esa-sceva/satcom-chunk-collection huggingface dataset:
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 2 Materials and Methods  
### Study Area  
The Turpan-Hami Basin is located in the eastern part of the Xinjiang Uygur Autonomous Region, China (86\({}^{\circ}\)40\({}^{\prime}\)00\({}^{\prime\prime}\)-96\({}^{\circ}\)23\({}^{\prime}\)00\({}^{\prime\prime}\) E, 40\({}^{\circ}\)21\({}^{\prime}\)00\({}^{\prime\prime}\)-43\({}^{\circ}\)43\({}^{\prime}\)00\({}^{\prime\prime}\) N) (Figure 1), and comprises two major tectonic units: the Turpan Basin and the Hami Basin. The basin's landscape is predominantly characterized by the Gobi Desert, stretching in an east-west direction with terrain higher in the north and lower in the south. Aydingkol Lake in the Turpan-Hami, with an altitude of \(-\)155 m, is not only the lowest point in China but also the third lowest depression in the world after the Dead Sea and Lake Assal. This unique terrain of extremely low altitude and the enclosed basin structure give rise to distinct regional climatic characteristics. The basin experiences a typical continental arid climate, marked by scarce precipitation (average annual precipitation is approximately 50 mm) and limited surface water resources, leading to a high dependence on groundwater for agricultural irrigation and domestic water supply. Especially in the Turpan region, the climate is hot and arid, with evaporation far exceeding actual precipitation (average annual precipitation is only 16.4 mm, while evaporation reaches as high as over 3000 mm). However, it is precisely these natural conditions that have endowed the region's unique light and heat resources (annual average sunshine duration \(>\)3000 h, and a day-night temperature difference of 14-16 \({}^{\circ}\)C). Together with the drought stress environment, these factors have shaped the area's distinctive ecological pattern and agricultural cultivation practices.
## 2 Materials and Methods
### Imaging System  
The photoacoustic endomicroscopy imaging system is illustrated in Figure 1. A 532 nm pulsed laser (2 ns, SPOT-10-200-532, Elforlight, Northamptonshire, UK) was used as the  
Figure 1: Schematic diagram of the photoacoustic endomicroscopy imaging system. The inset shows a photograph and a schematic of the transparent ultrasound sensor. DMD, digital micromirror device; L1–L2, convex lens (f = 50 mm); L3–4 convex lens (f = 100 mm); Obj1–2, objective lenses; BS, beamsplitter; DAQ, data acquisition card.  
excitation light source. The light wavefront was modulated by a digital micromirror device (DMD, DLP7000, Texas Instruments, Dallas, TX, USA) before projected onto the proximal tip of a multimode fibre (core diameter: 100 \(\upmu\)m; NA: 0.29; length: 30 cm) through a convex lens (AC254-050-A-ML, Thorlabs, Newton, NJ, USA) and an objective (0.4 NA, RMS20X, Newton, NJ, USA). The distal end of the multimode fibre was integrated within the brass tube with the fibre tip attached to the PVDF-ITO thin film at the centre.  
Since the light transport through a multimode fibre is scrambled by mode dispersion, a fibre characterisation process was implemented before imaging with wavefront shaping, which was detailed in our previous works [29; 30]. Here, briefly, a series of binary patterns were displayed on the DMD while the corresponding output speckles through the multimode fibre and the sensor were recorded with a camera. With these input-output pairs, the light intensity changes from the DMD to the distal fibre tip was modelled as a real-valued intensity transmission matrix (RVITM), achieved via direct matrix manipulations. Then, optimal DMD patterns that led to light focusing through the multimode fibre were calculated from the RVITM. As such, raster-scanning a tightly focused laser beam across the distal fibre tip was performed by sequentially displaying these optimal DMD patterns. The characterisation module comprising a camera, a beamsplitter and lenses was removed after fibre characterisation. For imaging, the sensor was immersed in a water tank, and samples were placed at various depths in front of the distal multimode fibre tip. The focal plane of the imaging probe was defined during the characterisation process by adjusting the focal plane of the camera accordingly. For each scanning position, a photoacoustic signal was excited from the sample, and then received by the sensor and recorded by a data acquisition card (M4i.4420, 250 MHz, Spectrum Instrumentation, Grosshansdorf, Germany) after amplification. Maximum intensity projection PAM images were formed by showing the peak-to-peak amplitudes of the ultrasound signals.
