esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 5 Conclusions  
This article proposes a new structural information restoration method for underwater image sequences using an image registration algorithm. Different from previous studies [20; 21], the proposed method follows a new registration strategy, that is, it gives higher priority to edge information before registration, and intentionally guides the registration to focus on the boundary area of interest. At first, the regions with less misalignment across the input warped sequence, which are picked by using SSIM as a metric, are fused into a single frame. Then the guided filter is employed to recognize and hold back the gradient border of the fused image while blurring other areas. During the iterative registration, using the output of the guided filter as a reference, most unexpected fringe anamorphoses can be corrected, and the structural edge information is restored to a greater extent. As iterations go on, the quality of the boundary improves progressively and tends to be stable with the increasing of the iteration. Finally, to dispel the random noise and produce an undistorted frame, the PCA algorithm and patches fusion technology are applied to the registered frames.  
From the experiment, we have tested and compared our method with other registration methods [20; 21]. In comparison with other registration strategies [20; 21], these methods fail to deal with the contour relationship between adjacent letters, bringing about fuzzy blocks that restrict character recognition. Instead, our method can effectively restore the more prominent letter boundary. Meanwhile, as the iteration of registration increases, the running time of our method is constantly shortened, which makes our method more advantageous in the real scene with unknown iterations. In comparison with a nonregistered method [24], our method can also cope better with the highly disturbed underwater scenes. In the future, the application of edge priority in underwater observing will be further studied.  
Figure 8: The comparison between the proposed method and the [PERSON] method [24]. (**a**) One frame of the original data set. (**b**) The result recovered by the [PERSON] method. (**c**) The result recovered by the proposed method.  
**Author Contributions:** Software, T.S.; validation, T.S.; conceptualization, Z.Z.; methodology, Z.Z.; project administration, Z.Z.; formal analysis, Y.T.; investigation, Y.T.; writing--original draft preparation, Y.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China under Grant No. 2018 YFB1309200.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Raman Spectroscopy  
Raman measurements were carried out with a Raman microspectrometer (Labram by Horiba Italia S.r.l., Roma, Italy) in the range of 980-1800 cm\({}^{-1}\). Before each spectrum acquisition, the single cell to be measured was visualized using a video camera in order to choose the cell region from which to collect the signal. In fact, for each cell, the signal was collected from two regions: one including the nucleus and the other excluding it (and including the cytoplasm). The measured spectra are referred to as nucleus spectra and cytoplasm spectra, respectively. The coverslips containing the cell samples were placed and maintained inverted on a well containing PBS during the Raman measurements. Each measurement was performed by focusing, through a 100\(\times\) oil-immersion Olympus objective (Olympus Italia S.r.l., Segrate, Italy), a laser beam (514.5 nm line of an Ar-ion laser) with a power of 6 mW. About 50 randomly selected cells from each cell type were measured. The acquisition time was 10 s, and 3 acquisitions were averaged for each spectrum. The backscattered radiation from the sample was analysed by a diffraction grating with 600 grooves/mm, which was detected by a CCD detector. The spectral resolution was 5 cm\({}^{-1}\)/pixel. The background signal was collected within regions of the coverslip where no cells were located.
## 2 Materials and Methods
### Study Area (AOI)  
The study area corresponds to the administrative boundaries of the Viterbo province in Italy (Figure 1), covering approximately 3615 square kilometers. Geologically, the area can be divided into a coastal sector with gentle slopes renowned for horticultural and olive oil production, and an inland hilly sector of volcanic origin, characterized by the remnants of extinct volcanic structures. Within this region, there are three distinct lakes occupying the volcanic crater. The elevations in this area reach a maximum of 1000 m and are covered by extensive forest systems of diverse mix, including vast expanses of chestnut trees, oaks, beeches and wild hazelnuts [57].  
