esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Work
### Bitumen Separation Techniques
#### 3.1.2 Thin Layer Chromatography (TLC)  
TLC separation technique was performed on Merck glass plates (silica gel coated with fluorescent indicator F254; gel thickness: 0.25 mm and 1 mm for qualitative and quantitative analysis, respectively), with MeOH/THF/CHCl\({}_{3}\) mixture (3:3:4 \(v/v/v\)) as eluent [24]. Preliminary tests on neutral alumina plates and silica gel plates were performed to select the stationary phase. The results confirmed a higher separation of bitumen samples by silica gel as stationary phase. Noteworthy, given that the bitumen stiffness and mechanical properties are related to the asphaltene content, the method has been applied for both bitumens with asphaltene and desaphaltenized bitumens. Dichloromethane (DCM) was used to solubilize the sample by mixing 0.04-0.05 g of bitumen to the amount of DCM needed to provide a homogenous solution (Figure 2A). The performed procedure is illustrated in Figure 2 and consists of some steps. Firstly, all prepared samples were spotted on the silica gel plate with a pipette as spotter (Figure 2B,C). Subsequently, the plate was placed in a glass eluent container (separation chamber) until the solvent rises through 90% of the plate (Figure 2D,E). Finally, the plate was removed from separation chamber and exposed to the UV lamp, Shortwave 254 nm (Figure 3). The visualization under ultraviolet (UV) light is surely the most used revelation technique to check molecules with chromophore groups and, for this reason, we chose to use it to observe the separated spots.  
Figure 3 shows three main zones (A, B and C) visualized under UV lamp that were separated by scraping off the silica with a scraper and transferring it to three flasks respectively. Methanol (100 mL) was added to each flask, the flasks were swirled for 30 min, and the contents were allowed to sediment. The supernatant solution was pipetted off and centrifuged. This procedure was repeated three times for each sample using same volume of methanol, and the combined extracts were evaporated to dryness in vacuo. Using this procedure, we obtained the three separated fractions as thick mass.  
Considering the complex composition of bitumen, in this first phase, we only hypothesized that Zone A (red frame) should match with the asphaltene fraction of bitumen, considering the absence of this spot in preliminary TLC tests on bitumen samples without asphaltene. Subsequently, all separated fractions have been analyzed by \({}^{1}\)H-NMR technique.
## 4 Discussion
### Influence of Artifacts in CT Image
The rigid body displacements are \(u_{rigid}=-0.0082\) voxels, \(v_{rigid}=-0.0204\) voxels and \(w_{rigid}=0.0167\) voxels, and the rotations are \(\theta_{x}=2.13\times 10^{-5}\), \(\theta_{y}=-3.15\times 10^{-5}\) and \(\theta_{z}=3.43\times 10^{-5}\). There are rigid body translations similar to the average measured displacements. Thus, when compared with the volumetric image of Scan 1, the volumetric image of Scan 2 incur very small rigid body translation and small rotation in all three directions. Compared with the deformation of the specimen in our study, the rigid body translation and rotation are very small, so we neglect the influence of the imperfect motion of the rotation stage in the data analysis. To assess the uncertainty of strain measurement, we use the above calculated imposed subvoxel displacement results to compute strains with the PLS approach. Three sizes of strain calculation cubic box surrounding the point of interest are used, and they are 23 \(\times\) 23 \(\times\) 23 voxels, 33 \(\times\) 33 \(\times\) 33 voxels and 43 \(\times\) 43 \(\times\) 43 voxels, respectively. Since only a rigid body translation is imposed, strains should be zero. However errors of displacement fields give rise to the error of strain calculation. The standard  
Figure 14: Influence of noise and artifacts on the imposed subvoxel displacement measurement (**a**) Mean bias error of displacement; (**b**) Standard deviation error of displacement. deviation errors of \(\varepsilon_{\text{zz}}\) is shown in Figure 15, which dictates that the uncertainty of strain measurement depends on the size of the calculation box, the larger is the box, the smaller the uncertainty. In PLS approach, too small calculation box is not enough to smooth the noise existing in local displacement fields, making strains having large variation. On the contrary, too large calculation box may result in local displacement fields oversmoothed. It is suggested that the size of the calculation box can be equal to or slightly larger than the size of the reference subset [46]. So in this study, we select the calculation box of 33 \(\times\) 33 \(\times\) 33 voxels. With this size, the uncertainty of strain measurement increases more than two times due to the influence of noise and artifacts, and it is 2.34 \(\times\) 10\({}^{-4}\).
