esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 4 Thermal-Structural Coupling Mathematical Model of the Subsea Connector
### Three-Dimensional Stress Caused by Internal Pressure Load
#### 4.3.3 Thermal-Structural Coupling Stress  
In order to obtain the stress caused by the combined action of multiple loads, it is necessary to couple various stresses. There are two conditions for stress superposition: one is that the system can be described as a linear second-order differential equation; the other is that the effects of various factors on the system cannot cause nonlinear phenomena [22]. The structural stress and thermal stress of the lenticular sealing gasket are both within the elastic range and can be expressed by linear second-order differential equations. With the condition for using the superposition principle, the coupling stress caused by various loads, i.e., radial stress \(\sigma_{r}\), circumferential stress \(\sigma_{\theta}\), and axial stress \(\sigma_{z}\), can be obtained by applying the superposition principle:  
\[\left\{\begin{array}{l}\sigma_{r}=\sigma_{r}^{\tau}+\sigma_{r}^{p}+\sigma_{ \theta}^{p}=\frac{\alpha^{*}E_{2}}{1-\mu_{2}}\frac{\tau_{i}-\tau_{r}}{2\ln( \tau_{r}/t_{i})}\left[-\ln\frac{\tau_{r}}{r}+\frac{r_{2}^{2}}{r_{2}^{2}-r_{r}^ {2}}\left(\frac{\tau_{r}^{2}}{r^{2}}-1\right)\ln\frac{\tau_{r}}{t_{i}}\right] \\ \quad+\frac{p_{r}^{2}}{r_{2}^{2}-r_{r}^{2}}-\frac{p_{r}^{2}\tau_{r}^{2}}{r_{2}^ {2}-r_{r}^{2}}\frac{1}{r^{2}}-aq_{\max}\cos^{2}\alpha\frac{1}{\sqrt{a^{2}+z_ {0}^{2}}}\\ \quad-aq_{\max}\sin 2\alpha\left[z_{0}-\frac{z_{0}^{2}}{\sqrt{a^{2}-z_{0}^{2}}} \right]-a^{-1}q_{\max}\sin^{2}\alpha\left[-2z_{0}+\frac{a^{2}+2z_{0}^{2}}{ \sqrt{a^{2}+z_{0}^{2}}}\right]\\ \sigma_{\theta}=\sigma_{r}^{\tau}+\sigma_{\theta}^{p}+\sigma_{\theta}^{p}= \frac{\alpha^{*}E_{2}}{1-\mu_{2}}\frac{\tau_{i}-\tau_{r}}{2\ln(\tau_{r}/t_{i} )}\left[1-\ln\frac{\tau_{r}}{r_{i}^{2}-r_{r}^{2}}\left(\frac{\tau_{r}^{0}}{r ^{2}-r_{r}^{2}}+1\right)\ln\frac{\tau_{r}}{t_{i}}\right]\\ \quad+\frac{p_{r}^{2}}{r_{2}^{2}-r_{r}^{2}}+\frac{p_{r}^{2}\tau_{r}^{2}}{r_{2} ^{2}-r_{r}^{2}}\frac{1}{r^{2}}-aq_{\max}\sin^{2}\alpha\frac{1}{\sqrt{a^{2}+z _{0}^{2}}}\\ \quad+aq_{\max}\sin 2\alpha\left[z_{0}-\frac{z_{0}^{2}}{\sqrt{a^{2}-z_{0}^{2}}} \right]-a^{-1}q_{\max}\cos^{2}\alpha\left[-2z_{0}+\frac{a^{2}+2z_{0}^{2}}{ \sqrt{a^{2}+z_{0}^{2}}}\right]\\ \sigma_{z}=\sigma_{z}^{\tau}+\sigma_{z}^{p}+\sigma_{z}^{\sigma}=\frac{\alpha^{ *}E_{2}}{1-\mu_{2}}\frac{\tau_{i}-\tau_{r}}{2\ln(\tau_{r}/t_{i})}\left[1-2\ln \frac{\tau_{r}}{r}-\frac{2\tau_{r}^{2}}{r_{2}^{2}-r_{r}^{2}}\ln\frac{\tau_{r}}{ r_{i}}\right]+\frac{p_{r}^{2}}{r_{i}^{2}-r_{r}^{2}}-\frac{2\mu_{2}q_{\max}}{a} \left[\sqrt{a^{2}+z_{0}^{2}}-z_{0}\right]\end{array}\right.\right. \tag{78}\]
## 3 Results and Discussion
### Cluster Analysis and Principal Components Analysis  
Cluster analysis is a statistical technique aimed at classifying wines based on their similarities. To this end, the parameters for such classification must be established. This study used aroma series and colorimetric parameters. As can be observed in Figure 5, vermicompost wines are clearly differentiated from compost and control wines. As per these two, the distance in between seems to indicate that there are no remarkable differences. This type of analysis is only descriptive and provides evidence of the variations among the different treatments.  
