esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Stress-Dependent Water and Oil Saturation  
[PERSON] and [PERSON] provide a semi-empirical model for determining the unsaturated hydraulic conductivity [66]. According to this method, the fluid saturation (\(S\)) of the phases can be expressed as:  
\[S_{o}=\frac{(S_{oi}-S_{ru})}{(1-S_{ru}-S_{rw})} \tag{15}\]  
\[S_{w}=\frac{(S_{wi}-S_{rw})}{(1-S_{ru}-S_{rw})} \tag{16}\]  
where the subscripts \(i\) and \(r\) represent initial and residual, respectively.  
Figure 9 depicts the stress-dependent water saturation (\(S_{w}\)) variations at the fracture-matrix interface for various injected water volumes (\(V_{w}\)) under different stress-loading conditions. As the injected water volume increases, water saturation also increases [67; 68]. This trend is observed for all different stress-loading conditions considered in this study. \(S_{w}=0.155\) at 6.9 MPa stress loading for \(V_{w}=0.1\) (or 10%). \(S_{w}\) increases to 0.166, 0.178, 0.191, 0.211, and  
Figure 8: Fracture permeability variations under different stress loadings.  
0.234 as the stress loading increases to 9 MPa, 11 MPa, 13.1 MPa, 15.2 MPa, and 17.2 MPa, respectively. The same trends have been observed in the case of other water injection values considered here. However, it is worth noting that after \(V_{w}\) = 0.5, the rate of increase in water saturation starts to decrease. This is less prominent at lower stress-loading values and more at higher stress-loading values. The reason for this is evident from the figure, i.e., as \(V_{w}\) increases, \(S_{w}\) increases, approaching \(S_{w}\) = 1, which is the maximum water saturation possible in the rock matrix. As \(S_{w}\) approaches a value of 1, the rate of increase in \(S_{w}\) decreases, and this happens at higher \(V_{w}\) and stress-loading values. Thus, beyond a certain \(S_{w}\) value, further injecting water may not significantly enhance oil recovery.
## 3 Results
### Profiles of Volatile Compounds Generated by Different FAs
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Compounds**} & \multicolumn{3}{c}{**Concentration of Volatile Compounds (\(\mathrm{\SIUnitSymbolMicro g}\)/L)**} \\ \cline{3-4}  & & **Blank** & **C16.0** & **C20.5 + LOX** \\ \hline
1 & Quinoline, 1,2-dihydro & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 1.28 \(\pm\) 0.24 b \\
2 & Decane, 2-methyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 4.39 \(\pm\) 0.32 b \\
3 & Decane, 4-methyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 13.76 \(\pm\) 1.03 b \\
4 & Cyclohexane, butyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 24.07 \(\pm\) 1.21 b \\
5 & Dodecane & 22.15 \(\pm\) 0.81 c & 6.72 \(\pm\) 0.40 a & 9.83 \(\pm\) 0.40 b \\
6 & Tetradecane & 5.10 \(\pm\) 0.08 c & 1.90 \(\pm\) 0.02 a & 2.35 \(\pm\) 0.19 b \\
7 & Undecane & 5.10 \(\pm\) 0.07 a & 59.16 \(\pm\) 2.46 b & 81.16 \(\pm\) 11.25 c \\
8 & Undecane, 2,6-dimethyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 4.53 \(\pm\) 0.23 b \\
9 & Cyclohexane, pentyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 9.71 \(\pm\) 1.08 b \\
10 & Benzene, 1,2,4,5-tetramethyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 5.71 \(\pm\) 0.24 b \\
11 & p-Xylene & 0.00 \(\pm\) 0.00 a & 11.67 \(\pm\) 0.73 b & 20.56 \(\pm\) 1.63 c \\
12 & Toluene & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 15.45 \(\pm\) 2.65 b \\
13 & Mesitylene & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 23.41 \(\pm\) 0.58 b \\
14 & o-Xylene & 0.00 \(\pm\) 0.00 a & 179.16 \(\pm\) 5.23 c & 161.18 \(\pm\) 6.58 b \\
15 & Benzene, 1-ethyl-2-methyl- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 17.02 \(\pm\) 2.32 b \\
16 & Benzene, 1-methyl-2-(1-methylmethyl)- & 0.00 \(\pm\) 0.00 a & 4.39 \(\pm\) 0.24 b & 0.00 \(\pm\) 0.00 a \\
17 & Ethylene & 0.00 \(\pm\) 0.00 a & 11.67 \(\pm\) 1.07 b & 14.78 \(\pm\) 0.20 c \\
