esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### DSC Characterization  
Figure 12 presents the relationship between the heat flow normalized per unit weight of the samples (\(Y\)-axis) versus temperature (\(X\)-axis) for the control, 5%PCM, and 5%GPCM binders. The purpose of using DSC was to measure the heat flow of the binders during the heat/cooling cycles.  
For each binder, the bottom curve represents the heating phase of the cycle, whereas the top curve represents the cooling phase of the cycle, as shown in Figure 12. Two main components were considered on the heating/cooling curves: the temperature at which the peaks occurred (phase transition) and its corresponding heat flow.  
It was found that the PCMs and GPCM had a visible effect on the thermal behaviour of the modified binders. As shown in Figure 12, the heat flow released from the 5%PCM was 0.335 W/g at \(-15\)\({}^{\circ}\)C. However, it was noticed that the released heat flow by the 5%GPCM binder was 0.175 W/g at \(-20\)\({}^{\circ}\)C. It was also found that the released heat flow from the 5%PCM binder was much higher than that of the 5%GPCM binder. The use of  
Figure 11: FTIR spectrum of the 5%PCM samples under different storage conditions.  
Figure 10: FTIR spectrum for all binder samples.  
GP could therefore have negatively affected the ability of the PCMs to employ its latent heat of fusion to release heat. A possible explanation is that when the PCM is trapped in the pores of the GP particles, the PCM has less tendency to release heat due to the low thermal conductivity of the GP. These findings indicated that PCM/GPCM would enhance the mechanical properties of the asphalt binder at low temperatures.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Extraction of Full-Field Displacement Time History
#### 2.3.2 Displacement Calibration Based on Adaptive Scale Conversion  
For larger structures such as long-span bridges, if the camera is set at a long distance to the bridge and the shooting range includes the entire bridge, different positions of the bridge will have different scale factors due to the effects of camera distortion and shooting angle. Some studies ignore this effect by assuming that the optical axis of the camera is perpendicular to the plane of structure vibration, while some studies consider this effect through camera calibration by mounting a target on the structural surface. Herein, this study presents the adaptive-scale conversion method to calibrate the displacement. The  
Figure 5: Displacement extraction of edge point.  
principle is that the physical width of the measured structure remains constant, which is used as a base to calculate the scale factor at each position, as detailed below.  
Choose a section as the reference position with the pixel width \(l_{0}(t)\). According to the assumption that the physical widths of both positions are equal, the scale conversion factor at position \(i\) can be calculated as  
\[l_{0}(t)=\Big{|}\widetilde{y}_{0}^{u}(t)-\widetilde{y}_{0}^{d}(t)\Big{|}, \tag{11}\]  
\[l_{i}(t)=\Big{|}\widetilde{y}_{i}^{u}(t)-\widetilde{y}_{i}^{d}(t)\Big{|}, \tag{12}\]  
\[k_{i}(t)=\frac{l_{0}(t)}{l_{i}(t)}, \tag{13}\]  
where \(\widetilde{y}_{0}^{u}(t)\) and \(\widetilde{y}_{0}^{d}(t)\) are the coordinates of the upper edge point and lower edge point at the reference position, while \(\widetilde{y}_{i}^{u}(t)\) and \(\widetilde{y}_{i}^{d}(t)\) are the coordinates of the upper edge point and lower edge point at the calibrating position \(i\), respectively, as shown in Figure 6. \(k_{i}(t)\) is the scale conversion factor of position \(i\), and the displacement of all the points at position \(i\), \(\overline{y}_{i}^{\prime}(t)\), is calibrated by multiplying the original displacement \(\overline{y}_{i}(t)\) with the scale conversion factor as follows:  
\[\overline{y}_{i}^{\prime}(t)=k_{i}(t)\cdot\overline{y}_{i}(t), \tag{14}\]  
It is noteworthy that the displacement obtained here is the pixel displacement and is only used to identify the natural frequencies and mode shapes of structures. Therefore, it is not necessary to obtain the physical displacement of the structure.
