esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The mean age of the participants in the study was 8.58 \(\pm\) 1.39 years old. The control group had a mean age \(\pm\) SD of 8.35 \(\pm\) 1.46 years old; for the acute pulpitis group, it was 8.8 \(\pm\) 1.06 years old; for the chronic pulpitis group, it was 8.52 \(\pm\) 1.68 years old. ELISA analysis was performed to establish the presence of the following biomarkers: TNF-\(\alpha\), SOD-3, osteoclast, and TGF-\(\beta\)1. Table 1 and Figures 1-4 show the protein expressionsof biomarkers in the control and experimental groups. Comparisons between groups were made according to the results of the Shapiro-Wilk tests, showing a non-parametric distribution for TNF-\(\alpha\) and a normal distribution for the rest of the variables (in which case, [PERSON]'s test showed an inequality of variances between groups). Differences for all biomarkers were statistically significant between groups (\(p<0.05\)). Post-hoc tests showed that all biomarkers were significantly lower in the control group in comparison to the acute/chronic pulitis groups, while differences between the acute and chronic pulitis groups were not significant (\(p>0.05\)), except for the protein levels of osteoclast, which were significantly higher in chronic pulitis compared to acute pulitis (\(p=0.002\)) (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Biomarker** & **Clinically Healthy** & **Acute Inflamed** & **Chronically Inflamed** & **_p_ **Value** \\  & **Pulp Samples** & **Pulp Samples** & **Pulp Samples** & **_p_ **Value** \\ \hline TNF-\(\alpha\) & & & & \\ (pg/mg prot.) & 5.6 (3.55–9) & 55 (52–61) & 49 (41–59) & \textless{}0.001 * \\ (median (IQR)) & & & & \\ \hline SOD-3 & & & & \\ (ng/mg prot.) & 1.56 \(\pm\) 0.44 & 27.45 \(\pm\) 8.31 & 26.78 \(\pm\) 7.16 & \textless{}0.001 ** \\ (mean \(\pm\) SD) & & & & \\ \hline Osteocalcin & & & & \\ (ng/mg prot.) & 2.51 \(\pm\) 1 & 5.87 \(\pm\) 3 & 11.23 \(\pm\) 3.13 & \textless{}0.001 ** \\ (mean \(\pm\) SD) & & & & \\ \hline TGF-\(\beta\)1 & & & & \\ (pg/mg prot.) & 13.24 \(\pm\) 1.48 & 18.07 \(\pm\) 4.5 & 31.06 \(\pm\) 15.3 & \textless{}0.001 ** \\ (mean \(\pm\) SD) & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Protein expression of biomarkers in the control and experimental groups.  
Figure 1: TNF-\(\alpha\) protein levels (pg/mg) in the control group versus acute/chronic pulitis. (* Indicates statistically significant differences). (The circle in the graphic is an indication that an outlier is present.)  
Figure 2: SOD-3 protein levels (ng/mg) in the control group versus acute/chronic pulitis. (* Indicates statistically significant differences).
## 4 Models and Algorithm Design
### Algorithm with Multiple Uncertainties
#### 4.5.3 Time-Varying Traffic Delay  
In real applications, traffic delays are inevitable when many agents communicate with each other. It will be noted that \(\tau_{ji}\) represents the unknown traffic delay on the communication line from agent \(i\) to agent \(j\). Traffic delay \(\tau_{ji}\) can be modeled using normal random distribution where \(\omega\) denotes the mean value and \(\sigma^{2}\) represents the variance.  
\[\tau_{ji}(k)\sim\mathcal{N}(\omega,\,\sigma^{2})\]It is assumed that the traffic delay is uniformly bounded, which means that there exists a positive value of \(\tau_{\max}\) for which:  
\[0\leq\tau_{ji}(k)\leq\tau_{\max},\quad j\in\mathcal{N}_{i}^{in}, \tag{35}\]  
where \(\tau_{ii}=0\), \(\forall i\) in graph \(\mathcal{G}\).  
