esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experiment Methods
#### 2.2.3 GC-MS Analysis  
Each sample was precisely weighed to 3 g and placed into an extraction bottle. Subsequently, 150 mL of boiling water was added. Next, 2.00 \(\upmu\)L of internal standard substance (12 mg\(\cdot\)L\({}^{-1}\) ethyl decanoate) was added. A head space solid-phase microextraction (HS-SPME) handset equipped with a 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) extraction head (the extraction head was aged for 15 min at 250 \({}^{\circ}\)C prior to the experiment) was then inserted into the extraction bottle through the rubber bottle cap, and the handset was placed in a 50 \({}^{\circ}\)C water bath for 10 min. After the extraction head had adsorbed analytes for 50 min, it was removed and immediately inserted into the inlet of the gas chromatograph apparatus to desorb for 3 min. The apparatus was then used to collect data. All tea samples were prepared and analyzed in three independent replicates.  
The conditions and apparatus used in gas chromatography were a 7890A gas chromatograph and 5975C mass spectrometer (Agilent, USA), a HB-5 MS (30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m) elastic quartz capillary column (Agilent Technologies), an inlet temperature of 240 \({}^{\circ}\)C, high purity helium (as the carrier gas), and a flow velocity of 1.0 mL\(\cdot\)min\({}^{-1}\). The column heating procedure was performed; the column was maintained at 50 \({}^{\circ}\)C for 5 min. Subsequently, the temperature was increased 3 \({}^{\circ}\)C/min until it reached 180 \({}^{\circ}\)C. The column was maintained at this temperature for 2 min after which the temperature was increased 10 \({}^{\circ}\)C/min until it reached 250 \({}^{\circ}\)C. Finally, the column was maintained at this temperature for 3 min. The conditions set for mass spectrometry were as follows: ionization energy (EI), 70 eV; quality scan range, 50-600 amu; ion source temperature, 230 \({}^{\circ}\)C; quadrupole temperature, 150 \({}^{\circ}\)C; and mass transmission line temperature, 280 \({}^{\circ}\)C.  
Volatile components were identified using the National Institute of Standards and Technology (NIST) library (11.L), and a mass spectrum fitness of \(>\)90% was set as the substance identification standard. The relative content of each volatile component was calculated using the equation _Ci = (Ai/Ait)_ \(\times\) 100%, where _Ci_ is the relative content of a particular volatile component (%), _Ai_ is the peak area of a particular volatile component, and _Ait_ is the summation peak area of the all volatile components.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## Materials and Methods
### Antioxidant Properties  
#### 2.5.1 DPPH Radical Scavenging Assay  
We detected DPPH radical scavenging activity according to the reported methods with minor modifications [23; 24]. In brief, polysaccharide samples at different concentrations were diluted with distilled water. The sample group consisted of an equal volume mixture of 0.1 mM DPPH solution and GPP sample solution in a 96-well plate, ascorbic acid (VC) as positive control and absolute ethanol as blank control. Absorbance was measured at 517 nm. DPPH radical scavenging activity was calculated with the following formula: DPPH scavenging activity (%) = [1 - (A\({}_{1}\) - A\({}_{0}\))]/A\({}_{2}\)\(\times\) 100%, where A\({}_{0}\)--absorbance without sample; A\({}_{1}\)--absorbance of the added sample; and A\({}_{2}\)--absorbance of distilled water.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 4 Experimental Results and Discussion  
### Data Description  
Provided by [PERSON] and [PERSON] [8], the NEU data set is utilized to establish a steel surface defect diagnostics model. The data set is made up of six different kinds of defect cases, i.e., rolled-in scale, patches, crazing, pitted surface, inclusion, and scratches. Figure 5 visualizes several example images of the steel surface defect cases used in the experiment. Besides, as described in Table 2, each class has 300 samples, where each sample is an image with a dimension of 200 \(\times\) 200. In the experiment, the entire data set is randomly split into training, validation and testing data set, where 70% of the whole images are used as a training set whilst 20% and 10% of them are, respectively, used as testing and validation set. The main challenging issue of the described data set is confusing spatial characteristics of the images. In some cases, it is difficult to identify spatial information as it sporadically appears in different aspects of the images. For example, patterns in the scratches differ from horizontal to vertical stripes. Also, the gray-scale of the images frequently varies due to the illumination effect [8].
