esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 2 Waveform Mapping Theory
### Identification of Blade Vibration Parameters Based on Waveform Mapping Method
The deviation between the traversal value and the actual measured value is expressed by root mean square of residual error, and the formula is as follows:  
\[S_{k}=\sqrt{\frac{\sum\limits_{i=0}^{n-1}e_{ki}^{2}}{n}}. \tag{16}\]  
It is assumed that the actual excitation order of blade synchronous vibration is \(N_{e}^{*}\) and the theoretical displacement residual is \(S^{*}=0\); that is, the coefficient matrix and solution vector corresponding to the excitation order \(N_{e}^{*}\) completely satisfy Equation (13). However, there are various errors in the actual blade vibration measurement; the condition for \(S_{k}\) to take the minimum is the traversal excitation order \(N_{ek}=N_{e}^{*}\). The excitation order \(N_{ek}=N_{e}^{*}\) corresponding to the minimum value \(S_{k}\) is obtained through the excitation order traversal process, and the nonlinear least square method in Equations (14)-(16) is used to solve the corresponding solution vector \(Z^{*}\). According to the vibration frequency, vibration amplitude, initial phase and constant offset formulae in Equations (17)-(20), the synchronous vibration parameters of blade are obtained. Vibration frequency:  
\[\omega=N_{e}\cdot\Omega. \tag{17}\]  
Vibration amplitude:  
\[A_{0}=\sqrt{z_{1}^{*2}+z_{2}^{*2}}. \tag{18}\]  
Initial phase:  
\[\varphi=\left\{ \begin{array}{cc}\arctan(\frac{z_{1}^{*}}{z_{2}})&z_{2}^{*}>0 \\ \arctan(\frac{z_{1}^{*}}{z_{2}})+\pi&z_{2}^{*}<0\end{array} \right. \tag{19}\]  
Constant offset:  
\[C=z_{3}^{*}. \tag{20}\]
## 3 Results and Discussion
### Milk and IF Fatty Fraction
As shown in Table 5, samples contained 78% of saturated fatty acids (SFAs), and 18% of these were short chain fatty acids (C4-C10), which usually have higher concentrations in sheep's milk with respect to cow's milk [9; 10]. These fatty acids are easily digested as they are hydrolyzed from the triglycerides and transferred directly from the intestine to the portal vein, providing a rapid energy supply [9; 10]. The most represented fatty acids were palmitic acid (C16:0; 30%), oleic acid (C18:1 9 _cis_; 11%), and myristic acid (C14:0; 14%). Polyunsaturated fatty acids (PUFAs) consisted mainly of linoleic acid (C18:2 _cis_ 12 _cis_) and \(\alpha\)-linolenic acid (C18:3 9 _cis_ 12 _cis_ 15 _cis_), both of which were just below 2%. These essential fatty acids are precursors of omega-6 (n-6) and omega-3 (n-3) PUFAs, respectively. A low n-6:n-3 ratio (between 1:1 and 4:1) is considered ideal in the prevention of cardiovascular diseases, inflammatory and autoimmune disorders, while values above 5 are considered unfavourable. Sheep's milk usually has a low n-6:n-3 ratio as shown by the optimal value close to 1 in the samples we examined [9; 10]. Another interesting feature of sheep milk fat is its content of conjugated linoleic acid (CLA), particularly in rumenic acid, the main CLA isomer (CLA 9 _cis_ 11 _trans_), and its precursor, the _trans_-vaccenic acid (C18:1 11 _trans_) (0.5% and 1%, respectively) for which various positive health effects have been demonstrated [7; 9]. Rumenic acid is an intermediate in rumen biohydrogenation of linoleic acid, whereas _trans_-vaccenic acid is a common intermediate in the biohydrogenation of both linoleic and linolenic acids. These latter precursor fatty acids are ingested by the animal through feed intake (grass, fodder, hay and concentrated feed). It has been demonstrated that a diet mainly based on the exploitation of pasture greatly increased the CLA content in sheep's milk [37]. However, the _trans_ configuration of the vaccenic acid enlists this fatty acid in the _trans_ fatty acids (TFAs) category. TFAs from industrial hydrogenation processes are associated with the onset of cardiovascular diseases, while no adverse effects due to the ingestion of natural TFAs, such as the milk vaccenic acid were registered. However, recommendations are given that limit the presence of TFAs, regardless their origin (industrial or natural), even in infant formulae and baby products. In this respect, both European and Chinese legislation limits the content of TFAs for foods intended for infant nutrition to a value lower than 3% of the total fatty acids [38; 39]. The data reported in Table 5 show that the level of TFAs in IF1 and IF2 were slightly over 3%. However, it is worth remembering that in the final FM to be marketed, both IF1 and IF2 will be mixed with vegetable oils (that should not contain _trans_ fatty acids) so lowering the final TFAs % content to the recommended limits.
