esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Safety
### Braking Distance  
The calculation of the robot's braking distance is done in two steps. Firstly, a deceleration value \(a_{B}\) is obtained from the braking force \(F_{b}\) (in Table 1) and friction force \(F_{t}\):  
\[F_{b} =\frac{2 jM_{b}}{r}, \tag{1}\] \[F_{t} =f_{0}mg,\] (2) \[a_{b} =\frac{F_{b}+F_{t}}{m}. \tag{3}\]  
The value of \(a_{b}\) is then used to determine braking distance \(s_{b}\) as follows. A momentum \(p\) of the vehicle with the total mass \(m\) moving with speed \(v\) is defined as:  
\[p =mv. \tag{4}\]  
The braking force \(F_{b}\) over the time \(\Delta t\) must be applied to change the momentum to zero:  
\[\Delta p =F_{b}\Delta t. \tag{5}\]  
Therefore, a braking time \(t_{b}\) needed to completely stop the vehicle from its initial speed \(v_{init}\) is  
\[t_{b} =\frac{\Delta p}{F_{b}}=\frac{mv_{init}}{F_{b}}. \tag{6}\]  
Finally, a braking distance \(s_{b}\) is calculated as  
\[s_{b} =v_{init}t_{b}+\frac{1}{2}a_{b}t_{b}^{2}. \tag{7}\]  
The value of braking distance \(s_{b}\) is needed for calculation of the size of safety zone \(S\), given by EN ISO 13855 and EN IEC 62046 standards together with the manufacturer's recommended setting of the safety laser scanner [20]. The following formula was used that is specifically related to the safety zone \(S\) of scanners placed on the mobile platform:  
\[S =v(t_{b}+t_{s})+Ls_{b}+s_{s}+s_{g}. \tag{8}\]  
Parameter \(s_{g}\) is determined in compliance with the manufacturer's recommendation. It increases the safety zone as the ground clearance of the vehicle increases (see Figure 6). Table 2 contains all values needed for the calculation of safety zone in Equation (8).
## 3 Results and Discussion
### Feature Set
From the diagonal subplots, none of the alloy phase distribution maps described by the individual features allow complete phase separation, which means that none of the features can be used to completely classify the alloy phases. Also, this indicates that the complex nonlinear relationship of  
Figure 2: Feature selection. (**a**) The importance of 145 features was evaluated using the LightGBM algorithm, and the LightGBM algorithm accuracy was evaluated by adding the feature sets in order of importance and using 10-fold. (**b**) Global searching of the feature set selected in (**a**) using GA. (**c**) Different feature importance levels of the LightGBM algorithms. (**d**) Heatmap of feature intercorrelation by PCC. alloy phases is difficult to fit using the linear ML algorithm. However, the distribution of the feature Col, which ranks third in feature importance as shown in Figure 2c, with other features in the non-diagonal plot indicates that the classification boundaries of Col are relatively clear in the two-dimensional linear space for the three classes BCC, FCC, and HCP. However, the BCC+FCC phase produces a large amount of confusion with the BCC and FCC phases in the two-dimensional linear space, which may be one of the reasons for the low classification accuracy of the classifier for the BCC+FCC phase. To further understand the decision-making mechanism of the model, we used the Shapley Additive Explanations (SHAP) [38] interpretable machine learning method to analyze how the model makes decisions. SHAP, as a post hoc interpretation method, is used to analyze a \"black box\" algorithm locally or globally by calculating the marginal contribution of each feature to the prediction results. Unlike the feature importance results in Figure 2c, SHAP takes the trained model as a whole to analyze the contribution of individual features to each alloy phase, which allows for a better assessment of the laws followed by the model in actual decision-making. As shown in Figure 4, just like the Col distribution presented in Figure 3, Col has the largest contribution in distinguishing the alloy phases in the ML model. Also, by observing the histogram of Col, it was found that Col has a good effect on the separation of BCC from other phases. It also has a good separation effect for the classification of HCP phases. In addition, \(\chi\) has good results for the classification of BCC+FCC, a mixed phase that is the most difficult to separate. L\({}_{7}\), on the other hand, has a good classification for FCC. It is the nonlinear combination of these features for features that focus on classifying different alloy phases using LightGBM's excellent nonlinear mapping ability that allows the model to show good classification results for the four alloy phases. After feature selection, the number of features decreases from one hundred and forty-five to nine, and the accuracy of the LightGBM classifier does not deteriorate because of the decrease in the number of features. At the same time, feature selection reduces the redundancy of data and improves the physical interpretability of the model.
