esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine
### Blade Profile  
One of the most significant improvements proposed in the literature consists of employing blade profiles different from the semicircular profile initially proposed by [PERSON] [7]. Some examples are the models of [PERSON] et al. [26], [PERSON] and [PERSON] [27], [PERSON] et al. [28], [PERSON] [29], [PERSON] et al. [30], and [PERSON] et al. [31], as illustrated in Figure 3a,f, respectively.  
The present work proposes an innovative nature-inspired blade profile based on the Fibonacci sequence. Geometrically, this sequence constitutes a spiral that is presented in many natural contexts such as vegetables, animals, and nature phenomena in general, as shown in Figure 4. Since the Fibonacci spiral is highly presented in nature, and thus is consequence of millions of years of evolution, it is appropriate to propose this geometry  
Figure 3: Some modified profiles proposed in the literature; (**a**) [PERSON] et al. [26]; (**b**) [PERSON] and [PERSON] [27]; (**c**) [PERSON] et al. [28]; (**d**) [PERSON] [29]; (**e**) [PERSON] et al. [30]; (**f**) [PERSON] et al. [31].  
Figure 2: Twist angle; (**a**) 3D view; (**b**) upper view.  
for VAWT wind turbines, obtaining a bio-inspired blade profile. In relation to this, some authors of the present work have proposed a bio-inspired shape for a marine propulsor elsewhere [32; 33; 34; 35], improving the performance of the traditional propellers for certain applications. These works demonstrate that sometimes nature can appropriately inspire mechanical devices with surprising results.  
The Fibonacci blade profile proposed in the present work and its modifications to improve the performance were analyzed numerically. In a previous work [38], the Fibonacci shape was proposed to investigate the effect of number of blades, aspect ratio, overlap, separation gap, and twist angle. The present work aims to analyze the effect of these parameters. To this end, a numerical model was carried out based on CFD (Computational Fluid Dynamics). This model was validated with experimental measurements obtained elsewhere [20].
The metabolism of carotenoids mainly depends on the catalysis of enzymes such as phytoene synthase (PSY), phytoene desaturase (PDS), carotenoid isomerase (CRTISO), zetacarotene desaturase (ZDS), lycopene \(\varepsilon\)-cyclase (LCYE), lycopene \(\beta\)-cyclase (LCYB), cytochrome P450 \(\beta\)-ring carotenoid hydroxylase (CYP97A), cytochrome P450 \(\varepsilon\)-ring carotenoid hydroxylase (CYP97C), \(\beta\)-carotene hydroxylase (CHYB), zeaxanthin epoxidase (ZEP), violaxanthin de-epoxidase (VDE), carotenoid cleavage dioxygenases (CCD), and 9-cis-epoxycarotenoid dioxygenase (NCED) [12]. Studies have shown that bagging affects the color of fruits. Bagging can significantly reduce the content of chlorophyll and carotenoids in kiwifruits [4]. However, the mechanism by which bagging affects chlorophyll and carotenoid metabolism in kiwifruits remains unclear. Ascorbic acid (AsA), also known as vitamin C, is essential for normal human growth and development [13]. However, as the human body cannot synthesize AsA, it can only be obtained from the diet, and fresh fruit is one of the most important sources of AsA [13]. Research on the biosynthetic pathways of AsA in plants has made great progress. Four possible pathways for the biosynthesis of AsA in plants have been proposed [14; 15], among which the L-galactose pathway is considered to play a dominant role, with the other pathways being complementary to AsA biosynthesis [16]. AsA biosynthesis mainly depends on the catalysis of some enzymes, such as glucose-6-phosphate isomerase (PGI), phosphomannose isomerase (PMI), phosphomannoutase (PMM), GDP-mannose pyrophosphorylase (GMP), GDP-mannose-3,5-epimerase (GME), GDP-L-galactose-1-phosphorylase (GGP), L-galactose-1-phosphatase (GPP), L-galactose dehydrogenase (GalDHH), and L-galactono-1,4-lactone dehydrogenase (GalLDH) [17]. AsA biosynthesis requires light, and the process would be blocked by shading, resulting from bagging or exposure to strong light [18]. Kiwifruit is rich in nutrients (e.g., AsA, dietary fiber, carotenoids, flavonoids, phenolics, and minerals), has a regular shape and a pleasant flavor, making it popular among consumers worldwide [19]. During the growth and development of kiwifruit, it is highly susceptible to infection by the pathogenic microorganisms present in the environment, resulting in a decline of fruit quality [20]. Therefore, bagging, which reduces exposure to pathogens, is an important kiwifruit-cultivation measure that helps to preserve the appearance of fruits, reduce pesticide pollution, and improve the commerciality of fruits [21; 22]. However, while bagging improves the appearance of the fruit, it also has an adverse effect on its quality [23]. In this study, yellow-fleshed kiwifruit (_A. chinensis_) was used to determine the effects of bagging and debugging on the external and internal quality of the fruit, as well as transcript levels of key genes involved in chlorophyll and carotenoid metabolism, to lay a foundation for analyzing the effects of light on the molecular mechanism of chlorophyll and carotenoid metabolism in kiwifruit.
