esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.5 In Vitro Release Studies  
Dissolution studies of benzoaine from selected ointments (P-6, P-E-30, E-24, E-E-36) were carried out by using a standard paddle Erweka DT 600 Dissolution Apparatus (ERWEKA GmbH, Langen, Germany) with a 500 mL dissolution medium at 32 \(\pm\) 0.5 \({}^{\circ}\)C and 50 rpm for 360 min. As dissolution media, a mixture of phosphate-buffered saline (PBS), pH 7.4 with 96% ethanol, in a ratio of 80:20 \(v/v\) was used. The excess ointment was placed in a cavity located at the base of the extraction chamber. The surface of the ointment was smoothed with a spatula, its excess was removed, and then the loaded amount of ointment was precisely determined by weight. A pre-wetted regenerated cellulose membrane Spectra/Por(r)2 Dialysis Membrane MWCO: 12-14 kDa, imitating the skin barrier (Fisher Scientific, Loughborough, UK) was placed on top of the ointment. At appropriate time intervals, 3 mL aliquots were collected with the replacement of equal volumes of temperature-equilibrated media filtered through a 0.45 \(\upmu\)m membrane filter. The amount of released drug was determined using a UV-VIS Cecil CE 3021 spectrophotometer (Cecil Instruments Limited, Cambridge, UK) by monitoring the absorbance at \(\lambda\) max = 289.5 nm. The concentrations of benzoaine in samples were determined according to the standard curves of the drug solution: y = 0.1023x \(-\) 0.0075, R = 0.999. The release profiles were made based on obtained results.  
In order to evaluate them, the model-independent method of similarity and difference was used [34].  
The dissolution profiles of the benzoaine ointment were compared using the coefficient of similarity _(f2)_:  
\[f_{2}=50\times\log\left\{\left[1+\left(\frac{1}{n}\right)\sum_{j-1}^{n}\left| R_{t}-T_{t}\right|^{2}\right]^{-0.5}\times 100\right\}\]  
where \(n\) is the number of dissolution time points, and \(R_{t}\), and \(T_{t}\) are the released value of the reference and the test batch at time \(t\), respectively.  
Dissolution profiles of test samples were considered similar to those of the reference samples if _f2_ > 50.
## 4 Experiments and Analysis
### Ablation Experiment
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Method** & **SEAM** & **FRFN** & **SC-Conv** & **Category** & **Precision** & **Recall** & **F1-Score** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline \multirow{2}{*}{None} & & & \multicolumn{2}{c}{Yawn} & 0.877 & 0.977 & 0.924 & 0.975 & 0.809 & \multirow{2}{*}{0.963} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.926 & 0.867 & 0.896 & 0.954 & 0.770 & 0.963 \\  & & & \multicolumn{2}{c}{Playing phone} & **0.982** & 0.916 & 0.948 & 0.961 & 0.632 & \\ \hline \multirow{2}{*}{A} & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.868 & 0.971 & 0.917 & 0.975 & 0.819 & \multirow{2}{*}{0.973} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.927 & 0.913 & 0.920 & 0.962 & 0.775 & 0.973 \\  & & & \multicolumn{2}{c}{Playing phone} & 0.955 & 0.951 & 0.953 & 0.981 & 0.667 & \\ \hline \multirow{2}{*}{B} & & \multirow{2}{*}{✓} & \multicolumn{2}{c}{Yawn} & 0.836 & **0.994** & 0.908 & 0.977 & 0.809 & \multirow{2}{*}{0.969} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.930 & 0.927 & **0.929** & 0.957 & 0.784 & 0.969 \\  & & & \multicolumn{2}{c}{Playing phone} & 0.956 & 0.944 & 0.950 & 0.973 & 0.657 & \\ \hline \multirow{2}{*}{C} & & \multicolumn{2}{c}{Yawn} & **0.904** & 0.966 & **0.934** & 0.977 & 0.826 & \multirow{2}{*}{0.970} \\  & & & \multicolumn{2}{c}{Yosedosed eyes} & 0.944 & 0.874 & 0.908 & 0.965 & 0.769 & \\  & & & \multicolumn{2}{c}{Playing phone} & 0.977 & 0.895 & 0.934 & 0.969 & 0.636 & \\ \hline \multirow{2}{*}{A} + B & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.882 & 0.986 & 0.931 & 0.977 & 0.823 & \multirow{2}{*}{0.971} \\  & & & \multicolumn{2}{c}{Closed eyes} & 0.917 & 0.911 & 0.914 & 0.962 & 0.775 & \\  & & & \multicolumn{2}{c}{Playing phone} & 0.964 & 0.935 & 0.949 & 0.974 & 0.667 & \\ \hline \multirow{2}{*}{A} + C & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.853 & 0.989 & 0.916 & 0.972 & 0.815 & \multirow{2}{*}{0.972} \\  & & & \multicolumn{2}{c}{Yosed eyes} & 0.907 & **0.937** & 0.922 & **0.965** & 0.788 & \multirow{2}{*}{0.972} \\  & & & \multicolumn{2}{c}{Playing phone} & 0.939 & **0.967** & 0.953 & 0.977 & **0.678** & \\ \hline \multirow{2}{*}{B} + C & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.865 & 0.989 & 0.923 & 0.978 & 0.820 & \multirow{2}{*}{0.973} \\  & & & \multicolumn{2}{c}{Yosed eyes} & 0.926 & 0.909 & 0.917 & 0.962 & 0.780 & 0.973 \\  & & & \multicolumn{2}{c}{Playing phone} & 0.955 & 0.965 & 0.960 & 0.978 & 0.665 & \\ \hline \multirow{2}{*}{A} + B + C & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.874 & 0.989 & 0.928 & **0.980** & **0.828** & \multirow{2}{*}{0.972} \\  & & & \multicolumn{2}{c}{Yosed eyes} & **0.953** & 0.887 & 0.919 & 0.961 & **0.797** & **0.976** \\ \hline \multirow{2}{*}{A} + \(\mathrm{B}\) + C & \multirow{2}{*}{✓} & & \multicolumn{2}{c}{Yawn} & 0.981 & 0.965 & **0.973** & **0.986** & 0.663 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Quantitative comparison of ablation experimental results on the YawDD-E dataset. The top results for each metric are shown in bold, with the next best results underlined.
## Theory
### Transverse Shear Stress Distribution Within a Cross-Section--Numerical Formulation  
The cross-section from Figure 4 can be discretized into a series of rectangular elements each with a height \(H\) as shown in Figure 5. As with the analytical formulation, the shape of the part is assumed to be symmetric about both the \(y\) and \(z\) axes. The width of an individual element is \(2z_{j}\) where \(j\) ranges from 1 to \(n\) total elements in the top half. The thickness \(t_{j}\) is defined in Equation (11).  
\[t_{j}=2z_{j} \tag{11}\]  
The numerical solution will approach the analytical solution as the number of iterations increases. If the total number of elements and height of the cross-section are known, the height of each element is calculated in Equation (12).  
\[H=\frac{h}{2n} \tag{12}\]The area \(A\) is calculated in Equation (13) by applying Equation (2) to a series of elements, each with an area \(A_{j}\).  
\[A=2\sum_{j=1}^{n}A_{j}=4H\sum_{j=1}^{n}z_{j} \tag{13}\]  
To calculate the area moment of inertia for each element \(I_{j}\), the parallel axis theorem for a given rectangle with width \(t_{j}\), height \(H\), area \(A_{j}\) and distance from the \(z\) axis to centroid axis \(d_{j}\) as shown in Figure 5, will be applied for each discretized element [7]. The area moment of inertia for each element is \(I_{j}\) and the summation for the cross-section is \(I\) as shown in Equations (14)-(16).  
\[I_{j}=\frac{t_{j}H^{3}}{12}+A_{j}d_{j}^{2} \tag{14}\]  
\[I_{j}=\frac{z_{j}H^{3}}{6}+2z_{j}Hd_{j}^{2} \tag{15}\]  
\[I=4H^{3}\sum_{j=1}^{n}z_{j}\left(j^{2}-j+\frac{1}{3}\right) \tag{16}\]  
The area \(A\) in Equation (13) and inertia \(I\) in Equation (16) are constants for the cross-section. The resultant shear force \(V\) will also be constant as mentioned previously.  
For the numerical method, the index \(a\) denotes the vertical position where the stress is calculated. The first moment of area \(Q_{a}\) will also depend on where the stress is being calculated as shown in Figure 6. Using Equation (4) for all the elements from the top down to element \(a\) yields \(Q_{a}\) in Equation (17):  
\[Q_{a}=H^{2}\sum_{j=a}^{n}z_{j}(2j-1) \tag{17}\]  
Applying Equation (7) for the numerical method yields the stress \(\tau_{a}\) at element \(a\) as shown in Equation (18).  
\[\tau_{a}=\frac{V\sum_{j=a}^{n}z_{j}(2j-1)}{8 Hz_{a}\sum_{j=1}^{n}z_{j}\left(j^ {2}-j+\frac{1}{3}\right)} \tag{18}\]  
Once all the transverse shear stress values are calculated for the elements, the maximum value is identified. The average stress \(\tau_{avg}\) shown in Equation (19) is the numerical application of Equation (9).  
\[\tau_{avg}=\frac{\sum_{a=1}^{n}\tau_{a}}{n} \tag{19}\]  
Figure 5: Discretized cross-section.  
