esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems
## 5 Conclusions and Further Research  
Multi-expert MCDM is a crucial issue in managerial practice, and directly influences the correctness of problem solving. In the process of information aggregation in multi-expert MCDM, information usually simultaneously contains certain, uncertain, and incomplete information provided by experts. However, traditional computing methods are unable to deal with uncertain or incomplete information, and this will cause information distortion and loss in the information aggregation process. To resolve these problems, this paper proposes a novel group DM method integrating an induced LOWGA operator and an HFLTS for solving group DM issues. In contrast to other methods, the proposed novel group DM method can simultaneously process certain, uncertain, and incomplete information in the information aggregation process. Moreover, CNC machine tool selection was used as an illustrative example to prove the practicality of the proposed novel group DM method. This paper also compared the calculated results with results calculated using the LOWGA operator approach and the induced LOWGA operator method. The calculation results showed that proposed novel group DM method provided a more accurate CNC machine tool ranking and more flexibly reflected actual world situations. To summarize, the main advantages of the proposed novel group DM method are as follows: (1) The proposed novel group DM approach can handle hesitant information provided by experts in the information aggregation process. (2) The proposed novel group DM approach can deal with incomplete information provided by experts in the information aggregation procedure. (3) The proposed method fully considers all information provided by experts, to avoid information distortion and loss. (4) The proposed method simultaneously considers the operator's weight and the attributes' weight. (5) The LOWGA operator method and induced LOWGA operator method were shown to be special cases of the proposed novel group DM approach. In future research, different practical cases will be examined to prove the practicality of the proposed novel group DM approach. Moreover, the extended concept of proposed novel group DM approach enables it to be combined with the preference relation approach for solving future group DM issues.  
Conceptualization, T.-C.W. and K.-H.C.; methodology, K.-H.C.; validation, T.-C.W., H.-H.L. and K.-H.C.; formal analysis, H.-H.L.; writing--original draft preparation, T.-C.W. and [PERSON]-H.C.; writing--review and editing, H.-H.L. and [PERSON]-H.C.; funding acquisition, K.-H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 108-2410-H-145-001, MOST 109-2410-H-145-002, and The APC was funded by MOST 109-2410-H-145-002.  
The authors declare no conflict of interest.
# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 4 Summary of Tools of Performance Assessment of ML Models
Nevertheless, the lack of normalization is considered a limitation for this metric in the cases where the scale of pressure coefficients changes [90]. The accuracy of some metric errors increases when their values approach one (i.e., coefficient of determination-R\({}^{2}\)), which means that the predicted data are close to the experimental data, and the accuracy of some others increases when their values are close to zero (i.e., root mean square error-RMSE). The correlation coefficient, R, is considered a reliable approach for estimating the prediction accuracy by measuring how similar two sets of data are, but its limitation is that it does not reflect the range nor the bias between the two datasets. The coefficient of efficiency, E, corresponds to the match between the model and observed data and can range from \(-\infty\) to 1, and a perfect match corresponds to E = 1 [89]. AIC is a mathematical method used to evaluate how the model fits the trained data and this is an information criterion used to select the best-fit model. One other error metric that has not been commonly used in the literature is the SI normalized measure of error, where a lower SI value indicates better performance for the model. Besides the error metrics that assess the performance level of the model, other factors are used to indicate the effect of input variables on the output. The most common example is the sensitivity analysis error percentage (Si) (Equation (18)) which computes the contribution of each input variable to the output variable [165; 166; 167]. The Si is an important factor to determine the contribution of each input value, especially when different inputs are used in the ML model training, which could be of great significance for informing and changing the assigned weight of neurons in neural networks. Overall, it is important to note that each error metric or factor usually conveys specific information regarding the performance of the ML model, especially in the case of wind engineering applications (due to variation of wall versus roof pressures for instance), and most of these metrics and factors are interdependent. Thus, our recommendation is to consider the following factors together: (1) use R\({}^{2}\) to assess the similarity between the actual and predicted set; (2) use MSE when the model includes the prediction of roof surface pressure coefficients only without walls, but use either MAPE or RMSE when pressure coefficients for walls' surfaces are included in the model; (3) use AIC to select the best fit model in case of linear regression. This recommendation is to stress the fact that using several metric errors together is essential to assess the performance of ML models for structural wind engineering as opposed to only relying on a single metric.
