esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Image Processing Approaches Used for EAT and PAT Segmentation
### Traditional Image Processing Techniques
#### 3.1.2 Region Growing  
Region growing is a type of pixel-wise image segmentation method [29]. Region growing begins with a selected pixel called the seed point. At each iteration, the neighbors of the selected pixel are analyzed, and if they satisfy a given criterion, the region is enlarged to include them. The criterion is usually based on pixel intensities, textures, colors, or similar features. When compared to other methods presented in this review, region growing is a simple image segmentation method. It can achieve clear segmentation edges and good results. However, it is highly dependent on selecting a precise seed point, which can often be hard to do automatically in medical imaging tasks. Region growing works well when the regions have relatively sharp transitions but often struggles when the differences between regions are subtle, like the difference between similarly dense tissues. Furthermore, region growth is very sensitive to noise. These problems can be alleviated by heavily pre-processing and thresholding the images before the region grows, to increase contrast, accentuate edges, and remove irrelevant portions. An example of region growth is shown in Figure 4.
## 2 Materials and Methods
### Characterization Techniques  
In order to make the analysis of X-ray diffraction, a Phillips diffractometer (X'Pert) was operated at 30 kV and 20 mA using a Cu-K\(\alpha\)1 radiation (\(\lambda\) = 0.1541 nm) and a rotation speed of 0.02\({}^{\circ}\)/min, the angle 20 changed between 20\({}^{\circ}\) and 80\({}^{\circ}\).  
Thermogravimetric analysis (TG) was made by loading 10 mg of boehmite in a horizontal balance with total capacity of 200 mg, with a sensitivity of 0.1 \(\mu\)g, (SDTQ-600 instrument) under a N\({}_{2}\) atmosphere (Infra-Air Products, 99.9%) and a flow rate of 100 cm\({}^{3}\)/min. The sample was introduced into the instrument without pre-treatment and the heating rate was 10 \({}^{\circ}\)C/min. The temperature increased from 25 to 1000 \({}^{\circ}\)C. All the weight changes were recorded as a function of temperature and the derivate (dW/dT) (DTG) was calculated in order to know the temperatures related with the inflexion points.  
The infrared spectra were obtained by using samples pulverized with KBr in a mortar and pressed up to 700 kgf/cm\({}^{2}\) to obtain a transparent disk in order to be placed in the infrared beam path of the instrument. The infrared spectrometer used was a Varian Spectra 220.  
Scanning electron microscopy was carried out with a scanning electron microscope FIB Mod. FEI Quanta 3D FEG Co. The qualitative chemical analysis and the corresponding spectra were obtained by EDS (X-ray energy dispersive spectroscopy) with a probe coupled to SEM microscope. The powder samples were spread on a graphite tape with adhesive on both sides, and then a mixture of Au and Pd atoms was evaporated on the samples to make them conductive.  
The specific surface area was determined by N\({}_{2}\) physisorption in an ASAP-2000 instrument (Micromerities). A sample of 500 mg was degassed at low pressure (\(1\times 10^{-3}\) Torr) and at 300 \({}^{\circ}\)C for 1 h. The sample was then cooled at -196 \({}^{\circ}\)C and the N\({}_{2}\) physisorption was made. The isotherm was determined and the surface area, pore volume and pore diameter were calculated from the BET method.  
For a more detailed analysis of the N\({}_{2}\) adsorption isotherm, the two terms of the Dubinin equation [26; 27] were fitted using the following equation:  
\[N_{m}=N_{m01}\ exp\ (-A/\beta E_{01})^{2}+Nm_{02}\ exp\ (-A/\beta E_{02})^{2} \tag{1}\]  
where \(Nm_{01}\), \(Nm_{02}\), \(E_{01}\), \(E_{02}\) are the maximum adsorption values in mmol/g and the characteristic adsorption energies in J/mol in the narrow and widest pores respectively, \(\beta\) is the affinity coefficient and A is the adsorption potential defined as:  
\[A=RT\ ln\ (Po/Pe) \tag{2}\]  
where \(Po\) is the vapor pressure at experimental temperature, \(Pe\) is the equilibrium pressure and \(R\) is the universal gas constant.  
[PERSON]'s two-term equation efficiently describes the performance of gas adsorption in adsorbents with two pore distributions as carbons [5; 6] and it was used in zeolites too [19; 28], but it was not reported his use in boehmite.