# Prediction of Tunnel Blasting Vibration Velocity Considering the Influence of Free Surface
## 5 Engineering Applications  
### Project Overview  
The new Jiujiawan tunnel on the Duyun-Anshun highway in Guizhou needs to be close to the adjacent existing tunnel with a minimum distance of 17.2 m (Figure 7). To avoid adverse effects of blasting vibration on the existing tunnel lining, blasting effects need to be controlled by predicting the peak blasting vibration velocity and determining the optimal blasting parameters.  
The total length of the new Jiujiawan tunnel is 230 m, with a section size of 21.5 \(\times\) 13.7 (length \(\times\) width). The tunnel mainly crosses the stratum of medium weathering tuff, with a rock density of 2660 kg/m\({}^{3}\), an elastic modulus of 6.5 GPa, and a P-wave speed of 4100 m/s. The tunnel construction is in the order of Part I to Part V. Part I is the construction of the middle guide tunnel, the size of which is 7.0 m \(\times\) 7.4 m (width \(\times\) height). The drilling and blasting methods were adopted on-site. The hole diameter was 38 mm, and the diameter of the rock emulsion explosive was 32 mm. It was detonated by a non-electric detonator. To better guide the blasting design, a test blast was first conducted at the site when the new  
Figure 6: Formula validation.  
Figure 7: Relative position of the new tunnel to the existing tunnel.  
tunnel was 20.0 m away from the existing tunnel. the blasthole is shown in Figure 8, and the blasting parameters are shown in Table 3.
# Mechanical Performance of Curved Link-Slab of Simply Supported Bridge Beam
## 4 Sensitivity Analysis of CLS
### Span of CLS  
Without changing the CLS pavement thickness and surface material, appropriately increasing the span can effectively reduce the bending and tension stiffness of the CLS structure. This behavior makes the isolation space between the lower part of the link-slab and the main beam larger, which is beneficial to the coordinated deformation of the beam end displacement. In this section, numerical analysis is used to evaluate the effect of the preset curve span length on the CLS's mechanical properties. The preset span lengths are 807, 1.5 m, 2.0 m, 3 m, and 4 m, which are 2.5%, 3.75%, 5%, 7.5%, and 10% of the main girder length, respectively. According to the numerical results, the maximum principal tensile stress of the upper and lower surfaces of the CLS pavement under different loads and load combinations are shown in Figure 16.  
Figure 16 shows that under the corresponding load conditions, the maximum principal tensile stress on the upper surface of the mid-span part of the CLS structure of the CLS decreases with the increase of the curve-shape span. As noted, when the span length increases from 1 m to 2 m, the maximum principal tensile stress of the upper surface under load combination I decreased from 2.13 MPa to 0.97 MPa (54.5% difference), while it decreased from 2.15 MPa to 0.97 MPa (54.9% difference). The maximum principal tensile stress of the lower surface decreases first and increases as the curve span length increases. When the span length increases from 1 m to 2 m, the maximum principal tensile stress of the lower surface increases from 1.45 MPa to 1.47 MPa under load combination II (1.4% difference), while it decreases from 1.82 MPa to 1.06 MPa (41.7% difference) under load combination IV.  
When the span length is 2 m to 3 m, the main tensile stress value of the CLS upper and lower surfaces tends to be stable. However, when the span length increases, the CLS stress does not change significantly, and it also affects the integrity of the CLS, which is not conducive to the structure's strength. Therefore, when the CLS span is 2 m to 3 m (about  
Figure 16: Curve of the maximum principal tensile stress vs. CLS span.  
5% to 7.5% of the main girder length), the main tensile stress on the CLS upper and lower surfaces tends to be stable and reasonable.
## 4 Simulation Experiments and Results
### Simulation Experiments
#### 4.1.2 Performance Index  
For a factory to achieve a better result, it needs to reduce the lot delivery time (\(LDT\)) as much as possible. Equation (1) defines the formula to calculate the \(LDT\) for a lot.  
Figure 13: The simulation model.  
\[LDT=TT+L\&ULT+WT+BT \tag{1}\]  
where  
_TT_: is the total transportation time for a lot from entering the system to exiting the system.  
_L&ULT_: is the total time of a lot being loaded and unloaded from machines.  
_WT_: is the total waiting for a lot to be loaded into a machine for processing.  
_BT_: is the total block time of a lot.  
Theoretically, the _L&ULT_ is fixed time and cannot be improved whereas the _TT_, _WT_ and _BT_ are variable times and can be improved by an effective dispatching method.  
