esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Verification of the Dynamic Model of Turbine Blades Coupled by a Sealing Strip
## 5 Numerical Results
### A Warning on the Inadequacy of the Full-Stick Linear Prediction  
The other linear FRFs produced whenever studying solid dampers are the full stick curves. In full stick, the strip is linked to the blades using the contact springs (see Figure 9b). In the case of solid dampers, these curves are representative of low excitation levels, i.e., the ratio between the excitation force on the blade and the centrifugal force on the damper is so low that the platforms' relative displacement is not sufficient to cause the damper to slip. These curves are often used in the damper design procedure as they can be easily computed using linear analysis and they serve as limit curves: they signal the maximum frequency shift produced by the damper.  
The IP and OOP full stick curves for the system at hand are shown in Figure 9a. These curves are quite different from those found experimentally (both in terms of amplitude and peak frequency), even at the lowest excitation level \(|F_{E}|\) = 0.1 N. The full stick assumption is incompatible with the testing conditions because the excitation level, here defined as the ratio between \(|F_{E}|\) and the centrifugal load on the damper is too high. In detail, the mass of a strip damper, and consequently the resulting contact pressure (i.e., 0.3 MPa), is more than one order of magnitude lower than those found on a solid damper. Therefore, even if the \(|F_{E}|\) is set at the lowest possible value compatible with the shaker and control system capabilities, the excitation level on the system is too high to be compatible with the full stick condition.  
It can therefore be concluded that the full stick assumption (depicted in Figure 9b) is not easily observable and therefore not a relevant indication in the case of strip dampers, as the high \(|F_{E}|\)/centrifugal load ratio causes the strip to partially detach in case of IP vibration and to slide in case of OOP vibration, as shown in Figure 5.  
The designer should therefore refer directly to the nonlinear calculation summarized in Section 5.2.
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 4 Outlook  
### New Etching Techniques  
The ever-increasing integration of integrated circuits demands TSVs with smaller feature sizes and diameters. However, the conventional TSV etching process is inadequate for future integrated circuits with feature sizes smaller than 3 nm. Therefore, researchers are exploring new etching techniques to realize TSV fabrication. Chemical or physicochemical combinations are promising approaches for TSV etching, which are expected to replace the traditional single etch mode. Several research groups are working on developing new etching techniques, such as using magnetic fields to attract metal catalysts deposited on silicon surfaces for faster etch speeds when fabricating high-magnification grating structures. In addition, thermal atomic layer etching is a viable solution for atomically accurate material removal and TSV fabrication on a very small scale.  
Furthermore, TSVs are not only becoming smaller, but also denser. The ever-increasing number of TSVs per unit area poses new challenges to TSV fabrication techniques. Therefore, new methods for controlling the depth and diameter of TSVs are being explored. For example, high-speed plasma beams can be used to etch TSVs with a high aspect ratio and uniform diameter. Moreover, novel materials with improved thermal and electrical properties are being investigated to enhance TSV performance and reduce power consumption. For instance, researchers are exploring the use of graphene, carbon nanotubes [73; 74], and other 2D materials [75] as TSV liners and interconnects due to their high thermal conductivity and excellent electrical properties.  
In addition to new etching techniques and materials, TSV reliability and quality assurance are critical factors for successful 3D IC integration. Non-destructive metrology techniques have been developed to measure TSV dimensions, integrity, and filling defects. Moreover, advanced simulation tools, including finite element methods and artificial intelligence and machine learning, are used to predict and optimize TSV performance, reliability, and yield. However, there is still a need for complete design tools and methodologies to address the challenges posed by TSV fabrication and integration in 3D ICs. Therefore, further research is needed to develop robust and efficient TSV fabrication processes, quality control techniques, and design methodologies to enable the realization of advanced 3D ICs [22; 23; 76].
## 3 Numerical Model of the Single-Stage FLTD
### Nonuniform Grid  
For complex models, to ensure the computation precision, a finer mesh is required for accurate simulation of the small geometrical details. However, this results in an increase in the total number of DoFs and a decrease in the time step of the explicit updating of TD-FIT. Thus, the required computer resources and computation time will increase dramatically.  