# The Less Known Cyclins--Uncovered
## 10 Cyclin M  
Encoded by the CCNM gene, cyclin M is characterized by strong homology in sequence to the transcriptional cyclin L [82]. This recently discovered cyclin binds and activates Cdk10. In normal cells, cyclin M together with Cdk10 plays an important role in the ciliogenesis process. As was confirmed by [PERSON] et al., 2017 [83] cyCM-Cdk10 regulates ciliogenesis by modulating actin dynamics. Actin network reorganization occurs through phosphorylation of protein kinase N2 (PKN2) and activation of the RhoA pathway. The cyclin M and Cdk10 complex bind to the N-terminal domain of PKN2, which is responsible for binding to RhoA. The phosphorylation of two residues, T121 and T124, promotes the binding and stabilization of RhoA, resulting in actin polymerization and repression of primary cilia formation [83; 84].  
Additionally, cyclin M together with Cdk10 is involved in the regulation of the protein C-ets-2 (ETS2) transcription factor. ETS2 participates in osteogenesis and its deregulation was confirmed in many cancer types, including breast cancer [85; 86]. The studies conducted by [PERSON] et al., 2013 [87] on MCF7 line (human breast adenocarcinoma) proved that cyCM-Cdk10 phosphorylates ETS2, but also positively regulates the degradation of ETS2 by proteasomes [83; 86].  
In normal cells, mutations in the FAM58A gene cause developmental defects associated with syndactyly, telecanthus, anogenital and renal malformations (STAR) syndrome. [PERSON] et al., 2008 [88] after analyzing patients with STAR syndrome, showed that the activity of the cyclin M and Cdk10 complex is impaired in some tissues in various stages of development [87; 88].  
There is no study showing the direct effect of cyclin M alteration in cancers; however, CDK10 has been found both overexpressed and downregulated in different cancer types. Low CDL10 expression was associated with poor prognosis in gastric, biliary tract and breast cancer [89; 90; 91]. It has been suggested that inactivation of CDK10 in breast cancerscauses MAPK pathway activation. On the contrary, inactivation of CDK10 resulted in decreased tumor growth and increased BCL-2 levels in colorectal cancer [92].
## 2 Methods
### GRNs Inference with DCNTC Algorithm
#### 2.3.1 Initialization of Regulatory Relationships  
The first step in the DCNTC algorithm is to initialize and pre-treat the regulatory relationships between the genes. As distance correlation is an effective way to quantitatively describe non-linear relationships between genes, a distance correlation matrix M is constructed for the input gene expression data based on Equation (2). The greater the value of the elements in this matrix, the greater the likelihood that the gene pair to whichit is addressed has a regulatory relationship. Considering the high noise level of gene expression data and the sparse nature of the regulatory network, elements of the M-matrix need to be pre-processed to eliminate some of the redundant regulatory relationships before the network structure can be inferred. This is usually done by setting a fixed threshold value. When the value of an element in the matrix is greater than the given threshold value, there is a preliminary regulatory relationship between the two genes to which the element corresponds; when the value of an element in the matrix is less than the threshold value, there is no regulatory relationship between the two genes to which the element corresponds, and the matrix element is set to zero.  
In the regulatory relationship matrix, we considered that there was no regulatory relationship between pairs of genes with small distance correlations, which can also be described as redundant relationships. In the process of selecting the threshold for initial de-redundancy, we further analysed the distribution of distance correlation values for each gene pair in the different datasets, as shown in Figure 1. In Figure 1, we separated the range of distance correlations (0-1) in steps of 0.1 and counted the ratio of how many values there were in each interval across the different datasets. We found a single peak in the distance correlation distribution plot. To initially remove redundant relationships from the gene regulatory network, we chose the left boundary value of the interval where the peak was located as the threshold value \(\theta\) for redundancy removal. This allowed us to initially remove redundant relationships from the initially obtained regulatory relationship matrix by threshold \(\theta\).
## 3 Analysis of Rotor Parameter Influence on Electromagnetic and Mechanical Stress Performance
### Pre-Treatment of Finite-Element Model
#### 3.1.1 Calculation Process of Rotor Stress  
The calculation process of rotor stress for the high-speed IPM motor is consistent with the sequence of static structure of toolkit, which is mainly divided into four parts: adding materials, establishing geometric model, setup, and solution. The first step, add the materials required for the rotor stress calculation to the engineering date meter according to the material properties in Table 3. The second step is to link the motor model drawn in the geometry structure to the geometry module.  
