esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Pixel-Based Classification  
This section contains the experimental results of the proposal for pixel-based classification. This scenario was considered in order to show the performances of the proposed augmentation techniques with a traditional pixel-based classification scheme. The scheme applied was the same one described in Section 2, albeit without the superpixel segmentation step. The patches are centered on pixels using a sliding window approach. Tests were run for the Salinas, PaviaU and PaviaC scenes. Table 7 displays the execution times of all the augmentation techniques reviewed in this study for the PaviaC scene. Based on the data obtained, we can observe that the total execution times are significantly higher in the case of pixel-based classification. Training times have a very strong linear correlation with the augmentation factor, and thus, with the number of samples being processed. Prediction times are only dependent on the dimensions of the image, and there is little variation across the different executions. It is worth noting that pixel-based classification is not practical for large images due to the large execution time required. The large number of pixels that need to be predicted, three orders of magnitude higher than the number of training samples, causes prediction times to be significantly higher than training times. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **Rotate** & **Inner-Rotate** & **Dual-Rotate** & **Flip** & **Inner-Flip** & **Dual-Flip** & **PVSA(+/\(-\))** & **RO** \\ \hline \hline Scene & 4\(\times\) & & 4\(\times\) & 4\(\times\) & 16\(\times\) & 4\(\times\) & 4\(\times\) & 16\(\times\) & 4\(\times\) & 16\(\times\) & 4\(\times\) & 16\(\times\) \\ \hline Salinas & 0.16 & 1.32 & \(-\)7.63 & 3.18 & 1.16 & 1.8 & \(-\)6.56 & 2.69 & 1.36 & 2.11 & 1.36 & 1.99 \\ PaviaU & 3.13 & 1.57 & \(-\)0.98 & 2.11 & 3.8 & 2.45 & \(-\)1.38 & 1.11 & \(-\)0.07 & 0.8 & \(-\)0.44 & 1.19 \\ PaviaC & 0.93 & 0.57 & \(-\)0.92 & 0.72 & 0.84 & 0.36 & \(-\)0.62 & 0.82 & 0.35 & 0.59 & 0.31 & 0.38 \\ \hline Average & 1.41 & 1.16 & \(-\)3.18 & **2.01** & 1.93 & 1.54 & \(-\)2.85 & 1.54 & 0.55 & 1.17 & 0.41 & 1.19 \\ \hline Ottawa & 0.95 & 1.16 & 0.72 & 1.97 & 0.98 & 1.33 & 0.94 & 1.96 & 1.09 & 1.52 & \(-\)0.42 & 1.45 \\ Ermidas & 0.63 & 0.88 & 0.5 & 1.12 & 0.81 & 0.92 & 0.71 & 1.37 & 0.62 & 0.85 & \(-\)0.01 & 0.88 \\ Eiras & 0.9 & 0.9 & 0.44 & 1.16 & 1 & 1.04 & 0.88 & 1.21 & 0.43 & 0.44 & \(-\)0.13 & 0.91 \\ Mestas & \(-\)0.29 & 0.27 & 0.01 & 0.62 & \(-\)0.3 & 0.22 & 0.1 & 0.65 & 0.26 & 0.3 & \(-\)0.33 & 0.42 \\ \hline Average & 0.83 & 0.98 & 0.55 & 1.42 & 0.93 & 1.09 & 0.84 & **1.51** & 0.71 & 0.94 & \(-\)0.19 & 1.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Superpixel-based overall accuracy delta (in percent) per augmentation technique over baseline performance for all the scenes from the standard and Galicia datasets. The best average result for each dataset is displayed in bold. RO denotes random occlusion.
