esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Country**} & \multirow{2}{*}{\begin{tabular}{c} **Population** \\ **(Million)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GDP** \\ **(Million)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Total Oil Supply** \\ **(Billion** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Proved** \\ **(Thousand** \\ **Barrels per Day)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Dry Gas** \\ **(Billion** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Proved** \\ **(Billion** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Cruude Oil** \\ **(Billion** \\ \end{tabular} } \\ \hline East Asia & Mongolia & 3.05 & 11.78 & 15.10 & NA & 0.00 & 0.00 & 0.00 \\ Central Asia & Kazakhstan & Kazakhstan & 18.25 & 258.23 & 1682.00 & 36.00 & 741.23 & 97 & 358.67 \\ ASEAN & Brunei & 0.43 & 19.23 & 143.44 & 1.42 & 457.34 & 14.56 & 9.41 \\ Indonesia & 259.23 & 951.23 & 962.34 & 4.35 & 2503.34 & 116.42 & 1137.31 \\ Malaysia & 32.34 & 340.32 & 942.34 & 4.92 & 2553.23 & 124.33 & 1132.45 \\ Myanmar & 52.34 & 63.78 & 21.78 & 0.07 & 475.34 & 11.00 & 60.00 \\ Philippines & 116.89 & 293.34 & 28.34 & 0.17 & 107.34 & 3.45 & 286.00 \\ Singapore & 5.62 & 324.34 & 28.34 & 0.00 & 0.00 & 0.00 & 1403.00 \\ Thailand & 69.23 & 428.34 & 546.34 & 0.55 & 1503.32 & 15.35 & 605.44 \\ Vietnam & 94.34 & 187.34 & 368.35 & 4.85 & 322.34 & 27.83 & 147.00 \\ South Asia & Bangladesh & 163.34 & 155.34 & 4.90 & 0.04 & 818.34 & 7.05 & 38.00 \\ India & 1315.34 & 1893.44 & 1097.34 & 6.45 & 1237.34 & 47.34 & 4072.34 \\ Pakistan & 191.23 & 246.34 & 87.34 & 0.30 & 1431.23 & 26.00 & 192.15 \\ Sri Lanka & 22.34 & 76.98 & \(-\)0.45 & 0.00 & 0.00 & 0.00 & 50.00 \\ West Asia & Bahrain & 1.36 & 35.33 & 66.23 & 0.17 & 565.34 & 3.92 & 267.34 \\ Cyprus & 1.17 & 26.34 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Egypt & 90.56 & 293.43 & 705.34 & 4.70 & 208.24 & 82.34 & 736.37 \\ Greece & 11.78 & 249.23 & 9.04 & 0.01 & 0.25 & 0.06 & 437.00 \\ Iran & 82.34 & 521.23 & 3264.33 & 163.23 & 5757.34 & 122.00 & 1485.00 \\ Iraq & 36.76 & 245.34 & 3221.23 & 160.23 & 45.34 & 119.36 & 652.34 \\ Israel & 8.62 & 303.23 & 6.05 & 0.01 & 239.23 & 10.22 & 235.00 \\ Jordan & 7.03 & 37.34 & 0.52 & 0.00 & 5.80 & 0.32 & 92.45 \\ Kuwait & 4.30 & 184.34 & 2846.34 & 112.00 & 586.34 & 71.96 & 947.00 \\ Lebanon & 4.62 & 47.98 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Oman & 3.97 & 82.67 & 961.23 & 5.80 & 1123.39 & 35.00 & 92.00 \\ Qatar & 2.62 & 214.23 & 2127.34 & 27.46 & 5657.23 & 905.00 & 344.30 \\ Saudi Arabia & 36.23 & 749.23 & 13,022.23 & 297.29 & 3648.31 & 296.02 & 2325.00 \\ Syria & 31.53 & 19.00 & 74.23 & 2.95 & 198.23 & 10.50 & 257.45 \\ Turkey & 79.23 & 847.23 & 65.43 & 0.34 & 19.76 & 0.42 & 728.34 \\ UAE & 9.95 & 398.23 & 3489.33 & 100.46 & 1963.34 & 227.67 & 763.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main statistics of 50 Nations alongside the China’s “Belt & Road Initiative in 2017”.
# Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
## 2 Related Work  
Existing research has explored the relationship between NASA-TLX scores and surgeons' eye blink rates. It was found that surgeons who blinked less frequently reported higher levels of frustration and overall workload compared to those who blinked more often [17]. Other studies have also investigated how subjective workload measures relate to physiological indicators. For example, heart rate and heart rate variability were linked to self-reported workload on the NASA-TLX during clinical reasoning tasks [18]. Higher workload has been found to potentially cause over-breathing [19]. Additionally, changes in skin conductance, which reflect sweat gland activity driven by the sympathetic nervous system, have been associated with varying levels of workload [20]. These findings indicate that physiological measures can enhance subjective assessments like the NASA-TLX, providing a more comprehensive evaluation of workload.  
Previous research has also made some progress in examining the relationship between pupil responses. Pupil size can serve as a bio-signal for occupational risk factors, including stress, fatigue and workload [21; 22]. The existing literature on pupil size and subjective workload measures has primarily focused on purely visual (cognitive) tasks [23; 24; 25] or visuomotor tasks performed under direct vision, such as manufacturing [25]. Many existing studies did not involve an imaging-guided environment [22; 26]. Although a few investigations did consider imaging-guided scenarios, some examined how pupil size varied according to operator expertise [27; 28]. Many researchers set predetermined difficulty levels and assessed subjective and objective (pupil size) measures separately, without establishing a direct correlation between the two [29; 30]. Moreover, focusing on a single task type does not accurately represent the complexity of real-world contexts, where individuals often switch between purely visual tasks and those requiring both visual perception and motor skills. This study addresses these gaps by integrating both purely visual and visuomotor (laparoscopic) imaging-guided tasks. This work would improve the generalizability of the findings and fill critical knowledge gaps in understanding workload assessments. A comparative table highlighting the main innovations and differences in the current study in relation to the literature can be found in Table 1.
## 4 Experimental Verification
### Experimental Device and Fault Setting  
The test equipment and instruments are all placed in the open workshop, so the noise is very close to the actual situation, and the obtained fault data is an approximate simulation of the actual fault. In the signals of multiple sensors, the time deviation is eliminated, the linear normalization is processed, the correlation function is used to obtain the adaptive weight, and the test data are used for verification. One motor, two three-direction acceleration sensors (sensor 1 and sensor 2) and one unidirectional acceleration sensor (sensor 3) are used in the experiment. The sampling frequency in the experiment was 25.6 KHz. As shown in Figure 6, Figure 6a is the composition of the test bench and Figure 6b is the position arrangement and coordinate axis of the three sensors. The gearbox contains two gears: planetary and solar. Specific gear parameters are shown in Table 2. The speed of the drive motor is 0-1500 rpm, the transmission ratio of the reducer is 3, and the output speed of the reducer is 0-4500 rpm. HRB6206 deep groove ball bearing specific parameters are shown in Table 3.  
seats in Figure 5(a), as shown in Figure 5(b) for specific positions. The coordinates are [0,0,20] and [40,0,20]. Sensor 3 is placed at the spatial coordinates [15,0,0] in centimeters. Unit of measurement: cm.  
Various fault data obtained through experiments can be used to obtain time domain graphs of different faults through signal processing methods, but only the time domain graphs cannot see the difference of different faults.  
Therefore, the time domain signal is mapped to the frequency domain through the HHT, and then the envelope spectrum of different faults is obtained, which can clearly see the difference of different faults. According to the test data at 1300 rpm, the planetary gear crack fault (3.6 mm) and wear fault (3.6 mm), 0.6 mm cracks in the inner ring and 0.6 mm cracks in the outer ring of the bearing, and the time domain diagrams and frequency domain envelope spectra of the four faults are shown in Figure 9. Among them, the horizontal axes of the time domain sampling point graph reveal the number of sampling points, the horizontal axes of the frequency domain envelope spectrogram reveal frequency (Hz), and the longitudinal axes of both reveal the amplitude (mv).  
According to the calculation formula of theoretical gear failure frequency and bearing failure frequency combined with the component layout of the test bench, the failure frequencies of these four types of failures can be obtained as: 21.67 Hz, 21.67 Hz, 53.4 Hz and 159.64 Hz. In Figure 9, the actual fault frequencies corresponding to the four types of faults are 18.36 Hz, 18.36 Hz, 55.08 Hz and 164.9 Hz. The theoretical fault frequency and the actual fault frequency are closely within the allowable range, so the data obtained by the test can be used normally.
# Icing Mitigation by MEMS-Fabricated Surface Dielectric Barrier Discharge
## 4 Icing Experiment
### De-Ling Results  
Subsequently to the anti-icing experiment, the de-icing experiment was performed with the MEMS-SDBDs. For this purpose, the plasma operation was stopped, and the samples were iced for additional 2 min with the same test conditions, see Figure 11.  