## 2 Materials and Methods
### Three-Points Direct Coordinate Determination  
In most robot systems involving motion, registration of preoperative 3D imaging and the actual patient or model is required. Our robot arm could detect its position and joint angle accurately within 0.1 mm. In this study, the robotic arm held the instrument and directly touched three reproducible points on the teeth of the mandible model to achieve registration.  
The robot uses a Cartesian coordinate system and the radians of \(\alpha\), \(\beta\) and \(\gamma\) are the value of Euler angles rotated by the z-, y- and x-axes, respectively. Euler angles are three angles introduced by [PERSON] to describe the orientation of a rigid body with respect to a fixed coordinate system [11]. Any orientation can be achieved by composing three elemental rotations.  
When we refer to the order of the unit coordinate transformations, the complex coordinate transformation matrix from the m-coordinate system to the i-coordinate system can be seen as follows.  
\[C_{m}^{i}=\left(\begin{array}{ccc}cos\beta cos\alpha&sin\gamma sin\beta cos\alpha -cos\gamma sin\alpha&cos\gamma sin\beta cos\alpha+sin\gamma sin\alpha\\ cos\beta sin\alpha&sin\gamma sin\beta sin\alpha+cos\gamma cos\alpha&cos\gamma sin \beta sin\alpha-sin\gamma cos\alpha\\ -sin\beta&sin\gamma cos\beta&cos\gamma cos\beta\end{array}\right) \tag{1}\]  
Figure 1: Overview of our autonomous robot osteotomy system.  
Likewise, Equation (1) shows the complex coordinate transformation matrix from the m-coordinate system to the i-coordinate system.  
One Cartesian coordinate system can be determined by three different points in space that are not on the same line. We used a robot arm holding the instrument to detect three points in space and use these 3D positioning values to calculate the new Cartesian coordinate system to match the preoperative image and actual position.  
When each of the three reference points were detected, the robot calculated the model coordination step by step. The first point represents the origin of the new coordinate. The second point is used for determining the x-axis. The direction from the first point to second point is the direction of the x-axis. Finally, the third point is used for determining the xy-plane by including all three points. The z-axis is defined as an axis passing through the origin and perpendicular to the xy-plane and calculated by cross product of the x-axis and y' vector, which is from first point to third point. The y-axis is defined automatically by the z- and y-axes and calculated by the cross product of them (Figure 2).
## 1 Introduction
In the case of a PV-fed grid connected inverter and due to the coupling between the two converters at the DC side by dynamics of the PV generator voltage and at the AC side by the dynamics of the grid current, such an approach cannot be applied and the differential inverter must be treated a whole system. The main contributions of this work are the derivation of a control-oriented model and the design of a three control loop strategy for a PV-fed grid-tied boost inverter as whole system without any decoupling between the two inverters hence dealing with its full-order model. The strategy is based on peak current mode control for the fast inner loop, a type III compensator for the grid current loop and a PI regulator with a pre-filter for the DC PV voltage loop. The main concern is the design of the three control loops for the inverter supplied from a series strings of PV panels and connected to the AC grid performing both Maximum Power Point Tracking (MPPT) at the DC side and Power Factor Correction (PFC) control at the AC side. For that, the study starts with a suitable mathematical modeling of the inverter. The design of its different control loops is carried out using simple approaches widely adopted for DC-DC converters while resulting in excellent system performances. At first, the state-space time-domain averaged model of the inverter is derived and the small signal frequency domain model is obtained using a quasi-static approximation in which the inverter is treated as a DC-DC converter with a slowly varying output voltage. Then, the controller is designed using a three-loop control strategy in which the boost inductor currents loop is used for suitable compensation by means of a single driving signal and its logic negation. The rest of this paper is organized as follows. Section 2 presents the system description consisting of a grid-connected PV system based on a differential boost inverter. Details about the operating principle of the system are provided in the same section. The mathematical model and the controller design are presented in Section 3. AC steady-state analysis is addressed in Section 4. Numerical simulations for the PV system grid-connected are presented in Section 5 validating the theoretical predictions. Conclusions of this work are summarized in the last section.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
## 4 Conclusions  
In the present study, heat transfer of different nanofluid types were investigated using two configurations: straight tube and straight microtube. Both geometries presented an enhancement in the Nu using nanofluids. In case of the straight tube with \(\phi_{b}=4\%\), the Nu increases 16% for Al\({}_{2}\)O\({}_{3}\)-water as compared to water, 12% for Al\({}_{2}\)O\({}_{3}\)-EG as compared to EG and 8% for Al\({}_{2}\)O\({}_{3}\)-turbine oil as compared to turbine oil. For the straight microtube with \(\phi_{b}=4\%\) the Nu increases 6% for Al\({}_{2}\)O\({}_{3}\)-water as compared to water, 12% for Al\({}_{2}\)O\({}_{3}\)-EG as compared to ethylene glycol and 5% for Al\({}_{2}\)O\({}_{3}\)-turbine oil as compared to turbine oil.  
