esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of the Numerical Results
### Separation Efficiency  
The separation efficiency in a cyclone is the relationship between the number of particles captured with regards to the number of particles that enter by its inlet section [9; 14]. This collection capacity is denoted in terms of its global efficiency or the cut-off diameter (d\({}_{50}\)), the latter being the particle diameter whose separation efficiency is 50% [10]. In this paper, the separation efficiency presented by each of the cyclone models is considered globally, as well as fractionally, by using a graph called grade efficiency curve, which relates the separation efficiency of each model with the diameter of the particle.  
Figure 11 shows the grade efficiency curves of the four cyclone models. The cut-off diameter for each model is 2.76 \(\upmu\)m, 2.98 \(\upmu\)m, 2.96 \(\upmu\)m, and 2.81 \(\upmu\)m, respectively. In comparison with the Stairmand high-efficiency model (Model A), all the other models show an increase of the cut-off diameter, being the model D, the one that increases the less, which coincidentally is the model that generated the higher pressure drop reduction, as was already shown in Table 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Pressure Drop (Pa)** & **a Percentage Difference in Pressure Drop** \\ \hline A & 1293.35 & – \\ B & 1243.40 & \(-\)3.86\% \\ C & 1222.30 & \(-\)5.49\% \\ D & 1215.67 & \(-\)6.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Values and percent change of pressure drop for cyclone models A, B, C, and D.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **A** & **B** & **C** & **D** \\ \hline
**Tangential velocity (m/s)** & 36.8 & 35.97 & 35.37 & 35.28 \\
**Velocity (m/s)** & 39.71 & 38.55 & 38.18 & 38.27 \\
**a Percentage change** & – & \(-\)2.26\% & \(-\)3.89\% & \(-\)4.13\% \\
**b Percentage change** & – & \(-\)2.92\% & \(-\)3.85\% & \(-\)3.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Values and percentage change of mean velocity and tangential velocity of cyclone models A, B, C and D.  
Additionally, Table 11 shows the values referring to the overall separation efficiency presented by all the cyclone models, along with their percentage change concerning the  
Figure 11: Grade efficiency curves of cyclone models A, B, C y D.  
Figure 10: Contour plots of static pressure of cyclone models; (**a**) Model A (Stairmand cyclone separator), (**b**) Model B cyclone separator, (**c**) Model C cyclone separator, (**d**) Model D cyclone separator.  
standard model (Model A). Table 11 shows that the application of circular cavities as a passive flow control technique does not favor the particle collection in the cyclones. The models B and C efficiency of separation decrease 4.35% and 3.81%, respectively, regarding model A. The separation efficiency values of models A and D differ only by 0.42% and, additionally model D presented the highest reduction in pressure drop too, therefore could be considered that this cyclone exhibits the same or almost the same particle collection capacity as Model A, having the advantage that its pressure drop is way less.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 1: Multi-Source Input  
A shift from the use of single-source remote sensing data (11 of 63) to multi-source remote sensing data (52 of 63) in urban forest studies has been observed thanks to the emergence of diverse airborne and spaceborne platforms and the rapid development of remote sensing techniques. Examples of these are the emerging very high spatial resolution (VHSR) and high spatial resolution (HSR) sensors, hyperspectral sensors and high point density LiDAR technology. Table 3 lists the remote sensing products/platforms used for urban forest studies. Two examples, as shown in Figure 1, are satellite imagery (WorldView-2 NDVI) and LiDAR point-cloud data (canopy height model) applied to urban forests at both macro- and micro-scale. Quantum leaps in computing power have enabled researchers to fuse, process and model data from various sources at an unprecedented level of spatial and temporal resolution.
