esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Tolerant Control of Multi-Joint Robot Based on Fractional-Order Sliding Mode
## 3 Fractional-Order Sliding Mode Controller Design
### Multi-Joint Robot Model  
This paper takes a double-joint manipulator as an example, and its physical equivalent model is shown in Figure 1.  
Figure 1: Double-joint manipulator model.  
Two joint position angles \(q_{1}\) and \(q_{2}\) are selected as state variables. According to [PERSON]'s theorem, the dynamic equation of the above double-joint manipulator can be obtained as follows [42].  
\[M(q)\ddot{q}+B(q,q)\dot{q}+G(q)=T+\omega \tag{7}\]  
In Formula (7), \(q=[q_{1},q_{2}]^{T}\) is the state variable of the double-joint manipulator, which represents the angle of the joint arm relative to the horizontal axis. \(G(q)\) is the gravity term, \(\omega\) is the external disturbance. \(M(q)\), \(B(q,\dot{q})\), and \(G(q)\) are the positive definite mass matrix, Coriolis force matrix, and gravity term, respectively. The calculation expressions are as follows.  
\[\left\{\begin{array}{l}M(q)=\left[\begin{array}{cc}v+q_{01}+2q_{02}\cos(q _{2})&q_{01}+q_{02}\cos(q_{2})\\ q_{01}+q_{02}\cos(q_{2})&q_{01}\end{array}\right]\\ B(q,\dot{q})=\left[\begin{array}{cc}-q_{02}\dot{q}_{2}\sin(q_{2})&-q_{02}( \dot{q}_{1}+\dot{q}_{2})\sin(q_{2})\\ q_{02}\dot{q}_{1}\sin(q_{2})&0\end{array}\right]\\ G(q)=\left[\begin{array}{cc}15g\cos(q_{1})+8.75g\cos(q_{1}+q_{2})\\ 8.75g\cos(q_{1}+q_{2})\end{array}\right]\end{array}\right. \tag{8}\]  
where parameter \(v=13.3\) is the item relative to the connector of joint1, \(q_{01}=8.98\) is the item relative to the connector of joint2, \(q_{02}=8.75\) are constants related to the joint mass and length, and \(g=9.8\) m/s\({}^{2}\) is the gravitational acceleration.
## 5 Interference Rejection Performance of CRPA Software Receiver
### Multiple Interferers  
In order to examine the rejection capabilities of the software receiver to multiple interferers, six interferers are injected to the data set as indicated by the sequence shown in Table 3.  
Using a four-element array, the maximum number of broadband interferers that can be nulled using spatial adaptation is three. In this scenario, two broadband and four narrowband interferers are injected to examine the performance of STAP. Figure 15 shows the sky plots, antenna gain patterns and angle-frequency responses of four cases. The antenna gain patterns and angle-frequency responses are averaged over all the channels for showing common nulls in the plots. The represented gains are obtained by fixing the elevation angle as 30\({}^{\circ}\).  
Each column in Figure 15 stands for each interferer case. The interferers are shown by red dots in the sky plot. In the first column, there is no interferer, so no obvious null or notch is present. In the second column, when one broadband and one narrowband interferers are injected, one null appear at the azimuth angle 0\({}^{\circ}\) in the gain pattern and one narrowband notch appear at the frequency of 0.42 MHz in the angle-frequency response. In the end, total four narrowband and two broadband interferers are injected. There are two nulls in the direction of broadband interferers and four notches at the frequency of the narrowband interferers. Note that adaptation using the time taps makes notches in  
Figure 14: **(left)** Positioning results of the software receiver without interference. **(right)** Positioning results of the software receiver with interference.  
the frequency domain instead of steering nulls in the spatial domain. It is the reason why the number of interferers which can be rejected is more than the number of elements in the antenna array minus one. Figure 16 shows all-in-view CRPA C/N\({}_{0}\) when adding multiple interferers. All channels continue tracking throughout the injecting sequence of interferers. This validates the CRPA software receiver is capable of mitigating multiple interferers.  
Figure 16: All-in-view CRPA channels C/N\({}_{0}\) under two broadband and four narrowband interferers  
Figure 15: Sky plots and angle-frequency responses of four cases with injected interferers marked by red dots in the sky plots.