Despite the better heat transfer enhancement in Al\({}_{2}\)O\({}_{3}\)-EG and Al\({}_{2}\)O\({}_{3}\)-turbine oil it is imperative to consider the associated higher values of the wall shear stress (e.g., quadruple of the corresponding based fluid for an Al\({}_{2}\)O\({}_{3}\)-EG nanofluid with high nanoparticle concentration \(\phi_{b}=10\%\)). It is important to notice that having nanofluids with high nanoparticle concentrations (\(\phi_{b}>4\%\)) is not recommended as the nanoparticle loadings increases the viscosity.  
The comparison between the different nanoparticles viz. Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), CuO, SiO\({}_{2}\) and ZnO revealed that the Al\({}_{2}\)O\({}_{3}\) nanoparticle with the three based fluids (water, ethylene glycol, and turbine oil) has a higher Nu due to the higher thermal capacity. The present results clearly showed that the addition of nanoparticles increases the Nu up to 16% as compared to the base fluid. For all the cases, the Nu enhancement becomes more pronounced due to nanoparticles. Two different boundary conditions were considered (constant heat flux and constant temperature), and thus two new convective heat transfer correlations were proposed for the straight tube covering a wide range of the process parameters (25 \(<\) Re \(<\) 1500, 0 \(<\)\(\phi_{b}<\) 10, 6 \(<\) Pr \(<\) 500) under laminar flow condition. Also, a new Nu correlation for the straight microtube was proposed for the same range of process parameters to that of straight tube. The new correlations can be applicable to a wide range of applications that will allow this new field of study to move a step forward.  
C.I.R.-S.: conceptualization, methodology and supervision. R.R.-T.: investigation, formal analysis, validation and writing--original draft. J.S.: formal analysis, writing--review and editing. K.D.P.N.: supervision. This research received no external funding.  
Figure 16: Comparison of Nu with literature: Al\({}_{2}\)O\({}_{3}\)-water nanofluid at \(\phi_{b}=4\%\) for SPH and SPD.  
**Acknowledgments:** This research was supported by the Energy and Climate Change Research Group at Tecnologico de Monterrey.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Conclusions  
In this study, we focus on utilizing vibration signals to diagnose wear faults in gearbox transmissions. We introduce a novel diagnostic approach that integrates VMD-PE and CSA-LSTM to enhance the accuracy and efficiency of identifying such faults. The outcomes and insights derived from implementing this innovative method led us to the following conclusions:  
1. In this work, we present an optimized LSTM fault diagnosis method tailored for efficient and accurate transmission vibration signal diagnosis. The CSA is harnessed to meticulously iterate and optimize the LSTM parameters, enhancing the pattern recognition of gearbox wear faults. Through a comprehensive comparative analysis with existing optimization algorithms, our proposed method emerges superior in fault pattern recognition, computational time complexity, and diagnostic accuracy, affirming its robustness and efficacy in the intricate domain of fault diagnosis.
2. This study introduces a feature extraction approach utilizing VMD and PE. This methodology enables adaptive mode decomposition of the transmission's vibration signals, facilitating the quantification of modes derived from the VMD-decomposed signal. By quantizing the state information encapsulated in the mod-al components of VMD decomposition, we effectively address the modal overlap issue associated with EMD. Our simulation test results for fault diagnosis under-score the efficacy of arrangement entropy in quantifying the state information embedded in the transmission component. Comparative analysis reveals that arrangement entropy outperforms multi-scale arrangement entropy, approximate entropy, and other time series complexity indicators in quantifying the transmission vibration signal, marking a significant advancement in the precision and reliability of fault diagnosis methodologies.  