Equation (2) defines the average lot delivery time (_ALDT_), where \(i\) indicates the _i_th lot and \(L\) is the total number of lots, which is used as the main performance index in this research.  
\[ALDT=\left(\sum_{i=1}^{L}LDT_{i}\right)/L \tag{2}\]  
To investigate different scenarios, the bay-loading ratio (\(R_{BL}(j)\)) and hot lots ratio (\(R_{h}(j)\)) are combined to represent a system configuration for an Intra-bay. The higher the \(R_{BL}(j)\) and \(R_{h}(j)\) indicates a higher degree of traffic-jam in a bay. A system configuration is denoted as XHYB, where X is the hot lot ratio and Y indicates the bay-loading ratio.  
Equation (3) is the formula for calculating \(R_{BL}(j)\) for an Intra-bay \(j\), which is the average number of hourly input lots divided by the maximum number of hourly output lots per bay.  
\[R_{BL}(j)=\sum_{i=1}^{m}\left(3600s/\ ST_{i}+PT_{i}+\varphi_{i}\right) \tag{3}\]  
where  
_m_: is the total number of machines in Intra-bay \(j\)  
_i_: the machine \(i\), \(i=1,\ldots,m\)  
_j_: the bay \(j\)  
_ST\({}_{i}\)_: is the setup time of machine \(i\)  
_PT\({}_{i}\)_: is the processing time of machine \(i\)  
_q_: is the loading and unloading time of machine \(i\)  
The bay-loading time of an Intra-bay is calculated for 1 h, and the time unit is second. In this research, three bay loading ratios 92%, 96% and 98% are used.  
Equation (4) defines the formula for calculating the hot lot ratio, \(R_{h}(j)\). The increase of \(R_{h}(j)\) can affect the delivery of normal lots. In this research three hot lot ratios 2%, 6% and 10% are used.  
\[R_{h}(j)=u_{j}/\rho_{j} \tag{4}\]  
where  
_u_: is the average number of hot lots  
_p_: is the average number of lots in Intra-bay \(j\)
## 5 Results of Experimental Test
### Combination of Configuration B and C  
Figure 27 shows the damping ratio for each diameter of orifices and configuration, calculated from the hysteresis graphs obtained during experimental testing.  
Configurations B and C, which feature four orifices located at the edge and center of the piston plate, respectively, display different damping ratios. As illustrated in Figure 27, the damping ratio for the damper with a 2 mm orifice diameter is higher than for the 1 mm orifice diameter. Although the damper with the 1 mm diameter orifices generates higher force due to the increased resistance to fluid flow, the position of the orifices significantly influences the damping ratio.  
In particular, the configuration with orifices at the edges of the piston plate (Configuration B) shows a higher damping coefficient and energy dissipation compared to Configuration C, where the orifices are positioned at the center of the piston plate. This indicates that the placement of orifices around the edges enhances the overall performance of the viscous plane damper.
## 3 Conclusions  
We investigated the polarization-selective electromagnetic (EM) transparent mesh-type E-shaped antenna unit-cell, which consisted of one S-band element being stacked on top of 3 \(\times\) 3 X-band elements in a shared aperture. To reduce the blockage effect, the S-band element was arranged to rotate 90 degrees with respect to X-band elements so that the polarizations between the S- and X-band elements were perpendicular to each other. In addition, to minimize performance degradation due to the blockage effect, a mesh structure was used for the S-band element, which improved EM transparency from \(-30\) dB to \(-1.5\) dB. The extended via cavity wall was also employed outside the nine X-band elements to minimize the mutual coupling and to reduce the antenna size. The simulated average bore-sight gain and bandwidth of the S-band antenna were 5 dBi and 756 MHz, respectively. The simulated average bore-sight gain and bandwidth of the X-band center element were 4.5 dBi and 1.96 GHz, respectively. Finally, array patterns were achieved using active element patterns of all nine X-band elements. The maximum array gains at 10 GHz by simulation and measurement were 13.6 dBi and 13.2 dBi, respectively. The results demonstrated that the proposed antenna was suitable for shared-aperture radar applications.  
Figure 9: Simulated and measured 2D array patterns of the nine X-band elements at 10 GHz.  
**Author Contributions:** Conceptualization, S.W., H.K. ([PERSON]) and H.C.; methodology, S.W., H.K. ([PERSON]) and H.K. ([PERSON]); software, S.W. and H.K. ([PERSON]); validation, S.W., H.K. ([PERSON]), H.C. and H.K. ([PERSON]); formal analysis, S.W.; investigation, H.K. ([PERSON]); resources, H.K. ([PERSON]); data curation, H.K. ([PERSON]); writing--original draft preparation, S.W.; writing--review and editing, S.W. and H.C.; visualization, H.K. ([PERSON]); supervision, H.C.; project administration, H.K. ([PERSON]); funding acquisition, H.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research has been supported by the Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea.  
