esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### EDC Removal Rate Against Mass Loading Rates  
The total mass removed of each EDC compound was plotted against its total inlet mass loading rate (Figure 3). The removal rate of BPA in the _Heliconia_-CW, unplanted-CW and _Phragmites_-CW increased as the mass loading rate increased as well. BPA removal efficiency in all three CW types investigated was not sensitive to the mass loading rate and was almost completely removed in all CWs investigated (Figure 3a). However, NP removal decreased at a high mass loading rate. Moreover, NP was poorly removed in the unplanted-CW both at low and high mass loading rates (Figure 3b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Removal Efficiencies (\%)**} & \multicolumn{3}{c}{**NP**} \\ \cline{2-4}  & _Heliconia_ & _Unplanted_ & _Phragmites_ \\ \hline \(\hat{\vartheta}\) & 62.8\% & 25.3\% & 52.1\% \\ Maximum & 90.0\% & 83.7\% & 80.2\% \\ Minimum & 28.0\% & \(-\)12.3\% & 20.4\% \\ \(\sigma\) & 20.1\% & 37.1\% & 23.2\% \\ C.V. & 0.32 & 1.46 & 0.4 \\ \(n\) * & 7 & 7 & 6 \\ \hline \hline \end{tabular}  
* The third _Phragmites_-CW sample was broken in shipping. Notes: \(\hat{\vartheta}\): mean value; \(\sigma\): standard deviation; C.V: coefficient of variation; and \(n\) number of samples.  
\end{table}
Table 3: Average effluent NP removal for _Heliconia_-CW, unplanted-CW, and _Phragmites_-CW.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 5 Case Study and Analysis
### Application Display and Analysis
#### 5.2.2 Abnormal Warning and Conclusion Analysis  
The data-processing layer analyzes the received data according to the warning rules. When the warning condition is met, the system pushes the alarm information to the manager's mobile APP, so that the manager can grasp the abnormal data as soon as possible. The data-processing layer will also convert the received data packets into tensioning records according to the tensioning rules. The tensioning record table presents the inspection results of each channel in all control stages of the tensioning process, which cover initial stress, control stress, elongation value, deviation, and other important control indicators, as shown in Table 5. During the tensioning operation, when the value of the deviation exceeds plus or minus 6%, the manager's mobile APP will receive an abnormal message.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Equipment** & **Parameter Requirements** & **Quantity** \\ \hline
1 & Jacks & & 4 \\ \hline
2 & Force sensor & The error is less than 0.5\%FS. & 4 \\ \hline
3 & Displacement sensor & The error is less than 0.25\%FS. & 4 \\ \hline
4 & Communication module & The data transport from sensors to PC & \\  & & console is supported, and wireless & 1 \\  & & transmission distance is not less than 200 m & \\ \hline
5 & PC control machine & Ordinary PC or notebook, with Win7 system, & 1 \\  & & is utilized. Data-pushing service is installed. & 1 \\ \hline
6 & Mobile communication terminal & Support 4G network. & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter requirements of tensioning equipment.  
Figure 13: Tensioning equipment and data monitoring. (**a**) The changing curves of force. (**b**) The changing curves of displacement.  
The tensioning process curve of channel ZN8 is the changing trend of tensioning force and displacement during the tensioning process, as shown in Figure 14. Comparing and analyzing the initial and control prestressing forces with the theoretical standard values during the job process can provide a favorable basis to determine the tensioning job results, as shown in Figure 14a,b. According to the requirement of tensioning operation, the tensioning force should be kept for at least 20 s when it reaches the theoretical initial force. When the tensioning force reaches the theoretical control force, it should be kept at least for 5 min. Therefore, we can use the curve in Figure 14a,b to analyze whether the tensioning work meets the requirements. In Figure 14c,d, the comparison and analysis of tensioning forces and displacement at both ends can also provide data support and decision-making basis for the subsequent adjustment of control parameters.
## 1 Introduction
### Voltage-Harmonics Mitigation
#### 3.6.3 Costs and Benefits  
The operation of a converter-interfaced DRES as active filter can be simply done by suitable control of the converter, thus, no additional investment cost is required, as mentioned above. However, the converter operates with PF \(\
eq\) 1, which means reduced efficiency [21]. This is an additional operational cost, which can be evaluated in a way similar to the case of fundamental-frequency reactive power mentioned above.  
