esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Phenolic Content and Antioxidant Capacity  
The total content of phenolic compounds in the raw materials ranged from 0.86 mg GAE g DM\({}^{-1}\) in the wheat flour to 2.15 mg GAE g DM\({}^{-1}\) in the YL flour. The extracts from these raw materials also exhibited the lowest and highest antioxidant activity, respectively (Table 7). Enriching bread with flour from legume seeds resulted in a slight but statistically significant increase in the phenolic content in the bread. The highest increase was observed  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Protein** & **Ash** & **Fiber** & **Fat** & **Carbohydrates** \\  & **(\% DM)** & **(\% DM)** & **(\% DM)** & **(\% DM)** & **(\% DM)** \\ \hline WF & \(11.13\pm 0.13\) & 0.59 \(\pm\) 0.02 D  & 1.22 \(\pm\) 0.02 C  \\ GP & \(31.99\pm 0.04\) & 3.31 \(\pm\) 0.02 C  & 0.32 \(\pm\) 0.02 D  \\ YL & \(34.01\pm 0.03\) & 3.66 \(\pm\) 0.00 B  & 6.42 \(\pm\) 0.03 A  \\ NL & \(48.18\pm 0.12\) & 3.83 \(\pm\) 0.01 A  & 26.58 \(\pm\) 0.13 D  & 0.87 \(\pm\) 0.02 h  & 1.29 \(\pm\) 0.02 h  \\ \hline GPB10 & \(13.70\pm 0.04\) i  & 2.21 \(\pm\) 0.02 i  \\ GPB15 & \(14.42\pm 0.00\) h  & 2.50 \(\pm\) 0.00 k  & 80.59 \(\pm\) 0.08 c  & 1.53 \(\pm\) 0.00 C  & 1.08 \(\pm\) 0.04 j  \\ GPB25 & \(16.04\pm 0.02\) & 1.60 \(\pm\) 0.02 b  & 0.99 \(\pm\) 0.01 k  \\ \hline YLB10 & \(13.42\pm 0.04\) j  & 3.41 \(\pm\) 0.01 h  & 80.01 \(\pm\) 0.04 d  \\ YLB15 & \(14.95\pm 0.03\) & 1.45 \(\pm\) 0.02 e  & 4.20 \(\pm\) 0.02 e  \\ YLB25 & \(16.89\pm 0.12\) & 1.63 \(\pm\) 0.00 b [FOOTTE:]Footnote : We use the same data as the data used in the analysis.[ENDFOOTNOTE] & 1.6 in the bread enriched with NL (from 0.72 mg GAE g DM\({}^{-1}\) (CB) to 1.12 mg GAE g DM\({}^{-1}\) (NLB25)), while the smallest increase was observed in the bread with GP (from 0.72 mg GAE g DM\({}^{-1}\) (CB) to 0.99 mg GAE g DM\({}^{-1}\) (GLB25)). The values of the EC\({}_{50}\) index were also significantly lower for the bread enriched with legume seed flour. This indicated higher antioxidant activity of the enriched bread. These relationships were observed for both antioxidant activities against DPPH and ABTS. The bread enriched with NL flour exhibited the highest activity against DPPH and consequently the lowest EC\({}_{50}\) values, while the bread with GP flour showed the highest activity against ABTS. On the other hand, the bread enriched with YL flour had the lowest antioxidant activity against ABTS, and the bread with GP flour had the lowest antioxidant capacity against DPPH. The increase in the content of phenolic compounds and antioxidant activity in the enriched bread resulted from the use of whole grain legume seed flour as an additive. Most of the bioactive substances were present in the fruit-seed coat of seeds [31]. Many authors observed that flour enrichment with different additives plant additives increased the antioxidant activity of bread. This effect was especially visible when raw materials that were rich in fiber were incorporated into wheat flour [10; 13; 14].
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 5 Performance Evaluation  
In this section, we analyze the performance of the proposed method through simulation studies. The default parameter values are as follows, if not specified otherwise. We deploy \(|N|=10\) MECSs randomly. We set \(|N_{i}|=6\) and select randomly the member of each \(N_{i}\) from \(N\). We set \(Q_{max}=100\), \(q_{th}^{ON}=0.7\), \(P_{m}=20\) Watt, and \(\alpha=0.5\). We also configure \(b_{i,j}=b_{i}=300\) Mbps. We vary the workload \((\rho)\) imposed on a MEC system. The workload is defined as a proportion of the total system capacity \(\sum_{i\in N}b_{i}\) during each time slot. Given \(\rho\), \(a_{1,j}(t)\) is configured randomly as \(\frac{1}{|N|}\sum_{i\in N}b_{i}\rho U[0,1]\), where \(U[0,1]\) is the Uniform distribution.  
