esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Transmission Fluids in Electrified Transmissions: Compatibility with Elastomers
## 3 Results and Discussion
### Changes in Volume  
Figure 1 shows the volume variations expressed as the ratio of the volume variation to the initial volume (as a percentage). Negative values indicate a reduction in volume after aging, while positive values indicate an increase in volume. The compatibility of the elastomers with the ATFs regarding volume change was initially tested after 168 h, as specified in standard ASTM D7216-15 [14], but effects of longer aging times are also reported in Figure 1. The dashed horizontal black lines indicate the range of volume variation accepted for each material by the manufacturer's specifications [9]. In the case of FKM, these dashed lines indicate the maximum swelling and shrinking accepted. For silicone, the upper line indicates the maximum accepted variation, and the lower line indicates the maximum recommended limit. In EPDM, there are no recommended values in the literature, so there are no acceptance values indicated in the figure. Silicone showed a volume variation lower than the maximum limit values recommended by the transmissions manufacturers [9]. On the other hand, the FKM constitutes a special case, as the volume of the specimens was reduced their after aging whereas, according to the results in Drobny [17], they were expected to swell. Although the Allison TES 439 specification [9] accepts up to a 2% volume decrease for this material, the FKM samples shrank between 2.5 and 3.5%, which is out of specification. The EPDM underwent a considerable increase in volume, the maximum values being obtained with oil A. Although no volume change specification exists for this elastomer, the values here reported lead to the conclusion that these oils are not compatible with this material.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aging Time (h)** & **FKM** & **EPDM** & **Silicone** \\ \hline
168 & 0.020 & 0.025 & 0.025 \\
336 & 0.015 & 0.025 & 0.030 \\
504 & 0.015 & 0.025 & 0.030 \\
672 & 0.015 & 0.025 & 0.030 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Traction speed (in m/s) by material and aging time.  
Regarding the long-term evolution of the volume variation (from 168 h onwards), the silicone samples hardly changed during the entire period of aging, with volume variations of around \(\pm\)1%. The EPDM increased in volume after aging in all of the ATFs used from 168 h onwards. However, it remained nearly constant after 336 h in oils B, C, and D. The highest volume change values were caused by oil A. EPDM increased in volume in this oil up to an aging time of 504 h, but after that time, it remained constant until the end of the test. The FKM showed a reduction in volume in all the ATFs, with values nearly constant after 336 h and outside all the specifications (Allison TES 439 has the lowest admissible volume change limit: \(-\)2%). So, the tested oils are not compatible with FKM in terms of volume change. Considering the available specifications for the volume changes of elastomers aged in ATFs, it could be concluded that only the silicone fulfils the volume change requirements.
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 3 Experiment and Results
### Detection Results
#### 3.4.1 Detection Results of the Denim Dataset  
The detection results by RetinaNet, Mask R-CNN, GA-Faster R-CNN and PRAN-Net are shown in Table 3. It can be seen that the inspection speed of RetinaNet is slightly faster than that of PRAN-Net, but the detection accuracy is much lower. Compared with Mask R-CNN, the ACC and the mAP of PRAN-Net have improved 2.5% and 3.9%, respectively, and the inspection speed is greatly improved. Compared with GA-Faster R-CNN, PRAN-Net also improves detection and position accuracy, while its detection speed is also faster than that of GA-Faster R-CNN.  
The detection results by PRAN-Net for the denim dataset are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**The Denim Dataset**} \\ \cline{2-6}  & **ACC (\%)** & **mAP (\%)** & **AR (\%)** & **IoUs (\%)** & **FPS (f/s)** \\ \hline RetinaNet & 84.7 & 53.7 & 46.7 & 63.8 & 10.2 \\ Mask R-CNN & 89.4 & 58.4 & 51 & 69.8 & 0.3 \\ GA-Faster & 87.1 & 60.2 & 50.9 & 71.4 & 7.2 \\ R-CNN & 91.9 & 62.3 & 53.3 & 72.9 & 9.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detection results of the four methods with the denim dataset.  
Figure 10: The training loss value curves and accuracy curve of PRAN-Net for the denim dataset. (**a**) the total loss value curve, (**b**) the priori anchor loss value curve, (**c**) the classification network loss value curve and (**d**) the accuracy curve of the test set.  