## 3 Results and Discussion
### Learning Curves and Model Features
In contrast, the baseline CNN-LSTM model, with 1,307,343 parameters, completed training in 30-45 min under the same conditions, yielding a MAPE of 1.88%. Less computationally intensive models, such as the DNN with 45,000 parameters (training time: 5 min), the standalone CNN with 301,319 parameters (training time: 5-15 min), and the DNN-LSTM with 150,000 parameters (training time: 10-15 min), demanded significantly fewer computational resources. This trend reflects the increased complexity introduced by combining convolutional layers, recurrent LSTM units, and an attention mechanism, which collectively elevate processing demands. Despite the prolonged training duration, the superior predictive performance of the proposed model justifies its computational cost, particularly in applications where precision in electricity demand forecasting is critical. These findings highlight a key trade-off: while simpler models offer faster training and lower resource requirements, the CNN-LSTM with Attention model delivers enhanced accuracy, making it a compelling choice for scenarios prioritizing forecast reliability over computational efficiency.
# Vibration Reduction of a Timoshenko Beam with Multiple Parallel Nonlinear Energy Sinks
## 1 Introduction
[32] optimized the parameters of NESs with both sensitivity analysis and the particle swarm optimization (PSO) method and obtained the best combination of mass and damping ratio of NESs. These studies indicate that parameter optimization is essential for improving the vibration reduction performance of NESs. Moreover, research has shown that optimization for the NESs' locations can effectively improve their vibration reduction performance. [PERSON] et al. [33] investigated the stochastic optimization of multiple parallel NESs when they are attached to a simply supported pipe; both parameters and locations were taken into account. The results showed that stochastic optimization had better robustness than deterministic optimization. [PERSON] and [PERSON] [34] performed parametric studies by varying the parameters and location of the NES; the results showed that the optimal position for the NES attachments is at the antinodes of the linear modes of the plate. [PERSON] et al. [35] investigated the effects of position and damping of the NES on a rotating beam, and the results showed that the optimal position for connecting the NES to a rotating beam is at the beam tip. In the above research, the number of NESs is predetermined. It remains unknown whether there is an optimal number of NESs, and the relationship between the optimal location and number of NESs is not clear. Multi-variable optimization, including the number, location, and parameters of NESs, is expected to achieve a better design. In this work, a short beam coupled with multiple parallel NESs is investigated, and a model is established based on Timoshenko beam theory. The parameter influences of the mass ratio, the damping ratio, cubic stiffness, and the locations of the NESs on vibration reduction performance are revealed. An optimization method is proposed based on a genetic algorithm for the multiple parallel NESs. The main contribution of this work lies in two aspects. (i) A continuum model is established for a beam coupled with multiple parallel NESs based on the Timoshenko beam theory, considering shear deformation; thus, the model applies to short beams. (ii) The number, locations, and parameters of the NESs are optimized simultaneously, and some new design guidelines for vibration reduction are provided based on the optimization results.
# The Wettability and Numerical Model of Different Silicon Microstructural Surfaces
###### Abstract  
Wettability is an important property of solid surfaces and is widely used in many industries. In this work, seven silicon microstructure surfaces were made by plasma immersion ion implantation (PIII) technology. The experimental contact angles and theoretical contact angles of various surfaces were compared, which indicated that the classical theory had great limitations in predicting the static contact angles of complex structures. A parameterized microstructure surface was established by computational fluid dynamics (CFD) with a volume-of-fluid (VOF) model to analyze the reasons for the differences between experimental and theoretical contact angles. Comparing the results of experiments and simulations, it was found that the VOF model can simulate the contact angle of these surfaces very well. The geometrical models of the different microstructures were simplified, and waveforms of the surfaces were obtained.  