## 3 Determination of PEMFC Parameter by COA
### Parameter Optimization of PEM Fuel Cell  
The optimization of the proposed problem aims to identify the unknown parameter of the electrochemical model for PEMFCs. The optimized model must be accurate as possible to converge with the actual model. The set of control variables (\(x\)) of the parameter estimation problem is represented by \(x=\{\xi_{1},\ \xi_{2},\xi_{3},\ \xi_{4},\ R_{c},\ \lambda,\ and\ b\}\). It is important to optimize these seven parameters affecting Equations (9)-(14) to obtain the stack output voltage of the modelled PEMFCs. The calculated stack voltage is required to converge with the experimental values of the actual stack. To realize the convergence, the sum of square error (SSE) between the measured and modelled stack voltage must be minimized. The Coyote optimization algorithm as a metaheuristic optimization algorithm is used to minimize the objective function of the problem.  
\[SSE=\sum_{k=1}^{n}[V_{measured}(k)-V_{stack}(k)]^{2} \tag{31}\]  
where \(n\) represents the number of real voltages at dissimilar load current. The problem can be stated as an optimization problem, as follows:  
\[OF=\min(SSE(x)) \tag{32}\]  
subject to  
\[\xi_{i}^{\min}\leq\xi_{i}\leq\xi_{i}^{\max}\ for\ i=1,2,3\ and\ 4 \tag{33}\] \[\begin{array}{c}\mathbb{R}_{c}^{\min}\leq\mathbb{R}_{c}\leq\mathbb{R }_{c}^{\max}\\ \lambda^{min}\leq\lambda\leq\lambda^{max}\\ b^{min}\leq b\leq b^{max}\end{array}\]  
All control variables are preserved within the lower and upper boundaries as presented in Equation (33).
## 3 Results
### Fatty acid Spectrum  
The fatty acid spectrum of the lipids produced was analyzed at the end of all fermentations (Figures S10-S24, Tables S2 and S3). In general, the share of the individual FAs in the lipids from the two different strains and at different scales was comparable. The highest share of the individual FAs was C18:1, reaching a share of approximately 42% in _P. kudriavzevii_ V194 and approximately 37% in _A. brassicae_ V134. Other FAs represented in larger proportions were C16:0, C18:0, C18:2, and C17:0 (see Figure 2, Table S4).  
When comparing the laboratory scale and pilot scale fermentations, a considerable change in the proportion of C17:0 and C18:0 is evident in both strains. This was expected, since a previous study [23] demonstrated that the share of odd-numbered FAs is strongly dependent on the share of HPr provided as a carbon source. Thus, the observed change in the FA spectrum is attributed to the described change of the HPr/HAc ratio in small pilot scale and pilot scale.  
Figure 2: Fatty acid spectrum of SCO produced from a mixture of HAc and HPr by _P. kudriavzevii_ V194 and _A. brassicae_ V134 in laboratory scale (starting volume = 400 mL, ratio HPr/HAc = 0.54 mol/mol), small pilot scale (starting volume = 45 L, ratio HPr/HAc = 0.27 mol/mol), and pilot scale for _A. brassicae_ V134 (starting volume = 500 L, ratio HPr/Hac = 0.27 mol/mol).
## 1 Introduction and Terminology  
In this paper, we review thermal properties of graphene, few-layer graphene (FLG), and graphene nanoribbons (GNR) and provide an example of a graphene application in thermal phase change materials (PCM). In many cases, when discussing graphene thermal applications, we use the term graphene even when the actual material consists of a mixture of single layer graphene (SLG), bilayer graphene (BLG), and FLG. The latter is because for thermal applications the difference between SLG and FLG is not as important as for electronic applications. It is sometimes difficult to distinguish between FLG and graphite films or between FLG and graphite nano-platelets (GnP) used in composite materials. The definition of SLG--a single atomic plane of sp\({}^{2}\)-bound carbon--is strict. The distinction between FLG and thin film of graphite or bulk graphite depends on the context. Investigating electrical properties one can consider the material to be FLG rather than graphite as long as it is thin enough for changing its carrier density via the electrostatic gating. In the thermal field, one can consider the film to be FLG as long as its Raman spectrum is different from that of bulk graphite.  
