esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Osteoblast Adhesion and Proliferation on Vacuum Plasma-Treated Implant Surface
## 4 Discussion
Given the fact that there is no extra gas supplied to our plasma device, the main discharge gas is air. In consequence, plasma will contain oxygen-related species such as O\({}^{+}\), O\({}^{2+}\), O\({}^{-}\), and O\({}^{3-}\)[23; 39; 40; 41]. In a number of investigations related to plasma cleaning, researchers have demonstrated that carbon contaminants react with these oxygen-based species and become dissociated and reduced, releasing CO\({}_{2}\) and H\({}_{2}\)O [23; 40; 41]. Furthermore, continuous pumping to maintain a vacuum in the package removes these by-products immediately after they are released, eliminating the possibility of re-contamination. More importantly, all of these plasma-bioactivation effects were achieved without causing any damage to the implant's existing calcium coating or microstructure. This is crucial in applying plasma treatment to the calcium-coated implant because if the plasma treatment damages the calcium coating, the osseointegration efficiency may be impaired rather than improved. According to many previous studies, calcium coating on the implant is known to promote osseointegration [42; 43]. [PERSON] et al. (2004) have demonstrated that calcium coating on the implant surface increases the adsorption of protein and improves the adhesion and proliferation of cells [42]. They reported that the calcium coating positively charged the implant surface with Ca\({}^{2+}\) ions, which created a favorable environment for FN and Vitronectin (VN) adsorption, leading in increased osteoblasts attachment [42]. This favorable effect of calcium coating was also seen in our results comparing SLA and CaSLA. Further, from such a benefit of calcium coating, we found that plasma treatment on the calcium-coated surface increased protein adsorption, cell adhesion, cell proliferation, and cell differentiation. Collectively, the plasma treatment on the calcium-coated SLA surface bioactivates it by improving its hydrophilicity and removing carbon impurities. This plasma surface modification resultingly improved FN protein adsorption, osteoblast adhesion, osteoblast proliferation, and osteogenic differentiation. We expect that these changes eventually can enhance osseointegration.
# Singular Phenomenon Analysis of Wind-Driven Circulation System Based on Galerkin Low-Order Model
## 2 Galerkin Procedure  
Large-scale numerical simulation and analysis represent the most economical and effective approach to studying the infinite-dimensional dynamical systems composed of continuum media, which exhibit a rich variety of nonlinear behaviors. However, when simplifying such systems using classical linearization methods, some nonlinear behaviors may not be accurately captured [3]. The direct numerical integration method is commonly employed to investigate dissipative infinite-dimensional dynamical systems, particularly those of second order in time. Nevertheless, simulating the long-term behavior of these systems demands substantial computational resources [44]. Consequently, it is crucial to reduce the complexity of the system by decreasing the number of degrees of freedom.  
In reality, the long-term dynamical behaviors of an infinite-dimensional dissipative dynamical system are often attracted to an invariant manifold of finite dimension. By projecting the solution of the governing equations onto this invariant manifold, the system can be significantly simplified. Therefore, the Galerkin procedure is introduced as a method to approximate the solution of the system [45].  
Considering the nonlinear equation,  
\[f(u)=0\text{ on boundary }\Omega\text{ and }B(u)=0\text{ on boundary }\Gamma \tag{1}\]  
where \(u=u(x,t)\) is a mapping on \(R\to H\) and boundary \(\Gamma\) corresponds to region \(\Omega\), and \(f\in H\) is given. The eigenfunction set \(\{\omega_{k}\}\) of the linear operator with orthogonality is selected, so \(u\) can be expressed as,  
\[u\approx\bar{u}=\sum_{k=1}^{N}a_{k}\omega_{k} \tag{2}\]  
where \(a_{k}\) is an unknown variable. There will be residuals generated from the corresponding differential equation and boundary conditions, Equation (1).  
\[f(\bar{u})=f\left(\sum_{i=1}^{N}a_{k}\omega_{k}\right)=R;B(\bar{u})=B\left( \sum_{k=1}^{N}a_{k}\omega_{k}\right)=\bar{R} \tag{3}\]  
Considering the basis function set \(\{\omega_{k}\}\), the [PERSON] procedure requires the weighted integral of residual Equation (3) over \(\Gamma\) and \(\Omega\) to satisfy the relationship,  
\[\int_{\Omega}\omega_{j}^{T}f(\bar{u})d\Omega+\int_{\Gamma}\omega_{j}^{T}B( \bar{u})d\Gamma=0,j=1,2,\cdots,N \tag{4}\]  
Then, a series of simple algebraic equations can be obtained and variables \(a_{k}\) can be obtained simultaneously. Furthermore, the desired variable \(u\) can be obtained.
