esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] [30] proposed an effective iterative inverse kinematic algorithm NIKA and compared the simulation results with the Newton-Raphson algorithm to verify the good performance of the NIKA algorithm. [PERSON] et al. [31] used particle swarm optimization (PSO) and least squares (LS) methods to estimate the inertial parameters of the Staubli RX-60 robot, and experiments showed that the particle swarm algorithm outperformed the LS method for parameter estimation. This paper, which mainly adopts the SQP (sequential quadratic programming) algorithm and BP-SQP (sequential quadratic programming based on back propagation neural network) algorithm, studies the inverse kinematics model of the UR10 robot. The SQP algorithm can solve nonlinear problems with constraints and reduce the complexity of the calculation. When establishing the inverse kinematics model of the UR10 robot, the SQP algorithm takes the minimum total rotation radians of the UR10 robot's joints as the constraint condition. The SQP algorithm is highly dependent on the initial values and will cause deviations. The time the SQP algorithm needs to find a solution and the accuracy of the SQP algorithm are impacted. In order to eliminate the limitations of the SQP algorithm, we propose the BP-SQP algorithm. On the basis of the SQP algorithm, a neural network algorithm, which is called the BP-SQP algorithm, is introduced to optimize the initial values. The BP-SQP algorithm is based on the error backpropagation method. Through the training process, the weights and offsets are continuously adjusted to reduce errors. The judgment of the condition for terminating the propagation is that the current number of iterations is the predetermined maximum number of iterations or is lower than the allowable accuracy. The BP (backpropagation) neural network has great advantages in solving inverse kinematics. The operation speed is greatly improved. The SQP algorithm optimized by the BP neural network can find the best inverse solution faster. Finally, the effectiveness of the SQP and BP-SQP algorithms is verified by simulation experiments.
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## Materials and Methods  
### Production of the Stents  
In this study, stents were produced in two stages: First, 316 L (Sigma Aldrich, St. Louis, MO, USA) stainless steel sheets with 100 \(\upmu\)m thickness were cut by femtosecond laser using a cut-out geometry. In the second step, tailored plates were rolled into a cylinder and laser spot welded at the joining line. For cut-out geometry, an optimized unit-cell geometry was determined based on previous work by [PERSON] et al. (2017) in [51], shown in Figure 2a. Here, the dimensions were: height of vertical rib (H) is 0.69 mm; the length of diagonal rib (L) 0.56 mm; the angle between vertical and diagonal ribs (\(\theta\)) 66.35 degrees and rib thickness (t) 0.1 mm. These dimensions were determined to prevent out-of-plane buckling of struts and provide sufficient radial and longitudinal stiffness. After preparing unit-cell geometry in SolidWorks design software, Victoria, Canada, (Figure 2a), the geometrical pattern was machined on stainless steel 316 L sheets (Figure 2b) using femtosecond laser cut-out. Tailoring the auxetic design was made by using a Ti:Sapphire femtosecond laser with 1 kHz repetition rate, 120 vfs pulse duration, and 800 nm central wavelength. The laser beam was directed into a microscope, and then it was focused by 20\(\times\) objective lenses with 0.42 numerical aperture (Mitutoyo, Victoria, Canada). The beam diameter was reduced to 3 mm using an iris diaphragm to achieve a thin cut-out. Turning on and off the laser beam was made possible by a computer-controlled electronic shutter. The final micromachined width and length of the plates were 15 mm and 20 mm, respectively. These plates were rolled and then were laser welded to fabricate the stent (Figure 2c). Thus, the diameter of the produced stents was approximately 5 mm (4.77 mm), the length is 20 mm.  
Figure 1: Challenges with mechanical properties of stents: longitudinal compression, bending, radial compression and torsion.
# The Impacts of Human Activities on Ecosystems within China's Nature Reserves
## 2 Methods and Materials
### Estimation of Ecosystem Qualities
#### 2.4.2 Model Simulation of Net Primary Production (NPP)  
NPP is a significant indicator in measuring ecosystem condition ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). In recent years, the model simulation of NPP based on remote sensing has led to extensive application in assessing vegetation ecosystem changes on a large scale ([PERSON] et al., 2018; [PERSON] et al., 2019). The simulation of NPP with a continuous time sequence and spatial attributes provides a data basis for the quantitative evaluation of the protection of a nature reserve ([PERSON] et al., 2019).  