18 & Benzene, (1-methylethyl)- & 0.00 \(\pm\) 0.00 a & 0.00 \(\pm\) 0.00 a & 2.68 \(\pm\) 0.39 b \\
19 & 5,8,11,14-Eicosataretaenoic acid,ethyl ester, (all-\(Z\))- & 0.00 \(\pm\) 0.00 a & 6.52 \(\pm\) 0.10 b & 0.00 \(\pm\) 0.00 a \\
20 & Decanoic acid, methyl ester & 0.00 \(\pm\) 0.00 a & 45.29 \(\pm\) 1.01 b & 50.07 \(\pm\) 1.09 c \\
21 & Octanoic acid, methyl ester & 0.00 \(\pm\) 0.00 a & 16.71 \(\pm\) 0.43 a & 17.55 \(\pm\) 1.95 a \\ \hline \end{tabular}
\end{table} 
Table 7: Changes in the concentrations of volatile compounds produced from C20:5.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
###### Abstract  
Imaging through wavy air-water surface suffers from uneven geometric distortions and motion blur due to surface fluctuations. Structural information of distorted underwater images is needed to correct this in some cases, such as submarine cable inspecting. This paper presents a new structural information restoration method for underwater image sequences using an image registration algorithm. At first, to give higher priority to structural edge information, a reference frame is reconstructed from the sequence frames by a combination of lucky patches chosen and the guided filter. Then an iterative robust registration algorithm is applied to remove the severe distortions by registering frames against the reference frame, and the registration is guided towards the sharper boundary to ensure the integrity of edges. The experiment results show that the method exhibits improvement in sharpness and contrast, especially in some structural information such as text. Furthermore, the proposed edge-first registration strategy has faster iteration velocity and convergence speed compared with other registration strategies.  
image restoration; guided filter; underwater images; image registration +
Footnote †: journal: Medical Imaging  
**2020**, _10_, 5670; doi:10.3390/app10165670
## 1 Current Role of CCTA in Clinical Practice and Guidelines
### CCTA in Previous Coronary Revascularization  
The role of CCTA is still debated in patients previously revascularized with stents. The effectiveness of CCTA \(\geq\)64 slices for diagnosing coronary in-stent restenosis (ISR) and the effect of specific procedural aspects on diagnostic accuracy were examined in a recent meta-analysis that included 35 trials and 4131 stents [35]. This meta-analysis showed that CCTA could provide precise information about ISR lesions and that assessing these lesions could help identify changes over time. This is particularly important because patients with ISR may finally be suitable for non-invasive angiographic follow-up using CCTA. Nevertheless, in this context, CCTA has only been observed to have a high sensitivity in a few instances. For example, in stents with a diameter of \(>\)3 mm compared to those with lower diameters (94% vs. 89%), for stents with metal struts \(<\) 100 \(\upmu\)m compared to thickening stents (96% vs. 84%), and for simple stents compared to stents implanted on bifurcations (95% vs. 88%) [19].  
On the other hand, the role of CCTA in the evaluation of coronary artery bypass grafts (CABG) is more defined. A recent meta-analysis including 1975 patients and 5364 grafts revealed the excellent diagnostic performance of CCTA in the identification of graft stenosis or occlusion compared with invasive coronary angiography, with a sensitivity of 96%, a specificity of 96%, and a negative predictive value of 99% [36]. CCTA could also be used to assess the disease progression of native nongrafted vessels in patients with recurrent angina after CABG. Although these segments are often massively calcified and challenging to assess with CCTA, several studies, such as that of [PERSON] et al., showed acceptable diagnostic accuracy for the detection of new coronary stenosis, with sensitivity and specificity of 83-100% and 77-100%, respectively [37].