## 3 Results
### ENN Identification Results  
The features of various disturbances were identified by ENN. In this paper, half of the voltage signals of power quality disturbances were taken as training samples and the other half as identification samples. Figure 7 shows the convergence curve of ENN. The error rate was zero after about eight iterations of 578 items of training data. The identification results are shown in Table 1. Other than the identification rate of voltage sags, which was 99.2%, the identification rates reached 100%.  
In order to verify the advantages of the proposed method, the disturbance voltage signals of the same training samples and identifying samples were used to identify power quality disturbances using  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PQD Type** & **Interruption** & **Swell** & **Sag** & **Flicker** & **Harmonic** \\ \hline Test pattern (items) & 138 & 112 & 126 & 100 & 102 \\ Accurate pattern (items) & 138 & 112 & 125 & 100 & 102 \\ Recognizing rate (\%) & 100 & 100 & 99.2 & 100 & 100 \\ Total recognizing rate (\%) & & & 99.84 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The classification and recognizing results of power quality disturbance.  
Figure 6: Energy feature curves with various disturbances: (**a**) power interruption energy feature curve; (**b**) voltage sag energy feature curve; (**c**) voltage swell energy feature curve; (**d**) voltage flicker energy feature curve; (**e**) power harmonics energy feature curve.  
Figure 7: Learning curve of ENN.  
BPN, PNN, LVQ network, and an extension method respectively, and compared with ENN. As shown in Table 2, it was found that the accuracy of the proposed ENN identification method was higher than that of BPN, PNN, LVQ, and the extension method and the required calculation time was also the least. The main reason was that ENN can execute features within a range, the extension distance can quickly obtain the correlation degree between the training data and clusters, and the network architecture is simpler. Therefore, the method proposed in this paper was applied to the identification of power quality disturbances, which had high accuracy and could quickly obtain the identification results.
## 2 Materials and Methods
### Data Sets and Processing Strategies  
In order to assess the PPP time transfer performance of the BDS/GPS/GLONASS/ Galileo/QZSS five-system combination, we selected 13 MGEX stations around the world, which are all equipped with high-performance hydrogen atomic clocks, for analysis. The station BRUX operated by the time laboratory was chosen as the central node to form 12 time links with other stations. Since the orbital repeat period is one day for GPS, QZSS, and BDS non-MEO satellites and several days for other satellites, the employed data sets span seven days (also taken in [23; 24]) from day of year (DOY) 248 to 254 in 2021. The geographical distribution of the selected 13 MGEX stations is shown in Figure 1, and Table 1 lists the detailed information for these stations. Table 2 summarizes the processing strategy of five-system integrated PPP time transfer.  
Figure 1: Geographical distribution of 13 MGEX stations employed for time transfer. The red point refers to the center node of all the time links.