We used Global Production Efficiency Model (GLO-PEM) to simulate NPP. GLOPEM uses satellite variables that include linked components expressing canopy radiation absorption, autotrophic respiration, utilization, and other environmental factors ([PERSON] et al., 2018; [PERSON] et al., 2019). To begin with, vegetation gross primary productivity (GPP) was estimated as follows:  
\[\text{GPP}=\text{PAR}\cdot\text{FPAR}\cdot\varepsilon_{g} \tag{4}\]  
\[\varepsilon_{g}=\varepsilon_{g}{}^{*}\cdot\sigma \tag{5}\]  
In which, PAR is photosynthetic active radiation, calculated by [PERSON] et al. (2018). FPAR is fraction of absorbed active radiation photosynthetically. FPAR was inverted by canopy radiative transfer algorithm, which was exploited by [PERSON] et al. (2019) on the basis of the NDVI. \(\varepsilon_{g}\) is efficiency of solar energy utilization. \(\varepsilon_{g}{}^{*}\) is the potential maximum radiation energy utilization under ideal conditions. \(\sigma\) is the impact on the environment including water pressure deficit, PAR, temperature, CO2, and soil moisture ([PERSON] et al., 2018; [PERSON] et al., 2019). Meteorological data was acquired from China Meteorological Administration ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn))) and then interpolated using ANUSPLIN.  
NPP is the subtraction of the GPP and autotrophic respiration (Ra). It was modeled with 1 km spatial resolution from 2000 to 2015 for NNRs in China by following equation:  
\[R_{a}=f(R_{m})+R_{g} \tag{6}\]  
\[\text{NPP}=\text{GPP}-R_{a} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Land Use and Land Cover** & **Cropland** & **Artificial Forest** & **Reservoir** & **Built-Up Areas** & **Others** \\ \hline Conversion factor & 0.2 & 0.133 & 0.6 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conversion factor of different land use and land cover types.  
where \(R_{g}\) is growing respiration and \(R_{m}\) is maintenance respiration.
## 4 Computational Experiments
### Instance Generation  
For the statistical test, all parameters are assumed based on the case study of a delivery service for convenience stores, as follows:  
* A set of customers, \(n=11\): \(i,j\in\{0,\dots,10\}\);
* A set of vehicles, \(v=3\): \(v\in\{1,2,3\}\);
* Capacity of all vehicles has a capacity \(Q=25\);* Average speed of a vehicle \(s=60\) km/h;
* Service time at each customer: 10 min;
* Penalty cost for violating time windows both early arrival and late arrival: W1000;
* Penalty cost for left over tray: W500;  
For the simple numerical examples, a new set of spatial coordinates for each customer is randomly generated as integers in a square of 100 units, and the location of depot is set at \((0,0)\) in the center of the square. The distances between all visiting points, including the depot and customers, are calculated with the Harversine method, because well-known methods such as the Euclidean method and the Manhattan distance method have limitations in calculating the actual distance based on the actual latitude and longitude coordinates. The driving speed of the vehicles is set as 60 km/hr. It has also been assumed that the fuel mileage is 6 km/L, and the unit oil price is 1000 Korean Won (KRW).  
The demands of customers, including pick-up and delivery, are randomly generated between 1 and 8 trays. It has been assumed that the number of trays follows a uniform distribution. The time window constraint for visiting each customer is determined by considering the number of visiting nodes on the path combined with the arrival time of the previous node; 30 min for operations like delivery and pick-up. If the vehicle could not meet the predefined time windows, the penalty, 1000 KRW, will be charged. Also, if the vehicle does not pick up a tray previously delivered to a certain node, the penalty for a left-over tray, 500 KRW per tray in a day, will be charged. The left-over penalty and time window penalty were set by the actual logistics company which supports convenience stores. To get rid of the effects of randomness, the experiment was repeated 30 times.