# Influence of Altitude on Phytochemical Composition of Hemp Inflorescence: A Metabolomic Approach
## 1 Introduction
Other phytocannabinoids detected in hemp inflorescences comprise main oxidation products of THCA and CBDA: cannabinol (CBN) and cannabinolic acid (CBNA) obtained from THC(A), and cannabielsoinic (CBE) and cannabielsoinic acid that derive from CBDA. The \"cannabivarin\" group, commonly following the above-mentioned ones, is produced from condensation of geranyl diphosphate with divarinic acid, which results in a propyl instead of the pentyl side-chain. Even though the attention of the scientific community has been focused on major phytocannabinoids, the phytochemical characterization of _C. sativa_ highlights the presence of various non-cannabinoids constituents including flavonoids [15]. Their characterization is scarce and random, especially when the inflorescences of industrial hep are concerned [13; 16; 17]. In any case, the characterization of the cannaflavones--compounds isolated exclusively from hemp--needs further elucidation, mainly when the inflorescences are concerned. One non-phytocannabinoid category of bioactive secondary metabolites that is studied in much more detail is the terpene family. They represent volatile components that has been claimed to have a synergic action with cannabinoids [18]. Many different monoterenes and sesquiterpenes are important components of _C. sativa_ essential oils [19; 20], as they define some of the unique organoleptic properties and may also influence nutraceutical potentials of different hep stringa and varieties [21]. Selecting a genotype appropriate for a particular end-use application that is adaptable to an environment is of principal importance for successful hemp cultivation. Hemp is a plant adaptable to various growing and ecological conditions, but there are no data available in the literature that concern the differences that may arise from cultivating the same chemovar contemporarily in the plains and mountain habitats. Different hemp varieties cultivated at high altitudes showed a characteristic phytochemical and ecological behavior [13]. In this research, our aim was to study whether two very different ecological environments (mountain and plains) would have a significant impact on phytocannabinoids qualitative and quantitative content, flavonoids presence, and terpenoids profile, in order to study the plant phytochemical behavior and its potential to provide nutraceutical substances. Four strains of industrial hemp (Kompolti) were selected and cloned to provide genetically uniform material for analyses of secondary metabolites (cannabinoids, terpen, and flavonoids) of clones of the same plant grown at different elevation in two sites representative of lowland (Vercelli Province 130 m ASL) and mountain (Alagna Valsesia 1200 m ASL) during the growing season 2018.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
## 3 Controller Synthesis
### Optimization Problem Implementation
The main steps of the implementation are briefly described as follows:  
* The continuous state space model is symbolically defined using C-code or the MATLAB interface, then it is simplified employing automatic differentiation tools and using zero entries in the Jacobian matrix. The result is an efficient real time C-code for the integration of the continuous nonlinear system which will be used for the prediction. * The optimization problem cost function and constraints are symbolically defined, parametrized by the aforementioned direct multiple shooting technique, and the resulting, large but sparse, Quadratic Problem (QP) is condensed. * The discretized QP is then solved using qpOASES solver. A representation of the usage of NMPC for path-following and collision avoidance is shown in Figure 4, and Algorithm 1 describes the NMPC usage as follows. ```
1:Set the time index \(k=0\), the sampling interval \(T_{s}\), the prediction horizon \(T_{p}=NT_{s}\), weight matrices \(Q\) and \(R\). 2:Measure the values of the states \(\bar{\mathbf{x}}(t_{k})\) or estimate them. 3:Solve the optimization Problem (16) over the discrete time instants \(T_{d}=[t_{k+1}\cdots t_{k+N}=T_{p}]\) and get the optimal control sequence \(\big{[}\bar{\mathbf{u}}(t_{k})=\mathbf{q}_{k}\cdots\bar{\mathbf{u}}(t_{k+N-1}=\mathbf{q}_{k+N- 1})\big{]}\) and the corresponding predicted states \(\big{[}\bar{\mathbf{x}}(t_{k})=\mathbf{q}_{k}\cdots\bar{\mathbf{x}}(t_{k+N}=\mathbf{q}_{k+N}) \big{]}\). 4:Apply only the first control element \(\bar{\mathbf{u}}(t_{k})\). 5:wait for the next sample and set the time index \(k=k+1\), then go to step 2. ```  
