esa-sceva/satcom-chunk-collection huggingface dataset:
# Bloch-Surface-Polariton-Based Hybrid Nanowire Structure for Subwavelength, Low-Loss Waveguiding
###### Abstract  
Surface plasmon polaritons (SPPs) have been thoroughly studied in the past decades for not only sensing but also waveguiding applications. Various plasmonic device structures have been explored due to their ability to confine their optical mode to the subwavelength level. However, with the existence of metal, the large ohmic loss limits the propagation distance of the SPP and thus the scalability of such devices. Therefore, different hybrid waveguides have been proposed to overcome this shortcoming. Through fine tuning of the coupling between the SPP and a conventional waveguide mode, a hybrid mode could be excited with decent mode confinement and extended propagation distance. As an effective alternative of SPP, Bloch surface waves have been re-investigated more recently for their unique advantages. As is supported in all-dielectric structures, the optical loss for the Bloch surface wave is much lower, which stands for a much longer propagating distance. Yet, the confinement of the Bloch surface wave due to the reflections and refractions in the multilayer structure is not as tight as that of the SPP. In this work, by integrating a periodic multilayer structure that supports the Bloch surface wave with a metallic nanowire structure, a hybrid Bloch surface wave polariton could be excited. With the proposed hybrid nanowire structure, a hybrid mode is demonstrated with the deep subwavelength mode confinement and a propagation distance of tens of microns.  
Keywords:Bloch surface polariton; surface plasmon polariton; waveguide; hybrid mode +
Footnote †: journal: Applied Physics Letters
## 3 Methodology
### Extraction of Drought Characteristics
#### 3.1.1 Spei  
SPEI [42] is calculated based on precipitation and potential evapotranspiration (\(PET\)). Its calculation proceeds in three main steps. Firstly, the monthly mean water balance \(D\) is calculated as follows [29]:  
\[D=P-PET, \tag{1}\]  
where \(P\) is the monthly precipitation (mm) and \(PET\) is the monthly potential evapotranspiration (mm) calculated with the Thornthwaite method [43].  
The cumulative monthly mean water balance value (\(D_{n}^{k}\)) at different time scales can be derived with Equation (2) [42]  
\[D_{n}^{k}=\sum_{i=0}^{k-1}(P_{n-1}-PET_{n-i}), \tag{2}\]  
where \(k\) is the cumulative number of months and \(n\) is the number of calculations.  
Secondly, water balance is standardized using a logarithmic probability distribution function, such as \(F(x)\). Thirdly, the SPEI index time series are calculated with Equation (3) [29]  
\[\begin{split} SPEI&=w-\frac{c_{0}+c_{1}p+c_{2}w^{2} }{1+d_{1}w+d_{2}w^{2}+d_{3}w^{3}}\qquad\quad P\leq 0.5,\\ SPEI&=-\Big{(}\frac{c_{0}+c_{1}w+c_{3}w^{2}}{1+d_{ 1}w+d_{2}w^{2}+d_{3}w^{2}}\Big{)}\quad P>0.5,P=1-P,\end{split} \tag{3}\]  
where the probability weighted moment is \(w=\sqrt{-2 ln(P)}\) and the probability of exceeding a certain value is \(P=1-F(x)\); the other parameters are \(c_{0}=2.515517\), \(c1=0.802853\), \(c2=0.010328\), \(d1=1.432788\), \(d2=0.189269\), and \(d3=0.001308\). If \(P>0.5\), then \(P\) should be replaced with \(1-P\) because the sign of SPEI is reversed.  
According to [PERSON] and [PERSON] et al. [3; 7], SPEI index values represent the degree of drought or humidity; large SPEI values usually indicate moist or wet conditions, and small values generally represent dry situations. SPEI index values can be generally divided into different categories that represent the degree of drought or humidity by setting different thresholds, as shown in Table 1.