The two hilly complexes are known as Monti Sabatini and Monti Cimini, with the latter being the focal area for Protected Designation of Origin (PDO) hazelnuts of Viterbo, named \"Nocciola Romana\". The hazelnut cultivar typically grown in these zones is \"Tonda Gentile Romana,\" with small quantities of the \"Nocchione\" variety. Hazelnut cultivation in this region is typically carried out using a multi-stemmed bush system, with a density of approximately 500 (5 \(\times\) 5 m inter and intra-row) trees per hectare; however, there has been a recent trend of establishing higher-density plantations [58]. In some areas, hazelnut groves 30 years of age with large plants are present. Periodically, hazelnut groves undergo pruning [59], which reduces canopy volume and can cause variation in reflectance due to the spectral mixing of soil and wood pixel signatures. The hazelnut is a deciduous plant, with an active vegetative growth phase during summer months, where irrigation intervention is often required, while pruning occurs during the vegetative rest period. These phenological parameters may vary based on the cultivar, environmental conditions, and diverse management practices.  
Figure 1: Area of Interest (AOI), encompassing the provincial boundaries of Viterbo in Italy, with a specific focus on the geographical positioning of the PDO (Protected Designation of Origin) within this AOI.
# Classroom Expression Recognition Based on Deep Learning
## 4 Experiment and Analysis
### Expression Recognition Effect Display
#### 4.2.2 Experimental Analysis  
To verify the effect of the MAF-ER algorithm on the CDE dataset, similar to Section 4.1, the confusion matrix is also used to evaluate the model accuracy of the algorithm. The confusion matrix results are shown in Figure 15.  
Figure 15 shows the confusion matrix of the CDE dataset. The vertical axis represents the true label, the horizontal axis represents the prediction result of the algorithm, and the diagonal of the matrix represents the probability of the model correctly predicting each type of label. Since most students are serious during class, the detected expression categories are mainly concentrated in Neutral and Understand. However, in some cases, students may not listen carefully, so the algorithm can also effectively classify some expression categories such as Confused and Exhausted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multicolumn{4}{c}{**Expression Categories and Prediction Probabilities**} & \multirow{2}{*}{**Identification**} \\ \cline{2-2} \cline{5-6}  & **Resist** & **Confused** & **Understand** & **Exhausted** & **Neutral** \\ \hline
0 & 0.0778 & 0.149 & 0.166 & 0.0172 & 0.59 & Neutral \\
1 & 0.0305 & 0.8208 & 0.0129 & 0.0328 & 0.103 & Confused \\
2 & 0.0168 & 0.134 & 0.0192 & 0.147 & 0.683 & Neutral \\
3 & 0.0211 & 0.117 & 0.0199 & 0.109 & 0.733 & Neutral \\ \hline \hline \end{tabular}
\end{table}
Table 7: Expression prediction probability based on MAF-ER.  
Figure 15: Confusion matrix of MAF-ER on the CDE dataset.  
To further assess the effectiveness of each MAF-ER module, an ablation experiment was conducted using the CED dataset. The same training procedure was repeated five times to record the results and calculate the average, as detailed in Table 8. The recording steps are as follows:  
1. The channel attention mechanism unit was added to the baseline network, denoted as CA;
2. The spatial attention mechanism unit was added to the baseline network, denoted as SA;
3. The self-attention mechanism unit was added to the baseline network, denoted as SFA;
4. Both the channel attention mechanism and the spatial attention mechanism were employed in the baseline network, denoted as CSA;
5. The complete multi-attention mechanism, as proposed in this paper, was applied to the baseline network, denoted as MLA.  
It can be concluded from Table 8 that the MLA (multi-level attention) model achieved the highest accuracy of 88.34%, indicating that the multi-level attention mechanism can maximize the capture and utilization of important features in the image and significantly improve the accuracy of expression recognition.
## 2 Results
### Sequencing and de novo Assembly  
Samples from C. chinense were sequenced using the BGISEQ-500 platform. After quality filtering, 30.51 Gb of clean reads were generated using an average Q30 of 90.32% (sequencing error rate \(<\) 1%), and 135,968 unigenes were obtained using the Trinity and TGI clustering tool (TGICL). The number of unigenes in leaf, stem, and root tissues were 64,540, 108,624, and 70,844, respectively. The N50 value was 1890 bp and the average length of the unigenes in C. chinense was 1195 bp; 72,498 (53.32%) of unigenes exceeded 500 bp, and 66,102 unigenes (48.61%) exceeded 1000 bp (Supplementary Figure S2).