## 4 Experimental Results
### The Planetary Gear Train Optimization
The best solutions in terms of the gearbox efficiency, among outlined is obtained for the case when the gears volume and efficiency is considered. Compared to the industrial gearbox reference, it leads to the 23.6% improvement in gearbox volume, 30% decrease in center distance, which directly correlate with gearbox size, and 2.2% improvement in gearbox efficiency. In addition, it can be observed that the best reduction in center distance is obtained in the case when the contact stress is considered to be one of the objectives. Furthermore, from Figures 3-6 it can be observed that the NSGA-II algorithm cannot obtain the complete Pareto front, which is especially true for non-convex Pareto fronts in Figures 3 and 4. Comparing the best solutions in Table 2 obtained by MHPSODE and NSGA-II algorithms, it can be observed the improvement of 30.4% in center distance, 10% improvement of gearbox volume and 0.25% improvement of gearbox efficiency for the solutions obtained by proposed MHPSODE algorithm. The availability of the full Pareto front is of great importance for the designer, as it provides a range of solutions from which to choose the most appropriate, and gives an insight into how the change of one objective function affects the other one. Furthermore, in Figure 7 the MOO problem was considered where center distance between sun and planet gear and volume have been taken as the objectives. From the results in Figure 7, it can be concluded that between two objectives exists linear correlation with the correlation coefficient 0.96. Therefore, we conclude that when considering MOO of gears only one of the objectives should be considered. This can  
Figure 7: Linear dependency between center distance between sun and planet gear and volume of gearbox. increase the efficiency and reduce the workload on the design process. Furthermore, in this sub case the MOO has been performed taking into consideration 5 different ISO grade oils in the numerical simulations. The analysis has been carried out volume and efficiency as the objective functions to determine the best performing oil for the formulated problem. Therefore, Figure 8 shows the obtained Pareto fronts, where four oil types have been employed e.g., ISO VG 100, ISO VG 150, ISO VG 680 and ISO VG 460. According to the results in Figure 8, it can be concluded that the ISO VG 460 oil performed better than other considered ISO grade oils and showed the minimum value for both considered objective functions. Comparing the compromise solutions obtained, the solution obtained using ISO VG 460 oil obtained 10% improvement in center distance and 1.2% improvement in power efficiency compared to the worst performing oil, e.g., ISO VG 100. The main reason for the poor performance of the other types of oil is lower viscosity, which lead to the lower power efficiency and increase of gearbox volume. This analysis emphasizes that the proper choice of lubricant does not only affect the power efficiency of gears, but also has impact on the overall dimensions of the gearbox.
## 3 Results
### The Performance of Geoman at the Site Scale
#### 3.1.2 Relative Importance of Environmental Factors  
In this study, we quantified the relative importance of each environmental variable for predicting the RE, using the dynamic attention weights. We analyzed the differences between seven grassland subtypes during the growing season and non-growing season (Figure 4, Table 3). Results demonstrate that vegetation type and soil texture are the two most important environmental factors for RE estimation in northern China's grasslands. The importance of other environmental variables also represents spatiotemporal characteristics with ecological significance. In the growing season, temperature was found to be more important than precipitation in alpine grasslands, whereas the opposite was true for temperate grasslands. This indicates that restrictive climatic conditions have a considerable effect on accurate estimation of RE. In the non-growing season, SOCD was more important than climatic variables for RE estimation. However, temperature was more important than SOCD in alpine meadows, which may be associated with the low temperature limit of the RE in the area.  
Evaluation of the relative importance of the environmental variables indicates that the importance of SOCD in the non-growing season exceeded that of the climatic factors. Therefore, we further evaluated the accuracy of the RE estimation for the low value period from December to January in the non-growing season. It was found that adding SOCD to the model could improve interpretability by 44% compared with removing this variable (Figure S2a,b). When the soil texture was removed from the model, the results appeared to have been overestimated during the low value period in the non-growing season (Figure S2c). Therefore, it is necessary to integrate soil texture into RE models because this can to a certain extent solve the problem of overestimation of low RE values.  
Figure 4: Relative importance of the environmental variables for predicting RE in the growing season (**left**) and non-growing season (**right**). KO: alpine _Kobresia_ meadow. SH: alpine shrub meadow. SW: alpine swamp meadow. AS: alpine meadow steppe. MS: meadow steppe. TS: typical steppe. DS: desert steppe.