The nonuniform-grid or sub-grid technique can be employed as a solution, i.e., fine mesh is introduced only in regions where the EM field changes drastically while coarse mesh is used in the rest of the computational domain, accomplishing a good computational balance between accuracy and efficiency [39]. In the nonuniform grid, to minimize EM reflections and numerical dispersion, a transition sub-region with gradually changed mesh size should be set between the fine- and coarse-uniform sub-regions. In general, magnetic field components are still located at the midpoints of primal cell facets, whereas electric field components are off-centered between the two adjacent magnetic field components. Application of uniform grid only changes the facet's area and edge's length in Equations (15)-(17) and time step \(\Delta t\).
## 4 Implementation Details
### Dataset Description  
The network presented in the previous section was applied to the whole heart segmentation task and is evaluated on a dataset provided by Multi-Modality Whole Heart Segmentation Challenge (MM-WHS) organized by MICCAI [32]. The MM-WHS dataset consists of 60 cardiac CT/CTA and 60 cardiac MRI volumes, whereas 20 volumetric images include corresponding ground truths, manually labeled by two clinical experts. In contrast, the remaining 40 volumetric images are used for testing purposes. Ground truths of the testing dataset are provided in encrypted form and can be decoded to evaluate algorithms using the procedure described in [32]. The data were collected on patients from the everyday clinical environment, and it has a various quality to preserve the robustness of the developed algorithms when it comes to real clinical usage. The cardiac CT/CTA data are obtained using 64 slice CT scanners using a standard coronary CT angiography protocol, and cardiac MRI data were acquired using a navigator-gated 3D balanced steady-state free precession (b-SSFP) sequence for free-breathing whole heart imaging. The axial slices' in-plane resolution is \(0.78\times 0.78\) mm, and the average slice thickness is 1.60 mm. The data include the following seven structures of the heart: (1) the left ventricle blood cavity (LV), (2) the right ventricle blood cavity (RV), (3) the left atrium blood cavity (LA), (4) the right atrium blood cavity (RA), (5) the myocardium of the left ventricle (Myo), (6) the ascending aorta (Ao), which is defined as the aortic trunk from the aortic valve to the superior level of the atria, and (7) the pulmonary artery (PA). An example of one slice from the used dataset is shown in Figure 3.
## 2 Computational Model
### Computational Domain and Boundary Conditions  
As referenced in the introduction section, the current study extended the author's previous work that involved optimizing the pump's geometry. Hence, the optimized geometry was utilized for the present study to report the impact of the particles' concentration, size, pump exit pressure, and cavitation on the erosion of the pump's blades and casing. The computational domain is shown in Figure 1, consisting of two sub-domains, i.e., the impeller (rotating at 1533 rpm) and the impeller's casing (stationary). The interface between the rotating and stationary domains was modeled as a frozen rotor to mollify the conservation of all fluxes from impeller to casing. The inlet of the impeller was assigned with total inlet pressure and mass fractions of both phases. At the same time, static pressure was used at the exit of the casing.  
The impact of the four parameters (solid particle's concentration, size, pump exit pressure, and cavitation) was evaluated on the erosion characteristics of the pump's impeller and pump's casing. Five values each for the first three parameters (solid particle's concentration, size, and pump exit pressure) were considered as listed in Table 1. Finally, to study the impact of cavitation, the inlet pressure was adjusted to ensure that the pressure values within the pump dropped lower than the saturation pressure of the water. For this simulation, four values of the particle's concentration, i.e., 5%, 7.5%, 10%, 12.5%, 15%, 20%, 25% were employed.  