## 5 Experimental
### Experimental Results and Analysis
#### 5.3.3 Ablation Experiments  
To verify the effect of different modules on MTAPSP, ablation experiments were performed on MTAPSP models. Where MTAPSP-M-A denotes the MTAPSP model after removing MHSA and ANN, MTAPSP-A denotes the MTAPSP model after removing ANN, and the MTAPSP-M model denotes the MTAPSP model after removing MHSA. The experimental results are shown in Table 7.  
Figure 7a-d shows the confusion matrix of the predictive model at the time of the ablation experiment, with the diagonal line being the number of correctly predicted categories. Combined with Table 7, it can be seen that compared to MTAPSP-M-A, MTAPSP-A's accuracy and precision are increased, but its recall and F1 score are decreased, a phenomenon that stems from the problem of imbalance in the distribution of the sample categories in the dataset. As shown in Figure 7b, MTAPSP-A is inferior to MTAPSP-M-A in identifying the categories \"Distinction\" and \"Withdrawn\", while the number of correct identifications is significantly higher than that of MTAPSP-M-A in the categories \"Fail\" and \"Pass\", and \"Pass\" is significantly higher than in MTAPSP-M-A. Although the overall accuracy increased, the overall recall and F1 score decreased due to the decrease in the performance of the metrics in the other categories. The significant difference in precision and accuracy of the MTAPSP-M model compared to the MTAPSP-M-A model is because the ANN can fit arbitrary nonlinear functions, which allows it to learn the assessment scores and demographic features well and improve the model performance. Compared with MTAPSP-M-A, the overall number of correctly predicted categories of MTAPSP is substantially increased and the overall performance metrics are significantly improved. This is because MHSA can focus on different dimensions of features such as student learning behavior, assessment scores, and demographic information, and, when paired with ANN, can better integrate student learning behavior features, assessment scores, and demographic features to further improve model performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline MTAPSP-M-A & 0.643 & 0.641 & 0.626 & 0.703 \\ MTAPSP-A & 0.650 & 0.588 & 0.594 & 0.719 \\ MTAPSP-M & 0.697 & 0.576 & 0.572 & 0.723 \\ MTAPSP & 0.692 & 0.656 & 0.668 & 0.744 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of ablation experiments.  
Figure 6: (**a**) Comparison of the accuracy of “weekly” models based on the WF-PD task, based on the F-PD task, and based on the W-PD task three months after the start of the course; (**b**) comparison of the F1-scores of “weekly” models based on the WF-PD task, based on the F-PD task, and based on the W-PD task three months after the start of the course.
## 2 Materials and Methods
### Developed Algorithm for Foreign Material Detection  
The core idea of the developed algorithm, called Fusion model, was based on spectral data fusion and multi-model decision fusion. The Fusion model included two binary classification models, called the VNIR model and the SWIR model, using the VNIR and SWIR spectral data, respectively. Once the VNIR and SWIR models independently predicted pre-sumptive FM pixels and thereafter blobs, we performed spatial matching of the FM blobs between the VNIR and SWIR modalities using a non-rigid image registration algorithm. The spectral data of the geometrically matched blobs were fused into one 400-2500 nm spectrum data, called Fusion data (VNIR + SWIR data fusion). All the matched blobs were classified with a machine learning model for the Fusion model, and the blobs detected only by a single modality were classified with the VNIR and SWIR data-based machine learning models, respectively. Then, the final classification result of the developed Fusion model was obtained through decision fusion at the image level. The overall flow chart is shown in Figure 3.  
