esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 4 Case Study and Discussion
### Initial Network Topology  
The power-side electrical network topology established on the basis of the IEEE 39-node system and the Chinese 132-node system is shown in Figure 4(a) and Figure 4(b), respectively. The IEEE 39-node system consists of a total of 10 generators, 39 busbars, and 12 transformers. In contrast, the Chinese 132-node system represents a more streamlined provincial network consisting of 25 generators, 101 loads, and 180 transmission lines. The degree distribution for both systems is shown in Figure 6, and the statistical characteristics of the networks are summarized in Table 1.  
The degree distributions in these two networks show notable differences. In the IEEE 39-node system, a significant proportion of nodes are connected to three or two other nodes, resulting in an intricate and highly clustered pattern. Conversely, in the 132-node system in China, the majority of nodes are connected to only one or two other nodes, resulting in a sparse and straightforward network structure. Referring to Table 1, there is a notable difference in driver node density and network clustering coefficient between the two networks. A higher minimum driver node density implies a higher number of required driver nodes. The network clustering coefficient [33] measures the degree to which neighboring nodes of a node are connected and is defined as the ratio of the actual number of connections between a node's neighbors to the maximum possible number of connections. A larger clustering coefficient indicates more connections between nodes and a denser network. Indeed, these two networks exhibit different characteristics, and using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Type** & **N** & **L** & \(\mathbf{n_{d}}\) & **Clustering Coefficient** \\ \hline IEEE 39-Node System & 39 & 39 & 0.0769 & 0.0385 \\ Chinese 132-Node System & 132 & 180 & 0.2273 & 0.0880 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristics of the networks analyzed in the paper.  
Figure 5: **(a)** The network topology of the IEEE 39-node system; **(b)** the network topology of the Chinese 132-node system.  
both the IEEE standard 39-node system and the Chinese 132-node system for simulation helps improve the universality of the models.  
To account for the uncertain topology of the information network, the simulation will be randomly run for 10,000 iterations. This approach aims to minimize the random errors stemming from the inherent randomness of the network.
# Influence of Oak Chips and Oak Barrel Ageing on Volatile Profile in Chardonnay Wine of Romania
## 2 Materials and Methods
### White Wine Ventilation and Sample Collection  
Chardonnay grapes (_Vitis vinifera_ L.) were manually harvested at maturity in Teaca winery (Lechintra, Bistrita Nasaud County, Romania) during the 2017 vintage (225 g/L sugar in grape juice, 8.95 g/L must total acidity expressed as tartaric acid and 104 g weight of 100 berries). The Lechintra grapevine growth area is known for the high acidity of wines, due to climatic conditions (oenoclimatic aptitude index of 4221) [23]. Grapes were crushed and destemmed (Enoitalia(r) WE223S, Cerreto, Italy) on the harvest day. A pneumatic press ([PERSON](r) RPS 50, France) was used, filled at 75-80% of its capacity. Potassium metabisulphite (4.5 \(\pm\) 0.5 g/hL) was added during the transfer of the must to the stainless steel tank (5000 L capacity). _Saccharomyces cerevisiae_ (Zymaflore VL1, Laffort(r), Bordeaux, France), a commercial active dry yeast was included at a rate of 20 g/hL to perform alcoholic fermentation at 16 \({}^{\circ}\)C. Malolactic fermentation (MLF) could take place spontaneously, no bacterial culture was added to performed MLF, and no supplementary sulfituation was made to inhibit the MLF. Three sets of experiments were performed depending on the container in which maturation took place (Table 1). When alcoholic fermentation ended, the control wine was transferred to new non-toasted oak barrels at full capacity (first experiment). The control wine was transferred to demijohns (glass container of 50 L) with 4 g/L non-toasted oak chips (second experiment) and 4 g/L light-toasted oak chips (third experiment). Wines were kept in oak barrels and glass containers with chips for ageing during 3 months at a controlled temperature of 14-16 \({}^{\circ}\)C. Sampling was performed at 1, 2 and 3 months of wood contact. Wine samples were stored at 4 \({}^{\circ}\)C until the moment of analysis. Each experiment had three sets of containers, meaning three non-toasted barrels, three glass containers with light toasted chips, and three glass containers with non-toasted chips.
