esa-sceva/satcom-chunk-collection huggingface dataset:
# Automation of a Hybrid Control for Electrohydraulic Servo-Actuators with Residual Dynamics
## 4 Controller Design
### Problem Statement
After that, feedback control is chosen as \(\Delta P=\alpha_{1}(. )\) :  
\[\alpha_{1}(e_{1},e_{2},y_{d},v_{d},a_{d})=\frac{1}{S}[-e_{2}(-b+c_{1}M)\,-e_{1 }\Big{(}bc_{1}-kM-c_{1}^{2}M\Big{)}+(bv_{d}+ky_{d}+Mg+Ma_{d})-c_{2}e_{2}] \tag{18}\]  
Subsisting the previous equation in the derivative of \(V_{12}\) :  
\[\dot{V}_{12}(e_{1},e_{2})=\dot{V}_{11}(e_{1})-c_{2}e_{2}^{2}=-c_{1}e_{1}^{2}-c _{2}e_{2}^{2}<0, \tag{19}\]  
where \(c_{2}\) is a positive constant: \(c_{2}[s^{-1}]>0\). From (14), we deduce that the origin \(e_{1}=e_{2}=0\) is stable. Step 3: Let us define a third variable error \(e_{3}\) as: \(e_{3}=\Delta P-\alpha_{1}(e_{1},e_{2},y_{d},v_{d},a_{d})\). From the defined variables errors, \((S_{1})\) can be re-written as follows:  
\[\left\{ \begin{array}{l}\dot{e}_{1}=-c_{1}e_{1}+e_{2},\\ \dot{e}_{2}=\frac{S}{M}e_{3}-c_{2}e_{2},\\ \dot{e}_{3}=\Delta P-\dot{a}_{1}(e_{1},e_{2},y_{d},v_{d},a_{d})\end{array} \right. \tag{20}\]  
Then, the Lyapunov function of the overall system can be expressed by adding \(V_{12}\) with the quadratic term in \(e_{3}\):  
\[V_{1}(e_{1},e_{2},e_{3})=\frac{1}{2}c_{1}e_{1}^{2}+\frac{1}{2}c_{2}e_{2}^{2}+ \frac{1}{2}c_{3}e_{3}^{2} \tag{21}\]  
where \(c_{3}\) is a positive gain: \(c_{3}[s^{-1}]>0\). Therefore, to ensure that \(V_{1}<0\), the control law \(u_{p}\) is chosen as:  
\[u_{p}=\frac{1}{\beta\Big{[}\frac{r\dot{y}_{1}(p_{1},sign(u))}{V_{1}(y)}+\frac{ r\dot{y}_{2}(p_{2},sign(u))}{V_{2}(y)}\Big{]}}\times\Big{[}\beta S\bigg{(} \frac{1}{V_{1}(y)}\,+\,\frac{1}{V_{2}(y)}\bigg{)}v\,+\dot{a}_{1}(e_{1},e_{2},y _{d},v_{d},a_{d})-c_{3}e_{3}\quad\Big{]} \tag{22}\]  
It is clear that the singularity of Equation (22) occurs when \(\psi_{1}(.)=\psi_{2}(. )=0\), which is the case when the pressures in the two chambers are equal, respectively, to the supply and the exhaust pressure. Or, according to the defined physical domain \(D_{\varphi}\), this problem is avoided. Hence, the origin of \((S_{1})\) is globally asymptotically stable.
## 3 Results and Discussion
### Statistical Analysis  
Statistical analysis was performed by calculating correlations between the parameters affecting sorption as independent variables (pH, ionic strength, temperature, sorbent dosage) and the dependent one--the sorption coefficient. Pearson's correlation coefficient \(r\) can range from \(-1\) to \(+1\), while \(-\) or \(+\) indicate the direction of correlation [61]. The sorption of IMT to all types of TiO\({}_{2}\) under different influences showed strong linear correlations with \(r\) higher than 0.84. A slightly weaker correlation was found for the influence of ionic strength on IMT sorption to TiO\({}_{2}\)-GF (Figure 5). A similar trend was also observed for CRZ, where a strong positive correlation (r = 0.87-0.98) indicated a proportional relationship between \(K_{d}\) and pH, temperature, and sorbent dosage. The sorption of CRZ showed a negative medium-strong correlation when the ionic strength of the solution was changed.