The financial benefits would be the deferral of investments by the DSO in order to mitigate voltage harmonic pollution beyond the acceptable limits; such investments would be the installation of passive or active harmonic filters in specific nodes. Other benefit is the reduction of the losses in the distribution transformers due to the reduction of the voltage THD.
## 3 Materials and Methods
### Text Vectorization
These embeddings capture the semantic and syntactic relationships between words. The word vectors can be averaged, summed, or combined for a sentence or document-level representation using more sophisticated techniques including weighted averages or the smooth inverse frequency method [39]. Word2 Vec consists of two primary architectures: Continuous bag of words (CBOW) and Skip-Gram. CBOW predicts a target word based on its surrounding context, while Skip-Gram predicts the context given a target word. There are several limitations to the Word2 Vec method: It requires substantial computational resources for training on large corpora. It focuses on word-level embeddings, which may not capture sentence- or document-level semantics. Despite these limitations, Word2 Vec does possess several benefits: It captures semantic and syntactic relationships between words. Generates dense continuous vectors, reducing dimensionality compared to sparse methods such as TF-IDF. Pre-trained models are available for various languages and domains. * GloVe (Global Vectors for Word Representation) is another word embedding method introduced by [PERSON] et al. (2014) [PERSON] et al. (2014). It generates word embeddings based on the global co-occurrence statistics of words in a corpus. Similar to Word2 Vec, GloVe embeddings can be aggregated to create a sentence or document-level representations similarly. * The bag-of-words (BoW) model is a simple text vectorization method that represents documents as fixed-size vectors based on the frequency of words they contain [PERSON] et al. (2014). While BoW does not capture the order of words or semantic relationships, it is computationally efficient. It can be effective for certain text analytical tasks. * Developed by [PERSON] et al. (2017) [PERSON] et al. (2017), FastText is an extension of the Word2 Vec model that generates embeddings for sub-word units (such as N-grams) instead of entire words. This approach enables capturing of morphological information and better handling of the rare and out-of-vocabulary words. The sentence or document-level embeddings can be obtained by aggregating the sub-word embeddings. * Doc2 Vec, also known as paragraph vectors, is an extension of Word2 Vec introduced by [PERSON] and [PERSON] (2014) [PERSON] and [PERSON] (2014). It generates dense vector representations for entire documents by considering both the words and the document as an input during the training process. This method can capture the overall semantic meaning of a document and can be directly used for document-level tasks. In summary, various text vectorization methods can serve as alternatives to sentence transformers and TF-IDF, each with their strengths and limitations. The choice of the appropriate method depends on factors such as the specific NLP task, data characteristics, and computational resources. In this work, we used sentence transformers models for the initial data vectorization and feature extraction.
# Modeling a Milling Dynamometer as a 3 DOF Dynamic System by Stiffness Identification
## 3 Static Stiffness Determination
### Dynamometer Assembly Stiffness Determination Using FEM  
The 3D model of the dynamometer was created using the same program ANSYS--Mechanical APDL 2021 R2. The base and upper plates and the rings were modeled  
Figure 11: Dynamometer assembly experimental stiffness curves: (**a**) longitudinal direction; (**b**) transverse direction. Blue—load; Red—unload; Yellow—Average.  
Figure 10: Static test of dynamometer: (**a**) longitudinal load; (**b**) transverse load.  
(Figure 12a). For the finite element model, the same type of element as in the case of the ring was used. The FE model consists of 9661 nodes and 21,131 elements (Figure 12b).  
To simulate the loads and to avoid local effects linked to the application of a force to a single node, a pressure was applied on the lateral surfaces of the upper plate in both directions: longitudinal (\(Ox\)) and transverse (\(Oy\)).  
To find the values of deformations, the nodes situated in the middle of lateral surfaces were considered, and we took an average of these values.  
The obtained values for the longitudinal load are presented in Table 6, and for the transverse load, in Table 7.  
