esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SBW Plant Dynamical Modeling
### Dynamical Modeling of the Nominal SBW Plant with Uncertainties
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Symbols** & **Descriptions** & **Values** & **Units** \\ \hline \(J_{e}\) & Equivalent inertial moment of the SBW system & 85.5 & \(\text{kgm}^{2}\) \\ \(B_{e}\) & Equivalent viscous damping friction of the SBW system & 218.8 & Nms/rad \\ \(\bar{\zeta}_{f}\) & Coulomb friction constant & 4.2 & Nm \\ \(\kappa_{1}\) & Scale factor to account for transmitting from the linear motion of the rack to the steering angle of front wheels & 6.0 & - \\ \(\kappa_{2}\) & Gear ratio between the pinion and rack system & 3.0 & - \\ \(\kappa_{3}\) & Gear ratio of the gear head & 8.5 & - \\ \(\kappa_{4}\) & Scale factor accounting for converting from the input voltage of steering motor to the output torque of the steering motor & 1.8 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Value of the SBW system. Regarding the model uncertainties in (2) with external disturbances \(d(t)\) that the SBW plant dynamic is subject to, the differential motion equation of this SBW plant in (1) can be expressed as  
\[(J_{\text{e0}}+\Delta J_{e})\dot{\theta}_{s}+(B_{\text{e0}}+\Delta B_{e})\dot{ \theta}_{s}=\kappa u-(\xi_{f0}+\Delta\xi_{f})\text{sign}(\dot{\theta}_{s})- \tau_{\text{sel}}+d(t) \tag{5}\]  
which can be concisely rearranged to have  
\[\ddot{\theta}_{s}=-\frac{B_{\text{e0}}}{J_{\text{e0}}}\dot{\theta}_{s}+\frac{ \kappa}{J_{\text{e0}}}u+D(\theta_{s},t) \tag{6}\]  
where  
\[D(\theta_{s},t)=\frac{1}{J_{\text{e0}}}[-\xi_{f0}\text{sign}(\dot{\theta}_{s} )-\Delta J_{e}\dot{\theta}_{s}-\Delta B_{e}\dot{\theta}_{s}-\Delta\xi_{f} \text{sign}(\dot{\theta}_{s})-\tau_{\text{sel}}+d(t)]\]  
and \(D(x,t)\in\mathbb{R}\) represents the lumped disturbance, including model uncertainties, external disturbances, etc. We define \(x_{1}=\theta_{s}\) and \(x_{2}=\dot{\theta}_{s}\) to be the SBW system states; thus, the state space form of the nonlinear second-order perturbed systems SBW modeled in (6) is as follows:  
\[ \begin{cases}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=F_{0}(t,x)+b_{0}u++D(x,t)\\ y=x_{1}\end{cases}  \tag{7}\]  
with  
\[F_{0}(x,t)=\frac{B_{\text{e0}}}{J_{\text{e0}}}\dot{\theta}_{s}=a_{20}\dot{ \theta}_{s};\text{ and }b_{0}=\frac{\kappa}{J_{\text{e0}}}\]  
where vector \(x=[x_{1},x_{2}]^{T}\in\mathbb{R}^{2}\) represents the system states, \(y\in\mathbb{R}\) stands for the output of the SBW plant, \(u\in\mathbb{R}\) is the AADRC input to be designed, and \(F_{0}(x,t)\in\mathbb{R}\) is the nominal dynamic of the SBW system.
## 3 Results and Discussion
### Instance-Level Feature Importance Analysis  
In this section, the traditional M-FIA method is first discussed. An RF model is constructed with 100 trees and trained by adopting a warm-start to speed up the training. The accuracy of the trained RF is 96.0%. Figure 4 illustrates the results of impurity-based M-FIA from RF. The calculation time of M-FIA is 0.019 s. In the M-FIA, \(Q_{AC42}\), \(Q_{AC27}\), \(P_{AC29}\) are at the top. The information of which feature contributes the most to the model prediction is given. However, this is just an overall analysis, rather than focusing on a specific instance or OC. Compared with the traditional method, our proposed I-FIA for DSA by SHAP has higher practical value. The detailed analysis is divided into local interpretation and global interpretation.  