Figure 1: Computational domain and boundary conditions and polylines used for the data postprocessing.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Simulations**} & \multirow{2}{*}{\begin{tabular}{c} **Exit Pressure** \\ _Pexit_ **[bar]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Particle** \\ **Concentration** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Particle** \\ **[\(\mu\)m]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **[\(\mu\)m]** \\ \
## 4 Conclusions  
In conclusion, we successfully synthesized an oxidation resistant bis-phenyl boronate derivative with a large Stokes shift and long excitation and emission wavelengths by adding an acetyl moiety to the anthracene ring. The Stokes shift was the largest among the reported diboronic anthracene dyes. Moreover, the excitation wavelength was 420 nm, which was the longest of the reported diboronic anthracene dyes. The large Stokes shift and long excitation wavelength would allow the easy selection of an excitation-detecting unit and in vivo glucose monitoring. Moreover, the acetylated diboronic acid anthracene demonstrated good binding affinity and selectivity for glucose and an identical resistance to oxidation compared to the non-acetylated analog. The improved optical and glucose-detecting properties of the newly synthesized acetylated trifluoromethyl diboronic anthracenes can allow easy fabrication and reduce the power consumption of the system. Therefore, we expect that the reported dye in this work will be utilized to develop an affordable and long-term usable implantable CGM system for widespread use.  
The followings are available online at [[https://www.mdpi.com/article/10.3390/app12062782/s1](https://www.mdpi.com/article/10.3390/app12062782/s1)]([https://www.mdpi.com/article/10.3390/app12062782/s1](https://www.mdpi.com/article/10.3390/app12062782/s1)). Figure S1: Excitation spectra in the presence of glucose for AcTDA (a) and TDA (b). Figure S2: The modified Benesi-Hildebrand plots of AcTDA with fructose (a), galactose (b), and mannitol (c), Figure S3: Association constant of AcTDA for mannitol.  
[PERSON] designed and synthesized the dyes; [PERSON] and [PERSON] carried out the characterization of the dyes; [PERSON] worked on the data presentation; [PERSON]-s.Y. and [PERSON] conceived these studies. All authors have read and agreed to the published version of the manuscript.  
This work was funded by a National Research Foundation of Korea grant funded by the Korean government (NRF-2019M3E5D1A02068242).  
Not applicable.  
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 Research Area and Research Method
### Test Method  
1. Total nitrogen and forms of nitrogen  
Total nitrogen: To determine the total nitrogen content, soil samples passed through a 0.25 mm sieve were weighed, placed into deionized water and boiled in concentrated sulfuric acid to convert all nitrogen-containing compounds into ammonium nitrogen. Then, a standard hydrochloric acid titration solution, distillation and titration were used in conjunction with an automatic nitrogen analyzer.  
Ammonia and nitrite were extracted by potassium chloride solution in alkaline and acidic environments, respectively. After the addition of the chromogenic agent, calibration curves were drawn at 630 nm and 543 nm by a Perkin Elmer Lambda 900 ultraviolet/visible/near-infrared spectrophotometer, and the absorbance was measured. The nitrate was reduced to nitrite, the total amount of nitrite was measured and the amount of nitrate was obtained by subtracting the amount of original nitrite obtained in the previous step [12].  
The detection limits for ammonia, nitrite nitrogen and nitrate nitrogen in the sample were 0.2 mg/kg, 0.15 mg/kg and 0.25 mg/kg, respectively, with an error range of 5%.  
2. Granularity  
Air-dried soil samples were passed through a 2 mm sieve, and hydrogen peroxide and hydrochloric acid were added to remove organic matter and CaCO\({}_{3}\). Finally, the soil samples were rinsed with deionized water to remove excess HCl and other chlorides; then, the soil particle size was determined by a Mastersizer 2000 laser particle size analyzer [13].  
While being heated, excess potassium dichromate-sulfuric acid solution was used to oxidize soil organic carbon. The excess potassium dichromate was titrated with a ferrous sulfate standard solution, and the organic carbon content was calculated from the amount of consumed potassium dichromate according to the oxidation correction coefficient amount multiplied by the constant 1.724 to obtain the organic matter content [14].  
pH  
Air-dried soil samples that had been passed through a 2 mm sieve were weighed, deionized water was added according to the soil-water ratio of 1:2.5 to prepare a solution and three standard buffer solutions of phthalate, phosphate and borate at room temperature were added. To measure the pH, a 3510 Portable Multiparameter Water Quality Analyzer for Determination of Solution pH was used [15].  