To delve into the variables responsible for the differentiation of the wines, a principal component analysis was carried out. Two main components were obtained that explained more than 94% of the variability observed (Figure 6).  
The first accounts for 65.4% and relates to the differentiation of vermicompost wine and the rest of the wines. The second relates to the differentiation between compost and control wines and accounts for 27% of the observed variability. In this sense, total  
Figure 5: Cluster analysis according to the Ward method using the aroma series and the colorimetric parameters of the wines as classifying variables.  
Figure 6: Principal component analysis using the aroma series and the colorimetric parameters of the wines as the classifying variables.  
polyphenol index, fruity, herbal, and waxy series significantly contribute to positive values of component 1, whereas absorbance 420 nm and creamy series contribute to the negative values of this component (Table 7). Regarding component 2, the series that contributed to control wine the most were chemistry and fatty, whereas citrus and green, in addition to absorbance at 520 nm, contributed to differentiating compost wine (Table 7).
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture
### Layer 3: Gated Recurrent Unit Model (GRU)  
The output of the RNN model is given to the next layer of the proposed architecture. At this layer, the GRU model is implemented. RNN faces exploding and vanishing gradient problems during backpropagation. GRU overcomes these problems using its gates. GRU is an RNN model with less memory and is faster than LSTM when the data have longer sequences. GRU is slower in training than ANN because GRU cell has more parameters than ANN cell. To address the gradient descent problem, GRUs employ the update gate and reset gate. Reset gates can recall previous knowledge learned and control what information is sent to the output. The model uses the update gate to determine how much information from the past should be transmitted to the future [31]. As the model can decide to replicate all the knowledge from the past, it eliminates the risk of the vanishing gradient problem, making this a very powerful model. As a first step, we used the following equation to get the updated gate state \(z_{t}\) for the time step \(t\):  
\[z_{t}=\sigma(W^{(z)}x_{t}+U^{(z)}h_{t-1}) \tag{1}\]  
The weight \(W_{(z)}\) of the network and the unit is applied to the input \(x_{t}\) when it is entered into the network. The same can be said for \(h_{t-1}\), which, after being multiplied by its weight \(U_{(z)}\), stores information about the time before it (\(t-1\)). The two sets of findings are combined and summed up, and then a sigmoid activation function is used to squeeze the total value into the range of 0 to 1. The purpose of the forget gate in the model is to determine how much of the information from the past should be ignored. The following equation was used to calculate the required information. This same formula describes the update gate, but the gate is used in a different way and with varying weights:  
\[r_{t}=\sigma(W^{(r)}x_{t}+U^{(r)}h_{t-1}) \tag{2}\]
## 3 Results and Discussion
### Investigation of Vorticity at Maximum Power Coefficient  
At high angles of attack and low TSR, VAWTs undergo dynamic stalls, leading to complex flow structures around the blades. This phenomenon involves large, separated flow regions, vortex shedding, and potential blade vortex interactions. Despite the challenging aerodynamics, VAWTs can still generate power at low TSR due to these flow characteristics. Understanding and accurately modelling these flow phenomena are crucial for optimizing VAWT design and performance under dynamic stall conditions.  
Vorticity behind the blades and turbine is a significant factor, performing at maximum power and torque coefficient, to enable the examination of the influence of different turbulence models. As seen in Figures 14-16, the vorticity measurements while the wind passes around the turbine and blades raise the value of vorticity around the blade as it rotates and entry to the path of the other blade flowing in the wind tunnel.  
Figure 13: Turbulence Intensity (%) around VAWT in different turbulence models for TSR 1.6 and \(\theta=180^{\circ}\).  
Figure 16: Vorticity coloured by field Velocity for LES-Smagorinsky turbulence models for TSR 1.2.  
Figure 14: Vorticity coloured by field Velocity for LES-WALE turbulence model for TSR 1.2.  
Figure 15: Vorticity coloured by field Velocity for RANS-Spalart-Allmaras turbulence models for TSR 1.2.  