## 2 Image Processing in Remote Sensing
### Image Segmentation
But it suffers from the limitation that it is complex in its size due to the excessive size of the decoder, which is an issue for researchers using this framework [21]. It has also the disadvantage of blurry nature of extracted boundaries. A firefly algorithm based on fuzzy entropy function is also used [17]. Fuzzy entropy function computes difference in adjacent entropies. The threshold levels are formed based on the minimum fitness function and all the regions will have same entropy values. The firefly algorithm is an effective search algorithm based on the behavior of unisex fireflies and its efficiency is based on the light intensity and the attraction between flies. Extraction of roads from an urban area scene is indeed difficult due to the complex building geometry. The Gabor filter enhances the image features to distinguish the textures in the image. It is followed by segmentation of the scene based on a graphical method [18]. This mainly involves two major steps where a graph representation of the segmented image is developed first followed by splitting and merging of similar segments based on color and shape. A new method using CS for segmenting color images is presented in Reference [22]. It uses Tsallis entropy for multilevel thresholding. This entropy function uses global property of the histogram of the image which is easier to implement. Also the entropy index acts as an adjustable parameter for tuning the threshold values. This method suffers from the limitation that the algorithm has very high complexity. The main aim in the case of object based analysis is to decide which technique will be useful for image segmentation which further decides the overall image performance. Object based analysis can be assessed using two stages. Firstly image segmentation is done followed by classification of the segmented image. The performance evaluation can be quantitative or qualitative. Compared to unsupervised methods, supervised methods are mostly used for quantitative analysis. Qualitative analysis is highly subjective but easier. An assessment technique using Under Segmentation Error (USE) and Over Segmentation Error (OSE) is used which is calculated for each reference in the image [23]. Finally once the OSE or USE of each reference in the image is computed, it is summed up to get the overall performance metric for segmentation problems. Some of the evaluation metrics for assessing the performance of image segmentation include pixel accuracy which is the percentage of pixels which were correctly classified, precision which is the correctness of the positive detections to ground truth and recall which is the completeness of positive predictions to ground truth.
# On Ghost Imaging Studies for Information Optical Imaging
## 4 Outlook
### Task-Oriented System  
Since GI systems can realize imaging directly in the high-dimensional light-field domain and have a higher image information acquisition efficiency, they are capable of demonstrating superiority in imaging applications for those time-varying dynamic scenes [127, 128]. In such scenarios, possible tasks to be performed can be roughly divided into object tracking, object classification, and complete imaging [129, 130]. Intuitively, sufficient information contents for those three kinds of tasks are varied, and actually ascending. As aforementioned, there have already been several studies on GI systems that aim at tasks of object classification and imaging. Then, in the next stage, one of the research topics worth paying attention to for task-oriented GI systems should be theories and methods to realize the ability of tracking tasks, especially the tracking-before-classification scheme [131, 132, 133] based on the GI framework.  
On the other hand, in the task-oriented imaging scheme, there are commonly considerable demands on the confidence and uncertainty of the retrieved task information [134, 135, 136]. Considering that GI is an imaging mode that utilizes light-field spatiotemporal fluctuations to encode the image information so that various kinds of tasks can be performed accordingly, for this kind of imaging mode that has a \"Codec\" operation different from the traditional \"direct point-to-point\" imaging mode, estimation on the confidence and uncertainty of the retrieved information is particularly important for its practical applications, remaining unaddressed. Hence, to give quantitative descriptions of these based on methods in statistical theory and information theory, such as the bootstrapping method [137, 138, 139], Fisher information [101, 136], and Bayesian Cramer-Rao bound [63, 140, 141], will also be one of the next expected areas of research focus.
## 5 Assessment Criteria for the Developed Models  
The coefficient of determination (\(\rm R^{2}\)), root mean squared error (RMSE), mean absolute error (MAE), and scatter index (SI) was used to assess the accuracy and efficacy of the proposed models. Their equations are:  
\[\rm R^{2}=\left[\frac{\sum_{p=1}^{p}\left(t_{p}-t^{\prime}\right)\left(y_{p}-y ^{\prime}\right)}{\sqrt{\left[\sum_{p=1}^{p}\left(t_{p}-t^{\prime}\right)^{2} \right]\left[\sum_{p=1}^{p}\left(y_{p}-y^{\prime}\right)^{2}\right]}}\right]^{2}, \tag{5}\]  
\[\rm RMSE\ =\sqrt{\frac{\sum_{p=1}^{p}\left(y_{p}-t_{p}\right)^{2}}{p}}, \tag{6}\]  
\[\rm MAE\ =\frac{\sum_{p=1}^{p}\left|\left(y_{p}-t_{p}\right)\right|}{p}, \tag{7}\]  
Figure 6: The ideal ANN structure has six hidden neurons and one hidden layer.  