One can imagine that the traffic delay cannot be negative. Therefore, the term log-normal distribution can sometimes be encountered. That is why its absolute value is then used for the following calculations. Since it is a discrete iterative algorithm, traffic delays can be integer values only. Unfortunately, normal distribution also returns decimal numbers. Therefore, the round function is used here, assuring that the result will be an integer. The resulting value is used for all generators in a given time step.  
\[\tau_{ji}(k)\sim\text{round}\big{(}|\mathcal{N}(\omega,\,\sigma^{2})|\big{)}\]
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Encoding Parameters
#### 4.2.1 Impact of Resolution  
From Figures 4-6, it can be seen that, when the QP is lower than 32, the difference in QoE between the 4 K (i.e., \(3840\times 1920\)) and 2.5 K (i.e., \(2560\times 1440\)) resolutions is insignificant. Specifically, when QP = 22, the difference averaged over content is 0.22 MOS for the perceptual quality, 0.28 MOS for the presence, and 0% for the acceptability rate.  
However, the QoE scores decrease rapidly as the resolution is reduced to the fHD and HD resolutions. In particular, the presence score of all three contents drops by 0.56 to 0.94 MOS when the resolution changes from 4 K to fHD (i.e., \(1920\times 1080\)). Also, the decrease of the perceptual quality is from 0.78 to 0.89 MOS. This finding is in-line with the results presented in [17,23] where the significant differences of the perceptual quality were found for the resolutions of 4 K and fHD. It is also worth noting that only two resolutions (i.e., 4 K and fHD) are used in [17,23]. From Figures 4-6, we can also see that, for all the versions encoded at the HD resolution, the acceptability rates are about 60% or lower. The perceptual quality and presence scores are also lower than 3 MOS. Therefore, 360 video should not be provided at the HD resolution when watching in the VR mode using HMD.
# Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers
###### Abstract  
Understanding the operation of complex assets such heavy-duty vehicles is essential for improving the efficiency, sustainability, and safety of future industry. Specifically, reducing energy consumption of transportation is crucially important for fleet operators, due to the impact it has on decreasing energy costs and lowering greenhouse gas emissions. Drivers have a high influence on fuel usage. However, reliably estimating driver performance is challenging. This is a key component of many eco-driving tools used to train drivers. Some key aspects of good, or efficient, drivers include being more aware of the surroundings, adapting to the road situations, and anticipating likely developments of the traffic conditions. With the development of IoT technologies and possibility of collecting high-precision and high-frequency data, even such vague concepts can be qualitatively measured, or at least approximated. In this paper, we demonstrate how the driver's degree of attention to the road can be automatically extracted from onboard sensor data. More specifically, our main contribution is introduction of a new metric, called attention horizon (AH); it can, fully automatically and based on readily-available IoT data, capture, differentiate, and evaluate a driver's behavior as the vehicle approaches a red traffic light. We suggest that our measure encapsulates complex concepts such as driver's \"awareness\" and \"carefulness\" in itself. This metric is extracted from the pedal positions in a 150 m trajectory just before stopping. We demonstrate that this metric is correlated with normalized fuel consumption rate (FCR) in the long term, making it a suitable tool for ranking and evaluating drivers. For example, over weekly periods we found a negative median correlation between AH and FCR with the absolute value of 0.156; while using monthly data, the value was 0.402.  
attention horizon; driver performance metric; fuel consumption rate; road safety +
Footnote †: journal: Sensors
## 3 Results  
### Yield and Total Protein Concentration of the Grain  
Under well-watered conditions, grain yield ranged from 51.5-65.0 dt/ha (Figure 1). The two Iranian cultivars had the lowest grain yield (Figure 1). Compared to the well-watered controls, grain yield was reduced only marginally by moderate drought stress (0-6.6%), but more strongly by severe drought, where the largest decline was observed for the genotypes Mihan (\(-\)24.0%) and Impression (\(-\)24.6%), followed by Hyberg (\(-\)16.6%), Discus (\(-\)13.8%), Rumor (\(-\)13.4%) and Alvand (\(-\)8.3%). However, due to relatively high variations in the field, these effects were not statistically significant. Even under conditions of severe drought, the three highest yields were observed for German genotypes.  
Under well-watered conditions, total grain protein concentrations ranged between 12.8% (Mihan) and 16.3% (Discus), and only Mihan had significantly lower protein concentrations compared to all other cultivars. No significant difference was observed between the German cultivars in any of the treatments.  
