esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RobustSTL  
In this study, we selected RobustSTL proposed by [PERSON] et al. [9], instead of standard STL, for the decomposition method. STL (Seasonal-Trend decomposition using LOESS) is a decomposition method utilizing LOESS (locally estimated scatterplot smoothing) to extract estimates of seasonality, trend, and residual components [12]. RobustSTL decomposition has reliability with abrupt changes and various noise compared to the existing methods. In RobustSTL, a residual or remainder component can be further extracted into spike and noise. This decomposition employs the least absolute deviations (LAD) loss with sparse regularizations to extract the trend component, non-local seasonal filtering to decompose the seasonal component, and bilateral filtering to denoise the data. The algorithm summary of RobustSTL to decompose a series data \(y_{t}\) into trend \(\tau_{t}\), seasonality \(s_{t}\), and remainder \(\tau_{t}\) is shown in Algorithm 1 [9].  
```
Input:\(y_{t}\), parameter configuration Output:\(\tau_{t}\), \(s_{t}\), \(r_{t}\) Step 1: Denoise the time series data using bilateral filtering, \(y^{\prime}_{t}=\sum_{j\in J}w^{\prime}_{j}y_{j}\) Step 2: Calculate the relative trend, \(\overline{\tau}^{t}_{t}=\left\{\begin{array}{cc}0,&t=1\\ \sum_{t=2}^{t}\
abla\hat{\tau}_{t},&t\geq 2\end{array},\,y^{\prime}_{t}=y^{ \prime}_{t}-\overline{\tau}^{t}_{t}\right.\) Step 3: Calculate the seasonality using non-local seasonal filtering, \(\overline{s}_{t}=\sum_{(t^{\prime}_{j})\in\varphi}w^{\prime}_{(t^{\prime}_{ j})}y^{\prime}_{j}\) Step 4: Adjust the trend, seasonality, and remainder components \(\tau_{t}=\overline{\tau}^{t}_{t}+\tau_{t},s_{t}=\overline{s}_{t}-\tau_{t},r_{t }=y_{t}-s_{t}-\tau_{t}\)
```  
**Algorithm 1** RobustSTL method summary  
where \(w^{\prime}_{j}\) and \(J\) in Step 2 are the filter weight and the filter window. Additionally, \(\
abla\hat{\tau}_{i}\) is the optimized first-order difference of the trend component and \(\phi\) is neighborhood parameter.
# Ultrafast Optical Heating Induced Polarization-Dependent Optical Switching in Gold Nanowires
## 3 Ultrafast Optical Switching Performance
### Measurement Results of the Optical Switching Processes
in Figure 1b, where simple waveguide resonance mode without coupling with plasmon resonance can be observed. This is because the thermal effect-induced change in the cross-plasmon can be observed only when the sample is excited by the pump pulses. In this case, 667 and 678 nm fall into the rising and falling edges of the spectral peak in Figure 1b, respectively. This is why a negative and a positive transient signal were observed at 667 and 678 nm, respectively, due to the optical excitation-induced redshift of the spectrum. However, for TM-polarized probe, we should use the black spectrum in Figure 1c to understand the transient spectroscopic change in the Fano-coupling process. Due to the broad-band spectral feature and the location of both wavelengths at the bottom of the spectral dip, small positive transient signals were observed for TM-probing as optical excitation induced electron-electron and electron-phonon scattering, which led to the redshift of the LSPR spectrum. Additionally, we need to note that the optical switching signal for TM-pump/TM-probe resulted from Fano coupling between the waveguide resonance mode and LSPR, while the wavelengths of 667 and 678 nm are located near the bottom of the coupling spectrum, as can be observed in the black spectrum in Figure 1c. The optical excitation induced redshift of LSPR and led to a complicated modulation on the spectrum of the coupled mode, which is also dependent on the pulse intensity that is varying with the pulse shape. The rising edge of the spectrum corresponds to a negative transient spectrum and falling edge to a positive value. Due to the complicated process at the bottom of the spectral dip, the dynamics may involve superimposition of pulse-intensity dependent processes. This is why dynamics at 667 and 678 nm for TM polarization have different rising times. Figure 2b shows a three-dimensional (3D) plot of the optical switching signal as a function of wavelength and time delay for the pump-probe scheme of TM-pump/TE-probe, which was measured at normal incidence of the probe beam. Negative TA spectra were measured at wavelength shorter than 672 nm, and positive ones were measured at wavelengths longer than 675 nm, which implies a red shift of the excited spectrum of plasmon resonance. Such a red shift took place within a spectral band as narrow as 20 nm, as shown in Figure 2b. These observations enable us to find out the mechanisms for the much-enhanced optical switching signal in the TM-pump/TE-probe scheme.
