esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and colleagues [20] present new perspectives on the application of Artificial Intelligence (AI) solutions to process Spacecraft (S/C) flight data in order to augment currently used operational S/C health monitoring and diagnostics systems. [PERSON] [21] used the Cessan172 flight simulator for flight data extraction to obtain an aerodynamic model, based on the idea of machine learning, a recurrent neural network was used to process multi-dimensional non-linear flight test data, and a real-time recursive learning algorithm was proved to be suitable for dynamic training, and some scholars have conducted combining multiple classifiers for the quantitative rank of abnormalities in-flight data, and applied in-flight data monitoring, flight control behavior analysis [22; 23; 24]. In terms of flight data analysis and application research, make the outlier detections with uncertain data from flight data for pilot performance and maintenance assessment [25; 26]. Many scholars have carried out a series of studies on linear and non-linear flight data fitting and correction, and the provision of various flight data correction and fitting methods shows important value, but he convenient and applicable methods are still worth discussing, especially in the research of correlation and prediction analysis two types of key flight data parameters between the engine exhaust temperature (EGT) and the revolutions per minute (RPM). Effectively explaining the relationship between flight data EGT and RPM also selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. For the fitting and correction of the missing flight data, the common and applicable methods include the extrapolation method of the least squares method, polynomial regression model method, and differential integrated moving average autoregressive method. The extrapolation method of the least square method is the most common method, which applies in engineering fields. Due to its convenience and practicability, the least square method has rich and mature applications in the field of engineering data processing and has a very strong method application universality. For the data of aircraft engine exhaust temperature is often lost or numerical deviation because of the immature detection technology in the general aviation,the polynomial fitting method based on least squares method is used to fit and correct the missing flight data for the relationship of aircraft engine exhaust temperature (EGT) and aircraft engine revolutions per minute (RPM). Conclusively, the algorithm is fitted and corrected with the example of aircraft engine speed data to fit and correct the missing flight data and seek out the most suitable fitting polynomial order through the numerical calculation of the fitting equation of the polynomial and the minimum deviation analysis.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 3 Results
### Dual Effects of PAM on HUVECs Activity and Cell Cycle  
After HUVECs cells were treated with PAM for 24 h, the effects on cell activity were evaluated with two different methods. MTT results showed that PAM treatment changed the cell viability. The cell viability was 110.53 \(\pm\) 2.49% and 113.67 \(\pm\) 2.53% when the cells were treated for 15 s and 30 s, respectively, and the percentage of living cells increased significantly. When the treatment time was 45 s, 60 s and 75 s, the cell viability decreased to 77.71 \(\pm\) 3.71%, 49.53 \(\pm\) 3.19% and 28.46 \(\pm\) 6.56% (\(p\) < 0.05) (Figure 2a). The results of PI single staining showed that the proportion of S phase cells in the control group was 33.50 \(\pm\) 0.29%. Compared with the control group (0 s), the proportion of S phase cells in the 15 s and 30 s-PAM groups increased to 34.52 \(\pm\) 0.43% and 35.62 \(\pm\) 0.74%, respectively. The proportion of cells in S phase in 45 s, 60 s and 75 s-PAM groups decreased to 32.23 \(\pm\) 0.61%, 31.35 \(\pm\) 0.36% and 28.19 \(\pm\) 2.63%, respectively, and the difference was statistically significant (\(p\) < 0.05) (Figure 2b).
## 4 FL-Based Controller: Formulation, Constraints and Algorithm
### Constraints and Formulation of Charging and Discharging Control  
A proper energy management in SGs is necessary to maintain the supply-demand power balance. In this regard, the FLC has been implemented to the hybrid PV-battery system and is connected to the GA (see previous section), as shown in Figure 5. Briefly, the initial population (size of battery capacity and PV) is generated by the GA and then the generated population is forwarded to the model to calculate VD and TCO for one year. Note that the battery tries to reduce the VD and shift demand based on on/off peaks according to its size, while the TCO should be reduced according to the investment costs, purchased, sold electricity from/to the grid, and so forth.  
Moreover, the FLC defines four membership functions. Briefly, both the charging and discharging power rates are controlled and adapted by the FL-based controller, according to the state of voltage, battery SoC, and on/off peak demand. It means that the battery acts as a regulator to stabilize the voltage and decrease the TCO through peak shaving to reach the optimal size that is based on the boundaries and defined and detailed in the following subsections.