The third step is setup. It mainly covers the settings of connections, mesh, boundary conditions, rotation speed, and thermal condition. In the pre-treatment of rotor stress simulation, the outer surface of the PMs is in bonded contact with the rotor core, and the inner surface of the PMs is set with frictional contact. The element size of rotor mesh is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Params** & **Value** \\ \hline \(TH_{B}\) & Bridge thickness (mm) & 2 \\ \(W_{R}\) & Rib width (mm) & 2 \\ \(TH_{S}\) & Stiffener thickness (mm) & 2 \\ \(TH_{PM}\) & PM thickness (mm) & 6 \\ \(W_{PM}\) & PM width (mm) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial design parameters of the rotor.  
Figure 3: IPM rotor structure with the three-segment PM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Property** & **Rotor Core** & **PM** \\ \hline Name & DW310-35 & N38 UH \\ Density (kg/m\({}^{3}\)) & 7850 & 7400 \\ Elastic modulus (GPa) & 200 & 160 \\ Poisson’s ratio & 0.3 & 0.24 \\ Coefficient of thermal expansion (10\({}^{-6}\)/K) & 11 & 8 \\ Yield strength (MPa) & 480 & 75 \\ Resistivity at 20 \({}^{\circ}\)C & \(4.4\times 10^{-7}\) & \(1.8\times 10^{-6}\) \\ Loss coefficient (_Kh/alpha/Kexc_) & 0.00393/5/0.00166 & - \\ Remanence \(Br\) at 100 \({}^{\circ}\)C (Tesla) & - & 1.139 \\ Coercive force \(Hc\) at 100 \({}^{\circ}\)C (A/m) & & \(-9.1\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rotor material properties.  
1 mm, as shown in Figure 4. The rotor stress is simulated at a 120% rated speed (21,600 rpm) at a hot state (100 \({}^{\circ}\)C). The fourth step is solving. The equivalent stress of the rotor core is selected for calculation. The yield strength of electrical steel is 480 MPa, while the PM can withstand a compressive stress of 800 MPa. Therefore, all rotor stress distributions are discussed only in relation to the rotor core in this paper.
## 5 Performance Evaluation
### Dataset Description
#### 5.1.1 Data Cleaning  
The obtained dataset has been already cleaned from implausible sensor values and the segments with issues have been excluded [28]. As a result, 420 segments from the original579 road segments have been kept and the dataset contains a total of more than 5 million observations.  
In addition, we verified that:  
* the obtained dataset does not contain empty cells, duplicates or wrong format;
* Observations are regularly and continuously recorded every 5 min for each parking segment during the considered 6 weeks.
# A Current-Mode Optoelectronic Receiver IC for Short-Range LiDAR Sensors in 180 nm CMOS
## 1 Introduction  
Recently, light detection and ranging (LiDAR) sensors have been popular in various areas such as advanced driver assistance systems for unmanned vehicles, remote sensing detection and navigation systems for robots, and monitoring systems for dementia patients in long-term care facilities [1; 2]. Most LiDAR sensors exploit the well-known pulsed time-of-flight mechanism that ensures the successful scan operations ranging up to several hundred meters. Figure 1 shows the block diagram of a typical LiDAR sensor, in which the transmitter includes a laser-diode driver to emit narrow light pulses to targets. The reflected pulses are detected by a receiver that comprises an optical detector, a transimpedance amplifier (TIA), a single-to-differential (S2D) converter, a post-amplifier (PA), and a time-to-digital converter (TDC).  
Optical detectors are realized as either a p-i-n photodiode or an avalanche photodiode (APD), depending upon the applications. In this work, APDs are preferred even with the characteristics of noise amplification because targets are usually located within a few-meter distance, hence providing large photocurrents. Nevertheless, the bond-wire interconnection between the APD chip and the receiver circuit inevitably causes severe signal distortions. Additionally, the length of bond wires cannot be precisely controlled, thus degrading thesensor performance in terms of factors such as gain, bandwidth, and noise. In addition, on-chip electro-static discharge (ESD) protection diodes are mostly necessary to avoid the chip damage from the ESD, which may yet shrink the receiver bandwidth considerably.  