Figure 2: Convergence property of the Delta activation level (the time slots \(t1\) and \(t2\) are randomly selected and \(t1\
eq t2\)).
## 3 Results
### Relative Retracking Algorithms Offsets  
It has been demonstrated in [PERSON] et al. [9] that _Ice-1_ gives more accurate results of water-levels of Lake Issykkul than the _Ocean_ retracker. An offset of \(\sim\)24 cm between the two algorithms had been found by [PERSON] et al. [10], using the Envisat and Jason-2 satellites data. The offset was due to differences in the algorithms: _Ocean_ was directly measuring the travel time of the signal, using the rising front of the footprint. It supposed that the received echo had a theoretical shape defined by [PERSON] et al. [36] who showed that the time needed to calculate the range between the satellite and the reflecting surface was found from halfway up the rising edge of the waveform.  
In contrast, the _Ice-1_ algorithm was much more robust for non-standard waveforms, such as those usually acquired over small lakes or rivers. It was based on an empirical estimation of the weighted Center of Gravity (CoG) of the waveform.  
Since the Envisat mission (launched in 2001), the GDRs of the altimetry missions distributed the range calculated from different algorithms. Here, we have calculated the relative bias between the two retrackers (_Ocean_ and _Ice-1_), again, by using the Jason-3 satellite, first, and then the Sentinel-3A. For this, we simply have calculated the lake Issykkul water-level over one and a half year, using altimetry data processed with each retracker [24]. We obtained the usual offset of 230 \(\pm\) 40 mm StD, using the Jason-3 satellite, which was in very good agreement with the previous results, based on Jason-2 or Envisat. However, the offset calculated using the Sentinel-3A satellite was significantly higher, since the value calculated was 300 mm \(\pm\) 25 mm StD. This was likely due to the SAR mode of the Sentinel-3A.  
Figure 7: Ellipsoidal height of the lake surface for Jason-3 with the _Ocean_ and _Ice-1_ retrackers, cycle 52 (July 2017) of track 131, and shipborne measurements.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Streaming Services
#### 3.3.2 Video Streaming Services  
Another video streaming technique is performed over CoAP in [17]. The proposed scheme adaptively changes the quality of video based on the available throughput, download time of the segment and size of the video segment for next video segment. In order to achieve high quality video streaming without experiencing any interruptions and needless quality switches, the CoAP client in the model selects the video rate from a set of video rates available on the CoAP server, for each segment of video. This rate is selected by estimating the throughput of next segment which is calculated using throughput variations in previously downloaded segments. The main reason to employ CoAP is the use of CoAP in video streaming services for constrained wireless environments.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 4 Case Studies, Results, and Discussion
### Balanced Load (Scenario I)
#### 4.1.2 Locating the Fault (Balanced Load)  
Table 3 presents the selected performance measures used to compare MLP and ELM when finding faults of different types. The values of \(R^{2}\), NSEC, and WIA in both cases were very close to 1 and MLP showed slightly better performance, whereas the performance measures of RMSE and MAPE showed very low values in both cases, indicating very good performance. Additional comparison can be made by considering the training time and testing time required for each tool. The MLP approach takes 8.78-14.32 s to train the network while the ELM training only takes a fraction of a second. The testing times in both cases were generally comparable. For some of the faults, the ELM approach showed a slightly faster testing time, and for the others, it showed a slightly slower testing time. It can be concluded that both ELM and MLP give comparably high performance in locating all types of faults and ELM shows a slightly higher performance than the MLP.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & \\ \cline{3-11}  & **AG** & **BG** & **CG** & **AB** & **BC** & **AC** & **ABG** & **BCG** & **ACG** & **ABC** & **No Fault** \\ \hline \multirow{6}{*}{**Cover**} & AG & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & BG & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & CG & 0 & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & AB & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 \\  & BC & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 & 0 \\  & AC & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 \\  & ABG & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 \\  & BCG & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 \\  & ACG & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 \\  & ABC & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 \\  & No & fault & & & & & & & & & & \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\ \end{tabular} Overall accuracy 100%  
\end{table}
Table 2: Detection and classification confusion matrix for scenario I.  
Figure 6: Useful features’ extraction from three-phase current signals and DWT: Approximate and detailed coefficients of phase A for a SLG fault applied on phase.
