esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Construction of Timbre Space
### Experiment C: Subjective Evaluation Experiment Based on Sample Dissimilarity  
Three factors were considered during sample selection to prepare the sound data needed in the subjective evaluation experiment [73]:  
1. _The appropriate number of samples:_ The number of samples must be sufficiently large to ensure the accuracy of the MDS algorithm and impose sufficient constraints on the model. In practice, however, it is difficult to establish precise rules for determining these data. However, empirical solutions do exist. In most MDS-based timbre studies, at least 10 sound samples are required for two-dimensional spaces and at least 15 sound samples are needed for three-dimensional spaces [51; 74; 75]. In this paper, 37 kinds of Chinese instruments were used as experimental materials, which ensured that sufficient constraints were provided to the MDS model.
2. _The range of timbre variation:_ The range of timbre varies depending on the subject of the study, with larger instrumental variety (i.e., orchestral music) providing better data [34]. Models constructed in this way can be applied more broadly to new timbre samples. In this study, 37 kinds of Chinese instruments were selected. As can be seen from Figure 4, compared with Western instruments, Chinese instruments had a wider distribution range in terms of their timbre evaluation scale. As such, the Chinese instrument samples selected in this paper ensured a diverse range of timbre samples.
3. _The uniformity of timbre sample distributions:_ The distribution of sound samples in each timbre perception attribute should be as uniform as possible. Timbre spaces are continuous perceptual spaces and a uniform distribution sample set is beneficial to the construction of continuous timbre spaces. Non-uniform sample distributions can degrade solutions to the MDS equations, preventing the structures between classes from being fully displayed [76]. As seen in Figure 4, the samples selected in this study covered a broad range of timbre attributes and they were distributed at varying psychological scales, providing a uniform distribution.  
Subjective evaluation of the experimental environment and the subjects was conducted as in experiment B. The process was as follows: while playing each experimental sample, the subjects judgedthe psychological scale of the sample on 16 timbre perception features (timbre evaluation terms) in turn, scoring each on a 9-point scale.
## 2 Materials and Methods
### Algorithm
#### 2.1.3 Segmentation  
The data were segmented as in the MARA algorithm. The segmentation vector contains \(N\) entries. There are two kinds of segments: segments containing MAs (\"MA\" segments) and segments containing no artifacts (\"ok\" segments). The beginning and end of each segment is identified based on the change of values in the vector \(\zeta_{x}(t)\). A change from \"0\" to \"1\" determines the beginning of an artifact (and end of an \"ok\" segment) and a change from \"1\" to \"0\" its end (and start of a new \"ok\" segment). The segmentation container _tsg.k_ contains the time points \(\vec{t}_{k}\) (vector addressing all time points for the segment) for each segment \(k\) and can be expressed as:  
\[t_{seg,k}=\{\vec{t}_{ok,1},\vec{t}_{MA,2},\vec{t}_{ok,3},\vec{t}_{MA,4}, \cdots,\vec{t}_{MA,N-1},\vec{t}_{ok,N}\},\ \ \text{for}\ k=\{1,...,N\} \tag{6}\]  
\[\textbf{acc}_{k}=\ \textbf{acc}(t_{seg,k}),\text{for}\ k=\{1,...,N\} \tag{7}\]\[x_{k}=\ x\big{(}t_{seg,k}\big{)},\ \text{for}\ k=\{1,...,N\} \tag{8}\]  
Furthermore, we define the MA segments to be  
\[\mathbf{acc}_{MA,m}=\ \mathbf{acc}\big{(}t_{seg,2m}\big{)},\text{for}\ m=\{1,...,(N-1)/2\} \tag{9}\] \[x_{MA,m}=\ x\big{(}t_{seg,2m}\big{)},\text{for}\ m=\{1,...,(N-1)/2\} \tag{10}\]  
The variable \(m\) is another index variable needed to address all even segments. The \"ok\" segments are defined in a similar way for the odd entries in \(\mathbf{acc}\) and \(x\). A classification condition \(C_{k}\) is defined based on the \(x\)-, \(y\)- and \(z\)-components of the acceleration signal \(\mathbf{acc}(t)\):  
\[C_{k}=\begin{cases}\ 1,\text{if}\ \mathbf{acc}\big{(}t_{seg,k}\big{)}\in\{\text{ condition free}\}\\ \ 0,\text{if}\ \mathbf{acc}\big{(}t_{seg,k}\big{)}\in\{\text{condition not free}\}\end{cases},\text{for}\ k=\{1,...,N\} \tag{11}\]  
whereas \(C_{MA,m}\) and \(C_{ok,n}\) (\(n=\{1,\ldots,\)(\(N\)-1)/2+1\(\}\)) can be defined in a similar way as above. The terms \"condition free\" and \"condition not free\" refer to ranges for the acceleration values of the \(x\)-, \(y\)- and \(z\)-axes. This classification gives trustworthy baseline regions. For example, during sleep, baseline shifts of the signal were observed whenever the subject lied on the NIRS sensor (pressure on the sensor: \"condition not free\") compared to when the sensor is free (not jammed between bed and subject: \"condition free\"). Thresholds for the determination of the two states were determined after examining the entire dataset manually. Video recordings were inspected to determine \"not free\" positions and to derive the corresponding acceleration thresholds. Since the sensor was always in a specific position during the position \"condition not free\", simple thresholds could be applied to the acceleration signals to determine the \"condition not free\" position. Furthermore, this information was used for the reconstruction of the signal.
