Graywater Treatment Model for Sustainable Wastewater Management Applied in a Small Scale
## 3 Results
### Characterization of Drained Graywater
#### 3.1.2 Characterization of Drained Graywater  
The results show that the average pH values were 7.69, 7.73, 7.92, and 7.94 for, respectively, RGW, GWT1, GWT2, and GWT3. These values comply with Tunisian regulation NT 106.03 and the World Health Organization's guidelines (WHO, 2006), which set the pH range between 6.5 and 8.5. The pH increased slightly with each treatment, though not significantly (Table 2). For electrical conductivity (EC), the average values were 2.41, 2.85, 3.43, and 3.78 dS/m for RGW, GWT1, GWT2, and GWT3, respectively, showing a significant increase with each treatment and meeting the regulatory standards. The dry residue followed the same trend as the EC, with average values of 1.4, 2.36, 2.60, and 2.97 g/L for RGW, GWT1, GWT2, and GWT3, respectively. Figure 2: Correlation between graywater volumes and treatment. RGW: raw graywater; GWT1: graywater drained from the first treatment; GWT2: graywater drained from the second treatment; and GWT3: graywater drained from the third treatment. The soluble salts showed significant variability in TGW quality due to the variability of raw graywater. The average values of anions and cations increased with each treatment. In addition, the ionic composition of the various graywater qualities reflected a sodium chloride facies, which remained unchanged after the different treatment stages. For SAR, the values were 5.6, 5.7, 6.4, and 7.1 for RGW, GWT1, GWT2, and GWT3, respectively, with slight, non-significant increases. SAR values for the treated graywater remained below 10. The measured values, calculated values, and the q factor for each parameter (U) are presented in Table 3. It can be observed that the different parameters also followed a geometric progression model:  
\[\text{U}_{\text{n}}=\text{q}^{\text{n}}.\text{U}_{\text{0}}. \tag{2}\]  
Following the application of the geometric progression, it is observed that all the deduced values closely aligned with the measured values. Significant correlations were observed between the different qualities of graywater for each parameter, as detailed in Table 3. The results indicate highly significant correlations across the various graywater qualities (RGW, GWT1, GWT2, and GWT3) for all the parameters analyzed.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 1 Introduction
* We developed a customized FFNN network architecture through selective feature-based network settings, which helped MIMONet to achieve optimal performance compared to benchmark MIMO detectors. * MIMONet was analyzed through the bit error rate (BER) and complexity against both traditional MIMO detectors (MMSE, ZF, MLD, GS, CG, OCDBOX, and ADMIN) and AI-based detectors (AIDETECT, AMP-DNN, OAMP-Net, OAMP-Net2, and DetNet), highlighting its relative performance and efficiency. The remaining part of the paper is structured in the following sections: Section 2 presents the related work based on traditional and AI-based MIMO detection methods. In Section 3, discuss the architecture of the MIMO system model, traditional MIMO detectors, and DL-based detection in MIMO systems. Section 4 presents the problem formulation and data preparation for the proposed MIMO detection method. Section 5 proposes the DLdriven MIMO detector called MIMONet for signal detection in MIMO systems. Section 6 presents a detailed discussion based on the simulation setup and results. Section 7 discusses the computational complexity of the proposed MIMO detector and other benchmark MIMO detection methods. Finally, Section 8 presents the conclusion of the paper and potential future direction.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
###### Abstract  
Non-orthogonal multiple access (NOMA) is considered a promising multiple access technique for fifth generation (5G) mobile networks and tactical internet due to its high spectral efficiency. Thanks to the high spectral efficiency of NOMA, it can be a strong candidate suitable for the limited channel bandwidth of underwater acoustic communication. The NOMA transmitter is employing superposition coding (SC). The NOMA receiver is based on the successive interference cancellation (SIC) technique. The multicarrier NOMA adopts orthogonal frequency division multiplexing (OFDM) as a multicarrier modulation (MCM) technique; however, conventional cyclic prefix OFDM (CP-OFDM) and zero padding (ZP-OFDM) have inefficient spectral efficiency. Thanks to efficient synchronization and high energy-spectral efficiency of the time-division synchronization OFDM (TDS-OFDM), it is a significant attractive candidate for underwater multicarrier communication. However, wasting the power transmission of long guard intervals in the battery-based underwater communication is represented as one of the TDS-OFDM main drawbacks. Harvesting energy and improving the energy efficiency of acoustic-based TDS-OFDM-NOMA represent high achievement goal battery recharging challenges due to the ocean environment. This paper proposes time switching simultaneous wireless information and power transfer (TS-SWIPT) to harvest the energy of transmitted power over the guard interval in the TDS-OFDM-NOMA scheme. The proposed energy harvested scheme harvests the energy from the wasted power in the long guard interval and improves the energy efficiency of the TDS-OFDM multicarrier scheme. This study demonstrates the superiority of the proposed TDS-OFDM-NOMA over the underwater acoustic channel by revealing high energy efficiency, high spectral efficiency, better bit error rate performance, and high system data throughput.  