## 3 Results
### Binary Logistic Regression Models with Different Features  
Table 3 presents the variables and parameters of BLR models. All the variables in the three models had a significant level of less than 0.05. The BLR models are shown as formulas 10-12.  
\[P_{spectral}=\frac{1}{1+\mathrm{e}^{-(1.421 NDVI+116.997 PSRI)}} \tag{10}\]  
\[P_{texture}=\frac{1}{1+\mathrm{e}^{-(0.232 Man1-5.751 Entory2)}} \tag{11}\]  
\[P_{spectral\_texture}=\frac{1}{1+\mathrm{e}^{-(76.28 NDWI-221.94 RDVI+1.313 Man1+6. 47 Enterropy2)}} \tag{12}\]  
The classification accuracies of the BLR models with the sample set are shown in Table 4. For the whole data set, Precision, Accuracy, Recall, and F-score were approximately equal in value and had the same trend among the three models, indicating that the classification sample distribution was balanced, and the accuracy rate could be used to evaluate the classification accuracy of each model. The Precision, Accuracy, Recall, and F-score of the three models exceeded 95% for the training set. The Precision was higher for the test set, while the Accuracy, Recall, and F-score were lower. Additionally, the Recall was less than 0.9 in the spectral and textural models for the test set. The classification accuracy of the spectral-texture model was slightly higher than that of the texture model or the spectral model.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 4 Mechanism of Microbial Decontamination
### Photo-Thermal Effect  
The photo-thermal effects of light are shown by wavelengths corresponding to IR regions. Thermal effects of PL may add to inactivation of microorganisms if final temperature reaches pasteurization temperature. Although higher temperature by intense PL treatment might add to inactivation, it has been shown that the food sensory qualities are adversely affected. It has been previously emphasized in several studies that light pulses heat the surface layers of foods. The temperature of surface layer increases, which can destroy superficial pathogenic organisms. The heat can also penetrate inside of the food by conduction, but as mentioned, due to a short process the heat gets dissipated before it reaches the bulk. Nevertheless, a study by [PERSON] [29] showed that light pulses may be absorbed into _A. niger_ cells when the molds heat instantaneously, which might lead to cell death. [PERSON] et al. [30] reported the possibility that light pulses to heat the food surfaces to destroy inhabiting bacteria. Changes in the structure and physiology of the cells might be the reason of cell death, what could be called photo-thermal stress. [PERSON] et al. [31] showed that regional overheating of bacteria due to PL exposure may cause damage and rupture leading to an 'explosion' of cellular contents. They showed scanning electron-microscope (SEM) images of PL-treated _A. niger_ spores, visualizing the release of overheated contents of spores. This happens due to the differential heating rate of cellular components leading to regional expansion and bursting of cell. [PERSON] et al. [50] demonstrated that in PL treatment of _Bacillus subtilis_, the cell wall got disrupted due to that photo-thermal stress. [PERSON] and [PERSON] [51] showed that, on PL treatment of _E. coli_, there were structural changes, possibly due to 'overheating, intercellular water vaporization and subsequent membrane disruption'. However, the studies showed the heat generation due to light pulses should only be a lesser contributing factor towards microbial inactivation, as compared to photochemical effects.
## 3 Seismic Response Spectra in Mexico
### Constant Acceleration Spectrum According to MDOC Sismo CFE 2015  
According to Table 3, the structures of the B2 subgroup include small and ordinary buildings, for which the budget is limited. Therefore, performing a detailed soil study of the site with a modal-spectral analysis may be economically nonviable. Hence, a constant acceleration spectrum procedure is selected, where the calculated spectral acceleration has a constant value for any structural period, proving that the structure vibration modes are not required and the acceleration will have a conservative result.  