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 4 Experiments and Analysis
### Performance and Discussion
It can be seen from the results of the image dehazing that people and furniture in the distance were affected by color deviation and low contrast were well-restored and were almost the same as the ground truth. Similarly, Figure 9 shows the restoration of traffic signs, cars, and sky regions blurred by haze in the outdoor environment. AOD-Net [10] and DehazeNet [9] cannot recover the color deviation or the contrast well. ReViewNet [28] generates a darker image than ground truth and cannot restore the appearance of the no parking sign. The proposed method effectively removes the haze on buildings, sky, and traffic signs. The same dehazing effect was also achieved for images with buildings and trees, as shown in Figure 10. It shows that ReViewNet [28] generates a darker dehazing result due to a color deviation problem. As illustrated in Figure 11, removing haze for objects far in the distance is a limitation of TheiaNet [20]. Obviously, the proposed method generates a clearer dehazing result for distant buildings than the other models. For a real hazy image, the proposed method achieves a visually pleasing result compared to those of the other models, as shown in Figure 12. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **SSIM (\% inc)** & **PSNR (\% inc)** \\ \hline DCP [4] & 0.7609 (29.36) & 14.84 (120.69) \\ CAP [5] & 0.8726 (12.75) & 21.53 (52.11) \\ HIDeGAN [17] & 0.8940 (10.10) & 28.04 (16.80) \\ AOD-Net [10] & 0.8973 (9.70) & 20.55 (59.37) \\ DehazeNet [9] & 0.9153 (7.54) & 24.48 (33.78) \\ ReViewNet [28] & 0.9582 (2.72) & 27.50 (19.09) \\ TheiaNet [20] & 0.9606 (2.47) & 30.30 (8.09) \\ Proposed Method & 0.9843 (best) & 32.75 (best) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of dehazing results on RESIDE HSTS dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ReViewNet [28]** & **TheiaNet [20]** & **Proposed Method** \\ \hline CPU & 3.6 GHz & 3.6 GHz & 3.0 GHz \\ AET 1 & 0.28 s & 0.247 s & 0.244 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of average execution time per frame on CPU. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ReViewNet [28]** & **TheiaNet [20]** & **Proposed Method** \\ \hline \hline GPU & NVIDIA TITAN V 12 & NVIDIA TITAN V 12 & GTX 1660 Super 6 GB \\  & GB & GB & GTX 1660 Super 6 GB \\ AET & 0.025 s & 0.018 s & 0.018 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of average execution time per frame on GPU. Figure 8: Comparison of dehazing results from RESIDE SITS indoor testing dataset with ground truth. Figure 9: Comparison of dehazing results of traffic signs, cars, and sky region [28]. Figure 11: Comparison of dehazing results of distant haze [20]. Figure 10: Comparison of dehazing results of urban road [28].