# Grating-like Terahertz Metasurface for Large-Deflection-Angle Beam Manipulations
## 3 Results and Discussion
Possible reasons for the deviations between the experimental and theoretical data are as follows: on the one hand, dimensional deviations of metasurface structure induced by the fabrication error will lead to the deviation of diffraction angle; on the other hand, the beam reflected by the sample will be broadened as a result of fabrication error and non-uniform thickness of quartz substrate, which will also contribute to the deviation in the angle measurement. The measured beam scanning range is around 10.7\({}^{\circ}\) for the +1 st order diffraction mode in the 0.87-1.02 THz band, which is slightly smaller than the theoretical value of 12.2\({}^{\circ}\). We remark that a liquid-crystal (LC) layer was embedded into the metasurface element as a dielectric spacer. The LC layer contributes to the performance of the large deflection angle of the metasurface grating to some extent, as the loading of LC induces the redshift of the effective working frequency band of the metasurface grating (compared to the air  
Figure 3: (**a**) The optical micrograph of the fabricated metasurface grating; the measured results of the diffraction wave. (**b**) +1 st order mode; (**c**) \(-\)1 st order mode. with the same thickness) [32; 33], which will lead to the increased deflection angle according to Equation (2). In addition, LC possesses the electrically tunable dielectric constant, and hence the switchable grating effect can be expected in this LC metasurface grating. More importantly, we wish to develop a kind of bifunctional metasurface device, that is, the metasurface behaves as an electrically controlled programmable metasurface in the low-frequency portion of the \(y\)-polarized incident wave and functions as a frequency-scanning grating in the high-frequency band for the \(x\)-polarized incident wave. Thus, we also utilize the LC-integrated metasurface structure in this work. According to the aforementioned analyses, the grating-like metasurface can steer the terahertz beams in a large deflection-angle region, which indicates the potential applications in terahertz frequency-scanning antennas. Compared with phased-array antennas, frequency-scanning antennas commonly possess higher response speeds and lower energy consumption. Additionally, the metasurface can also be applied to beam splitting. At optical frequencies, beam-splitting prisms and gratings are usually utilized to design beam splitters based on the dispersion effect, which is widely used in spectrometers. Nevertheless, in the terahertz regime, beam-splitting components are not widely reported. Thus, the grating-like metasurface can be extended to terahertz spectral separation. It is worth noting that the working bandwidth can be further broadened by designing multilayer devices or constructing composite metasurfaces consisting of sub-lattices with different operating frequency bands. Additionally, hence, broadband terahertz spectral separation can probably be achieved based on metasurface grating.
## 3 Recent WHO Recommendations for the Treatment of MDR/RR TB  
In the last decades, WHO made great efforts to facilitate and improve the treatment of patients with MDR-TB in high burden countries using various actions including the Directly Observed Treatment Strategy (DOTS)-Plus, to stress the use of second-line drugs in low- and middle-income settings, but the cure rate was lower than the WHO 2015 target of at least 75% to 90% [6]. For instance, treatment success for MDR/RR-TB cases started on treatment in 2016 in India, China and Russian Federation was 48%, 52% and 54%, respectively [1].  
In 2018, the results from an individual patient data meta-analysis involving 12,030 patients from 25 countries showed that treatment success and death of pulmonary MDR-TB were significantly reduced after the administration of the newer or repurposed drugs linezolid (LZD), later generation FQs, bedaquiline (BDQ), clofazimine (CFZ) and carbapenems [7]. On the basis of this and other studies, in March 2019, WHO released a new drug classification and new recommendations for the treatment of MDR-TB [8, 9, 10]. The second-line drugs were reorganized into three groups, including priority drugs [Group A: LFX or MFX, BDQ, LZD], preferentially used drugs [Group B: CFZ, cyclosporine (CS) or terizidone (TRD)], and other drugs [Group C: EMB, delamandi (DLM), PZA, impenem-clastatin (IPM-CLN) or meropenem (MPM) (administered with clavulanic acid, CLV), AM or streptomycin (SM), ethionamide (ETO) or protonamide (PTO), para-aminosalicylic acid (PAS)].  
In summary, WHO recommended an injection-free therapy (groups A and B drugs) at the initiation of MDR-TB treatment. Group C agents (oral and parenteral) should be administered when groups A and B drugs cannot be used. The commonly used second-line injectable drugs KM and CM were associated with worse outcomes [7], and were no longer recommended for the treatment of MDR-TB; AK and SM may be administered only if drug susceptibility testing (DST) confirms susceptibility.  