## 1 Introduction
### Figures of Merit
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Formula** & **Variables** \\ \hline [PERSON] et al. [12] & \(\eta_{1}=\frac{0.5\text{k}_{1}^{2}}{1-\text{k}_{1}^{2}}/\left(\frac{1}{\hat{ \omega}}+\frac{0.5\text{k}_{1}^{2}}{1-\text{k}_{1}^{2}}\right)\), \(k_{1}^{2}=\frac{\text{k}^{2}}{C_{p}c+\text{k}^{2}}\) & \\ Efficiency \(\eta_{2}\) & \(\eta_{2}=\frac{20\text{k}^{2}}{(\text{$\pi\hat{\omega}$}+\frac{\text{$\pi$}}{ 2})^{2}+20\text{k}^{2}}\) & \
\begin{tabular}{l} \(Q\): mechanical quality factor \\ \(C_{p}\): capacitance \\ \(\alpha\): coupling coefficient of units \\ \([\text{N/V or C/m}]c\): stiffness \\ \(\tilde{\omega}\): ratio between excitation \\ frequency and resonance \\ frequency \\ \(\omega_{\tilde{\omega}}\): resonance frequency \\ \(\omega_{\tilde{\omega}}\): phase angle \\ \(R\): applied load resistance \\ \(k^{2}=\frac{\text{$\tilde{\omega}$}^{2}}{\text{$\pi$}^{2}\text{$\pi$}^{2}}\) \\ \(r=C_{p}\omega_{n}R\) \\ \end{tabular} \\ \[PERSON] et al. [15] & \(\eta_{4}=\frac{rQ\text{k}^{2}}{1+\text{$\pi$}^{2}\text{$\pi$}^{2}\text{$\pi$}^{ 2}\text{$\pi$}^{2}}\) &
\begin{tabular}{l} \(Q\): mechanical quality factor \\ \(C_{p}\): capacitance \\ \(\alpha\): coupling coefficient of units \\ \([\text{N/V or C/m}]c\): stiffness \\ \(\tilde{\omega}\): ratio between excitation \\ frequency and resonance \\ frequency \\ \(\omega_{\tilde{\omega}}\): resonance frequency \\ \(\theta\): phase angle \\ \(R\): applied load resistance \\ \(k^{2}=\frac{\text{$\tilde{\omega}$}^{2}}{\text{$\pi$}^{2}\text{$\pi$}^{2}}\) \\ \(r=C_{p}\omega_{n}R\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Expressions to estimate the efficiency of the energy harvesters. The ultimate goal of vibration energy harvesting is to produce a standalone generator. Therefore, generating the maximum possible power from the available volume is a necessity. In this study, important criteria for the effectiveness of piezoelectric energy harvesters will be theoretically investigated in order to help develop an expression to appraise the different structures proposed for piezoelectric energy harvesters.
## 2 Design of the Six-Element MIMO Antenna Structure
Co-polarization and cross-polarization 2D radiation pattern for the different port excitation conditions such as **(b)** (P1 = 1, P2 = 1), **(d)** (P3 = 1, P6 = 1), and **(f)** (P1 = 1, P5 = 1, P6 = 1). **(g)** three-dimensional radiation pattern along with the electric field distribution for the all-port excitation conditions. **(h)** Co polarization and cross polarization 2D radiation pattern for all port excitation conditions. Different electric field distributions for two-port and three-port excitation are shown in Figures 9-11 as co- and cross-polarization radiation patterns. It is observed that the changes in the port excitation in horizonal/vertical lines are largely affected by radiation beam generations. The radiation pattern can also be controlled by exciting the different ports at the same time. In many of the cases, the overall radiation pattern is observed as multibeam radiation. Figure 9g,h, Figure 10g,h and Figure 11g,h show the overall radiation pattern in two- and three-dimensional space, while all ports are excited for their respective antenna structures. It is also observed that the major changes in the radiation pattern occur when the top antenna structure changes from a circular patch to a split ring resonator. In the case of the scattered rectangular ground plane observed in Designs 4 and 5, the radiation pattern is distributed over all angles with less specific directional peaks under all port excitation conditions. In the case of Design 3, the radiation pattern is multibeam, which is caused by the continuous ground plane over the antenna element structures.