# Experimental Study of Explosion Mitigation by Deployed Metal Combined with Water Curtain
## 1 Introduction
Consequently, this blast mitigation method appears promising but still needs some further work and improvement. The porosity of the plate is an important factor to take into account in the blast mitigation performance and a water cover on the plate increases even more the blast attenuation. The plate should have a relatively low porosity. The use of a configuration with two plates could also represent an option for improvement. Indeed, it has been shown that shock wave trapping between two perforated plates enhanced the shock wave attenuation downstream from the grids [8]. In this paper, the assessment of this blast mitigation solution made of perforated plates with or without a water cover was further investigated in order to have a better understanding of its mechanism and to improve its performance. Grids made of expanded metal, with or without a water film, served as an obstacle. Expanded metal has an interesting geometry that enhanced the reflection of the shock wave, and a low porosity. First, an explosive-driven shock tube (EDST) was used to visualize the interaction of a blast wave profile with one or two plates made of expanded metal, using a retroreflective shadowgraph technique. Indeed, the propagation of blast waves in complex media is an important topic of shock wave research and there is a need to study wave phenomena in complex environments. EDSTs generates high dynamic loadings compared to conventional shock tubes. Their mitigation capacity could be assessed under a load comparable to that produced by several kilos of high explosives, located several meters from a target. Secondly, free-field air blast experiments were performed in order to evaluate the protection system under real conditions. Configurations with either one or two grids were investigated. The case in which a film of water was added on the grid, or on the first grid in the case of a two-grid configuration, was also studied. The transmitted pressure was measured on a wall placed behind the plate(s) and the shock wave reflection by the plate was assessed using a sensor located on the ground in front of the first grid.
## 2 Results and Discussion  
### Isolation of APs from Water-Soluble Protein Hydrolysate (RSH) of Red Stingray Cartilages  
#### 2.1.1 Fractionation of RSH by Ultrafiltration  
Bioactive peptides are inactive in the sequence of their parent proteins and they can be released by enzymatic hydrolysis either during gastrointestinal digestion in the body or during food processing [2].  
Bioactive peptides may act as regulatory compounds with diverse biological activities once they are liberated from protein sources by proteolysis, such as antioxidant, antihypertensive, and enhancing immunity activities [1]. Subsequently, water-soluble proteins of red stingray cartilages were hydrolyzed using trypsin in the experiment.  
Three fractions RSH-I (MW \(<\) 3 kDa), RSH-II (3 \(<\) MW \(<\) 10 kDa), and RSH-III (MW \(>\) 10 kDa) were prepared from protein hydrolysate (RSH) of red stingray cartilages using 3 and 10 kDa ultrafiltration membranes, and their radical scavenging activities are shown in Figure 1. The 2,2-diphenyl-1-picrylhydrazyl radical (DPPH\(\bullet\)) and HO\(\bullet\) scavenging rates of RSH-I were \(40.12\pm 1.57\%\) and \(51.43\pm 1.88\%\) at the concentration of 10 mg protein/mL, which were significantly (\(p<0.05\)) higher than those of RSH, RSH-II, and RSH-III at the same concentration. Short chain peptides easily access free radicals and act as electron donors to convert them into the stable state and inhibit the chain reactions [25; 28]. The results were in agreement with previous reports that fractions from protein hydrolysates with low molecular size had high antioxidant activities, such as fractions from protein hydrolysates of miuuy croaker muscle [9], cod muscle [29], skate cartilage [21], bluefin leatherjacket head [30], and blue mussel [2; 31]. Therefore, RSH-I was chosen for the preparation of APs with high activity.
## 2 Related Works
### Test Scheme  
The evolution of Intelligent Transportation System (ITS) research has traversed a pathway from experimentation, through evaluation, to commercialisation, mirroring the progression of test solutions: from simulation testing to Hardware-in-the-Loop (HIL) testing, Digital Twin (DT) testing, and, ultimately, field and large-scale testing.  
Simulation testing allows for cost-effective and efficient experimentation, yet it fails to fully capture real-world scenarios and individual behaviours. Furthermore, relying solely on simulation results in a disconnect between standard formulation and hardware device development [7]. The actual operation of devices or vehicles also lacks standardised verification methods. Consequently, HIL testing and DT testing have attracted significant attention. Compared to simulation testing, these two testing schemes incorporate factors from the actual physical world into the virtual realm, thereby accelerating the commercialisation of V2X applications.  
Even though these testing methods, which emphasise simulation, align closely with the physical world, adhering to the principle of safety first necessitates field testing. However, the field testing of V2X applications faces numerous challenges. For example, some functional tests cannot be supported in the field, and tests are generally not repeatable due to the randomness deriving from both the wireless environment and operators [8].  
The biggest challenge faced in field testing is the large-scale character of ITS, which involves numerous traffic participants. For instance, channel congestion can affect application response performance in high-vehicle-density situations. This means that testing V2X applications requires consideration of the communication background. However, arranging dense traffic flow tests in natural environments is time-consuming, laborious, and inefficient. Therefore, achieving large-scale field testing in terms of cost, scalability, and efficiency, thereby advancing the commercialisation of ICVs, is currently a focus of attention for academia and industry.