## 1 Introduction
Computational docking is useful modeling for predicting bound conformations and binding free energies for small-molecule ligands to macromolecular targets instead of time- and energy-consuming experimental research. Our group has studied the interaction of human blood proteins with membrane models using molecular docking [10, 16, 17, 35, 36]. Those studies were crucial to providing insights and highlighting the functional group(s) that are responsible for the interactions with human serum proteins. Based on the computational results, our research group has developed novel ZW membranes that reduce the inflammatory biomarkers released in dialysis patients' serum [33, 34, 35]. Furthermore, our research group has recently conducted in-depth molecular docking studies on biocompatible Trametes versicolor and versicolor and melanocarpusalbomyces lacases to remove protein-bound uremic toxins (PBUT) [37]. This study sought to explore the influence of the dipole orientation of the ZW chain on the interactions between the PES membrane and essential human serum proteins, with the goal of enhancing PES hemocompatibility. While the zwitterionization of membranes is a well-documented method for improving hemocompatibility, and the dipole moment is a crucial attribute of zwitterions, our research marks the first systematic examination of how the dipole orientation of ZW impacts membrane hemocompatibility. The objectives of the study were as follows: (i) study the ligand-protein interactions between two opposite dipole orientation zwitterionic structures and three vital human serum proteins, Fibrinogen (FB), Albumin (HSA), and Transferrin (Tr), using molecular docking; (ii) investigate the influence of the ZW dipole orientation on its interactions with the three different human serum proteins; (iii) identify the functional groups responsible for perceived interactions between 2-methacryloyloxyethyl phosphorylcholine (MPC), 2-((2-(methacryloyloxy)ethyl)dimethylammonio)ethyl methyl phosphate (MMP), and butyl (2-((2-(methacryloyloxy)ethyl)dimethylammonio)ethyl) phosphate (MBP) zwitterions, MPC-PES, MMP-PES and MBP-PES with HSA, FB, and Tr human serum proteins; and (iv) evaluate the effect of different ZW moieties on binding energy and their affinity with human serum proteins. Based on the author's knowledge, this is the first study to investigate the influence of zwitterion dipole orientation on the interactions of hemodialysis membranes with human serum proteins.
# Fast Minimum Error Entropy for Linear Regression
## 2 The Related Algorithms  
### Mee  
MEE minimizes the information contained in the error and maximizes the information captured by the estimated model. Usually, the second-order Renyi entropy [39] is used to measure the entropy contained in the error of the model.  