# Impacts of Thermal Maturity on the Carbon Isotopes of
## 4 Conclusions  
Based on the semi-open pyrolysis experiments, the \(\delta^{13}\)C values of monomeric propane hydrocarbons during thermal evolution were analyzed. The results show that thermal maturity can somewhat affect the \(\delta^{13}\)C value of monomeric propane hydrocarbons, with amplitude variations reaching up to 21%. However, the effects of thermal maturity on the \(\delta^{13}\)C values of various propane monomers are different. For the \(\delta^{13}\)C of C\({}_{29}\)\(\alpha\)\(\beta\) and C\({}_{29}\) Ts, they first become slightly heavier and then become lighter with increasing thermal maturity, reaching the lightest value around 350 \({}^{\circ}\)C (\(R_{\rm o}\) = 1.05%). After 350 \({}^{\circ}\)C, the \(\delta^{13}\)C values become heavier, around 350-365 \({}^{\circ}\)C (1.05% \(<R_{\rm o}\) < 1.18%), and finally become lighter when the temperature is greater than 365 \({}^{\circ}\)C (\(R_{\rm o}\) > 1.18%). For the \(\delta^{13}\)C of Ts, Tm, 22S-homohopane, and 22R-homohopane, they exhibit an opposite trend, initially becoming slightly lighter and then becoming heavier, reaching the maximum value at 350 \({}^{\circ}\)C (\(R_{\rm o}\) = 1.05%); then they becomes lighter and finally heavier with the increasing temperature. Meanwhile, the \(\delta^{13}\)C values of C\({}_{29}\)\(\beta\)\(\alpha\), C\({}_{30}\)\(\alpha\)\(\beta\), C\({}_{30}\)\(\alpha\), and non-hopane gammacerane remain almost constant with increasing thermal maturity. Therefore, it is essential to consider not only their precursors but also the effects of thermal maturity when these parameters are used for the study of oil-source correlation. In addition, the change of carbon isotopes of monomeric propane hydrocarbons is closely related to the hydrocarbon generation process, with a notable inflection point occurring near the peak of oil generation. The substantial variation in carbon isotopes of monomeric propane hydrocarbons may be attributed to the generation and cracking of propane compounds during the early stages of hydrocarbon generation.  
Conceptualization, Y.L., X.W. and Z.W.; Methodology, L.L., Y.L., X.W. and Z.C.; Investigation, H.C., G.Y. and Z.G.; Resources, Y.X.; Writing--original draft, L.L.; Writing--review & editing, L.L.; Visualization, Z.L.; Supervision, Z.W.; Funding acquisition, Y.L. and X.W. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (Grant No. 42272175; 42203058; 42472175) and by the Natural Science Foundation of Hubei Province (Grant No. 2025 AFA106). The authors declare no conflicts of interest.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors acknowledge anonymous reviewers for their assistance. The authors declare no conflicts of interest.
## 2 Motivation  
Among the precursory phenomena associated with earthquake predictions, the ionospheric TEC enhancement preceding earthquake events has attracted significant scientific attention. Regarding stress--rocks activate positive holes as charges that carry and generate electric currents [52]. Positive hole charge carriers accumulate at the Earth's surface and charged \(O_{2}^{+}\) ions from field-ionization accumulate in the air near the region of stressed rocks [53]. As rocks are subjected to stress, they activate hole charge carriers. Except for pure white marble, every igneous and high-grade metamorphic rock tested has produced hole charge carriers when stressed [54]. The positively-charged carriers can spread through less stressed (and even nominally unstressed) rocks. The unstressed rocks become positively charged and the stressed rocks negatively charged due to the loss of the charge carriers in the stressed region. Regarding oceanic regions-the 2011 Tohoku-oki earthquake charge carrier location had higher mobility than on land because of the higher conductivity of water [55]. The accumulated surface charge over land or ocean drives the current outward. After the charge neutralization time, it is possible that some surface charges are transported into the ionosphere. The ultimate effect is the current flowing into the ionosphere. The direction of the dynamo current flowing in the atmosphere depends on the sign of the generated charges over the Earth's surface near the stressed rock region: downward to negative surface charge regions, and upward from positive ones.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 3 Materials and Methods
### Methods
#### 3.2.3 Properties of Specimens with 100% RAP  
Previous studies have found that the ITS values at 25 \({}^{\circ}\)C from the indirect tensile test (ITT) have a good correlation with the activation degree of RA binder [28; 38]. This study performed some other test methods (i.e., Marshall stability, 15 \({}^{\circ}\)C splitting tensile strength, \(-\)10 \({}^{\circ}\)C splitting tensile strength and freeze-thaw splitting tensile strength ratio) to see if similar conclusions can be drawn. This study is regarded as an initial stage of research, with the purpose being to pave the way for future research. Therefore, only the volumetric and basic mechanical properties of specimens with 100% RAP were tested and used to indicate the activation of RA binder.  
