esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
The measured EEG data were analyzed using the Bio-scan (Bio-Tech, Daejeon, Korea) program. The collected EEG raw data were analyzed using power spectrum analysis to identify the spectral edge frequency as 50% of alpha (ASEF50) [26]. ASEF50 is an index indicating brain comfort and is calculated as a band corresponding to 50% of the alpha (8 to 13 Hz) power. Data were collected by measuring the average of the EEG during the experiment, and a brain map program (Biotec Analysis Software, Daejeon, Korea) was used.  
MS data processing and multivariate statistical analysis were performed as previously described [25]. For MS data processing, raw data obtained from GC-TOF-MS were converted to a netCDF (*.cdf) format using LECO ChromaTOF software (version 4.44, LECO Corp., St. Joseph, MI, USA). Subsequently, the peak selection, retention time, and peak alignment were determined using MetAlign software (RIKILT-Institute of Food safety, Wageningen, The Netherlands). The results of alignment data were exported to a Microsoft Excel file. Multivariate statistical analysis was performed using SIMCA-P+ ver. 12.0 software (Umetrics, Urea, Sweden). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to compare different VOCs and serum metabolites. The significance levels of the PLS-DA and OPLS-DA models were defined by analysis of variance of cross-validated predictive residuals (CV-ANOVA) using the SIMCA-P+ program. The significantly discriminant metabolites were selected on the variable importance in the projection (VIP) values of the PLS-DA and OPLS-DA models. The selected metabolites were identified through comparing mass spectra (MS) and their retention time to the available databases, such as the National Institute of Standards and Technology (NIST) database (version 2.0, 2011, FairCom, Gaithersburg, MD, USA), the Human Metabolome Database(HMDB; [[http://www.hmdb.ca/](http://www.hmdb.ca/)]([http://www.hmdb.ca/](http://www.hmdb.ca/)) (accessed on 27 February 2022)), and our in-house library of standard compounds.  
The processed EEG data, SDM, and NRS for each stimulus were analyzed via paired _t_-test, which was performed using the SPSS (Version 25 for Windows; IBM, Armonk, NY, USA) program. For the demographic information collected, descriptive statistics were obtained for the mean, standard deviation, and percentage of each item using Microsoft Excel (Office 2007; Microsoft Corp., Redmond, WA, USA).  
The serum BDNF and CRP levels were expressed as the mean \(\pm\) SD using Graph Pad Prism 8.0.1 software (Graph Pad software Inc., San Diego, CA, USA). The results were analyzed statistically via paired _t_-test. Differences with a p-value less than 0.05 were considered statistically significant.
# Single-Shot Compressed Imaging via Random Phase Modulation
###### Abstract
proposed the correlation imaging of moving targets based on phase modulation. The spatial random phase modulator was introduced into the imaging optical path to modulate the three-dimensional spectrum data of the target, and then the detector was used to obtain the two-dimensional aliasing signal to realize the reconstruction by single exposure [7]. In this paper, both the theoretical and applications of compressed sensing and several compressed imaging systems using random phase modulation are reviewed. First, the fundamentals of compressed sensing are presented. Second, the principle of phase modulation is described. Finally, several strategies of compressed sensing and compressed imaging using random phase modulation are reviewed. The rest of the paper is organized as follows: In Sections 2 and 3, the fundamentals of compressed sensing and the principles of phase modulation are both described. In Section 4, different schemes of compressed imaging using random phase modulation are presented, which include: (i) single-shot compressed imaging with spatial-domain single random phase mask (CI-SSRPM). (ii) Single-shot compressed imaging with a random convolution using a double random phase mask (CI-DRPM). (iii) Single-shot compressed imaging with Fourier-domain single random phase mask (CI-FSRPM). (iv) Single-shot compressed imaging with double random phase encoding (CI-DRPE). Section 5 is the conclusion.
## 5 Example Simulation and Analysis
### Results Analysis
#### 5.4.1 Different Scheduling Scenes Analysis
Figure 7: System load curve before and after demand response. In Figure 8, the optimal dispatching plan under the comprehensive scene is shown. From 19:00 to 20:00, the photovoltaic station output decreases to 0, and wind power output begins to decline. At this moment, the output of the energy storage station is relatively large, and the thermal power unit is an upward climbing state. From 22:00 to 24:00, the system load decreases continuously. In addition, thermal power units change into a downward climbing state, and the output of the energy storage power station gradually decreases. It can be seen from the figure that using the complementary characteristics of multiple generations can be used to promote the economic and low-carbon dispatching for the collaborative dispatching power system.
# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 4 Discussion
### EEG Source Localization  
Several methodological approaches have been used to assess the neural correlates of vocal production and control. They include, for example, the analysis of local field potentials with cortical electrodes [83] and the use of transcranial magnetic stimulation [85; 95].  
While these procedures enable the role of anatomically restricted brain regions to be investigated, the analysis of the whole brain activity is facilitated by methods to solve the EEG inverse problem [96; 97]. The latter approach was used in this study to estimate the neural generators of the ERP elicited by self-produced speech (Figure 6C). Feedback perturbations resulted in increased activity of frontal, temporal and regions that have been traditionally associated with speech production and speech motor control (Table S3, Supplementary Materials). This group of regions include the precentral gyrus, the supplementary motor area, and the Rolandic operculum (frontal lobe), the insula (limbic lobe), the Heschl gyrus as well the inferior and superior temporal gyri (temporal lobe), and the postcentral gyrus (parietal lobe) [86].  
Furthermore, differences in activity were also obtained in the occipital lobe and other limbic areas. Although this result needs to be validated, evidence suggest that speech-driven spectrotemporal receptive fields that are sensitive to pitch are located in the calcarine area, an occipital cortical region that display strong functional connections with early auditory areas [98]. Likewise, the medial and the posterior cingulate cortices have been proposed as hubs of the syllable and speech production network, respectively [99]. These networks also comprise the hippocampus, the amygdala and the insula (limbic areas), as well as the cuneus, the lingual gyrus and the inferior, middle and superior occipital gyri (occipital areas) [99].
## 4 Results and Discussion
### Comparison of the Main Effects
#### 4.1.3 Main Effects of Factors on End-to-End Delay  
Figure 5 shows the main effects of factors with regard to average end-to-end delay. The three methods, FFM, PBM and TM, produce similar results regarding the trends of the main effects. Indeed, the plots report that the change in the levels from a low to high level leads to an increase in average end-to-end delay for factors ND, NC and WMH. The conclusions with respect to ND and NC factors are justified by the mechanism of route discovery established by the AODV routing protocol that adds delays before data transmission. Likewise, the two colluding attackers in the wormhole attack create a distant link between them, claiming that all routes must be formed through this wormhole link, causing a failure in node connectivity and resulting in infinite delays.  
In summary, the effects of ND, NC and the WMH are the same from the three methods. The main difference is in the effect of BKH. The FFM and Taguchi method outline that the black hole attack has no direct impact on average end -to-end delay. In that regard, the findings of both the FFM and Taguchi method seem to be more logical due to the fact that the time spent in the AODV route discovery phase is reduced when a black hole attack is  
Figure 4: Main effects on the packet loss ratio (PLR) from FFM, PBM and TM.  
Figure 5: Main effects on AODV end-to-end delay (E2 ED) from FFM, PBM and TM.  
initiated because the malicious node sends a fast RREP message to the source node in the AODV route reverse path.
## 3 The Proposed Method  
### 3D-LAMS Algorithm  
Dense urban scenarios require new approaches that consider large variations in the heights and shapes of buildings, which significantly affect path loss values, as opposed to the low and nearly identical average building heights in suburban scenarios. The first step of the new approach would be to design a new CNN image generation algorithm that can extract the 3D topographical information with a good representational power of the wave propagation environment. Our proposed algorithm is motivated by the LAMS algorithm designed to generate 2D CNN images for path loss modeling in suburban and indoor scenarios [10; 11].  
Based on Digital Elevation Map (DEM) data, the 3D-LAMS algorithm (Algorithm 1) generates 3D images which include the topographical information of the propagation environment between Tx-Rx pairs. As shown in Figure 1, 3D-LAMS algorithm collects 3D topographical information by placing a number of 2D scan planes (perpendicular to the ground) of the parallelepiped connecting a Tx and a Rx within a 3D map data. As shown in Table 1, different kinds of topographical objects are encoded into different pixel values (color values) of the grayscale.  