Figure 11: Images taken by the machine vision camera of the de-icing study with the glass and the zirconia SDBD. The SDBDs were turned off for 2 min to ice the former active surface. Pictures (**b–d**) present the study after the de-icing time displayed in the lower left corner.  
Without discharge the ice started to cover the SDBD area. At the first five electrodes of the glass sample the ice accumulation followed the electrode pattern, see the green mark in Figure 11a. See also Supplementary S11 for the development of the glass SDBD icing. The ice appeared on both sides of the electrodes and the ice structure became less pronounced with an increasing distance from the leading edge. This icing pattern strongly differs from the icing of the FR-4 sample on Figure 10, where the ice was dominantly accumulated at the impact edge of the electrodes along the whole SDBD area. Therefore, the icing pattern of the glass sample is not related to the thickness of the MEMS-electrodes. However, the reason for the icing pattern to be seen is still unclear. Possibly, this effect can be connected to an inhomogeneous cooling of the sample which is less pronounced with the distance from the leading edge due to the residual heat which is transported by the wind in downstream direction. Additionally, it has to be noted that the ice that is accumulated in front of the SDBD area acts as mechanical shield for the ice wind. The shape of the shield is parallel to the electrode structure. The shielding of the ice wind is well visible for zirconia sample, see Figure 11c, where the height of the shield is higher and, thus, the the first zirconia SDBD electrodes depicted a minor icing.  
Switching on the plasma after 2 min of icing leads to a recognizable de-icing of both SDBDs. For the glass sample the icing is still strong at the front half of the SDBD because the ice structures are linked to each other. Along the downstream direction of the SDBD, the ice-free area increases gradually. Over all experiments, the initial crack in the glass sample grew and after 4 min of plasma activation during the shown de-icing test the SDBD was damaged which was the cause for aborting the glass sample experiment.  
A visible de-icing was evident at the zirconia SDBD 20 s after the plasma activation, which is marked by a green circle in Figure 11d. Similar to the glass SDBD, de-icing was first detected in the right half of the SDBD. On closer inspection, one can see that the ice started to melt near the area that has already been de-iced. After 20 s of plasma activation during the de-icing test of the zirconia sample an electrical breakdown occurred which led to the termination of the experiment.
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 4 Performance Evaluation  
This section briefly introduces the evaluation indicators used; then, the simulation and algorithm are described; and finally, we conduct an in-depth analysis of the simulation results.
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Average Thickness of Coal Seams  
China's GSER technology originated from thin coal seams and gradually spread to medium-thick coal seams. As the thickness of the coal seam in the working face increases, the activity range of the overburden in the mining area generally increases, resulting in a greater intensity and impact range of the corresponding ground pressure during the GSER period. In thick coal seams, GSER technology needs higher requirements for roadway support technology and entry-side structure performance. Among 587 cases of GSER engineering projects, 497 cases provide data on the average thickness of coal seams. The thinnest coal seam in the GSER engineering projects is found in the Bolin Coal Mine of Sichuan Coal Group, with a thickness of 0.48 m. The thickest coal seam in the GSER engineering projects is in the Wangjiashan Coal Mine of Gansu Jingmei Company (Baiyin, China), with an average thickness of 15.5 m. However, this working face used steeply inclined horizontal segmented fully mechanized mining, with a retained entry height of only 2.5 m. Using the average thickness of 0.5 m coal seam as the statistical interval, the application of GSER engineering with different average thicknesses of coal seams is shown in Figure 6.  
As shown in Figure 6, there are 115 cases of GSER engineering projects in coal seams with an average thickness of less than 1.5 m and 239 cases in medium-thick coal seams with a thickness of 1.5-3.5 m, accounting for 48.10% of the total. Besides, there are 136 cases of coal seams with a thickness of 3.5-8.0 m and 7 cases of coal seams with a thickness greater than 8.0 m. Among them, the majority of the average coal seam thickness is 1.0-1.5 m in GSER engineering projects, accounting for 16.50% (82 cases) of the total. It can be seen that when the average thickness of the coal seam exceeds 2.5 m, the number of GSER engineering projects significantly decreases with the increase in the average thickness of the coal seam. At present, the average thickness of coal seams in China's GSER engineering projects generally does not exceed 4 m. Even when the thickness of coal seams exceeds 4 m in GSER engineering projects, the mining parameters of the working face are adjusted to reduce the mining height of the coal seams at the position of the entry-side structures in order to optimize and control the size of the filling body and ensure the effectiveness of GSER technology and entry retaining speed. Therefore, the current GSER technology in China is more suitable for coal seams with medium thickness.