To better present the location accuracy of defects by PRAN-Net, IoU examples of the detected defect and the GT boxes by RetinaNet, GA-Faster R-CNN and PRAN-Net are shown in Figure 12. (Mask R-CNN was not considered because its detection speed was too slow for online inspection.) The blue rectangles are the GT boxes of the defects, the yellow rectangles are the detected location corresponding to the defects and the green intersection regions are the IoU of the GT boxes with the detected defects of three methods, which are 55.1% for RetinaNet, 72.4% for GA-Faster R-CNN and 80.6% for PRAN-Net. Their IoU values are consistent with the tendency of IoUs as shown in Table 3, which are 63.8% for RetinaNet, 71.4% for GA-Faster R-CNN and 72.9% for PRAN-Net, respectively. All these indicate the better location precision of PRAN-Net for extreme shape defects.
## 5 Results
### Experimental Analysis
#### 5.2.2 Experiment 2: Comparing the Speeds of the Different Enhancements
It can be seen that, in the case of larger files (with the size of gigabytes), there was no improvement in the compression rate of the examined methods. Bzip2 and PPMd have the best rates, with 95.823% and 94.543%, respectively, while our algorithm falls slightly behind them (approximately 1%). The proposed algorithm also outperforms LZMA with 1.75%.
## 6 Discussion  
LiDAR is an active measurement technique that can be applied in day or night conditions. The range accuracy depends on the measuring distance and the intensity of the reflected beam. The light reflectance ratio of the measuring surface affects the maximum measuring distance. The higher reflectance enables measurement from a further distance away. For laser measuring instruments, it is an important reason for preference that surfaces with low reflectivity can be measured from longer distances. This provides an important advantage in detecting black vehicles and other surfaces from longer distances, especially in mobile mapping and navigation. LiDAR point cloud is collected from mobile or static platforms. Multi-beam scanners and LiDAR cameras can be used in mobile mapping. In laser scanning, the scan point density can be adjusted, while in a LiDAR camera, the point density is fixed and only changes according to the measuring distance. The 3D position accuracy of the scan points is largely related to the divergence angle, backscattered intensity, and the object interaction angle of the laser beams.
# Influence of Cryogenic Grinding on the Nutritional and Antinutritional Components of Rapeseed Cake
###### Abstract  
We investigated the influence of cryogenic grinding on the quality of rapeseed cake. Rapeseed cake is a good source of valuable proteins (30%) and oil (14%), with a balanced fatty acid composition and a fair amount of sterols, which may reduce the risk of cardiovascular diseases. However, the presence of antimutrittive compounds prevents its use as a food source. Grinding under cryogenic conditions is much more efficient than grinding at room temperature in terms of particle size reduction. The additional cryogenic grinding of the cake had little effect on the nutritional components, as phytosterols and soluble dietary fiber increased slightly. It had no effect on insoluble dietary fiber, polyphenols, and tannins. Prolonged grinding time, both at room and subzero temperatures, reduced the total amount of glucosinolates by 34 and 43%, respectively. However, the reduction in undesirable components is not sufficient to use cryogenic grinding as the sole treatment for the cake, but it could be a good pretreatment for chemical or biological treatments.  
rapeseed cake; cryogenic grinding; dietary fibre; polyphenols; glucosinolates
## 5 Experiments and Results
### Topology Test  
Chain-typed network has two categories: single-chain and multi-chain. In single-chain structure, if a node in sleeping or in failure status has a significant effect on data transmission of others. Multi-chain topology, just the simple replication of single-chain, has the same challenge of chain breaking. The sensor nodes can be classified into two classes under the double-chain topology, namely, the end node and the sink node. The double-chain topology helps to reduce cost on batteries on the end nodes because this type of nodes operates under sleep mode for most of the time. The end nodes acquire data and send them to the sink node and finally to the base station. As an example, a sensing network with 9 nodes is illustrated in Figure 8. The sensing network is made up of eight end nodes and one sink node. In normal situation, the pathway of data collection is arranged as N1-N2-N3-N4-S or N5-N6-N7-N8-S. Concerned with possible failure of some node link due to unexpected reasons, an alternative pathway for each node is added, shown in dash lines in Figure 8. For instance, if node 3 fails, node 2 will automatically transfer its data to node 7; meanwhile, node 2 receives the data from node 1 as well as ones from node 5. Based on the double-chain topology, a symmetrical double-chain topology is proposed in our study to enhance the lifetime and reliability of data transmission. Through the symmetrical double-chain topology, wireless data links in a sensing cluster can be enhanced greatly.  