# The Role of PIXE and XRF in Heritage Science:
###### Abstract  
Analytical techniques play a fundamental role in heritage science. Among them, Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence (XRF) techniques are widely used in many laboratories for elemental composition analysis. Although they are well-established, a strong effort is put on their upgrade, making them suitable for more and more applications. Over the years, at the INFN-LABEC (the laboratory of nuclear techniques for the environment and cultural heritage of the Italian National Institute of Nuclear Physics), the INFN-CHNet group, the network devoted to cultural heritage, has carried out many technological improvements to the PIXE and XRF set-ups for the analysis of works of art and archaeological finds. Among the many, we recall here the scanning external microbeam facility at the TANDEM accelerator and the MA-XRF scanner. The two instruments have shown complementary features: the former permits quantitative analysis of elements heavier than sodium, which is not possible with the latter in most of the case studies. On the contrary, the scanner has the undeniable advantage of portability, allowing it to work in situ. In this framework of technological developments in heritage science, INFN, CERN, and OPD are jointly carrying on the MACHINA (Movable Accelerator for Cultural Heritage In-situ Non-destructive Analysis) project for on-site Ion Beam Analysis (IBA) studies on cultural heritage.  
P XRF; MA-XRF; IBA; material analysis; heritage science +
Footnote †: journal: _Sciences_
thanks to the six beamlines of the TANDETRON accelerator: the external beamline for aerosol measurements [7]; the external microbeam line [8]; the pulsed beamline [9; 10]; the ion beam analysis beamline in vacuum [11]; the external beamline for cultural heritage measurements; and the atomic mass spectrometry beamline for \({}^{14}\)C dating [12]. Reviews on past and current activities at the INFN-LABEC are found in [13; 14].  
On the other side, especially in the field of HS, the possibility of performing measurements on site is more and more required due to the problems of transporting a work of art and the invaluable support of analytical techniques. For this reason, in 2011, a transportable XRF device was developed. Moreover, thanks to the expertise within the INFN-CHNet group, in the last ten years, a constant effort has been put in R&D for cultural heritage applications [15; 16]; therefore, several instruments have been developed and others are presently in progress.  
In this review, after a brief recall of the PIXE and XRF techniques, salient aspects of the dedicated facilities (XRF device and microbeam facility) at the INFN-LABEC laboratory are presented towards several applications. The INFN-CHNet MA-XRF scanner, a transportable device capable of mapping non-planar surfaces, is then described. The article closes with an introduction of the MACHINA project, for the development of the first transportable accelerator for IBA inside conservation centres and museums.
## 4 Tuning Microwave Absorption for Superior EMI Shielding in Porous Materials
The characteristic input impedance can also be expressed in terms of relative complex permittivity (\(\varepsilon_{\text{r}}\)) and complex permeability (\(\mu_{\text{r}}\)), as follows [97]:  
\[Z_{\text{in}}=\sqrt{\frac{\mu_{0}}{\varepsilon_{0}}}\ \sqrt{\frac{\mu_{ \text{r}}}{\varepsilon_{\text{r}}}} \tag{12}\]  
where \(\varepsilon_{0}\) and \(\mu_{0}\) are the permittivity and permeability of the vacuum. It is well-known that \(\varepsilon_{\text{r}}\) is usually larger than \(\mu_{\text{r}}\) for any absorber material. Therefore, if the value of \(\varepsilon_{\text{r}}\) decreases, it will help to bring the \(Z_{\text{in}}/Z_{0}\) value close to 1 and result in better impedance matching and enhanced microwave absorption. The attenuation constant (\(\alpha\)) is another parameter that determines the extent of microwave absorption performance. It can be expressed in terms of the dielectric and magnetic losses in an absorber using the following equation [97]:  
\[\alpha=\frac{\sqrt{2\pi\text{f}}}{\text{c}}\sqrt{\mu^{\prime\prime}\ \varepsilon^{\prime\prime}-\mu^{ \prime}\varepsilon^{\prime}+\sqrt{(u^{\prime\prime}\varepsilon^{\prime\prime }-\ \mu^{\prime}\varepsilon^{\prime})^{2}+(u^{\prime}\varepsilon^{\prime\prime}+u^{ \prime\prime}\varepsilon^{\prime})^{2}}} \tag{13}\]  
where f = frequency of electromagnetic wave propagation and c = velocity of light.