Figure 3: The single line diagram of the CEPRI36 benchmark system.
## 3 Results and Discussion
### Functionalization and Characterization of Gold Surfaces  
Ultra-flat gold surfaces prepared according to [PERSON] and coworkers [28] were functionalized with four alkanethiols in order to obtain SAM (see Section 2.2.1 and Table S1) with different charge distribution (i.e., negative, positive or neutral) and hydrophobicity. MCOOH, MOH, MNH\({}_{2}\) and M11 CH\({}_{3}\), which share the same alkane chain, and carry differing terminal groups, were selected. In this way the formation at physiological pH of negatively charged SAM (MCOOH) or positively charged (MNH\({}_{2}\)) or even neutral but hydrophilic (MOH) or hydrophobic (M11 CH\({}_{3}\)) SAM was possible at physiological pH. Functionalized surfaces were first of all characterized by AFM for the possible change in morphology with respect to the bare gold surface and by XPS for the change in chemical composition (Table 1).  
Ultra-flat gold surfaces prepared according to Section 2.2.1, presented large flat areas, ranging from hundreds of nanometers to a few \(\upmu\)m, with surface average roughness \(S_{a}\) equal to 0.15 \(\pm\) 0.01 nm (Table 1). After functionalization, the original gold substrate morphology changed, showing objects with an height of 1-2 nm, while some higher peaks were also present. The average roughness of the four surfaces (Table 1) is however very similar to gold, i.e., very low and therefore suitable for the direct observation of ribosomes (expected height around 10 nm, imaged in air [25]) and nucleic acids by AFM (expected height about 1 nm [29]).  
The functionalized surfaces were also characterized from the chemical point of view and compared to the nonfunctionalized gold surface (Table 1). As expected, the bare gold surface present a minor contamination of hydrocarbon and oxygen, as expected, while sulfur is present in all the four functionalized surfaces, witnessing the successful SAM formation. Moreover, oxygen and carbon increase on the MCOOH surface and nitrogen appears only when gold is modified by MNH\({}_{2}\), as expected. In addition, the chemical composition of MOH or M11 CH\({}_{3}\) surfaces reflects the presence of the respective SAM.  
The morphological and chemical characterization of the functionalized surfaces confirm not only the presence of the desired SAM but also the SAM smoothness, which are required for the AFM analysis of biological macromolecules.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 5 Discussion
### Insights from Ablation Studies  
The ablation study provides valuable insights into the contributions of different components and feature types to the EOR performance. The component ablation study results demonstrate that combining SDF and SSC achieves the highest accuracy across translation, rotation, and IoU dimensions. Removing both components severely impacts performance, particularly in the z-axis and yaw angle, emphasizing their importance. SDFis more effective for z-axis accuracy due to its analysis of spatial object distribution, while SSC significantly enhances rotation accuracy by enforcing geometric consistency.  
The feature type ablation study results indicate that lane markings alone provide reasonable localization accuracy but are insufficient for longitudinal alignment. Adding poles significantly enhances overall accuracy, especially at intersections, due to their spatial stability. Combining lane markings with traffic signs leads to higher z errors and a lower IoU, indicating that traffic signs are less effective for robust registration compared to poles. These findings emphasize the importance of a well-balanced combination of feature types, such as lane markings, poles, and traffic signs, for optimal localization performance.
## 4 Some Possible Reasons to Explain the Strength Recovery upon Reactivation
### Thixotropy  
If ageing takes place under constant volume and soil composition, the subsequent increase in strength and stiffness is classified as a thixotropic effect [47; 48; 49]. As stated by [PERSON] [49], if a thixotropic material is subjected to a constant shearing speed, its viscous resistance will decrease until stabilisation at a steady value. If shearing is stopped and an ageing cycle is carried out, the measured strength at reactivation will be initially higher, but will then decrease again towards the steady value previously attained. The recovered initial strength mainly depends on the time spent in the rest condition.  