The constant acceleration spectrum is obtained with the following equation:  
\[S_{a}=c\beta \tag{1}\]  
where \(S_{a}\) is the spectral acceleration (cm/s\({}^{2}\)), \(c\) is the maximum spectral acceleration (cm/s\({}^{2}\)), and \(\beta\) is the damping factor (dimensionless). The value of \(c\) is obtained according the following equation:  
\[c=F_{sit}F_{res}a_{0r} \tag{2}\]  
where \(F_{sit}\) is the site factor, \(F_{res}\) is the response factor, and \(a_{0r}\) is the maximum acceleration in the rock layer according to the reference spectrum (ER), which is obtained directly from PRODISIS [4]. The factors \(F_{sit}\) and \(F_{res}\) correspond to the dynamic amplifications due to the soil physical conditions at the site, with values shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Seismic Zone** & \(\mathbf{F_{sit}}\) & \(\mathbf{F_{res}}\) \\ \hline A & 3.0 & 4.2 \\ B & 3.0 & 4.2 \\ C & 2.7 & 3.9 \\ D & 2.3 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Site and response factors for the B2 subgroup structures, reproduced with permission from [PERSON], Comision Federal de Electricidad Manual de Diseno de Obras Civiles; published by Comision Federal de Electricidad (CFE), 2015 [1].  
Figure 3: Seismic spectra results obtained from PRODISIS software for the city of Puebla, Mexico.  
The damping factor \(\beta\) is obtained using the following equation:  
\[\beta=\left(\frac{0.05}{\xi_{e}}\right)^{0.45} \tag{3}\]  
where \(\xi_{e}\) is the structural damping ratio. According to the previous equation, if the damping ratio used is \(\xi_{e}=0.05\), then the damping factor is \(\beta=1\).  
According to the procedure, the spectral acceleration, \(S_{a}\), shows a single value, since the described equations do not require the fundamental vibration periods, \(T_{e}\), avoiding any modal analysis on the structure. Therefore, the graph \(S_{a}\) vs. \(T_{e}\) is not required.
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 2 Drill Floor Design and Risk Assessment for Gas Explosion  
### General Information and Design Procedure for Drill Floor  
The drill floor is generally located in the middle of a drillship or drill rig due to minimize motion during the drilling operation. It primarily consists of a main deck supported on four cruciform columns and eight supporting diagonal box girders. Another type consists of four rigid box girder columns to support the drill floor. The decks of drill ships and drill rigs support the columns of the drill floor and are reinforced to resist the force during operation. On the drill floor module, there is various equipment such as a rotary table, eight matched pairs of riser tensioner, drawworks, winches, derrick, BOP sea fixing, X-mas crane, etc. The mud module stores and provides mud to the drilling systems, and risers are stacked in the riser deck. Several tests of oil and gas reservoir are carried out during the drilling operation in a well test area. Figure 1 shows the general arrangement of the drilling unit explained above.  
Table 1 presents the drill floor weight and the weight distribution in analysis, respectively. This weight is assumed based on a previously conducted project data. The weight of the drill floor is adopted in -finite element (FE) analysis with factorization to meet the total weight.  
For structural analysis, FE modeling mass (1372 ton) of drill floor is adjusted to actual mass (4000 ton) by multiplying the gravity factor (2.915).  
Figure 2 shows the general drill floor design flow chart. The overall dimension of the drill floor is determined by the drilling capacity and the general arrangement of the drilling facility. High tensile strength steel is preferred for use in the drill floor structure since it sustains heavy drilling load and equipment. The design temperature is also an important factor for choosing the material grade. Once the dimension and material are determined, the detailed geometry is created, and FE modeling is performed. Assessment of the design load involves many loadings on the drill floor. They include a combination of permanent, variable, functional, environmental, and accidental loads. The structural analysis is carried out by linear and non-linear analyses. These analysis outputs are investigated to meet the drill floor design criteria. If it meets the criteria, the design process is terminated, otherwise some improvement in the steel structure design such as reinforcement is to be considered.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Discipline** & **Weight (ton)** \\ \hline Machinery & 1500 \\ Structure & 2000 \\ Steel outfitting & 150 \\ Outfitting machinery & 10 \\ Electrical & 70 \\ Architectural & 10 \\ HVAC \({}^{*}\) & 10 \\ Piping & 250 \\ \hline Total & 4000 \\ \hline \hline \end{tabular} \({}^{*}\) HVAC: heating, ventilation, and air conditioning unit.  