## 3 Statistical Properties
### Order Statistics  
Let \(X_{1},X_{2},...,X_{m}\) be a random sample selected from PZD with cdf (4) and pdf (5) and \(X_{(1)}\), \(X_{(2)},...,X_{(m)}\) be its order statistics; then, the pdf of the \(i\)th \((i=1,2,...,m)\) order statistic, \(X_{(i)}\), is given by  
\[\begin{split} f_{(i)}(x;\theta,\beta)=\frac{m!}{(i-1)!(m-i)!}[F(x ;\theta,\beta)]^{i-1}[S(x;\theta,\beta)]^{m-i}f(x;\theta,\beta)\\ =\frac{\beta\theta^{3}m!x^{2\beta-1}\left(x^{\beta}+1\right)e^{- \theta x^{\beta}\left[\frac{\alpha^{6}\left(\theta+\alpha^{6}\beta^{2}\right) +\theta+2}{\theta+2}\right]^{i}\left[\theta+2-e^{-\theta x^{\beta}\left( \theta+\alpha^{6}\beta^{2}+2\right)+\theta+2}\right]}^{m-i}}{\Gamma(i)\Gamma(1 +i+m)(\theta+2)^{m}\left[\theta+\alpha^{6}\beta^{6}\left(\theta+\alpha^{6} \beta^{2}+2\right)+2\right]}\end{split} \tag{10}\]  
For \(i=1\) and \(m\) in (12), the pdfs of the smallest and largest order statistics, respectively, are given by  
\[f_{(1)}(x;\theta,\beta)=\frac{\beta\theta^{3}mx^{2\beta-1}\left(x^{\beta}+1 \right)e^{-\theta x^{\beta}\left\{\theta+2-\left[\theta x^{\beta}\left(\theta+ \alpha^{6}+2\right)+\theta+2\right]e^{-\theta x^{\beta}\right\}}^{m-1}}}{ \left(\theta+2\right)^{m}},\]  
and  
\[f_{(m)}(x;\theta,\beta)=\frac{\beta\theta^{3}mx^{2\beta-1}\left(x^{\beta}+1 \right)e^{-m\theta x^{\beta}\left[\theta x^{\beta}\left(\theta+\alpha^{6} \beta+2\right)+\theta+2\right]^{m-1}}}{\left(\theta+2\right)^{m}}.\]  
Figure 4 represents the three-dimensional plots of the minimum and maximum pdfs with \(m=5\), \(\beta=2\) and \(1\leq\theta\leq 4\).  
Figure 4: Three-dimensional plots of maximum and minimum pdf of the PZD for \(m=5,\ \beta=2\).
# Electroencephalography (EEG) Technology Applications and Available Devices
## 5 Discussion
To obtain high-quality data and better results, artifacts such as muscle and eye movement, eye blink, and line noise need to be pre-processed and artifacts should be omitted before doing any data analysis;
* **Price:** Most of the EEG devices designed for medical purposes like Neurofeedback and neuroscience are expensive;* **API/Software Used by Device:** The software which accompanies an EEG headset can be complicated for researchers without prior extensive knowledge about brain activity, as well as knowledge of filtering and analysis techniques. The software, which is utilized by an EEG device, can have adverse effects on the ease and reliability of experiments, as well as the overall cost. In addition to research on the quality of the EEG device itself, care should be taken to understand if the software it makes use of is within acceptable cost and quality levels. Open Source software tends to be more secure, but has less built-in support for newer users, whereas integrated proprietary software tends to have better support, but is more costly. Depending on the EEG software, users may be given access to raw EEG data that has not been modified, processed data that has been modified after recording by the software in some way, or to both raw and processed data;
* **Comfort to user:** Wireless dry or saline solution EEG devices are more convenient for the user because of their flexibility of movement, lower setup time, and no need for cleaning the user hairs after the experiment like in gel-based solution;
* **CE/FDA approved:** Most of the listed commercial EEG sensors have not been CE/FDA approved. A list of EEG headsets that can be utilized for clinical treatments is given in the \"MD\" column of Table 3.