Several research works have been conducted to develop on-chip optical detectors implemented in bulk CMOS processes [2; 3; 4; 5; 6; 7; 8]. Although they inherently suffer from low responsivity and narrow bandwidth [2], the on-chip CMOS APDs can be an effective solution to overcome the aforementioned shortcomings, particularly for the applications of short-range LiDAR sensors [3; 4; 5].  
Section 2 describes three on-chip CMOS APDs with their measured results. Section 3 presents a conventional voltage-mode optoelectronic receiver with the on-chip CMOS APDs. Section 4 introduces the proposed current-mode optoelectronic receiver, of which measured results are demonstrated in Section 5. Then, a conclusion follows.
## 2 Carbon-Carbon Forming Reactions  
### Oxidative Homocoupling of Terminal Alkynes  
Carbon-carbon bond forming reactions are essential tools for building the carbon skeleton of organic compounds, and therefore they represent an elemental content of organic synthesis. Copper-catalysed oxidative homocoupling of terminal alkynes known also as Glaser coupling [8] introduce a suitable method for the preparation of 1,3-diynes, which are needful basic building blocks in the synthesis of many natural and pharmaceutical products, as well as some advanced functional materials [9]. A successful application of the heterogeneous catalyst intended for this reaction can be documented on the example of recyclable catalyst **1**, which is based on the complex consisting of \(N\),\(N\),\(N^{\prime}\),\(N^{\prime}\)-tetraethyldiethylenetriamine and CuSO\({}_{4}\), anchored on polystyrene cross-linked with 1% divinylbenzene (DVB) (Scheme 1) [10].  
The efficiency of catalyst **1** was confirmed from the syntheses of 20 substituted symmetrical 1,3-diynes, where high yields of isolated products were achieved (58-99%). During the reuse of catalyst **1**, release of small amount of Cu(II) ions was observed, however this did not affect significantly the yield of 1,4-diphenylbutane-1,3-diyne, even after nine cycles [10]. Another recently published successful heterogeneous catalyst [11] for Glaser coupling was based on the Cu(II) complex of copolymer \(N\)-(1,10-phenanthroline-5-yl)acrylamide and DVB. This catalyst was also used for Huisgen 1,3-dipolar cycloaddition [11]. For oxidative C-C homocoupling of acetylenes, a heterogeneous catalyst based on copper nanoparticles (3-9 nm), stabilized onto nitro functionalized polystyrene resin, was prepared. This catalyst was tested in the synthesis of seven 1,4-disubstituted-1,3-diynes (92-95%) [12].
## 3 Results
### BPNN Model  
The commonly used BPNN model for LSP is mainly composed of input, hidden, and output layers. Related studies have shown that this model structure can fit any nonlinear function and predict many kinds of complex time series and pattern recognition problem [62]. Therefore, this study also adopted a single-hidden-layer BPNN model to carry out LSP in Shicheng County. Each layer of the BPNN model was composed of a certain number of neurons which connected the input, hidden, and output layers by weight values. Generally, the error back-propagation algorithm was applied to determine these weight values.  
In this study, the input-output variables used for PSO-MLP were used again for BPNN model training and testing, and the optimal number of neurons in the hidden layer was set to 20 based on the minimum prediction error method [64]. The other parameters of the BPNN model were set to default values. Finally, the landslide susceptibility map produced by the BPNN model is shown in Figure 6c, which had very low (25.7%), low (22.4%), moderate (24.9%), high (16.1%), and very high (10.9%) susceptibility levels (Table 2).
