esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Model Predictive Control  
Model predictive control (MPC) is currently the state-of-the-art for artificial pancreas systems [50; 51; 52], and is used in commercial systems including the recently FDA approved Control-IQTMadvanced hybrid closed loop technology [53]. In general, MPC is a collection of algorithms where a model of the process is used to predict the system's future behavior. Optimal actions are then computed, while using an objective function, to ensure that the predicted behavior matches the optimal desired behaviour [54]. Algorithms typically differ in the type of model and the objective function used [54]. MPC has the advantage of incorporating constraints in the objective function. This is particularly beneficial for the artificial pancreas system case that is characterized by long delay times [54]. Because the quality of the results is completely driven by the ability of the model to describe the true process state, most MPC algorithms are used in conjunction with state estimation techniques, such as the Kalman filter [24]. The main drawback of MPC is that adapting the model to each patient individually and accounting for intra-day variability is completely dependent on the structure of the predictive model [25]. If the model is not expressive enough to capture the situation, the algorithm will fail. Several recent works have tried to lessen this burden by using multiple predictive models [55; 56; 57].
## 2 Materials and Methods
### Methods
#### 2.3.4 Standard Deviational Ellipse (SDE)  
The standard deviation ellipse (SDE) method is used to analyze the distribution pattern of a point set with spatial coordinates [53]. It was first proposed by [PERSON] to analyze geographic concentration [54]. This method uses an ellipse to simplify spatial distribution characteristics. The area represents the degree of distribution and concentration, the relationship between the major axis and the minor axis represents the concentrated direction and the center represents the spatial centroid. Many GIS applications allow for its use. In ArcGIS, three-level ellipses can be generated, representing 68%, 95% and 99% of the sample points, respectively [55]. In this study, the PLES transferring patches were extracted as points, and their scale was defined as the weight. The weighted SDE of level 1 in ArcGIS 10.3 was used to identify transferring type distribution.
## 3 Results and Discussion
### Prediction of Elongation at Break and Comparison of Models' Performances  
Similarly to trends observed for Nominal Stress in Angle orientation, the elongation at break in Angle orientation also has a linear correlation with the investigated features. As can be seen in Table 7, models in the XYZ, XZY, and ZYX orientations have relatively low prediction accuracies and therefore cannot be used at this moment. This phenomenon can be explained by looking at the previously published results [12], which state that material properties have a significant impact on mechanical properties, including elongation at break. In order to improve developed models, material properties in terms of viscosity and virgin/used powder ratio should be determined.  
However, when data is not separated on the orientation-based groups, prediction accuracy increases significantly. Thus, one can assume that the number of data points is an important factor. Besides, AdaBoost Regressor has outperformed all other algorithms with a prediction accuracy 0.987 out of 1 with relatively low MSE comparing with other methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\# Data Points**} & \multirow{2}{*}{**Orientation**} & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**GBR**} & \multicolumn{2}{c}{**DTR**} & \multicolumn{2}{c}{**ABR**} \\  & & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** \\ \hline
110 & XYZ & 0.261 & 0.0907 & 0.0146 & 0.121 & \(-\)0.168 & 0.143 & 0.165 & 0.103 \\
40 & XZY & 0.476 & 0.667 & 0.46 & 0.681 & 0.453 & 0.696 & 0.486 & 0.655 \\
142 & ZYX & 0.269 & 0.963 & **0.67** & **0.434** & 0.587 & 0.545 & 0.638 & 0.476 \\
74 & Angle & 0.889 & 0.500 & 0.795 & 0.927 & 0.749 & 1.129 & **0.903** & **0.439** \\ \hline
325 & All & 0.965 & 0.739 & **0.987** & **0.284** & 0.985 & 0.326 & 0.971 & 0.616 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prediction of Elongation at break with a help of machine learning techniques. Linear means linear regression models, GBR stands for Gradient Boost Regressor, DTR—Decision Tree Regressor, and ABR—AdaBoost Regressor.  
