esa-sceva/satcom-chunk-collection huggingface dataset:
# Induced Effect of Gold Nanoparticles (AuNPs) and Halide Ions on Pyridoxine Molecule Stability
## 3 Results and Discussion
### The Influence of Fluoride Ions on Electrochemical Behavior of Pyridoxine
The study of the processes that take place was continued by applying the kinetic models of the zero- and first-order reactions. The experimental data obtained by recording the variation of UV-Vis absorption spectra over time, of electrolyte solutions at constant current density, were used in the kinetic approach of the processes (Figure 4). Thus, the rate of electrochemical degradation processes of pyridoxine (\(\lambda\) = 326 nm) and gold nanoparticles (\(\lambda\) = 570 nm) was calculated (Table 1). Figure 3: UV–Vis absorption spectra of work electrolyte solution as the electrochemical processes progress over time in NaF/B\({}_{6}\)/AuNP systems (i = 50 mA \(\times\) cm\({}^{-2}\)). According to Figure 4a and Table 1, it is observed that the rate of electrochemical degradation of pyridoxine is higher in the presence of gold nanoparticles (there is a 7-fold increase). At the same time, a change in the initial absorbance value is highlighted, which proves a spontaneous chemical interaction between the pyridoxine molecules and the gold nanoparticles. Another important feature is that, in the presence of gold nanoparticles, it fits a first-order reaction model, which means that the rate is proportional to the concentration of a single reactant [57]. Gold nanoparticles exhibit opposite electrochemical behavior, both in the absence and in the presence of pyridoxine molecules. The difference between the value of the initial absorbance is very small (0.73 \(\pm\) 0.04). In this situation, gold nanoparticles are characterized by high stability, their spectrum being modified to a very small extent. The presence of pyridoxine molecules in the electrolyte solution leads to a decrease in the rate of AuNP degradation. In conclusion, it can be said that pyridoxine molecules delay the oxidation of AuNPs, while AuNPs accelerate the oxidation of pyridoxine molecules.
## 5 Conclusions  
In this research, an optimized multiparameter satellite component health assessment method based on the Wasserstein distance and spectral clustering is presented. This method offers four advantages over the original method (K
# LoRa 2.4 GHz Communication Link and Range
## 4 Path Loss Modeling
### Indoor Environment  
In the second scenario, we evaluate the maximum communication range in an indoor environment. To this end, we slightly adapt a heuristic algorithm that was developed based on real measurements in an office-like environment [28]. The path loss model is based on the Indoor Dominant Path (IDP) model, which focuses on the dominant path between the TX and RX location. In general, the total path loss is the sum of the distance loss, accumulated wall loss and interaction loss and can be calculated as  
\[L_{p,in}(d)=L_{p_{0}}(d_{0})+10*n*\log_{10}(\frac{d}{d_{0}})+\sum_{i}L_{W_{i}}+ \sum_{j}L_{B_{j}}, \tag{12}\]  
where \(L_{p_{0}}(d_{0})\) represents the path loss at a distance \(d_{0}\) and \(n\) is the path loss exponent. The accumulated wall loss is the sum of losses \(L_{W_{i}}\) caused by each wall along the dominant path. Finally, the interaction loss is the sum of losses \(L_{B_{j}}\) caused by all directional changes of the propagating signal.  