\end{table}
Table 1: Assumed drill floor weight in drilling operation.  
Figure 1: Overview of drillship and drill floor.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 3 Data Collection and Processing
### Data Filtering Process  
During the data collection process by onboard sensors, the external environment is complex and variable, with many irrelevant factors causing interference. These interferences can contaminate the collected data, resulting in redundant and erroneous points. Although these interferences may seem minor, they can significantly impact subsequent modeling work, sometimes leading to substantial deviations between the model's predictions and actual values. Therefore, to obtain more accurate natural driving data and construct precise models, it is necessary to filter the data to eliminate noise pollution. To efficiently handle outliers in naturalistic driving data from vehicle sensors, an adaptive mean-standard deviation filter is used for noise attenuation. This filter dynamically adjusts the size of the data filtering window, utilizing the Z-score of the data within the window to identify outliers. Based on this, it then smooths these anomalous values to ensure data integrity. The Z-score is defined as:  
\[Z=\frac{|x-\overline{x}|}{s} \tag{2}\]  
where \(x\) is the value of the sample point, \(\overline{x}\) is the mean, and \(s\) is the standard deviation of the data. The mean \(\overline{x}\) and standard deviation \(s\) are defined as:  
\[\overline{x}=\frac{\sum\limits_{i=1}^{n}x_{i}}{n} \tag{3}\]  
\[s=\sqrt{\frac{\sum\limits_{i=1}^{n}(x_{i}-\overline{x})^{2}}{n-1}} \tag{4}\]  
where \(x_{i}\) represents the value of the \(i\) sample point within the window, and \(n\) is the size of the window.
# Travel-Time Inversion Method of Converted Shear Waves Using RayInvr Algorithm
## 3 Types and Applications of Converted S-Waves Phases  
In the OBS surveys, only the incident P-wave can reach the seafloor and propagate for the seismic waves that are generated by firing an air-gun in seawater. The S-wave phases recorded by the OBS are converted from the P-wave at different subsurface interfaces, such as the seafloor, sedimentary basement, and crust-mantle boundary [4; 9; 22]. The energy of converted S-waves depends on the angle of incidence [23]. The same parameters of filtering and reduction velocity were used to plot seismic profiles of the vertical and radial components. The converted S-wave phases were determined by comparing the kinematic (e.g., travel times, apparent velocity) and dynamic features (e.g., particle motion paths) of arrival phases in the vertical and radial components [4]. Compared to the vertical component, the converted S-wave phases in the radial component have lower apparent velocities and slower travel times (Figure 4a-d). The particle motion paths of the 710 th trace in the OBS09 vertical component (Figure 4e) show that the energy of the vertical component (V) is strongest at 2.88-3.48 s, suggesting that particle motion was dominated by P-waves. The energy at 4.66-5.26 s in the radial component (R) is the largest (Figure 4f), denoting that this particle motion was dominated by S-waves. Two modes of S-wave phases [16] generally observed on OBSs are PPS and PSS phases (Figure 5). The apparent velocity of PPS arrivals is similar to corresponding P-waves, whereas the apparent velocity of PSS arrivals is much slower (Figure 6).  
Figure 4: **(a)** Vertical and **(b)** radial component seismic profiles of OBS 09, reduced by 6 km/s. **(c)** and **(d)** show the waveform of the 710 th trace in the vertical and radial component profiles, respectively. **(e,f)** correspond to the particle motion paths of the 710 th trace in the vertical component (2.88–3.48 s) and radial component (4.66–5.26 s), respectively.