## 3 Results and Discussion
### Effects of US on Rheological and Structural Characteristics of KGM  
Figure 3 displays the apparent viscosity versus shear rate of KGM solutions as a function of time under US treatment. All the original KGM fractions exhibited shear-thinning flow behavior at concentrations over 0.25% (\(w/v\)) and the apparent viscosity exceeded 1000 Pa-s at shear rate of 0.01 s\({}^{-1}\). The apparent viscosity decreased rapidly upon US treatment. As the US treatment exceeded 4 min, the apparent viscosity was flat versus the shear rate, indicating that the KGM solution was changed to a Newtonian fluid (0.5%) with a constant viscosity independent of shear rate.  
Figure 4 shows the FTIR spectra of KGM fractions before and after US treatment (for 10 min and 60 min). The broad absorption peak at 3500-3000 cm\({}^{-1}\) was attributed to O-H stretching vibration of the inter- and intra-molecular hydrogen bonds of hydroxyl groups. The band around 2889 cm\({}^{-1}\) was due to the C-H stretching and bending vibration of methyl groups. Peaks at 1733, 1369, and 1254 cm\({}^{-1}\) were assigned to the valence vibration of C=O, the symmetric C-H bending vibration of the methyl group, and the C-O vibration of \(O\)-acetyl groups, respectively [44,45]. Most of these characteristic peaks were preserved after US treatment, suggesting that there was no significant alteration in the functional group types of KGM before and after US treatment.  
Figure 4: Fourier-transform infrared (FTIR) spectra of original KGM and US-treated KGM (KGM-US-10 and KGM-US-60).  
Figure 3: Apparent viscosities of original KGM and US-treated KGM. (**a**) Apparent viscosity of KGM; (**b**) apparent viscosities of US-treated KGM (KGM-US-2, KGM-US-4, KGM-US-6, KGM-US-10, KGM-US-20, KGM-US-30, and KGM-US-60). All samples were tested three times by flow sweep in the shear rate range of 0.01–1000 s\({}^{-1}\) at 25 \({}^{\circ}\)C. All values are expressed as means \(\pm\) SD (\(n\) = 3).
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 3 Algorithm Improvement
### Path Planning of Dual-Arm Based on GA_RRT
#### 3.2.1 The Goal Probability Bias  
The traditional RRT algorithm is a random search of space, which ensures the effectiveness of the RRT algorithm. However, excessive blind search reduces the convergence speed and consumes a large amount of computation power. Therefore, we adopt a goal probability bias strategy. This method sets a parameter, \(P_{a}\), in sampling judgment. Before each extension, a random value, \(P_{rand}\), (between point 0, 1) is randomly generated. When \(0<P_{rand}<P_{a}\), the random tree grows toward the target point, making the random tree expansion more objective. When \(P_{a}<P_{rand}<1\), \(X_{rand}\) points are generated randomly.  
For the dual-arm manipulator environment, when the goal point is taken as a random point with probability \(P_{a}\), we need to read the real-time position of the manipulator, and detect whether there are static obstacles and dynamic obstacles of the manipulator under the path. When there are obstacles, the cost of expansion is infinite. We abandon the random point and restart the random process.
# In Situ Interaction of Enterocin A/P with _Staphylococcus aureus_ SA5 in Goat Milk Lump Cheese
## 3 Results  
### Microbial Analysis and pH Values  
The difference in staphylococcal count between the CV (inoculated with the SA5 strain) and the EV (SA5 and Ent A/P) was 1.1 log cycle at the start of the experiment (0 h, Table 1). This mathematical difference (1.5 log cycle) was slightly higher at 4 h, which was almost stable up to 24 h. During processing and storing (at 96 h), a 1.7 log cycle difference was found comparing the CV and the EV. Finally, at day 168 (1 week) the highest difference was noted (2.5 log cycle). It appears that Ent A/P indicated a reductive effect against the amount of SA5 cells, although direct bacteriocin activity of Ent A/P in cheese has not been detected. Colonies picked up from the BP agar were genotyped using PCR and the appropriate primers. Taxonomical allocation to the species _Staphylococcus aureus_ was confirmed (420 bp).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Goad Milk/Cheese Vat** & **S/0 h** & **0 h** & **4 h** & **24 h** & **96 h** & **168 h** \\ \hline CV & 5.2 \(\pm\) 0.3 & 5.2 \(\pm\) 0.3 & 5.6 \(\pm\) 0.3 & 5.6 \(\pm\) 0.6 & 5.6 \(\pm\) 0.8 & 4.6 \(\pm\) 0.6 \\ EV & 5.2 \(\pm\) 0.3 & 4.1 \(\pm\) 0.2 & 4.1 \(\pm\) 0.2 & 4.1 \(\pm\) 0.2 & 3.9 \(\pm\) 0.4 & 2.1 \(\pm\) 0.3 \\ \hline \end{tabular}
\end{table}
Table 1: The counts of staphylococci during goat cheese manufacturing and storing expressed in colony forming units per mL and/or per gram \(\pm\) SD.  