## Appendix B Quantization of Free Electromagnetic Field on A Cylindrical Basis  
The purpose of this Appendix is the derivation of commutation Relation (7). We will consider the homogeneous space with permittivity \(\varepsilon\) using the cylindrical basis. Let us present the positive frequency component of the operator of vector potential in the form  
\[\hat{A}^{+}(\mathbf{x},t)=\int\limits_{0}^{\infty}\frac{1}{\omega_{k}}\hat{U}_{ k}(\mathbf{x})e^{-i\omega_{k}t}dk. \tag{100}\]  
A similar relation may be written for the negative frequency component of the electric field:  
\[\hat{E}^{-}(\mathbf{x},t)=-i\int\limits_{0}^{\infty}\hat{U}_{k}^{+}(\mathbf{x} )e^{i\omega_{k}t}dk, \tag{101}\]  
where \(\hat{U}_{k}(\mathbf{x})\), \(\hat{U}_{k}^{+}(\mathbf{x})\) is the pair of Hermially conjugated operators of spectral density. The problem is finding the commutation relation for the operators of spectral density, which gives the canonical bosonic commutation relation for the EM field. Let us present the operator of spectral density as a superposition of cylindrical waves:  
\[\hat{U}_{k}(\mathbf{x})=\sqrt{i}\widetilde{E}_{k}\sum\limits_{n=-\infty}^{ \infty}\frac{1}{i^{-n}}J_{n}(k\rho)e^{-in\varphi}\hat{b}_{nk}, \tag{102}\]  
where \(\widetilde{E}_{k}=\sqrt{\hbar\omega_{k}k/4\pi\varepsilon}\) is a normalization coefficient, \(J_{n}(x)\) is Bessel function, and \(\hat{b}_{nk},\hat{b}_{nk}^{+}\) is a pair of creation-annihilation operators of cylindrical modes. We assume that they satisfy the conventional bosonic-type commutation relation  
\[\left[\hat{b}_{nk},\hat{b}_{n^{\prime}k^{\prime}}^{+}\right]=\delta_{nn^{ \prime}}\delta\big{(}k-k^{\prime}\big{)}. \tag{103}\]  
Let us consider the commutator \(\big{[}\hat{U}_{k}(\mathbf{x}),\hat{U}_{k^{\prime}}^{+}\big{(}\mathbf{x}^{ \prime}\big{)}\big{]}\). Substituting in it Relation (102) and its Hermitian conjugate, we obtain  
\[ \begin{array}{c}\big{[}\hat{U}_{k}(\mathbf{x}),\hat{U}_{k^{\prime}}^{+}\big{(} \mathbf{x}^{\prime}\big{)}\big{]}=\\ -\frac{\hbar\sqrt{\omega_{k}\omega_{k^{\prime}k^{\prime}k^{\prime}}}}{4\pi \varepsilon}\sum\limits_{n}\sum\limits_{n^{\prime}}i^{(n-n^{\prime})}J_{n}(k \rho)J_{n^{\prime}}(k^{\prime}\rho^{\prime})e^{i(n\rho-n^{\prime}\sigma^{ \prime})}\left[\hat{b}_{nk},\hat{b}_{n^{\prime}k^{\prime}}^{+}\right].\end{array}
## 1 Introduction
The analysis results show that the proposed TF analysis by using reassigned spectrogram eliminates the cross-term artifacts present in the quadratic TF distribution and presents good resolution of time and frequency in the TF plane during the identification and detection of interfering signals for GNSS receivers. This developed TF analysis technique by using a reassigned spectrogram in interference detection makes the TF characteristics of the interfering term sharply distinguishable among the received GNSS signal, which provides improved readability and localization properties in the TF plane and presents enhanced interference detection performance for GNSS receivers with respect to the state-of-the-art TF analysis approaches. In particular, the ROC curves have been adopted as metrics for verifying the effectiveness of the interference mitigation technique for GNSS receivers. The adoption of the notch IIR filter in interference mitigation for GNSS receivers has led to a significant acquisition performance improvement in the disturbing scenarios.