## 2 Materials and Methods
### Assessment of HM Contamination, Ecological and Health Hazards  
HM contamination in soil was assessed with a geo-accumulation index, I\({}_{\text{geo}}\)[15], developed based on a reference value. \[\text{I}_{\text{geo}}=\log_{2}\biggl{(}\frac{C_{\text{m}}}{1.5C_{\text{r}}} \biggr{)} \tag{1}\]  
where C\({}_{\text{m}}\) is the measured concentration of the element in soil samples and C\({}_{\text{r}}\) is the reference value of the element. Contamination degree based on I\({}_{\text{geo}}\) values was classified into the following categories: uncontaminated (I\({}_{\text{geo}}\) < 0), uncontaminated to moderately contaminated (0 \(\leq\) I\({}_{\text{geo}}\) < 1), moderately contaminated (1 \(\leq\) I\({}_{\text{geo}}\) < 2), moderately to heavily contaminated (2 \(\leq\) I\({}_{\text{geo}}\) < 3), heavily contaminated (3 \(\leq\) I\({}_{\text{geo}}\) < 4), heavily to extremely contaminated (4 \(\leq\) I\({}_{\text{geo}}\) < 5), and extremely contaminated (I\({}_{\text{geo}}\) \(\geq\) 5; [15]). The ecological risks of the investigated HMs in the surface soil were assessed with the potential ecological risk index (PERI; [16]). \[\text{C}_{\text{r}}^{\text{i}}=\frac{\text{C}_{\text{s}}^{\text{i}}}{\text{C} _{\text{n}}^{\text{i}}}, \tag{2}\]  
\[\text{E}_{\text{r}}^{\text{i}}=\text{C}_{\text{r}}^{\text{i}}.\text{T}_{\text {r}}^{\text{i}}, \tag{3}\]  
\[\text{PERI}=\sum\text{E}_{\text{r}}^{\text{i}}, \tag{4}\]  
where C\({}_{\text{r}}^{\text{i}}\) is the pollution factor of a single element in soil, C\({}_{\text{s}}^{\text{i}}\) is the measured concentration of a single element in soil, C\({}_{\text{n}}^{\text{i}}\) is the background concentration of a single element in soil, E\({}_{\text{r}}^{\text{i}}\) is the potential ecological risk of a single element, and T\({}_{\text{r}}^{\text{i}}\) is the toxic response factor for a single element. The reference value of each element was considered the background. The T\({}_{\text{r}}^{\text{i}}\) values for Cu, As, Pb, and Zn were derived from [17] and for Mo from [18]. The potential ecological risk of a single element based on the E\({}_{\text{r}}^{\text{i}}\) values was classified into the following categories: low risk (E\({}_{\text{r}}^{\text{i}}\) < 40), moderate risk (40 \(\leq\) E\({}_{\text{r}}^{\text{i}}\) < 80), considerable risk (80 \(\leq\) E\({}_{\text{r}}^{\text{i}}\) < 160), high risk (160 \(\leq\) E\({}_{\text{r}}^{\text{i}}\) < 320), and very high risk (E\({}_{\text{r}}^{\text{i}}\) \(\geq\) 320; [16]). The overall potential ecological risk based on the PERI values was classified according to [16] as follows: low risk (PERI < 150), moderate risk (150 \(\leq\) PERI < 300), considerable risk (300 \(\leq\) PERI < 600), and very high risk (PERI \(\geq\) 600). Human health risks of HMs in surface soil were examined with the risk assessment methodology adopted from the USDOE (2011) [19] and USEPA (2011) [20].
## 2 Materials and Methods
### Sample Characterization
#### 2.2.2 Laser Damage Testing  
The laser-induced damage threshold (LIDT) measurement was performed at the laser damage test bench located at the RhySearch optical coating laboratory. Herewith the cleaned bare substrates and the subsequently coated AR samples were laser damage tested. The laser irradiation at 1064 nm was performed using a diode-pumped Nd:YAG laser system. The effective pulse duration was about 10 ns, and the repetition rate was 100 Hz. In accordance with ISO norm 21,254, the Gaussian beam profile's effective beam diameter was above 200 \(\upmu\)m (1/e2). An S-on-1 measurement strategy [15] was applied whereby S = 5000. The centered test area was 20 mm in diameter and divided into a test matrix. According to the beam diameter and the ISO norm, each test site's distances were larger than 5 times  
Figure 1: Measured reflectivity of the AR-coated fused silica test sample (AOI = 7\({}^{\circ}\)).  
the beam diameter. A set of three samples per cleaning process were laser damage tested under an angle of incidence of 5\({}^{\circ}\). The measurement was performed in filtered air and at room temperature. The detection of damage events was done by a scattering detector that was placed in front of the test sample. To verify the detected damage events, visual inspection by means of differential interference contrast DIC-microscopy was performed.