The comparison of the obtained values from the experiments and from the FEM is presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Force [N]** & **Pressure [N/mm\({}^{2}\)]** & \
\begin{tabular}{c} **Average** \\ **Displacement [mm]** \\ \end{tabular} &
\begin{tabular}{c} **Average Stiffness** \\ **[N/m]** \\ \end{tabular} \\ \hline
98.1 & 0.0329 & 0.000050 & 1.928 \(\times\) 10\({}^{9}\) \\
196.2 & 0.0658 & 0.000101 & 1.931 \(\times\) 10\({}^{9}\) \\
293.32 & 0.0983 & 0.000152 & 1.929 \(\times\) 10\({}^{9}\) \\
390.44 & 0.131 & 0.000202 & 1.932 \(\times\) 10\({}^{9}\) \\
487.56 & 0.163 & 0.000252 & 1.934 \(\times\) 10\({}^{9}\) \\
585.66 & 0.196 & 0.000301 & 1.943 \(\times\) 10\({}^{9}\) \\ \hline \end{tabular}
\end{table}
Table 6: Synthesis of the stiffness values of dynamometer in \(Ox\) direction.  
Figure 12: The solid 3D model created in ANSYS: (**a**) isometric view; front view; side view; (**b**) the FE model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Force [N]** & **Pressure [N/mm\({}^{2}\)]** & \
\begin{tabular}{c} **Average** \\ **Displacement [mm]** \\ \end{tabular} &
\begin{tabular}{c} **Average Stiffness** \\ **[N/m]** \\ \end{tabular} \\ \hline
98.1 & 0.0164 & 0.000051 & 1.908 \(\times\) 10\({}^{9}\) \\
196.2 & 0.0327 & 0.000102 & 1.909 \(\times\) 10\({}^{9}\) \\
293.32 & 0.0488 & 0.000154 & 1.901 \(\times\) 10\({}^{9}\) \\
390.44 & 0.0650 & 0.000206 & 1.898 \(\times\) 10\({}^{9}\) \\
487.56 & 0.0813 & 0.000253 & 1.921 \(\times\) 10\({}^{9}\) \\
585.66 & 0.0976 & 0.000303 & 1.932 \(\times\) 10\({}^{9}\) \\ \hline \end{tabular}
\end{table}
Table 7: Synthesis of the stiffness values of dynamometer in \(Oy\) direction.
# Effects of Driving Frequency and Voltage on the Performances of Vibrating Mesh Nebulizers
###### Abstract  
The functional components of vibrating mesh nebulizers are a piezoelectric ceramic with a mesh mounted on one side, a reservoir, and a driving circuit. The piezoelectric material vibrates at a specific intrinsic frequency, and when the mechanical resonance frequency of the piezoelectric ceramic and the frequency of the applied electrical signal match, the vibration amplitude of the ceramic is greatest. In the present study, nebulizing performances were tested with respect to driving voltage amplitude after automatic resonance frequency tuning (ARFT) and/or impedance matching (IM) for salbutamol and glycerol solutions. A 1% mismatch of resonance frequency reduced the output rate by 11.0-30.1% and increased particle size by 1.6-7.7% and power consumption increased by 6.6-13.6%. Driving at 30 VPP after AGFT and IM increased output rate by 45% and decreased power consumption by 31% compared with operation at nominal resonance frequency without IM at 50 VPP. Nebulization of viscous solutions was also enhanced by applying AGFT with IM. The study shows the application of AGFT with IM improves vibrating mesh nebulizer performance and reduces power consumption.  