```
Input:  GPS locations of Tx and Rx  Size of a 3D-LAMS image: \((w,h,d)\)  Rasterized 3D map image \(MR\) Output:  A 3D-LAMS image
1. Divide the interval between Tx and Rx by \(d\),\(P_{1}\sim d\)
2. Determine the slope \(S\) of a straight line perpendicular to the line between Tx and Rx and parallel to the XY plane
3. Make empty 3D-array, \(C[w][h][d]\)
4.for\(i=1\) to \(d\)do 4.1. Make scan plane \(SP_{i}\)(center: \(P_{i}\), size: \(w\times d\), slope: \(S\) and perpendicular to the XY plane) considering \(MR\)
4.2. Copy \(SP_{i}\) to \(C[][][i]\)
5.endfor
```  
**Algorithm 1** 3D-LAMS Algorithm  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Class** & **Grayscale** \\ \hline
1 & Building & 240 \\
2 & Terrain & 200 \\
3 & Sky & 0 \\
4 & Ground & 255 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grayscale value of class in 3D-LAMS.  
The size and number of scan planes are the user-defined parameters and should be determined empirically.
## 3 Results
### Evaluation of the Backward Approach Compared with the SCOPE Model  
Although the forward estimation of GPP by SIF observations was also designed for its temporal variation through seasons, we assessed the backward approach from GPP for the estimation of diurnal SIF dynamics. Figure 8 illustrated that the SIF\({}_{\text{BACK}}\) using the direct backward approach did not significantly correlate with SIF\({}_{687}\) (R\({}^{2}\) = 0.00) or SIF\({}_{760}\) (R\({}^{2}\) = 0.03). It was also found that the high value of SIF\({}_{\text{BACK}}\) was usually accompanied by high VPD. Considering the coefficient \(c\) in the raw forward approach was obtained under the given PAR, the correction factor \(k\) was used in our study to improve the approach. Using the improved backward approach, the SIF\({}_{\text{BACK}^{*}}\) accounted for 21% and 36% of temporal variations of SIF\({}_{687}\) and SIF\({}_{760}\), respectively (R\({}^{2}\) = 0.21, \(p\) < 0.001; R\({}^{2}\) = 0.36, \(p\) < 0.001) (Figure 9a,b). The parabola regressions showed higher coefficients of determination (Figure 9), which was caused by the interference of high VPD on their positive correlation. After removing the abnormal points under drought stress (VPD > 2.7), the R\({}^{2}\) between SIF\({}_{\text{BACK}^{*}}\) and SIF\({}_{\text{SCOPE}}\) improved to 0.53 for SIF\({}_{687}\) (\(p\) < 0.001) and 0.72 for SIF\({}_{760}\) (\(p\) < 0.001). The high VPD appeared on 22 July, when the subtropical forest experienced the seasonal drought. That also explained the lower GPP on this day. Thereby, the improved backward approach offers a new insight to estimate the diurnal canopy SIF dynamics without the water stress for subtropical coniferous forests.  
Figure 7: Logarithmic relationship between canopy chlorophyll concentration (CCC) and canopy SIF simulated by the SCOPE model (SIF\({}_{\text{SCOPE}}\)). (**a**) CCC and SIF\({}_{687}\); (**b**) CCC and SIF\({}_{760}\).  
Figure 8: Comparison between canopy SIF estimated by the backward approach (SIF\({}_{\text{BACK}}\)) and SIF\({}_{\text{SCOPE}}\). (**a**) SIF\({}_{\text{BACK}}\) and SIF\({}_{\text{687}}\); (**b**) SIF\({}_{\text{BACK}}\) and SIF\({}_{\text{60}}\).  
Figure 9: Comparison between canopy SIF estimated by the improved backward approach (SIF\({}_{\text{BACK}}\)) and SIF\({}_{\text{SCOPE}}\) in all conditions (black line) or without drought (red line). (**a**) SIF\({}_{\text{BACK}}\) and SIF\({}_{\text{687}}\); (**b**) SIF\({}_{\text{BACK}}\) and SIF\({}_{\text{760}}\).
# Miniaturized PIFA for 5G Communication Networks
## 6 Conclusions  
A compact multi-band internal antenna for mobile handsets which can cover WCDMA800, WCDMA850, GSM900, DCS, PCS, WCDMA2100, LTE2100 communication services, 5G communication networks is modeled, simulated, and measured in this paper. This multi-band internal antenna shows a satisfactory performance for a common application. The demonstration compared with the actual results of the antenna produced confirmed the possibility of commercializing the PIFA in the proposed way. The beam width at E-plane radiation pattern is about \(120^{\circ}\), and the gain is 5.6 dBi at 960 MHz. The radiation patterns at E-plane from 824 MHz to 2.17 GHz have similar patterns, and a side lobe is formed at H-plane as the frequency increases. However, this side lobe is considered not to be a big problem because it is comparatively very small to the main beam which is vertical to the antenna side. Therefore, the designed multi-band internal antenna in this paper can satisfy the broadband characteristics in the view of an impedance, radiation pattern, and gain.  