silicon microstructure surface; wettability; contact angle; volume of fluid (VOF); numerical simulation +
Footnote †: journal: Applied Physics Letters
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 2 Numerical Simulation of Pile-Supported Embankments
### Model Verification  
As shown in Figure 4a, in order to verify the accuracy of the model, the calculated values of the model were compared with the measured results of the field test. It can be seen that the calculated value of the pile-soil stress ratio is basically the same as the measured value. When the embankment height was greater than 3.5 m, the calculated value of the model was greater than the measured value. The main reason for this is that there are many uncontrollable factors in the field test, so it was impossible to accurately measure the average stress of the soil between the piles and the top of the pile cap. With the increase in the embankment filling height, the stress concentration effect gradually increased, so the difference between the measured value and the calculation gradually increased. In the model calculation, in order to ensure the accuracy of the data, the average stress of the soil between the piles and the top of the pile cap was calculated in different regions. As shown in Figure 4b, according to the pile spacing, the treatment scope of a single pile could be  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Thickness** & **Volume Weight** & \multirow{2}{*}{**E [MPa]**} & \multirow{2}{*}{**Poisson Ratio**} & **Internal Friction** & **Cohesion** \\  & **(Length) [m]** & & **[kh/m3]** & & & **Angle [\({}^{\circ}\)]** & **[kPa]** \\ \hline Pile & 2 & 25 & 20,000 & 0.2 & - & - \\ Geogrid & - & - & 0.5 & 0.2 & - & - \\ Cushion & 0.3 & 20 & 20 & 0.3 & 37 & 6 \\ Embankment & 5 & 18.5 & 15 & 0.35 & 30 & 6 \\ Soft soil & 2 & 0.25 & 4 & 0.34 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters used in finite element model of piled embankment.  
calculated, and the calculation Formula (1) of the pile-soil stress ratio, \(n\), could be obtained by conversion.  
\[\begin{array}{c}n=\frac{\sigma_{\text{p}}}{\sigma_{\text{s}}}\\ \sigma_{\text{s}}=\sigma_{\text{s4}}+2\sigma_{\text{s2}}\end{array} \tag{1}\]  
In the equations: \(\sigma_{\text{p}}\) is the average stress at the top of the pile cap, \(\sigma_{\text{s}}\) is the average stress of the soil between piles within the single-pile treatment scope, \(\sigma_{\text{s2}}\) is the average stress of the soil between two piles, and \(\sigma_{\text{s4}}\) is the average stress of the soil between four piles. The theoretical value of the model was calculated according to BS 8006-1 [2] and the Nordic Geotechnical Group [6]. Since the theoretical basis of each specification was different, the obtained theoretical values were different. By comparison, it can be seen that the variation trend of the calculated value and the theoretical value was basically the same. This proved the rationality of the model.  
Figure 3: Three-dimensional finite element model of geosynthetic-reinforced piled embankment: (**a**) model planar graph; (**b**) model grid partition diagram.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 5 Prototype Fabrication and Measurements
### Prototype Fabrication  
The top view and bottom view of the wrist-worn wireless SpO2 sensor with no top enclosure are shown respectively in Figure 10a,b. In Figure 10c the bottom view of the antenna PCB is depicted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component Label** & **ATC Part Number** & **Value** \\ \hline \(C_{1}\) & ATC 600L 1R5 BT [71] & 1.5 pF \\ \(C_{2}\) & ATC 600L 5R6 BT [71] & 5.6 pF \\ \(L_{1}\) & ATC 0603 WL8R2J [72] & 8.2 nH \\ \hline \hline \end{tabular}
\end{table}
Table 6: List of the component values used in the matching network.  
Figure 10: _Cont_.  
Figure 9: \(\pi\)-type matching network implemented in the sensor device.  