Thus, thixotropic hardening is a time-dependent processes that may explain some increase in strength at reactivation after ageing has occurred at constant volume.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**v (mm/min)** & \(\tau_{\text{reac }}\) (kPa) & \(\tau_{\text{r}}\) (v) (kPa) & \(\tau_{0}\) (kPa) & \(\tau_{\text{reac}}\)/\(\tau_{\text{r}}\)(v) & \(c_{\text{reac }}\) (kPa)/(mm/min) & \(\beta\) & \(\delta\) \\ \hline
0.018 & 36.50 & 35.70 & 34.30 & 1.022 & 122.22 & 77.78 & 1.10 & 1.30 \\ \hline
0.089 & 39.70 & 36.30 & 34.30 & 1.094 & 60.67 & 22.47 & 0.55 & 1.28 \\ \hline
0.445 & 43.40 & 37.00 & 34.30 & 1.173 & 2
## 5 Experimental Implementation and Results  
In order to examine the robot navigation experimentally, an indoor office room is chosen. The dimensions of this room are 863 cm \(\times\) 406 cm. The blue rectangle, which is shown in Figure 4a, is the working area of the 4 OWMR. The working area is divided into small squares 10 cm \(\times\) 10 cm for capturing the actual path. Different static obstacles are added to this map in different configurations to test the robot's maneuverability and the applied path-planning technique.  
The generated paths are additionally optimized by using a multi-objective genetic algorithm [39] to minimize the path length and the number of turns of the 4 OWMR. The multi-objective optimization problem is converted to a single-objective optimization process by using a weighted sum, as shown in Equation (2).  
\[fitness=(A\times f_{1})+(B\times f_{2}) \tag{2}\]  
where \(A+B=1\), \(f_{1}=1/\)path and \(f_{2}=\)C/S  
\(fitness\) is the total fitness function, \(f_{1}\) is the fitness function of the path length, \(f_{2}\) is the fitness function of path smoothness, and \(A\) and \(B\) are the weights of the two fitness functions. C is the precision coefficient, and S is the smoothness of the path. \(f_{1}\), \(f_{2}\), and C are controlled to maintain the same magnitude order.  
The fitness function's responsibility is to rank the chromosomes for the motion problem. Any specific path would be composed of n waypoints. Hence, the path length is expressed as \(path\), as shown in Equation (3).  
\[path=\sum_{i=1}^{n-1}\sqrt{(x_{i+1}-x_{i})^{2}+(y_{i+1}-y_{i})^{2}} \tag{3}\]  
Figure 4: (**a**) The proposed indoor map for experimental validation of path planning techniques on the 4 OWMR (**b**) An example of an inflated map.  
Figure 5: These three maps are used in the validation of the experimental work; the static obstacles are marked in red.  
The generated path has a travel distance = 1.31 \(\times\) 10\({}^{2}\) cm, an average velocity of 0.1189 m/s, a travel time of 13.621 s and a maximum acceleration of 0.141 m/s\({}^{2}\). The real final point is varied from the planned as the absolute error point on the X- and Y-axes and is 0.3 cm and 2.1 cm, respectively.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Single Node Upset Recovery Analysis  
#### 2.5.1 Single Event Transient Occurs at Node Q  
In the case of an SNU, only five sensitive nodes (Q, QB, S0, S1, S3) are considered. In Figure 4, the recovery process of the five sensitive nodes (Q, QB, S0, S1, S3) is presented. Node S2 stores a strong '0', and the possibility of it being '1' is not considered, as it is surrounded by NMOS transistors. When a SET occurs at node Q, Q changes from '1' to '0'. This activates transistor P3. Since the other nodes are unaffected by the SET at Q, they maintain their original values. As a result, the SEU that occurred in Q does not affect S3, and N4 remains activated. Since the width of the transistor N4 is larger, QB maintains '0' despite the influence of P3. Therefore, QB activates P4. Since S0 is storing '0', N5 is deactivated. Ultimately, Q is restored to '1' through P4.  
