esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Comparision of Computational Complexity  
The computational complexity is compared to demonstrate the superiority of the proposed method. The method of obtaining PSR by point-wise calculation of eigenvalues is called method 1, and the proposed method is called method 2. The consuming times are obtained via a desktop with Intel Core i5-4590, CPU at 3.30 GHz 8.00 GB, Windows 7 operation system plus MATLAB R2014a, where h\({}_{\text{p3}}\) is 0-5, b\({}_{\text{p1}}\) is 0-5 and the intervals are 0.01 and 0.001, respectively, for the point-wise eigenvalue method.  
In the case of the 0.01 interval, the times taken for each method are in Table 5.  
In the case of the 0.001 interval, the times taken for each method are in Table 6.  
Figure 5: The PSR of h\({}_{\text{p3}}\)-b\({}_{\text{p1}}\) (parameter equilibrium point: h\({}_{\text{p3}}\) = 2.41, b\({}_{\text{p1}}\) = 0.12).  
Figure 6: The extended PSR of h\({}_{\text{p3}}\)-b\({}_{\text{p1}}\).  
Tables 5 and 6 show that the computational complexity of method 2 increases very slowly, while that of method 1 increases rapidly with the increase in accuracy. The greater the accuracy, the more obvious the advantage of method 2, which demonstrates the merit of the proposed method in computational efficiency and its potential for online application.
## 3 Method of Prediction and Optimization
### Multi-Objective Particle Swarm Optimization (MOPSO)  
#### 3.2.1 MOPSO Introduction  
Pareto optimality is a classical method to optimize conflicting objectives which is impossible to make any individual or preference criterion better off without making at least one individual or preference criterion worse off. The result of Pareto optimality is a set of Pareto front, which is known as the Pareto frontier. The algorithm we applied in this paper is MOPSO, which is used to find the Pareto frontier.  
MOPSO is a popular algorithm in the field of real parameters processing and conflicting objectives optimization. It is derived from the classical PSO [28], which is based on the update formula:  
\[\begin{array}{c}V^{y+1}=\omega\times V^{y}+C_{1}\times rand_{1}(\cdot)\times (pbest-X^{y})+C_{2}\times rand_{2}(\cdot)\times(gbest-X^{y})\\ X^{y+1}=X^{y}+V^{y+1}\end{array}, \tag{28}\]  
where \(X\), \(V\) denote the position and velocity of a particle in the solution space, respectively; \(pbest\), \(gbest\) denote the personal best and the global best of \(X\), respectively; \(\omega\), \(C_{1}\) and \(C_{2}\) denote the velocity update rate, the \(pbest\)-based position evolution rate, and the \(gbest\)-based position evolution rate, respectively. \(y\) in superscript is the generation index. MOPSO evolves from (28), but it has two fitness functions and a set of Pareto optimality standards. In each generation, MOPSO not only updates particles' position and velocity, but also the Pareto frontier.
# An Optical Encoder Chip with Area Compensation
## 5 Conclusions  
This paper proposes a photodiode area-compensation method applied to an optical encoder chip. This method compensates the area of the photodiode arrays of the M-code according to the light intensity distribution characteristics of the light source to achieve a high consistency of the output signals of the M-code. Using this method, a photodiode array chip for a reflective hybrid optical encoder is designed based on the 0.35 \(\upmu\)m CMOS process. When the LED is working normally, the duty cycle error of the square wave signals output by the M-code is within 2%, and even when the LED light intensity changes, the duty cycle error of the square wave signals output by the M-code is within 3.1%. The experimental results show that the output signals of each channel of the M-code have high consistency. Therefore, the same readout circuit can be used for each channel of M-code, which greatly simplifies the design complexity of the processing circuit. Moreover, when the LED luminous intensity changes due to temperature, aging, and other reasons, it is relatively simple  
Figure 10: (**a**) Phase relationship of A6 and B6 output signals. (**b**) Phase relationship of A11 and B11 output signals.  
Figure 9: (**a**) Signals output by A1, A6 and A11 channels when LED luminous intensity decreases. (**b**) Signals output by A1, A6 and A11 channels when LED luminous intensity increases.  
to adjust and not easy to generate error codes. Although the area compensation method has high requirements for the calculation of light intensity distribution and the accuracy of chip installation, it is a relatively simple method to achieve the consistency of output signals and reduce the bit error rate. This method has certain reference significance for applying reflective hybrid optical encoders practically.  