The integrated Antenna Under Test (AUT) has been fabricated on a 1.53 mm Isola 370-HR substrate [73]. Moreover, no soledermask has been deposited on the top of the radiator, where instead an Immersion Gold finish 3-7 \(\upmu\)m is applied. Using this method, a reduction of the dielectric losses associated with the soledermask can be obtained. The matching network is placed close to the antenna feed point in the section highlighted also in Figure 7. The antenna is connected to the PU through two standard six-pin headers. The SpO2 sensor is placed underneath the processing board. The overall structure is secured to the enclosure using screws. The antenna is fed through a 50 \(\Omega\) SMA connector for characterization purposes. From the bottom view, an aperture applied in the enclosure can be noticed. This aperture allows the SpO2 sensor measurements described in Section 3.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 2 MIMO-QSM Transmission System
### ML Detection  
The MIMO transmitted signal is:  
\[\mathbf{r}=\sqrt{\gamma}\mathbf{H}\mathbf{x} \tag{3}\]  
where the transmitted symbol \(\mathbf{x}\) can be split into two real valued signals, \(s_{\Re}\) and \(s_{\Im}\), according to the QSM mapping rule described in Table 1. Since the QAM symbol \(s\) is expanded by the matrix \(\mathbf{H}\in\mathbb{C}^{n_{\mathcal{R}}\times n_{T}}\), (3) can be expressed as:  
\[\mathbf{r}=\mathbf{h}^{(\ell_{\Re})}\cdot\mathbf{s}_{\Re}+j\mathbf{h}^{(\ell_ {\Im})}\cdot\mathbf{s}_{\Im}, \tag{4}\]  
where \(\mathbf{h}^{(\ell_{\Re})}\) and \(\mathbf{h}^{(\ell_{\Im})}\) denote the \(\ell_{\Re}^{\text{th}}\) and \(\ell_{\Im}^{\text{th}}\) columns of \(\mathbf{H}\), respectively, with \(\ell_{\Re}\), \(\ell_{\Im}\in\{1,2,\cdots,n_{T}\}\), and the vectors \(\mathbf{s}_{\Re}=\Re\left\{\mathcal{S}\right\}\) and \(\mathbf{s}_{\Im}=\Im\left\{\mathcal{S}\right\}\) of dimension \(q_{\Re}\) and \(q_{\Im}\), respectively, represent the different real and imaginary parts of the symbols belonging to the \(M\)-QAM constellation \(\mathcal{S}\); finally, the symbol \(\cdot\) represents the product among the \(\ell_{\Re}^{\text{th}}\) and \(\ell_{\Im}^{\text{th}}\) columns of \(\mathbf{H}\) and each element of the vectors \(\mathbf{s}_{\Re}\) and \(\mathbf{s}_{\Im}\).  
Assuming that the receiver had perfect channel state information, the ML estimation compared the distance between the received signal and all possible received signals. The ML criterion is defined as:  
\[\hat{\mathbf{x}}=\underset{\mathbf{x}\in\mathcal{X}}{\text{argmin}}\parallel \mathbf{y}-\sqrt{\gamma}\mathbf{H}\mathbf{x}\parallel_{F}^{2}, \tag{5}\]  
where \(\|\|_{F}^{2}\) is the Euclidean distance among two vectors and \(\mathcal{X}\in\mathbb{C}^{n_{T}\times 2^{\text{mQSM}}}\) is the full spatial modulation QSM used in the transmitter; therefore, the ML detector jointly estimated the two possible active Tx antenna indices, \(\hat{\ell}_{\Re}\) and \(\hat{\ell}_{\Im}\), and the corresponding real valued signals \(\hat{s}_{\Re}\) and \(\hat{s}_{\Im}\). Therefore, (5) can be written as:  
\[\left[\hat{\ell}_{\Re},\hat{\ell}_{\Im},\hat{s}_{\Re},\hat{s}_{\Im}\right]= \underset{\ell_{\Re},\ell_{\Im},\ell_{\Im},\ell_{\Im},s_{\Im}}{\text{argmin}} \|\mathbf{y}-\mathbf{h}^{(\ell_{\Re})}\cdot\mathbf{s}_{\Re}-j\mathbf{h}^{( \ell_{\Im})}\cdot\mathbf{s}_{\Im}\|_{F}^{2} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **QAM Symbol** & **Position Antenna** & **Output Vector** \\
**a** & \(\boldsymbol{s}\) & \(\boldsymbol{(\ell_{\Re},\ell_{\Im})}\) & \(\boldsymbol{x_{i}}\) \\ \hline
0000 & \(1+j\) & \((1,1)\) & \(1+j\), 0 \\
0001 & \(1+j\) & \((1,2)\) & \(1,+j\) \\
0010 & \(1+j\) & \((2,1)\) & \(j,+1\) \\
0011 & \(1+j\) & \((2,2)\) &
# Physics-Based Vehicle Simulation Using PD Servo
## 7 Conclusions  
In this paper, we introduced a novel system for physics-based vehicle simulation from input trajectory. While previous approaches focus on representing large-scale traffic flow, the proposed system approximates the physical movements of a real vehicle using a PD servo and deforms its shape to generate convincing interaction with other objects. Using the path library, the system avoids expensive simulation calculation and creates responsive vehicle scenes where multiple vehicles run on the specified paths, change lanes, pass each others, and collide with other objects. Utilizing camera data for an input trajectory, such a responsive simulation is useful for reproducing real traffic scenes such as reenacting a car accident for review. In the proposed system, the overall simulation process is fully automated and designed for a general user who is not familiar with the dynamic properties of moving vehicles.  