## 4 Estimation of Impact Angle  
If a train derails from the rail and simply moves in a straight direction from the derailment location shown in Figure 7, it will move a distance of L and collide with the DCP at impact location 1. At this time, the impact angle between the DCP and the vehicle  
Figure 5: Collision in the Olson model.  
Figure 6: Olson model applied for the DCP Type 1.  
can be estimated as \(\theta_{1}\). The impact angle of the curved track can be calculated Equation (3) as follows.  
\[\alpha=tan^{-1}(\frac{R}{\sqrt{\left(R+d\right)^{2}-R^{2}}})=>\theta_{1}(Impact \Angle)=90-\alpha \tag{3}\]  
Here, \(R\) is the track radius, and \(d\) is the distance between the DCP and the wheel surface.  
Impact location 2 in Figure 7 is an impact location that takes into account the additional angle \(\beta\) determined by the curve radius, cant, speed, etc. Assuming that the centrifugal acceleration is constant, the constant acceleration can be obtained by \(v_{c}^{2}=2a_{c}d\to v_{c}=\sqrt{2a_{c}d}\), and \(a_{c}=\frac{v^{2}}{R}\). At this time, the additional angle \(\beta\) can be obtained by \(\beta=tan^{-1}\big{(}\frac{v_{c}}{v}\big{)}\) and the impact angle of the impact location 2 can be estimated using the sine rule as in Equation (4). This condition could become the worst-case impact on the DCP because the full cant deficiency and a frictionless wheel-track are assumed.  
\[\gamma=sin^{-1}(\frac{R}{R+d}\text{sin}(\beta+90))=>\theta_{2}(Impact\Angle)=90-\gamma \tag{4}\]  
The impact angle can be calculated using the previous process. Theoretically, it is possible to calculate the impact angle considering cant, etc., but in this study, the cant was not considered to calculate the most severe impact force by applying the full cant deficiency condition. Table 1 is a table that calculated collision angles using Equations (3) and (4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Curve Radius** & **Wheel Distance** & **Cant** & **Impact Angle \(\theta_{1}\) (\({}^{\circ}\))** & **Impact Angle \(\theta_{2}\) (\({}^{\circ}\))** \\
**(m)** & **from DCP (m)** & **(mm)** & **(\(\beta=0\))** & **(\(\beta\
eq 0\))** \\ \hline R400 & & & 2.640 & 3.732 \\ R1600 & & & 1.320 & 1.867 \\ R2400 & & & 1.078 &
# Anticipatory Troubleshooting
## 3 Troubleshooting with Survival Analysis
### Integrating Survival Functions to Improve Diagnosis
In general, we limit our discussion in this paper to the single-fault case. In Section 8 we discuss how to extend our approach to multiple-fault scenarios. Now, consider the case where the car does not start (\(C=False\)) and the water level is low (\(W=Low\)). Given this evidence, we apply Bayesian reasoning to get the likelihood of each component to be faulty. The likelihoods obtained for \(lg\), \(B\), and \(R\) to be faulty are 0.16, 0.33, and 0.52, respectively. Thus, an intelligent troubleshooter would sense \(R\) first. Now, assume that the ages of the ignition (\(lg\)), battery (\(B\)), and radiator (\(R\)) are 3, 12, and 5 months, respectively, and that they all follow an exponential survival curve \(e^{-0.09\cdot t}\). Thus, based on the components' ages and the survival curves the probabilities of \(lg\), \(B\), and \(R\) being faulty are 0.24, 0.66, and 0.36, respectively. By adjusting the priors for \(lg\), \(B\), and \(R\) using Equation (1) and the given survival curves, we get the priors shown in Figure 2. Setting these priors dramatically affects the result of the Bayesian inference, where now the probabilities of \(lg\), \(B\), and \(R\) to be faulty are 0.16, 0.56, and 0.28, respectively. As a result, a troubleshooter that is aware of both BN and survival curves would choose to sense the battery (\(B\)) rather than the radiator (\(R\)). Figure 2: The BN for our car diagnosis running example. In Section 6, we demonstrate experimentally the benefit of integrating the survival functions in the BN inference process, showing significant gain.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Multi-Step Speckle Tracking Settings