The values of pH were well balanced during goat cheese processing; the pH value measured at 24 h was 6.69 in the CV, EV, and RV (Table 2). Reduction in pH was noted in all vats/cheeses at the 72 h compared to 24 h; the lowest pH values (4.38, 4.39) were noted in the EV cheese and the RV to be almost the same. In the CV (with SA5), the pH reached the value 4.66. At 96 h, the pH was 3.93, which was the lowest pH that was measured in the EV; it was lower by comparison than in both the CV and the RV.
## 2 Materials and Methods
### Methylene Blue Removal by Adsorption and Photocatalytic Degradation  
Methylene blue is the model dye compound used to evaluate the photocatalytic activity of the synthesized composite materials under UV irradiation following ISO procedure for photocatalytic characterization [35]. According to previous experiments [19; 36], the initial MB concentration was 6.0 \(\times\) 10\({}^{-5}\) M, the photocatalyst dosage was 1.25 g\(\cdot\)L\({}^{-1}\), and the temperature was fixed at 20 \({}^{\circ}\)C using a thermostatic bath. The following two types of experiments were carried out: (a) the mixture MB solution and photocatalyst was maintained under stirring in the dark for 30 min to achieve the adsorption equilibrium before starting the irradiation, and (b) irradiation was started just after preparing the mixture that was kept under magnetic stirring. In both cases, a 100 mL Pyrex photochemical reactor with a 125 W high-pressure mercury lamp, operating between 180 and 420 nm with a peak at 366 nm, was used. The photon flux was measured by using a Delta OHM radiometer HD2302.0 leaned against the external wall of the photoreactor containing only pure water. After switching on the lamp, 2 mL aliquots of the aqueous suspension were collected from the reactor and filtered through a 0.45 \(\upmu\)m PTFE Millipore disc to remove the catalyst powder.  
A Shimadzu UV-2450 UV/V spectrometer was used to determine the dye concentration after calibration, by measuring the absorbance at 660 nm. Since the degradation pathway for the selected dye is known with high reliability [37], the eventual formation of byproducts was checked, monitoring the overall UV-vis spectrum of the solutions recovered at different times during the degradation experiments.  
The rate constant \(k\) was calculated according to the following Equation (1):  
\[\ln\frac{\text{C}}{\text{C}_{0}}=-\text{kt} \tag{1}\]  
where C is concentration after time t, C\({}_{0}\) represents the initial concentration and k is the pseudo first-order rate constant (min\({}^{-1}\)), calculated from the slope (\(-\)k/2.303) of the MB concentration and time curve (log-linear scale) as follows (2):  
\[\text{k}=2.303\times\text{slope} \tag{2}\]
## 3 AE Data and Methods
Hence, we designed a complex triggering logic for the waveform recording system. First, three pairs of sensors were selected as triggering sensors. Signals from the triggering sensors were fed to a trigger unit. An OR logic was used within each pair, and an AND logic was used among the three pairs. In order to avoid biased triggering, the three pairs were required to be homogeneously distributed on the specimen surface. The triggering threshold level, which is normally approximately 10 times higher than that for the peak detector, was determined. As a result, the AE hypocenter data constituted a subset of relatively large events among the AE count data. The higher threshold and complex triggering configuration ensured that AE events were collected without significant bias throughout the specimen space. Axial load and strain data were digitized at a sampling rate up to 10 KHz. While confining pressure, which was kept constant during the experiments, was recorded at 5 Hz. Both shear stress (\(\tau\)) and normal stress (\(\sigma\)) can be calculated from axial stress and confining pressure. Hence, an apparent frictional coefficient (\(\mu\) = \(\tau/\sigma\)) could be estimated from the shear stress and normal stress. The obtained coefficient was considered as either static or dynamic ones according to the slip velocity of the fault. The axial shortening of the specimen was estimated from piston displacement and was calibrated using the elastic portion of the mean axial strain. Since the results for the intact specimen were used for the calculation of elastic shorting, it is possible to make rough fault slip in the pre-cut specimens.