# Inertial Parameter Identification in Robotics: A Survey
## 3 Inverse Dynamic Identification Model (IDIM) and Least-Squares (LS) Estimation Methods
### Ordinary, Weighted and Iteratively Reweighted Least-Squares (IDIM-OLS, -WLS, -IRLS)
The resulting IDIM-IRLS method takes the form of an iterative process, which consists of applying additional penalty to the outliers, in the form of a dedicated weight vector \(\mathbf{v}^{i}\in\mathbb{R}^{n\cdot N}\) and a weight matrix \(\mathbf{Y}^{i}=[\mathbf{v}^{i},\cdots,\mathbf{v}^{i}]\in\mathbb{R}^{(n\cdot N)\times b}\) for each iteration \(i\), in order to eventually mitigate their contribution to the final result  
\[\mathbf{W}^{si} =\mathbf{Y}^{i}\cdot*\mathbf{W} \tag{22}\] \[\mathbf{y}^{si}_{\tau} =\mathbf{v}^{i}\cdot*\mathbf{y}_{\tau}\]  
where \(*\) refers to the element-wise multiplication operator. Accordingly, the IDIM-IRLS estimate \(\hat{\mathbf{\beta}}^{i}_{IRLS}\) at iteration \(i\) is given by  
\[\hat{\mathbf{\beta}}^{i}_{IRLS}=(\mathbf{W}^{si\top}\mathbf{\Sigma}^{-1}\mathbf{W}^{si})^{-1} \mathbf{W}^{si\top}\mathbf{\Sigma}^{-1}\mathbf{y}^{si}_{\tau}\,. \tag{23}\]The weight vector \(\mathbf{v}^{i}\) is updated following  
\[\mathbf{v}^{i}=\text{Min}\left(\mathbf{v}^{i-1},\Lambda(\mathbf{\varepsilon}^{i}_{IRLS})\right) \tag{24}\]  
where \(\text{Min}(\cdot)\) is the element-wise min operator and \(\Lambda:\mathbb{R}^{n\cdot N}\to[0,1]^{n\cdot N}\) is a tailor-made weight function. The process is iterated until convergence of the weights. This method was successfully tested on robotic systems in [23, 51]. It is critically important to note that the LS estimates in general, will be unbiased if and only if the observation matrix \(\mathbf{W}\) is uncorrelated with the error term \(\mathbf{\varepsilon}\), or in other words, that the following equality holds  
\[\tau=\Upsilon_{\beta}\big{(}\bar{q},\dot{q},q\big{)}\beta+\mathbf{e}\,. \tag{18}\]0  
Unfortunately, the presence of uncorrelated random components within the observation matrix does not allow this hypothesis to be validated in practice. Noise sensitivity is especially problematic in the context of robotic systems as the joint accelerations, obtained by double time differentiation of the noisy encoder data, often exhibit poor signal-to-noise ratio with multiple outliers. A workaround to this issue is to filter the joint measurement signals as suggested by [2] where a pragmatic and tailor-made data-filtering is proposed. Nevertheless, this requires the knowledge of the bandwidth of the position closed-loop and special attention due to the bias induced by the filter, see [7, 43, 52] for more details. It is worth noting that in the case of periodic excitation trajectory with a known characteristic frequency, it is possible to use Fourier analysis tools to perform frequency domain filtering as suggested in [11, 53]. The other alternative is to use identification methods that are robust against a violation of (25).
# Variable Selection in Time Series Forecasting Using Random Forests
## 2 Methods and Data
### Methods
#### 2.1.3 Forecasting Using ARMA and ARFIMA Models  
Here we present how we can use ARMA and ARFIMA models in one-step ahead time series forecasting. The specific applications of the forecasting methods are presented in Section 2.1.8. We consider a time series of \(n\) observations. Let us also consider a model fitted to the \(n\) observations and subsequently used to forecast \(x_{n+1}\). Let \(x_{n}\) and \(\psi\) represent the last observation and the forecast of \(x_{n+1}\), respectively. The methods using ARMA and ARFIMA models can be used as benchmarks in the simulation experiments. In fact, these methods are expected to perform better than the rest, when applied to the synthetic time series, since the latter are simulated using ARMA or ARFIMA models (see Section 2.1.8). We examine two cases.  