Since the Linear regression model in the Angle orientation has relatively high prediction accuracy (0.889 out of 1), this model can be used in cases when the mathematical description is required:  
\[y_{eb}=49.54*x_{1}-2.757*x_{2}-25.02*x_{3}+5.516*x_{4}-\] \[-9.45e-06*x_{5}-24.52*x_{6}-2.757*x_{7}+ \tag{2}\] \[+2.497e-03*x_{8}-2.41e-02*x_{9}\]  
where \(y_{eb}\) is the Elongation at break in the Angle orientation, \(x_{1}\) is the central ordinate x, \(x_{2}\) is central coordinate y, \(x_{3}\)--min coordinate x, \(x_{4}\)--min coordinate y, \(x_{5}\)--min coordinate z, \(x_{6}\)--max coordinate x, \(x_{7}\)--max corradiance y, \(x_{8}\)--max coordinate z, and \(x_{9}\)--surface.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 7 SCO Production from Hydrolyzed Lignocellulosic Biomass  
The production of SCO by oleaginous species, principally yeasts, filamentous fungi and bacteria, cultivated on lignocellulosic hydrolysates has been studied by many authors, (selected papers are included in Table 4). Likewise, in recent years, the conversion of lignocellulosic hydrolysates into SCO by heterotrophically growing oleaginous microalgae, such as the green microalgae _Auxenochlorella_ (_Chlorella_) _protothecodes_ has been studied [257]. Based on the information included in the current review and taking into account the International Patent WO 2015/162568 Al, a flow chart was designed showing the various stages of an industrial process for the production of microbial oils by cultivating oleaginous microorganisms on lignocellulosic substrates (Figure 3). In addition to the general conditions required for SCO production by oleaginous microorganisms, their cultivation in lignocellulosic hydrolysates may require additional treatment of the substrate, e.g., detoxification, due to the presence of inhibitory compounds generated during pretreatment. Nevertheless, some microorganisms are able to grow well even on undetoxified hydrolyzed lignocellulosic media and accumulate satisfactory amounts of lipids. Biomass and lipid production by selected oleaginous microorganisms, cultivated in both detoxified and undetoxified lignocellulosic hydrolysates (DLH and ULH, respectively) derived from different plant biomasses are show in Table 4.  
Several investigations have shown that inhibitory compounds generated during lignocellulosic biomass pretreatment negatively affect growth and lipid production (Table 3), making the application of additional treatments often necessary to reduce toxicity. A conceptual scheme proposed for SCO production using lignocellulosic hydrolysates as substrate is shown in Figure 4.  
Figure 3: A simplified flow chart of an industrial process for the production of lipids from lignocellulosic biomass using oleaginous microorganisms.
## 4 Discussion
### Effect of the Studied Cut-Off Frequencies on Signal Features
The sEMG signals from the m. extensor carpi radialis in a more distal position, as well as signals from the m. extensor digitorum lateralis and m. extensor carpi ulnaris in more proximal positions, were significantly more contaminated by noise and movement artifacts compared to signals recorded at the positions reported in this study. It is worth noting that the only study to date on optimizing electrode placement [5] did not identify a consistent safe zone for recording signals from the m. extensor carpi radialis; each of the three horses tested in that study showed a different optimal zone. Our electrode placement corresponded with the optimal zones reported for horses 1 and 3 but differed from that of horse 2 in [PERSON] et al. [5]. For the m. extensor digitorum communis, our electrode placement aligned with the common safe zone proposed by [PERSON] et al. [5], located between 30-40% of the proximal segment of the distance between anatomical landmarks. However, no similar recommendations were provided for the m. extensor digitorum lateralis of the forelimb or the m. extensor carpi ulnaris, as these muscles were not included in the referenced study [5]. Furthermore, no alternative recommendations for optimal electrode placement for these two muscles have been published to date. The observed variation in signal features following distal shifts in electrode placement may suggest the presence of alternative sources of noise and artifacts that contaminate the raw signal and may affect the extraction of signal features. Potential contributors to these differences include skin displacement noise [1; 26; 27; 28], movement artifacts [5; 6] caused by high vertical forces during equine gaits [29; 30], crosstalk from larger, proximally located forelimb muscles [4; 31; 32], and muscle fiber-type composition [33]. In human medicine, sEMG signals are typically filtered with a high-pass cut-off at 20 Hz and a low-pass cut-off in the range of 400-450 Hz to remove noise and movement artifacts, which generally appear in the spectral component between 0 and 20 Hz [16]. In equine sEMG studies, the higher body weight, faster movement, and unique gait characteristics of horses are suspected to generate greater perturbations at the electrode-skin interface compared to humans [5; 6]. Consequently, movement artifacts in horses have been observed not only in the spectral component between 0 and 20 Hz but also between 20 and 40 Hz, leading to a shift to a high-pass cut-off frequency toward 40 Hz [5]. As a result, a recommended bandpass filtering range for equine sEMG signals has been proposed between 40 Hz [5] and 450 Hz [6]. Therefore, identifying the sources and types of noise and artifacts targeted for reduction is essential, as both noise and its reduction introduce modifications to sEMG signal characteristics, potentially affecting data analysis.