Drought stress did not significantly affect total grain protein in any of the six cultivars. However, in four out of six cultivars a slight increase in the protein concentration was observed at the severe stress level (Mihan, Impression, Discus, Rumor). As a result, protein concentrations of Discus and Rumor were significantly higher than those of the other cultivars under severe drought (Figure 2). Only in Hybery was the total protein concentration reduced by drought stress, while it was not affected in Alvand.  
Figure 1: Grain yield (dt/ha) depending on the treatments (well watered: WW, moderate drought stress: MD, severe drought stress: SD) of the Iranian (Alvand, Mihan) and German wheat genotypes (Impression, Discus, Rumor and Hybery). The error bars represent the standard errors of the measured values. Different letters within a treatment represent significant differences (small letters: within WW treatment; capital letters: within MD treatment; italic letters: within SD treatment). \(p\) < 0.05; \(n\) = 4. Two-way ANOVA results are shown in the upper right corner of the diagram. G: genotype; T: different water treatment; GxT: Interaction between genotype and different water treatment; ns: not significant; *: significant effect.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Pose Estimation on Antennas
#### 6.3.3 Performance on Working Antennas  
In this subsection, we report an evaluation of our overall downtilt/azimuth estimation pipeline on working antennas to assess whether it satisfies the minimum accuracy requirements for industrial applications, i.e., to access whether the errors are lower than 1.5/15 degrees.  
The parameter configurations and evaluation method used are the same as those applied in the evaluations presented in Section 6.2.2. As demonstrated in Table 6, the largest errors on the antenna downtilt and azimuth angles estimated using our method are all below the tolerance values, demonstrating the applicability of our method in an industrial environment. Two typical visual pose estimation results are presented in Figure 17, where the red curves represent the projections of the 3D model based on the estimated poses.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Mean** & **Std.** & **Max** \\ \hline
**Downtilt** & 0.61 & 0.31 & 1.13 \\
**Azimuth** & 8.7 & 2.25 & 12.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Downtilt and azimuth estimation results for our method when applied to working antennas.  
Figure 16: Three views of a situation in which the projection of the recovered 3D principal axis (on the red contour) is far from its real position and its center falls outside of the antenna silhouette.  
Figure 15: The rotation angles between the SfM calibrations and the pattern-based calibrations.
# Microbial Life on the Surface of Microlgistics in Natural Waters
## 3 Microplastics as Substrate for the Microbial Colonization in Marine and Freshwater Environments
There are only a few reports about the presence of fungi in biofilms of microplastic in water systems. Species form fungal phylum Ascomycota were found on plastic debris in marine environments [75]. Members of _Chytridiomycota_, _Cryptomycota_ and _Ascomycota_ dominated in community composition of fungi found on the surface of polyethylene (PE) and polystyrene (PS) particles, which were incubated in different aquatic systems including sea, river, and a wastewater treatment plant [76]. So, the role of fungi in microplastic biofilms is one of the topics for future study. Additionally, formation of biofilm could lead to change of distribution of microplastics in the water column. So, biofilms covered microplastic particles of polyethylene terephthalate (PET) and polyvinyl chloride (PVC) being placed for 44 days in freshwater systems contained different biomass levels, and increased MPs density which, supposedly, can change MPs particles buoyancy, and stimulate their sinking and transport behavior [77]. A possible influence of biofilm formation on change of polyethylene density and sinking in coastal waters of the North Sea was also shown [78]. However, the authors suggested that formation of bacterial biofilms is not sufficient to induce sinking of microplastic, and only the presence of multicellular organisms can cause sinking of plastic pieces. The difference between freshwater and marine habitats may play an important role in formation and activities of the microbial biofilm community on microplastics floating in water bodies. However, there is no systematic study to compare microbial communities of microplastic biofilm in different surroundings. Plastic having a long life in comparison with natural surfaces might transport biofilms for long distances [74]. Wind, current, water movement, and weather conditions also affect movement of MPs with adsorbed microbial cells, so, MPs may play the role of vectors for microorganism's distribution.