Acoustic phonons determine the thermal properties of graphene and graphite at room temperature (RT) \(T=300\) K, while the optical phonons define their Raman spectrum [1; 2; 3; 4; 5]. In both cases, the crystal lattice dynamics is essential for the distinction between FLG and graphite. The Raman spectrum of FLG differs from that of graphite for the thickness \(H\) less than seven to 10 atomic planes [6; 7; 8; 9; 10; 11]. In this review, we consider the materials to be FLG when its phonon properties are different from those of graphite (\(H\leq 10\approx 3.5\) nm). In most of cases, FLG flakes have larger lateral sizes (up to a few micrometers) than their thickness \(H\).  
The initial interest in the thermal properties of graphene and FLG was driven by the exotic physics of two-dimensional (2D) phonon transport [1; 2; 3; 4; 5]. Recently, the studies turned to thermal properties of graphene and related composite materials from the position of practical applications. In this review, we briefly outline the state-of-the-art and new developments in the field of thermal properties of graphene. In addition, as an example of a practical application of graphene in thermal management, we will describe graphene-enhanced phase-change materials (PCMs). Readers interested in details of the phonon thermal transport in graphene and FLG are referred to other recent reviewers [1; 2; 3; 12; 13].
###### Abstract  
The aim of this study was to evaluate the impact of ethanol extracts from sea buckthorn and Cornelian cherry fruits and leaves on physiology of gastrointestinal tract cells. We used three cell lines relevant to the types of cells, which are exposed to bioactive compounds after oral administration, namely intestinal absorptive cells (Caco-2/HT-29 MTX model), hepatocytes (HepG2 cells) and immunocompetent cells (RAW 264.7 and P38 BD1 monocytes). The contents of antioxidant and bioactive polyphenols, such as cinnamic, caffe and \(p\)-cumaric acids, rutin, myricetin, resveratrol, quercetin, apigenin and kaempfer), were assessed in the extracts using HPLC chromatography. The application of the extracts to Caco-2/HT-29-MTX cultures increased entrocyte differentiation markers (alkaline phosphatase and villin1 level) and goblet cell markers (mucins) over a fortnight. The extracts reduced lipid droplet size in hepatocytes challenged with hyperglycaemic glucose concentration, insulin and palmitate. Sea buckthorn leaf, fruit and Cornelian cherry leaf extracts blocked oxidative burst in the PMA-stimulated monocytes, while the sea buckthorn leaf and Cornelian cherry fruit extracts downregulated lipopolysaccharide-induced NO and IL-1\(\beta\), respectively. The results indicate that the tested extracts modulate the behaviour of cells in the gastrointestinal tract in a beneficial way, especially regarding lipid accumulation and innate immunity actions.  
Keywords:intestinal epithelial cells; hepatocytes; monocytes; phenolic acids; quercetin; rutin; resveratrol; apigenin; myricetin; kaempfer +
Footnote †: journal: Applied sciences
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Node Ranking Based on Network Resource Attributes and Topology Attributes
#### 4.1.4 Closeness Centrality  
Closeness centrality is a method of measuring the importance of a node from a global topological perspective.  
The closeness centrality of a node is obtained by first calculating the sum of the shortest paths from the node to all other nodes in the graph and then taking the reciprocal of the sum. The normalized closeness centrality is:  
\[CC(v_{i})=\frac{|V|-1}{\sum\limits_{i\
eq j}d(v_{i},v_{j})}, \tag{15}\]  
where \(d(v_{i},v_{j})\) is the length of the shortest path between node \(v_{i}\) and node \(v_{j}\).  
Thus, the nodes that are near the geometric center of the graph have higher closeness centrality.