## 4 Conductive Polymers  
Conductive polymers (CPs) refer to polymers with a conjugated \(\pi\)-bonded long-chain structure. The polymer main chain is composed of alternating single and double bonds. CPs exhibit excellent conductivity, redox activity, environmental stability, and biocompatibility due to their conjugated structure. They are easily synthesized and can be used for enzyme immobilization [129]. Commonly used CPs include polypyrrole (PPy), poly(3,4-ethylene-dioxytthiophene) (PEDOT), polyaniline (PANI), polythiophene (PTH), etc. [130]. A summary of one-step in situ enzyme immobilization in conductive polymer electropolymerization is presented in Table 3. The term \"one-step in situ enzyme immobilization in conductive polymer electropolymerization\" typically refers to the simultaneous presence of CP monomers and enzymes during the in situ polymerization process to form immobilized enzymes. In this process, the formation of CPs is usually achieved through electrochemical polymerization [131] or enzyme-catalyzed polymerization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Polymer** & **Enzyme** & **Applications** & **Improved Performance** & **Ref.** \\ \hline PTh/Ppy/PANI & GOx & Glucose detection biosensor & Enhanced stability & [132] \\ \hline NMPY & ChOx & Cholesterol biosensor & Enhanced charge transfer & [133] \\ \hline PANI & PyOx & Glucose detection biosensor & Enhanced activity and stability & [134] \\ \hline PANI & GOx/Ur & Glucose and urea enzymatic biosensors & Enhanced stability and reusability & [135] \\ \hline Nafion®117 & FDH & Formaldehyde detection biosensor & Enhanced stability and reusability & [136] \\ \hline \
\begin{tabular}{c} Chitosan derivatives \\ (CS-Fc) \\ \end{tabular} & GOD & Glucose detection biosensor & Enhanced electronic conductivity,
\begin{tabular}{c} electrode surface \\ area and electrochemical stability \\ \end{tabular} & [137] \\ \hline \hline \end{tabular}
\end{table}
Table 3: One-step biosensors based on enzymes trapped inside a polymer.
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 3 Importance of Carbon Nanomaterials for the Corrosion Mitigation
### Use of Nanomaterials for Corrosion Mitigation in Space  
Coatings (metals [67], metal oxides [68; 69], metal carbides [35], ceramics [70]), self-healing materials [38] and various types of surface modification [71; 72] are examples of technologies that could be adapted for the mitigation of corrosion on satellites. However, coatings could be damaged by micrometeorites, space debris and handling. Additionally, internal factors such as density differences and thermal expansion may cause coating defects [73]. _AO_ can penetrate through the protective coatings and damage the internal layers. Materials such as SiO\({}_{2}\) can efficiently act against _AO_ corrosion, but the lack of conductivity causes electrostatic build-up on SiO\({}_{2}\)-based materials. Electrically conductive coating materials such as indium tin oxide is an alternative, but the limited flexibility and brittle nature is an issue [39]. The use of boron nitride films as a substitute for graphene was demonstrated, with the graphene-like chemical and physical properties proven, but electrical conductivity was low [74; 75]. MoS\({}_{2}\) is a commonly used solid lubricating material used in many space missions including the ESA Infrared Space Observatory and James Webb Space Telescope [76] to prevent cold welding that arises as a result of friction between moving parts; however, this material is also susceptible for _AO_-induced oxidation and degradation. Efforts were made to mitigate the effects of _AO_ by making highly ordered nanosheet composite of MoS\({}_{2}\)[77] and, e.g., by the use of montmorillonite and poly(p-aminostyrence) against _AO_ erosion on Kevlar fibres [70]. Nevertheless, the above materials do not ensure the necessary level of efficiency; thus, carbonous nanomaterials are currently under investigation due to their attractive properties such as thermal and oxidation resistance, high tensile strength-to-weight ratio and high tensile modulus-to-weight ratio. Carbon nanomaterials demonstrated excellent results as composite materials on both simulated and in situ experiments.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 5 Experimental Results and Analysis