CEC (cation exchange capacity)  
Air-dried soil samples that had been passed through a 2 mm sieve were weighed, and ammonium acetate solution was added repeatedly to treat the soil. The excess ammonium acetate was rinsed off with 95% ethanol; then, deionized water was used to wash all the soil sample residues into the NKB-3200 automatic nitrogen analyzer for digestion. The tube was distilled, the absorption solution was titrated with a 0.05 mol/L hydrochloric acid standard solution and a blank experiment was performed at the same time [16].
## 3 Optimisation Approach
The use of a numerical approach introduces an error that depends on the level of discretisation. On the other hand, the greater the accuracy, the narrower the discretisation and the larger the computational time. A trade-off is to be sought between the performance and computational cost. For the simulations presented in this paper, the curvilinear abscissa was discretised with steps of around 0.1 m, resulting in calculation times of up to a few seconds, always lower than the actual lap times. The step size is in line with suggestions from the literature [13].
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 3 Materials and Methods
### Interpretable KAN Model
For the arrhythmia classification task, the input dimension of the KAN corresponds to the length of the ECG signal (187), the classification output dimension is 5, and the intermediate node count is 128. The residual activation function serves as the core component of the KAN model. During training, the quadratic spline residual value sampling method  
Figure 4: Schematic diagram of the 2-layer framework of the KAN model in the ECG signal classification task. iteratively samples and fits the function, thereby learning an optimal activation function. A critical step in optimizing the KAN layer involves defining the activation function as the sum of the basis and the spline function. This means that the activation function \(\varphi(x)\) is represented as the sum of the basis function \(b(x)\) and the spline function [5]. The calculation formula is as follows:  
\[\varphi(x)= b(x)+\text{spline}(x) \tag{6}\] \[b(x)=\text{silu}(x)=\frac{x}{1+e^{-x}} \tag{7}\]  
The spline function is usually parameterized as a linear combination of splines:  
\[\text{spline}(x)= \sum_{i}c_{i}B_{i}(x) \tag{8}\]  
In the classification of arrhythmia diseases by ECG signals, \(c_{i}\) is a trainable parameter used to adjust the weight of each spline function. \(B_{i}(x)\) is the spline function of \(B\), which is used to form a spline combination. In addition, we further explored the characteristics of the KAN in adapting to ECG signal classification of arrhythmia diseases. It includes three parts, as described below:  
1. KAN incorporates a learnable univariate function. As demonstrated in Equation (8), the weight of each edge is not a fixed value but rather a dynamically learnable univariate function. This design enables flexible application of complex nonlinear transformations on each edge, thus enhancing the capture of characteristic distributions in ECG signals. 2. Feature visualization facilitates the interpretation of ECG signal feature distributions. In the arrhythmia classification task, when different categories of ECG signals are input, key features--such as QRS waveforms or characteristic signal change curves--exhibit progressively significant classification differences. 3. The tree structure diagram illustrates the forward propagation process. As illustrated in Figure 4, the tree structure represents each node as a feature combination, with edges denoting the dependency relationships between features. This representation strategy aids in understanding the model's decision-making mechanism and offers potential clinical interpretability, such as elucidating synergies among ECG signals. In summary, the KAN's [5] dynamic nonlinear modeling capabilities and robust interpretability render it particularly well-suited for ECG signal classification tasks, offering new opportunities for precise intelligent diagnosis of arrhythmia diseases and clinical decision-making support.
# Experimental Setup and Graphical User Interface for Zero-Length Column Chromatography
## 2 Mathematical Model of ZLC  
Basic theory of ZLC has been extensively discussed in previous contributions [14] and references therein. However, the introduction of the equations to be used later may facilitate the understanding of the analyses performed. That is why we will introduce them in this section.  