Conceptualization, X.W. and Y.L.; methodology, X.W. and W.Z.; software, X.W. and X.Y.; validation, X.W. and D.H.; data curation, X.Y.; formal analysis, X.W.; funding acquisition, W.Z.; investigation, X.W.; project administration, Y.L.; resources, X.W. and Y.L.; supervision, Y.L.; visualization, X.W.; writing--original draft preparation, X.W.; writing--review and editing, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 4 Evaluation
### System Performance Analysis
For example, when the payload size was 2000 bytes, the RTT time of the HTTP GET operation was 18 ms and the RTT time of the CON CoAP GET operation was 53 ms. When the payload size was 3000 bytes, the RTT time of the HTTP GET operation as 22 ms, but the RTT time of the CON CoAP GET operation was 76 ms. In conclusion, when the payload size was small, CoAP is preferred over HTTP. The performance of the CoAP GET and CoAP PUT operations under different data block sizes was tested based on Equation (2) with the Fireox browser. The RTT was measured by setting the CoAP block size to 64 bytes and 1024 bytes, respectively. In Figure 9, it can be seen that the larger the data block, the smaller the RTT. When the package size exceeded 64 bytes or 1800 bytes, the RTT grows faster because the data are transmitted in several segments. This also shows that when the CoAP protocol is integrated with other wireless sensor networks, the transmission performance will be affected if the other network data frames are small. For a block size of 64 bytes (the default size), the CON CoAP GET RTT for Equations (1) and (2) were compared, as shown in Figure 10. The T\({}_{\text{render}}\) was much larger than both the RTT, indicating that the application process time of Firefox was much larger than the CoAP protocol transmission needs. Figure 8: CoAP vs. HTTP: **(a)** CoAP GET and HTTP GET; and **(b)** CoAP PUT and HTTP POST. Figure 11 shows the availability of the Eclipse Kura gateway under different load conditions. In Figure 11a, it can be seen that, when the payload size was 2000 bytes, the system success rate decreased quickly with increasing concurrency number. When the number was 60, the success rate decreased to 80. When it exceeded 100, the success rate was reduced to less than 50%. At the same time, the memory usage of the Eclipse Kura gateway remained stable, and the CPU usage increased rapidly when the number of concurrencies exceeded 400. In Figure 11b, the payload was set to 1000 bytes. Figure 10: The CON CoAP GET performance comparison between Equations (1) and (2). Figure 9: Impact of different CoAP data block size on performance: (a) CoAP GET RTT; and (b) CoAP PUT RTT. In this case, the success rate of the system was 100% when the concurrency was less than 400. Even if it was increased to 1000, the success rate was still above 96%. Therefore, it can be concluded that the success rate was high and the system stable and reliable with small payload data. In both cases, the system memory usage was stable and the CPU usage increased rapidly when the concurrency number exceeded 200.
# Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence
## 2 Materials and Methods
### Statistical Analysis
Contrary to the delta and theta bands, the RD group presented lower coherence values in the alpha frequency band in Fp1-F3 (t = 3.2876, \(p\) = 0.0200, d = \(-\)0.741), Fp1-F7 (t = 3.1549, \(p\) = 0.0324, d = \(-\)0.660), Fp1-T3 (t = 3.0781, \(p\) = 0.0374, d = \(-\)0.761), and F7-T3 (t = 3.1629, \(p\) = 0.0320, d = \(-\)0.684) than the TD group. In the beta band, there were no significant differences between groups. In the right hemisphere, significant differences were observed only in the alpha band. Children with reading disorders obtained lower coherence values in Fp2-F4 (t = 3.2664, \(p\) = 0.0238, d = \(-\)0.725), Fp2-F8 (t = 3.8603, \(p\) = 0.0038, d = \(-\)0.862), and F4-F8 (t = 3.2980, \(p\) = 0.0226, d = \(-\)0.764) in the alpha band than the TD group. In the delta, theta, and beta bands, no differences were observed between groups. 3.1.2. Interhemispheric Coherence  
Coherences between homologous occipital leads were significantly higher in the RD group than in the TD group in the delta (t = \(-\)2.5737, \(p\) = 0.0428, d = 0.646) and theta (t = \(-\)2.8898, \(p\) = 0.0188, d = 0.637) frequency bands. In the alpha band, the RD group had lower coherences between the F7 and F8 leads (t = 3.3646, \(p\) = 0.0096, d = \(-\)0.853). The results are shown in Figure 3.  