### The Experimental Results of BCSO
slowly, and the optimum solution is worse than the BCSO algorithm. Therefore, it can be concluded that the convergence speed of BCSO algorithm is also of large advantage compared with others. Figure 2 shows the mean values of the four contestant methods for solving different unit numbers. In Figure 2, the blue column of BCSO is the shortest, forming the point of economic cost, and the experimental results of the BCSO are the best. Combining the Table 4 and Figure 2, it can be proved that the BCSO algorithm is a competitive tool in solving UC problem. From Figure 2, it can be observed that the height difference gradually increases with the unit number increase, which means that the difference of optimum solution between the BCSO with other algorithms is increasing with the dimension scales increase. Thanks to the evolutionary advantage of BCSO, the proposed method is even more competitive in solving the large scales cases. The run time curve of four heuristic algorithms with different unit numbers is shown in Figure 3. From Figure 3, it can be seen that the curve of BCSO (blue curve) is at the bottom, and it reflects that the run time of BCSO is the least. Comparing the interval between different curves, we find that the curves of BPSO, BLPSO and NBPSO have smaller intervals, for example, when the unit is 10 and 20, the three curves almost coincide, but the interval between BCSO and another algorithm is larger and the interval increases with the unit-scales increase. For example, when the unit is 10, the run time difference between BCSO and NBPSO is 8.54 s; when the unit increased to 100, the difference is increased to 35.55 s. It can be concluded that the calculating time of BCSO algorithm is not affected by high-dimensional issues. Therefore, the convergence speed and applicability of the proposed BCSO algorithm are proved again. From the experimental results shown above, it can be concluded that the BCSO is able to improve the optimization accuracy and improve the economic effect. The BCSO algorithm converges quickly and obtains competitive results. This may due to the fact that the character of the UC problem is a high dimension and complicated problem, and the BCSO algorithm provides chances for further exploitation in each dimension, and is therefore more suitable for solving this kind of problem. Figure 2: The mean comparison of different methods for different unit scales. BCSO: Binary competitive swarm optimizer; BPSO: Binary particle swarm optimization; BLPSO: Best parallel particle swarm optimization; NBPSO: New binary particle swarm optimization.
## 4 Results and Discussion
### Stabilized Dredged Material
#### 4.3.2 Compressive Strength  
Following curing times of 7 and 28 days, the specimens were tested for compressive strength (CS) in accordance with the New Mexico Code. For each mix, three samples were tested, and the average value was determined. To assess the durability of the samples, they were submerged in water for four hours after 28 days of curing and then tested for their CS. The average data of all these tests are presented in Figure 8, and the values are provided in Table 4. The term \"remaining CS\" refers to the CS of the submerged brick divided by the naturally cured 28-day SC. The remaining UCS column shows that in the most critical condition, which occurred for the mix 15/34.5/10, above 40% of the strength was retained after submergence.  
Figure 8 helps us to compare all samples and shows that a higher content of additives results in higher CS. Strength values varied between 4.1 to 7.8 MPa after 7 days of curing. All samples demonstrated higher CS after 28 days, which indicates the effect of time on strength gain. The CS after 28 days varied between 5.2 MPa to 9.4 MPa. We note that major strength improvement occurs during the first week of curing. According to the data, between 78 to 85% of the 28-day UCS is achieved in just 7 days. This is attributed to the presence of Portland cement (PC), which hardens faster than the combination of hydrated lime (HL) and fly ash (FA). Conversely, as reported by other researchers, the combination of HL and FA takes longer to harden, with the process continuing for 90 days or even longer [3,34,38]. Results from Grubb indicated that where an abundant content of HL and FA was available, a substantial increase in strength was recorded after 90 days.  
Figure 8 also compares the UCS averages of brick cured for 28 days before and after being submerged. A major drop in UCS is observable for stabilized bricks after they were submerged in water for four hours. These results are indicative of the durability of the stabilized dredge material brick (SDMB). Comparing all three types of samples, Figure 8 reveals that the strength values of the bricks after submergence were lower than those of 7 days for all samples. The submerged strength of SDMB decreased to as low as half of their strength after 7 days, specifically for mixes 15/34.5/10 and 15/34.5/15. As expected for all samples, as the amount of addition is increased compared to DM, the UCS increased, with the effect being more pronounced as the ratio of PC increased.  