In the first case we assume that the time series are modelled by an ARMA(\(p\), \(q\)), in which \(p\) and \(q\) are predefined, while the parameters must be estimated. We fit ARMA models to the data using the arima built in R function. During the implementation process, the values of \(p\), \(q\) are set equal to the respective values used in the corresponding simulation experiment. The function applies the maximum likelihood method to estimate the values of the AR and MA parameters \(\phi_{1}\),..., \(\phi_{p}\), \(\theta_{1}\),..., \(\theta_{q}\) of the models. We use the fitted ARMA model in forecast mode by implementing the predict built in R function [52].  
In the second case, we assume that the time series can be modelled by an ARFIMA(\(p\), \(d\), \(q\)) model with unknown parameters. We fit an ARFIMA model to the data using the arfima function of the R package forecast [55,56]. The values of \(p\), \(d\), \(q\) are estimated using the Akaike Information Criterion with a correction for finite sample sizes (AICc), with \(d\) ranging between \(-0.5\) and \(0.5\). The function applies the maximum likelihood method to estimate the values of the AR and MA parameters \(\phi_{1}\),..., \(\phi_{p}\), \(\theta_{1}\),..., \(\theta_{q}\) of the models. The order selection and parameter estimation procedures are explained, for example, in [57] (Chapter 8.6). We use the fitted ARFIMA model in forecast mode by implementing the forecast function of the forecast R package.
# Stress Corrosion Cracking Analysis of a Hot Blast Stove Shell with an Internal Combustion Chamber
## 3 Results and Discussion
### Crack Propagation Velocity Prediction Results from SCC SMM  
In the cases of HBSs, the apparent chemical composition could be more complex. The internal space of the HBS experiences a significant rate of NO\({}_{2}\) formation when the temperature falls below 650 \({}^{\circ}\)C. On the metallic shell of the stove, the coldest area is where the formation of NO\({}_{2}\) is most apparent. During the on-blast period, the temperature of the metallic shell may readily fall below the water dew point. Consequently, NO\({}_{2}\) will dissolve in the water and undergo a reaction to form nitric acid. According to [PERSON] et al. [39], the nitric acid formation, combined with the dissolution of various compounds from the insulation material, results in an acidic condensate that produces a nitrate solution, which is responsible for the SCC of the stove shell.The brick layer in the HBS can result in the production of a calcium nitrate solution as a byproduct. The presence of sulfates in the deposits may lead to the condensation of sulfuric acid inside of the refractory linings near the gas side. In regions close to the shell with low temperatures, NO\({}_{2}\) can either liquefy in the water that has condensed below the dew point or be converted into nitric acid and condense below the dew point. Ammonium and nitrate ions were detected in the condensed liquid, despite the absence of ammonium ions or ammonia in the gas [40]. Therefore, the chemical composition of acidic condensation and their melting temperature were reviewed in the table in the appendix, and the target melting temperature to use in the SMM was estimated at 770.8 \({}^{\circ}\)C.  
Figure 5 shows the maximum principal stress field and crack propagation velocity \(V_{p}\) caused by the temperature and maximum principal stress for the reference blast temperature. The crack propagation velocity is the average value through an operation cycle. The duration of the operation cycle is 180 min. The regions at which crack propagation velocity based on the original SCC SMM was expected to be the largest are the conical region of  
Figure 4: Temperature distributions of the shell at the midpoint of the on-blast period (\(t=130\) min) for (**a**) the reference blast temperature [28], and for the blast temperature increased by (**b**) 45 K and (**c**) 90 K.  
the upper combustion chamber and part of the hot blast outlet. The reason for this could be summarised as follows: the conical part of the upper combustion chamber was caused by the high temperature; the part of the hot blast outlet caused the maximum principal stress. In addition, this distribution means that the performance of characteristics of the SCC SMM are caused by temperature and stress. Meanwhile, these results could be used to evaluate some simple cases in the situation of acid concentration distributed evenly.