This research was funded by Sangmyung University, grant number 2017-A000-0024.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
Figure 9: Measurement result (Radiation Pattern).  
WCDMA  
Wideband Code Division Multiple Access  
GSM  
Global System for Mobile Communications  
DCS  
Digital Cross-connect System  
PCS  
Personal Communication Services  
SMA  
Sub Miniature version A  
FINM  
Finite Integration Method
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 4 Proposed Architecture
### Merging-Squeeze-Excitation Feature Fusion  
Conventional feature fusion methods [19; 20; 21; 22; 23; 24; 25; 26; 27; 28] combine all feature maps from all branches equally without considering which feature maps are useful. However, some feature maps in \(\mathbf{A}^{(n)}\) may be unhelpful for the classification and they should be suppressed while the informative feature maps in \(\mathbf{A}^{(n)}\) should be emphasized. Therefore, in this work, inspired by the SE mechanism [16], we propose a feature fusion method called the merging-squeeze-excitation, which is aware of this issue. As shown in Figure 1, all sets of feature maps \(\mathbf{A}^{(n)}\), for \(n=1,2,\ldots,N\), are firstly combined in the pre-merging step to create a set of pre-merged feature maps \(\mathbf{B}\). Unlike [16], in the squeeze step, the channel-wise statistics \(\mathbf{h}^{(n)}\) used to compute the channel weights \(\mathbf{s}^{(n)}\) are computed according to the feature maps \(\mathbf{A}^{(n)}\) and pre-merged feature maps \(\mathbf{B}\). This implies that the importance of each feature map in \(\mathbf{A}^{(n)}\) is measured not only from \(\mathbf{A}^{(n)}\) but also from \(\mathbf{B}\). Accordingly, we find the corresponding channel weights \(\mathbf{s}^{(n)}\), multiply them to \(\mathbf{A}^{(n)}\), and obtain the channel-weighted feature maps \(\mathbf{P}^{(n)}\) in the excitation step. Finally, in the post-merging step, we recombine \(\mathbf{P}^{(n)}\), for \(n=1,2,\ldots,N\), using the same merging method in the pre-merging step to obtain the channel-weighted and merged feature maps \(\mathbf{Y}^{mse}\), which will be used in the classification process later. The details of these steps are explained as follows.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Pol-SAR Features Extraction
#### 2.1.1 Cross Polarized Ratio  
The cross polarized ratio is the echo ratio between the cross channel and co-polarized channels. It can be defined as follows with respect to the coherency matrix [20]:  
\[r_{\text{xv}}=\frac{1}{\sqrt{2}}\left|\frac{C_{22}}{C_{33}}\right| \tag{6}\]  
\[r_{\text{xh}}=\frac{1}{\sqrt{2}}\left|\frac{C_{22}}{C_{11}^{2}}\right|, \tag{7}\]  
where \(r_{\text{xv}}\) and \(r_{\text{xh}}\) are the cross polarized ratios in vv channel and hh channel, respectively. The cross polarized ratio reflects the effect of surface roughness on scattering, which is proportional to the surface roughness, within a certain roughness range.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 5 The Proof of Concept: A Case Study on Oil Spill Detection
### Context Reasoning in the Case Study
#### 5.3.3 MEBN Reasoning
However, it showcases that the MEBN reasoning can cater to real-world situations that are partially observable and can deal with uncertainty reasoning given unknown data. In this scenario, only two spill instances are exemplified as influence factors for the severity level. It is worth noting that the built MEBN can be flexible to accommodate reasoning that involves more spill instances due to its modularity. Therefore, the same MEBN could be instantiated to deal with a varying number of entities.