The authors declare no conflict of interest.
## 3 System Model  
### Model of HAPS  
In Figure 1, we show a typical HAPS communication system model. We think about the scenario of \(M\) HAPSs serving multiple users in the mMIMO mmWave networks. In more detail, each HAPS is equipped with \(N\) antenna arrays to generate \(N\) beams. \(M\) HAPSs assist the BS to serve multiple users. HAPSs relay the signal transmitted from the BS to users to improve the throughput of users.  
\begin{table}
\begin{tabular}{l l} \hline \(M\) & The number of HAPSs \\ \hline \(N\) & The number of antenna arrays in each HAPS \\ \hline \(K\) & The number of users \\ \hline \(\theta_{AB}\) & The vertical beam half power beam width (HPBW) \\ \hline \(\phi_{MB}\) & The horizontal beam HPBW \\ \hline \(\phi_{ni1t}\) & The horizontal tilt \\ \hline \(\theta_{bilt}\) & The vertical tilt \\ \hline \(p\) & The transition probability \\ \hline \(\gamma\) & signal-to-interference plus noise ratio \\ \hline \(\alpha\) & Reward discount factor \\ \hline \(\beta\) & Learning rate \\ \hline \(\mathcal{S}\) & The state space \\ \hline \(\mathcal{A}\) & The action space \\ \hline \(A\) & The action of the HAPS \\ \hline \(a_{ul}\) & The \(l\)-th action of the \(i\)-th antenna array \\ \hline \(s_{t}\) & The state at the time \(t\) \\ \hline \(A_{j}\) & The \(j\)-th action \\ \hline \(\mathcal{R}(s_{t},A_{j})\) & The reward with the \((s_{t},A_{j})\) \\ \hline \(\mathcal{Q}(s_{t},A_{j})\) & The \(\mathcal{Q}\)-value with the \((s_{t},A_{j})\) \\ \hline \(\mathcal{F}\) & The neural network \\ \hline \(\mathcal{D}\) & The experience reply memory \\ \hline \(\mathcal{X}\) & The randomly generated antenna parameters \\ \hline \(E\) & The number of epochs \\ \hline \(T\) & The number of steps in each epoch \\ \hline \(p\) & The number of particles \\ \hline \(gbest\) & The optimal solution \\ \hline \(pbest\) & The sub-optimal solution \\ \hline \end{tabular}
\end{table}
Table 1: Key Notations.  
Moreover, we also consider the wind-caused HAPS movement, as shown in Figure 2. Figure 2a shows the HAPS with shifting and Figure 2b shows the HAPS with rotation. Whatever shifting or rotation, it will cause the degradation of users' throughput.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
###### Abstract
The methods that were used in [15] included the use of an impinge air quality testing apparatus, which is very expensive and requires a huge budget, which makes it heavy and difficult for developing countries governments to fund [10]. The other approach is that samples of fine particulate matter (2.5) is collected at the end of each sampling period, stored in plastic petri dishes, sealed, and transferred to the U.S.A. for analysis. Therefore, in this paper, a fuzzy based prediction model for predicting AQI based on the air pollutant concentrations in Kampala city is designed in order to provide the public with information about the quality of air and also the responsible authorities to take the precautions and decisions from an informed point of view of the levels of air pollutants. Fuzzy logic modeling is known to give accurate results in solving non-linear problems.