## 2 Mathematical Formulation
### Coordination Criterion and Operation Time  
The coordination criterion between the acting times of a main and back up relay is given by Equation (4). In this case, \(CTI\) stands for coordination time interval, which typically ranges between 0.2 and 0.3 s. \(t_{bf}\) is the operation time of the backup relay and \(t_{rf}\) is the operation time of the main relay. Equation (5) establishes the time limits of any relay \(r\).  
\[t_{bf}-t_{rf}\geq CTI \tag{4}\]  
\[t_{rmin}\leq t_{rf}\leq t_{rmax} \tag{5}\]
# Study on Preparation and Properties of Sintered Brick from Multi-Source Solid Waste
###### Abstract  
The recycling of construction waste and the use of a new sintering process in the field of sintered bricks can greatly solve the problems of clay resource depletion, soil structure destruction, and high CO\({}_{2}\) emissions that always limit the development of the sintered brick field. The study was carried out using an orthogonal experiment to derive the optimal mix ratio for the preparation of sintered bricks, and subsequently, the sintered bricks were prepared using the optimal mix ratio. The experimental results show that the maximum compressive strength of construction waste sintered brick (MRB sintered brick) prepared using high-temperature sintering is 8.1 MPa, and the water absorption is 11. When the waste glass slag is mixed with 10%, it can show a better fluxing effect in the preparation of sintered bricks by mixing construction waste with waste glass slag (MGB sintered bricks), so that the MGB sintered bricks have a higher densification. The compressive strength is 32.9% higher and the water absorption is 3.5% lower than that of MRB sintered brick. MGS sintered bricks were prepared by mixing Yellow River sedimentary sand into MGB sintered bricks. The strength of MGS sintered bricks increased with the replacement rate of Yellow River sedimentary sand, and when the replacement rate of Yellow River sedimentary sand reached 16%, the strength of the MGS sintered bricks increased by 88.9%, and the water absorption rate was reduced by 4.6% compared with the MGB sintered bricks. The sintering mechanism had significant effects on the compressive strength, weathering resistance, and frost resistance of the sintered brick. The microwave sintering process has the characteristics of high efficiency, uniform heating, selective heating, and low thermal inertia, which can increase the compressive strength of MGS sintered brick by 4.6%, reduce the water absorption by 12.9%, shorten the sintering time by 43.6%, and improve the frost resistance.  
construction waste; sintered brick; Yellow River sedimentary sand; electric furnace sintering; microwave sintering +
Footnote †: journal: Applied Sciences
# Cross-Domain Access Control Model in Industrial IoT Environment
###### Abstract
Blockchain has a range of built-in features, such as tamper-proof, decentralized, and traceable data, which can build trust in an untrustworthy environment [11]. Blockchain is increasingly used in many IIoT scenarios to address the access control problem [12]. However, many current scholars have found that single-chain structure has problems such as weak data management capability and poor scalability. Therefore, it is difficult to meet the demand for multi-domain cooperation in the IIoT environment. In addition, the process of cross-domain access has a large number of query operations and multiple types of data processing. In this case, the shortage of the chain data structure itself results in the low efficiency of access. All these problems lead to reducing the efficiency of access. To solve these problems, the authors of this paper designed a master-slave multi-chain structure and propose a trusted cross-domain access control model with high scalability. The main contributions of this paper are as follows:  
1. The authors of this research paper designed a master-slave chain based on a cross-domain access control model for Industrial IoT that has high scalability by extending the slave chains;
2. A reputation-based master chain node-selecting mechanism is proposed, which selects safe and reliable nodes as the master chain nodes by evaluating their reputation values. Therefore, the security and reliability of the master chain are ensured;
3. A grouping strategy retrieval method based on the combination of attributes and roles is proposed to improve the matching speed of attributes and strategies;
4. The authors designed a detailed process for intra-domain access control and cross-domain access control based on a master-slave chain. The rest of this article is organized as follows: Section 2 provides some work related to this article. The Section 3 introduces the system architecture and access control model. Section 4 will conduct a security analysis of the system in this article, and Section 5 validates the approach through experiments. Finally, Section 6 gives the conclusions and outlook of this article.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 3 Research on Mechanical Performance of Porous Asphalt Mixture
### Research on Mechanical Performance of High-Viscosity Modified Asphalt Mixtures
#### 3.2.2 Low Temperature Cracking Resistance  
According to the Chinese specification JTG E20-2011, in a low-temperature environment, when the temperature contraction stress in asphalt mixtures surpasses the resistance stress, the mixture is likely to experience low-temperature cracking damage [46]. This study employed a low-temperature beam bending test to evaluate the low-temperature performance of porous asphalt mixtures. Standard rutted plate specimens (300 mm \(\times\) 300 mm \(\times\) 50 mm) prepared through indoor rolling were cut into 250 mm \(\times\) 35 mm \(\times\) 30 mm standard beam specimens. The beam specimens were placed in a \(-10\)\({}^{\circ}\)C thermostat for 5 h to ensure that the internal temperature of the specimens stabilized at the \(-10\)\({}^{\circ}\)C test temperature. The bending test was conducted using a UTM-100 universal testing machine. A loading rate of 50 mm/min was applied at the mid-span position of the beam specimen until the specimen failed. The test procedure is illustrated in Figure 14, and the test results are presented in Figure 15.  