## 3 Results and Discussion
Figure 4: The effect of fluid type and immersion ratio on average battery surface temperature for various discharge rates of (**a**) 1C, (**b**) 2C, (**c**) 3C and (**d**) 4C (T\({}_{\rm inlet}\) = 25 \({}^{\circ}\)C). Figure 5: \(Cont\). Figure 7 represents the effect of coolant inlet temperature on the average surface temperature of batteries for various discharge rates. The comparison for air cooling results is given in Figure 7a, and it can be concluded that inlet coolant temperature has no significant effect on average battery temperatures working in an air medium. Because of the low specific heat capacity of air, it is difficult to condition working fluid efficiently. The results for 50% immersion cooling experiments show that average surface temperatures are slightly lowered with the reduction of cooling water inlet temperature, as seen in Figure 7b. Because of direct contact between distilled water and the cooling tube, the decrease in average temperature can be significantly observed in the 100% immersion experiments. For all discharge rates, the average temperature gradually increases for the coolant temperature of 25 \({}^{\circ}\)C, but the temperature curves are different for the cooling tubes. The temperature curves are different for the cooling tubes. the coolant temperature of 15 \({}^{\circ}\)C. Figure 7c demonstrates that the average temperature of the battery initially decreases and then increases at the discharge rate of 1C. It takes the lower value of 20.5 \({}^{\circ}\)C and rises to 21.2 \({}^{\circ}\)C at the end of discharge. In addition, the average temperature of the battery initially decreases to 23.3 \({}^{\circ}\)C and 24 \({}^{\circ}\)C and then increases up to 24.1 \({}^{\circ}\)C and 25.11 \({}^{\circ}\)C for 2C and 3C, respectively. Figure 6: The effect of discharge rate on average battery surface temperature for (**a**) air, (**b**) 50% immersion, (**c**) 100% immersion (T\({}_{\rm inlet}\) = 25 \({}^{\circ}\)C). 100% immersion. As a result, the coolant inlet temperature has a significant impact on the average battery temperature for 100% immersion. Figure 8a,b depicts the maximum temperature differences with the application of air, 50% immersion 100% immersion cooling for different discharge rates and coolant water inlet temperatures, respectively. Both figures show that the maximum temperature difference of the battery working in 100% immersion is under 5 \({}^{\circ}\)C, which is the desired value for battery performance. In addition, air and 50% immersion cooling are adequate at the discharge rates of 1C and 2C since they are lower than 5 \({}^{\circ}\)C; however, it is obvious that it is difficult to keep the maximum temperature difference in desired value for 3C and 4C. This result shows that 50% immersion shows the highest maximum temperature gradients, which will potentially accelerate the degradation and limit the performance of the cells when cells are discharged at high C-rates. Such a case may happen due to leakage or evaporation of coolant in real applications and should be actively monitored.
###### Abstract
Researchers often need to systematically evaluate and improve novel segmentation methods by comparing them to existing techniques or by selecting the most suitable for existing image content. Different environmental monitoring, urban planning, or disaster management applications demand specialized segmentation methods to extract the required information. Less research is published focusing on developing methodologies that actively consider the unique content of satellite images to adapt segmentation methods, ensuring higher accuracy and applicability. The main contributions introduced in this research can be summarized as follows:  
* We developed a methodology for selecting the most appropriate segmentation algorithm for satellite images using the PROMETHEE multi-criteria decision-making (MCDM) method extended with intuitionistic fuzzy sets (IFSs). In connection with this, we also applied the SWARA weight selection method and proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. * In the suggested methodology, we proposed an extension of the classical PROMETHEE MCDM method by incorporating intuitionistic fuzzy sets (IFSs). Since traditional fuzzy sets do not define negative numerical values, two arrays have been used to separately contain positive and negative outranking flow differences of the proposed PROMETHEE-IFS method. * An innovative approach to constructing a decision-making matrix is introduced. In this methodology, the fuzzy fusion of the image content vector and segmentation algorithm matrix allows for assessing and comparing different segmentation algorithms based on their performance against each visual criterion of image content. The structure of this research is as follows. Section 2 provides an overview of related works. Section 3 consists of a methodology framework summary, analysis of the visual features' set of the satellite image content, segmentation algorithm selection, description of criteria weights, description of the fundamental concepts of intuitionistic fuzzy sets (IFSs), and PROMETHEE modification via intuitionistic fuzzy sets (IFSs). Section 4 demonstrates a practical application of the PROMETHEE-IFS methodology to select the optimal segmentation algorithm for the RGB satellite images. The discussion section (Section 5) addressesthe study's findings and implications, while Section 6 presents concluding observations and outlines potential aspects for future work.