Conceptualization, B.-C.Z., S.-Q.S., X.-J.Y., W.-D.H. and Z.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Department of Jilin Province under Grant 20210201050 GX, in part by the Major Science and Technology Projects of Jilin Province and Changchun City under Grant 20210301033 GX.  
Not applicable.  
The data used in this article are inconvenient to declare.  
The authors declare no conflict of interest.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Drift Overview  
Given a data stream \(\left(S=\left\{\left(x_{t},y_{t}\right)\right\}_{t=1}^{\infty}\right)\), drift denotes any time-dependent departure of the stream's data-generating distribution from the one that prevailed when a predictive model was trained. Formally, let \(\left(\mathcal{D}_{t}\right)\) be the joint distribution \(\left(P_{t}(X,Y)\right)\) at time \(\left(t\right)\). Drift occurs iff \(\left(\exists\,t_{1}<t_{2}:\ \mathcal{D}_{t_{1}}\
eq\mathcal{D}_{t_{2}}\right)\). Such nonstationarity violates the classical i.i.d. assumption and can erode model performance, sometimes catastrophically. A change in \(\left(\mathcal{D}_{t}\right)\) can manifest in two principal ways.
# A Novel Optimization Design Method of Form Ginding Wheel for Screw Rotor
## 2 Theoretical Background
### Form Wheel Profile Generation Model  
Once the screw rotor profile is known, the profile of the form wheel can be derived. The contact line equation can be expressed as [3]:  
\[\left(\overrightarrow{k}_{c}\times\overrightarrow{k}\right)\overrightarrow{ n}=0 \tag{7}\]  
where \(\overrightarrow{R}=\overset{\rightarrow}{O_{c}M}\) is the radial vector of \(M\) in \(O-X_{c}Y_{c}Z_{c}\), and \(\overrightarrow{n}\) is the normal vector at \(M\). The component of the normal vector \(\overrightarrow{n}\) at three coordinate axes can be calculated based on the following equation:  
\[\overrightarrow{n}=\frac{\partial\overrightarrow{r}}{\partial t}\times \frac{\partial\overrightarrow{r}}{\partial\theta}=\left|\begin{array}{ccc} \overrightarrow{i}&\overrightarrow{j}&\overrightarrow{k}\\ \frac{\partial X}{\partial t}&\frac{\partial Y}{\partial\theta}&\frac{ \partial Z}{\partial t}\\ \frac{\partial X}{\partial\theta}&\frac{\partial Y}{\partial\theta}&\frac{ \partial Z}{\partial\theta}\end{array}\right| \tag{8}\]  
Substituting \(t\) and \(\theta\) in Equation (1) with their corresponding partial derivative, the following relationship can be obtained:  
\[\left\{\begin{array}{c}\frac{\partial X}{\partial t}=\frac{dx_{t}}{dt}\cos \theta-\frac{dy_{t}}{dt}\sin\theta\\ \frac{\partial Y}{\partial t}=\frac{dx_{t}}{dt}\sin\theta+\frac{dy_{t}}{dt} \cos\theta\\ \frac{\partial Z}{\partial t}=0\end{array}\right. \tag{9}\]  
\[\left\{\begin{array}{c}\frac{\partial X}{\partial t}=-x_{t}\sin\theta-y_{t} \cos\theta\\ \frac{\partial Y}{\partial\theta}=x_{t}\cos\theta-y_{t}\sin\theta\\ \frac{\partial Z}{\partial\theta}=p\end{array}\right. \tag{10}\]  
Substituting Equations (9) and (10) into Equation (8) yields the following:  
\[\left\{\begin{array}{c}n_{x}=p\Big{(}\frac{dx_{t}}{dt}\sin\theta+\frac{dy_{t }}{dt}\cos\theta\Big{)}\\ n_{y}=-p\Big{(}\frac{dx_{t}}{dt}\cos\theta-\frac{dy_{t}}{dt}\sin\theta\Big{)}\\ n_{z}=\Big{(}\frac{dy_{t}}{dt}\cos\theta-\frac{dy_{t}}{dt}\sin\theta\Big{)}(x_{ t}\cos\theta-y_{t}\sin\theta)-(-x_{t}\sin\theta-y_{t}\cos\theta)\Big{(}\frac{dy_{t }}{dt}\sin\theta+\frac{dy_{t}}{dt}\cos\theta\Big{)}\end{array}\right. \tag{11}\]  
The substitution of Equations (5) and (7) into Equation (7) then yields:  