Figure 3: Flow chart of the overall classification model.
## 2 Cruise Experiment
At few stations, the measurements were affected by the Sun, which was removed easily because the intensity of radiation emitted by the Sun was high (~10 K or higher) and the reflectivity of the sea surface was large (in the order of 0.7). Although the effect of radio-frequency interference (RFI) is another possible source of error for the measured brightness temperature, the shipborne radiometer was deployed about 6 m above the sea surface (unlike the airborne radiometer), which could avoid potential sources of RFI. Moreover, the radiometer was pointed towards the east direction at most of the time to avoid the RFI effect from the land. In addition, it was observed that, even under the wind speeds up to 11 m/s, the foam coverage in the radiometer's field of view was less than 5%, and thus, the foam effect was neglected in this study. Figure 2: Shipborne radiometric measurement setup. (**a**) L-band polarimetric radiometer and (**b**) stabilized platform for amounting the radiometer.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 6 Simulation Results  
In this part, we employ C programming language for simulation and give performance comparison among the lifetime-extended PO-CMAC protocol, direct transmission (DT) protocol and energy efficiency cooperative relay (EE-CR) protocol proposed in [34]. To measure the effect of different protocols on the energy efficiency and network lifetime, we investigate the network lifetime, the average number of data packets transmitted by each node during the network lifetime, network energy utilization and throughput. Herein, the network lifetime is defined as the duration time from the network initialization to the time when the first node exhausted its energy [36; 43]. The network energy utilization is defined as the ratio of the energy used during the network lifetime to the initial network energy. High network energy utilization indicates the energy of the network is fully used. The throughput is defined as the ratio of the time on the channel to be used for successful data packet transmissions by all the nodes in the network to the total elapsed time.
# Investigation of Electromagnetic Angle Sensor Integrated in FR4-Based Scanning Micromirror
## 3 Experimental Results and Discussion
Without closed-loop control, the optical scan angle decreases from 4.6\({}^{\circ}\) to 1\({}^{\circ}\) as the temperature rises from 20 to 50 \({}^{\circ}\)C. On the other hand, with closed-loop control, the optical scan angle decreases from 4.6\({}^{\circ}\) to 2.5\({}^{\circ}\). By comparison, the micromirror operated with closed-loop control possesses relatively better temperature stability. It indicates that the feedback control, based on the integrated angle sensor, is workable and is beneficial to improve the micromirror's temperature stability. However, due to the limited adjustable voltage range of this analog closed-loop control circuit, and the lack of tracking-resonance function, the maximum variation of scan angle still reaches 45.65%. Therefore, it is highly desirable to develop a more precise digital closed-loop control circuit that can regulate the amplitude and frequency of the driving signal in follow-up work. Figure 7: Frequency response function of the micromirror measured by the angle sensor and position sensitive detector (PSD) and phase curve of the angle sensor. The phase represents the phase difference between the sensor output and driving voltage.
# Non-Classic Atmospheric Optical Turbulence: Review
## 2 Measurements and Synthesis
### Presence of Refractive Index Gradients  
Yet another effect pertinent to a non-classic turbulent regime is that due to the presence in the same region of space of the refractive index fluctuations and the constant (or very slowly varying) refractive index gradients. The refractive index is typically in the inverse dependence with the average temperature. For example, over a hot ground surface, the average refractive index increases with height. Therefore, an image received by an observer would be shifted down from its actual direction and possibly be overlapped with the direct line-of-sight image. In addition, the turbulent air may add image jitter and diffraction. Recent measurements of this turbulent mirage phenomenon [44] indicate that over the 13-15 km path, the vertical image shift might reach several meters. See also [45; 46] for related analytical and numerical calculations.