\[\text{SI}\,=\frac{\text{RMSE}}{\text{t}^{\prime}}. \tag{8}\]  
The predicted and measured path pattern values are denoted by \(\text{y}_{\text{P}}\) and \(\text{t}_{\text{P}}\), respectively, and the average value of the measured and predicted values is denoted by \(\text{y}^{\prime}\) and \(\text{t}^{\prime}\). The terms tr and tst, which stand for training and testing datasets, denote the number of patterns (collected data) in each set. When evaluating any equation or model, one is the ideal value for \(\text{R}^{2}\), but the lowest value is preferable for other parameters (MAE and RMSE), and zero is the ideal SI value. However, the SI parameter has several limitations, and when it is greater than 0.3, it indicates that the models are performing poorly.
## 3 Magnetic Field Generated by the Single Thin Current Lead  
As the double-circuit 400 kV transmission line is comprised of six long, thin current leads, the magnetic field generated by it can be calculated using the method of superposition. We therefore analyze how the magnetic field generated by a single current lead can be calculated. When the magnetic permeability of all points in the space is the same and equal to the permeability of free space \(\mu_{0}\), the calculation of the magnetic field of the given symmetric current system uses the LaPlace-Poisson equations. When current is passed through the current lead with the element \(idI\), the length is \(l_{i}\) if \(r\) represents a radius-vector pointing from the current element \(idI\) to the point of observation, and the electrical current is distributed within the volume of the current lead \(Vj\), the vectors of the magnetic field strength \(H\) and of the magnetic field density \(B\) at the point of observation M are calculated as follows [17; 18; 19; 20; 21; 22]:  
\[\mathbf{H}=\int_{Vj}\mathrm{d}\mathbf{H};\,\mathrm{d}\mathbf{H}=\frac{(\mathbf{r})\mathrm{d}V }{4\pi r^{3}};\,\mathrm{d}\mathbf{H}=\frac{\mathrm{J}\mathrm{d}V}{4\pi r^{2}} \mathrm{sin}\,\angle\mathbf{r},\mathbf{j};\,\mathbf{B}=\mu_{r}\mu_{0}\mathbf{H}. \tag{1}\]  
The magnetic field generated by the current lead can be calculated using a vector potential. When the electric current flows through the lead, making a circuit \(l_{i}\), and the element \(\mathrm{d}\mathbf{I}\) and electric current density \(\mathbf{J}\) within the volume of the current lead \(Vj\) are known, the vector potential \(\mathbf{A}\) is expressed as follows:  
\[\mathbf{A}=\int_{Vj}\frac{\mu_{r}\mu_{0}\mathrm{J}\mathrm{d}V}{4\pi r}. \tag{2}\]  
The density of the magnetic field generated by all the current elements \(idI\) of the current lead is expressed based on the superposition principle by summing up the constituents of the flux generated by individual current elements [7; 8; 9; 10; 11; 12; 13; 14; 15; 23].  
Due to axial symmetry, the magnetic field strength line of a long and thin current lead with an electric current passing through it have a circular shape, with their centers on the geometrical axis of the current lead, and they are located in planes that are perpendicular to this axis. At any point of such a circle, the directions of the magnetic field strength vector \(\mathbf{H}\) and distance element \(\mathrm{d}\mathbf{I}\) match.
## 4 Discussion  
### Model Performance and Structural Advantages  
The deep learning model proposed in this study, which integrates BERT, BiGRU, attention mechanism, and CRF, performs excellently in the task of named entity recognition in Chinese marine coral reef ecosystem texts, achieving an F1 Score of 86.54%, significantly surpassing existing methods. The model's outstanding performance is primarily attributed to its unique structural design. Firstly, by utilizing the BERT model, our model can capture rich, context-relevant word vectors, providing a solid foundation for understanding the meaning of the text [32]. Then, the use of BiGRU further enhances the model's ability to process sequence dependencies, enabling the model to effectively capture the global semantics at the sentence level [33]. Moreover, the incorporation of the attention mechanism allows the model to focus on key information related to coral reefs, significantly improving the accuracy of entity recognition [34]. Finally, the application of the CRF layer optimizes the output of the label sequence, further improving the model's performance by considering the transition probabilities between labels. This structural innovation is specially optimized for the recognition of long entities and nested entities in marine coral reef ecosystem texts. Ref. [35] significantly enhances the ability to recognize these complex entity structures, addressing the challenges faced by existing named entity recognition methods when dealing with complex marine texts.  