## 3 Results and Discussion
The obtained Friedman statistic of 6.6545 and a \(p\)-value of 0.0359 provide statistical evidence of a significantly different performance. Additionally, we compared NEAT-Python and Cellular-NEAT using the Wilcoxon signed-rank test. The test produced a statistic of 15.00 with a \(p\)-value of 0.0186, indicating significant differences between the two algorithms. Cellular-NEAT achieved a higher mean and median. These results suggest that  
Figure 6: Processing cells’ structure. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Cellular-NEAT** & **Cellular-NEAT with PCELL** \\ \hline banknote\_auth & 59.30\% & 60.30\% \\ divorce & 98.12\% & 96.81\% \\ iris & 98.0\% & 98.0\% \\ pulsar\_stars & 89.31\% & 90.76\% \\ car\_evaluation & 96.0\% & 95.85\% \\ contact-lenses & 83.76\% & 84.90\% \\ weather.nominal & 84.10\% & 84.33\% \\ diabetes & 89.20\% & 89.10\% \\ weather.numeric & 69.34\% & 71.14\% \\ QSAR Bioconcentration & 67.10\% & 67.30\% \\ seeds & 66.20\% & 66.82\% \\ cryotherapy & 53.30\% & 57.20\% \\ Heart\_disease & 59.70\% & 60.04\% \\ glass & 68.0\% & 67.72\% \\ abalone & 20.40\% & 21.18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Cellular-NEAT accuracy. Cellular-NEAT consistently outperforms NEAT-Python in terms of accuracy. Moreover, the Wilcoxon signed-rank test between NEAT-Python and Cellular-NEAT with PCELL algorithms produced a statistic of 18.00 with a \(p\)-value of 0.0303. In that case, Cellular-NEAT with PCELL achieved a higher mean and median. Finally, the statistical test of Cellular-NEAT and Cellular-NEAT with PCELL produced a statistic of 19.00 with a \(p\)-value of 0.0355, and Cellular-NEAT with PCELL achieved a higher mean. These results suggest that Cellular-NEAT with PCELL outperforms Cellular-NEAT in terms of accuracy. The PCELL algorithm significantly alters the network phenotype by adjusting the number of neurons and layers in each processing cell, consequently improving the accuracy of the results. Each of the six processing cells uses the initial phenotype as a baseline to independently modify the number of neurons or layers, creating various structural configurations. These changes enable the algorithm to explore a more exhaustive solution space and optimize its performance. Table 7 shows that, for the diabetes dataset, cell 6 modified its structure to [8; 6; 2; 2] and reached an accuracy of 89.20%, surpassing the initial phenotype. Moreover, the overall results indicate that integrating the PCELL algorithm into the proposed model enhances accuracy across most tested datasets, as shown in Table 8. Statistical tests, such as the Wilcoxon test and Friedman test, confirm the significance of this improvement and demonstrate that Cellular-NEAT with PCELL outperforms the compared algorithms in average accuracy. This evidence suggests that adaptive phenotype modification through processing cells optimizes neural network performance and more efficiently explores the solution space.
## 3 Results and Discussion  
### MPPT Test  
A solar photovoltaic simulator and a three-point fuzzy MPPT algorithm with a variable step size were used for the tracking experiments. The power point tracking process can be clearly observed. The corresponding tracking results are shown in Figure 11.  
Figure 11: _Cont._  
Figure 10: MPPT tracking effect under the condition of illumination disturbance.  
It can be observed from the figure that the maximum power point can be found steadily and quickly at low power and high power. The working voltage and current of the maximum power point are consistent with the actual output characteristics of the photovoltaic panels.
## 5 Factors Influencing the Antimicrobial Efficacy of NTP
### Role of the Gas or the Gas Mixture with its Flow Rate  
As mentioned earlier, different gases and mixtures have been used in plasma jets to increase antimicrobial efficacy. Argon requires lower ionization energy than helium and it is cheaper, whereas helium possesses better thermal conductivity thereby preventing thermal instabilities [107]. Moreover, the noble gases increase the antimicrobial properties and plasma stability, whereas the addition of oxygen aids in producing chemically active species [111; 112; 113]. One study demonstrated more inactivation of spores of _Bacillus_ genus when He was combined with 3% O\({}_{2}\) in comparison to 100% He [114]. This is due to the generation of oxygen species such as singlet O and O\({}_{3}\) in a He/O\({}_{2}\) plasma environment, as mentioned by the study [114]. These oxygen species play a significant role in the sterilization process because these species have strong oxidative effects on the outer membrane of the bacterial cell [39; 54; 115]. The addition of oxygen in the working gas contributes to the deactivation efficacy of the plasma jet by the combined action of plasma-induced endogenous ROS and the plasma generated ROS as reveal by the study [116]. This combined effect damages the bacterial membrane leading to biofilm deactivation [116]. Nitrogen has also been added to the noble gases to increase the formation of reactive nitrogen species [107]. Up to 3% of oxygen maintains the discharge stability, which produces reactive metastables that are long-lived and capable of traveling tens of centimeters at the nozzle exit velocity. This occurs even though the usual lifetime of atomic oxygen is of the order of 1 ms [117]. However, if more oxygen is added to the helium, the discharge becomes unstable due to the quenching effect of the oxygen gas, and this would decrease the plasma efficacy as a result of a decrease in plasma density [111].  