drug delivery; nebulizer; resonance frequency; piezoelectric
## 1 Introduction
According to the types of deriving equations, meshfree methods can be classified into three types: meshfree strong-form method, meshfree weak-form method and meshfree weak-strong (MWS) form method [36]. Compared with the strong-form methods, the mesh weak-form methods have some advantages in stability and calculation accuracy. In terms of weighted residual method, different meshfree weak form methods can be obtained by using different trial functions and weight functions. RPIM is a typical global weak-form meshfree method proposed by [PERSON] and co-workers in 2001 [30; 31]. Using radial basis functions (RBF) as trial functions, RPIM is robust for irregular nodal distributions. The RPIM has been successfully used to solve many problems [37; 38; 39; 40; 41]. In addition, some improved RPIMs and coupling technologies based on RPIM have emerged [42; 43; 44; 45]. Unfortunately, the stress solutions at weld toe obtained by RPIM are strongly influenced by the parameters used in RPIM as a result of singularity at notch, which makes the conventional RPIM unable to effectively and consistently determine the stress concentration factor and stress intensity factors at the weld toe. The rest of this paper is composed of the following items. A brief description of conventional RPIM for two-dimensional linear elasticity problem was presented. Then, the improved RPIM which modified the stress calculation process of conventional RPIM was developed for achieving stress calculation consistency at weld toe where existing singularity. Cantilever beam, a typical plate lap fillet welded joint and a T-butt welded joint were employed for numerical examples. The numerical example of cantilever beam was carried out to validate the consistency and accuracy of the improved RPIM based stress solutions at the general location of cantilever beam without concentration effect. The numerical example of plate lap fillet welded joint was to validate insensitivity and effectiveness of SCF at weld toe based on improved RPIM. The numerical example of T-butt welded joint was to validate the consistency and effectiveness of the stress intensity solution at weld toe based on improved RPIM.
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
## 2 Channel Model
### Atmospheric Turbulence Model  
The irradiance \(I_{a,m}\) at the \(m\)-th receiver follows the MG distribution, which is a linear combination of gamma distributions, with a probability density function PDF [5]:  
\[f_{I_{a,m}}(I_{a,m})=\sum_{i=1}^{N}w_{i}f_{i}(I_{a,m})=\sum_{i=1}^{N}a_{i}I_{a,m }^{b_{i}-1}e^{-\zeta_{i}I_{a,m}}, \tag{3}\]  
where \(N\) is the number of the summation terms, \(f_{i}(x)=\zeta_{i}^{b_{i}}x^{b_{i}-1}e^{-\zeta_{i}x}/\Gamma(b_{i})\) is the probability density function (PDF) of a Gamma distribution, and \(a_{i}\), \(b_{i}\)\(\zeta_{i}\), are the parameters of the \(i\)-th Gamma component, while \(w_{i}=a_{i}\Gamma(b_{i})\zeta_{i}^{-b_{i}}\) with \(\sum\limits_{i=1}^{N}w_{i}=1\) and \(\Gamma(.)\) stands for the Gamma function [6] (equation (8.310.1)). The MG distribution can efficiently approximate some of the well-known turbulence models by setting proper parameter values for \(a_{i  
Moreover, \(A=\frac{2\alpha^{\prime}m^{\frac{d^{\prime}\prime}{2}}}{\gamma^{\prime}{}^{1+ \frac{\alpha^{\prime}m}{2}}\Gamma(\alpha^{\prime}{}_{m})}\left(\frac{\gamma^{ \prime}\beta^{\prime}{}_{m}}{\gamma^{\prime}\beta^{\prime}{}_{m}+\Omega^{ \prime}}\right)^{\beta^{\prime}{}_{m}+\frac{\alpha^{\prime}m}{2}}\), \(a^{\prime}{}_{k^{\prime}}{}_{m}=\left(\begin{array}{c}\beta^{\prime}{}_{m}-1 \\ k^{\prime}-1\end{array}\right)\frac{(\gamma^{\prime}\beta^{\prime}{}_{m}+ \Omega^{\prime})^{1-\frac{\alpha^{\prime}m}{2}}}{(k^{\prime}-1)!}\left(\frac{ \Omega^{\prime}{}_{m}}{\gamma^{\prime}}\right)^{k^{\prime}-1}\left(\frac{ \alpha^{\prime}{}_{m}}{\beta^{\prime}{}_{m}}\right)^{\frac{k^{\prime}}{2}},\]  
and \(\Omega^{\prime}=\Omega+2\rho b_{0}+2\sqrt{2\rho b_{0}\Omega\cos(\varphi- \varphi)}\). \(\Omega\) is the average power of the LOS component, \(2b_{0}\) represents the average power of the total scatter components, \(\beta^{\prime}{}_{m}\) is a natural number representing the amount of turbulence, \(\gamma^{\prime}=2b_{0}(1-\rho)\), and \(\alpha^{\prime}{}_{m}\) is a positive parameter depending on the effective number of large-scale cells of the scattering process. Furthermore, \(\rho\), with \(0<\rho<1\), is the amount of scattering power coupled to the LOS component, and \(\varphi_{A}\), \(\varphi_{B}\) are the deterministic phases of the LOS and the coupled-to-LOS components.