\[H(E)=-\log Ep(p_{E})=-\log\int p_{E}^{2}(e)de, \tag{1}\]where \(E\) is the error random variable of MEE, and \(H(E)\) is the entropy of \(E\) and \(p_{E}(e)\) donates the PDF of \(E\). In this paper, \(Ep(\cdot)\) donates the expectation in order to avoid misunderstanding. Then, the PDF of \(E\) can be estimated by Parzen windowing:  
\[\hat{p}_{E}(e)=\frac{1}{n}\sum_{i=1}^{n}K_{h}(e-e_{i}), \tag{2}\]  
where \(n\) is the number of the observed data, \(K\) is the kernel function, and \(h\) is the bandwidth. Usually, the Gaussian kernel is chosen, and \(K_{h}(z)=\frac{1}{\sqrt{2\pi h}}\exp(-\frac{z^{2}}{2h^{2}})\). Then, the empirical information of error \(\hat{H}(f)\) can be calculated as:  
\[\hat{H}(f)=-\log\frac{1}{n^{2}}\sum_{i=1}^{n}\sum_{j=1}^{n}K_{h}(e_{i}-e_{j}). \tag{3}\]  
As the logarithmic function is monotone increasing, it can be removed without any influence on the minimizing process. Then, the transformed empirical error information can be obtained as Equation (4):  
\[R(e)=-\frac{1}{n^{2}}\sum_{i=1}^{n}\sum_{j=1}^{n}K_{h}(e_{i}-e_{j}). \tag{4}\]  
For the linear regression, \(y=\mathbf{w}^{\mathrm{T}}\mathbf{x}+e\), the target of the model is to estimate \(\mathbf{w}\) from the limited samples. As \(e_{i}=y_{i}-\mathbf{w}^{\mathrm{T}}\mathbf{x}_{i}\), the corresponding empirical error information is calculated as Equation (5):  
\[R(\mathbf{w})=-\frac{1}{n^{2}}\sum_{i=1}^{n}\sum_{j=1}^{n}K_{h}\big{(}(y_{i}-\mathbf{w }^{\mathrm{T}}\mathbf{x}_{i})-(y_{j}-\mathbf{w}^{\mathrm{T}}\mathbf{x}_{j})\big{)}. \tag{5}\]  
Then, the MEE estimator \(\hat{\mathbf{w}}\) can be obtained by minimizing \(R(\mathbf{w})\) with gradient descent. The time complexity of MEE is \(O(n^{2})\) due to the double summation operation, as shown in Equation (5). As the time consumption of MEE is quadratic to the number of the observed data, the running time of MEE would increase dramatically with the increase in the amount of the observed data.
## 3 Results
### Scattering in 2-D Plane Strain Models
#### 3.1.2 Non-Symmetric Smooth Defect  
The second study examines the scattering from one-sided thickness reductions (Figure 1b), which more accurately represent real defects. Figure 7 shows that the reflection amplitude increases as the defect becomes deeper and shorter. In the Mindlin model, the reflection amplitude is smaller compared to the FEM model, with a maximum relative difference of 12% for the deepest and shortest defect. This suggests that the Mindlin model is less accurate in representing reflectors with higher contrast changes. However, it is important to note that Mindlin plate theory can simulate reflection information, which is an advantage over widely used acoustic modeling techniques that cannot model reflections from such defects [22].  
Figure 8 illustrates that the amplitude of the transmitted wave for the same defect parameters is larger in the Mindlin model than in the FEM model, with transmission decreasing as the defect depth increases and length decreases. Although the relative  
Figure 6: Transmission from symmetric smooth defect. (**a**) transmission coefficients from FEM model (FEM), (**b**) transmission coefficients from Mindlin model (FDM), (**c**) relative difference between the reflection coefficients.  
Figure 7: Reflection from non-symmetric smooth defect: (**a**) reflection coefficients from FEM model (FEM), (**b**) reflection coefficients from Mindlin model (FDM), (**c**) relative difference between the reflection coefficients.  
difference between the models is greater than in the case of symmetric defects discussed in the previous section, the difference remains small, indicating that the Mindlin model is still effective for non-symmetric smooth defects.
## 3 Results & Discussion
### Ca and Sc Desorption  
The XPS signal associated with Ca was observed from almost every region of the glass envelope, although the signal was generally moderate. Ca was also observed in the exterior reference (G9). This implies that in these regions (everywhere except G6) Ca is either present in the top 10 nm of deposited material or that the deposited layer is less than 10 nm thick. The absence of peaks associated with Ca at G6, the Ba-Al flash region, is notable, especially given that elemental mapping of the cross section show that Ca is plausibly present in at least the first few hundred nanometers of the deposit on the glass substrate at G6 (Figure 8). Scanning electron imaging of the entire FIB lift-out showed the deposit layer to be 2.5-3 \(\upmu\)m thick. The fact that Ca is not detected in the top 10 nm of material deposited at G6 (based on XPS, see Figure 3), but _is_ detected in the first few hundred nanometers of deposit, may suggest that Ca-containing deposits were formed prior to the Ba-Al flash--that is, before both activation and operation, possibly during pre-activation. These initial Ca-containing deposits would have then been covered by the Ba-Al flash residue.  