Figure 4: Single node upset recovery when SET occurs at node (**a**) Q (**b**) QB (**c**) S0 (**d**) S1, and (**e**) S3.
## 2 Numerical Simulations and Wind Tunnel Tests
### Computational Domain and Boundary Condition Setting  
The scale ratio of numerical simulation is 1:1000, and ANSYS FLUENT 19.0 is used to carry out the numerical simulation of the downburst over a three-dimensional hill. To reduce computational cost, a one-fourth circle is used to model the computational domain, as shown in Figure 2. The outlet diameter is D = 600 mm, and the distance from the outlet nozzle to the ground is H = 1.0D = 600 mm. The center of the jet to the farthest radial distance position is 7.0D, and the outlet wind velocity, \(u_{j}\), is 10 m/s. In the numerical simulation, the hill model is placed at \(r/h\) = 0 (where \(r\) represents the radial coordinate, and \(h\) represents the height of the hill model) and at a circular position of 45\({}^{\circ}\). The hill model is quadratic, as shown in Equation (10).  
\[z=h\biggl{(}-\frac{\bigl{(}x^{2}+y^{2}\bigr{)}}{L^{2}}+1\biggr{)} \tag{10}\]where \(L\) represents the horizontal distance from the foot of the hill to the hilltop and \(x\) and \(y\) represent the distances to the center of the hill. In this study, two hill models of different sizes are employed. The Quad-D300-H075 hill model has a diameter \(d_{h}\) of 300 mm and a height \(h\) of 75 mm. The Quad-D176-H075 hill model has a diameter \(d_{h}\) of 176 mm and a height \(h\) of 75 mm.  
As shown in Table 1, the velocity inlet condition is used for the inlet, the pressure outlet condition for the outlet and top, the symmetric boundary condition for the sides, and the no-slip wall condition for the bottom and wall of the computational domain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Position** & \multicolumn{2}{c}{**Boundary Conditions**} \\ \hline \multirow{2}{*}{Inlet} & \multirow{2}{*}{Velocity inlet} & Inlet wind velocity \(u_{j}=10\) m/s \\  & & Turbulence intensity = 1\% \\  & & Inlet diameter D = 600 mm \\ Outlet and top & Pressure outlet & Turbulence intensity = 1\% \\ Sides & Symmetry & \(v=0,\partial(u,\ v,\ w,\ k,\ \epsilon)/\partial z=0\) \\ Bottom & No-slip wall & \(u=v=w=0,\partial(u,\ v,\ w,\ k,\ \epsilon)/\partial z=0\) \\ Wall & No-slip wall & \(u=v=w=0,\partial(u,\ v,\ w,\ k,\ \epsilon)/\partial z=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions settings.  
Figure 2: (a) Computational domain and boundary conditions; (b) definition of circular coordinates and profile of 45\({}^{\circ}\) in circular direction.
## 2 Materials and Methods
### Processing Software
#### 2.2.2 ZPD Retrieval
Many processing parameters were left at their default values [30]. The cut-off of the satellite observation angle was 10 degrees to achieve higher mitigation of reflections and interference, according to [35], which was valid for processing in the absence of weighting functions of satellite elevation. The ZPD values used in this work were obtained using the final orbits produced by CODE (Center for Orbit Determination in Europe) and downloaded from [36]. Specifically, the multi-GNSS orbit and clock solution (COM) were used, produced by the CODE analysis center for the IGS within the framework of the Multi-GNSS Experiment (MGEX). The selected products contained the position and clock of the GPS, GLONAS, Galileo, QZSS and BeiDou satellites at a resolution of 300 s for the orbits and 30 s for the clock [37]. The implemented processing system on a daily basis produced the ZPD at a 30 s time interval (the same as clock solutions) for each GNSS station whether fixed or moving (as listed in Table 1). The Saastamoinen model [38] was used in combination with the mapping function VMF1 (Vienna Mapping Function 1) [39] for the first guess estimation of the tropospheric hydrostatic delay. The SRIF recursive filter was adopted for random walk estimation of the zenith wet delay (ZWD): the first guess associated variance was 10 m, and the subsequent time variance was about 2 mm h\({}^{-1/2}\). The surface air temperature and pressure needed by the Saastamoinen model were given by the meteorological stations installed close to the GNSS receivers in each ship-based or fixed installation. When occasionally missing, such information was derived from GPT 3.5 gridded data (Global Model of Pressure and Temperature) [40]. All ancillary data were automatically downloaded during the processing.