The current system archives the simulated results into a library to reduce the optimized trajectory search time. As the size of the library grows larger, there can be many similar trajectories stored in it, which can reduce the search time. By comparing the shape similarity between the trajectories, we plan to remove any redundant trajectories and maintain the optimal size of the library. In addition, the weight skinning method used for the deformation only approximates the mesh surface shape. For better visual quality, a more sophisticated method can be applied for the deformation process. Finally, the proposed system mainly attempts to generate traffic scenes with several vehicles. Its performance can degrade for large traffic scenes with tens or hundreds of vehicles. For such large-scaled simulation, vehicles on input trajectories can be parallelized to expedite the optimization process. Handling a dynamic trajectory to generate extreme scenes like car acrobatics is also an ongoing work.  
Figure 16: Input trajectory estimated from GPS data extracted from dashboard camera.  
**Author Contributions:** Conceptualization, D.K. and J.J.; Data curation, D.K. and J.J.; Formal analysis, D.K., J.J., S.-w.K., T.K., and Y.K.; Funding acquisition, T.K.; Investigation, J.J. and Y.K.; Methodology, D.K., J.J., and Y.K.; Project administration, T.K.; Software, J.J. and S.-w.K.; Supervision, T.K. and Y.K.; Validation, S.-w.K.; Visualization, D.K.; Writing--original draft, D.K. and Y.K.; Writing--review and editing, Y.K.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(NRF-2017R1D1A1B03034889) and by Institute for Information & communications Technology Promotion (IITP) grant funded by Korea government (MSIT) (No. 2019-0-01849, Development of Core Technology for Real-Time Image Composition in Unstructured In Outdoor Environment).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Protein disulfide isomerase (PDI) is a member of the thioredoxin superfamily and is a multifunctional enzyme that catalyzes rate-limiting reactions such as disulfide bond formation, isomerization, and reduction in the endoplasmic reticulum (ER) [1, 2]. In addition, PDIs also act as molecular chaperones which assist the refolding of denatured proteins without disulfide bonds, such as citrate synthase [3, 4], and their molecular structure and biological activity have been well characterized in yeast, humans, and some parasitic protozoa [5, 6].  
A typical PDI consists of four domains, including two thioredoxin-like catalytic domains (a and a') each containing a Cys-Gly-His-Cys (CGHC) active site motif and two non-catalytic domains (b and b') [7]. Moreover, the PDI contains a signal peptide at the N-terminus and the ER retention signal is located in the small C-terminal domain (c). While some PDI gene members do not contain ER sequences, it has been noted that the amino acid composition of some ER sequences differs [8]. PDI also plays an important role in host-pathogen interactions [9]. For example, studies have reported higher expression of a PDI gene (LmPDI) in the parasitic _Leishman major_ indicating that PDI expression is correlated with infection and might be a causative agent [10]. A PDI gene (PpPDI1) from _Phytophthora parasitic_ contributes to plant infection and causes necrosis of tobacco leaves when transiently expressed in _Nicotiana benthamiana_[11]. HsPDI, a candidate effector, is involved in the interaction between _Heterodera schachtii_ and host plants and plays an important role in protecting _H. schachtii_ from the harm of reactive oxygen species (ROS) released by host plants [12].  
_Meloidogyne graminicola_ is one of the most important plant-parasitic nematodes (PPN) that attacks rice. It is also a quarantine pest that has caused substantial destruction in most rice-growing countries [13; 14; 15]. Studies of the interaction between rice and _M. graminicola_ revealed that rice enacts defense responses during nematode infections. It was observed that basal defense in rice increased during \(\beta\)-aminobutyric acid-induced resistance against _M. graminicola_, which includes the expression of genes related to hormones, ROS generation, and callose deposition [16]. Recent studies suggested that the effectors secreted by _M. graminicola_ can interfere with the host immune responses such as the effectors MgGPP and MgMO237 observed to promote the _M. graminicola_ parasitism by inhibiting cell death and manipulating rice basal immunity, respectively [17; 18].  
Recently, a PDI in _M. graminicola_ (MgPDII) was reported to be involved in protection against oxidative damage as a nematode effector [19]. In this study, we characterized a second PDI from _M. graminicola_ (MgPDI2) via analyses of the expression pattern, enzymatic activity, physiological effects associated with gene silencing and necrosis induction in _N. benthamiana_. Together, our data indicate that MgPDI2 contributes to _M. graminicola_ infection.