Ca signal is observed at A2 and A3, which are in or near the primary deposition region (the green ring in Figure 7b). Elemental mapping of an anode lift-out sample originating from A3 shows evidence that Ca deposition did not occur uniformly, seemingly exhibiting layers of Ca-rich deposits (Figure 9). Assuming that the impregnate desorbs relatively uniformly during operation, these striations in deposition composition suggest the possibility that Ca desorption is nonuniform in time. As cathode environmental conditions are uniform during operation this may, in turn, again suggest that Ca is primarily desorbed during pre-activation and activation.  
The detection of Sc given the XPS and EDS results is plausible, but highly ambiguous. G2 is the only point on either the glass or the anode to exhibit an isolated peak around 400 eV--the binding energy associated with Sc 2p3/2. On the anode, Sc signal is observed at this energy; however, it is only present at A4-A8, which are away from the cathodes or on the opposite side of the anode. It is more likely that this signal originates from Mo, which is the primary component of the anode. If Sc was desorbing from the cathode, it would be observed in A1-A3. As it is not, the immediate implication is that, unlike Ba, Ca, and Al, either Sc/Sc2O3 desorbs so slowly that even after pre-activation, activation, and \(\sim\)26,000 h of operation Sc is not detectable, or that Sc does not desorb from the cathode. A dissensus remains in the literature as to the behavior of Sc during operation: some report the presence of Sc at the surface of high-performing scandate cathodes after operation [20], while others hypothesize that Sc readily desorbs based on studies of model Ba-Sc-O on W surfaces [21].
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Influence of Branch (Tap) Length on Throughput  
To see the impact of the branch (tap) length on the throughput, the following scenario was selected: constant length between transmitter and receiver of 1200 m and varying length of the branch (15, 25, 35 and 45 m). The increasing length of the branch caused decreasing channel capacity, but for the distance between transmitter and receiver of 1200 m it is not significant. The drop of 0.23 Mbps from 3.15 to 2.92 Mbps is caused by increasing the length of the branch from 15 to 45 m (see Figure 7).
## 5 Antintutitional Factors and Reduction Strategies
Lectins in legumes can interfere with the absorption of nutrients and crucial minerals like zinc, calcium, phosphorus, and iron. Therefore, lectins may interact with intestinal epithelial cells, modifying intestinal permeability and causing hypertrophy and hyperplasia [123]. Thus, lectins can causedamage to the lining of the digestive tract, mainly in the small intestine, potentially causing a decrease in protein digestibility. On the other hand, oxalates are ANFs found in certain foods such as spinach, rhubarb, and beet greens [122]. They exhibit potent acidic properties and can form water-soluble salts by binding with minerals like sodium or potassium and water-insoluble salts by binding to calcium, iron, or zinc [124]. Consequently, in susceptible individuals, this can lead to the formation of kidney stones. In addition, the results of Dogra [125] on buckwheat suggest a negative correlation between protein digestibility and oxalate content, which shows that a higher content of oxalates leads to lower protein digestibility. Tannins are a polyphenol commonly found in tea, coffee, fruits, and certain vegetables. These compounds are categorized as ANFs because of their capacity to hinder the absorption of essential minerals, particularly iron, and can result in digestive disturbances when consumed in excessive quantities [126]. Furthermore, in conjunction with phytic acid, tannins are widely recognized ANFs for their significant interaction with proteins, reducing their digestibility [127]. It is important to note that many of these ANFs may also exert some beneficial properties [120; 128; 129]. However, the beneficial effects of ANFs depend on molar ratios between nutrients and antinutrients [11]. For instance, phytates can act as antioxidants and have anticancer properties [130]. In addition, new studies have shown the possible beneficial effects of these compounds on the risk of osteoporosis or age-related cardiovascular calcifications [131; 132]. However, the negative effects of antinutritional factors can often be minimized through various processing methods, such as soaking, fermentation, cooking, or heat treatment, which can either reduce their levels or deactivate them.
# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 2 Methodology and Materials  
### Methodology  
This research aimed to compare the ability of different ML algorithms and a gray box model to predict indoor temperature. During this investigation, data on the thermal environment were first collected from a heating experiment in a closed room in the LGCgE laboratory using an intelligent monitoring system. The recorded datasets served as a basis for the development and training of ML algorithms, and the evaluation of their predictive performance in terms of root mean square error (RMSE) and coefficient of determination (R\({}^{2}\)). For detailed information about the dataset see the Supplementary Material.  
A gray box model was also established and compared to the ML algorithms to cover the statistical and hybrid aspects of data-driven modeling.  
Figure 1 summarizes the methodology applied in this study. More explicit descriptions of the experiment, intelligent algorithms, and evaluation criteria are presented below.  
Figure 1: Research Methodology.
# Retrofitting Masonry Walls against Out-Of-Plane Loading with Timber Based Panels
## 2 Material and Method
### Experimental Program
#### 2.2.2 Stage 2: Small-Scale Test (Flexural Bond Strength of Masonry Prism)  
Since an experimental program with full-scale testing is expensive, small-scale testing such as the one presented in stage 2 (see Table 2) is ideal for insight when proposing a new retrofit technique for masonry walls. In this stage, flexural bond strength test according to the provisions of [16; 17] was conducted on nine 615 \(\times\) 215 \(\times\) 102.5 mm masonry prisms (MP). Three of these were tested as plain MP while the remaining six specimens were retrofitted with an 18 mm-thick OSB timber panel using two different types of connections: C1 (adhesive anchor--a threaded dry rod with an injectable chemical adhesive) and C2 (mechanical connection--a threaded dry rod with a plastic anchor). The purpose of this test [18] is to provide a simplified means of gathering data on the flexural strength of plain and timber-retrofitted MP. Precisely, the experiment evaluates the out-of-plane performance of OSB panel in retrofitting URM prisms by comparing the toughness, flexural strength, out-of-plane load capacity and displacement of both plain and OSB-retrofitted masonry prisms. This small-scale test allowed initial understanding of the composite action between masonry and timber panels, and it allowed for identifying the best-performing connection types for the proposed application.
# Metaverse Meets Smart Cities--Applications, Benefits, and Challenges
## 5 Results
### Potential Applications in the Smart City
Virtual platforms for strategic decision-making provide policymakers with interactive dashboards, scenario modeling tools, and simulation environments. These enable them to explore policy options, assess their potential impacts, and make informed decisions [29]. Simulation tools allow stakeholders to simulate legislative processes, test policy proposals, and evaluate their effectiveness in addressing complex urban challenges. Using advanced analytics and artificial intelligence, cities can identify trends, anticipate future scenarios, and develop proactive policies and strategies that promote sustainability, resilience, and equitable growth [6]. The metaverse also serves as a platform for civic engagement, public participation, and transparency. It allows citizens to contribute to policymaking, hold elected officials accountable, and advocate for their interests and priorities. By utilizing the metaverse, cities can create more inclusive, responsive, and effective governance structures that empower citizens, foster trust, and improve the overall quality of life for residents. Integrating these technologies into policymaking can lead to more transparent, participatory, and efficient governance responsive to citizens' needs and concerns [29]. Cultural Heritage Preservation: Through virtual spaces, heritage preservation initiatives can protect and promote the rich cultural heritage of cities. Virtual presentations of local cultural assets offer immersive experiences that allow citizens and visitors to explore historic sites, artifacts, and traditions in unprecedented detail. Community-based cultural and heritage preservation offerings provide citizens with platforms to share stories, memories, and traditions, fostering a sense of belonging and connection to their cultural heritage. Cities can preserve and pass on their cultural heritage to future generations through digital archives, interactive exhibitions, and educational programs to ensure its continued relevance and appreciation [22]. In addition, the metaverse serves as a platform for cultural exchange, collaboration, and innovation. It enables artists, historians, and community leaders to celebrate diversity, foster dialog, and explore new ways to preserve and communicate cultural heritage [30]. By embracing virtual heritage preservation initiatives, cities can enrich their cultural landscape, promote social cohesion, and strengthen their identity as vibrant and inclusive cultural centers [31].