Figure 7: Stabilized mixed at a fixed dosage of HL and FA (for PC = 10, 15, and 20).  
Table 4 presents the remaining unconfined compressive strength (UCS) after submergence. Unsurprisingly, increasing the amount of stabilizing agent increases the remaining UCS. With a fixed amount of HL and FA, an increase in PC led to higher UCS retention. These observations are possibly explained by the fact that the presence of PC creates a denser matrix with fewer pores and lower water absorption. To evaluate the extent of these bonds, the microstructure of the samples was investigated using SEM.
## 4 Measurement Results and Analysis
### Comparisons of Propagation Models  
Figure 5(a),b show the comparison of measured model, free-space path loss model, two-ray model and COST231-Walfish-Ikegami model in the flat concrete road scenario with 5 cm and 1 m antenna heights, respectively. The one-slope model was applied when the antenna height was extremely low (5 cm), while the two-slope model was adopted otherwise. As shown in Figure 5(a), the path loss values predicted by FSPL model and COST231-Walfish-Ikegami model were much lower than the values predicted by the one-slope model. This indicated that the wireless channel in near ground was far from a free space. In contrast, the two-ray model was close to the one-slope model in this scenario. However, when antenna heights varied to 1 m, the two-ray model became fairly inaccurate. The path loss predicted by two-ray model varied drastically with the change of antenna height. It can be concluded that these three models are not suitable to represent the characteristics of near-ground channels, due to their unrealistic assumptions and simplifications. The adoption of these models may lead to overestimation or underestimation of path loss.
# A Stronger Adaptive Local Dimming Method with Details Preservation
## 2 Related Work
### Backlight Extraction  
As an early and fundamental approach, the max method [4] employs the maximum luminance of an image block to determine the luminance level of the corresponding BLU. It also explores replacing the maximum luminance with the average luminance. However, the former is sensitive to noise and suffers from light leakage [1] problem. The latter reduces image luminance and suffers from losing details in bright areas. To achieve a trade-off between the above two methods, the methods proposed in [1, 5, 6] consider both the maximum luminance and the average luminance to improve display quality. In [1], a decision rule is proposed to determine optimal backlight by comparing the light leakage and the clipping of image blocks. This method is effective for images with bright objects in a dark area. In [5, 6], the difference between the maximum and the average luminance value of a block is used to adjust backlight based on the average value. In [7], the global information of the image is used to extract backlight. It proposes a threshold method using a Cumulative Distribution Function (CDF) to ensure the distortion of the compensated image within a certain range. Besides, the methods proposed in [8, 9] are effective to reduce power consumption. Based on Otsu [18], [PERSON]; [PERSON], [PERSON] [10] introduced a local dimming algorithm to separate foreground and background pixels for backlight extraction. In [11], the Peak Signal to Noise Ratio (PSNR) = 30 is considered as the lowest standard to guarantee the image quality. In [12], a Gaussian distribution model is proposed to reduce power consumption and improve image quality. In Swarm Intelligence (SI), the authors of [13, 14] transformed the local backlight dimming to an optimization problem to preserve the image quality with low power consumption. A guided firework algorithm proposed in [14] achieves higher performance than the one in [13]. Although there are other local dimming algorithms [15, 16], most of them favor only specific characteristic of an image. Therefore, we propose a backlight extraction method to adapt to images with diverse characteristics, acquiring preferable display quality.
## 3 Results and Discussion
### Day of the Week Variability
As already discussed in Section 3.2, the diurnal variability patterns of tropospheric NO\({}_{2}\) are rather different for the two time periods, mainly due to different NO\({}_{x}\) emission strengths and the lifetime of NO\({}_{2}\) in the troposphere, in addition to the different boundary layer structure that has a strong effect on the accumulation and dispersion of NO\({}_{2}\).