## 4 Model Optimization Based on Stepwise Regression
### Stepwise Regression  
The most common approach to eliminate multicollinearity is to re-select the independent variables, including the forward method, the backward method, and the most commonly used stepwise regression [32]. As shown in Figure 8, variables are introduced sequentially and required to be removed when the original variables no longer become significant due to the later ones. Notably, only one variable can be introduced or excluded at a time. Furthermore, to guarantee that only significant variables are incorporated into the regression equation, an F-test is essential for each procedure. This operation continues until no more variables are imported or deleted. That is, the idea of stepwise regression is essentially in and out, making it possible to introduce the elements that strongly relate to the dependent variable first and then eliminate those with overlapping contributions. In short, the stepwise regression compensates for the defects of the forward and backward methods, making the final regression subset optimal.  
full parameter model (FPM) and the traditional mount model (MM). An electro-optical telescope carried out 4 observations of stars randomly distributed throughout the sky within 2 days, in which researchers collected a total of 54 valid data sets. Moreover, during each observation, the telescope mounted on a sailing ship pointed intermittently to a star and made 10 samples per 'pointing'.
## 3 Results and Discussion  
### Physicochemical Characterization of the Coated Surfaces  
The studied surfaces were prepared from recyclable and environmentally-friendly materials with the aim of providing a low cost, sustainable solution, with potential application to large surfaces [22].  
Superhydrophobic modification was performed by organic-inorganic mixed coating with fluoropolymer blend in combination with funnel silica nanoparticles (SHS1) and by polystyrene solution with dispersed polytetrafluoroethylene particles (SHS2). To avoid side effects on cells, all the materials have been selected and used according to the safety data sheet provided. Moreover, in the case of fluoro-based coatings, they contain non-toxic and inert compounds insoluble in water, without a further release detectable, in case, by adsorbing at liquid-solid interface then influencing the wettability properties. In case of coatings based on polystyrene, it is used on conventional TCPS (Tissue Culture Polystyrene) plates, as a substrate where cells come and grow completely in contact with the surface. The highest hydrophobicity and insolubility in water of polytetrafluoroethylene avoid the contact with the water-based medium used in the experiments.  
Since in such conditions, hydrophobicity drops typically roll off the SHS surface, CA was then measured with the drop still hanging from the capillary tip (Figure 1). Hysteresis measured by advancing-receding technique was less than \(5^{\circ}\) for SHS, while for uncoated fabric samples complete wettability was observed. Moreover, the hysteresis value address to the absence of release of soluble substances from the coatings affecting, by adsorption, the wettability properties.
# Static Bearing Capacity Investigation of a Novel Prefabricated Light-Steel Beam-Column Connection
###### Abstract  
In this study, a novel prefabricated light-steel beam-column connection consisting of a thin-walled rectangular hollow section column and two cold-formed steel truss beams is proposed and investigated by carrying out experimental tests. Eight cruciform beam-column connection specimens with different configurations are fabricated and tested to failure under monotonic static loading. First, failure mode and the loading-displacement curve of each specimen are investigated. Consequently, the effect of three variables, including truss-beam configuration, truss-beam type, and with or without sleeve tube reinforcing the column, on the static bearing capacity of the proposed connection and the deflection of the truss beams are investigated. It is found that plug welding the sleeve to the column can significantly increase the static bearing capacity of the proposed connection. In addition, filter welding connecting the column and the channel connectors to accommodate the end of the truss beams is crucial to the static bearing capacity of the proposed beam-column connection. Because beam-column connections with single-truss beams have a higher load-bearing capacity and require less material and assembly work, it is recommended to adopt this type of configuration for the proposed connection.  
light steel; beam-column connection; truss beam; rectangular hollow section; prefabricated