# Effects of Velocity and Permeability on Tracer Dispersion in Porous Media
## 2 Materials and Method
### Experimental Setup and Procedure  
A schematic of the experimental setup for the tracer transport experiments is shown in Figure 1. The main experimental components included a constant flux pump, core holder, pressure gauge, tracer fluid tank, and formation water tank. The core sample (either natural or sand-filled) was placed in the core holder. The core was then saturated with water using a vacuum pump to ensure no air bubbles were present. A flask was used to collect the produced liquid. Finally, an oven was used to heat the core to determine its porosity.  
Two sets of core displacement experiments were designed. The first set consisted of high-velocity (0.04 cm\({}^{3}\)/s) and low-velocity (0.02 cm\({}^{3}\)/s) displacement experiments performed on natural cores (Nos. 1-3) from the same formation and block. The second set consisted of four sand-filled cores (Nos. 4-7) with similar permeabilities subject to displacement experiments with different velocities (0.02, 0.03, 0.05, and 0.08 cm\({}^{3}\)/s). The permeabilities of cores Nos. 1-3 were 1570, 610, and 230 mD, respectively. The permeabilities of cores Nos. 4-7 were between 574 and 699 mD, representing the typical permeability range of inter-well waterflooding channels in eastern oilfields in China. The fluid veloc  
\begin{table}
\begin{tabular}{c c} \hline
**Test Item** & **Test Results** \\ \hline Background concentration of SCN\({}^{-}\) in formation water & 0 mg/L \\ \hline Compatibility test & No precipitation and lost concentration of 1\% and 2\% for days 3 and 10 in formation water, respectively \\ \hline Static adsorption test & Adsorption ratio of 2.2\% \\ \hline \end{tabular}
\end{table}
Table 2: Tracer evaluation results.  
ities in the experiments were selected based on the equivalent water injection velocities calculated from the injection rate.  
The experimental procedure for each group of experiments was as follows:  
(1) A water displacement test was conducted to make sure there are no leaks in the system.  
(2) A steady water displacement was carried out using a constant flux pump in order to calculate the absolute permeability of the core using the data from the pressure gauge and the injection flow rate.  
(3) Once the absolute permeability was determined, the tracer, \(50\,\mathrm{mg/LSCN^{-}}\) solution, was injected at a constant flow rate. In total, two pore volumes (PV) of tracer were injected.  
(4) Once the displacement experiments were completed, the core was heated in an oven and its porosity was determined.
# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## Challenges and Approach
### Arabic Preprocessing  
The morphological complexity of Arabic language makes it difficult to achieve high quality and accuracy translation between Arabic and various languages. To facilitate the translation process, we first need to analyze the morphological and orthographic features contained in the Arabic word  
Figure 1: Example of the sentence order in Arabic and Chinese.  
by using preprocessing techniques; it is aptly called the preprocessing schemes. The advantages of morphological preprocessing have been proven useful for Arabic [12], this procedure helps reduce data sparseness, thereby improving translation quality. In this work, we use three preprocessing schemes to compare effects across different techniques on Arabic-to-Chinese translation.  
For the baseline approach, we test the tokenization script _tokenizer.perl_ of [PERSON] [34], which simply separates the punctuation marks of Arabic side using the same default as English tokenization.  
We also experiment with MADAMIRA [35], a state-of-the-art morphological analyzer for the Arabic language to implement the Penn Arabic Treebank (ATB) scheme as morphology-aware tokenization. Except for the definite article, the ATB scheme tokenizes all clitics on the Arabic side. It also has a default normalization for '(' and ')' characters into \"-RRB-\" and \"-LRB-\", in addition to the well-known Alif/Ya Arabic normalization. This scheme performs better in Arabic-English MT compared with other schemes [36].  