The influence of preheating temperature on the activation of RA binder was the primary objective of this study. In order to explore the effect of increasing temperature on the improvement of binder activation, three sources of RAP (RAP-A, RAP-B and RAP-C) and three preheating temperatures (180 \({}^{\circ}\)C, 150 \({}^{\circ}\)C and 120 \({}^{\circ}\)C) were used. For RAP-A, three lower preheating temperatures (90 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 30 \({}^{\circ}\)C) were also implemented to study the influence of preheating temperature on binder activation in a larger temperature range.  
The influence of aging of RA binder is another key concern in this study. In general, the stiffness of RA binder obtained from the old pavement tends to be higher than that of the virgin binder. As the viscosity of asphalt is related to aging, the adhesive properties and coating ability of aged asphalt  
Figure 2: Preheating, mixing and compaction procedure.  
binder will also be affected. In this study, three kinds of artificial RAP with different aging conditions (unaged, with short-term aging and with long-term aging) were preheated at two temperatures (120 \({}^{\circ}\)C and 180 \({}^{\circ}\)C) to evaluate the influence of aging on binder activation.
## 3 Results  
### Prelude of the Results  
#### 3.1.1 Thermal Annealing: Cases A0-A5  
In the Venticell 111 dryer, heat treatment tests were performed on samples that were 30 cm \(\times\) 30 cm and 1 cm thick. I performed two series of heat treatments, during which I measured the thermal conductivity factors in each stage and followed the changes in the physical and thermal engineering characteristics. The samples were heat treated at 343 K for 6 weeks in a Venticell 111 dryer at atmospheric pressure, and then the thermal conductivity coefficients were measured. I found that the results fluctuated around an average reading (0.0171 W/mK), which corresponds to the thermal conductivity of the above-mentioned fully dried to constant weight (as-received) sample; this measurement value became A0. For the same period (24 h), but at different temperatures, I heat treated the same sample with the Venticell 111 and then measured the change in thermal conductivity for each temperature. I treated the samples first at 373 K for 1 day, then at 423 K for 1 day, at 453 K for 1 day and finally at 483 K for 1 day. The reached thermal conductivities were assigned as A1, A2, A3, A4 and A5, respectively. The thermal conductivity factors are shown in Figure 3.  
Apparently, the thermal conductivity of the sample was stable up to 373 K, and then it increased with the increasing temperature [10]. Results presented by [PERSON] in [16] and by [PERSON] in [30] showed that thermal annealing had as significant effect as artificial ageing both in structural and thermal properties of materials.  
Previously in [31], structural changes in the samples were revealed after the heat treatments. For clarity purposes, this paper only highlights them.  
The following changes were observed after heat treatment and were presented in [31]:  
* Crystallization and grain growth processes were revealed by X-ray diffractometry;
* Grain separation, as well as the growth of the grains, were manifested with a scanning electron microscope;
* Changes in the sorption isotherm were evinced, showing increasing moisture sorption after heat treatments;
* The decrease in density with increasing specific heat was also determined by calorimetry.  
The study by [PERSON] and colleagues [32] presented well how the results of the structural changes of the materials by thermal annealing could be followed well by X-ray diffraction. They reached a similar phenomenon to that presented earlier in [31], showing that the aerogel grains separated down from the fibers and, at the same time, cracks appeared and the crystallization process took place.