To further reduce the burden of drug-resistant TB in the near future, in December 2019, WHO also released a Rapid Communication to inform countries and stakeholders that a regimen containing BDQ, pretomandi (PRT, formerly PA-824) and LNZ (BPaL regimen) may be used under operational research conditions conforming to WHO standards for the treatment of XDR-TB patients [11]. This communication was released after a previous announcement of the Global TB Alliance in the second half of 2019, following the decision of the United States Food and Drug Administration (FDA) to administer BPaL (Nix-TB trial by the Global TB Alliance) to adults with pulmonary XDR-TB or intolerant/not responsive MDR-TB [12].
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Decision Tree (DT)**} & \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**Performance Metrics (Accuracy)/Dataset**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Variation**} \\ \cline{3-8}  & & **CM1** & **KC1** & **KC3** & **MW1** & **PC2** & **(\%)** & **(\%)** \\ \hline
**NO Feature Selection** & DT & 81.04 & 74.18 & 79.38 & 90.40 & 97.51 & 84.50 & 0 \\ \hline
**DT + CFS + Heuristic Search** & DT+GA & 85.32 & 75.82 & **82.99** & 89.6 & 97.51 & 86.25 & 2.06 \\
**DT + CFS + Heuristic Search** & DT+BAT & 87.16 & 74.96 & **82.99** & 90.4 & **97.78** & **86.66** & **2.55** \\
**DT + CFS +** & DT+PSO & 86.54 & 75.65 & 81.96 & 89.2 & 97.51 & 86.17 & 1.97 \\
**Heuristic Search** & DT+FS & 85.93 & 75.22 & 80.41 & 88.4 & 97.65 & 85.52 & 1.21 \\
**DT + AS** & 85.93 & 75.13 & 82.47 & 89.6 & 97.51 & 86.13 & 1.92 \\ \hline
**DT + CFS + Exhaustive Search** & DT+GSS & 86.54 & 74.10 & 80.41 & 89.2 & 97.51 & 85.55 & 1.24 \\
**Exhaustive Search** & DT+BF & 86.54 & 75.47 & 81.44 & 89.6 & 97.51 & 86.11 & 1.91 \\ \hline
**DT +**GA & 86.24 & 73.75 & 80.93 & 89.2 & 97.65 & 85.55 & 1.24 \\
**DT +**BAT & 85.63 & 75.30 & 80.41 & 89.2 & 97.51 & 85.61 & 1.31 \\
**DT +**PSO & 86.24 & 74.44 & 80.93 & 88.4 & 97.65 & 85.53 & 1.22 \\
**Heuristic Search** & DT+FS & 83.49 & 74.44 & 78.87 & 89.6 & 97.65 & 84.81 & 0.36 \\
**DT +**AS & 85.93 & 74.44 & 80.93 & 89.2 & 97.37 & 85.57 & 1.27 \\ \hline
**DT +** CNS + Exhaustive Search** & DT+GSS & 87.16 & 75.56 & 80.41 & **91.6** & 97.65 & 86.47 & 2.33 \\
**Exhaustive Search** & DT+BF & 86.85 & 74.44 & 80.93 & 89.6 & 97.78 & 85.92 & 1.68 \\ \hline
**DT +**Filter Method** & DT+IG & 86.54 & 74.96 & 82.47 & 90 & 97.65 & 86.32 & 2.16 \\
**DT +**RF & DT+RF & 86.24 & **76.25** & 79.90 & 87.6 & **97.78** & 85.55 & 1.24 \\
**DT +**GR & 86.24 & 75.04 & 81.96 & 90.4 & **97.78** & 86.28 & 2.11 \\
**DT +**CV & **87.46** & 74.01 & 78.87 & 88.8 & **97.78** & 85.38 & 1.04 \\ \hline \hline \end{tabular} Boldface typeface indicates the highest value for each dataset.  