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 4 Results and Discussion
### Normalizing the Arias Intensity along the Structure
The results are reported in Table 5 showing \(x\) and \(y\) for all the nodes of the beam and bridge. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & **Node 1** & **Node 2** & **Node 3** & **Node 4** & **Node 5** & **Node 6** & **Node 7** & **Node 8** & **Node 9** \\ \hline \(\beta\) (for bridge) & 1049 & 1306 & 1256 & 1427 & 1252 & 1354 & 1381 & - & - \\ \(\beta\) (for beam) & 294 & 392 & 365 & 402 & 382 & 342 & 442 & 397 & 297 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(\beta\) calculated for all signals under moving load in the laboratory for no-damage condition considering the speed as v = 0.3 m/s. Figure 14: Normalizing factor for the structure under moving load at (**a**) node 1 of the intact bridge and (**b**) node 1 of the intact beam. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & **Node 1** & **Node 2** & **Node 3** & **Node 4** & **Node 5** & **Node 6** & **Node 7** & **Node 8** & **Node 9** \\ \hline \multirow{2}{*}{Beam} & \(x\) & \(-203\) & \(-498\) & \(-241\) & \(-377\) & \(-348\) & \(-228\) & \(-457\) & \(-444\) & \(-238\) \\  & \(y\) & 356 & 535 & 441 & 514 & 475 & 432 & 567 & 525 & 378 \\ \hline \multirow{2}{*}{Bridge} & \(x\) & \(-1116\) & \(-1954\) & \(-1687\) & \(-2249\) & \(-1689\) & \(-1874\) & \(-1949\) & - & - \\  & \(y\) & 1389 & 1901 & 1769 & 2112 & 1766 & 1925 & 1975 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The coefficients \(x\) and \(y\) calculated for all nodes of the beam and bridge for the no-damage condition. Equation (12) and Table 5 provide a baseline for these laboratory models and normalize the AI at different velocities.
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 2 Methods
### Performance Degradation Modeling Based on Inverse Gaussian Process
The CDF and PDF of residual life Tr can be formulated as:  
\[\mathrm{F}_{\mathrm{Tr}}(t)=\Phi\left(\sqrt{\frac{\lambda}{\omega}}\frac{ \sigma_{\mu}\Lambda(t)-\alpha_{\mu}\sigma_{\mu}\omega}{\sqrt{\sigma_{\mu}^{2}+ \lambda\omega}}\right)-\exp\left(2\alpha_{\mu}\lambda\Lambda(t)+\frac{2\lambda ^{2}\Lambda^{2}(t)}{\sigma_{\mu}^{2}}\right)\times\Phi\left[-\sqrt{\frac{ \lambda}{\omega}}\frac{\left(\sigma_{\mu}^{2}+2\lambda\omega\right)\Lambda(t) +\alpha_{\mu}\sigma_{\mu}^{2}\omega}{\sqrt{\sigma_{\mu}^{4}+\lambda\omega \sigma_{\mu}^{2}}}\right], \tag{7}\]  