## 3 Simulation Results and Discussion
Table 2 shows the processing time required for beam training in the conventional and proposed methods when a different set of parameters is used. Here, the processing time is normalized by the sampling interval. This table shows that the processing time in the conventional method increases linearly in proportion to \(N_{Tx^{\prime}}N_{Rx^{\prime}}\) and \(M\). However, the number of neighboring BSs has little effect on the processing time when the proposed method is used. In the table, \(M\) is \(N_{SA}-1\). The processing time of the proposed method is mainly affected by \(N_{SA}\) and \(N_{Rx}\). In the table, the ratio of the processing time of the proposed method to that of the conventional method is \(N_{SA}/M/N_{Tx}\) (1.6-6.25%). Figure 4: The performance of AoD estimation when the conventional and proposed methods are used (3 BSs, Rician, \(K\) = 15 dB, 1 RF). Figure 3: The performance of CID detection when the conventional and proposed methods are used (3 BSs, Rician, \(K\) = 15 dB, 1 RF).
# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 4 Experiments and Results
### Performance Comparison with 2D and 3D-IQA Metrics
These results show that the proposed metric performs better than the compared method for most individual distortion types. Similar observations were made when evaluated using other performance parameters, i.e., SROCC, KROCC, RMSE, and MAE, which are not shown here to save space.
## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships
### Nuclear Contamination Management  
Operational experience from nuclear power plants has shown that both large- and small-scale accidents--or even illegal activities--can result in nuclear contamination, which can gradually spread over time. In the harsh and dynamic environment of the ocean or sea, containment becomes even more challenging, as contamination can disperse rapidly with currents, making mitigation efforts significantly more difficult [93].  
Nuclear contamination in aqueous environments is a topic of interest but is only indirectly related to nuclear maritime vessels. Contamination would occur only in the event of an onboard nuclear system accident that leads to the release of nuclear material into the water [94]. In such cases, AI solutions can play a crucial role in managing nuclear contamination by conducting the following:  
1. Predicting the dispersion of radioactive material in the water;
2. Controlling unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) to monitor radiation levels;
3. Operating autonomous vehicles for decontamination efforts in affected ocean areas.  
Driven by these cases, maritime nuclear ships should be equipped with a fleet of autonomous vehicles and robots designed to activate when contamination is detected. AI algorithms will enable these systems to operate independently, executing their designated tasks with precision, whether for monitoring radiation levels, containing the spread, or conducting decontamination efforts.  
Figure 5: Block diagram of potential use of foundation model for identifying critical digital assets (CDAs) in nuclear maritime ships.
# Simulation Study of Dynamic Rotation and Deformation for Plasmonic Electric Field-Skyrmions
###### Abstract  
The topological properties of optical skyrmions in confined electromagnetic fields are perfectly presented through spin vectors and electric-field vectors. However, currently, electric-field optical skyrmions in surface plasmon polaritons are mostly presented in the form of a Neel type. Most control strategies involve linear directional movement, and topological manipulation methods are monotonous. We specifically propose a multi-arc symmetric slit array, which generates skyrmions from the surface plasmon polariton (SPP) field under excitation of a linearly polarized Gaussian light-source array and exhibits strong dependence processes on the rotation, deformation, and phase distribution of the incident light source. We also discuss the independence and synthesis of deformation and rotation related to phase difference and positions of regulation, respectively, which provide the possibility for rich deformations under different rotation states. Our work extends new ideas for the dynamic control of plasmonic skyrmions, which is of great significance to fields such as spin photonics and nano-positioning.  