The error for the velocity angle estimates was calculated as the absolute error, i.e., error (i) = \(|EST_{mean}(i)-GT(i)|\). The precision for the velocity magnitude and inter-frame displacement estimates was assessed by calculating the relative standard deviation:  
\[\text{SD (i) }=\frac{\sigma_{EST}(i)}{GT_{max}}*100\% \tag{2}\]  
with \(\sigma_{EST}\) the standard deviation of the same n estimates, and \(GT_{max}\) the maximal ground truth value. The precision of the velocity angle estimates was calculated as the standard deviation (\(\sigma_{EST}\)). For the analysis of the experiments, the center of the disk (r < 1 mm) was excluded from the calculations to avoid velocities or displacements close to zero. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Step**} & **Blood Flow** & **Vessel Wall** & **Blood Flow** & **Vessel Wall** \\  & & **Simulations** & **Simulations** & **Experiments** & **Experiments** \\ \hline Template1 & 1 & 0.84 \(\times\) 2.20 & 0.93 \(\times\) 0.77 & 0.9 \(\times\) 1.6 \\ - & 2 & 0.63 \(\times\) 2.00 & 0.32 \(\times\) 0.38 & 0.42 \(\times\) 1.0 \\ Search window1 & 1 & 2.31 \(\times\) 8.14 & 1.01 \(\times\) 1.63 & 1.34 \(\times\) 3.0 \\ - & 2 & 0.74 \The performance for all methods for the vessel wall simulation was quantified by calculating the root-mean-squared error (RMSE) between estimated and ground truth axial and lateral displacements:  
\[\text{RMSE }=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(EST(i)-GT(i))^{2}} \tag{3}\]  
with \(N\) the number of estimated samples within the ROI, \(EST(i)\) the estimated value for sample \(i\), and \(GT(i)\) the ground truth value for sample \(i\).
# Viscoelastic Model and Synthetic Seismic Data of Eastern Rub'Al-Khali
## 3 Development of Digital Depth Models  
There are almost no published data about the eastern parts of the Rub' Al-Khali basin in Saudi Arabia. Only limited published data on the geological or geophysical properties were available for the Rub' Al-Khali basin in Saudi Arabia. In this study, we performed an extensive literature review to compile one geophysical and geological model in the eastern Rub' Al-Khali area using references specific to Rub' Al-Khali. In cases where data are not available in these references, we obtain the missing data from references studying the same formations in nearby areas of Saudi Arabia, UAE, or Oman. In terms of geologic column in the Rub' Al-Khali basin, we found the following twenty-three formations: Quaternary (F-1), Hofuf Dam Hadrukh (F-2), Dammam (F-3), Rus (F-4), Umm Er Radhuma (F-5), Aruma (F-6), Wasia (F-7), Shuaiba (F-8), Biyadh (F-9), Hith (F-10), Arab (F-11), Hanifa & Tuwaiq Mountain (F-12), Dhruma (F-13), Marrat (F-14), Minjur (F-15), Jih (F-16), Sudair (F-17), Khuff (F-18), Unayzah (F-19), Qusaiba (F-20), Qasim (F-21), Saq (F-22), and Basement (F-23) (Figure 2). Eventually, twenty-three layers and two major faults were defined and digitized at variable intervals (Figure 2). The Phanerozoic stratigraphy column of the eastern Arabian Peninsula is given in Figure 3[25]. The stratigraphy of layers and faults of the model were built mostly based on the geologic information provided by [19]. The elastic properties such as densities and velocities were determined using median values from digitized well-log data provided by several published articles as reported in Table 1[18; 22; 23; 26; 27; 28; 29; 30; 31; 32; 33; 34], and available Vp and density data were plotted in Figure 4. If logs are not available, values for these parameters from nearby areas are used. Due to the lack of availability of S-wave velocities for some formations, we use Vp-Vs relations to complete the whole stratigraphic column. We used the relation developed by [26] for carbonate lithologies, and relations presented by [22] for sandstones and shales. The remaining properties, which are P- and S-wave quality factors (Qp and Qs), were calculated using a method proposed by [27], which involves taking the square root of the corresponding value of P- and S-wave, respectively. The complete geophysical properties including P-wave velocity, S-wave velocity, density, lithology, P- and S- wave quality factors of each formation are listed in Table 1. Moreover, Figure 5 shows plots of the compiled Vp, Vs, \(\rho\), Qp, and Qs models. \begin{table}