3.2. Comparison between NF and Sham Groups with Respect to Pre-Post Treatment Changes  
No significant differences were found when the groups were compared regarding the changes produced by the respective treatment. This was true for treatment-induced learning, ENI reading scores, and coherence values. However, it is of interest to know if significant changes occurred in each group, considered independently. Figure 3: Significant differences between the RD and TD groups in interhemispheric coherence between homologous pairs. (**Top**): Bar graph showing the significant changes in coherence z values by frequency band (indicated at the superior part). (**Bottom**): topographic representation of significant changes at the scalp; red lines represent that coherence is higher in the RD group, and the blue line indicates that coherence is lower in the RD group when compared with the TD group. Figure 2: Significant differences between the RD and TD groups in intrahemispheric coherence. (**Top**): Bar graphs showing the significant changes in coherence z values by frequency band (indicated at the **top**) when comparing intrahemispheric connectivity between RD and TD groups; left or right hemisphere is displayed below the graph. (**Bottom**): topographic representation of significant differences in z-coherence between groups; red color represents that coherence is higher in the RD group, and blue color means that it is lower compared to the TD group.
# Improving Accuracy of Impervious Surface Extraction Based on a Threshold Hierarchical Method (THM)
## 6 Conclusions  
This study considered Guangzhou city as an example. A hierarchical thresholding extraction method was designed to address the underestimation of impervious surfaces obtained through the LSMA. It was proved that the THM can achieve a higher ISA extraction accuracy than the LSMA.  
The threshold is the key to determining the capacity of the THM. On selecting a GF-2 image, the optical threshold value was 0.5, whereas the highest level of precision through the THM was 0.1871; the accuracy of THM could be increased by 3.83% compared to the LSMA. The threshold was also found to be related to the selection of high-resolution, and the threshold increased as the resolution of the imagery improved.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1.0** \\ \hline Panchromatic & & & & & & & & & & \\ GF-2 & & & & & & & & & \\ GF-2 & & & & & & & & & \\ GF-1 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the RMSE of various high-resolution images at different thresholds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RMSE** & **THM** & **LSMA** \\ \hline (a) & 0.1347 & 0.1514 \\ (b) & 0.1904 & 0.1782 \\ (c) & 0.3225 & 0.2597 \\ (d) & 0.1927 & 0.2355 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the RMSE of the four zones.  
It was proved that the THM has a suitable accuracy when an entire image was considered; however, the method was found to be unsuitable for areas such as the CBD zone because of the existence of the shadows of tall buildings; moreover, the OOM also has limitations in dealing with this problem. On the one hand, detecting and removing shadows by using object-based detection methods [36; 37], building shadow indices [38] or SVM classifiers [39] is an effective step to improve the extraction accuracy of THM. On the other hand, choosing another threshold variable is also a feasible approach to deal with this problem.  
[PERSON] proposed the research idea; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments. The manuscript was written by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (Grant No. 41901347), the China Postdoctoral Science Foundation (Grant No. 2018M643109), and Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515010562).  
We sincerely thank the reviewers for their helpful comments and suggestions about our manuscript.  
The authors declare no conflict of interest.
## 6 Performance Evaluation
### Simulation Results
It is interesting to observe that improvements of the proposed solution compared to the considered benchmark schemes are still significant under less favourable settings in terms of service popularity, i.e., \(\alpha=0.4\). The service provisioning time increases since more distinct services need to be executed, wasting processing resources of nodes. For NDN-fog, the metric achieves values are well below the ones provided by the benchmark schemes (see Figure 7). The larger processing load at NDN-fog nodes results in a larger percentage of services offloaded to the cloud compared to the previous case with \(\alpha=0.8\); see Figure 8. The impact of different  
Figure 7: Service provisioning time when varying the service request arrival rate (\(\lambda\)) for different content size settings (\(\sigma\)), \(\alpha=0.4\). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Content size** & \multicolumn{2}{c}{**NDN-fog**} & \multicolumn{2}{c}{**Cloud**} & \multicolumn{2}{c}{**CE**} & \multicolumn{2}{c}{**LLE**} \\ \hline  & \(\alpha=0.4\) & \(\alpha=0.8\) & \(\alpha=0.4\) & \(\alpha=0.8\) & \(\alpha=0.4\) & \(\alpha=0.8\) & \(\alpha=0.4\) & \(\alpha=0.8\) \\ \hline
500 packets & 2012 & 1567.4 & 1945 & 1497.5 & 496.4 & 384.54 & 2018 & 1517.4 \\
1000 packets & 3572 & 2787.1 & 3884 & 2990.7 & 981.3 & 757.85 & 4024 & 3019.6 \\
2000 packets & 6759 & 5132.6 & 7764 & 5977.2 & 1951 & 1504.5 & 8053 & 6043 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Intra-edge exchanged traffic for different content size settings (\(\sigma\)), \(\lambda=80\) requests/s. round-trip-time (RTT) values towards the cloud on the metric is also evaluated. The larger the RTT, the lower the metric: it is more convenient to execute the service at the edge to reduce the service provisioning time. This is especially true as the input content size increases. For what concerns the intra-edge traffic, the same trends already discussed for \(\alpha=0.8\) can be observed in Table 3. However, a higher traffic is exchanged due to the presence of less popular services.