and  
\[ \begin{array}{c}f_{\mathrm{Tr}}(t)=\sqrt{\frac{\lambda}{\omega}}\frac{\sigma _{\mu}}{\sqrt{\sigma_{\mu}^{2}+\lambda\omega}}\Lambda^{\prime}(t)\times\Phi \left[\sqrt{\frac{\lambda}{\omega}}\frac{\sigma_{\mu}\Lambda(t)-\alpha_{\mu} \sigma_{\mu}\omega}{\sqrt{\sigma_{\mu}^{2}+\lambda\omega}}\right]-\exp\left(2 \alpha_{\mu}\lambda\Lambda(t)+\frac{2\lambda^{2}\Lambda^{2}(t)}{\sigma_{\mu}^ {2}}\right)\\ \times\left(2\alpha_{\mu}\lambda\Lambda^{\prime}(t)+\frac{2\lambda^{2}\Lambda ^{2}(t)}{\sigma_{\mu}^{2}}\Lambda^{\prime}(t)\right)\Phi\left[-\sqrt{\frac{ \lambda}{\omega}}\frac{\left(\sigma_{\mu}^{2}+2\lambda\omega\right)\Lambda(t) +\alpha_{\mu}\sigma_{\mu}^{2}\omega}{\sqrt{\sigma_{\mu}^{4}+\lambda\omega \sigma_{\mu}^{2}}}\right]\\ +\sqrt{\frac{\lambda}{\omega}}\frac{\left(\sigma_{\mu}^{2}+2\lambda\omega \right)}{\sqrt{\sigma_{\mu}^{4}+\lambda\omega\sigma_{\mu}^{2}}}\times\Lambda ^{\prime}(t)\exp\left(2\alpha_{\mu}\lambda\Lambda(t)+\frac{2\lambda^{2}\Lambda ^{2}(t)}{\sigma_{\mu}^{2}}\right)\\ \times\Phi\left[-\sqrt{\frac{\lambda}{\omega}}\frac{\left(\sigma_{\mu}^{2}+2 \lambda\omega\right)\Lambda(t)+\alpha_{\mu}\sigma_{\mu}^{2}\omega}{\sqrt{ \sigma_{\mu}^{4}+\lambda\omega\sigma_{\mu}^{2}}}\right].\end{array}  \tag{8}\]
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1
### GTPase Dynamin  
The GTPase dynamin contributes to the retrograde transport of STxB/STx1B. The early endosome-to-Golgi transport of STxB/STx1B is inhibited by overexpression of a dominant-negative dynamin mutant that is unable to bind and hydrolyze GTP [31].
## 4 Experimental Results
### Cross-Validation Experiments
For the \"Voting\" dataset, the results are illustrated in Table 9. The highest average predictive accuracy for both the model and the rules was obtained by GB (96.6% and 96.2%, respectively). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc.** & **Fid.** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant.** \\ \hline RF (100) & 91.4 (0.4) & 89.6 (1.8) & 83.5 (1.5) & 91.9 (0.7) & 78.4 (3.6) & 5.1 (0.1) \\ RF (150) & 91.0 (0.6) & 89.3 (1.5) & 83.4 (1.4) & 91.7 (0.6) & 78.1 (3.1) & 5.1 (0.1) \\ \hline RF-3 (25) & 86.1 (1.6) & 97.3 (0.6) & 86.8 (1.3) & 87.5 (1.4) & 18.5 (1.3) & 3.4 (0.2) \\ RF-3 (50) & 87.0 (1.4) & 97.6 (0.9) & 87.1 (1.2) & 88.0 (1.3) & 17.4 (1.3) & 3.4 (0.1) \\ RF-3 (100) & 87.3 (1.1) & 97.5 (0.7) & 87.4 (1.0) & 88.3 (1.2) & 17.2 (0.7) & 3.4 (0.1) \\ RF-3 (150) & 87.3 (0.8) & 97.1 (0.8) & 87.5 (1.3) & 88.6 (1.2) & 16.6 (0.9) & 3.3 (0.1) \\ \hline GB (1,25) & 92.0 (0.2) & **100.0** (0.0) & **92.0** (0.2) & 92.0 (0.2) & **2.0** (0.0) & **1.0** (0.0) \\ GB (1,50) & 91.8 (0.3) & 100.0 (0.1) & 91.8 (0.2) & 91.8 (0.3) & 2.4 (0.1) & 1.1 (0.0) \\ GB (1,100) & 91.7 (0.4) & 99.2 (0.4) & 91.3 (0.5) & 91.8 (0.4) & 9.1 (0.5) & 2.3 (0.1) \\ GB (1,150) & 91.4 (0.6) & 98.3 (0.5) & 90.8 (0.5) & 91.8 (0.5) & 14.1 (0.6) & 2.7 (0.0) \\ \hline GB (2,25) & 92.0 (0.2) & **100.0** (0.0) & **92.0** (0.2) & 92.0 (0.2) & **2.0** (0.1) & **1.0** (0.0) \\ GB (2,50) & 91.6 (0.3) & 99.3 (0.3) & 91.1 (0.4) & 91.7 (0.3) & 10.0 (0.8) & 2.3 (0.1) \\ GB (2,100) & 91.4 (0.6) & 97.6 (0.9) & 90.8 (0.7) & 92.1 (0.6) & 20.5 (0.6) & 3.2 (0.0) \\ GB (2,150) & 91.4 (0.6) & 96.6 (0.9) & 90.2 (0.9) & 92.3 (0.4) & 25.8 (0.3) & 3.5 (0.1) \\ \hline GB (3,25) & 91.0 (0.4) & 99.1 (0.5) & 90.7 (0.6) & 91.2 (0.4) & 10.7 (0.8) & 2.5 (0.1) \\ GB (3,50) & 91.1 (0.8) & 97.6 (0.5) & 90.8 (1.1) & 92.0 (0.8) & 19.8 (0.8) & 3.2 (0.0) \\ GB (3,100) & 91.2 (0.8) & 97.0 (0.7) & 90.2 (0.9) & 92.0 (0.7) & 28.1 (0.6) & 3.6 (0.0) \\ GB (3,150) & 91.0 (0.7) & 96.2 (0.9) & 90.0 (1.0) & 92.1 (1.0) & 32.8 (3.0) & 3.8 (0.1) \\ \hline SR (25) & — & — & 91.0 (0.4) & — & 13.9 (0.8) & 2.5 (0.0) \\ SR (50) & — & — & 90.9 (1.0) & — & 21.2 (1.3) & 2.6 (0.0) \\ SR (100) & — & — & 90.9 (0.5) & — & 29.5 (1.0) & 2.6 (0.0) \\ SR (100) & — & — & 90.7 (0.8) & — & 35.8 (1.0) & 2.6 (0.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc. ** & **Fid. ** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant.
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
## 3 Finite-Time Convergent Observer Design
\right]\\ &=2[\frac{1}{2}\mathrm{sgn}(e_{i1})p_{11}e_{i2}+\frac{1}{2}\frac{1}{|e_{i1}|^{ \frac{1}{2}}}e_{i2}p_{12}e_{i2}-\frac{1}{2}p_{11}l_{i1}|e_{i1}|^{\frac{1}{2}} \mathrm{sgn}^{2}(e_{i1})-\\ &\frac{1}{2}e_{i2}p_{12}l_{i1}\mathrm{sgn}(e_{i1})+\ddot{k}_{i}|e_{i1}|^{ \frac{1}{2}}\mathrm{sgn}(e_{i1})e_{i2}p_{12}+e_{i2}^{T}p_{22}\ddot{k}_{i}e_{i2} -\\ &l_{i2}p_{12}|e_{i1}|^{\frac{1}{2}}\mathrm{sgn}^{2}(e_{i1})-l_{i2}e_{i2}^{T} \mathrm{sgn}(e_{i1})p_{22}+\\ d_{i}|e_{i1}|^{\frac{1}{2}}\mathrm{sgn}(e_{i1})p_{12}+d_{i}e_{i2}^{T}p_{22}]. ```  