Given the semi-empirical nature of this path loss model, some parameters need to be set to commonly used values in order to provide a generally applicable model that can predict ranges in other indoor environments. Therefore, \(L_{p_{0}}(d_{0})\) is set to \(40\,\mathrm{dB}\) at a distance \(d_{0}=1\) m, as suggested in [28]. The path loss exponent is set to \(n=5\), which is generally used for obstructed paths inside buildings [29]. For the accumulated wall and interaction loss, values of 6 and 3 dB have been taken into account, as found specifically for the office-like environment in [28]. Consequently, the path loss model can be simplified to  
\[L_{p,in}(d)=40+5*10*\log_{10}(d)+6+3. \tag{13}\]  
Thus, the range can be empirically estimated based on the path loss:  
\[d=10^{(L_{p,in}(d)-49)/50}. \tag{14}\]
## 1 Introduction
(2017) extended a new version of the CSA to estimate the proton-exchange membrane fuel-cell model parameter. Owing to its effectiveness in many problems, the CSA was used in this study. However, the classical CSA has a problem of low convergence speeds and becomes trapped in the local minimum easily when tackling a real-world problem. Therefore, in this work, we have introduced three mechanisms, including the gradient search rule (GSR), local escaping operator (LEO), and Levy mutation mechanism (LM) into the CSA to improve its search capability, and then the proposed GLLCSA is used to simultaneously optimize the parameters of the KELM classifier and feature space in the data. The results show that the proposed GLLCSA-KELM-FS model can effectively predict the entrepreneurial intentions of the school students, with an accuracy rate of 93.2%. In addition, the experimental results have found that the major selected, campus innovation and entrepreneurship practice experience, and a positive personality are key factors that influence the entrepreneurial intentions of the school's students. Therefore, this research plays a key role in guiding relevant departments to guide students' entrepreneurial intentions. The main contributions of this study are as follows:  
1. Incorporating the gradient search rule (GSR), local escaping operator (LEO), and Levy mutation mechanism (LM) into the CSA to improve their search capabilities. 2. The performance of GLLCSA is effectively verified through benchmark function experiments. 3. The GLLCSA-KELM-FS model is proposed to predict students' entrepreneurial intentions. 4. Achieve an effective prediction of students' entrepreneurial intentions and screen out the key features. This paper is organized as follows. Section 2 briefly describes KELM and the CSA. Section 3 introduces GLLCSA. Section 4 presents the GLLCSA-KELM-FS model. Section 5 evaluates the performance of the proposed GLLCSA. Section 6 uses the GLLCSA-KELM-FS model to predict graduate entrepreneurial intentions. Section 7 draws conclusions and outlooks.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review
### Copper Nanowires
#### 2.4.2 Ultra-Thin Metal Films  
Transparent ultra-thin metal films have been reported as good candidates for transparent electrodes because they are easy to process and feature high conductivity. Metal candidates for ultra-thin metal electrodes are mainly Ag and Cu. Ag is the most commonly used film material because it has the lowest electrical resistance and optical loss in the visible spectrum of known metals [32].  
AgHT commercial product, which is widely used, is a silver-coated highly conductive ITO film and the value 4 at AgHT-4 is a sheet resistance of the product [33]. A unique combination of high visible light transmission, which is nearly a neutral color, and low electrical resistance makes it ideal as an EMI shield for electronic displays and membrane switches [33].  
In Reference [34], a dual-band coplanar-waveguide (CPW)-fed transparent antenna operating at both 2.45 GHz and 5.8 GHz is investigated using AgHT-8 films in Figure 9a. The designed antenna featuring a size of 36 mm \(\times\) 39 mm \(\times\) 0.175 mm is printed on 2-mm thick glass with a dielectric permittivity of 7. The AgHT-8 thin film features a high transmittance of above 80% and a high electrical conductivity of \(\sigma\) = 1.25 \(\times\) 10\({}^{5}\) S/m, but it has a sheet resistance of \(R_{\rm s}\) = 8 \(\Omega\)/sq, which is quite high compared to other conductive films like multilayer TCOs or nanowires. The fabricated antenna shows good dual band performance such that the return loss value has a reasonable value (less than \(-\)10 dB) at the frequency of interest, as illustrated in Figure 9b.  
Figure 8: (a) Prototyped transparent micro metal mesh conductive film antenna; (b) simulated and measured S-parameters of the proposed antenna [30]. (With permission from IEEE).  
Figure 9: (a) Photograph of the fabricated antenna using the AgHT-8 transparent thin film; (b) simulated and measured return loss of the proposed antenna [34]. (With permission from IEEE).  
In Reference [35], a CPW dual band meander-line transparent antenna with AgHT-4 film is introduced. A meander-shaped resonant radiator is combined with an inverted U-slot CPW ground plane to take advantage of the compact size and dual band of the antenna. This transparent antenna is printed on a glass substrate with a 2-mm thickness. This AgHT-4 film with dimensions of 30 mm \(\times\) 30 mm \(\times\) 0.175 mm has \(\sigma\) = 2.2 \(\times\) 10\({}^{5}\) S/m conductivity and \(R_{\mathrm{s}}\) = 4 \(\Omega\)/sq resistance. The proposed antenna has a performance below \(-\)10 dB at the dual band frequency referring to the return loss and it yields gains of 2.42 and 2.85 dBi at 2.4 and 5.8 GHz.