Similar to the analytical method, Equation (10) is applied to the maximum transverse shear stress value that is identified and the average transverse shear stress values from Equation (19) in order to calculate the efficiency of the cross-section.
## 2 Materials and Methods
### Stimuli and Procedure  
A total of 133 dissimilar 3D stereoscopic models with different semantics were modeled and uniformly de-colored with the software Blender 3.0. Given that previous studies have demonstrated that both familiarity and concreteness affect users' perceptions of complexity [49; 50], we conducted a 5-point Likert scale rating on these factors prior to the formal experiment to rule out these potential influences (see Figure 2a). Sixty volunteers were recruited to rate the complexity, concreteness, and familiarity of each 3D object. The instructions here were similar to those used in a previous study [51; 52]. For complexity ratings, objects were regarded as complex if they contained a large amount of detail or intricacy (1 = definitely simple, 5 = definitely complex); for concreteness ratings, objects were regarded as concrete if they depicted real objects (1 = definitely abstract, 5 = definitely concrete); for familiarity ratings, objects were regarded as familiar if they frequently appeared in daily life (1 = definitely unfamiliar, 5 = definitely familiar). Based on the rating results, we only selected 3D objects with high concreteness (mean rating > 3) and high familiarity (mean rating > 3). Then, three groups of 60 3D virtual objects (20 low-complexity 3D objects, 20 medium-complexity 3D objects, and 20 high-complexity 3D objects) were selected based on the scoring results. Examples of low-complexity, medium-complexity and high-complexity 3D objects are shown in Figure 2b.  
Subsequently, one-way analysis of variance (ANOVA) was conducted to ensure the validity of the differences in ratings [47]. The results showed that there were significant differences in the complexity ratings of three levels of objects (F = 457.480, \(p\) = 0.000, \(p\) < 0.001), while there were no significant differences in the familiarity ratings (F = 0.117, \(p\) = 0.890, \(p\) > 0.05), and no significant differences in concreteness ratings (F = 0.124, \(p\) = 0.780, \(p\) > 0.05). Figure 2c and Table 1 show the mean ratings of complexity, concreteness, and familiarity for three complexity subgroups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Object Complexity**} & \multicolumn{5}{c}{**Object Characteristic Ratings**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Complexity**} & \multicolumn{2}{c}{**Concreteness**} & \multicolumn{2}{c}{**Familiarity**} \\ \cline{2-7}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Low & 1.97 & 0.20 & 3.48 & 0.21 & 3.80 & 0.27 \\ Medium & 2.98 & 0.09 & 3.52 & 0.16 & 3.76 & 0.17 \\ High & 3.54 & 0.17 & 3.64 & 0.10 & 3.78 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean ratings and standard deviations for complexity, concreteness, and familiarity in each 3D virtual object set.
In a similar pattern, the NDVI was assessed using SPOT(Satellite Pour I'Observation de la Terre) images to develop a vegetative recovery index [23]. However, the method based on NBR with the vegetation index NDVI is vulnerable to noises due to atmospheric effects and soil brightness. This study utilizes several vegetative indices, including NDVI, SAVI, EVI, and NBR. The integration of vegetative indices can calculate the degree of vegetative regeneration robustly. Furthermore, this study employs clustering analysis to detect the accurate spatial boundary of vegetative regeneration areas.
## 3 Results and Discussion
### Level Status of Insulator String Condition in Field Test  
The results of implementing the insulator string condition level categorization procedure (as described in Section 3.1) for field tests are shown in Tables 3 and 4. The UV frame sample is given as evidence for the visual assessment. A summary of assessment results for all insulator strings, without the UV frame sample, is presented in Table 5. The category with the least number of strings was level one, that was two strings (one string for S phase tension and one string for T phase tension). The most insulator string condition level was at level two, that was 27 strings. The remaining insulator strings (22 strings) fell into level three.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**File Name** & **Zoom** & **Did UV Appear?** & **Was UV Connected between Cup-and-Pin Insulators?** & **Cond. Level** & **Sample of Frame** \\ \hline
06\_20\_03\_37\_32 & 1\(\times\) & Yes & No & 2 \\
06\_21\_21\_03\_50 & 0.5\(\times\) & Yes & No & 2 \\
06\_22\_03\_28\_36 & 0.5\(\times\) & Yes & Yes & 3 \\
07\_06\_20\_15\_14 & 1\(\times\) & Yes & Yes & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Visual-based assessment results for S phase suspension string at the GIS area.  