## 2 The Novel Transducer  
A novel configuration of the piezoelectric transducer was designed and manufactured at the Institute of Dynamics and Vibration Research (Leibniz University Hannover, Germany). The transducer consisted of one set of piezoelectric elements, an Al alloy cover part, a horn part, and a ring sonotrode. The ring sonotrode was made of Al alloy and the diameter of inner ring was 21.5 mm. The PZT (lead zirconate titanate ceramic) plates were assembled between the cover and the horn to excite longitudinal vibration, achieving the elliptical vibration of the ring sonotrode. The harmonic simulation result is shown in Figure 1. The resonance frequency of the transducer was about 21,034 Hz. Low-frequency ultrasound, such as 21 kHz, can introduce more violent cavitation at the same vibration amplitude compared to high-frequency ultrasound. The reason is that at low-frequency ultrasound, the bubble growth period becomes longer, resulting in higher bubble energy accumulation. As shown in Figure 1, when the ring sonotrode vibrates, there are four small areas with high vibration amplitude, which leads to the generation of stronger cavitation near the four areas. Thus, there are four areas of the workpiece surface that can be peened when they are put into the center of the ring sonotrode. In this way, the efficiency of the ultrasonic cavitation peening for cylindrical workpieces is improved four times over the traditional longitudinal vibration transducer.  
In order to verify the real vibration shape and resonance frequency of the ring sonotrode, we built a measurement setup using a 3D Laser Doppler vibrometer (MSA-100-3D, Polytec Company, Waldbronn, Germany). The laser head of the vibrometer can move in the \(Z\) direction and the XY positioning stage can achieve the two-dimensional motion in the horizontal plane. The transducer was placed on two support blocks to make the plane of circular ring horizontal, and then they were fixed on a XY positioning stage for obtaining vibration characteristics of all points on the vibrating system. The number of the points on the edge of the ring was set to 80. During the experimentations, the [PERSON] transducer and the circular ring were tested with the excitation signals of 1 V. As shown in Figure 2, the resonance frequency was 21,025 Hz, which is very close to the simulation results. It also can be seen that the vibration amplitude of the ring sonotrode in the \(x\) direction was almost the same as that in the \(y\) direction. At the resonance frequency, a relationship of approximately 0.012 \(\upmu\)m/V existed between the vibration amplitude and the driving voltage in the range of 0 V and 400 V according to the measurements.
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
## 2 Methods
### Micropile Design Calculation Method and Pile Length Optimization Study
As illustrated in the figure, it can be observed that there is a decrease in the deformation coefficient with an increase in pile diameter for micropiles ranging from 0.09 m to 0.3 m; however, when using the product of the deformation coefficient \(\alpha\) and the embedded length \(h_{2}\) of the pile to determine the type of micropile (k method: rigid pile when \(\alpha h_{2}\)\(\leq\) 1 and elastic pile when \(\alpha h_{2}\)\(>\) 1; m method: rigid pile when \(\alpha h_{2}\)\(\leq\) 2.5 and elastic pile when \(\alpha h_{2}\)\(>\) 2.5), based on data shown in Figure 1, when the embedded length of the micropile exceeds 6 m (calculated using the \"m method\" for a pile diameter of 0.3 m), it can be regarded as an elastic pile regardless of the soil conditions, and this length can be achieved in practical engineering. Micropiles can be considered elastic based on both experimental phenomena and engineering theoretical calculations. Therefore, the mature calculation theory of elastic piles can serve as a reference for calculation, forming the theoretical basis for the method described in this chapter. As shown in Figure 2, first, multiple rows of micropiles and the soil between them are considered a whole, forming a combined anti-sliding system. The system is assumed to share the sliding force generated above the pile position, and then the force is distributed among the micropiles according to the ratio of sliding force that each pile can bear, which is determined by model tests and numerical simulations. Consequently, the consolidation of multiple rows of micropiles into a single row is undertaken for computational purposes. Subsequently, within a given calculation width, each individual micropile is segmented based on soil layers, and the ground coefficient method is employed to determine parameters such as pile displacement, bending moment, and shear force, among others, in each segment. Ultimately, reinforcement calculations are conducted based on the distribution of internal forces along the pile.