The following can be obtained:  
\[ \begin{split}& U_{i}\leq-\frac{1}{|e_{i1}|^{\frac{1}{2}}}\Big{[} \begin{array}{cc}|e_{i1}|^{\frac{1}{2}}\mathrm{sgn}(e_{i1})&e_{i2}\end{array} \Big{]}\times\\ &\left[\begin{array}{cc}|l_{i1}p_{11}+l_{i2}p_{12}&l_{i2}p_{22}+\frac{l_{i 2}}{2}p_{12}-\frac{1}{2}p_{11}\\ l_{i2}p_{22}+\frac{l_{i1}}{2}p_{12}-\frac{1}{2}p_{11}&-p_{12}\end{array}\right] \times\\ &\left[\begin{array}{cc}|e_{i1}|^{\frac{1}{2}}\mathrm{sgn}(e_{i1}) \\ \dot{e}_{i2}\end{array}\right]+(p_{12}|e_{i1}|^{\frac{1}{2}}\mathrm{sgn}(e_{i1}) +e_{i2}^{T}p_{22})\times\\ &(\ddot{k}_{i}e_{i2}+d_{i}).\end{split}  \tag{11}\]\[\text{Denote }Q_{i}=\left[ \begin{array}{cc}l_{i1}p_{11}+l_{i2}p_{12}&l_{i2}p_{22}+ \frac{l_{i1}}{2}p_{12}-\frac{1}{2}p_{11}\\ l_{i2}p_{22}+\frac{l_{i1}}{2}p_{12}-\frac{1}{2}p_{11}&-p_{12}\end{array} \right]\text {, the following inequality can obtain:}\] \[\dot{U}_{i}\leq-\frac{1}{|e_{i1}|^{\frac{1}{2}}}\xi_{i}^{T}Q_{i}\xi_{i}+m_{ i}, \tag{12}\]  
where \(m_{i}=2p_{M}\|\xi_{i}\|(\bar{k}_{i}\|e_{i2}\|+\|d_{i}\|)\), \(p_{M}=\max\{p_{12},p_{22}\}\). Moreover, observe that \(\|\xi_{i}\|^{2}=|e_{i1}|+e_{i2}^{2}\), \(|e_{i1}|^{\frac{1}{2}}\leq\frac{\mu^{\frac{1}{2}}}{\lambda_{\min}^{\frac{1}{2 }}(p)}\), then \(\dot{U}_{i}\leq-\frac{1}{|e_{i1}|}\frac{\lambda_{\min}(Q_{i})}{\lambda_{\min} (P)}U+m_{i}\leq-\frac{\lambda_{\min}(Q_{i})}{\lambda_{\min}^{\frac{1}{2}}(P)} U^{\frac{1}{2}}+m_{i}\). According to Lemma 1, the observer error will converge to the region \(\Omega=\{(e_{i1},e_{i2}):|e_{i1}|+e_{i2}^{2}\leq(\frac{\lambda_{\min}^{\frac{ 1}{2}}(P)m_{i}}{\mu_{i\min}(Q_{i})})^{2}\}\) in the finite time \(T_{1}=\frac{2\lambda_{\min}^{\frac{1}{2}}(P)\|U(\xi_{i},0)^{\frac{1}{2}}}{(1- \mu_{i})\lambda_{\min}(Q_{i})}\), where \(0<\mu_{i}<1\), and the matrices \(P\) and \(Q_{i}\) can be selected to make the error sufficiently small. \(\forall\delta_{i}>0\), when \(t<T_{1}\):  
\[\left|\hat{d}_{i}-d_{i}\right|<\delta_{i}. \tag{13}\]  
When \(t>T_{1}\), \(e_{i1},e_{i2}\) will converge to the region \(\Omega\) near the origin. According to (7), the estimated value of the unknown composite disturbance can be obtained as follows:  
\[\lim_{t\to T_{1}}\hat{d}_{i}=l_{i2}\text{sgn}(e_{i1})-e_{i2}. \tag{14}\]  
The proof is completed.