## 5 Demonstration Using TerraSAR-X
### Inhomogeneous Scene: Urban Region in China  
In the following, a more challenging scene is analyzed. The scene consists of a mountainous region in the upper and lower part of the image; some urban areas and a river are found in the middle. It has been acquired over central China and is considered representative for various inhomogeneous areas on Earth. The two images generated from the individual SAR data streams acquired with the boresight and the notch beam pattern are shown in Figure 8. From this, the difference dataset is generated which is presented in Figure 9 in amplitude (left) and phase (right). In this scene it becomes evident that the size of the antenna pattern in elevation, especially the notch pattern, was not optimal for the small swath width realizable with the high PRF required to acquire the two datasets at once. Due to the experimental nature of the data, a larger swath was illuminated than the timing allows to record non-ambiguously and additional range ambiguities appear in the image. Some distinct ambiguities can be seen as bright areas in the otherwise black region of the notch or as area of inappropriate colors in the phase image. These ambiguities can be largely avoided in a carefully designed system. However, after averaging in azimuth direction, the notch position in the derived range profile, as shown in Figure 10, can still be easily determined and used for antenna pointing estimation (Figure 11).  
Figure 8: Original input dataset acquired using a boresight and notch beam pattern over China.  
Figure 9: Image of the difference dataset generated from the acquisition of a nominal and a notch beam as shown in Figure 8. The data is visualized in amplitude on the left and in phase on the right.  
In addition, the same correlation technique as for the rainforest was also applied to this more challenging scene. The results are similar, with a retrieved mispointing of \(-27.8\) mdeg. The sample Pearson correlation coefficient as a figure for mispointing estimation accuracy is 0.90 for a complex-valued evaluation. The gain-only correlation value of 0.98 is again higher. Thus, a good agreement between model and measurements can be confirmed.  
Figure 11: Output of the correlation process for pointing determination for the scene in China: logarithmic gain of the \(\gamma\)-profiles from model (green) and measurement (blue); aligned \(\gamma\)-profile in gain (green and blue) and phase (yellow and red).  
Figure 10: Range profile over an inhomogeneous scene in China.
## 4 Results and Discussion
### Visualization of the Slug Flow  
Before collecting the pressure and vibration data, a visualization of the slug flow within the subsea jumper was performed to qualitatively understand the evolution of the flow patterns of the slug flow through the jumper and the associated flow characteristics. Images of a typical slug flow are illustrated in Figure 10. As shown in Figure 10, the flow was dominated by a slug flow in the rising section (S1 section) of the jumper. As shown in Figure 10a, a bullet-shaped bubble characterizing a Taylor bubble appeared in the jumper, with a spherical head and a flattened tail. As can be seen in Figure 10b, when the liquid continued to flow upward and reached the horizontal section S2 after the elbow, the liquid phase accumulated at the elbow and occupied a large part of the volume due to the steering effect of the elbow. The subsequent bubbles continued to be pushed upward, causing the instability of the gas-liquid flow. An alternating gas-liquid flow was observed at the S2 pipe section, which is an obvious characteristic of a segment plug flow between the states of a wave flow and a slug flow, but a small amount of gas was still observed at the top of the liquid slug. The gas-liquid continued to flow under the effect of gravity when it reached S3 because the S3 pipe section was located in the descending pipe section, as shown in Figure 10c. In this section, the flow pattern changed to the churn flow, and the pipe was filled with small-scale broken bubbles in irregular shapes scattered throughout the liquid phase. The gas-liquid continued to be pushed forward, and when it reached the longer overhanging horizontal section S4 after the elbow, the gas-liquid flow gradually stabilized and showed a stratified flow pattern, as shown in Figure 10d. After an elbow, the flow entered the S5 rising portion after the gas and liquid flow passed through the overhanging area, and the flow pattern redisplayed a slug flow pattern, as shown in Figure 10e. However, different from the flow pattern in S1, the length of the Taylor bubble was smaller than that through S1. The flow pattern in section S6 of the pipeline was similar to that in section S2. However, since the rising section of S5 was longer than the rising section of S1, less liquid was accumulated at the elbow, as shown in Figure 10f, compared to Figure 10b, and the flow pattern resembled a wave flow. In the falling section of S7, the gas-liquid flow rate decreased due to energy loss, as can be seen in Figure 10g. A liquid film formed on both sides of the jumper wall and the flow pattern was an annular flow.  