## 4 Results and Discussion
### Effect of Treatment Cycle on Disintegration  
To analyze the effect of the treatment cycle on the disintegration process, the disintegration curves of soil samples treated with different treatment cycles are plotted in Figure 15. At the given bacterial solution concentration (BS) and cementation solution concentration (CS), the disintegration amount of 1, 3, 5, and 7 treatment cycles can be then analyzed. Series A, Series B, Series C, and Series D correspond to the treatment cycles of 1, 3, 5, and 7 in Figure 15, respectively.  
Figure 14: Disintegration curve with seven treatment cycles of different bacterial solution concentrations: (**a**) 0.25, (**b**) 0.5, (**c**) 0.75, and (**d**) 1.0.  
Figure 15: _Cont._Figure 15: _Cont._  
At a given bacterial solution concentration and cementation solution concentration, the more cycles, the longer the disintegration time. The ability to resist disintegration improves with the increase in treatment cycles. There is no significant change in the disintegration characteristics of the sample after one cycle of treatment. The sample undergoes complete disintegration. After three cycles of treatment, the sample presents incomplete disintegration. The ability to suppress disintegration is further improved after five cycles of treatment. The formation of microorganism surface crusts becomes more apparent when increasing the number of treatments [39]. The samples of BS 0.75, CS0.8; BS0.75, CS1.2; BS1.0, CS1.2; BS1.0, and CS1.6 present non-disintegration. The resisting disintegration ability of the seven-cycles treatment has a smaller improvement compared to the five-cycle treatment. The reason for this is that after a five-cycle treatment, the sample shell forms a sufficient cover layer, making it difficult for the liquid to continue infiltrating. Five cycles of treatment can effectively complete the process of MICP reinforcement of granite residual soil. Meanwhile, the soil structure is not changed by spraying, and a hard crust is formed on the soil surface, making it more suitable for practical applications [40]. The mechanical stability and ability to resist erosion by heavy rainfall will be investigated in the future.
## 3 Experimental Results and Discussion
### Load and Deflection Relationship
#### 3.2.3 End Anchorage  
Figure 10 shows flexural performances of T2, T2E, T2A, and T2 AP according to the type of end anchorage. The yield and ultimate load of T2E were 40.69 kN and 42.29 kN, respectively, which were 17% and 16% higher than that of T2, respectively. The yield load of T2A was 40.19 kN, which was 15% higher than that of T2. The yield and ultimate load of T2 AP were 38.34 kN and 40.19 kN, respectively, which were 10% higher than that of T2. In the case of T2E, the load dropped two times after the ultimate load, unlike other TRM specimens. The first load drop was caused by the rupture of the outer filaments due to increase in the stress in the crack. In general, the slippage of textile inside the matrix occurred if there was no clamping device with sufficient compressive force such as an anchor [25]. The textile of T2E was fixed to both ends of the beam, hence the inner filaments in the textile were fixed by anchor and the load was once again increased slightly. The slippage of inner filaments had occurred before the ultimate load was reached but was limited by bonding of the end anchorage until the second load drop occurred. Therefore, T2E improved the load-bearing capacity more than the non-end anchorage specimens but did not completely prevent slippage. The T2A and T2 AP with bolt anchor prevented all slippage of the textile, and all the filaments of textile resisted the load simultaneously so that the stiffness and load-bearing capacity increased significantly.
## 3 Results and Discussion
### Detection of Protein Overexpression in Gels Using the IPBBIS Algorithm  
#### 3.3.1 Application of the New Intensity Profile on the Complete Gel Image  
The IPBBIS method was used to identify GPN protein overexpression in the gel shown in Figure 8A. In this Figure, lane 1 contains the ladder or molecular weight control. Lane 2 is the negative control, lane 3 is the positive control for GPN protein expression, lane 4 is the concentrated cell extract control, and lanes 5 to 15 represent extracts with the same amount of endogenous proteins to which different concentrations of recombinant protein have been added. The thickness of the spots indicates a higher concentration of GPN in lanes 5, 6, 11, 12, and 14, while lanes 9 and 10 show a lower concentration of the protein. These values were identified in the plot of the new image intensity profile (Figure 8B,C), where maximum peaks are observed in lanes 5, 6, 11, 12, and 14, and lower peaks correspond to the bands with lower concentrations of recombinant protein, i.e., lower overexpression (lanes 9 and 10). Image analysis was performed by equalizing the image (Figure 8B) and not equalizing it (Figure 8C).  