# Recent Advances in Optical Detection of Aminoglycosodies
## 4 Other Optical Assays
### Resonance Rayleigh Scattering  
Resonance Rayleigh scattering (RRS), determined by using a commonly used fluorescence meter, has been applied to determine biomolecules and inorganic ions [81]. Metal NPs have RRS effects and the resonance scattering peak is correlated with the size of the NPs. A higher signal is obtained with a larger size of NPs. Cu\({}_{2}\)O nanocubes have been reported to have a good RRS response at 370 nm. Well-dispersed Au NPs can catalyze the Fehling reaction in which Cu\({}^{2+}\) is reduced by glucose to form small particles of Cu\({}_{2}\)O, while the aggregated Au NPs do not have such effects. Based on the recognition of aptamers and the salt-induced aggregation of Au NPs, the in situ generation of the Cu\({}_{2}\)O strategy was employed for the RRS detection of tobramycin with a LOD of 0.19 nM [82]. RRS energy transfer (RRS-ET) refers to the resonance energy transfer process between the RRS and the acceptor. [PERSON]'s group established an RRS-ET method for the detection of neomycin. neomycin could induce the formation of blue-black Cu(II)-NEO complexes with a strong absorption peak at 350 nm, which acted as an acceptor to adsorb the scattering light from Cu\({}_{2}\)O nanocubes. The RRS signal of Cu\({}_{2}\)O at 370 nm decreased with the increase of neomycin concentration. The detection limit was 0.4 uM [83].
## 3 Proposed System  
### An Outline of the Proposed Technique--PMIPv6/AAND  
The proposed technique, PMIPv6/AAND, would introduce a novel Multiple Virtual Interface (MVI) technique in PMIPv6 to enable multi-homing. The technique supports effective inter-technology handover and solves the LL-ID swapping problem described in further sections. Additional flag fields, auxiliary advertisement of neighbor detection (AAND)) fields, i.e., 'D' and 'R' are displayed in PMIPv6 message format. The new technique is known as PMIPv6/AAND, adapted from F-PMIPv6. The message format of this proposed paper is designed based on the paper [9].  
Figure 3: Limitation of Single virtual Interface.
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 3 Experimental Results and Analysis
### Single Defect Inverse Imaging  
In the 320 mm \(\times\) 320 mm square region, white represents air, the relative dielectric constant is set to 1 and the air impedance is set to 120, with 100 \(\times\) 100 imaging resolution. The yellow region represents the two-dimensional cross section of xylem, which is shaped as a circle with a radius of 10 m. The relative dielectric constant is set to five. The gray area represents the defect, which is the circle of the radius 20 mm, and the relative dielectric constant of the defect is set to 40.  
Figure 4 shows the inversion imaging effect of each algorithm when the defect is in the center of the xylem. The boundary transition phenomenon in the inverse map of the contrast source is serious, which makes the inverse defect area larger than the defect area set in the model, and the _IOU_ value is 0.885. The BP neural network has a better inversion of the defect size and location, and the _IOU_ value reaches 0.964, but the boundary between wood and air in the inverse map is not clear enough. The model-driven deep learning inverse map has the highest _IOU_ value of 0.977, which not only inverts the defect size and location but also clearly inverts the media boundary between wood and air.  