underwater acoustic communication; NOMA; TDS-OFDM; energy harvesting; energy efficiency
## 2 Materials and Methods
### Cell Live Assay  
The proliferation of the MG63 cells was analyzed with the Cell Counting Kit-8 (CCK-8; Sigma, Taipei, Taiwan) assay. In this assay, a highly water-soluble tetrazolium salt solution containing 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium is reduced to Formazan dye (yellow) by dehydrogenase. The color of the Formazan dye depends on the cell viability. Thus, 100 \(\mu\)L of 1 \(\times\) 10\({}^{4}\) cell/mL of the MG63 cell suspension was cultured on a 96-well plate at 37 \({}^{\circ}\)C (5% CO\({}_{2}\) in the humidified incubator) for 24 h. Before the CCK-8 test, the MBG and EGCG-MBG scaffolds immersed in Dulbecco's Modified Eagle Medium (DMEM) solution for 24 h. After 1 day, the medium was centrifuged (2000 rpm, 10 min) and sieved through a 0.22-\(\mu\)m filter. The extraction medium was prepared with the MBG and EGCG-MBG scaffolds as the experimental group. Furthermore, the same amount of medium (no cells) was present to indicate background performance and the medium with cells in a well as the control group. After adding 100 \(\mu\)L of the medium for 1 day, 10 \(\mu\)L of the CCK solution was provided to each well and incubated for 2 h. The absorbance was measured at different time intervals (24 and 48 h) using a microplate reader (BioTek Epoch, Winooski, VT, USA) at 450 nm. The percentage of cell viability was estimated by the following equation:  
\[\text{Survival rate (\%) = (A - B)/(C - B) \times 100}\]  
in which A is the mean absorbance value of the experimental group in the culture plate, B is the mean absorbance value of the background group (only medium without cells in a well) in the culture plate, and C is the mean absorbance value of the control group (medium and cells in a well) in the culture plate.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
###### Abstract  
In body-centric communications, energy efficiency is a critical performance metric, while the achievable data rate is not of primary concern. In this paper we present a novel modulation scheme, which can be efficiently used in body-centric terahertz (THz) nanonetworks. The proposed scheme is a combination of the time-spread On-Off keying (TS-OOK) and the pulse position modulation (PPM) and presents lower energy consumption, compared to other existing methods as TS-OOK, at a minor cost to the data rate. Furthermore, another important aspect is that the proposed modulation scheme can be effectively used to mitigate the impact of the specific kind of noise in THz body-centric communications, thus leading to better error performance. Finally, we present analytical and simulation results in order to compare the new scheme with the existing TS-OOK.  
Keywords:terahertz band; nanosensors; nanonetworks; femtosecond pulses; body-centric communications +
Footnote †: This paper is an extended version of our paper published in 2018 7 th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 7–9 May 2018.