Figure 9: Slug flow conditions of the present study based on the flow regime map according to [PERSON] and Ishii [32].  
Figure 10: Cont.
## 2 Theories and Methods
### Feature Extraction  
Feature extraction reduces the dimension of the sEMG signal by mapping the object to a smaller dimensional space [15]. It is a feature vector constructed from a part of features selected from the original sEMG signals. The selection of features plays a decisive role in the accuracy and reliability of the results, and its impact is even greater than the choice of pattern recognition methods [16]. Ideally, these features should be simple to extract, not interchangeable, insensitive to noise, and easy to distinguish between motion patterns [17]. Therefore, in this work, because it has been widely used in recent research and has been simplified in the calculation process, features are extracted in the time domain and frequency domain.  
Root mean square (RMS) is a relatively common sEMG signal time-domain feature, and to a certain extent, reflects the contribution of each muscle group in the process of motion. RMS is defined as [18]:  
\[\text{RMS}=\sqrt{\frac{1}{N}\sum_{k=1}^{N}X_{k}^{2}} \tag{1}\]  
For the sEMG signals, integral electromyography (IEMG) is a traditional way to detect the level of muscle contraction, and is often used in the study of signal feature extraction:  
\[\text{IEMG}=\frac{1}{N}\sum_{k=1}^{N}|x(k)| \tag{2}\]  
where \(N\) is the number of data samples in the time-window and \(X_{k}\) is \(k\)-th data sample in the time-window.  
During dynamic contractions, median frequency (MDF) and mean frequency (MNF) of the sEMG signal are the important process parameters that reflect the complex physiological phenomena inside the muscle. They can be expressed as [19]:  
\[\text{MNF}=\sum_{k=1}^{N}f_{k}P_{k}/\sum_{k=1}^{N}P_{k} \tag{3}\]  
\[\sum_{k=1}^{MDF}P_{k}=\sum_{k=MDF}^{N}P_{k}=\frac{1}{2}\sum_{k=1}^{N}P_{k} \tag{4}\]  
where \(P_{k}\) is the sEMG power spectrum at a frequency bin \(k,f_{k}\) is the frequency of the spectrum at a frequency bin \(k\), and \(N\) is the total number of frequency bins. To eliminate the effects of individual factors, each feature was normalized to the maximum muscle contraction measurement (RMS, IEMG, MDF, and MNF) with values between 0 and 1.
## 3 Test Scheme
### Feedback Control System Scheme  
The control system exhibited a full state feedback scheme. The controller host computer was installed with a PCI 8335 data acquisition board. It can complete the whole process of data collection, calculation, and instruction. The data collection board using Visual Basic (VB) compiler transforms the two control algorithm into VB executable programs for structural signal collection, online calculation and control signal generation and communication. The interface of the control operating system is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Duration (s)**} & **Record** & **Feak Acceleration (gal)** & **Earthquake** & **Seismic Wave Description** \\ \cline{3-8}  & & & **Interval (s)** & & **14.695 (NS)** & 2.12 \\ \hline \multirow{3}{*}{El-Centro wave} & \(Y\) direction & \multirow{3}{*}{53} & \multirow{3}{*}{0.02} & 210.142 (EW) & 11.44 & \multirow{3}{*}{The seismic wave is recorded in El Centro city, California, USA.} \\ \cline{2-8}  & \(Z\) direction & & & \(-\)206.34 (UD) & 0.98 & \\ \hline \multirow{3}{*}{Taft wave} & \(X\) direction & \multirow{3}{*}{54} & \multirow{3}{*}{0.02} & 175.9 (NS) & 9.3 & \\ \cline{2-8}  & \(Y\) direction & & & 152.7 (EW) & 9.1 & \\ \cline{1-1} \cline{2-8}  & \(Z\) direction & & & 102.9 (UD) & 9.76 & \\ \hline \multirow{3}{*}{Tianjing wave} & \(X\) direction & \multirow{3}{*}{19} & \multirow{3}{*}{0.01} & 145.805 (NS) & 7.65 & \\ \cline{1-1} \cline{2-8}  & \(Y\) direction & & & 104.18 (EW) & 7.59 & \\ \cline{1-1} \cline{2-8}  & \(Z\) direction & & & 73.14 (UD) & 9.03 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Earthquake waves table in the experiment.  