Furthermore, we use a new tokenization scheme of MADAMIRA, the D3* scheme proposed by [22], which tends to exhibit better performance in Arabic-to-Chinese translation and other multiple languages. Compared with the ATB scheme, D3* first requires tokenization according to the D3 scheme, which tokenizes all clitics, splits \(\mathcal{O}\)(Al) (Arabic transliteration is presented according to the Habash-Soudi-Buckwalter scheme [37]) \"the\" definite article, and then removes the definite article from the Arabic context.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 6 Discussion and Open Challenges
### From Software to Hardware  
In this work, we have explored how GNSS algorithms developed in a high-level programming language (Python) can be ported to hardware with a limited effort (C2). Our conversion process required partial re-development in an intermediate language (C/C++) to use HLS to produce HDL descriptions. We have not reviewed libraries to convert Python code into C/C++ code, such as Cytthon, for two reasons: (1) successful application of the HLS synthesis flow requires knowledge of the C code and (2) retaining a certain level of code quality from the beginning might not be possible with such tools. Moreover, one of our reasons for writing SyDR in a high-level language was to simplify the validation of any processing stages that would later be converted into lower-level languages. In particular, we use unit tests for each algorithm whose expected values are generated in Python.  
Naively using HLS generally shortens the development time and requires less domain-specific knowledge of hardware design than using any traditional HDL would. The latter point renders it particularly interesting to GNSS algorithm researchers, who often come from a software development background. Despite some initial hiccups in our conversion due mostly to inexperience, we still believe that using HLS rather than handwriting HDL has accelerated the progress of Hard SyDR (C3).  
Figure 9: Power consumption of the Hard SyDR at idle and execution.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Item** & **Time (s)** & **Mean Power (mW)** & **STD Power (mW)** & **Mean Energy (mJ)** \\ \hline System Idle & — & 3360.1 & 45.3 & — \\ FPGA & 44.3 & 236.8 & 48.3 & 10,490 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Power consumption of the design and the energy consumption of FPGA.  
Nevertheless, we acknowledge that using HLS is non-trivial. As mentioned before, it has well-known overheads [41; 45], which require understanding of hardware design and the targeted FPGA architecture to mitigate. As shown in Section 5.1, for example, fine-tuning details of a design can reduce resource utilization or improve performance significantly. In line with this, we have a version of the Hard SyDR code that is fully synthesizable but consumes far more BRAMs than are available in the KV260's FPGA [35].  
Following this work, we plan to explore the efficiency of the HLS flow by developing HDL versions of key GNSS algorithms and, in parallel, optimizing our current design better. Moreover, we will consider the feasibility of using power consumption estimates from post-implementation simulations instead of on-board measurements. If deemed sufficiently accurate, this would allow for a truly portable algorithm evaluation flow without physical hardware.
## 3 EMI NO\({}_{2}\) Retrieval Algorithm
### Stratosphere-Troposphere Separation (STS)  
The STREAM STS method directly estimates the stratospheric concentration based on the satellite measurements over remote regions and under cloudy conditions where tropospheric contribution is considered to be negligible [32], which provides a good reference for STS on EMI. The STREAM method defined three weighting factors:  
(1) Pollution weight, which was calculated using the multi-year monthly averaged climatological tropospheric NO\({}_{2}\) column to provide lower weights for the measurements over polluted regions;  
(2) Cloud weight, which was calculated based on the cloud radiation fraction and cloud pressure to provide higher weights for cloudy measurements;  
(3) Tropospheric residue weight, which was calculated during the iteration process to adjust the total weight.  
The total weight was obtained on the basis of the weighting factors above. Then, two convolution kernels defined for the polar and equatorial regions, respectively, were used for the weighted convolution of the initial total column with total weight on a \(1^{\circ}\times 1^{\circ}\) grid to get the stratospheric NO\({}_{2}\) field. The initial total column used in the convolution was \"latitudinally corrected\" by subtracting the corresponding mean column over clean regions for each latitude band.  