# Multi-Robot Trajectory Planning and Position/Force Coordination Control in Complex Welding Tasks
## 3 Task-Oriented Multi-Robot Trajectory Planning  
### Problem Formulation  
The schematic diagram of the multi-robot cooperating to complete the welding task is shown in Figure 2.  
Where _robot_1_ and _robot_2_ are used for transfer robots, _robot_3_ is used for a welding robot. The transfer robots grab the workpiece \(R_{a}\) and \(R_{b}\), respectively. The welding robot is equipped with a welding torch at the end-effector, and the workpiece \(R_{a}\) and \(R_{b}\) are saddle-shaped after splicing.  
The relevant coordinate system shown in Figure 2 is defined as follows. \(\mathbf{T}_{w}\) represents the world coordinated system, \(\mathbf{T}_{o}\) represents the reference coordinate system of the object. \(\mathbf{T}_{bi}\) denotes the base coordinate of the \(i\)-th robot, \(\mathbf{T}_{i}\) denotes the coordinate system of the \(i\)-th robot end-effector. \(\mathbf{T}_{weld}\) is the weld coordinate system of the workpiece, and \(\mathbf{T}_{object}\) is the coordinate system of the workpiece.  
Figure 1: The diagram of multi-robot cooperation welding system.  
To facilitate the coordinate conversion, it is usually assumed that the world coordinate system coincides with the base coordinate system of _robot_1_.  
The requirements for the multi-robot cooperation to complete the welding task of the tube include:  
* The welding torch should meet the requirements of the ship-type welding posture;
* The configuration of each robot is reachable, and the configuration has no singularity in the welding process;
* The motion planning results of each robot are in a common collaborative space;
* There is no collision between the robot itself and another robots in the welding process.
## 4 Discussion
The bactericidal ability of the acidified NaOCl solution alone in the present study was not in agreement with previous reports, which indicated that the bacteria killing was maximized at lower pH [6; 9; 15]. [PERSON] et al. [9], proved that the antibacterial activity of a 4.2% NaOCl solution is enhanced by weak acidification of the solution to pH 6.5. In the present study, when used alone, NaOCl at pH 6.5 showed apparent enhanced bactericidal activity compared to NaOCl at pH 12, with 70% and 63% dead _E. faecalis_ cells, respectively, but with no significant difference was found by the effects of the two solutions. When these two solutions were used in succession, one after the other, a significantly higher bactericidal effect was found (79% dead _E. faecalis_ cells) compared to each solution alone. None of the tested solutions were able to completely eradicate the biofilm. It may be assumed that the first NaOCl solution at pH 12 had a disruptive effect on the biofilm matrix [11; 14], which allowed the acidified solution (pH 6.5), with its enhanced bactericidal activity [12; 14], better access to the bacteria in the biofilm, thus enhancing the total bactericidal effect. Nevertheless, this assumption should be further investigated. In the current study the biofilm was grown on flat dentin surface, similarly to [PERSON] et al. [1]. This was done to allow optimal observation conditions for the confocal laser scanning microscopy and avoid potential distortions that may result from growing the biofilm on a concave dentin surface of a root canal [23]. The exposure time to the NaOCl solutions in the present study (4 min) was similar to that used in previous studies: R\(\ddot{\text{o}}\)[PERSON] et al. [21], (3 min) and [PERSON] et al. [24], (5 min). The results of the present study show a promising direction in the use of NaOCl. It should be noted that this is a laboratory study and hence its limitations. We did not attempt to mimic the clinical situation. Additional studies using tooth models, a multi-species biofilm model and different exposure times are needed to confirm the promising results observed in the current study.