From Figure 15, the maximum bending and tensile strains of asphalt mixtures were found to be directly proportional to the low-temperature cracking resistance, while the stiffness modulus was inversely proportional to the low-temperature cracking resistance. The maximum bending and tensile strains of both mixtures exceeded 2500 \(\upmu\)  
Figure 14: Porous asphalt mixture low temperature beam test.  
Figure 15: Porous asphalt mixture low temperature trabecular test results.  
that both mixtures possessed good low-temperature cracking resistance. Mixture 10-A exhibited higher maximum bending and tensile strains and a lower stiffness modulus, suggesting that 10-A had a greater capacity to withstand deformation at low temperatures and demonstrated superior low-temperature performance. The results also indicated that the adhesion of high-viscosity modified asphalt to aggregate was superior to that of SBS-modified asphalt under low-temperature conditions and with the same mixture gradation. This finding is consistent with the study by [PERSON] et al. (2019) [47], which indicated that the quantity of asphalt modifier can significantly influence the rheological properties of modified asphalt. However, our results also reveal that when the dosage of 10-A was increased to 15%, the high-temperature performance of the asphalt mixture was notably compromised, likely due to an increase in resin content leading to a reduction in the softening point of the asphalt material. This suggests that the optimal dosage of the modifier may depend on precise control according to service conditions. Furthermore, excessive use of modifiers may necessitate further investigation into their economic benefits and impacts on other road properties.
## 4 Discussion
To elucidate the effect of MFSCE on the amyloidogenic pathway, we observed that MFSCE significantly downregulated levels of the amyloidogenic pathway-related proteins, such as APP, BACE, PS1, and PS2. Treatment with MFSCE at doses of 1, 2.5, and 5 \(\upmu\)g/mL inhibited APP. MFSCE at a high concentration (5 \(\upmu\)g/mL) effectively decreased BACE, compared with other concentrations. Moreover, cells treated with MFSCE dose-dependently downregulated PS1 expression. In addition, MFSCE at all concentrations (0.5-5 \(\upmu\)g/mL) effectively downregulated PS2 among other amyloidogenic pathway-related proteins. Previous studies have reported the deposition of A\(\beta\) in stem cells. Adipose tissue-derived stem cells inhibited the accumulation of A\(\beta\) by stimulating A\(\beta\)-degrading enzyme, neprilysin, during A\(\beta\) metabolism [52]. In addition, human umbilical cord-derived stem cells decreased A\(\beta\) deposition and led to improved cognitive function in an AD mouse model [53]. Furthermore, we confirmed the neuroprotective effect of MFSCE derived from adipose tissue via the regulation of the amyloidogenic pathway in neuronal cells induced by A\(\beta_{25-35}\). This study investigated the effects of MFSCE against neurotoxicity in A\(\beta\)-induced SH-SYSY cells, but it has several limitations, such as the comparison of neuroprotective effects between MFSCE and regular stem cells. Previous studies have demonstrated the neuroprotective effects of stem cells against A\(\beta\)-induced neurotoxicity in neuronal cells [10; 11; 54]. The treatment of stem cells increased cell viability and decreased A\(\beta\) levels, compared with A\(\beta\)-induced SH-SYSY neuronal cells [11]. In addition, the treatment of stem cell attenuated the neuronal apoptosis by the regulation of the mitogen activated protein kinase signaling pathway in A\(\beta\)-induced hippocampal neuronal cells [10]. However, for the clinical application of disease therapy of MFSCE, further study on the comparison of the neuroprotective effects between MFSCE and stem cell has to be supported.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 2 Related Works
It adds flexibility in controlling the overall displacement in the network. \[m(i,j)=m_{ij}= \begin{cases}m,&r_{i}<r_{j}\\ \frac{m+1}{2},&r_{i}=r_{j}\\ 1,&r_{i}>r_{j}\end{cases}  \tag{1}\]  