The insulator string condition with level three was dominated by the suspension type that was 19 strings. This revealed that the suspension type was more prone to flashover due to contamination since its vertical position made it easier for contaminants to accumulate at the bottom area of the string (due to contaminants falling from the higher cup-and-pin insulator to the lower cup-and-pin insulator). In other words, the self-cleaning for insulator strings of suspension type was less effective.
###### Abstract  
In this study, a numerical approach was carried out to analyze the effects of different geometries of microchannel heat sinks on the forced convective heat transfer in single-phase flow. The simulations were performed using the commercially available software _COMSOLMultiphysics_\(5.6^{\text{\text{\textregistered}}}\) (Burlington, MA, USA) and its results were compared with those obtained from experimental tests performed in microchannel heat sinks of polydimethylsiloxane (PDMS). Distilled water was used as the working fluid under the laminar fluid flow regime, with a maximum Reynolds number of 293. Three sets of geometries were investigated: rectangular, triangular and circular. The different configurations were characterized based on the flow orientation, type of collector and number of parallel channels. The main results show that the rectangular shaped collector was the one that led to a greater uniformity in the distribution of the heat transfer in the microchannels. Similar results were also obtained for the circular shape. For the triangular geometry, however, a disturbance in the jet impingement was observed, leading to the least uniformity. The increase in the number of channels also enhanced the uniformity of the flow distribution and, consequently, improved the heat transfer performance, which must be considered to optimize new microchannel heat sink designs. The achieved optimized design for a heat sink, with microchannels for nanofluid flow and a higher heat dissipation rate, comprised a rectangular collector with eight microchannels and vertical placement of the inlet and outlet.  
heat transfer; microchannel heat sinks; flow geometries; nanofluids; numerical study +
Footnote †: journal: Applied Sciences
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 2 Study Area and Data Sets  
### Study Area and Pretreatment  
Five SAR oil slick scenes acquired by Radarsat-2 (fine quad-polarized mode) are described in Table 1, and some information on those data (e.g., wind direction, water temperature, etc.) is described in detail in [PERSON]'s studies [19]. Radarsat-2 images of the Mexico Bay area (No.1 and No.4) were acquired on 8 May 2010 and 24 August 2011, respectively. The dark spots in Figure 1 were interpreted as crude oil. North Sea of Europe area data sets (No.2 and No.3) were acquired from 6-9 June 2011. There are three substances (i.e. crude oil, oil emulsion, and plant oil) in the two scenes, and the acquisition interval of No.2 and No.3 was about 12 hours. The data No.5 was obtained in the South China Sea on 18 September 2009. The experimental data contain a small amount of crude oil and plant oil, which were poured with 15-minute intervals.  
Quad-polarization SAR images are susceptible to noise. Pauli decomposition has the advantages of anti-interference and a general high adaptability [20]. In general, the Pauli decomposition images are clearer than original quad-polarization SAR images. The image preprocessing stages are as follows:  
(1) The original quad-polarization SAR data are decomposed by Pauli.  
(2) The obtained Pauli decomposition images are filtered by 3 \(\times\) 3 Boxcar filtering.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Scene ID** & **No.1** & **No.2.** & **No.3** & **No.4** & **No.5** \\ \hline Location of & & & & & \\ Center & 26\({}^{\circ}\)49\({}^{\prime}\)N/92\({}^{\circ}\)01\({}^{\prime}\)W & 59\({}^{\circ}\)59\({}^{\prime}\)N/2\({}^{\circ}\)25\({}^{\prime}\)E & 60\({}^{\circ}\)09\({}^{\prime}\)N/2\({}^{\circ}\)19\({}^{\prime}\)E & 27\({}^{\circ}\)54\({}^{\prime}\)N/90\({}^{\circ}\)55\({}^{\prime}\)W & 18\({}^{\circ}\)06\({}^{\prime}\)N/109\({}^{\circ}\)25\({}^{\prime}\)E \\ \hline Wind Speed & \(\sim\)6.5 m/s & 1.6–3.3 m/s & 1.6–3.3 m/s & \(\sim\)15 m/s & \(\sim\)10 m/s \\ \hline Types of Dark & & Plant oil/oil & & & Oil \\ Spot & Crude oil & & emulsion & & & \\  & & & oil & & \\ \hline Sample size & 420 & 420 & 420 & 420 & 420 \\ \hline \end{tabular}
\end{table}
Table 1: Information of the five quad-polarization SAR images of oil spill.  