One of the basic schemes, vorticity coloured by field velocity, was used to observe the wake structures in the flow field. It is beneficial when compared with RANS, LES turbulence models reveal existing wake structures and velocities behind the VAWT. Furthermore, LES models, WALE and Smagorinsky, demonstrate individually the different wake structures even if they are in the same class. The visualization in Figures 14-16 depicts the turbulent structures, showcasing distinct vortex shapes formed behind the rotating blades under varying turbulence models (WALE, Smagorinsky, Spalart-Allmaras). These figures provide insights into the characteristic features of the vortices when the wind originates from the left side. In the figures, when the TSR is 1.2, the WALE turbulence model shows long vorticity intensity compared to the RANS-Spalart Allmaras and Smagorinsky turbulence models. Thus, it was clear that LES WALE turbulence models enable us to observe the wake predictions behind the VAWT.
# Evaluation of Mechanical Properties of Recycled Material for Utilization in Asphalt Mixtures
## 3 Experimental Work
### Laboratory Tests
#### 3.2.2 Particle Shape Test  
The results of the studies on aggregate have shown that the aggregate physical shape properties significantly affect both the strength and stability of asphalt mixes [54]. Therefore, in order to design asphalt mixtures with long service lives, the aggregate must have the proper gradation and shape. The particle shape of aggregate substantially influences the mechanical stability of asphalt mix. The presence of excessive flaky and elongated particles is undesirable in asphalt mixtures as they tend to break down during the production and construction, and thus affect the durability of HMAs. Therefore, it is preferable to have rough and angular aggregates rather than smooth and round aggregates.  
In this study, the proportion of misshapen aggregates, including the flat particles, elongated particles and, flat and elongated particles found in coarse aggregate is evaluated through the Particle Shape Test (Figure 4). The particle shape test is carried out by proportional caliper, using a 2:1 calliper ratio and based on AS 1141.14 (2007). The results of this test on three samples for each aggregate type (i.e., RCA, RAP and basalt) and the average value are given in Table 2.  
As presented in Table 2, basalt materials show more of misshapen particles than RAP and RCA while still below the 35% limit of the Australian standard.
## 3 Unique Contributions of Ka-Band Information in AltiKa Data
### Relationship between Surface Soil Moisture and Radar Altimetry Backscattering Coefficients
In spite of the low temporal resolution of the SARAL/AltiKa mission with its repeat-period of 35 days, altimetry-derived SSM at Ka-band would be useful for the monitoring of the seasonal variations of this key variable along the altimeter tracks at high spatial resolution (\(\sim\)165 m using the high-frequency 40 Hz data) and could be used for the calibration and validation of land-surface models and low resolution active (scatterometry) and passive (radiometry) microwave-based products. The characteristics of the backscattering coefficient at Ka-band illustrated in this study is also used to study the snow and ice state and important scientific results are given in [3].
## 4 Materials and Methods
### Solid Phase Microextraction (SPME) Fibers and Gas Chromatography-Mass Spectrometry (GC-MS) System  
The SPME device, including a manual holder and four different types of fiber coatings (carbowax-polyethylene glycol (PEG) 60 \(\upmu\)m, polyacrylate (PA) 85 \(\upmu\)m, divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m, and carboxen/polydimethylsiloxane (CAR/PDMS) 75 \(\upmu\)m) were obtained from Supelco (Sigma-Aldrich). Fibers were conditioned in the GC injector as suggested by the supplier.  
The GC-MS system was a Finnigan TRACE GC ultra-gas chromatograph (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a split/splitless injector, interfaced to an ion trap MS (Finnigan Polaris Q, Thermo Fisher Scientific). The capillary column was a Supelco SPB-5 fused silica (30 m, 0.25 \(\upmu\)m i.d., 0.25 \(\upmu\)m film thickness) with helium (purity \(>\) 98%, Rivoira, Bari, Italy) as carrier gas (flow rate 1 mL/min).  