Many researchers have chosen argon as the working gas because of its inertness and have succeeded in achieving planktonic and biofilm sterilization [118; 119; 120; 51]. One of the study observed the sterilization effects of microwave-induced argon plasma on _E. coli_ and MRSA and suggested the generation of free radicals, and UV light, as well as the etching process, are responsible for the sterilization efficacy [118]. Coupled to effect of the choice of gas, its flow rate determines the velocity at which the active species are delivered to the target [121]. The study by [PERSON] et al., uses a He plasma with a flow rate of 2 and 4 SLM and found that the shape of the inhibition zone and homogeneity were compromised at higher flows with no significant difference in the size of inhibition zones [121]. With higher gas flow rates, flow dynamics such as turbulent mixing and buoyancy effects play major roles in the formation and distribution of active species [122].
## 1 Introduction
Due to these factors, there is a very large trend to encapsulate and control the release of GA to improve its stability and bioactivity over time [14]. To this end, the composition of the encapsulating material is a major determinant of both the capsule's functional properties and the way it can be applied to improve the performance and release of a particular bioactive compound [15]. Novel methods are, therefore, pursued in a particular effort to incorporate GA in sustainable packaging materials avoiding losses in antioxidant activity during film formation or packaging structure development. In this regards, the electrohydrodynamic processing (EHDP), also called electrospinning when fibrilar structures are produced, has been recently suggested as being a simple and straightforward method of generating nanostructured encapsulation structures for a variety ofbioactive agents [16]. The electrospinningning technique makes use of electrostatic forces to produce electrically charged jets from viscoelastic polymer solutions that upon drying, by the evaporation of the solvent, give rise to submicro- and nano-sized polymer-based fibers [17]. In this sense, EHDP is a promising technology that can be easily performed at room temperature so that it has recently attracted increased interest both for the nanoencapsulation and stabilization of thermolabile and light-sensitive substances [18; 19; 20] and for the development of fiber-based active coatings [21; 22; 23; 24]. As a result, various bioactive-loaded electrospun biopolymer nanofibrous mats have been recently intended for food biopackaging purposes [25; 26; 27]. Moreover, in a more packaging application context, the electrospun fiber mats can be further converted into continuous films by the application of a thermal post-treatment below the biopolymer's melting point, the so-called annealing [28; 29; 30]. According to the above, the aim of this research work is to originally develop innovative multilayer structures of PLA with controlled release capacity of antioxidant GA that could serve as bioactive films for food and pharmaceutical packaging. To develop the bilayer structures, the electrospun PLA fibers containing GA were applied in the form of coating on PLA films previously prepared by cast extrusion. For the multilayers, a cast film of PLA was, first, coated on one side with different amounts of electrospun GA-loaded PLA fibers and then sandwiched with another PLA film on the other side. Thereafter, the three layers were annealed to generate a continuous film of PLA containing GA in the interlayer. The morphology, transparency, thermal properties, release profile, and antioxidant activity of the resultant PLA bi- and multilayer films were evaluated to ascertain their potential in bioactive packaging applications.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Multi-Scale FAPAR Truth Data
#### 3.4.1 Estimating Fine-Resolution FAPAR  
The normalized difference vegetation index (NDVI) is widely believed to be significantly related to FAPAR [8; 49]. The linear or approximately linear relationship between NDVI and FAPAR has been testified by satellite data [13; 50], radiative transfer models [20; 51; 52; 53] and field data [13]. The NDVI data were calculated from ASTER reflectance. An empirical regression approach was employed to transfer ASTER NDVI to FAPAR, as conducted in [26], in the form as:  
\[\mathrm{FAPAR}=\mathrm{c}_{1}\times\mathrm{NDVI}+\mathrm{c}_{2} \tag{8}\]  
where \(\mathrm{c}_{1}\) and \(\mathrm{c}_{2}\) are the coefficients converting NDVI to FAPAR. To reduce the effects of outliers, a robust regression using iteratively reweighted least squares (IRLS) [54; 55] was applied. It is used to find the optimized estimates of a linear model by an iterative method in which each step involves solving a weighted least squares problem. Compared to the ordinary least square regression, the regression using IRLS is advantageous to mitigate the influence of outliers that derive large residual errors and are assigned lower weights in the iteration. After the fit, the derived regression coefficients were used to estimate FAPAR values from ASTER NDVI.