Figure 4 show the stratospheric VCD results derived from the STREAM algorithm for OMI orbit 75,102 and EMI orbit 1629, respectively. The spatial distribution of stratospheric VCD from EMI agrees quite well with the OMI results, and the latitudinal dependencies are clearly visible in the stratospheric VCD for both OMI and EMI. The stratospheric VCD results from EMI in the mid-high northern hemisphere are a little higher than those from OMI, which could be attributed to the relatively higher total VCD in these regions. The tropospheric residue results of EMI and OMI are, overall, in the same order of magnitude, and the mean tropospheric residue over the Pacific region for EMI (OMI) is \(12\ (9)\times 10^{13}\) molec cm\({}^{-2}\), which is within reasonable limits.  
Figure 4: Total vertical column densities (VCD) (**top**), stratospheric VCD (**middle**), and tropospheric residue (**bottom**) derived from the STRatospheric Estimation Algorithm from the Mainz (STREAM) algorithm for OMI 75,102 orbit (**left**) and EMI 1629 orbit (**right**).
## 3 Results and Discussion
### NMR Analysis  
The chemical structures of the PET cyclic oligomers (dimer up to heptamer), the \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra, as well as their respective signal assignments are presented in Figures 1 and 2.  
Figure 1: Chemical 3D structures of the PET cyclic dimer and trimer.  
From the \({}^{1}\)H NMR analysis results (Table 2 and Figure 3) we observed that the protons from the aliphatic chains and close to the carboxy groups presented a difference between the dimer and all the other structural cyclic oligomers (Table 2). This observation can be attributed to the size and geometry of the dimer, which seem to differ from all the others, and this may be related to the increased solubility of this molecule in all organic solvents as already presented in Table 1 and reported for other PBT cyclic oligomers [18]. Moreover, of particular interest were the aromatic protons, as it could be observed that they have a different chemical shift (singlet) when compared with the other PET cyclic oligomers. The \({}^{1}\)H NMR chemical shifts for PET oligomers are in accordance with the study by [PERSON] et al. [18], where a clear difference exists between the PBT cyclic dimer and all the other oligomers, suggesting a similar physicochemical behavior and a strong relation with the geometry of the molecule and its internal interactions.  
Some differences were nevertheless observed on the chemical shifts, due to the different intermediate aliphatic chains connecting the terephthalatic acid units [18]. For \({}^{13}\)C NMR (Table 2, Figure 3), we observed a clear difference between the cyclic PET dimer and all the other cyclic oligomers, where an increase of the chemical shift (\(\delta\)) occurred for either aliphatic or aromatic carbons. In this case, the results for PET cyclic oligomers were in in accordance with previously reported work for PBT [18].  
Figure 2: \({}^{1}\)H NMR spectrum of the PET cyclic dimer (**1**), trimer (**2**), tetramer (**3**), pentamer (**4**), hexamer (**5**) and heptamer (**6**), recorded in CDCl\({}_{3}\)/TFA.