The temperature of the transfer line was 220 \({}^{\circ}\)C, while the injector (splitless mode) was kept at 200 \({}^{\circ}\)C (headspace solid phase microextraction, HS-SPME) and 250 \({}^{\circ}\)C (direct immersion solid phase microextraction, DI-SPME). The oven temperature program was: 40 \({}^{\circ}\)C held for 5 min; ramp 1: 12 \({}^{\circ}\)C/min from 40 \({}^{\circ}\)C to 160 \({}^{\circ}\)C; 160 \({}^{\circ}\)C held for 1 min; ramp 2: 5 \({}^{\circ}\)C/min from 160 \({}^{\circ}\)C to 200 \({}^{\circ}\)C; 200 \({}^{\circ}\)C held for 5 min; ramp 3: 10 \({}^{\circ}\)C/min from 200 \({}^{\circ}\)C to 220 \({}^{\circ}\)C; 220 \({}^{\circ}\)C held for 1 min. The mass spectrometer was operated in the electron impact positive ion mode (EI+) with the ion source temperature set at 250 \({}^{\circ}\)C. The electron energy was 70 eV and the filament current 150 \(\upmu\)A. Total ion current (TIC, \(m/z\) range 30-300) or selected ion monitoring (SIM, the \(m/z\) ions useful for the determination are listed in Table 2) acquisition modes were used. The National Institute of Standards and Technology (NIST) library allowed us to identify the revealed molecules.
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 2 Materials and Methods
### Chitosan Coating Preparation and Treatments  
Treatments included (1) UVA, Gavita(r) UVR LED UVA (Gavita North America Hawthorne, Vancouver, WA, USA), with the irradiation wavelength between 320 nm to 400 nm and the fluence rate of 20 W/m\({}^{2}\) during storage; (2) 1% chitosan coating, listed as 'Chito' in this study; (3) 1% chitosan coating combined with UVA treatment, listed as 'UVA-Chito' in this study; and (4) the control/diH\({}_{2}\)O (deionized water) treatment, in which uncoated samples were treated with deionized water as the control.  
As the chitosan treatment, 1% chitosan (Cellar Science, Pittsburg, CA, USA) was mixed with 0.3% (\(w/v\)) glycerol-water. Glycerol (99.5%) was purchased from Sigma-Aldrich (Sigma-Aldrich, Burlington, MA, USA). The coating solution was stirred well at 300 rpm with a magnetic stirrer (VEVOR, Rancho Cucamonga, CA, USA). The solution was stored in a 4 \({}^{\circ}\)C refrigerator and used within two days.  
Fresh blackcurrants were completely immersed in the chitosan coating solution for 1 min before being air-dried at room temperature for about 3 h. Afterward, spread single layer coated blackcurrants were put onto trays under UVA light ('UVA-Chito' treatment)/darkness (chitosan treatment) for storage quality assessment. The control treatment was processed in a similar way to the chitosan treatment process; instead of using chitosan solution for the immersion of blackcurrants, deionized water was used.
## 3 Results
### First Experiment  
The first experiment examined the efficiency of the above-mentioned data particle creation methods in the process of imbalanced data sets classification, in which a linear decision boundary does not exist, the centroids are close to each other and objects belonging to various classes overlap in a feature space. As a preliminary point, the target numbers of data particles were determined, which may be the basis for the GD and UGD methods in the classification process of individual imbalance data sets having aforesaid features. Taking into consideration that the examined data sets have two classes, and the GD method conducts the division of each data particle in a single iteration, the established values were powers of two and they are presented in Table 3.  
The results expressed by precision, recall, and F-measure and obtained by 1 CT1P, GD and UGD on the individual imbalanced Moons and Circles data sets are presented in Table 4.  
Analysing the data presented in Table 4, it can be observed that the Unequal Geometrical Divide method reaches the highest F-measure values in 9 of the 16 imbalanced Moons and Circles data sets. Furthermore, it gains the mean result of F-measure, which within the entire experiment equals F-measure = 0.633. The Geometrical Divide method obtains the best results on the seven remaining data sets and it reaches the mean value of F-measure = 0.642. It can be seen as well that the 1 CT1P approach obtains the lowest results of F-measure on all analysed data sets and it reaches the mean value F-measure = 0.214. Taking into consideration the characteristics of data, it can be remarked that each time the Unequal Geometrical Divide method gives the best results on the data sets in which the objects belonging to other classes overlap in a feature space (names with postfix NU). Based on values of precision obtained by individual approaches, it can be observed that the UGD obtained the highest precision on all data sets. It should be emphasized that on four data sets the results of that method were identical as those obtained by using the GD approach. Analysis of recall values allows to observe that the Geometrical Divide method achieved the highest results of this measure on the all data sets. In the second part of the first experiment, the sensitivity to change the F-measure (p) function value was examined, depending on the change of the applied number of data particles. With this purpose, the methods were parametrised a few times. Given that the examining data sets have two classes, and the GD method conducts the division of each class, in the first iteration the number of data particles was equal to the value of the exponential function with base = 2 and the exponent x = 2.