# Numerical Study on the Evolution Mechanism of the Crater under a Millisecond Laser
## 4 Conclusions  
In this paper, a two-dimensional numerical model for studying the crater morphology and its melted zone was established. In addition to the Marangoni effect, surface tension and volume force, recoil pressure, and the Hertz-Knudsen ablation rate were also considered. The model was verified experimentally, and the significant conclusions of this research are summarized as follows:  
The effect of ablation velocity on the crater morphology can be ignored at a lower peak power density (\(<\)\(3\times 10^{9}\) W/m\({}^{2}\)) and lower laser duration (\(<\)1 ms). However, when the peak power density increases or the laser duration increases, the effect of the ablation rate on the crater formation becomes more evident.  
With the increase of peak power density and laser duration, the material evaporates and the molten pool expands because of heat accumulation. This makes the diameter and the center depth of the crater texture increase after the cooling period. However, with the increase of laser duration, the height of the edge bulge decreases, which is due to the fact that the central depth of the molten pool morphology is too large in comparison with the edge bulge at the end of heating, which requires more molten reflow during the cooling period.  
The laser peak power density and pulse width obviously affect the melted zone. A numerical model is used to explain that in a crater with the same depth, a larger melted zone can be obtained with a higher laser duration time than with higher peak power density, and this model is effective for studying the formation of the crater under a millisecond laser.  
**Author Contributions:** The contributions of the individual authors were the following: methodology, D.Z. and P.Z.; numerical simulation, D.Z., P.Z., C.C., X.X., and S.X.; experimental validation, D.Z., Z.T., and P.Z.; writing--original draft preparation, D.Z. and P.Z.; writing--review and editing, D.Z., P.Z., and Z.T.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No. 51975252), High-technology Research Key Laboratory Project of Zhenjiang City (SS2018007), Major Scientific and Technology Project of Zhenjiang City (ZD2018001), Major Scientific and Technological Achievement Transformation Project of Taizhou City (SCG201901).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Ontology Construction of Digitization Domain for Ancient Architecture
## 4 Application Case
### Experimental Process and Results  
Based on the ontology model in the field of ancient architecture digitization, this digitization work firstly used on-site investigation and the literature review to clarify the elements required for digitization in the digitization process of the wooden tower, such as murals, beams, statues, roofs, the dougong, etc., as shown in Figures 6 and 7. It can be seen that the key elements of the wooden tower can be classified into three main categories: structural components, decorative elements, and accompanying cultural relics. The structural components include beams, columns, bucket arches, fangs, doors, windows, and other components. The decorative elements include caissions, murals, and other such features, like ancillary cultural relics such as plaques and Buddha statues, etc. Second, the digitization elements of the wooden tower were defined, after which the corresponding components were selected from the aforementioned digitization ontology model of ancient architecture. Then, the digitization technology and corresponding equipment that meet the required accuracy were then selected according to the defined attribute information in the digitization ontology model of ancient architecture, see Table 4  
Figure 5: Overview image of the wooden tower. for details. This section describes the relationship between digitization elements, digitization equipment, the storage format, and specifications in the digitization process of the wooden tower from both semantic and spatial perspectives. It also presents a complete ontology model of the digitization process of the wooden tower, as shown in Figure 8. The complex structure of the wooden tower and the limitations of the working environment necessitate frequent changes of station for the traditional fixed laser scanner, resulting in a markedly low data acquisition efficiency. To solve this problem, a handheld scanner based on SLAM technology was selected in the digitization ontology model of ancient buildings for integrated data acquisition inside and outside the tower, thereby obtaining the point cloud data. Concurrently, the appropriate UAV equipment was selected in the ontology database to obtain the image data in the protected area of the wooden tower and establish the digitization document. Figure 6: The wooden tower digitization elements. Figure 7: The wooden tower formal ontology description fragment. Ultimately, a digitization representation of the wooden tower was developed according to the aforementioned digitization ontology, and its details are shown in Figure 9. It can be seen that the ontology model of the digitized domain of ancient architecture constructed in this article can not only clearly and completely express the digitization process of a certain building, but also fully describe the relationship between the various elements, thus enabling the construction and visualization of the digitized domain ontology of ancient architecture.