esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Operation and Modelling of the Bearingless Switched Reluctance Motor
### Rotor Modelling (Suspension Control)  
The suspension forces required for the levitation of the rotor in both the X and Y directions are given below at the standstill position:  
\[Fx=m\frac{d^{2}x}{dt^{2}}+kx \tag{1}\]  
\[Fy=m\frac{d^{2}y}{dt^{2}}+ky+mg \tag{2}\]  
Here, \(X_{1}\), \(X_{2}\), \(Y_{1}\) and \(Y_{2}\) are chosen as the state variables from Equations (1) and (2). Hence, the desired tracking rotor displacement states are modelled as:  
\[\begin{array}{c}\overset{\bullet}{X}_{1}=X_{2}\ ;\overset{\bullet}{Y}_{1}=Y_{2}\\ \overset{\bullet}{X}_{2}=-\frac{k_{x}}{m}X_{1}+\frac{F_{x}}{m}+F_{dx}\ ; \overset{\bullet}{Y}_{2}=-\frac{k_{y}}{m}Y_{1}+\frac{F_{y}}{m}+F_{dy}+g\end{array} \tag{3}\]  
The net suspending forces produced in the X-Y directions are written in electrical equivalence, as given in Equation (4):  
\[\left[\begin{array}{c}F_{x}\\ F_{y}\end{array}\right]=\left[\begin{array}{cc}K_{\text{xxp}}&K_{\text{xyp}} &K_{\text{xxn}}&K_{\text{xyn}}\\ K_{\text{yxp}}&K_{\text{yyp}}&K_{\text{yxn}}&K_{\text{yyn}}\end{array}\right] \left[\begin{array}{c}i_{\text{xp}}{}^{2}\\ i_{\text{yp}}{}^{2}\\ i_{\text{xn}}{}^{2}\\ i_{\text{xn}}{}^{2}\end{array}\right] \tag{4}\]  
By equating the above Equations (3) and (4), we get the following Equations (5) and (6):  
\[F_{x}=m\frac{d^{2}x}{dt^{2}}+kx=[K_{X}][I_{x}] \tag{5}\]  
\[Fy=m\frac{d^{2}y}{dt^{2}}+ky+mg=[K_{Y}][I_{Y}] \tag{6}\]  
where \(K_{X}=diag\!\left[\begin{array}{ccc}Kxxp&Kxyp&Kxxn&kxyn\end{array}\right]\), \(K_{Y}=diag\!\left[\begin{array}{ccc}Kyxp&Kyyp&Kyxn&Kyyn\end{array}\right]\) and \(I_{x}=\left[\begin{array}{c}I_{\text{xp}}{}^{2}\\ I_{\text{ym}}{}^{2}\end{array}\right]\) and \(I_{Y}=\left[\begin{array}{c}I_{\text{yp}}{}^{2}\\ I_{\text{ym}}{}^{2}\end{array}\right]\).  
The equivalent desired tracking state space equations for rotor displacements are given as follows in Equation (7):  
\[\left[\begin{array}{c}\overset{\bullet}{X}_{1}\\ \overset{\bullet}{X}_{2}\\ \overset{\bullet}{Y}_{1}\\ \overset{\bullet}{Y}_{2}\end{array}\right]=\left[\begin{array}{cccc}1&0&0&0 \\ -\frac{k}{m}&0&0&0\\ 0&0&1&0\\ 0&0&-\frac{k}{m}\ -\ g&0\end{array}\right]+\left[\begin{array}{cccc}0&0&0&0 \\ Kxxp&Kxyp&Kxxn&Kxyn\\ 0&0&0&0\\ Kyxp&Kyyp&Kyxn&Kyyn\end{array}\right]\times\left[\begin{array}{c}i_{\text{ xp}}{}^{2}\\ i_{\text{yp}}{}^{2}\\ i_{\text{xn}}{}^{2}\\ i_{\text{xn}}{}^{2}\end{array}\right] \tag{7}\]
# Research on Vibration Reduction in the Transmission System of Plastic Centrifugal Pump
## 5 Conclusions  
1. Key hydraulic parameters for the plastic centrifugal pump were determined. An equivalent model of the pump shaft transmission system was developed, and an undamped forced vibration model was analyzed, revealing the system's inherent frequency to be 151.84 Hz, with an amplitude of 1.4 \(\times\) 10\({}^{-3}\) rad and a phase angle of either 0 or \(\pi\).