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Classification Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline benign & DenseNet121 & 0.78 & 0.92 & 0.67 & 0.77 \\ malignant & DenseNet121 & 0.83 & 0.62 & 0.91 & 0.74 \\ normal & DenseNet121 & 0.93 & 0.76 & 0.89 & 0.82 \\ AVERAGE & DenseNet121 & **0.8467** & **0.7667** & **0.8233** & **0.7767** \\ benign & EfficientNet\_b3 & 0.74 & 0.88 & 0.62 & 0.73 \\ malignant & EfficientNet\_b3 & 0.77 & 0.54 & 0.91 & 0.68 \\ normal & EfficientNet\_b3 & 0.94 & 0.88 & 0.75 & 0.75 \\ AVERAGE & EfficientNet\_b3 & **0.8167** & **0.7667** & **0.76** & **0.72** \\ benign & GoogleNet & 0.75 & 0.88 & 0.63 & 0.74 \\ malignant & GoogleNet & 0.8 & 0.59 & 0.91 & 0.71 \\ normal & GoogleNet & 0.9 & 0.69 & 0.71 & 0.7 \\ AVERAGE & GoogleNet & **0.8167** & **0.72** & **0.75** & **0.7167** \\ benign & ResNet50 & 0.75 & 0.95 & 0.58 & 0.72 \\ malignant & ResNet50 & 0.78 & 0.56 & 0.95 & 0.71 \\ normal & ResNet50 & 0.93 & 0.75 & 0.86 & 0.8 \\ AVERAGE & ResNet50 & **0.82** & **0.7533** & **0.7967** & **0.7433** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of model performance of Brest dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline glioma\_tumor & DenseNet121 & 0.85 & 0.98 & 0.42 & 0.59 \\ meningioma\_tumor & DenseNet121 & 0.91 & 0.76 & 0.99 & 0.86 \\ no\_tumor & DenseNet121 & 0.93 & 0.78 & 1 & 0.88 \\ pituitary\_tumor & DenseNet121 & 0.97 & 0.97 & 0.88 & 0.92 \\ AVERAGE & DenseNet121 & **0.915** & **0.8725** & **0.8225** & **0.8125** \\ glioma\_tumor & EfficientNet\_b3 & 0.86 & 1 & 0.44 & 0.61 \\ meningioma\_tumor & EfficientNet\_b3 & 0.9 & 0.74 & 1 & 0.85 \\ no\_tumor & EfficientNet\_b3 & 0.93 & 0.79 & 1 & 0.88 \\ pituitary\_tumor & EfficientNet\_b3 & 0.97 & 1 & 0.84 & 0.91 \\ AVERAGE & EfficientNet\_b3 & **0.915** & **0.8825** & **0.82 & **0.8125** \\ glioma\_tumor & GoogleNet & 0.84 & 1 & 0.38 & 0.55 \\ meningioma\_tumor & GoogleNet & 0.89 & 0.72 & 1 & 0.84 \\ no\_tumor & GoogleNet & 0.94 & 0.81 & 1 & 0.9 \\ pituitary\_tumor & GoogleNet & 0.98 & 1 & 0.92 & 0.96 \\ AVERAGE & GoogleNet & **0.9125** & **0.8825** & **0.825** & **0.8125** \\ glioma\_tumor & ResNet50 & 0.82 & 1 & 0.28 & 0.44 \\ meningioma\_tumor & ResNet50 & 0.86 & 0.68 & 1 & 0.81 \\ no\_tumor & ResNet50 & 0.94 & 0.81 & 1 & 0.89 \\ pituitary\_tumor & ResNet50 & 0.98 & 1 & 0.91 & 0.95 \\ AVERAGE & ResNet50 & **0.9** & **0.8725** & **0.7975** & **0.7725** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of model performance of BT datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline  
AVERAGE & ResNet50 & **0.932** & **0.672** & **0.49** & **0.522** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of model performance of DR datasets.