The data of the average values resulting from the multiple maxima of the graph corresponding to the different samples in Figure 8C are shown in Table 1. It can be corroborated that the maximum intensity value was obtained in lane 5, indicating the highest overexpression of GPN protein in that sample. The lowest expressions were recorded in lanes 9 and 10, with maximum values of 23 and 29, respectively. The maxima assigned to lanes 1 to 4 were not shown as they correspond to the ladder, controls for GPN, and GPN with total protein extract expression.  
Figure 7: A new image intensity profile was obtained by plotting the array’s values containing only the white pixels generated by the IPBBIS method.
## 3 Results and Discussion
The enthalpy of formation (\(\Delta\)H\({}_{\text{f}}\)) of the major crystalline phases As\({}_{2}\)Se\({}_{3}\) (-83.7 kJmol\({}^{-1}\)) [55] and PbSe (-96.3 kJmol\({}^{-1}\)) [56] measured via drop calorimetry at 298 K, also supports the proposed mechanistic evolution of species available to participate most easily (lowest activation energy barrier) for crystallization. Figure 6a depicts the change in hardness measured in both the droplet-free (DFPG) commercial melt and the PSPG lab melt glasses, with each iteration of heat treatment. At first, the hardness of the heat-treated lab melt GC increased consistently until 270 \({}^{\circ}\)C, 30 min, due to formation of both Pb-rich (major phase) and As\({}_{2}\)Se\({}_{3}\) (minor) crystallites. However, with extended heat-treatment beyond this time, only a small increase in hardness was observed. This can be explained by the presence of cubic Pb-rich crystallites as the principal secondary phase. The presence of a high concentration of large cubic crystallites can result in an increase in residual stresses, causing strength degradation in GCs. However, no evidence of stress-induced birefringence was ever observed in these GCs. The homogeneous droplet-free commercial melt on the other hand achieved a maximum hardness at a growth temperature of 270 \({}^{\circ}\)C for 60 min. For the same times and temperature, this corresponds to a hardness increase of \(\sim\)1.1x times that seen in the lab melt. However, with further treatment up to 150 min at 270 \({}^{\circ}\)C, only a slight increase (within one standard deviation) in the hardness was observed, and further hardness changes reached a plateau. This further substantiates our interpretation that constituents available to form (nucleate) new crystals may be depleted at this point and any further heat-treatment only results in existing crystal growth. The improved hardness in the commercial melt over that seen in the lab-melt can be attributed to the presence of a greater quantity of needle-shaped As\({}_{2}\)Se\({}_{3}\) crystallites as the major secondary phase in the GC. The needle-shaped crystallites on their own, and upon extensive growth into clusters, can form interlocking microstructure inside the glass matrix, therefore slowing and/or preventing the crack propagation. Figure 6b shows the change in [PERSON]'s modulus in the two samples post heat-treatment. The extended heat-treatment of the commercial melt also resulted in a \(\sim\)1.1x increase at 270 \({}^{\circ}\)C for 150 min. In both the commercial melt as well as the lab melt, a consistent increase in [PERSON]'s modulus was observed, likely due to the increase in volume fraction crystals generated, at the expense of the parent glass phase. Figure 6: (**a**) Illustrates the variation in Hardness (in both kgf/mm\({}^{2}\) and MPa), and (**b**) [PERSON]’s modulus (GPa) of commercial and lab melt, following identical heat-treatment protocol.
## 2 Overview of Blockchain Technology
### Blockchain Architecture
#### 2.1.2 Merkle Tree  
A trustworthy and immutable distributed ledger may be created using the Merkle tree idea [77]. Figure 3 illustrates the Merkle tree's structure.  
Figure 3 makes it clear that the bottom of the tree is made up of a group of transactions. Note that each operation's/transaction's hash digest falls within the first tier, while subsequent upper layers are formed by computing the hash value of two components of the preceding layer. Finally, the Merkle tree root contains a single hash value representing the content of all validated transactions [78]. Note that, even if a minute change occurs for one transaction, the block's hash code is different due to the modification of the base of the Merkle tree, such that the whole blockchain is affected by a single transaction modification. Since hash functions have the characteristic that a minor alteration to the input induces an enormous modification in the output, even for very small transaction modifications, the links in the blockchain are broken.
## 2 Background Study
### Slant Path in Rain Cell
#### 2.4.2 Effective Slant Path  
The effective path length is the length of a conceptual path derived from radio data by dividing overall attenuation by actual attenuation exceeded over the same percentage of the time. The total attenuation is determined as [35]:  
\[A(dB)=\gamma(dB/{\rm km})\times L_{eff}({\rm km}) \tag{9}\]  
where \(A(dB)\) is the total attenuation due to rain, and specific attenuation of the link is \(\gamma\), then the \"effective path length\" is defined by \(L_{eff}\). Table 4 shows a list of additional techniques to determine 'effective path length.