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### Inertial Navigation Error Analysis and Modeling
#### 3.1.3 Position Error Equation  
The differential of the curve position with respect to time is  
\[\left\{\begin{array}{l}\dot{L}=\frac{V_{N}}{R_{n}+h}\\ \dot{\lambda}=\frac{V_{E}}{R_{n}+h}\sec L\\ \dot{h}=-V_{D}\end{array}\right.. \tag{22}\]  
Since the changes in \(R_{m}\) and \(R_{n}\) are small, they can be regarded as constant values. Therefore, we can take the total differential of Equation (22), ignoring the second-order infinitesimal expression, and obtain the position error propagation equation as  
\[\left\{\begin{array}{l}\delta\dot{L}=\frac{\delta V_{N}}{R_{n}+h}\\ \delta\dot{\lambda}=\frac{\delta V_{E}}{R_{n}+h}\sec L+\frac{V_{E}\delta L}{R_ {n}+h}\tan L\sec L\\ \delta\dot{h}=-\delta V_{D}\end{array}\right., \tag{23}\]  
where \(f\) is the measured value of the accelerometer, so the position error is calculated by Equation (23).
## 1 Introduction
A stark example is the Wenchuan M8.0 earthquake on 12 th May 2008. The consensus is that strong and destructive earthquakes are more likely to happen in a gradient zone with positive and negative changes in the gravity field [16]. The dynamic difference of the gravity field in the TGR area before and after the 135 m impoundment was calculated using the test-retest data of mobile gravity. It showed that the maximum change in the dam area was about \(200\times 10^{-8}\) ms\({}^{-2}\)[17]. [PERSON] et al. further employed the ZY-3 high-resolution multispectral remote sensing image to obtain the lake water data library. Results from the integration of the Newton load model showed that the gravity increment on either side of the TGR bank could reach \(1000\times 10^{-8}\) ms\({}^{-2}\)[18]. The changes could increase the background of the regional seismicity. However, the current theoretical calculation models, such as the cuboid model [19] or the negative Green's function integral method, only consider the direct effect of the gravity change of the massive water load and fail to assess the gravity change effect caused by underground water infiltration. Therefore, it would be difficult to determine the impact of water impoundment on the gravitational field using a simple theoretical model and obtain reliable results. Yet, the results of gravity observations obtained are precisely the superposition of the two effects above. Facing such significant mobile gravity anomalies, as a typical local area where earthquakes occur frequently in reservoirs, these long-term re-measured gravity data combined with rich earthquake catalog data will certainly provide a rare opportunity to reveal reservoir-induced earthquakes. However, we have yet to see any report that gives a comprehensive analysis about the changes in the gravity field and seismic activity in the reservoir area induced by the TGR's impoundment over a long period of time. First, we used the earthquake monitoring catalog and test-retest data of the relative gravity field, which were collected by the reservoir-induced earthquake monitoring system for the Yangtze River Three Gorges Project (Figure 1) from 2001 to 2009 during the reservoir's impounding process, focusing on the key monitoring areas of the Three Gorges Project (110.2-111.1\({}^{\circ}\) E 30.6-31.2\({}^{\circ}\) N), to study the impact of the rising reservoir water level on the regional gravity field and seismicity. Secondly, the cross-correlation and reshuffling tests were used to calculate the time series correlation between the monthly frequency of the earthquake and the water level. Based on this, we further discuss the mechanism of the TGR earthquakes by establishing a three-dimensional finite-element model and tried to gain a new understanding of the relationship between the reservoir's seismicity and the water level.