The topology of sensing cluster can be designed as star, tree, chain or other irregular styles. Start-typed network consisting of one core node and peripheral nodes has high reliable data links. But due to limit radio power in a single node, its network coverage is usually small so that more star-typed subnets are cascaded to extend network range. Tree and chain topology both are suitable for our  
Figure 8: The double-chain topology.  
Figure 7: Average packet loss rate.  
situation, we take account the lifetime of different topologies by using ESPIN to further test. So we set another two monitoring groups with ESPIN in the same farmland to study the lifetime of symmetrical double-chain topology in comparison with tree topology. Figures 9 and 10 show the topology of two groups in detail.  
The practical test result is shown as Figure 11 and we can see that the SDC topology can support the our monitoring system for more than 11 months continuously, which is about 2 months longer than tree topology. The energy consumption of each node with SDC topology is also a bit less than tree topology in general. The reason that the network of both two topology collapsed is the cells of N1 nodes which directly connected with sink node ran out early.  
Figure 11: The battery consumption.  
Figure 10: Tree topology.  
Figure 9: Symmetrical double-chain topology.
## 3 Results and Discussion
### Accumulation and Translocation of PTEs
Up to this point, 450 plant species have been classified as hyperaccumulators. The most common accumulated heavy metal is Ni and the least common are Mn, Cd, and Pb [56]. The use of plant residues after phytoremediation may be problematic due to the presence of PTEs. However, a trace level of some PTEs is necessary for the proper functioning of microbial metabolism because many of them, such as Cu, Mo, Fe, Ni, etc., are responsible for proper enzymatic function [57]. The presence of PTEs in higher concentrations can negatively affect processes of anaerobic metabolism [57; 58]. [PERSON] et al. [58] suggested that acid-forming bacteria are the most affected. The effect of PTEs on the production of methane depends on the type and amount of the metal. The inhibitory effect of Ni\({}^{2+}\) on methane production was recorded only in concentrations above 30 mg/L, whereas the highest cumulative methane production was observed when the Ni\({}^{2+}\) concentration was 4 mg/L [59]. The present study shows low effectiveness in the translocation of Ni to the shoot tissue (TF = 0.755), and even lower effectiveness in the accumulation of this heavy metal in tissues (BCF = 0.041; BAC = 0.003). Similarly, low Cu concentrations (5 mg/L) had a stimulatory effect on methanogenesis. On the other hand, with the rising concentration of Cu, the methane production was decreasing [60]. [PERSON] et al. [60] found that a half maximum inhibitory concentration (IC50) for Cu was 18.32 mg/L. Similarly, a Cu concentration of 13.5 mg/L caused reduced the methane production by 50% [61]. Different results were obtained when copper was added to the bioreactor with the residues of plants used for phytoremediation. A Cu concentration of 100 mg/kg not only promoted anaerobic digestion and required a shorter anaerobic digestion time, but also increased the methane content in biogas. Biogas production decreased when Cu in a concentration of 500 mg/kg was added [62]. Zinc is a microelement which is essential for many enzymes involved in anaerobic reactions [57]. This is why Zn has a common stimulatory effect on biomethane production [55]. [PERSON] et al. [55] evaluated the effect of Zn supplementation on biogas production. The authors found that the concentration of Zn in the fermenter increased from 50 to 100 mg Zn\({}^{2+}\)/L and positively affected methane yield. The total yield of methane increased by 30-65%.
## 6 Real Data Applications
### COVID\(-\)19 Patients, Dataset 3  
The NGLog-Weib distribution was evaluated on the COVID-19 patients' dataset. The key measures were: minimum = 1.516, 1 st quartile = 2.788, mean = 3.282, median = 3.178, 3 rd quartile = 3.637, maximum = 6.868, skewness = 3.384, kurtosis = 16.559, var = 93,286.41, and range = 1763.8. Figure 8 depicts the histogram, kernel density, TTT plot, violin plot, and box plot for the COVID-19 patients' dataset 3. Similarly, the values of the parameters, i.e., \(\hat{\beta}_{MLE}\), \(\hat{\alpha}_{MLE}\), \(\hat{\delta}_{MLE}\), \(\hat{\delta}_{MLE}\), \(\hat{b}_{MLE}\), and \(\hat{\theta}_{MLE}\) of the NGLog-Weib model with the other competing probability distributions are displayed in Table 12, whereas the GFMs with \(p\)-values and DMs for selecting the best distribution of the competing distributions are recorded in Tables 13 and 14. Furthermore, the graphical representation of the estimated PDF, CDF, SF, PP, and QQ plots of the NGLog-Weib distribution is shown in Figure 9.  