In order to express the relationship between the output data of the model result and the true value, the mean square error is introduced:  
\[C(\omega,b)\equiv\frac{1}{2n}\sum_{x}\|y(x)-a\|^{2} \tag{38}\]  
The weight set of network connections is expressed as \(\omega\), offset to \(b\). The total amount of input data is \(n\). When the input is \(x\), the output vector of the last layer is \(a\). \(C(\omega,b)\) is the average generation value obtained from the total input data.  
Table 3 shows the average detection time of each algorithm and the maximum mean square error of the relative permittivity in the inverse map of each algorithm versus the relative permittivity in the model. Among the algorithms, the CSI has the largest mean squared error and the model-driven deep learning network has the smallest mean squared error. In terms of time cost, the CSI takes the longest time for a single detection, reaching more than 100 s; the BP neural network and the model-driven deep learning network require the shortest detection time, both around 0.06 s.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 3 Intelligent Lighting Control System Design
### Lighting Control Algorithms  
This section introduces a control algorithm that utilizes natural lighting and occupancy as inputs, with the dimming factor K serving as the output. During the daytime, we suppose six luminaire units in office turned on at the maximum power (the dimming factor K = 1). The occupancy sensor detects both occupied and unoccupied work planes. The work plane expected the illuminance to be 500 lx when occupied (\(\mathrm{E_{o}}\)), and 300 lx when unoccupied (\(\mathrm{E_{u}}\)). The mapping relationship between the level of natural light level, the occupancy status, and the expected illuminance of the work plane is outlined below:  
\[S_{n}^{o}=E_{all}\times\frac{E_{o}}{E_{n}}, \tag{4}\]  
\[S_{n}^{u}=E_{all}\times\frac{E_{u}}{E_{n}}, \tag{5}\]  
where \(S_{n}^{o}\) represents the target illuminance when the n\(-\)th work plane is occupied, \(S_{n}^{u}\) denotes the target illuminance when the n\(-\)th work plane is unoccupied, and \(\mathrm{E_{all}}\) is the average illuminance throughout the office, while \(\mathrm{E_{n}}\) refers to the illuminance measured by the lighting sensor at each work plane:  
\[\sum_{j=1}^{n}S\big{(}x_{j},y_{j}\big{)}=\sum_{j=1}^{n}\big{[}K_{i}E_{i} \big{(}x_{j},y_{j}\big{)}\big{]}\big{(}1\leq i\leq n\big{)}, \tag{6}\]  
where \(\mathrm{S}\) (\(\mathrm{x_{j}}\), \(\mathrm{y_{j}}\)) represents the total illuminance at the point. \(\mathrm{E_{i}}\) (\(\mathrm{x_{j}}\), \(\mathrm{y_{j}}\)) denotes the illuminance contribution from luminaire I at the same point (\(\mathrm{x_{j}}\), \(\mathrm{y_{j}}\)), and \(\mathrm{K_{i}}\) is the dimming factor of luminaire \(i(0\leq K\leq 1)\).  
By integrating Equations (4)-(6), a function model can be established that connects the user's expected illuminance, the natural light level, the occupancy status of office workers, and the dimming coefficient K.  
\[\left\{\begin{aligned} & E_{1}(x_{1},y_{1})K_{1}+E_{2}(x_{1},y_{1})K_{2}+ \cdots+E_{n}(x_{1},y_{1})K_{n}\\ & E_{1}(x_{2},y_{2})K_{1}+E_{2}(x_{2},y_{2})K_{2}+\cdots+E_{n}(x_{ 2},y_{2})K_{n}\\ &\vdots\\ & E_{1}(x_{n},y_{n})K_{1}+E_{2}(x_{n},y_{n})K_{2}+\cdots+E_{n}(x_ {n},y_{n})K_{n}\\ \end{aligned}\right\}=\left\{\begin{aligned} & S_{1}^{\circ}\\ & S_{2}^{\circ}\\ &\vdots\\ & S_{n}^{\circ}\\ \end{aligned}\right\}(0\leq K_{1},K_{2},\cdots,K_{n}\leq 1), \tag{7}\]