where \(m\in[0,1]\) is a user-defined value. To determine the location to which a device will move, authors introduced two rules (called the \"empty-arc\" rules). A device \(D_{i}\) has \(2\pi\) empty arc when there is no other device \(D_{j}\) such that \(d_{ij}<r_{i}+r_{j}\). In other words, it has no intersection area with another device on its circumference. An empty arc has initially the value \(2\pi r_{i}\). The value of the circumference starts to decrease when other devices start to intersect with the device \(D_{i}\) until it reaches zero. It is worthy to mention that the device \(D_{i}\) starts to fill this empty arc during the maximization process and any overlapping before the maximization process is ignored by the device \(D_{i}\). If a device \(D_{i}\) has empty arc the minimum required overlapping between \(D_{i}\) and another device \(D_{j}\), is defined by \(\tau_{ij}=m_{ij}\cdot r_{i}+m_{ji}\cdot r_{j}\). \(D_{i}\) moves to a position that guarantees \(\tau_{ij}\). This is called \"circle-circle\" connection. Another rule is \"circle-two circle\" connection. Given two intersecting circles, a new coming device \(D_{i}\), will be able to connect to them if it has an empty arc. \(D_{i}\) moves along the line through the two crossing points of the crossed circles. It chooses the closest point of intersection and connects to the closest circle that has free arc on that point [27]. Simulation results showed a significant improvement in the coverage gain.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### The Bottoms-Up Correlated Error Model
#### 2.2.1 Model Assumptions  
Several assumptions are made regarding the overall model and each individual \(Ecorr\) term. The first is that the decorrelation scales of interest are on the order of seconds to minutes. This restricts the problem to errors that decorrelate on timescales that would be of relevance to numerical weather prediction users. CYGNSS observations may have a correlated error structure that evolves during longer timescales, say, daily or seasonally. However, this structure would probably be better resolved using other calibration and processing techniques and would not add significant value to a correlated error matrix. As a result, all errors between CYGNSS samples \(i\) and \(j\) are assumed to be 100% uncorrelated if there is more than 10 min of separation in observation time. This corresponds to roughly 4500 km of distance, where observations could reasonably be treated as wholly uncorrelated.  
Further, all error terms \(Ecorr_{n}\) are assumed to be uncorrelated with each other. This assumption simplifies the implementation and calculation of the model but is less supported theoretically. There are a number of plausible rationales for why many of these error sources are correlated. However, this model hopes to correct for these empirically with the implementation of tuning parameters.  
Finally, there is a general assumption that all the error sources in the model exhibit wide-sense stationarity during the timescales of interest. Therefore, the error magnitude values \(E(n)\) are treated as constants. In practice, the magnitude of errors and their correlation time and space scale sizes may well vary (e.g., due to seasonal dependence). A fully operational implementation of the methodology developed here would take these dependencies into account, e.g., by an appropriate parameterization of the properties of the errors. Here, we only consider stationary error properties in order to illustrate how they are determined and how they would be combined to be used by a DA scheme.
# Portable Prototype of Hydrogen Fuel Cells for Educational Training
## 4 Experimental Setup
### First and Second Experiment: Familiarity with the Equipment  
The first and second experiments introduce learners to the prototype. These two experiments illustrate the connection between the electrolyzer, fuel cells, and the motor as a load. First, the electrolyzer is turned on, producing oxygen and hydrogen, which are brought to the fuel cells through pipes in a gaseous state. Then, in the cells, the chemical energy of the gases is converted into electricity, which drives the connected engine. In the second part, voltage, current, and power are measured, and the V-I characteristic of series-connected fuel cells is recorded.  
The voltmeter measurements show the result of the second part, in which the voltage of the motor connected to the series connection of the fuel cells is measured. The result is \(V=1.65\) V. It is written on the model that one cell gives a voltage of 0.9 V. The measurements show a voltage drop of 0.15 V on the measuring instruments. The current measurement indicates that \(I=0.012\) A. The active power of the consumer is determined by using a wattmeter, which indicates that \(P=0.02\) W. This reading can also be checked using the power equation \(P=V\times I=1.65\times 0.012=0.0198\) W, which is close to the one indicated by the wattmeter.