## 4 Results and Discussion
It is seen that the error of the solutions obtained from the newly developed equations for \(Pd\) is lower than the error obtained with the default equation and even the median is very close to zero. Estimates based on \(T_{p}^{max}\) with default relations resulted in a mean magnitude error of \(-0.88\), a standard deviation of \(1.15\), and a median of \(-1.1\). When newly developed relations are used, the mean magnitude error is \(0.0\), the standard deviation is \(0.41\), and the median is \(0.0\). An improvement was observed for \(T_{p}^{max}\) when the magnitudes obtained using the default and newly developed equations were compared. However, when \(Pd\) and \(T_{p}^{max}\) are compared, we think \(Pd\) provides more reliable results as a magnitude estimation measure, as mentioned before. Figure 6: Magnitudes calculated from \(Pd\) and \(T_{p}^{max}\) versus the observed magnitude (**a**) magnitudes calculated using default equations for \(Pd\), (**b**) magnitudes calculated using newly developed for \(Pd\), (**c**) magnitudes calculated using default for \(T_{p}^{max}\), (**d**) magnitudes calculated using newly developed for \(T_{p}^{max}\).
## 2 Materials and Methods
### Handling Imbalanced Datasets
#### 2.4.1 Resampling Techniques  
The main objective of balancing datasets is to increase the number of the minority class or decrease the number of the majority class. This is performed to obtain an equivalent frequency of instances for both classes. The most familiar resampling approaches for resolving the imbalanced-dataset problem are oversampling the minority class and undersampling the majority class. The well-known undersampling method is called random undersampling, and it randomly deletes window signals from the major classes of the training dataset. The most common and simplest method of this oversampling of the minority class is random oversampling, which simply duplicates random sequences of window signals from the minority class in the training datasets. Undersampling for the majority class loses some of the information, whereas oversampling for the minority  
Figure 2: Sliding Multi-window feature engineering.  
class does not lose any data. Undersampling helps to improve the run-time and storage problems by reducing the number of training data samples, even though it loses some of the information, whereas oversampling for the minority class does not lose any data, but it increases the likelihood of overfitting since it replicates the minority class events.  
Thus, these duplicated examples do not provide new information to the model. Due to the above reason, we also try to compare another method called the Synthetic Minority Oversampling Technique (SMOTE) [37] to random undersampling and random oversampling. This approach of balancing datasets helps us to avoid overfitting, which occurs when exact replicas of minority instances are added to the main dataset. A subset of data is taken from the minority class as an example, and then new similar synthetic instances are created. These synthetic instances are then added to the original dataset. The new dataset is used as a sample to train the classification models. SMOTE works by selecting random examples from the minority class that are close to most of the feature variables. Subsequently, its k nearest minority-class neighbors are found, and then the line segment in the feature space is formed to obtain a synthetic example generated randomly in this line segment [37].
## 3 Results
### Nutrient Status  
Drought stress changed the chemical composition of plants and nutrient uptake (Table 2). MA application increased N uptake in PEG by 5% and Sa and MA improved the N status of plants in PEG by 7.5%. SA and MA treatments improved the uptake of P for PEG by 5%. All the tested acids positively affected the case plants grown under high PEG stress. A positive effect of spraying plants on uptake was found for K (both for PEG 5% and PEG 7.5% SA, MA). A direct effect of spraying was not found for Ca, Mg, and Na.  