# Experimental Investigation on the Dynamic Characteristics of Bubble-in-Chain Near a Vertical Wall
## 2 Materials and Methods
### Bubble Tracking
The results of the edge detection are presented in Figure 2e. The feature points in a continuous image frame are matched using a feature matching device, and the corresponding pairs of matched feature points are recorded. The bubble is tracked by evaluating the matching ratio between the selected bubble and the bubble in the subsequent frame, which represents the ratio of successfully matched characteristic points to the total number of characteristic points within the bubble. Once this matching rate exceeds a predefined threshold, the bubble with the highest matching rate is chosen as the tracking object. Figure 2f illustrates the tracking results, where variations in color depict velocity and trajectory is represented by curves on each bubble profile. The bubble equivalent diameter can be calculated based on the results of bubble edge detection [19]:  
\[d_{eq}=\sqrt[3]{{d_{h}}^{2}d_{v}} \tag{7}\]  
The long and short axes of the bubble are represented by \(d_{h}\) and \(d_{v}\), respectively. Figure 3 illustrates the probability density function of bubble sizes corresponding to different generation frequencies. The probability density function of the equivalent diameter of bubbles exhibits an approximate Gaussian distribution. As the frequency of bubble generation increases, there is a slight increment in their diameters, with average equivalent diameters measuring 3.1 mm for frequency \(F1\), 3.2 mm for frequency \(F2\), and 3.8 mm for frequency \(F3\). Figure 3: Probability density function of equivalent bubble diameter with varying bubble generation frequency. (**a**) have a frequency of \(F1\) and an average equivalent diameter measuring 3.1 mm, (**b**) have a frequency of \(F2\) and an average equivalent diameter measuring 3.2 mm, and (**c**) have a frequency of \(F3\) and an average equivalent diameter measuring 3.8 mm. Uncertainties in the experimental process arise from the edge-detection process involved in bubble recognition [20]. In cases where the bubble does not collide with the wall, there is a maximum error of \(\pm 1\) pixel in identifying the bubble's edges, resulting in an uncertainty of less than 3% in its diameter. However, when a collision occurs between the bubble and the wall, this maximum error increases to \(\pm 2\) pixels, leading to an uncertainty of less than 5% in its diameter.
# Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion
## 2 Materials and Methods
### The Vehicle  
#### 2.2.1 General Description  
The vehicle is a cylindrical AUV, with an annular wing and propelled by a double electric motor. Let us look at its detailed description (see Figure 1 below).  
In the front bay, there is a radome with a \"nostril\" that houses the sensors kit: The data collected are managed by a computer. The nostril is inclined at 20\({}^{\circ}\) so that its flow is the least disturbed possible and its discharge flow does not create turbulence or disturbance to the flow of the elliptical wing. Below, there is the corresponding window of a digital camera (GoPro class) for visual inspection and automatic recognition of objects at depths, which, although modest, could be lacking in sufficient light; on the first bulkhead, a 10\({}^{6}\) candle, flat LED has been mounted [11; 12; 13; 14; 15; 16]. Lighting for the camera is important in the case of operation in the low waters of the ports, notoriously turbid or, in co-currentsources, coming from silty river mouths [17]. The general characteristics and performances (estimated) of our underwater vehicle are shown in Table 1.  