For a linear equilibrium system, perfectly mixed, with uniform spherical particles of radius \(R\) and negligible gas hold-up in the voids of the ZLC bed, the relative concentration of effluent gas (\(C/c_{0}\)) as a function of time is given by [20]:  
\[\frac{C(t)}{C_{0}}=\sum_{n=1}^{\infty}\frac{2L\exp\Bigl{(}-\beta_{n}^{2}\frac{ D}{R^{2}}t\Bigr{)}}{\beta_{n}^{2}+\left(\gamma\beta_{n}^{2}+1-L\right)^{2}+\gamma \beta_{n}^{2}+L-1} \tag{1}\]  
where: \(C(t)\) is the concentration at any time \(t\), \(C_{0}\) is the initial concentration, \(D\) is the diffusion coefficient, \(\gamma\) is one-third of the ratio of the accumulation in the fluid phase relative to that of the adsorbed phase:  
\[\gamma=\frac{1}{3}\frac{V_{\text{g}}}{KV_{\text{g}}} \tag{2}\]\(V_{\rm g}\) is the mobile phase volume, \(V_{\rm s}\) is the volume of the solid bed, \(K\) is the dimensionless Henry's law constant, \(L\) is the time constant ratio:  
\[L=\frac{FR^{2}}{3 KV_{\rm s}D} \tag{3}\]  
\(F\) is the interstitial gas velocity and \(\beta_{n}\) is given by the roots of the auxiliary equation:  
\[\beta_{n}\cot(\beta_{n})+L-1-\gamma\beta_{n}^{2}=0 \tag{4}\]  
For gaseous systems \(\gamma<0.1\) and the effect of extra particle retention is negligible [21; 22]. In the limit when \(\gamma\) tends to zero, the parameter \(L\) is also the maximum dimensionless gradient of concentration inside the particle and determines in which mode the ZLC is operated. When \(L<1\) the solid is effectively at equilibrium with the fluid while for \(L5\) the response is controlled by kinetics inside the particle. Further details about the theory describing the ZLC experiment are given in the supplementary information.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 2 Load Period Partitioning Method Based on Improved Fuzzy C-Means Clustering
### Time-Weighted Similarity Matrix Calculation Method  
The comprehensive similarity evaluation approach proposed in this paper--based on both load characteristics and optimal network topology--only requires static reconstruction for each initial time period. This substantially reduces computational complexity compared to traditional methods that perform repeated reconstruction across all time segments to minimize network loss. Furthermore, unlike clustering techniques that rely solely on load profiles, the proposed method also accounts for cases where significant load variation exists but the resulting optimal network structures remain identical. This enhances the feasibility of applying unified network configurations across merged time periods.  
Assuming the current time period is \(t\), a time weighting factor is introduced to include the information of the previous and subsequent time periods in the calculation. The specific formula is shown in Equation (7):  
\[S_{ab}^{time}=\varepsilon S_{ab}^{base}+\lambda S_{a-1,b}^{base}+\gamma S_{a+1,b}^ {base} \tag{7}\]  
where \(S_{ab}^{base}\) is the comprehensive similarity of the current time period, \(S_{a-1,b}^{base}\) and \(S_{a+1,b}^{base}\) are the similarity between the previous and subsequent time periods, \(\varepsilon+\lambda+\gamma=1\) and usually \(\varepsilon>\lambda\geq\gamma\).  
In Equation (7), the temporal weight coefficients \(\varepsilon,\lambda,\gamma\) are designed to reflect the decaying importance of historical data over time. Specifically, we adopt an exponential decay scheme to assign higher weights to more recent load data. These coefficients are normalized such that \(\varepsilon+\lambda+\gamma=1\), ensuring consistency across different time lengths. This weighting mechanism allows the algorithm to focus more on current trends while still incorporating useful historical information.  
By introducing a time-weighted similarity matrix to fuse time-series information, it is possible to smooth out abrupt load features, reduce the impact of outliers, and ensure the continuity of time period division results, thereby making the time period division results more accurate and reliable.