## 4 Case Study and Results Analysis
### KELM for Load Forecasting
Practice has proved that using FWA algorithm to select KELM method \(C\) and \(\sigma\) can obtain better optimization effect, while KPCA method can reduce redundant data while ensuring the integrity of input information, so as to achieve ideal forecasting effect. In summary, the method proposed in this article optimizes the KELM method by the FWA algorithm and obtains the appropriate parameters \(C\) and \(\sigma\) in the KELM method, which can effectively decrease the load forecasting error. On the one hand, KPCA method can ensure the integrity of input information, on the other hand, it can decrease the noise in the input variables to enhance the effectiveness of input variables, so as to enhance the accuracy and stability of distributed energy system load prediction. The data calculation results prove the effectiveness and stability of the load prediction method proposed in this article.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 3 Circuital Model
### Transmission Line  
SPIDER TL is represented as a multiconductor transmission line, by means of per unit length equivalent R-L-C blocks, with inductive and capacitive mutual couplings. To correctly simulate the behavior of the TL at the frequency of interest (1 MHz) with the equivalent R-L-C lumped representation, more than 50 elements per wavelength were used in the model [13]. The reference conductor is chosen to be the outer conductor, described in Section 2.3. Considering the geometry represented in Figure 3, the parameters per unit length were computed with FEMM. Details are described in the following section.  
* Capacitances: the 2D planar electrostatic problem of the TL geometry was analyzed, iteratively applying a unitary voltage to one conductor and fixing the others to zero potential, thus obtaining the Maxwell capacitances matrix. This matrix was then used to obtain the mutual capacitances between conductors.  
Figure 3: SPIDER transmission line considered in the circuital model. (**a**) SPIDER TL cross-section (not in scale). (**b**) Equivalent pi-representation of the multiconductor TL.  
Figure 2: Detail of ISRF unit. The original CVD configuration (CVD II) and the modified one (CVD I) are both represented.  
* Inductances: the approach of partial inductances was used to compute the self and mutual inductances of the TL conductors. In FEMM, a Dirichlet boundary condition was imposed at the outer conductor of the TL; thus, we consider that all the currents flowing in the system are contained within that boundary.
* Resistances: the RF resistances were computed in FEMM solving the 2D planar magnetic problem, at a frequency of 1 MHz. The current was applied singularly to the conductors.  
The distributed core snubber elements were modeled with a multiwinding transformer.
## 3 Results and Discussions
### Comparison with Existing Techniques  
Unlike the Izod and Charpy techniques, which primarily measure energy without load and displacement data, this setup provides a more comprehensive assessment of material behavior. The drop-weight technique is limited by gravity's effect, as the weight continues impacting the specimen until all the energy is absorbed, making it unsuitable for free-collision scenarios. In contrast, our spring-actuated system allows for controlled and repeatable impacts, independent of gravity, providing accurate measurements of load and displacement.  
A key advantage of the proposed setup is its versatility, allowing the testing of various materials and specimen configurations. The specimen mounting frame (Figure 1) accommodates different support systems, such as the three-point bending system used in this study (Figure 4), as well as steel and reinforced concrete frames. This flexibility is not available in the Izod, Charpy, or drop-weight techniques, which offer limited configurations for support systems. Additionally, the device can test materials like ceramics, composites, and metals, expanding its applicability across multiple fields. A brief comparison of capabilities and limitations is provided in Table 7.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Specification** & \begin{tabular}{c} **Proposed** \\ **Spring-Actuated Setup** \\ \end{tabular} & **Izod Technique, [20]** & **Charpy Technique, [20]** & **Drop-Weight Technique, [21]** \\ \hline Measurement Parameters & \begin{tabular}{c} Impact velocity, force, \\ displacement \\ \end{tabular} & Energy only & Energy only & Impact load, force \\ \hline Impact Energy, J & 0.005-130,000 & Limited (<100) & Limited (<100) & Moderate \\ \hline Load and Displacement Data & Yes & No & No & Yes \\ \hline Free-Collision Scenario & Yes & No & No & No \\ \hline Material Flexibility & \begin{tabular}{c} Steel, ceramics, \\ composites, metals \\ \end{tabular} & \begin{tabular}{c} Limited to specific \\ materials \\ \end{tabular} & \begin{tabular}{c} Limited to specific \\ materials \\ \end{tabular} & Limited to certain materials \\ \hline Support System Flexibility & \begin{tabular}{c} Supports various systems \\ (3-point, etc.) \\ \end{tabular} & Fixed setup (not versatile) & Fixed setup (not versatile) & Fixed (limited flexibility) \\ \hline Key Limitations & \begin{tabular}{c} Requires accurate friction \\ calibration \\ \end{tabular} & \begin{tabular}{c} Does not provide \\ load/displacement data \\ \end{tabular} & \
\begin{tabular}{c} Does not provide \\ load/displacement data \\ \end{tabular} &
\begin{tabular}{c} Repeated impact due to \\ gravity, not free-collision \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed spring-actuated setup with existing low-velocity impact testing techniques.  