These findings directly respond to the research questions by demonstrating that our model can effectively handle the unique characteristics of marine domain texts, particularly the complexity and variability of entity structures. The model's superior performance highlights its potential as a powerful tool for NER in specialized fields.
## 3 Results
### Detection of THz Radiation in GaSe:S Crystals  
#### 3.3.1 Experimental Setup  
The study of the detection efficiency in GaSe:S crystal samples was carried out using a specially assembled separate THz-TDS (Figure 4). The source of femtosecond radiation is an erbium fiber laser with \(\lambda=1.55~{}\upmu\)m (\"Toptica Photonics\", Munich, Germany). The pulse width and repetition rate are \(\sim\)80 fs and \(\sim\)78 MHz, respectively. The output laser beam with a power of about 220 mW is divided into two parts using a polarizing beam-splitting cube (PBS) for THz generation (90%) and detection (10%). The first one passes through an acoustic-optical modulator (AOM) and is directed to a THz pulse generator represented by a 200-\(\upmu\)m-thick p-InAs wafer placed in a magnetic field (B = 0.8 T) of permanent magnets [57]. The generated THz beam is collimated by an off-axis parabolic mirror, passes through a low-pass filter (LPF) that blocks the residual laser radiation, and is focused by a similar parabolic mirror onto the detector electro-optic crystal (EOC). The probing laser beam passes through an optical delay line (ODL), represented by a motorized  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{x}\) & \(\boldsymbol{L_{\text{coh}}}\) (\(\
u\) = 1 THz), \(\upmu\)m & **Sample Thickness \(\boldsymbol{d_{\text{,}}}\)\(\upmu\)** \\ \hline
0 & 329.3 & 361 \\
0.03 & 347.5 & 351 \\
0.12 & 354.2 & 342 \\
0.16 & 370.0 & 324 \\
0.22 & 391.5 & 317 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated coherence lengths \(L_{\text{coh}}\)(\(\
u\) = 1 THz) for GaSe:S crystals and measured thicknesses of the prepared samples.  
Figure 3: Coherence length \(L_{\text{coh}}\) of the interaction of terahertz and laser radiation with a wavelength of \(\lambda\) = 1547 nm in GaSe:S crystals.  
linear positioner (8 MT173-50-20 from \"Standa\", Vilnius, Lithuania) with an installed corner-cube retroreflector, and simultaneously with the THz wave, it arrives at the EOC: GaAs, thickness 0.7 mm, with optical anti-reflective coating.  
Detection is carried out by the free-space electro-optic sampling technique based on the Pockels effect [23; 58; 59]. The change in the polarization of the probe laser beam is measured using a detection system built on a quarter-wave plate, a Wollaston prism (WP), and balance photodetector (BPD). The signal from BPD is fed to a lock-in amplifier (SR844 from \"Stanford Research Systems\", Sunnyvale, California, USA). It acquire the signal at the modulation frequencies of AOM and THz radiation, respectively. The PC controls the ODL and receives the digital signal from the lock-in amplifier.
## 4 Experiment Results  
### Dielectric Properties  
The average values of the relative permittivity and dissipation factor are summarized in Table 1. Additionally, the graphs of the relative permittivity and dissipation factor at each temperature are presented in Figure 6. The results of the relative permittivity and dissipation factor measurements exhibited temperature-dependent characteristics. As the temperature increased from 30 \({}^{\circ}\)C to 150 \({}^{\circ}\)C, both the relative permittivity and dissipation factor showed increases. The relative permittivity showed a limited increase (less than 5\(\%\)) when the temperature increased from 30 \({}^{\circ}\)C to 60 \({}^{\circ}\)C and 90 \({}^{\circ}\)C. However, the values measured at 120 \({}^{\circ}\)C and 150 \({}^{\circ}\)C showed increases of approximately 5\(\%\) and 6.8\(\%\), respectively, compared to value measured at 30 \({}^{\circ}\)C. Regarding the dissipation factor, measurements at different temperatures were found as follows. Compared to the dissipation factor value of 0.0041 measured at 30 \({}^{\circ}\)C, the value measured at 60 \({}^{\circ}\)C and 90 \({}^{\circ}\)C showed increases of approximately 9.8\(\%\) and 24.4\(\%\), respectively. The values measured at 120 \({}^{\circ}\)C and 150 \({}^{\circ}\)C showed a noticeable increase compared to the value measured at 30 \({}^{\circ}\)C (approximately 68.3\(\%\) and 109.8\(\%\), respectively).  