# The Objective Space and the Formulation of Design Requirement in Natural Laminar Flow Optimization
###### Abstract  
Design requirement is as important in aerodynamic design as in other industries because it sets up the objective for the samples in design space to approach. Natural Laminar Flow (NLF) optimization belongs to the type of aerodynamic design problems featured by the combination of distinct aerodynamic performance, where the design requirement is often formulated in form of summation of laminar-related performance and pressure drag performance with different weight assignment according to different perspectives. However, the formulations are rather experience-oriented and are decided non-quantitatively. Inspired by data manipulation approaches in design space (spanned by design variables of geometrical representation parameters) in many aerodynamic designs, this paper proposes new formulations of design requirement in NLF optimization via consideration of objective space (projection of design space through aerodynamics) and shows the impact of the corresponding formulation of design requirement to the result of NLF optimization in cases of transonic airfoil and aero engine compressor blade design from two perspectives: Pareto front convergence and improving effect of accessory performance. The paper uses Principal Component Analysis (PCA) to obtain the eigenvectors of objective space to extract the intrinsic information about specific problem. The method is realized in two cases with satisfactory result.  
design requirement; objective space; design objectives; engineering design; natural laminar flow; aerodynamic performance; principal component analysis +
Footnote †: journal: Applied Sciences
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 7 Underwater AIS Relay
### Reducing the Load for Underwater Communications
Using a spreading factor SF = 32, the configuration \(8\times 4\) is better than \(4\times 8\) because it reduces the bit error rate and packet error rate for NOF1 channel. For NCS1 channel, performance is rather similar whatever the configuration. Increase the spreading factor improves the performance. OFDM is not able to provide a reliable link on these channels for the considered SNR.
## 5 Experimental Results  
In this section, the MATLAB R2020b software is used for the calculation experiment to evaluate the performance of the SFLA-uGSA. Firstly, a Design of Experiment (DOE) is carried out to determine the optimal parameter combination. Secondly, the algorithm is tested by a single objective with standard examples. The results are compared with those of other articles, and the effectiveness of this algorithm is obtained. Thirdly, compared with single-objective optimization, the superiority of the algorithm in multi-objective optimization is analyzed. Finally, an example with the setup time and the handling time is designed to analyze the experimental results. The implementation has been performed on a computer with a 2.40 GHz Core (TM) i5 CPU with 16 GB RAM memory.
## 2 Basic Concepts
### The Combined Heat and Power Plant  
The research is designed to evaluate the efficiency of enterprises in the fuel and energy complex. Namely, the study is carried out for combined heat and power plants (CHPPs) of the heating system. Therefore, a description of the CHPPs will be presented below.  
A combined heat and power plant is an enterprise whose products are electricity, as well as heat released in the form of steam and hot water, and the \"raw material\" is organic fuel (coal, gas, fuel oil, etc.) [49]. The equipment of the power plant is used for the economic conversion of chemical energy into electrical energy. Steam is supplied directly for industrial and household needs or is partially used for preheating water in special boilers (heaters), from which water is sent through the heating network to consumers of thermal energy. The steam condensate given to the thermal consumer is returned to the CHPP by a reverse condensate pump.  
The connection of the CHPP to other stations of the power system is carried out at high voltage through step-up transformers.  
Below is the composition of the combined heat and power plant:  
* Fuel economy and fuel preparation system;
* Boiler plant: a combination of the boiler itself and auxiliary equipment;
* Turbine plant: steam turbine and its auxiliary equipment;
* Installation of water treatment and condensate treatment;
* Technical water supply system;
* Ash removal system (for CHP plants operating on solid fuel);
* Electrotechnical equipment and electrical equipment control system.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### SEU Recovery Analysis
#### 2.2.3 SEMNU at Node Pair Q-S0  
If both nodes Q and S0 are simultaneously affected by an SEMNU, resulting in transitions from \"1\" \(\rightarrow\) \"0\" and \"0\" \(\rightarrow\) \"1\", respectively, transistors P5 and N4 are turned on, while P2 is turned off. This causes node S1 to discharge through P5 and N4, leading to a state change from '1' to '0'. Subsequently, P1 turns on and remains active, allowing node Q to be recharged from '0' to '1' via P1 and N1. However, during this process, the internal state of the feedback loop changes, resulting in a corruption of the originally stored data. Hence, if a sufficiently large charge is deposited simultaneously at nodes Q and S0, self-recovery to the original logic state becomes impossible.  