Figure 10: A comparison of the measured and predicted (**a**) mid-span displacements and (**b**) impact forces.
# Network Visualization of Cooperative Games
## 6 Examples
### Myerson Games  
#### 6.5.1 Introduction to Myerson Games  
Traditional cooperative games assign a value \(v(S)\) to every coalition \(S\subseteq N\) without considering any underlying structure that might restrict cooperation. In contrast, a Myerson game incorporates a communication network \(\gamma\) that restricts which subsets of players can feasibly cooperate. Specifically, the value of any coalition \(S\) is computed by first partitioning \(S\) into its connected components with respect to \(\gamma\), and then summing the contributions of these components as in Equation (4).  
This formulation reflects real-world scenarios where cooperation is limited by physical connectivity, leading to strategic interactions that depend on the network topology. Therefore, solution concepts, such as the Myerson value in Equation (5), are developed to allocate the total game value by accounting for the structural constraints imposed by \(\gamma\).  
To illustrate the network representation of a Myerson cooperative game \((N,v,\gamma)\in\mathcal{CS}^{N}\), we present examples using three graphical representations (cycle, chain, and star), two symmetric characteristic functions (a unitary cost share game, as shown in Equation (12) and a message game, as in Equation (13), and ten players (\(n=10\)).  
\[v^{\gamma}(S)=-\sum_{C\in S/\gamma}1 \tag{12}\]  
In Equation (12), each connected component \(C\) of \(S\) incurs a unit cost. Thus, if \(S\) is connected (i.e., \(S/\gamma\) consists of one component), then \(v^{\gamma}(S)=-1\), and if \(S\) splits into \(k\) connected components, then \(v^{\gamma}(S)=-k\).  
\[v^{\gamma}(S)=\sum_{C\in S/\gamma}\binom{|C|}{2} \tag{13}\]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \(s_{i}\) & \(R=I^{+}\) & \(R=I^{-}\) & \(R=I\) & \(R=I^{|-|}\) \\ \hline Maximum & 2.0317 & 1.0317 & 1 & 3.0634 \\ Q3 & 2.0317 & 1.0317 & 1 & 3.0634 \\ Mean & 2.0317 & 1.0317 & 1 & 3.0634 \\ Median & 2.0317 & 1.0317 & 1 & 3.0634 \\ Q1 & 2.0317 & 1.0317 & 1 & 3.0634 \\ Minimum & 2.0317 & 1.0317 & 1 & 3.0634 \\ Standard Deviation & 0 & 0 & 0 & 0 \\ Skewness & 0 & 0 & 0 & 0 \\ Kurtosis & 0 & 0 & 0 & 0 \\ Gini Index & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Descriptive metrics for the Shoe Game relationship matrices.  
Meanwhile, in Equation (13) \((\frac{|\mathcal{C}|}{2})\) counts the number of unordered pairs of players in \(C\), so that \(v^{\gamma}(S)\) equals the total number of potential pairwise interactions within all connected components of \(S\).