# Quality Assessment of Honey Powders Obtained by High- and Low-Temperature Spray Drying
## 2 Materials and Methods  
### Materials and Feed Solution Preparation  
Both raw materials: rapeseed honey (RH) and honeybee honey (HH) were purchased from a local market (Pasieka Warminska, Gietrzwald, Poland). Moisture content in honey was measured by refractometry and was 15.2 and 14.4%, RH and HH, respectively. Nutriose FM06 (Roquette, Lestrem, France), a water-soluble compound produced from partially hydrolyzed (by heating, in the presence of food-grade acid) maize starch, with recognized prebiotic properties was used as a carrier material in this study. Nutriose fits well in the \"clean label\" trend, contrary to typical carriers such as maltodextrin or gum Arabic, which are not perceived well by consumers [15; 16; 17].  
The types of feed solutions (and obtained powders) are presented in Table 1. In all feed solutions, the same ratio of the honey to the carrier solids (1:1) was utilized. The powders differed in the concentration of the feed solution. The reduction in the amount of water in the feed solution (increment of solids concentration from 30 to 60%) was required in the case of the process performed by dehumidified air, at a reduced temperature (100 \({}^{\circ}\)C), as it was observed in the preliminary experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **RH-DASD** & **HH-DASD** & **RH-SD** & **HH-SD** \\ \hline Honey solids & & 50:50 & 50:50 & 50:50 & 50:50 \\ to carrier solids ratio & & & & \\ Feed solution & 60 & 60 & 30 & 30 \\ concentration (\%) & & & & \\ Inlet/outlet air & & & & \\ temperature (\({}^{\circ}\)C) & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Experimental variants during traditional spray drying (SD) and dehumidified air spray drying (DASD) of rapeseed honey (RH) and honeybee honey (HH).
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### State of-the-Art Onset Detection Algorithms
#### 2.2.1 Librosa  
Librosa is a well-known library for sound analysis and feature extraction [23]. It has three different methods to estimate onsets. The first method (referred to as \"LibRaw\" in this paper) locates the note onsets based on peaks in the onset strength envelope. The onset strength envelope is calculated by finding the spectral flux, which is the difference in power spectrum between two consecutive frames, applying a threshold, and returning a one-dimensional array representing the change in spectral energy for each frame. Then, based on the onset strength, it peaks where the energy is a minimum based on the heuristic described by [PERSON] et al. [27]. Another two methods rely on backtracking from the nearest preceding minimum energy point [28]. The second method (called \"LibBt\") works by backtracking using the onset strength profile, while the third method (named \"LibBRMS\") depends on backtracking with the Root Mean Square (RMS) or amplitude value. All these three methods are offline; they have not been designed to work in real time.
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
## 5 Conclusions  
The development of GPS, BDS, GLONASS and Galileo marks a new era for our GNSS community. How to take advantage of multiple-frequency and multiple-constellation signals to improve the availability, reliability and accuracy of GNSS navigation and positioning service has become a research focus. This study focuses on the research of taking full advantage of every system and improving the efficiency of ambiguity resolution. A new single-epoch GNSS AR method for a short-range baseline based on triple-frequency signals is developed in this study. To test the performance of the new technique, real GPS/GLONASS/BDS data are processed. From the discussions above, we can obtain the conclusions below:  
(1) Triple- or multiple-frequency signals can form an EWL and WL(Wide Lane) linear combination; their ambiguities can be fixed much more easily. The test results show that the AR of the GEOs' observation is more reliable than that of the MEO and IGSOs' observation by rounding the float ambiguities to integers.  
(2) Using BDS carrier phase observation with known ambiguity to compute the prior information will not only enhance the dependability of the GNSS observation equation but also significantly increase the efficiency of the AR of GNSS double-frequency signals.  
(3) Least squares with robust estimation can effectively resist the effect of the residual from the mis-fixing BDS ambiguity.  
In conclusion, the new method can realize single-epoch GNSS AR for a short-range baseline based on triple-frequency signals.  
This method is based on triple-frequency signals. Note that 20 BDS satellites are currently in operation: 6 GEOs, 8 IGSOs and 6 MEOs. The full constellation is scheduled to comprise 35 satellites. According to its overall planning schedule, a BDS Navigation Satellite System with global coverage will be in place by 2020. Consequently, customers in the Asia-Pacific region can use this method right now, and it will be available for global users by 2020.  
This research was supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (No. 22016 RCJJ014) and Public Science and Technology Research Funds Projects of Surveying and Mapping (grant No. 201512034) and Natural Science Foundation of Fujian Province (project No.2015J01176).  
For this study, [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analysed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.