Figure 8: For dataset 3, the (**a**) histogram, (**b**) kernel density plot, (**c**) TTT plot, (**d**) violin plot, and (**e**) box plot.  
The results presented in Tables 13 and 14 display that the NGLog-Weib distribution has the least GFM and DM values and highest \(p\)-values, as compared to the GFM and DM values and \(p\)-values of the other distributions. For the proposed NGLog-Weib distribution, the AMs and \(p\)-values are: CM = 0.092, AD = 0.536, KS = 0.104, AIC = 102.028, BIC = 106.778, CAIC = 102.778, HQIC = 103.686, and \(p\)-value = 0.813, respectively. In terms of the CM, AD, KS, and \(p\)-value, the second most optimum distribution is the Kumar-Weib model. For the Kumar-Weib model, the values of the CM, AD, KS, and \(p\)-value are 0.093, 0.539, 0.106, and 0.795, respectively. Similarly, in terms of the AIC, BIC, CAIC, and HQIC, the second-best distribution is the APTra-Weib distribution because it's AIC, BIC, CAIC, and HQIC values are 103.616, 108.366, 104.366, and 105.274, respectively.  
Based on the justification of the numerical description in Tables 13 and 14, the visual graphical illustration in Figure 9, and the above comprehensive discussion, we can observe that the NGLog-Weib model is a good competitor among the other compared distributions for the COVID-19 dataset (Data 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **CM** & **AD** & **KS** & \(p\)**-Values** \\ \hline NGLog–Weib & 0.092 & 0.536 & 0.106 & 0.813 \\ \hline APTra–Weib & 0.103 & 0.582 & 0.123 & 0.649 \\ \hline NExpo–Weib & 0.137 & 0.794 & 0.144 & 0.446 \\ \hline MO–Weib & 0.182 & 1.035 & 0.141 & 0.474 \\ \hline FRL–[PERSON] & 0.163 & 0.937 & 0.148 & 0.411 \\ \hline Kumar–[PERSON] & 0.093 & 0.539 & 0.106 & 0.795 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The GCMs and \(p\)-values of the competing models for COVID-19 data (dataset 3).
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 7 Concluding Remarks
Multi-hazard probabilistic framework for reliability assessment of offshore wind turbines is relatively well-established. Monte Carlo simulation-based frameworks for multi-hazard fragility assessment are recently emerging. For land-based wind turbines, several studies have highlighted the role of aerodynamic damping in response to combined action of wind and earthquakes. Most of the published work on vibration control of wind turbines focuses on structural fragility of the supporting tower. Offshore wind turbines subjected to wind and waves is the most investigated scenario. The most reported control device is passive TMD placed on the nacelle, although use of TMD on the platform of a barge-type floating turbine has also been reported to be effective. Most of the studies conclude that control devices are effective in reducing multi-hazard fragility. Recent advances in multi-hazard control of wind turbines include interesting innovations such as braced viscous dampers, and semi-active control systems. Effect of ground motion variability on control performance is an area that needs to be studied better. Control performance against impulsive loads caused by, for example, near-fault ground motions (see, for example, [PERSON] et al. [186]; [PERSON] et al. [187]; [PERSON] et al. [188], [PERSON] et al. [189]) also need to be investigated better. In addition, fragility of rotor blades and effects of drivetrain dynamics need more attention. In most of structural vibration control studies reported in the literature, the structure is assumed to remain elastic, which may not be realistic in extreme loading conditions. Inelastic deformations of the structure can result in de-tuning of the control device resulting in lower performance. Control optimization and performance assessment of yielding structures subjected to multi-hazard scenarios, as well as damage accumulation due to multiple hazards occurring over the useful life of a structure, need to be investigated and better understood to facilitate practical applications of control systems in actual engineering projects. M.J.: Conceptualization, research, literature study, writing, and review; R.R. : methodology, writing, and review; S.E. : review; B.B. : writing, and review; J.T.S. : writing and review. All authors have read and agreed to the published version of the manuscript. [PERSON] is supported by a doctoral grant from the University of Iceland. RR acknowledges support from the University of Iceland Research Fund.