## 3 Results and Discussion
### Tensile and Cracking Properties  
Typical stress-strain responses of PE-SHCC dumbbell-shaped specimens with different dosages of steel fibers in direct tension were provided in Figure 4a. All specimens show the representative strain-hardening and multiple cracking behaviors under monotonic tension. Figure 4b shows typical stress-strain relationships of PE-SHCC dumbbell-shaped specimens with a 4 mm diameter round bar in direct tension. In Figure 4, the tensile stress was calculated with division of the introduced tensile force by a cross-sectional area (30 \(\times\) 30 mm) of the narrowed middle region of the specimen. Additionally, the tensile strain was obtained from dividing the average displacement measured with both SDTs by the gauge length (100 mm).  
The average tensile strengths of three PE-SHCC tensile specimens with different contents (0, 0.5 and 1.0%) of steel fibers were 1.98, 2.10 and 2.43 MPa, respectively. Average strain capacities, defined as strain at tensile strength, for the tensile specimens with 0, 0.5 and 1.0% steel fibers were 1.02%, 0.85% and 0.53%, respectively. As shown in Figure 4a, the addition of steel fibers into PE-SHCC mixtures enhanced the tensile strength while strain capacity decreased as the steel fibers contents increased.  
The typical stress-strain responses of PE-SHCC dumbbell-shaped specimens with a 4 mm diameter round bar were presented in Figure 4b. PE-SHCC-SF00-4, PE-SHCC-SF05-4 and PE-SHCC-SF10-4 mean the PE-SHCC tensile specimens with 0, 0.5 and 1.0% steel fibers. As shown in Figure 4b, the elastic stiffness (the inclination of stress and strain curve) of PE-SHCC tensile specimens reinforced with a 4 mm steel bar was steeper due to the  
Figure 4: Typical stress–strain curves of PE-SHCC dumbbell-shaped specimens in direct tension: (**a**) PE-SHCC tensile specimens without steel bar for 0, 0.5 and 1.0% steel fibers; (**b**) PE-SHCC tensile specimens with a 4 mm diameter round bar for 0, 0.5 and 1.0% steel fibers.  
contribution of steel fibers in resisting the tensile stresses across the macro cracks [31] as the steel fiber contents increased. The tensile responses of PE-SHCC tensile specimens with a 7 mm diameter round bar are very similar to those of PE-SHCC specimens with a 4 mm round bar. The tensile tests for PE-SHCC specimens with the steel reinforcing bar were stopped before tensile failure at the narrowed middle region due to local failure at the clamping end of the specimens.  
Figure 5 presents typical cracking process of plain PE-SHCC specimens with different content of steel fibers. The figure indicates that the addition of steel fibers has a significant effect on the cracking process of PE-SHCC specimens in direct tension. However, multiple micro cracks were observed in all PE-SHCC specimens.
## 3 Results and Discussion
### Fatty Acid  
The fatty acid composition of the sausage after production and storage is shown in Table 5.  
It was found that the changes in the fatty acid profile in individual samples are small but statistically significant. Treatment significantly affected the total saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA) proportions of the sausage (\(p<0.05\)). Analysis of the fatty acid profile in the C treatment after production (time 0) revealed the highest PUFA content (18.03%) and lowest MUFA content (44.68%), significantly (\(p<0.05\)). The opposite trend was observed in the V1 and V3 treatments, where the lowest PUFA content (17.60% and 17.55%, respectively) and the highest MUFA content (45.25% and 45.08%, respectively) were observed. In the V3 treatment, the SFA content was higher than in the V1 and V5 treatments after production (\(p<0.05\)) (Table 4). The significantly higher SFA and lowest MUFA content in the control (C) treatment after production was mainly caused by oxidative changes because unsaturated fatty acids are oxidized faster [49]. The composition of fatty acids in food is subject to change during processing and storage, thereby influencing both the taste and nutritional quality of the food. Unsaturated fatty acids are particularly susceptible to oxidation [48]. In light of our study, it is observed that the use of apple vinegar treatment (V1, V3, V5) resulted