The tasks of a single AUV are various: From the autonomous surveillance of the networks (thanks to the recognition system from self-learning - neural network) to the sentinel function for oil spills or biological contaminations. Furthermore, it could be able to identify fish schools or the presence of boats or great ships. The vehicle also has a peculiar use: In the case of biochemical contamination, it is able to recognize it and, partially emerging, take a picture above the free surface of the water of all the ships present at that time in the area [18].
# Effect of Wind Direction and Velocity on PV Panels Cooling with Perforated Heat Sinks
## 3 Materials and Methods
### Studied Cases  
A total of seven cases were studied. The first one is the base case (Case 0), while the other six consist of the heat sinks analyzed:  
* no cooling;
* heat sink and non-perforated horizontal fins;
* heat sink and perforated \(\Phi=30\) mm horizontal fins;
* heat sink and perforated \(\Phi=60\) mm horizontal fins;
* heat sink and non-perforated vertical fins;
* heat sink and perforated \(\Phi=30\) mm vertical fins;
* heat sink and perforated \(\Phi=60\) mm vertical fins.  
The analysis was realized on a fixed PV panel, oriented to the south, with an inclination of 45\({}^{\circ}\) from the horizontal position, and wind direction was varied towards the back, front and side of it, as shown in Figure 4.  
Figure 3: The geometry and set-up of the numerical simulation domain.  
The designed heat sinks consisted of a rectangular metal (copper) sheet with horizontal or vertical equidistant fins (Table 4). The main geometric characteristics of the heat sinks are: \(h_{fins}=100\) mm (height of all fins implemented), \(L_{fins}=1520\) mm (length of vertical fins) and \(L_{fins}=860\) mm (length of vertical fins)--Table 4.  
The fins, made of copper, are studied for both perforated and non-perforated cases. The base sheet of the heat sink is in direct contact with the backside of the PV panel. The diameter of the circular perforations of the fins were of 30 mm or 60 mm (Table 4). The holes were placed at an equal distance of \(d=100\) mm from one to another. In order to assure good air circulation, there was imposed an offset of 50 mm between two successive fins.  
The main cases of cooling solutions analyzed are presented in Table 4. Cases 1-3 represent the horizontal (transversal) ones with no perforations or with perforations of 30  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **3D Perspective View** \\ \hline \hline
**Plate of the heat sink** & **P**y panel \\ \hline
mm and 60 mm. Cases 4-6 are the vertical (longitudinal) ones, both with and without perforations.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 1 Introduction
### Hexagonal Ferrites  
Hexagonal ferrites (also referred as hexaferrites) represent a large class of magnetic ceramics with a hexagonal structure, which could be seen as an Fe\({}_{2}\)O\({}_{3}\) network distorted by the presence of larger cations, usually Sr, Ba, or Pb. Compared to their spinel relatives, they generally (i) require higher temperatures to be synthesized, (ii) form larger particles easily, and (iii) exhibit typical hard-ferromagnetic behavior, with a large value of magnetic remanence and coercivity [66]. The hexaferrite family might be classified into six sub-categories, depending on the complexity of their crystal structure: M-type, Z-type, Y-type, W-type, X-type, and U-type ferrites [67]. However, regarding their photocatalytic applications, only M-type materials, with the general formula of MFe\({}_{12}\)O\({}_{19}\), have been studied so far. Their crystal structure, using barium hexagonal ferrite (BaFe\({}_{12}\)O\({}_{19}\)) as an example, is presented in Figure 3.  
Figure 2: Spinel ferrite structure (inverse) for Fe\({}_{3}\)O\({}_{4}\), presented along the a direction and as a 3D projection (not marked octahedra for clarity). Reprinted with permission from [63]. Copyright (2021) Creative Commons Attribution.