\[\begin{array}{c}\big{[}(x_{t}\cos\theta-y_{t}\sin\theta)(\cos\theta-K\sin \theta)+(x_{t}\sin\theta+y_{t}\cos\theta)(\sin\theta+K\cos\theta)\big{]}\\ \big{[}Y-T-p\cot\omega\big{]}+\big{[}p(\cos\theta-K\sin\theta)\big{]}\text{:}p \theta\\ +\big{[}p(\sin\theta+K\cos\theta)\big{]}\text{:}T\cot\omega=0\end{array}\right. \tag{12}\]where \(K\) is the first-order derivative of \(y_{t}\) versus \(x_{t}\), which can be obtained by MATLAB (The MathWorks, Inc., Natick, MA, USA). As can be seen from the above equations, the only unknown variable \(\theta\) can be calculated using \(T\). Therefore, both the contact line and grinding wheel profile can be solved.
## 4 Discussion
Up to now, many Basidiomycetous sesquiterpene synthases have been characterized, exemplified as putative STSs from _O. olearius_, _C. puteana_, _L. rhinocetotus_, and _A. aegerita_[8; 9; 10; 11]. With the recent revolution in genome-sequencing technology, a large number of Basidiomycetous genomes have been completed. Bioinformatic mining indicates that most genes/clusters of the phylum _Basidiomycota_ are silent or cryptic under laboratory conditions [4; 9; 10]. The terpenoids and their synthases represent largely untapped \"dark matter\" that awaits to be characterized [1; 2; 4]. What are the ecological functions of STSs that play important roles in secondary metabolism during the lifecycle of mushrooms? What is the underlying mechanism that controls the functional evolution of STSs? Are these STSs unique to specific evolutionary lineage (taxa) or widely distributed? To answer these questions, functional characterization of increasing STSs encoded in the fungal genome is essential and indispensable. As a cutting-edge discipline that drives original breakthroughs in the field of biopharmaceutical, the rise of synthetic biology can promote drug leads discovery by designing new biosynthetic pathways. Therefore, mining and discovering of biosynthetic elements are urgently needed in synthetic biology technology [10]. Also, bioinformatic analysis and functional prediction of specific STSs would facilitate the screening of ideal candidate synthetic elements or bioprospecting for new sesquiterpenes. Since most Basidiomycetous mushrooms are difficult to manipulate in genetic transformation, _S. cerevisiae_ yeast has emerged as a powerful host cell for production of fungal secondary metabolites and pathways reconstitution [11; 56]. To our knowledge, this is the first report to document the endeavor of the yeast expression for mining the sesquiterpene repertoire in _Desarmillaria_ species and _Armillaria_ affinities. Phylogenetic analysis of characterized STSs reinforcedthat certain group of STSs shares highly conserved sequences, suggesting that they might catalyze the likely cyclization mechanism [9; 10]. Thus, phylogenetic analysis might be useful to mine novel terpenoid synthases. In this study, noteworthy, half members STSs from the strain CPCC 401429 were distributed in minor group Clade IV. This group of STSs belongs to a new subfamily and only few STSs have been characterized. Our report on STSs derived from _D. tabscens_ enriches the number of STSs of this subfamily and expands our knowledge of STSs diversity for _Desarmillaria_ or close genus. Yeast cells expressing DtSTS9 and DtSTS10 yielded diverse sesquiterpene molecules, suggesting that this subfamily might be highly promiscuous producers. This highlights the potential of _Armillaria_ in generating novel sesquiterpenoids. Accessing the basidiomycetous pool of STSs can be an immediate source of novel biocatalysts, or yield STSs that could be further specialized by directed evolution or combinatorial engineering. This also lays foundation for downstream in-depth deciphering catalytic mechanism of novel STSs.