**Algorithm 1** NMPC Algorithm for Path Following. **Remark 1**. : _There are many state estimation techniques in the literature that could be used to get the state vector \(\bar{\mathbf{x}}(t_{k})\). A survey for these techniques is given in [27]._  
Figure 4: Representation of Nonlinear Model Predictive Control (NMPC) usage for path following and collision avoidance.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 3 Case Study
### Time Autocorrelation Analysis  
Selecting appropriate lag days is critical for deformation prediction, as it ensures the model effectively captures the key temporal patterns and historical dependencies within the deformation process. Figure 11 show the partial autocorrelation for wall deflections at different lags (days). Each graph represents different deflection measurements. The significance threshold, marked at 0.2, is calculated based on 95% confidence using the formula threshold = \(\pm\)1.96\(\times\)\(1/\sqrt{N}\), where \(N\) is the sample size. From the analysis, it is observed that Lag 1 consistently shows significant partial autocorrelations across all wall deflections, indicating that the value at any given point in the time series is closely related to the value at the previous point, and this relationship is not influenced by the effects of other points in between. Hence, Lag 1 is selected as a feature input. Meanwhile, for wall deflections 2 and 3, the partial autocorrelations at Lag 3 and Lag 4 exceed or approach the threshold of 0.2. Therefore, a time window of size 4 is used to capture the time correlations from Lag 1 to Lag 4.  
Figure 10: Non-linear correlation analysis of axial forces and deflections at different monitoring points.  
Figure 9: Linear correlation analysis of axial forces and deflections at different monitoring points.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 3 Designing MEC Model for MFL System
### Calculation of the MEC Model Permeances
#### 3.1.2 Calculation of the Magnetic Branches Permeances  
The general representation of a magnetic permeance can be expressed as follows:  
\[P_{br}=\frac{A_{br}\mu_{B}(.)}{l_{br}} \tag{16}\]  
where \(l_{br}\) is the length of a branch, \(A_{br}\) is the cross-sectional area of a branch, and \(\mu_{B}\)(.) refers to the variable permeability of a branch. The calculation method for the variable permeabilities will be explained in Section 4 in detail. The permeance equations of the magnetic branches can be presented as follows:  
\[P_{yoke}=\frac{l_{c}w_{e}\mu_{B}(.)}{w_{s}+w_{i}} \tag{17}\]  
\[P_{ub}=\frac{l_{c}w_{e}\mu_{B}(.)}{d_{h1}} \tag{18}\]  
\[P_{mb}=\frac{l_{c}w_{e}\mu_{B}(.)}{(d_{h}-d_{h1})+d_{h2}} \tag{19}\]  
\[P_{lb}=\frac{l_{c}w_{e}\mu_{B}(.)}{w_{w}-(d_{h}+d_{h2})} \tag{20}\]  
\[P_{sp}=\frac{l_{c}w_{m}\mu_{B}(.)}{w_{s}+w_{e}} \tag{21}\]
# A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution
## 3 Experiments
### Experimental Results
Also, in Figure 6, a comparison for the regression datasets is outlined between the proposed method with the application of the genetic algorithm in second phase and the genetic algorithm on the original regression datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(f=2\)} & \multicolumn{2}{c}{\(f=3\)} & \multicolumn{2}{c}{\(f=4\)} \\ \hline
**DATASET** & **RBF** & **GENETIC** & **RBF** & **GENETIC** & **RBF** & **GENETIC** \\ \hline ABALONE & 4.361 & 3.518 & 4.159 & 3.839 & 4.859 & 3.786 \\ \hline AIRFOIL & 0.003 & 0.001 & 0.003 & 0.001 & 0.003 & 0.001 \\ \hline BASEBALL & 66.00 & 53.74 & 60.79 & 57.04 & 66.19 & 61.69 \\ \hline BK & 0.022 & 0.031 & 0.021 & 0.029 & 0.019 & 0.023 \\ \hline BL & 0.413 & 0.0001 & 0.019 & 0.007 & 0.043 & 0.011 \\ \hline CONCRETE & 0.008 & 0.006 & 0.007 & 0.005 & 0.008 & 0.004 \\ \hline DEE & 0.259 & 0.252 & 0.339 & 0.286 & 0.609 & 0.5 \\ \hline DIABETES & 0.611 & 0.832 & 0.634 & 1.411 & 0.857 & 1.157 \\ \hline HOUSING & 22.387 & 15.583 & 18.614 & 13.602 & 14.83 & 13.208 \\ \hline FA & 0.056 & 0.011 & 0.015 & 0.011 & 0.015 & 0.012 \\ \hline MB & 0.258 & 0.087 & 0.115 & 0.078 & 0.342 & 0.072 \\ \hline MORTGAGE & 0.621 & 0.046 & 0.65 & 0.037 & 0.078 & 0.04 \\ \hline PU & 2.894 & 0.14 & 0.936 & 0.029 & 0.724 & 0.031 \\ \hline QUAKE & 0.069 & 0.036 & 0.057 & 0.037 & 0.04 & 0.037 \\ \hline TREASURY & 0.912 & 0.088 & 0.874 & 0.084 & 0.173 & 0.076 \\ \hline
**AVERAGE** & **6.59** & **4.96** & **5.82** & **5.10** & **5.92** & **5.38** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results on the regression datasets using the proposed method. The numbers in the cells denote the average regression error as measured on the test set.The variable \(f\) corresponds to the number of artificial features created by the proposed method. Figure 4: Comparison of the proposed genetic classification algorithm for the construction of \(2,3\), and \(4\) artificial features (blue color) versus a Welkin genetic classification algorithm (gray color) on the 24 different datasets. Finally, using the Wilcox on a sandbank test, a comparison was made between the proposed method and all mentioned machine learning methods for the classification datasets. This comparison is graphically outlined in Figure 7.  
These results suggest that the proposed method has a distinct advantage over the Welkin classification methods when it comes to constructing artificial features for classification tasks. It offered improved performance and provided a more effective solution for these datasets. Figure 5: Comparison of the proposed RBF classification network for the construction of two, three, and four artificial features (blue color) versus a Welkin RBF classification network (gray color) on the 24 different datasets. Figure 6: Comparison of the proposed algorithm for the construction of two, three, and four artificial features (blue color) versus the genetic algorithm (gray color) for the regression datasets.
## 4 Investigation of the Influence of Major Parameters on the Pipeline Damping Performance
### Effect of Particle Materials on the Pipeline Damping Performance
#### 4.2.2 Comparison of Three Particle Materials at an 80% Filling Rate  
Figure 10 shows the spectral responses of damped pipelines with different materials at an 80% filling rate. When the cavity of the damper was filled with steel balls, the corresponding resonant amplitude was reduced by 88.8%, while the resonant amplitude was reduced by 70.0% and 61.4% due to aluminum and glass, respectively. The RMS values of responses in the time domain are compared in Table 2. The experimental phenomenon was similar to that observed with the 20% filling rate.  
The test results show that particles of different materials have a great impact on the vibration reduction effect of the particle impact damper at the two filling rates of 20%  
\begin{table}
\begin{tabular}{c c c} \hline
**Pilot Protocol** & **RMS/g** & **Vibration Damping Effect \(\Delta\)** \\ \hline Bare pipe & 78.7 & — \\ Steel ball & 41.8 & 46.9\% \\ Aluminum ball & 50.5 & 35.8\% \\ Glass ball & 54.2 & 31.1\% \\ \hline \end{tabular}
\end{table}
Table 2: Three materials with an 80% filling rate under random excitation.  
Figure 10: Spectral responses of damped pipeline with different materials at an 80% filling rate.  
Figure 9: Spectral responses of damped pipeline with different materials at a 20% filling rate.  
and 80%. The first-order amplitude and root mean square value of filled steel balls are the smallest, which means that the vibration reduction effect is the best. The first-order amplitude and root mean square value of filled aluminum balls are next, which implies that the vibration reduction effect is second. However, the first-order amplitude and root mean square value of filled glass balls are the largest, which means that the vibration reduction effect is relatively poor. Table 3 shows that the steel ball mass of the three kinds of particles is large at the same filling rate, which has a certain impact on the energy dissipation of vibration reduction. However, the mass of glass and aluminum is a little different, and the difference in vibration reduction effect between glass balls and aluminum balls is mainly due to the coefficient of restitution of the particles. According to the experimental results and Table 3, the smaller the coefficient of restitution of the particle materials, the better the vibration reduction effect.