## 6 Performance Evaluation
### Training Efficiency of the DETO Scheme  
In this subsection, we examine the effectiveness of the DETO optimization method's training. DETO's training progress is shown in Figure 3. As the number of training episodes increased, we can see that the total number of rewards continues to rise progressively from the beginning. With a total of 100 episodes, total rewards experience a notably significant increase after the 40 th episode. This occurs because the agent initially explores new positions at random using the \(\epsilon\) probability, and these new positions may not yield optimal performance. Moreover, experience replay memory initially has fewer feasible solutions. However, as the neural networks begin training, experience replay memory gathers more useful solutions. Thus, UAVs are able to execute actions iteratively and can learn from mistakes, to increase the reward, which ultimately leads to a better system objective. Even though the curve shows fluctuations and lacks convergence, it continually moves upward.  
In Figures 4 and 5, we plot the UAVs' ellipsoidal trajectories and the corresponding rewards, respectively, during the algorithm's iterative operations after the end of the DETO model's training. With the advancement of the learning process, the UAVs gradually optimize their ellipsoidal movements, minimizing transmission latency and energy consumption. The distribution of MDs and the deployment positions for UAV ellipsoidal movements are shown in Figure 4. Figure 5 illustrates the system's improvement over  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of episodes & 100 \\ Time steps & 80 \\ Learning rate & 0.001 \\ Discount factor & 0.95 \\ \(\epsilon\) with starting value & 1 \\ \(\epsilon\) with final value & 0.1 \\ Epsilon decay & 0.9997 \\ Memory size & 2500 \\ Batch size & 32 \\ Optimizer & Adam optimizer \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters of the DETO Model.  
Figure 3: Training curve of DETO.  
time, as the reward increases in each step. Remarkably, within the first 25 steps, the reward stabilizes and stays relatively constant. This implies that after an adequate number of steps, the system achieves a balanced state, to reduce the system's overall transmission delay and energy consumption in providing MEC services to the MDs with maximum throughput.  
Figure 4: Deployment positions of UAVs’ ellipsoidal movement.  
Figure 5: Reward during the algorithm’s iterative operations.
# Utilizing Network Structure to Accelerate Markov Chain Monte Carlo Algorithms
## 4 Numerical Demonstration
The parameters will be as follows: \(m\), the number of sets in the partition, is \(2\), since the partition is \((A,\overline{A})\). The matrices \(L,U\) become the following:  
\[L=\left[ \begin{array}{cc}1-p_{0}-\epsilon&p_{0}\\ q_{0}&1-q_{0}-\epsilon\end{array} \right]\qquad U=\left[ \begin{array}{cc}1-p_ {0}&p_{0}+\epsilon\\ q_{0}+\epsilon&1-q_{0}\end{array} \right]\]Furthermore, we can take \(\rho=1-p_{0}-q_{0}-\epsilon\) as an upper bound on the ergodic coefficient of \(L\). Then we obtain from Theorem 2, expressing the estimation in terms of the total variation distance:  
\[D_{TV}(\hat{\pi}_{t},\hat{\pi})\leq(1-p_{0}-q_{0})^{t}+\epsilon\frac{1-(1-p_{0}- q_{0})^{t}}{p_{0}+q_{0}}\]  
where the distributions \(\hat{\pi}_{t},\hat{\pi}\) are over the sets of the partition \((A,\bar{A})\), not on the original state space. Note that in our case we actually have \(D_{TV}(\hat{\pi}_{t},\hat{\pi})=|\pi_{t}(A)-\pi(A)|\), due to the fact that \(|\pi_{t}(A)-\pi(A)|=|\pi_{t}(\bar{A})-\pi(\bar{A})|\). Therefore, we obtain the estimation directly for the set \(A\):  
\[|\pi_{t}(A)-\pi(A)|\leq(1-p_{0}-q_{0})^{t}+\epsilon\frac{1-(1-p_{0}-q_{0})^{t} }{p_{0}+q_{0}} \tag{11}\]  
If \(p_{0}+q_{0}\) is not extremely small, then the term \((1-p_{0}-q_{0})^{t}\) will quickly vanish, as it approaches 0 at an exponential rate. Therefore, after a reasonably small number of steps, we reach a distribution \(\pi_{t}\) from _any_ initial state, such that approximately the following bound is satisfied:  