## Materials and Methods
### Chemicals  
The analytical standards used to identify the aroma compounds were of high-purity grade (GC grade, \(\geq\)98%). Ethyl acetate, ethyl propanoate, ethyl 2-methylpropanoate, 2-methylpropyl acetate, ethyl butanoate, ethyl 2-methylbutanoate, ethyl 3-methylbutanoate,3-methylbutyl acetate, ethyl pentanoate, ethyl hexanoate, hexyl acetate, 3-methylbutyl butanoate, ethyl heptanoate, ethyl lactate, ethyl octanoate, 3-methylbutyl hexanoate, octyl acetate, ethyl nonanoate, diethyl malonate, ethyl decanoate, ethyl benzoate, diethyl butanedioate, ethyl phenylacetate, 2-phenylylethyl acetate, ethyl dodecanoate, ethyl 3-phenylpropanoate, ethyl tetradecanoate, 2-butanol, 1-propanol, 2-methylpropanol, 1-butanol, 3-methylbutanol, 1-pentanol, 1-heptanol, 2-heptanol, 1-hexanol, 3-hexen-1-ol, (Z)-, 1-heptanol, 1-octen-3-ol, 1-octanol, 2,3-butanediol, 1-nonanol, 2-phenylethanol, acetic acid, propanoic acid, 2-methylpropanoic acid, butanoic acid, 2-methylbutanoic acid, 3-methylbutananoic acid, pentanoic acid, hexanoic acid, octanoic acid, decanoic acid, 2-methylpropanal, 3-methylbutananal, hexanal, nonanal, decanal, benzaldehyde, (E,Z)-2,6-nonadienal, phenylelectaldehyde, naphthalene, 2-pentylfuran, 2-furaldehyde diethyl acetal, furfural, 5-methyl furfural, 2-furanmethanol, 2(5H)-furanone, 2-heptanone, 3-hydroxy-2-butanone, 2-nonanone, \(\gamma\)-hexalactone, \(\gamma\)-nonalactone, guaiacol, 4-methylguaiacol, phenol, 4-ethylguaiacol, 4-ethylphenol, 2,6-dimethylpyrazine, trimethylpyrazine, dimethylyl disulfide, dimethyl trisulfide, 2-thiophenencarboxaldehyde, \(\beta\)-damascenone, geosmin, geranylacetone, \(\beta\)-ionone, 1,1-diethyloxyethane were obtained commercially from Sigma-Aldrich Co., Ltd. (Shanghai, China), J&K Scientific Co., Ltd. (Shanghai, China), and Alfa Aesar (Tianjin, China). A mixture of C\({}_{6}\)-C\({}_{30}\) hydrocarbons (Sigma-Aldrich, Shanghai, China) was employed to determine linear retention indices (RIs). Sodium chloride (NaCl) and anhydrous sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) were acquired from China National Pharmaceutical Group Corp (Shanghai, China). Ethanol (high-performance liquid chromatography (HPLC) grade) and dichloromethane (CH\({}_{2}\)Cl\({}_{2}\), HPLC grade) were bought from Titan Scientific Co., Ltd. (Shanghai, China). Ultrapure water was obtained from a Milli-Q purification system (Millipore, Bedford, MA, USA). 2-Methyl-2-butanol (IS1), n-pentyl acetate (IS2), dl-menthol (IS3), octyl propanoate (IS4), trimethylacetic acid (IS5) (Sigma-Aldrich, Shanghai, China) were used as internal standards in quantitative analysis.  
The reagents used in QDA were high-purity grade (GC grade, \(\geq\)98%), while the references of the retronasal aroma were food grade (except for geosmin, \(\geq\)95%). They were all purchased from Sigma-Aldrich (Shanghai, China). Purified water (Wahaha Group Co., Ltd., Hangzhou, China) was used to clean the mouth between samples in sensory analysis.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Acoustic Signal Processing Techniques
#### 4.2.2 Frequency Domain  
The most recurrent frequency features and main applications in the literature are detailed in Figure 6. After the signals were captured, to carry out analysis, data were transformed into the frequency domain. Several studies analyzed pitch, f0, or harmonics [29; 41; 42; 49; 51; 56; 57; 59; 60; 70; 73; 85; 89; 94; 95; 104; 106; 107; 108].  
Other studies used MFCC for their analyses [40; 41; 42; 44; 52; 56; 58; 60; 61; 63; 65; 72; 73; 77; 80; 87; 94; 100; 101; 107; 109; 110; 111; 112], also combined with other techniques, such as LFCC [113]. Several researchers used CPP [84; 97] and CPPS [45; 73; 74; 75]. The most recurrent SBFs were spectral centroid [49; 52; 67; 77; 80; 93], followed by spectral spread [67; 80; 93], and, finally, spectral roll-off [52; 67; 77; 93].  
A small number of researchers concentrated their analyses on voice formats, another frequency feature related to vocal tract characteristics and voice analysis [37; 46; 47; 48; 49; 59; 60; 80; 86; 89; 107; 114].  
Further, [PERSON]'s method for the short-term average spectrum and the Fourier transformation of the Hilbert envelope for the modulation spectrum were used to investigate speech production in noisy environments [33].  
Lastly, energy was a feature analyzed in time and frequency domains across several studies [49; 56; 80; 94; 107; 115].  
Figure 6: Commonly used acoustic features in the frequency domain from the revised literature. The main applications for each group of features are shown inside the hexagons.
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Analysis  
In order to evaluate the data obtained in the experiment, different complementary approaches were adopted: a survey, a focus group, an analysis of the communication flow between the participants and of the video uploads to the RTVE platform, and their distribution. All the screens, with the locations and chats in the goTenna app, were also recorded.