## 2 Preliminaries  
**Theorem 1** ([8]).: _(Existence and Uniqueness Theorem)._  
Let \(f(x,\;y)\) and \(\frac{\partial f}{\partial y}\) be continuous functions of \(x\) and \(y\) at all points \((x,\;y)\) in some neighborhood of the initial point \((x_{0},\;y_{0})\). Then, there is a unique function \(y(x)\) defined on some interval \([x_{0}-\varepsilon,\;x_{0}+\varepsilon]\) satisfying  
\[y^{\prime}=f(x,y(x)),\;y(x_{0})=y_{0},\;x\;\in\;[x_{0}-\varepsilon,\;x_{0}+ \varepsilon],\;\varepsilon>0 \tag{3}\]  
**Definition 1** ([9]).: _A Taylor sum or Taylor series is a mathematical function representation in the form of a series consisting of terms calculated using the values of the function's derivation at a specific point and given by_  
\[\psi_{T}(x_{n},y_{n};h)=\sum_{r=0}^{\infty}\frac{h^{r}}{(r+1)!}\bigg{(}\frac{ \partial}{\partial x}+f\frac{\partial}{\partial y}\bigg{)}^{r}f(x,y) \tag{4}\]  
**Definition 2** ([10]).: _Let \(1\subseteq\mathbb{R}\) be an open interval and \(f:I\rightarrow\mathbb{R}\) be a \(n\)-times differentiable function at \(\alpha\in I\). The Taylor polynomial \(T_{n}^{\alpha}f\) of degree \(n\) at a point \(\alpha\) of \(f\) is the polynomial_  
\[T_{n}^{\alpha}f(\alpha)=f(\alpha)+(x-\alpha)ft(\alpha)+\frac{(x-\alpha)^{2}}{ 2!}f^{\prime\prime}(\alpha)+\frac{(x-\alpha)^{3}}{3!}f^{\prime\prime\prime}( \alpha)+\cdots+\frac{(x-\alpha)^{n}}{n!}f^{(n)}(\alpha) \tag{5}\]  
**Definition 3** ([11]).: _The difference between the numerical solution_  
\[y_{n+1}=y_{n}+h\psi(x_{n},y_{n};h) \tag{6}\]  
_and the exact solution \(y(x_{n+1})\) is called the local truncation error (L.T.E.) for a one-step method \(x_{n}\) with step size \(h\) is given by_  
\[L.T.E.=y(x_{n+1})-y_{n+1} \tag{7}\]  
**Definition 4** ([12]).: _The numerical method is said to be stable if there exists \(h>0\) for each differential equation such that changing the initial values by a fixed amount produces a bounded change in the numerical solution for all \(h\)_  
**Definition 5** ([13]).: _A numerical method is said to be consistent when all step sizes \(h\to 0\), as it will converge to the differential equation. In other words, we say the method is consistent if and only if_  
\[\underset{h\to 0}{\text{Lim}}\frac{\text{L.T.E.}}{h}=0\text{ for }h=\frac{b-a}{N} \tag{8}\]
## 4 Time-Frequency Analysis for Feature Extraction
### Short-Time Fourier Transform (STFT)  
STFT is a prevalent technique in TFA to evaluate non-stationary signals. It partitions these signals into brief segments that overlap for the Fourier transformation, producing a spectrum varying over time. For a continuous-time signal \(x(t)\), the STFT involves multiplying the signal by a window function \(w(t)\) followed by the Fourier transform:  
\[X(t,f)=\int_{-\infty}^{\infty}x(\tau)w(\tau-t)e^{-j2\pi f\tau}d\tau \tag{7}\]  
where \(w(\tau-t)\) isolates a segment of the signal around time \(t\) and \(X(t,f)\) represents the frequency content of the signal at time \(t\).
## 3 Result and Analysis
### Effect of Erosion under Different Conditions
#### 3.2.2 Effect of Sediment Concentration on Erosion  
The sediment concentration in the spillway reservoir area is greatly affected by the season, and the sediment concentration is also an important index affecting erosion. Combined with the actual situation, six different conditions of sediment concentration of 1 kg/m\({}^{3}\), 2 kg/m\({}^{3}\), 3 kg/m\({}^{3}\), 4 kg/m\({}^{3}\), 5 kg/m\({}^{3}\), 6 kg/m\({}^{3}\) were taken for simulation analysis. Sediment particle size is 0.025 mm, the water depth before the spillway is set at 6.5 m, the opening degree of the gate is 1 m, and the flow velocity is 5 m/s. The shapes of erosion cloud diagrams corresponding to different sediment concentrations are consistent, and the erosion conditions are basically the same as those in Figures 6e and 7e, except that the amount of erosion varies with different sediment concentrations. The maximum value of erosion amount corresponding to different sediment concentrations is shown in Figure 10. The value of erosion amount shows an obvious linear relationship with the increase in sediment concentration, and this distribution rule is also obviously consistent with the conclusion of Hu [6].  
Figure 6: Erosion cloud diagram of spillway bottom under sediment with different diameters.  
Figure 8: Relationship between erosion and sediment diameter.  
Figure 7: Erosion cloud diagram of gate leaf under sediment of different diameters.