# Development of Passive Fire Protection Mortars
## 3 Results and Discussion
### Short Steel Columns Specimens
Concerning the thermal performance of the mortars developed in the laboratory, it was found that the specimens protected with the DCM had a FRR of R60. When NS and MS were added to its composition, a FRR increased to R90. The specimens protected with the DGMP, with and without NS and MS, presented a FRR of R90. The findings results obtained from the short steel columns protected with the different mortars types were in agreement with the ones obtained in the tests carried out on the protected steel plates, which makes these exploratory tests useful for preliminary selection of promising fire protection materials. Figure 14: Photos of the specimens of SSC before and after tested, as an example. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Average Temperature**} & & & & \\ \cline{3-8}
**Different Types of** & **Specimens** & \multicolumn{3}{c}{**of Specimens (\({}^{\circ}\)C)**} & & & & & \\ \cline{3-8}
**Tested Columns** & **Designation** & **15** & **30** & **60** & **90** & & & \\ \cline{3-8}  & & & **(Minutes)** & & & **(Failure Time)** & **(Minutes)** & & \\ \hline Columns without & SSC1 & 525 & - & - & - & 560 \({}^{\circ}\)C (17 min) & & & R15 \\ passive fire protection & SSC2 & 572 & - & - & - & 617 \({}^{\circ}\)C (17 min) & & & R15 \\ \hline Columns coated & SSC3 & 78 & 111 & 351 & - & 560 \({}^{\circ}\)C (85 min) & & & R60 \\ with CM & SSC4 & 77 & 108 & 360 & - & 531 \({}^{\circ}\)C (7) min & & & R60 \\ \hline \multirow{3}{*}{Column coated with DCM} & Without MS & SSC5 & 96 & 220 & 465 & - & 566 \({}^{\circ}\)C (82 min) & & \\  & and NS & SSC6 & 103 & 242 & 481 & - & 585 \({}^{\circ}\)C (84 min) & & \\ \cline{2-8}  & With & SSC11 & 96 & 193 & 447 & - & 582 \({}^{\circ}\)C (88 min) & & \\  & MS and NS & SSC14 & 92 & 186 & 425 & 557 & 559 \({}^{\circ}\)C (91 min) & & & R90 \\ \hline \multirow{3}{*}{Column coated with DGMP} & Without MS & SSC7 & 79 & 115 & 341 & 535 & 576 \({}^{\circ}\)C (98 min) & & \\  & and NS & SSC8 & 75 & 119 & 342 & 545 & 572 \({}^{\circ}\)C (95 min) & & & R90 \\ \cline{1-1} \cline{2-8}  & With & SSC12 & 76 & 116 & 328 & 516 & 566 \({}^{\circ}\)C (102 min) & & & \\ \cline{1-1}  & MS and NS & SSC13 & 76 & 114 & 328 & 525 & 564 \({}^{\circ}\)C (98 min) & & 100 & R90 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average temperature value obtained on the specimens of SSC.
## 3 Results and Discussion
### Effects of Arc Length Correction on Bead Penetration and Porosity  
#### 3.2.1 Effects of Arc Length Correction on Bead Penetration  
ALC is used to adjust the arc length. The change in ALC affects the heat transfer, arc shape, and droplet properties [11]. Additionally, changes in ALC also affect the mechanical properties [20]. For this investigation, ALC was varied at intervals of 15% from +30% to \(-30\%\) for specimen preparation using CMT+P. The resulting five deposited samples after grinding and polishing were observed under the microscope, and the results are presented in Figure 7.  
Weld bead penetration was measured using ImageJ; the results are plotted in Figure 8. As the ALC was altered from +30% to \(-30\%\), the bead penetration increased. A similar trend was observed in CMT-based welding of AISI 316L by [PERSON] et al. [21], where the penetration depth increased as the ALC was changed from \(-5\%\) to \(-20\%\). Moreover, [PERSON] et al. [12] also observed in mild-steel welding that as the arc length increases, the  
\begin{table}
\begin{tabular}{c c c c} \hline
**PC (Unitless)** & \(\mathbf{-5}\) & \(\mathbf{0}\) & \(\mathbf{+5}\) \\ \hline Penetration (mm) & 2.38 & 2.89 & 3.87 \\ \hline Porosity area (\%) & 3.35 & 6.69 & 3.39 \\ \hline \end{tabular}
\end{table}
Table 3: Variation of porosity with pulse correction.  
Figure 7: Optical images of Cu1897 samples prepared by varying ALC.  
penetration depth reduces. This is because the arc length reduces with a reduction in ALC, and the arc cone is concentrated on a smaller area [22]; hence, the bead's penetration increases. The highest penetration of weld bead was observed at ALC = \(-\)30%, which is 4.47 mm.  
Furthermore, the penetration area of the weld bead, as shown in Figure 9, was examined by altering the ALC. These measurements are presented in Table 4, and it can be observed that as ALC reduces, the penetration area increases. The results indicate that at ALC +30%, the lowest penetration area of 7.29 mm\({}^{2}\) was observed, compared to the maximum penetration area of 20.63 mm\({}^{2}\) at ALC \(-\)30%.