## 3 Results and Discussion
### Proximate Composition  
_D. viridis_ TAV01 biochemical characterization is presented in Table 2. The results obtained were compared with other _Dunaliella_ strains. Ash content (23.7 g 100 g\({}^{-1}\) DW) was similar to that of _D. bardawil_ and may be related to the high salt content of the medium [45]. Total dietary fibre (TDF) content (9.06 g 100 g\({}^{-1}\) DW) was higher than other _Dunaliella_ species but lower than species from other genera, such as _Haematococcus pluvialis_, _Arthospira platensis_, or _Chlorella vulgaris_: 58.52, 42.82, 35.04 g 100 g\({}^{-1}\) DW, respectively. Comparing with other species, _D. viridis_ TAV01 showed the highest protein and lipid contents (35.75 and 21.31 g 100 g\({}^{-1}\) DW, respectively) (Table 2). These results may be related to the cultivation conditions, since medium salinity can influence the proximate composition of microalgae [46]. Overall, _Dunaliella_ species have high protein contents in relation to those of lipids and carbohydrates, as shown by other studies in which _Dunaliella_ microalgae displayed protein contents of 36.2% [14], or _Microchloropsis gaditana_ with 33.2 g 100 g\({}^{-1}\) DW. _Porphyridium cruentum_, however, exhibited significantly lower protein levels (19.6 g 100 g\({}^{-1}\) DW) [47]. Instead of a rigid cell wall, _Dunaliella_ cells are known to have a glycocalyx-type structure, which is fragile and easily breakable; this may increase the bioavailability of the biomass nutrients as they would be more accessible for human absorption [48]. TAV01 presented a higher lipid content (21.3%) than other _Dunaliella_ spp. (3.49-18.02%). This could be related to the cultivation conditions applied in this study, as several authors reported that high salinity significantly induced lipid production compared to normal seawater salinity (\(\approx\)0.3 M) [14]. This conclusion is corroborated by results shown by [PERSON] and [PERSON] [21], who obtained higher lipid contents for _D. salina_ grown under salt stress (3 M NaCl) [21]. Furthermore, an increase in NaCl concentration from 0.5 to 1 M allowed for increased intracellular lipids and triacylglycerglycerol concentration in _Dunaliella tertiolecta_[23]. _Dunaliella_ spp. are known to be halotolerant and capable of easily adapting to high salinity, leading to increased contents of some biocompounds such as fatty acids and lutein, that can be used as nutrients in a response to abiotic stress, which is vital for the survival under such detrimental growth conditions [23].  
Figure 3: Comparison of ITS2 secondary structures of _Dunaliella viridis_ TAV01 **(A)**, showing a 99% similarity with that of _Dunaliella viridis_ (ID: AF313419.1; **(B)**) and _Dunaliella salina_ (ID: EU932917; **(C)**).  
The mechanisms involved in saline stress are complex and affect different metabolic and physiological pathways. Furthermore, the lack of a rigid cell wall in these microalgae might facilitate the maintenance of the cell shape and the osmotic balance, allowing an early response and the development of strategies to adapt to high salt stress, which is typical of _D. viridis_ and _D. salina_ cells [52; 53; 54].
## 2 Materials and Methods
### Multivariate Data Analysis
#### 2.6.2 Data Partition  
A total of 208 samples were collected to ensure an appropriate range of TWC values. The dataset was manually divided into a training set consisting of 172 samples (83%) and a test set consisting of 36 samples (17%). The statistical values of TWC in the training and the test set for this study are shown in Table 2.  
The PLSR models were built with the training data using a full cross-validation method (leave-one-out), in which one validation set was removed at one time from the training set until all samples have been removed once. The PLSR model was then established based on the remaining samples of the training set. It should be noted that six tissue samples were taken from each kidney. The samples from one kidney were selected together as one validation set from the training set, as these samples are not independent of each other. The number of LVs and the accuracy of PLSR models in training were estimated by the root-mean-square error resulted from cross-validation (RMSECV). Finally, this established model was used to predict the TWC in the independent test set and evaluate the performance of the algorithm. A good PLSR prediction model is characterized by a high coefficient of determination in prediction (\(\text{R}\text{P}^{2}\)) and a low root-mean-square error of prediction (RMSEP).