Figure 1: Five oil spill RadarSat-2 scenes.
###### Abstract
This also provides improved tolerance for imaging acoustically heterogeneous tissue with a large aperture [16]. The method has been experimentally demonstrated using two probes; initially with 1D (linear) arrays that were constrained to lie in a common plane [11; 16], and more recently 2D sparse arrays were used to demonstrate the feasibility of 3D CoMTUS imaging [17]. Nevertheless, the large discontinuous aperture created by CoMTUS presents challenges for existing beamforming algorithms, developed for small and continuous apertures [18; 19]. Indeed, preliminary findings have shown that CoMTUS imaging performance is affected by the discontinuous effective aperture distribution and the scan sequence [16]. For example, increased separation between the transducers can extend the aperture and improve resolution, however, these discontinuities can reduce contrast due to grating and side lobes. Furthermore, the use of multiple synchronized arrays can result in interactions among the beams and possible direct transmissions between the individual arrays that generate undesirable cross-talk artifacts. Like in coherent PW imaging [20], in CoMTUS it is expected that coherently compounding the images obtained from the transmission of multiple steered PWs may reduce the amplitude of side lobes and thus, improve contrast. However, it is unclear how much the interplay between the discontinuities and the scan sequence affect the global performance of the method. If the scan sequence is not properly preset according to the spatial configuration of the arrays, these factors might have a negative impact on CoMTUS performance. Likewise, the apodization laws also play an important role in imaging quality [21]. For example, in multi-line transmit beamforming, where the images suffer from inter-beam cross-talk artifacts, the use of a Tukey apodization window in both transmission and receive lowers the cross-talk artifacts, however, at the expense of lateral resolution [22; 23]. It is yet unclear what the corresponding trade-offs are for CoMTUS. The aim of this work is to investigate, both in simulation and experimentally, the role of the distribution of the individual arrays, scan sequence, and apodization on CoMTUS image quality, and specifically to explore how to attain the best point spread function (PSF) images with either minimum-side lobe energy (an indicator of contrast) or minimum main lobe width (an indicator of resolution) and the highest frame rate possible. For the first time, this study explores the imaging performance of coherent dual-array systems, taking into account the relative position between arrays, the scan sequence, and the apodization. The cross-talk artifacts are also investigated for the first time in this context. This work can, thus, provide a guideline to determine imaging performance trade-offs in different applications and to guide further studies on multi-transducer beamforming.
## 1 Introduction
Although the absorption coefficient of 808 nm is higher compared to 888 nm wavelength, it shows different values between the two crystallographic axes that result in the varying and irreproducible absorption if without the polarization controlling of pump light. The lower pump absorption efficiency can be optimized by using a longer crystal for high output power that allows the heat load to be spread in a larger volume, thus minimizing stress and thermal gradient [24]. Additionally, the 888 nm wavelength shows a higher absorption band width than 808 nm, which decreases the sensitivity to the shift in the pump wavelength. In this study, we demonstrate a highly stable and high power industrial grade picosecond Nd:YVO\({}_{4}\) amplifier with the adjustable repetition rate from 200 to 500 kHz. A combination of an 888 nm optical fiber pumped Nd:YVO\({}_{4}\) regenerative and a traveling-wave amplifiers have been utilized. To eliminate the leakage of the regenerative amplifier in high repetition rate operation, double BBO crystal Pockels cells are adopted as one switch to improve the responsivity of the switching. The maximum output power of 24.5 W is obtained at 500 kHz with the pulse width of 16.9 ps, corresponding to the pulse energy and the peak power of 0.05 mJ and 2.9 MW, respectively. The beam quality factor \(M^{2}\) is measured to be 1.25 at the full power operation. When operated at 200 kHz, the maximum pulse energy of 0.11 mJ and the maximum peak power of 6.5 MW is obtained. Our measurements show that this method effectively realizes the suppression of leakage and improves the purity of the regenerative amplified pulses. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wavelength (nm)** & **Output Wavelength** (nm)** & **Fractional Thermal Loading** & **Absorption Coefficient** & **Absorption Band Width** \\  & **(nm)** & **Fractional Thermal Loading** & **(cm\({}^{-1}\))** & **(nm)** \\ \hline
808 & 1064 & 0.241 & 10 (a-axis) 37 (c-axis) & 0.8 \\
888 & 1064 & 0.173 & 1.5 (a-\& \(c\)-axis) & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Nd:YVO\({}_{4}\) pumped by 808 nm and 888 nm.