Keywords:distributed phase; arc-shaped slit array; electric field skyrmion; deformation and rotation +
Footnote †: journal: Applied sciences
## 3 Results and Discussion  
### Aroma Profiles of Fresh and Frozen PJ  
QDA is a common approach for investigating the impacts of various processing treatments on the overall flavor profile of food products [17, 23]. The aroma quality of four PJs, both before and after freezing, was assessed by QDA, as illustrated in Figure 1. Six attributes (fruity, sweet, woody, grassy, floral, and earthy) and one comprehensive matrix (favorability) were chosen for evaluating and comparing the different PJs. As shown, the fresh control sample FFJ0 showed the highest intensities of grassy, woody, fruity, and floral notes, but this was not found for the earthy aroma. In contrast, all frozen samples (\(-\)20, \(-\)40, and \(-\)80 \({}^{\circ}\)C) displayed an inverse trend: significant reductions in grassy, woody, fruity, and floral attributes (\(p<0.001\)), accompanied by increased earthy notes (\(p<0.001\)). This alteration may lead to a reduced favorability of frozen PJs. This phenomenon may be attributed to substantial volatile loss in PJs during frozen storage, leading to a progressive decline in grassy, woody, fruity, and floral aromatic intensities. The loss of volatiles can be caused by sudden extreme changes in temperature (both high and low), which can denature the chemistry, and thus change the aroma profile of the frozen sample [24]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Aroma Active** \\ **Compounds** \\ \end{tabular}  } & \multirow{2}{*}{**Linear Equations**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**OTS**} & \multicolumn{4}{c}{**Concentration (\#/kg)**} & \multicolumn{4}{c}{**OAVs**} \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{\ \begin{tabular}{c} Hexanal \\ (E)-2-Hexanal \\ \end{tabular}  } & y = 0.0667\(\mathrm{\SIUnitSymbolic}-0.1147\) & 0.9963 & 6.9 & 19.215 & 48.196 & 42.792 & 34.348 & 2.785 & 6.985 & 6.202 & 4.978 \\  & y = 0.0869\(\mathrm{\SIUnitSymbolic}-0.0279\) & 0.9999 & 17 & 1.121 & 0.000 & 0.815 & 0.000 & 0.006 & 0.000 & 0.048 & 0.000 \\ Nonanal & y = 0.0144\(\mathrm{\SIUnitSymbolic}+0.0123\) & 0.9998 & 1 & 0.000 & 5.041 & 5.559 & 4.493 & 0.000 & 5.041 & 5.559 & 4.493 \\ Linalool & y = 0.0899\(\mathrm{\SIUnitSymbolic}-0.1\) & 0.9984 & 0.22 & 6.029 & 1.917 & 4.308 & 3.542 & 27.403 & 8.714 & 19.583 & 16.101 \\ Hexanal & y = 0.0524\(\mathrm{\SIUnitSymbolic}-0.0539\) & 0.9976 & 5.6 & 124.986 & 88.859 & 1009.149 & 143.915 & 231.231 & 158.725 & 19.491 & 186.592 \\ \multirow{2}{*}{ \begin{tabular}{c} (Z)-3-Hexanal-1 \\ 1-Octron-3-3-3 \\ \end{tabular}
# A Software Defined Radio Evaluation Platform for WBAN Systems
## 4 Implementation and Demonstration
### Implementation Architecture of Proposed Evaluation Platform for WBAN Systems
Therefore, HD decoding, soft-decision (SD) decoding, and maximum-likelihood (ML) decoding methods for BCH (63,51) configurations have been simulated as demonstrated on the right side of Figure 11. The blue line is the performance results, Block Error Rate (BLER) vs. \(E_{b}/N_{0}\), running in the proposed evaluation platform for the soft-decoding method, which meets the simulation results. Furthermore, Table 5 demonstrates the hardware cost of implementing the baseband processing module in the proposed evaluation platform for WBAN systems. Moreover, the security scheme proposed in [8] has been utilized and implemented in the evaluation platform as the authentication and encryption module for the demo communication. The communication level was set to Level 2, \begin{table}
\begin{tabular}{c c} \hline
**FMC111/110** & **Value** \\ \hline Radio Frequency & 1800 MHz \\ Middle Frequency & 30.72 MHz \\ ADC Sample Rate & 250 Msps \\ Baseband Symbol Rate & 31.25 Msps \\ \hline \end{tabular}
\end{table} 
Table 4: RF front-end configuration. which requires both authentication and encryption. Hardware utilization of the security scheme performing in the proposed evaluation platform is shown in Table 6. It could be found that the FPGA platform hardware resource utilization was quite low, which means the SDR platform can support more complicated functional tests and validations at the same time. Figure 11: Communication performance executing in the proposed SDR evaluation platform for WBAN systems. Figure 10: Constellation maps performed in the proposed evaluation platform for [7] ((a) Short distance; (b) long distance; (c) frequency-offset (max. 20 ppm in IEEE 802.15.6); (d) corrected frequency-offset).