Nevertheless, it is widely recognized that charge sharing between transistors can be effectively suppressed by ensuring a minimum spacing of 1.62 \(\upmu\)m between PMOS transistors and 0.6 \(\upmu\)m between PMOS and NMOS transistors [14]. As shown in Figure 2, the proposed EWS-16T cell layout maintains a spacing of 2.0 \(\upmu\)m between the critical node pair Q and S0, which sufficiently exceeds these minimum requirements, thereby effectively enhancing immunity to SEMNUs at this node pair. Among the six possible double-node upset combinations, five cases--those involving at least one non-sensitive node, such as Q-QB, Q-S1, QB-S0, QB-S1, and S0-S1--are inherently recoverable through the SEU self-correction mechanism. The remaining case, Q-S0, is also addressed at the layout level by ensuring sufficient node separation, thus confirming full robustness against SEMNU-like scenarios in the proposed design.  
Figure 5: Self-recovery behavior of (**a**) node Q and (**b**) node S0.
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Model Catalysts as Tips
#### 3.2.1 Engineering the Surface Composition of Pt-Based Alloys
At 673-773 K, a Pd-rich thin oxide is formed at the surface, which evolves into a thick PdO oxide layer at 873 K. Beneath the PdO layer, a Pd-depleted region is present with an interfacial PtO\({}_{\mathrm{x}}\) layer. Finally, above 973 K, the surface became metallic and Pt-rich. To understand this behavior, the heat of formation of Pt- and Pd-oxides, the formation of volatile oxides and kinetic effects of oxides evaporation was considered. Solid oxide phases are absent, presumably due to volatility, leading to a surface enriched in Pt. This system highlights that oxidation treatment at different temperatures allows tuning of the surface composition of Pt-Pd to be either Pt-rich or Pd-rich, forming two different types of core-shell structures (Figure 5c). Along with Pt-Rh and Pd-Rh, the Pt-Pd example shows a third way of engineering the surface of catalytic materials and highlights the fact that similar conditions can lead to dramatic differences in oxidation behavior. Finally, the oxidation and oxidation/reduction behavior of Pt-Ru and Pt-Rh-Ru samples has been investigated [114]. Pt-8.9 at.% Ru and Pt-23.9 at.% Rh-9.7 at.% Ru specimens have been produced with the same electropolishing method as Pt-Rh specimens. The oxidation of Pt-Ru specimens at 773 and 873 K leads to Ru-enriched surfaces with a Ru/(Ru + Pt) ratio of 22% instead of the initial 8.9%. Beneath this Ru-enriched region is a Ru-depleted region, suggesting bulk diffusion. Inversely, at 973 K, the surface is enriched in Pt with only traces of Ru (composition: 99.6 \(\pm\) 0.3 at.% of Pt and 0.4 \(\pm\) 0.08 at.% of Ru). However, the Ru-depleted region is still present and greater than at lower oxidations temperature, suggesting the volatilization of the Ru species during oxidation. This is not surprising, as Pt and Ru oxides are not stable at high temperatures, and the rate of Ru volatilization is known to be higher. Therefore, from a homogeneous Pt-Ru sample, it is possible to generate a Ru-rich or a Pt-rich (Pt-rich(shell)-Pt/Ru(core) structure to be exact) surface as a function of the temperature of oxidation (Figure 5d), as in the Pt-Pd case. On the Pt-Rh-Ru samples, the oxidation initiates at grain boundary regions with a MO\({}_{2}\) stoichiometry. Further oxidation leads to separate Rh-rich (stoichiometry: (Rh,Ru)\({}_{2}\)O\({}_{3}\)) and Ru-rich (stoichiometry: (Ru,Rh)O\({}_{2}\)) oxide phases, the structure of which is retained upon reduction in hydrogen at low temperature, leading to isolated phases as in the Pd-Rh case (Figure 5e). Analysis of Pt-10.1 at.% Ir [86] using a reaction cell leads to Ir enrichment upon oxidation, as well as competitive Ir and Rh surface segregation in the Pt-17.3 at.% Rh-14.0 at.% Ir alloy, consistent with previous experiments focusing on {111} and {001} facets only [90].