Figure 7: Relation diagram for current and damping force.
## 3 Results and Discussion
### Advanced Oxidation Process UV/H\({}_{2}\)O\({}_{2}\)
#### 3.1.2 Behavior of Physicochemical Parameters  
The pH levels in the system remained in acidic condition, below 2.5 units during the AOP (Figure 6a). Such conditions enhance organophosphate pesticides degradation through advanced oxidation processes [37]. The H2O2 oxidation generates OH, and in turn releases hydronium ions, responsible for the medium acidification. According to the study results, pH and TOC removal decrease over time (Figures 4, 5 and 6a).  
Figure 6b indicates that oxidative conditions were maintained throughout the treatment. Although ORP values did not show a clear trend linked to irradiation time, higher ORP values were observed for the 65 mM of H2O2, which is mainly because H2O2 determines oxidative conditions of the treatment regardless organic matter degradation and/or oxidation of dissolved solids [38; 39]. Indeed, efficiencies of malathion and TOC removal with 65 mM were higher than with 82 mM H2O2, despite no statistical difference (\(p\) = 0.11). Ref. [40] have evaluated the doses of peroxide in advanced oxidation processes, finding that when the kinetics of decomposition, manifested as a greater amount of OH, are greater than degradation kinetics, there is competition for UV light.
# Estimating Natural Frequencies of Cartesian 3D Printer Based on Kinematic Scheme
###### Abstract
The magnitude of the resulting vibrations directly depends on the rigidity of the structure, which is also affected by many other factors [26]. Nevertheless, reducing the vibration amplitude of the system and increasing the natural frequency of vibrations can improve the quality of 3D printing. Several recent studies are dedicated to detecting and decreasing these effects [24; 27; 28]. The most effortless way to do that is to increase the natural frequency of the printer head vibration due to proper mechanical design, leading to decreasing mode shape displacements [29]. A more elaborate solution is using active vibration control, which is a common practice in CNC milling machines [30; 31]. For 3D printing, some solutions based on feed-forward control have been reported recently applied to vibration-prone 3D printers [32; 33]. In addition, open-source Klipper firmware allows using one of the feed-forward resonance compensation algorithms for all compatible 3D printers [34]. However, how does an engineer understand which design is the most vibration-tolerant at the stage of preliminary design, where neither a complete CAD/CAE model exists nor any experimental specimens are available? This paper proposes using natural frequencies as the feature for distinguishing vibration-tolerant designs from vibration-prone ones. This feature depends on the relative positions and mobility of 3D printer parts. We propose a method for natural frequencies estimation from the 3D printer kinematic scheme, study in detail its bounds of applicability and give several illustrative examples including decision making on the 3D printer construction. The paper is organized as follows. Section 2 describes the approach to calculating the natural frequency for a given Cartesian design. Section 3 presents examples of natural frequency analysis for Anycubic i3 Mega (Shenzhen Anycubic Technology Co., Ltd., Shenzhen, China) The natural frequencies have been obtained theoretically using the proposed approach and experimentally, confirming its applicability. Section 4 gives brief conclusions.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
###### Abstract  
In order to improve the speech enhancement ability of FullSubNet model, an improved method FullSubNet-pMix is proposed. Specifically, pMix module is added to the structure of full-band frequency domain information processing, which realizes the information interaction between amplitude spectrum and phase spectrum. At the same time, the hyperparameters used in training are optimized so that the full-band and sub-band structure of the system can play a better role. Experiments are carried out on selected test sets. The experimental results show that the proposed method can independently improve the speech enhancement effect of the model, and the effect on the four evaluation indicators of WB-PESQ, NB-PESQ, STOI, and SI-SDR is better than the original model. Therefore, the FullSubNet-pMix method proposed in this paper can effectively enhance the ability of the model to extract and use voice information. The impact of different loss functions on the training performance was also verified.  
speech enhancement; signature processing; phase information; information exchange; hyperparameter optimization Article
## 4 Simulation Verification of the Method
### Comprehensive Verification of the Effectiveness of the Method
Based on the results shown in Figure 6, it can be concluded that the accuracy of estimating the variance of the dynamic error signal under the experimental conditions depends mainly on the frequency domain resolution of the DFT algorithm. For the same value of \(N\), both the spread and the expected value of the analyzed parameter do not change significantly with an increase in the number of harmonics of the processed signal.