## 1 Introduction
[11; 20] employed background profiles and error covariance matrix under cloud scenario conditions in 1 DVAR, so the retrieval results of the area within TCs had been obviously improved, particularly in the lower layers. Furthermore, based on the work of [PERSON] and [PERSON] [20], [PERSON] and [PERSON] [21] designed a dynamic background error covariance matrix, which can select and use a corresponding covariance matrix based on different atmospheric conditions. In comparison to the static covariance matrix, the retrieved humidity and temperature profiles under TC conditions exhibited significantly reduced errors. Using the Advanced Radiative Transfer Modeling System (ARMS) as the observation operator in 1 DVAR can improve the retrieval results. Therefore, the experiment for warm core retrieval in this study is also conducted on this basis. The remaining chapters and their main contents of this article are as follows: Section 2 introduces the MWHS and MWTS onboard FY-3F and FY-3E and ATMS onboard NOAA-20. Moreover, the polarization of FY-3F MWHS has been revised. Section 3 evaluates the instruments. Section 4 shows the TC warm core structure retrieved from FY-3F observation data. Section 5 concludes and discusses the findings of this paper.
## 2 Materials and Methods
### Determination of Phenolic Compounds of Eggplant
#### 2.5.2 Anthocyanin Compounds  
Extraction procedure. The lyophilized samples (1 g) were extracted using the conventional methodology applied by this research group [20]; ethanol/water (80:20, v/v) acidified with 0.5% of citric acid was used as extraction solvent.  
Analytical method. The chromatographic analysis was made using a system previously described in Section 2.5.1 and the separation was achieved using an AQUA(Phenomenex) reverse phase C18 column (5 um, 150 \(\times\) 4.6 mm i.d.) thermostatted at 35 \({}^{\circ}\)C, using a UPLC-DAD-ESI/MSn system (Ultra-Performance Liquid Chromatography coupled with Diode Array Detection and an Electrospray Ionization Mass Spectrometry) previously mentioned. Detection was carried out using a DAD (520 nm) and a mass spectrometer (Linear Ion Trap LTQ XL Thermo Finnigaj equipped with an ESI source, operating in positive mode. Compounds identification was performed using the retention time, UV-Vis, and mass spectra data in comparison with available standards and literature review. For quantitative analysis, calibration curves were obtained by injection of standard solutions with known concentrations (0.25-50 ug/mL): cyanidin-3-O-glucose (y = 97,787x - 743,469; R2 = 0.9999), penoidine (y = 110,391x - 1 \(\times\) 10\({}^{6}\); R2 = 0.9999), and pelargonidin-3-O-glucoside (y = 43,781x - 275,315; R2 = 0.9989). The results were expressed in mg of anthocyanin/g of extract [19; 20].  
Evaluation of the Bioactive Properties of Solanum melongena L. Fruits: Epicarp, Pulp, and Whole Fruit, through In Vitro Tests  
The lyophilized extract, provided by the extraction procedures (Section 2.5.1), was re-dissolved: (i) in culture medium (10 mg/mL) for antimicrobial activity assay; (ii) in distilled water at a concentration of 8 mg/mL for the evaluation of cytotoxic, hepatotoxic, and anti-inflammatory activity; and (iii) in a hydroethanolic solution (ethanol/water; 80:20, v/v), in a concentration of 5 mg/mL, for antioxidant activity evaluation. These solutions were diluted successively in order to obtain the working concentrations.
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 3 Finite Element Method
### Choice of the Finite Element Spaces  
The choice of Finite Element spaces \(V_{h}\) and \(Q_{h}\) must satisfy the well-known inf-sup (or LBB) condition (2). The lowest order pair uses \(P_{1}\) functions for the space \(Q_{h}\) and \(P_{2}\) functions for \(V_{h}\). This type of choice is sometimes referred to as Taylor-Hood approximation and it will be the one used in this work. Figure 1 shows the two kind of elements used for the spaces \(Q_{h}\) and \(V_{h}\).