Such results seem to be well aligned with previous studies. For example, [PERSON] et al. reported that the relative permittivity and the dissipation factor showed limited change in the value when the measurement temperature was increased to 90 \({}^{\circ}\)C [44]. When the temperature was further increased up to 150 \({}^{\circ}\)C, however, the results of [44] reported that both the relative permittivity and the dissipation factor dielectric properties showed more apparent changes in the value as a result of structural  
\begin{table}
\begin{tabular}{c c c} \hline
**Temperature (\({}^{\circ}\)C)** & **Relative Permittivity** & **Dissipation Factor** \\ \hline
30 & 4.94 & 0.0041 \\
60 & 5.04 & 0.0045 \\
90 & 5.13 & 0.0051 \\
120 & 5.19 & 0.0069 \\
150 & 5.28 & 0.0086 \\ \hline \end{tabular}
\end{table}
Table 1: Dielectric properties of epoxy/Al2O3 micro-composites at different temperatures.  
changes in the epoxy resin after being exposed to increased thermal stress. Meanwhile, other previous studies have reported on the temperature-dependent dielectric properties of composites filled with Al\({}_{2}\)O\({}_{3}\) micro-fillers. [PERSON] et al. [45] investigated the thermal and electrical behavior of epoxy-based micro-composites filled with micro-sized silica (SiO\({}_{2}\)) and alumina (Al\({}_{2}\)O\({}_{3}\)) particles. The study demonstrated that as the filler content increased and the measurement temperature rose, the relative permittivity of the composites also increased. These results suggest that the concentration of filler particles and temperature can significantly impact the electrical properties of the composites, supporting the findings of this study.
## 3 Methodology
### Advantages of Combined Application of the Suggested Methods  
CRITIC provides criteria weights in an objective way by analyzing their variability and contrast intensity among alternatives, giving more substantial discriminative criteria greater weight. This reduces subjective bias and strengthens decision-making. It works well in complicated situations where interdependencies between criteria and variances affect the results.  
WASPAS combines WSM and WPM to improve decision-making accuracy and dependability. This enables modification of the additive and multiplicative weights for robust rankings. It reconciles conflicting criteria in complex decision-making scenarios and is computationally efficient.  
TOPSIS is user-friendly and identifies options nearest to the best solution and furthest from the worst-case scenario, ensuring optimal decision-making. It enables the integration of quantitative and qualitative criteria and the control of evaluation differences by means of distance calculations. Its computational efficiency and scalability make it suitable for complex, multicriteria decision-making.
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 2 Materials and Methods
### Antioxidant Activity Evaluation
#### 2.5.4 Trolox Equivalent Antioxidant Capacity (TEAC) Microassay  
This method was based on ABTS (2, 2\({}^{\prime}\)-azino-bis(3-ethylbenzthiaazoline-6-sulphonic acid)) radical decolorization adjusted to a microplate reader [32]. Stock solutions of ABTS (14 mM) and potassium peroxodisulfate (4.9 mM) in phosphate buffer (pH 7.4) were prepared and mixed in equal volumes.  
The mixture was left at room temperature in the dark (1216 h) to allow free radical generation. On the day of analysis, the ABTS radical solution was diluted with phosphate buffer in order to achieve an absorbance of 0.7 \(\pm\) 0.02 at 734 nm (approximately 1:80, _v/v_). The method was performed in the following way: diluted samples and Trolox solutions (20 \(\upmu\)L) were added to 280 \(\upmu\)L of the ABTS radical solution in wells of MTP in triplicate. Trolox solutions in the concentration range 0.02-0.15 mmol L\({}^{-1}\) were used for calibration curve (y = 283.25x + 0.2318), with good linearity (r\({}^{2}\) = 0.998). LOD and LOQ were determined (0.1 and 0.5 \(\upmu\)mol L\({}^{-1}\), respectively). After exactly 6 min, the absorbance readings were taken at 630 nm. Results were expressed as mM Trolox Equivalents (TE) per gram of dry weight (mM TE g\({}^{-1}\) DW).