in lowered levels of PUFA, coupled with increased levels of TBARS and ORP immediately after production. This observation suggests that vinegar fails to provide adequate protection against lipid oxidation, unlike sodium nitrite employed in the C treatment. Sodium nitrite provides better lipid protection, which is supported by the existing literature [48]. According to a study conducted by [PERSON] et al. [52], the inclusion of acetic acid in pork marinades is reported to promote the oxidation of the free polyunsaturated fatty acids present in the meat. However, no significant differences in the PUFA content were found among the tested samples in other studies [11]. These results confirm that vinegar and curing salt protect polyunsaturated fatty acids against oxidative changes. There are many factors which determine the susceptibility of meat  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Treatment** & \multicolumn{2}{c}{**Storage Time (Days)**} & \multicolumn{2}{c}{**Treatment**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Treatment \(\times\) Time**} \\ \cline{2-7}  & & **0** & **7** & **14** & **\(p\)** & **\(p\)** & **\(p\)** \\ \hline SFA & C & 37.25 \(\pm\) 0.09\({}^{\text{\,\text{\,\text{\,\text{\,\text{\,\text{\,\,\text{\,  
to lipid oxidation. These factors include, but are not limited to, the balance between antioxidant and pro-oxidant factors, as well as the fatty acid composition of the meat lipid fractions [48; 49]. The processing of meat has the potential to disrupt cell structures which can result in the interaction of polyunsaturated fatty acids and prooxidants, leading to lipid oxidation [48; 49].
## 6 Conclusions  
To accurately reflect the propagation characteristics of near-ground WSNs, we measured the received RSSI values of near-ground deployed sensor nodes under four terrains, including two derived scenarios with different terrain factors. We established the statistical path loss models, and analyzed the influence of different antenna heights and terrain factors. In near-ground scenarios, when the antenna height is extremely low (e.g., lower than 50 cm), the one-slope model is more practical. As the antenna height increases, the precision of the two-slope model becomes higher. Measurement results also show that simplistic theoretical models may lead to overestimation or underestimation of path loss, and thus result in inaccurate estimation of network connectivity. Based on our analyses, we took the first attempt to propose a propagation model selection strategy in near-ground scenarios. In addition, we implemented these propagation models on the Cooja simulator, demonstrating the huge difference in their impact on network connectivity.  
Conceptualization, W.T. and J.W.; Data curation, X.M.; Formal analysis, W.T.; Funding acquisition, J.W.; Investigation, W.T. and X.M.; Methodology, W.T.; Project administration, J.W.; Resources, X.M.; Software, W.T. and X.M.; Supervision, J.W. and Z.W.; Validation, W.T.; Visualization, W.T.; Writing--original draft, W.T.; and Writing--review and editing, W.T. and Z.W. This research was funded by National Key R&D Program of China (2016 YFC0801505) and China Scholarship Council. The authors acknowledge the support provided by the Safety and Emergency Lab of Shanghai Advanced Research Institute (SARI) for this study. The authors declare no conflict of interest.
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 4 Mathematical Analysis of Atmospheric Turbulence
### Beam Spreading  
When a beam propagates through the turbulent atmosphere, beam spreading which is defined as the broadening of the beam at the receiver surface beyond vacuum diffraction, occurs. In this section, we describe the Gaussian beam spreading of a beam propagating through turbulence at a distance \(L\) from the source. To estimate the amount of beam spreading, the effective average beam waist, \(w_{eff}(L)\), is defined as follows [39]:  
\[w_{eff}(L)^{2}=w(L)^{2}\bigg{[}1+1.33\sigma_{I}^{2}\bigg{[}\frac{2L}{kw(L)^{2} }\bigg{]}^{\frac{5}{6}}\bigg{]} \tag{8}\]  
where \(w(L)\) is the beam waist at a propagation distance \(L\).  
\[w(L)^{2}=w_{o}^{2}+\bigg{(}\frac{2L}{kw_{o}}\bigg{)}^{2} \tag{9}\]  
where \(w_{0}\) is the initial beam waist at \(L=0\)\(m\).