\[|\pi_{t}(A)-\pi(A)|\leq\frac{\epsilon}{p_{0}+q_{0}} \tag{12}\]  
It is quite interesting to note that neither the precise estimation (11), nor its approximate version (12) depend on the size of the state space. Now we demonstrate via numerical results that the obtained bounds indeed hold. Moreover, they are achievable after a small number of Markov chain steps, that is, with fast convergence. We simulated the example with the following parameters: \(n=100\) states, \(p_{0}=q_{0}=0.25\), and \(\epsilon=0.1\). The set \(A\) was a randomly chosen subset of 50 states. The transition probabilities were also chosen randomly, with the restriction that together with the other parameters they had to satisfy conditions (A), (B), (C). Figure 1 shows the relative frequency of visiting \(A\), as function of the number of Markov chain steps. It is well detectable that the chain converges quite slowly. Even after many iterations the deviation from the stationary probability \(\pi(A)\) does not visibly tend to 0. On the other hand, it indeed stays within our error bound:  
\[|\pi_{t}(A)-\pi(A)|\leq\frac{\epsilon}{p_{0}+q_{0}}=\frac{0.1}{0.25+0.25}=2 \times 0.1\]  
as promised. Having observed this, it is natural to ask, how soon can we reach this region, that is, how many steps are needed to satisfy the bound? This is shown in Figure 2. We can see that after only 10 iterations, the error bound is already satisfied. Note that this is very fast convergence, since the number of steps to get within the bound was as little as 10% of the number of states.
## 3 Results and Discussion
### Spectroscopic Characterization
#### 3.3.1 Detected Species in Plasma
The intensities of the atomic emission lines, that depend on the populations of an upper excited state, can be used to determine the electron temperature. In this work, five lines of the Balmer series of hydrogen, H\({}_{\varepsilon}\), H\({}_{\delta}\), H\({}_{\gamma}\), H\({}_{\beta}\), and H\({}_{\alpha}\), were used for this purpose (Section 3.3.3). The gas temperature, or temperature of heavy particles, is another important parameter for the study of the application of this type of plasma discharge. The heavy particles acquire translational energy by collisions with energetic electrons. When the equilibrium among these species is reached, their temperatures are equal. In a non-homogenous plasma, like the filamentary discharge, the conditions are far from this equilibrium and the electron temperature is different to the gas temperature. It is called a 2-temperature plasma condition. In this sense, it should be mentioned that the LPG conversion to hydrogen is more effective when the difference between these temperatures is higher. This is due to a part of the energy of the electron being used for gas heating with a less efficient LPG conversion. The gas temperature is usually associated with the rotational temperature that gives the distribution of the vibro-rotational states of molecules in the discharge, because these are mainly populated by collision with heavy particles. The simulation of a vibro-rotational spectrum and its fitting to the experimental data was used to determine the rotational temperature. The C\({}_{2}\left(d^{3}\Pi_{g}\to a^{3}\Pi_{u}\right)\) band has enough intensity in all study conditions and was used for the determination of this temperature (Section 3.3.2). Table 3 presents a summary of the species and techniques used in this work for the determination of the different parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **Parameter** & **Technique** \\ \hline
**H\({}_{\alpha}\)** & Electron density, \(n_{e}\) & Stark Broadening \\ \hline
**H\({}_{\varepsilon}\), H\({}_{\delta}\), H\({}_{\gamma}\), H\({}_{\beta}\), H\({}_{\alpha}\)** & Electron Temperature & Boltzmann Plot \\ \hline
**C\({}_{2}\left(d^{3}\Pi_{g}\to a^{3}\Pi_{u}\right)\)** & Gas temperature & Fitting to simulated spectrum (SPECAIR) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of techniques used for spectroscopic characterization of the plasma.