## 2 Materials and Methods
### MD Simulations Details  
Prior to MD simulations, the protein geometry was optimized in three cycles with different constraints. In the first cycle (2500 steps), only water molecules and ions were relaxed, while the protein and ligand (cholesterol (moiety), progesterone and estradiol) were restrained by the harmonic potential with a force constant of 32 kcal mol/A\({}^{2}\); in the second (2500 steps) cycle, the force constant of 12 kcal mol/A\({}^{2}\) was applied to the protein backbone; and in the third cycle (4500 steps), all atoms were free to move. Followed heating (250 ps, protein and ligand were restrained with a force constant of 10 kcal mol/A\({}^{2}\)), density equilibration (1 ns, protein backbone and ligand were restrained with a force constant of 10 kcal mol/A\({}^{2}\)) and productive MD simulation. During heating from 0 to 300 K, the _NVT_ ensemble was used, while density equilibration and productive MD simulations were performed using a _NpT_ ensemble. The temperature (300 K) was held constant using Langevin dynamics [41], with a collision frequency of 1 ps\({}^{-1}\). Pressure was regulated by using a Berendsen barostat [42]. The time step was 1 fs during equilibration and 2 fs during productive MD simulations. Bonds involving hydrogen atoms were constrained by using the SHAKE algorithm. Simulations were performed with the GPU version of the pmemd (pmemd.cuda) program [43; 44] from the AMBER software suite version 20 ([[https://ambermd.org/](https://ambermd.org/)]([https://ambermd.org/](https://ambermd.org/)) (accessed on 1 April 2021)) [45], using periodic boundary conditions (PBC) with a cutoff value of 11 A, and the particle mesh Ewald (PME) method was used to calculate the long-range electrostatic interactions. The 0.5 \(\upmu\)s-long MD simulations were performed for ER\(\alpha\) and PR in complexes with their natural agonist (estradiol and progesterone, respectively) and cholesterol, as well as AR in a complex with the SHH-N-cholesterol protein. The SHH-N-cholesterol in a complex with ER\(\alpha\) and PR was subjected to microsecond-long MD simulations. More precisely, in order to obtain a stable complex structure, the ER\(\alpha\)-SHH-N-cholesterol complex was subjected to 2 \(\upmu\)s-long MD simulation. In the case of the PR-SHH-N-cholesterol complex, two 1 \(\upmu\)s-long MD simulations were conducted (designated as simulations a and b). Notably, the first half of both simulations was identical; the structure sampled after 0.5 \(\upmu\)s was used as the starting structure for two additional independent 0.5 \(\upmu\)s-long MD simulations, employing different seed numbers.
# Drag and Attitude Control for the Next Generation Gravity Mission
## 4 Drag and Attitude Control Impacts on Mission Performance  
The drag and attitude control results just presented above feed into the performance and error budget model through their impact in several of the error terms in Figure 9: in particular, the accelerometer-to-satellite coupling contributors (C) and the transformation contributors (T), as mentioned in Section 3.3, weighted by some of the parameters in Table 3. In addition, in Section 3.1: the non-gravitational linear acceleration ASDs resulting from the E2E simulator shown in Figure 16 are fed directly into the contributors for the coupling error estimates (scaled appropriately by the scale factors) and the transformation error estimates (through the pointing misalignments between the accelerometer and satellite reference frames) under several assumptions of drag-free capabilities. Spectral densities for the simulated drag-free performance are also employed for the axes enabled with the drag-free control. The resulting non-gravitational acceleration measurement error ASDs,  
Figure 16: Amplitude spectral density of the drag acceleration components at 396 km and 492 km, computed at the epoch of maximum solar activity with 95 th percentile on F\({}_{10.7}\) and A\({}_{\text{P}}\) prediction. The control requirement (magenta horizontal line, taken from Table 4\({}^{\prime}\)s control requirement for the spectral densities of the linear accelerations in the MBW) is abundantly exceeded below 50 mHz by all components.  
shown in Section 4.1, are then employed to obtain the errors in the gravity field retrieval resulting from the propagation of said measurement errors on the constellation's ground track pattern, as shown in Section 4.2.