## 1 Introduction  
Plants and plant-based foods are rich in antioxidants, such as polyphenic compounds, well-known for their beneficial effects on human health, due to their antioxidant, cardioprotective,anticancer, anti-inflammatory, and antimicrobial properties [1; 2; 3; 4; 5; 6; 7]. In such a context, ginger represents a really challenging bioresource.  
_Zingiber officinal_ (ginger) is a rhizome that has been used in traditional medicine to treat different diseases like colds, bronchitis, cough, vomiting, and nausea [8; 9; 10]. Its composition includes components of phenolic (6-gingerol and its related compounds like shogaol) and terpene types (\(\alpha\)-zingiberene, \(\beta\)-bisabolene), which have antioxidant properties [11; 12; 13]. Such properties are of interest from a technological and nutritional point of view. For this reason, the extraction of this kind of compounds from natural sources has encouraged researchers to look for more innovative strategies to maximize the extraction rates from different plant materials due to their promising impact in improving health. To evaluate the use of ginger as a natural source of antioxidants, several studies have reported the use of methods involving different solvents for extraction such as ethanol, methanol, methanol-water, and water, as well as wide range of temperatures and extraction times [14; 15]. Among the analyzed variables, temperature is the physical factor that influenced the most in the release of antioxidant compounds, regardless the time of extraction and the quantity of the sample [16]. Recently, emerging technologies such as ultrasound have been successfully used as an alternative method for the extraction of thermosensitive bioactive compounds from plant and fruits [17; 18; 19]; nevertheless, there are few studies related to the application of this method for the extraction of antioxidant compounds from ginger in aqueous media.  
Ultrasound-assisted extraction (UAE), compared with other techniques, offers some advantages such as significant reduction of extraction time and the use of solvents. This is because the ultrasound has a mechanical effect that allows a better penetration of the solvent into the matrix, expanding the surface of contact between the sample and the liquid phase [20; 21; 22]. In recent years, ultrasound has also been used in the extraction of phenolic compounds aiming to preserve its antioxidant activity, avoiding the effect that high temperatures may have when applying conventional solid-liquid extraction methods (infusion, maceration, Soxhlet, etc.) [23; 24; 25; 26; 27]. Therefore, the aim of this work was to evaluate the influence of ultrasound-assisted extraction technique on the release of antioxidant compounds from ginger in aqueous media as well as their in vitro bioaccessibility. For that purpose, a central composite rotatable design (CCRD) was used to obtain the optimal physical conditions for antioxidant compounds extraction from ginger in aqueous media.
## 2 Materials and Methods
### Analytical Methods  
The quantification of reducing sugars was achieved through a colorimetric assay based on the redox reaction between reducing sugars and sodium dinitrosalicylate, yielding a chromogenic product exhibiting maximum absorbance at 530 nm, measured by UV-Vis spectrophotometer [18]. Protein concentration was determined utilizing the Bradford assay, which relies on the spectral shift of Coomassieie Brilliant Blue G-250 upon protein binding, measured at 595 nm [19]. Cellulose content was assessed spectrophotometrically following sequential extraction with an acetic-nitric reagent to eliminate interfering compounds, dissolution in 67% sulfuric acid, and subsequent reaction with anthrone. The resulting chromophore's absorbance was quantified at 620 nm [20]. Total solids (TS) and volatile solids (VS) were determined gravimetrically [21]. To mitigate volatilization losses during ashing due to high organic content, a controlled, gradual temperature ramp was employed to ensure slow oxidative decomposition. The heating, annealing, and desiccation procedures were iterated until successive mass measurements exhibited negligible variation. Biogas production was measured volumetrically using a water displacement gas holder and graduated cylinder. Methane and carbon dioxide concentrations were determined using a Drager X-am7000 gas analyzer, (Oslo, Norway) which employs infrared sensors for volumetric percentage measurement. Hydrogen and carbon dioxide concentrations were determined using a Gasboard 3100P gas analyzer, also utilizing infrared sensors for volumetric percentage measurement. Gas yields are based on total mass.