\end{table} 
Table 7: Accuracy Values of Decision Tree Classifier on Full and Reduced Datasets.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 3 Wireless Power Transfer of Power Industry Optical Wireless Communications
### Optical Wireless Power Transfer
In [104], by using high brightness white LED, a collimating large parabolic mirror and a-Si solar panel placed at 5 m, an OW link was created. Measure the maximum link efficiency and harvest power were only 0.1% and 18.3 mW, respectively [100]. [PERSON] and his researchers in [90] proposed a solar cell wireless light energy transmission system based on optical antenna, as detected in Figure 26. As can be shown, the light source is located on the ceiling of the room and emits light signals that comply with the US Federal Communications Commission (FCC) human safety regulations which is less than 1 mW/cm\({}^{2}\)[89]. The central station connected with the light source receives the guide signal from the equipment to be charged and controls the activation and deactivation of the light source. The mobile device to be charged contains an optical  
Figure 25: System block diagram of OWPT [100]. antenna array and a DC conversion circuit integrated on top of the solar cell, which can be anywhere in the house to receive energy from the light source. The system will realize a safe and efficient intelligent charging system. In order to realize this system, a nano-structured optical antenna using the enhancement effect of plasmonic electric field was designed and analyzed [90]. In addition, an efficient single-junction GaAs solar cell was designed to maximize the output power of the solar cell. In the rechargeable batteries of portable devices, a step-up DC-DC converter is used to provide a stable current and convert it to the required DC voltage. Because the optical antenna can enhance the plasma electric field effect, thereby promoting the efficiency of the energy transfer system without conflict with human safety rules. They simulated the realization of optical antennas, solar cells, and DC-DC conversion circuits. On the basis of this simulation, the solar cell output of the proposed technology is 40 times higher than that of the traditional solar cell system.
# Efficient Wireless Charger Deployment for Wireless Reshapeable Sensor Networks
###### Abstract  
A wireless rechargeable sensor network (WRSN) consists of sensor nodes that can harvest energy emitted from wireless chargers for refilling their batteries so that the WRSN can operate sustainably. This paper assumes wireless chargers are equipped with directional antennas, and are deployed on grid points of a fixed height to propose two heuristic algorithms solving the following wireless charger deployment optimization (WCDO) problem: how to deploy as few as possible chargers to make the WRSN sustainable. Both algorithms model the charging space of chargers as a cone and calculate charging efficiency according power regression expressions complying with the Friis transmission equation. The two algorithms are the greedy cone covering (GCC) algorithm and the adaptive cone covering (ACC) algorithm. The GCC (respectively, ACC) algorithm greedily (respectively, adaptively) generates candidate cones to cover as many as possible sensor nodes. Both algorithms then greedily select the fewest number of candidate cones, each of which corresponds to the deployment of a charger, to have approximate solutions to the WCDO problem. We perform experiments, conduct simulations and do analyses for the algorithms to compare them in terms of the time complexity, the number of chargers deployed, and the execution time.  
wireless rechargeable sensor network (WRSN); energy harvesting; charger deployment; sustainability; greedy algorithms; directional antennas; Friis transmission equation +
Footnote †: journal: Accepted: 22 August 2016;
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 5 System Evaluation
### \(\mathcal{D}\) Model Verification
The case with \(F_{conq}\) outperforms the case without \(F_{conq}\) independent to \(|M|\), since \(F_{conq}\) not only equalizes the WLAN congestion but pushes the edge-located UAVs out to search the unconnected devices. Also, the case with \(F_{conq}\) and 15 UAVs cover greater number of the devices than the case without \(F_{conq}\) and 20 UAVs, which means that \(F_{conq}\) can result the better coverage ratio with fewer UAVs. Figure 7(b) shows the results of the formation control algorithm. Figure 7: The results of the formation control algorithm. shows the standard deviation of \(|C_{i}|\). As shown at the example of Figure 7, standard deviation of the case with \(F_{coneq}\) is much less than the case without \(F_{coneq}\). Also, as \(|M|\) increases, standard deviation of the case without \(F_{coneq}\) increases, but the case with \(F_{coneq}\) does not change since \(|C_{i}|\) keeps equalized. Estimated throughput refers to the average of the devices' throughput calculated by Equation (5) with \(|C_{i}|\), assuming that each device sends data packet with rate \(1 Mb\). Due to the network capacity of the AP and the congestion, estimated throughput has no choice but to decrease as \(|M|\) increases. However, as previously verified in Figure 6, decreasing the number of \(|C_{i}|\) can reduce the loss of the throughput. The case with \(F_{coneq}\) and 20 UAVs results in the best throughput on the evaluation, while the difference grows as \(|M|\) increases. To provide the better quality of the devices, increasing the number of the UAVs could be the easiest way, but it might not bring the meaningful changes in terms of the coverage (Figure 7(a)) or the standard deviation (Figure 7(b)). Dynamically adjusting the number of the UAVs can be the promising strategy to improve the UAV-based network infrastructure.