## 1 Introduction
Some studies [16; 17] propose utilizing high-speed interconnections between RSUs to form a local area network (LAN), enabling collaborative computation between MECs to improve computational resource utilization efficiency. Specifically, tasks from the high-load MEC can be offloaded to the low-load MEC for processing through LAN to reduce computation delay. While collaborative sensing can reduce communication resource overhead to some extent, several key issues remain to be addressed. In practice, the sensing range of the RSU usually does not fully overlap with that of the vehicle [18]. As a result, the sensed data obtained by the RSU and the vehicle often differ, complicating the use of collaborative sensing to improve resource utilization efficiency. Thus, accurately identifying redundant data within the overlapping sensing ranges of the RSU and the vehicle is essential. Furthermore, decisions regarding whether tasks are processed locally on the vehicle or offloaded to the RSU significantly affect task computation latency. Additionally, the allocation of communication resources by the RSU to the vehicle directly impacts task transmission delay. Therefore, making reasonable task offloading decisions and efficiently allocating communication resources are crucial to meeting the low processing latency requirements of vehicle tasks. In this paper, we propose a joint task offloading and resource allocation scheme based on RSU and vehicles' collaborative sensing for ISCC-enabled IoV, aiming to minimize the processing delay of all vehicle sensing tasks. The main contributions of our work are as follows:  
* We construct a sensing model to demonstrate the redundant sensing data generated by collaborative sensing between RSUs and vehicles. Based on this model, we propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism for task offloading between vehicles and RSUs. Specifically, vehicles only need to transmit the sensing data that are not redundant with the RSU during task offloading, rather than all the raw sensing data. * We formulate an optimization problem for joint task offloading and communication resource allocation, with the goal of minimizing the processing delay of all vehicle sensing tasks while satisfying the task processing delay constraints for each vehicle. Since the optimization problem is a mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes it into a task offloading decision subproblem and a communication resource allocation subproblem. These subproblems are solved using the Deep Q-Network (DQN) algorithm and the convex optimization algorithm, respectively. Finally, the iterative optimization of these two subproblems obtains a suboptimal solution to the original optimization problem. * To evaluate the performance of our scheme, we conduct simulations by varying several key parameters, including task data size, available bandwidth, and computing resources. The simulation results validate the superiority of our scheme compared to other benchmark schemes.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
## 4 Discussion
### Comparison with AH Plus  
The resin-based sealer AH Plus, selected for comparison with CSCs, has been widely utilized in previous studies and is considered a gold-standard sealer, due to its excellent physicochemical properties and strong adhesion to dentin [19].  
The observations indicate that AH Plus demonstrates greater bond strength compared to CSCs such as EndoSequence BC, EndoSeal MTA, Tech Biosealer Endo, GuttaFlow Bioseal, Endo CPM, Apexit Plus, BioRoot RCS, and MTA Fillapex [23; 24; 25; 27; 29; 33; 41; 43; 45]. This difference may be attributed to covalent bonds between the epoxy resin and the amino groups of dentin collagen, resulting in a stronger bond between AH Plus and dentin compared to the interaction of calcium silicates with dentin. The micromechanical interaction between the root canal wall and the calcium silicate-based sealer, facilitated by tag-like structures, and the chemical interaction via the \"mineral infiltration zone\", establish a weaker bond with dentin compared to epoxy resins [25].  
Contrastingly, [PERSON] et al. [31] and [PERSON] et al. [32] concluded that EndoSequence BC and BioRoot RCS exhibited higher adhesion strengths than AH Plus. The routine use of NaOCl and EDTA, employed to dissolve organic and inorganic components, plays a role in this variation. While NaOCl negatively affects resin cement adhesion, EDTA creates favorable conditions for resin infiltration by exposing dentin collagen. This sequential use of solutions may be effective for hydrophobic epoxy cements but may not be suitable for calcium silicate-based cement types [32]. [PERSON] et al. [32] proposed exploring alternative methods like photodynamic therapy for root canal disinfection when using calcium silicate cements, as NaOCl/EDTA reduces adhesion to dentin [32].