## 5 Numerical Applications and Discussion
### Benchmark  
In this subsection, the rationality of the self-compiled PD codes is verified through a benchmark without considering the influence function. The model results will be in contrast with the finite element (FEM) results.  
As displayed in Figure 6, there is a prefabricated circular hole with diameter of 0.01 m in the center of an isotropic plate. The bottom of the plate is fixed, and the top is subjected to 0.0003 m displacement load along the \(y\) direction. Material properties and PD model parameters are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**E(GPa)** & \(\mathbf{v}\) & \(\mathbf{\rho}\) **(kg/m\({}^{3}\))** & \(\mathbf{\Delta x}\) **(m)** & \(\delta\) **(m)** & **Influence Function** \\ \hline
192 & 1/3 & 8000 & 0.0005 & \(2\Delta x,3\Delta x,4\Delta x,5\Delta x\) & \(g_{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties and PD model parameters.  
Figure 6: Scheme of the plate.  
To explore the effect of horizon radius (\(\delta\)) on calculation accuracy, two column points along the \(x\)-axis and \(y\)-axis are selected, as depicted in Figure 7. The horizon radius has a certain impact on the computational precision of the PD model, and the calculation cost increases with the increase in horizon radius. The reason is that the horizon needs to contain a certain number of material points due to nonlocal characteristics. Moreover, the result is also related to the convergence of the PD model. In this model, the PD solutions are in good agreement with the FEM solutions when the horizon radius is equal to or greater than \(3\Delta x\).  
Except for the above local verification, the displacement cloud maps of \(u_{x}\) and \(u_{y}\), which can better reflect the overall deformation, are also compared, as shown in Figure 8 (e.g., \(\delta=3\Delta x\)). It is evident that the deformation distribution obtained by the PD model is well in line with the FEM results. There is little difference in displacement, \(u_{y}\), between results from the two methods (as depicted in Figure 8b,d). Moreover, the maximum relative error of displacement, \(u_{x}\), is no more than 0.8% (as revealed in Figure 8a,c). It indicates that the PD model established has the characteristics of rationality and accuracy.  
Figure 8: Displacement contours (unit: m).  
Figure 7: Results along the \(x\)-axis and \(y\)-axis.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 3 Experimental Verification  
In order to verify the effectiveness and accuracy of the proposed algorithm, a length measurement experiment of a buried metal pile was conducted. In the experiment, the stress wave was generated by striking the end face of the buried pile with an excitation hammer, and the stress wave was reflected when it met the other end face. A PZT sensor was used to receive the reflected wave. The length of the buried metal pile was calculated after the reflected wave period was extracted by the proposed algorithm in this paper.  
The corrugated steel beam guardrail is the most important safety facility in highway construction, and the buried metal piles are the main load-carrying parts of the guardrail. The buried depth or defect of the column directly determines whether the guardrail can provide protection in the event of an accident. In view of the importance of the buried metal piles, it is crucial to accurately determine the buried length of the metal piles in service. By measuring the length of the buried pile, the method of extraction of the period is experimentally verified in this paper. The reflection period was obtained by the algorithm proposed in this paper and substituted into the reflection distance Equation (7) to obtain the column length.  
\[s=T\times\
u\div 2, \tag{7}\]  
where \(T\) represents the reflection period, \(\
u\) represents the propagation speed of the wave in a uniform single medium, and \(s\) represents the measured distance.
## 4 Experiments
### The Effect of Different Modules on the Detection Results
#### 4.5.1 The Effect of RASPP  
We conduct ablation experiments with the RASPP module as a variable. The results are shown in Table 4. Thanks to the multi-scale information captured by RASPP and the enlarged receptive field, the detection performance can be improved after embedding the RASPP module, whether it is a top-down feature pyramid or a top-down and bottom-up feature pyramid. It shows that the RASPP module is effective for multi-scale defect detection of steel strips. The mAP showed a larger drop after replacing the RASPP module with ASPP. The reason is that ASPP can capture a wider range of feature information than RASPP. The extra information does not always work but interferes with the useful feature information and causes model confusion, which leads to a decrease in detection performance. It also shows that RASPP is sufficient to cover defective feature information at all scales. The adaptive residual learning added in RASPP can independently judge whether the learned features are effective, so as to avoid the confusion caused by ASPP. It also helps to shorten the information flow path and enhance the robustness of the model. Nevertheless, RASPP requires fewer parameters than ASPP, so it has a faster detection speed.