## 3 Deep Feature-Based Adaptive Level Set Model (DFALS)
### Implementation of the DFALS  
As aforementioned, the energy functional of the proposed method is defined as Equation (7). By introducing the time variable \(t\) and minimizing the energy functional with respect to \(\phi_{1}\) and \(\phi_{2}\), we can deduce the following evolution equations for \(\phi_{1}\) and \(\phi_{2}\) by the gradient descent flow:  
\[\begin{array}{rl}\frac{\partial\phi_{1}(u)}{\partial t}&=\gamma_{1}\frac{( \phi_{1}(u)-\phi_{2}(u)|-\mu)}{\partial^{2}}+\delta(\phi_{1}(u))\int_{\Psi} \delta(\phi_{1}(v))((I(v)-I_{inner})^{2}-\big{(}I(v)-I_{outer})^{2}\big{)}dv \\ &+\gamma_{2}\delta(\phi_{1}(u))div(\frac{\
abla\phi_{1}(u)}{\phi_{1}(u)})+ \gamma_{3}(\delta(\phi_{1}(u))\cdot((\hat{I}(u)-\xi_{1})^{2}-H(\phi_{2})(\hat {I}(u)-\xi_{2})^{2}))\\ \end{array} \tag{9}\]  
\[\begin{array}{rl}\frac{\partial\phi_{2}(u)}{\partial t}&=\gamma_{1}\frac{( \phi_{1}(u)-\phi_{2}(u)|-\mu)}{\partial^{2}}+\delta(\phi_{2}(u))\int_{\Psi} \delta(\phi_{2}(v))((I(v)-v_{inner})^{2}-\big{(}I(v)-v_{outer})^{2}\big{)}dv \\ &+\gamma_{2}\delta(\phi_{2}(u))div(\frac{\
abla\phi_{2}(u)}{\phi_{2}(u)})+ \gamma_{3}(\delta(\phi_{2}(u))\cdot((\hat{I}(u)-\hat{c}_{2})^{2}(1-H(\phi_{1} ))-(\hat{I}(u)-\hat{c}_{3})^{2}))\end{array} \tag{10}\]  
Figure 7: Auroral image and the corresponding time step map. (**a**) Auroral oval image. (**b**) The corresponding time step map.  
The evolution equation can be discretized as:  
\[\phi^{k+1}=\phi^{k}+\Delta t\frac{\partial\phi^{k}}{\partial t} \tag{11}\]  
where \(k\) refers to the \(k^{th}\) iteration. The adaptive time step \(\Delta t\) is introduced into the evolution equation. The main procedures of the proposed DFALS model are summarized in Algorithm 1 as follows:  
```
Preprocessing: Train the CNN with the training samples, and then construct each UVI image's corresponding deep feature map with the trained CNN. Input: An original UVI image and the corresponding deep feature map Output: The segmentation result of the auroral oval image Step 1: Initialize the level set functions \(\phi_{1}\) and \(\phi_{2}\), and set the coefficient parameters \(\gamma_{1}\), \(\gamma_{2}\) and \(\gamma_{3}\). Step 2: For\(k=1:K\) (\(K\) is the total number of iterations). Step 3: Calculate the gradient descent flow for \(\phi_{1}\) and \(\phi_{2}\) by Equations (9) and (10). The gradient descent flow of the shape energy term, the regularization term, and the local information term are calculated with the original UVI image, and the gradient descent flow of the global energy term is calculated with the corresponding deep feature map. Step 4: Calculate the adaptive time step according to Equation (8). Step 5: Calculate the level set evolution equation using Equation (11). Step 6: If the evolution of the curves is stable or the predetermined iteration number is reached, then output the segmentation result; otherwise, return to Step 4. Step 7: End for Step 8: Output the segmentation result.
```  
**Algorithm 1********Deep feature-based adaptive level set model (DFALS).
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 9 Ultrasonic Method
### Introduction and Principle of the Application
#### 9.1.1 Quality of Concrete  
[PERSON] et al. [121] investigated the quality of concrete using UPV. They measured the velocities of acoustic waves travelling through the manufactured concrete samples and found a correlation between the UPV and the compressive strength of the concrete. An experiment has also been conducted to investigate the relationship between ultrasonic pulse velocity (UPV), porosity and compressive strength of lightweight foam concrete samples [122]. In contrast to the results by [PERSON] et al. [121], it was mentioned that several factors such as composition, presence of cracks or defects of the concrete do not affect the compressive stiffness of the concrete to the same extent as they affect the magnitude of the UPV. Thus, the UPV results alone are not reliable for the prediction of the compressive stiffness of the concrete. Instead, the combination of several NDT methods should be employed for results that are more accurate [122]. [PERSON] and [PERSON] [123] used ultrasonic wave propagation waves to assess the quality of polymer-cement concrete with polyethene terephthalate (PET) as an aggregate. They found that the more PET was added to the polymer-cement concrete, the more the ultrasonic wave velocity reduced. The results also showed that the mechanical properties, namely flexural and compressive strengths, were in correlation with the ultrasonic wave velocity. Thus, the higher the mechanical property of the composite, the higher the wave velocity. Ultrasonic guided waves were utilized by [PERSON] and [PERSON] [124] to monitor the progression of corrosion in steel reinforcement in chloride and oxide environment.