The uptake of most elements depended on the PEG level but did not depend on the kind of foliar spray (Table S2). Only in the case of magnesium, its uptake depended on both the level of applied water stress and the type of spraying. Positive correlations were found between plant yielding, dry mass plant yielding, and all elements [Table S3]. Moreover, the results were statistically significant (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} PEG \\ Level \\ \end{tabular} } & \multicolumn{5}{c}{**Foliar Spray**} \\ \cline{2-5}  & **Non Treated** & **H\({}_{2}\)O** & **SA** & **MA** & **CA** \\ \cline{2-5}  & \multicolumn{5}{c}{**Plant Yielding (g-Plant\({}^{-1}\))**} \\ \hline Control & 17.45 \(\pm\) 1.77 a & 16.32 \(\pm\) 3.02 a & 18.09 \(\pm\) 2.36 a & 19.46 \(\pm\) 0.74 a & 17.24 \(\pm\) 1.61 a \\ PEG 5\% & 12.67 \(\pm\) 3.81 a & 10.95 \(\pm\) 4.11 a & 12.05 \(\pm\) 5.57 a & 10.67 \(\pm\) 0.13 a & 12 \(\pm\) 2.44 a \\ PEG 7.5\% & 11.31 \(\pm\) 0.56 a & 10.65 \(\pm\) 0.7 a & 11.76 \(\pm\) 1.96 a & 12.8 \(\pm\) 1.32 a & 11.78 \(\pm\) 0.63 a \\ \hline \hline \end{tabular}
\begin{tabular}{c} **Dyne plant plant yielding (g-plant\({}^{-1}\))** \\ \hline Control & 1.25 \(\pm\) 0.13 ab & 1 \(\pm\) 0.01 ab & 1.15 \(\pm\) 0.14 ab & 1.29 \(\pm\) 0.06 b & 1.08 \(\pm\) 0.09 ab \\ PEG 5\% & 0.92 \(\pm\) 0.28 ab & 0.81 \(\pm\) 0.23 ab & 0.86 \(\pm\) 0.11 ab & 0.96 \(\pm\) 0.06 ab & 0.87 \(\pm\) 0.17 ab \\ PEG 7.5\% & 0.89 \(\pm\) 0.04 ab & 0.75 \(\pm\) 0.05 a & 1.06 \(\pm\) 0.08 ab & 1.01 \(\pm\) 0.12 ab & 0.8 \(\pm\) 0.04 ab \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of the foliar application of salicylic acid (SA), maleic acid (MA), and citric acid (CA) on plant yielding (g-plant\({}^{-1}\)).
## 3 Methods  
### Geometry Reconstruction  
Geometry reconstruction consisted of creating models in the end-systolic and end-diastolic phases occurring in a patient with mitral valve regurgitation. The geometric data of the hearts came from imaging studies performed on patients who were involved in ongoing research projects at the Medical University of Gdansk. Patients consented, and approval was obtained from the Bioethics Committee. The cardiac CAD models were developed from selected CTA (computed tomography angiography) data of three patients with varying degrees of chronic post-myocardial infarction heart failure associated with mitral valve regurgitation with different LVEF (left ventricle ejection fraction), i.e., the percentage of blood ejected from the heart with each contraction:  
* LVEF 40%--a heart muscle of very small size. It was verified that the developed ventricular balloon would cooperate in the left ventricle of this heart;
* LVEF 30%--small sized myocardium, which was the basis for the development of the ventricular balloon shape;
* LVEF 17%--heart muscle of very large size. It was verified that the developed balloon would cooperate in the left ventricle of this heart.  
It should be noted that normal LVEF takes values greater than 50%. This means that the latter two geometries can be classified as cases with chronically low ejection fraction (less than 30%).  
CTA imaging of the moving heart (in 10 frames) at a resolution of 0.4238 \(\times\) 0.4238 \(\times\) 1 mm was used to perform the segmentation (extracting the shape of the heart). From these data, images of the heart in the diastolic and systolic states were extracted in 3D Slicer [47]. Standard procedures in 3D Slicer were used for initial segmentation, followed by manual shape correction on approximately 1000 tomographic sections.  
Figure 3 shows the combined image of the heart muscle shape in a single 3D image, in the systolic and diastolic states (red corresponds to the systolic phase and blue to the diastolic phase). The heart models in the systolic and diastolic states were then exported to STL format. Furthermore, these models, after reducing the number of nodes to around 60,000, were used for further CAD processing.  
Figure 3: Reconstruction process.  
Figure 4a shows the initial design of the balloon set, including the pneumatic supply line against the background of the reconstructed heart model. Three superimposed heart models of three patients considered in systole were the basis of the ventricular balloon design.  
Because an elastic heart model was used in the laboratory studies, a mechanical artificial valve (1) (see Figure 4b) was utilized in order to replace the aortic valve. The mitral valve with regurgitation was replaced by a cylindrical orifice (2), and the pulmonary arteries were reduced to a single conduit to the atrium (3). Further details on the construction of the heart model are given in [46].