###### Abstract
Besides, thermal effects reduce material damage [9; 25], thus generally making it impossible to use heat treatment to determine specimen damage proceeding from data on elastic modulus evolution. At high deformation temperatures, relaxation processes associated with dynamic recovery and recrystallization occur in metal materials. This forms new grains and decreases texturization and residual stresses [26; 27; 28; 29]. Thus, the deformation of metal materials under conditions of dynamic structure formation can form material microstructure suitable for the determination of damage in metallic materials from microindentation data. Numerous studies have reported on the effect of thermomechanical conditions on damage accumulation [9; 25; 30; 31; 32; 33] and damage healing [9; 25; 34; 35] in metals and alloys in terms of both the mechanics of solids [9; 25; 31; 32; 33] and physical material science [25; 30; 34]. Unlike the case with alloys, interface boundaries between the phase constituents of metal matrix composites act more clearly as sources for the formation of discontinuities and barriers for micro- and macrocrack growth. As a result, the models relating the history of changes in temperature and the stress-strain state to damage accumulated in a metal matrix composite due to the presence of reinforcing particles may in general differ from previously constructed models for alloys. A limited number of studies deal with this problem [36; 37; 38; 39], and this makes it relevant to study damage evolution in metal matrix composites under various thermomechanical conditions. The goal of this article is to evaluate the applicability of data on elastic modulus evolution at the micro-level to the study of damage in metal matrix composites under high-temperature deformation conditions. The best type of metal matrix composites used to estimate the damage measuring method is a composite produced by powder technology since microcracks can be much more easily detected in it than in a composite made by liquid-phase technology. This is what determined the choice of the method of composite manufacturing.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography
### Liver
#### 5.1.1 Elastography Techniques in Liver Disease Diagnosis  
Shear wave imaging has proven to be the most accurate type of imaging for chronic liver diseases, primarily relying on 1D-TE, but can be detected with pSWE and 2D-SWE. However, due to the fact that pSWE and 2D-SWE are both newer techniques in contrast to 1D-TE, the latter method is predominantly used following its extensive research and diagnostic accuracy in the detection of fibrosis. The use of 1D-TE in liver pathology detection is mostly referred to as Fibroscan. The device measures the propagation speed of the shear waves generated by the vibration, and this information is used to estimate liver stiffness. Throughout the use of 1D-TE, research has shown that histopathologic fibrosis stages correspond with liver stiffness. In multiple studies conducted on liver fibrosis, using liver biopsy as the reference, they found that 1D-TE was the most authentic way to diagnose F4 fibrosis rather than F2 or F3. These results are indicative of how poorly the 1D-TE technique can differentiate between the various stages of fibrosis, but it still has the gift of being the most useful method to detect cirrhosis, unlike the pSWE technique that can detect F4 cirrhosis but is slightly less exact than 1D-TE. Some physicians encourage the use of pSWE to gather stiffness information from both the liver and the spleen, given the fact that pSWE may help cover a larger area than 1D-TE [74; 75; 76].  
Many other studies compared 1D-TE and 2D-SWE and found that 2D-SWE was better at detecting significant (F2) and severe (F3) fibrosis than 1D-TE and pSWE. 2D-SWE uses ARFI, which aids in the inducement of shear waves in the organ of interest at more than one point. We conducted a comparison study among all three modalities to determine the accuracy of each individual method. A total of 349 patients participated in this study, and we used a liver biopsy as the reference [76]. Following studies conducted across all patients and modalities, 2D-SWE (also referred to as supersonic imaging) has shown the highest authenticity when it comes to detecting significant (F2) and severe fibrosis (F3) compared to both pSWE and 1D-TE, making it the only modality with the ability to fully differentiate between the four stages of fibrosis. Even though 2D-SWE has more benefits than 1D-TE, the latter is still being used primarily due to the recent development and new nature of 2D-SWE [77].