# Numerical Study on Dynamic-Stall Characteristics of Finite Wing and Rotor
## 2 Numerical Methods
### Flowfield-Solver Verification  
In order to verify the accuracy of the CFD method employed in this research, the comparisons of the simulated lift coefficient (\(C_{l}\)) of the NACA00112 airfoil (2D) and the finite wing (3D) with test data [30] under a dynamic-stall condition are shown in Figure 2. In this case, the Mach number (Ma) was 0.286, reduced frequency (k) as 0.151, and the AoA was \(\alpha=14.91^{\circ}-9.88^{\circ}\sin(\omega t)\), where \(\omega\) represents angular velocity. It can be seen that the numerical results of the 2D airfoil were well-correlated with the test data. However, the numerical results of the finite wing had a larger stall angle and the maximum \(C_{l}\) compared with the 2D numerical results due to the influence of the three-dimensional effect.  
Another comparison of numerical normal force coefficient \(C_{n}\) of the SA349/2 rotor with the test data [31], at an advance ratio of 0.26, is shown in Figure 3; \(\psi\) represents the azimuthal angle of rotor blade. It is illustrated that the numerical data were also in agreement with the test data at a blade section of \(\mathrm{r}/\mathrm{R}=0.75\). Therefore, it is indicated that the present CFD method could effectively simulate the aerodynamic characteristics of a finite wing and rotor under dynamic-stall conditions.  
Figure 2: Comparison of numerical \(C_{l}\) with test data.
# Automated Destination Renewal Process for Location-Based Robot Erands
## 3 Methods
### Robot Positioning Based on Objects
#### 3.2.1 Gvg  
Firstly, a topological map is constructed to enable the reliable self-localization and autonomous navigation of a mobile robot in a complex, dynamic, and extensive environment. This method fundamentally represents the indoor environment using nodes and edges, where nodes are positioned at destinations or junctions, and edges connect nodes based on the connectivity structure of the indoor environment. Moreover, it has the capability to incorporate specific sub-concepts such as doors, rooms, corridors, etc., allowing the previously built indoor environment perception information to be integrated with the grid map.  
The GVG is a method based on the grid map to create a topological map. It involves constructing the generalized Voronoi diagram (GVD) from the grid map, selecting endpoints or intersections of the GVD as nodes, and forming edges based on the connectivity between nodes. The GVD traces the paths of grids at the same distance from occupied grids, forming a configuration resembling lines traversing the spaces between free cells. The GVD represents the set of cells that, when a circle is drawn around each unoccupied cell, can touch the boundary between at least two occupied and unoccupied cells. This structure resembles the thinned lines of the grid map. Nodes in the GVG are designated through a validation process of each GVD cell. This validation determines whether a cell corresponds to an endpoint or an intersection of the GVD strands. Edges are formed by connecting the selected GVD cells according to their existing connectivity, and the remaining cells are removed. The resulting nodes and edges represent the main points of the indoor space as nodes, and represent their connectivity as edges, thus completing the graph representation. Through practical information for LBSs, this integration can be effectively applied to interfaces for user errand tasks and intelligent navigation.  
Figure 4 shows the nodes and edges created from the map representing one floor of a building. The mobile robot navigates along these GVG nodes, collecting the object information necessary for performing errand services. While recognizing objects, it optimally updates the nodes. As the process includes exploring the indoor environment, the initial GVG skeletonization stage can be utilized as exploration nodes for the mobile robot to gather comprehensive information about the indoor environment.