## 3 Results and Discussion
### Stage 2--M-BGS Anaerobic Digestion
Anaerobic digestion of microalgae (_Chlorella_ mixture) and excess sewage sludge performed by [PERSON] et al. (2017) [50] for 28 days under laboratory conditions allowed the production of approximately 0.26 \(\pm\) 0.02 dm\({}^{3}\)CH\({}_{4}\)/gVS under standard conditions of 101.3 kPa and 273.15 K [50]. [PERSON] et al. (2016) [52] optimized the process of methane co-digestion of _Chlorella sorokiniana_ and wastewater activated sludge (WAS) by testing various ratios of substrates (0% WAS-100% microalgae; 25% WAS-75% microalgae; 50% WAS-50% microalgae; 75% WAS-25% microalgae; 100% WAS-0% microalgae). They obtained the highest methane yield, i.e., 442 cm\({}^{3}\)CH\({}_{4}\)/gVS, using the 75% WAS-25% microalgae mixture [52]. Different substrate ratios were established by [PERSON] (2014) [53], who investigated co-digestion of _Chlorella vulgaris_ and WAS. The results of their study demonstrated the linear correlation of the microalgae addition to WAS with methane yield until algae reaches a share of 75% in the co-digested mixture, when methane yield reached 369 cm\({}^{3}\)CH\({}_{4}\)/gVS. This finding formed grounds for developing a laboratory-scale CSTR in order to identify possible operational parameters and challenges likely to be encountered during continuous reactor work. The microalgae to WAS ratio of 75:25, OLR at 4 g VS/dm\({}^{3}\)-d, and HRT of 20 days ensured the highest methane yield at 434 cm\({}^{3}\)CH\({}_{4}\)/gVS, suggesting a balance between the substrates, one which in turn promotes methanogenic activity [53]. Important issues to consider for the full-scale installation include ensuring stable work and optimal biogas production without compromising the post-fermentation characteristics of the residue. The composition of substrates is essential to a stable degradation process. A too low C/N ratio, as in the case of microalgal biomass, may lead to a high level of ammonia which inhibits biomethane production, especially at high process temperatures [54]. Further research is needed to identify optimal operation conditions (ratios, feeding rate, retention time), and to assess the impact of substrate characteristics on the methane fermentation of M-BGS.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 1 Introduction  
Among the ferroic systems, ferroelectrics are subject to intense scrutiny by the scientific community due to their promising impact on current and future energy-efficient technologies [1; 2; 3; 4]. The low-energy consuming electric field driving their polarization direction, and therefore their functionality, makes ferroelectrics of interest for novel device applications. In the drive for power-efficient oxide electronics, the recent development regarding the control of thin film ferroelectricity spans from the determination of critical thicknesses in the ultrathin regime [5; 6] to ferroelectric domain engineering using epitaxial strain [7; 8]. Since all ferroelectrics are piezoelectric, their range of applications can even be extended beyond charge-based memory devices to include mechanical device concepts such as stress sensors, etc. [2]. Furthermore, in the case of coexisting electric and magnetic orders in multiferroic thin film materials, new avenues toward increased data storage density can be realized [9; 10; 11]. A memory bit could indeed be stored independently in the magnetic and in the ferroelectric state. In addition, when the two ferroic orders are coupled, themagnetoelectric coupling can enable the implementation of energy-efficient, electrically writable magnetic memories [12; 13; 14; 15].  
Reviews on SHG and its potential for probing ferroic states in bulk crystals are available in the literature [16; 17; 18]. Here we place the emphasis on recent development in SHG for investigating ferroelectric and multiferroic thin films and heterostructures. Therefore, the first part of this review is devoted to ferroic domain design in thin layers and superlattices. In the following sections, we review the progress made in SHG as tool to access the properties and challenges posed by ferroelectric and multiferroic domains in thin film architectures. By its symmetry sensitivity and its non-invasive nature, SHG appears as a complementary tool to the traditional approach by scanning probe microscopy (SPM) and electron microscopy.