## 5 Performance Evaluation of Our Routing Protocol
### Comparison with Different Number of Flows  
Figure 9 shows the performance of the schemes with different number of flows, where the number of channels is assumed to be 12. For Figure 9, we assume that the transmission rate is 185 KBps. From Figure 9, we can observe that JRCA performs significantly better than a single channel scheme in terms of throughput, delivery ratio, delay and jitter. With four NIC per router, JRCA improves the network throughput by about three times, compared to the single channel case. On the other hand, the packet delivery ratio improves by \(\sim\)40%, and the end-to-end delay is reduced to about six times. This significant improvement in delivery ratio is achieved as a result of reduced network interference due to efficient channel selections. On the other hand, using multiple channels also results in reduced channel contention with the neighboring transmitting nodes, which significantly reduces the end-to-end packet delay and jitter. These factors result in significant improvement in the network throughput.  
Figure 8: Joint route and channel assignment (JRCA) scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Values** & **Parameter** & **Values** \\ \hline Max queue length & 200 & Data packets size & 1000 bytes \\ Propagation Model & Two Ray Ground & Traffic Generation & Exponential \\ Antenna gain & 0 dB & Transmit power & 20 dBm \\ Noise floor & \(-\)101 dBm & SINDRatacapture & 10 dB \\ Bandwidth & 6 Mbps & PowerMonitor Thresh & \(-\)86.77 dBm \\ Average ON time & 0.5 s & Average OFF time & 0.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation environment.  
From Figure 9, we can also observe that the network performance improves with the increase in the number of NICs. Due to efficient channel selection, JRCA achieves better network performance in comparison to random channel selection scheme. Compared to the random channel selection scheme, JRCA improves the network throughput by \(\sim\)20%. On the other hand, the packet delivery ratio is improved by \(\sim\)10% and the packet delay is almost halved because of better channel management in JRCA.
## 3 Results and Discussion
### Bioactive Compounds and Antioxidant Potential of the Buritirana Fruit Fractions
#### 3.5.1 Phenolic Compounds Profile
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Phenolic Compounds**} & \multirow{2}{*}{**Mass**} & \multirow{2}{*}{**RT**} & \multirow{2}{*}{**WS**} & **PU** & **PE** & **SE** \\  & \(\mathbf{[M-H]}\) & & & & **Concentration (\(\mathrm{\SIUnitSymbolMicro mL^{-1}}\))** & \\ \hline Quercetrin & 447 & 3.90 & n.m & \textless{}LOQ & \textless{}LOQ & \textless{}LOQ \\ Ferulic acid & 193 & 3.60 & 0.04 \(\pm\) 0 b & 0.05 \(\pm\) 0 a & 0.03 \(\pm\) 0.01 c & n.m \\ Sinapic acid & 223 & 3.50 & \textless{}LOQ & n.m & \textless{}LOQ & \textless{}LOQ \\ Rutin & 609 & 3.40 & 0.61 \(\pm\) 0 b & 1.14 \(\pm\) 0.01 a & 0.37 \(\pm\) 0.01 c & n.m \\ _p_-Coumaric acid & 163 & 3.25 & 0.01 \(\pm\) 0 a & n.m & 0.01 \(\pm\) 0 a & n.m \\ Epicathecin/Cathecin & 289 & 2.70 & 0.03 \(\pm\) 0 b & 0.01 \(\pm\) 0 c & 0.10 \(\pm\) 0 a & 0.01 \(\pm\) 0 c \\ Chlorogenic acid & 353 & 2.25 & 0.06 \(\pm\) 0 b & n.m & 0.10 \(\pm\) 0 b & 2.51 \(\pm\) 0.1 a \\ Protecatechuic acid & 153 & 1.75 & 0.06 \(\pm\) 0 c & 0.08 \(\pm\) 0 b & 0.04 \(\pm\) 0 d & 0.14 \(\pm\) 0.01 a \\ Quinic acid & 191 & 0.70 & 6.77 \(\pm\) 1.43 b & 5.20 \(\pm\) 0.11 c & 8.25 \(\pm\) 0.24 a & 1.91 \(\pm\) 0.07 d \\ \hline \hline \end{tabular}  
* RT: retention time; LOQ: limits of quantification; WS: whole without seed; PU: pulp; PE: peel; SE: seed; Average with different letters in the line indicate statistical differences according to Turkey (_p_ < 0.05). n.m: not measured; quercetin LOQ: 0.88 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\); sinapic acid LOQ: 0.43 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\).  
\end{table} 
Table 3: Phenolic compounds found by UHPLC-ESI-MS in the buritirana fruit fractions.
# Color CCD High-Temperature Measurement Method Based on Matrix Searching
## 3 Experiments
### Calibration Experiments  
Firstly, with the lens cap installed, the MER2-134 camera is used to acquire a pure black image (lens cap image) to obtain the dark current noise D in 0-12 grayscale units existing in different pixel positions. To obtain the s current-gray conversion coefficient of the camera, the color CCD calibration method described in reference [25] is used to calibrate the MER2-134 camera with the LS1050 standard blackbody of Electro-optical Company as the standard radiation source. The schematic diagram of the experimental set-up is shown in Figure 4.  
In the process of calibration, the focal length of the camera is 25 mm, the aperture is f/2.8, and the distance between the camera and the blackbody is 27.5 cm. The calibration begins when the blackbody temperature is 823 K, and then the calibration temperature increases by 50 K, and a total of 11 sets of data are obtained.
## 3 System Model
### Propagation and Antenna Models  
The value of SNR at the UE can be written as  
\[S(x)=P_{T}G_{A}G_{U}\Big{[}\frac{x^{-\zeta_{T}}\epsilon^{-Kx}}{N_{0}+M_{T}} \Big{]},\]  
where \(\zeta_{T}\) is path loss exponents in LoS non-blocked state, \(M_{T}\) is the constant capturing interference and shadow fading on LoS non-blocked state, \(N_{0}\) is density of noise, \(P_{T}\) is emitted power, \(G_{A}\) and \(G_{U}\) are the BS and UE gains.  
To define the path loss, \(L(y)\), for mmWave systems one utilize the urban-micro (UMi) providing path loss in the following form  
\[L_{dB}(y)=\begin{cases}32.4+21\log_{10}y+20\log_{10}f_{c},\text{ non-bl.},\\ 32.4+31.9\log_{10}y+20\log_{10}f_{c},\text{ blocked},\end{cases} \tag{1}\]  
where \(f_{c}\) is the carrier measured in GHz.  
The path loss defined in (1) can also be converted to the linear scale by utilizing the generic representation \(A_{ij}v^{-\zeta_{i}}\), where \(A_{i}\), \(\zeta_{i}\), \(i=1,2\), are the propagation coefficients corresponding to LoS non-blocked (\(i=1\)) and blocked (\(i=2\)) conditions, i.e,  
\[A=A_{1}=A_{2}=10^{2\log_{10}f_{c}+3.24},\,\zeta_{1}=2.1,\,\zeta_{2}=3.19.\]
## 2 System Structure  
This paper studies a community microgrid demonstration system that includes 24 villas. This community is a typical rural microgrid demonstration system in Qinghai Province, China. An aerial image of the demonstration community is shown in Figure 1. Photovoltaic system installation adopts the form of decentralized installation, taking full advantage of the roof area of the building, forming a small-scale power supply system for self-use and sale of surplus electricity to the utility.  
The heating method for users in the community is decentralized electric heating, and each villa is designed as a renewable energy-based cogeneration microgrid. In order to improve the power supply reliability of the community microgrid system, the community has set up a centralized ESS. During grid-connected operation, the ESS relies on the time-of-use electricity price policy to improve the economics of the microgrid system; when the utility is out of power, it acts as a backup power source to ensure that the system is continuously powered. The microgrid system structure is shown in Figure 2.  
There are 24 villas in the community, which are powered by renewable energy. The community uses the forms of \"photovoltaic + carbon fiber electric heating\" and \"photovoltaic + air source heat pump\" to realize the combined supply of heat and electricity to the main energy users. There are two types of heating load: the carbon fiber geothermal electric heating load, and the air source heat pump heating load. The system consists of the utility, distributed photovoltaic power generation system,  
Figure 1: Aerial image of demonstration community.  
Figure 2: System structure diagram of the community microgrid.  
battery ESS, and the internal load of the microgrid system. It is a typical \"source-utility-load-storage\" microgrid system structure. The system is a grid-connected microgrid system, which can realize power exchange with the utility and, as a user, settle electricity charges and distributed generation subsidies with the utility. The grid has a time-of-use electricity price incentive policy for the microgrid systems.