2. The introduction of a silicone oil damper into the pump shaft transmission system transformed it into a damped forced torsional vibration model. Analysis identified an optimal damping value of 0.22 for the system. The silicone oil damper's structural design was completed, and its 3D model and 2D engineering drawings were produced. Experimental analysis demonstrated that the optimal damping value of the silicone oil damper is 0.22, reducing the vibration frequency by 10.45% and the amplitude by 49.29%, and thus achieving excellent damping effects.
3. Employing a single-span cantilever structure for experimentation, the deflection values of the cantilevered rotor were recorded. Polynomial fitting methods were used to derive the speed-deflection mathematical expression and curve under the first critical speed, resulting in an ideal speed-deflection relationship.  
Conceptualization, L.Z., L.T. and Q.W.; methodology, L.Z.; software, Q.W.; validation, Q.W., L.Z. and L.T.; formal analysis, L.T.; investigation, L.Z.; resources, L.Z.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.T.; visualization, L.T.; supervision, L.T.; project administration, L.T.; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript.  
The Open Research Fund of AnHui Key Laboratory of Detection Technology and Energy Saving Devices (Project No: JCKJ2022B02).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **SSE** & **R-Squared** & **RMSE** \\ \hline
1 & 10850.87 & 0.493733 & 17.60752 \\
2 & 4424.173 & 0.793582 & 11.40714 \\
3 & 2504.61 & 0.883143 & 8.711904 \\
4 & 2428.948 & 0.886673 & 8.712326 \\
5 & 1687.732 & 0.921256 & 7.378547 \\
6 & 1181.688 & 0.944866 & 6.276112 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of R-squared parameters for each polynomial order.
## 3 Proposed Controller Design and Stability Analysis
### Construction of the AESO Estimator  
In this subsection, the novel AESO is designed for estimating the unmeasured states and the lumped disturbances, \(\zeta(x,t)\). Defining \(\zeta(x,t)\) as a new extended state \(x_{4}(t)\) with \(h_{o}(x,t)\triangleq\zeta(x,t)\), nonlinear SBW system (8) is extended as follows:  
\[\begin{cases}\dot{x}_{1}(t)=x_{2}(t)\\ \dot{x}_{2}(t)=x_{3}(t)\\ \dot{x}_{3}(t)=x_{4}(t)+f_{o}(x,t)+b_{0}u(t)\\ \dot{x}_{4}(t)=\hbar_{o}(x,t).\end{cases} \tag{23}\]  
For the extended system dynamics given in (23), the AESO is designed as  
\[\begin{cases}e_{1}(t)=x_{1}(t)-z_{1}(t)\\ \dot{z}_{1}(t)=z_{2}(t)+4(\omega_{o}+g_{o}(e_{1}(t)))e_{1}(t)\\ z_{2}(t)=z_{3}(t)+6(\omega_{o}+g_{o}(e_{1}(t)))^{2}e_{1}(t)\\ z_{3}(t)=z_{4}(t)+4(\omega_{o}+g_{o}(e_{1}(t)))^{3}e_{1}(t)+b_{0}u(t)\\ z_{4}(t)=(\omega_{o}+g_{o}(e_{1}(t)))^{4}e_{1}(t)\end{cases} \tag{24}\]  
where \(e_{1}(t)\) is the estimation error, \(z_{i}(t)\), \(i=1,2,3,4\) are the estimates of system state \(x_{i}\), \(i=1,2,3\), lumped disturbance \(x_{4}=\zeta\), and observer bandwidth \(\omega_{o}>0\). In addition, adaptive gain \(g(e_{1}(t))\) is defined as  
\[g_{o}(e_{1}(t))=\eta_{o}|e_{1}(t)| \tag{25}\]  
where \(\omega_{o}>0\) represents the basic observer bandwidth and \(\eta_{o}>0\) is the observer accuracy factor which is used to increase the estimation accuracy of the AESO.  
Figure 2: Configuration of the proposed AADRC for the SBW system.
## 2 Methodology
### Unsupervised Anomaly Detection Using Spherical Detectors and Local Neighborhood Sampling
#### 2.4.1 Input  
* An example, \(x\in\mathbb{R}^{n}\), represented as a feature vector.
* A set of spherical detectors, \(D=\{(c_{j},r_{j})\mid j=1,2,\ldots,M\}\), where \(c_{j}\in\mathbb{R}^{n}\) is the center and \(r_{j}\) is the radius of detector \(j\).
* Number of random points, \(N_{\text{rand}}\), to sample in the neighborhood of \(x\).
* Minimum and maximum values for each feature dimension, \(\text{mins}\in\mathbb{R}^{n}\) and \(\text{mas}\in\mathbb{R}^{n}\).
* Percentage parameter, \(\text{perc}\in(0,1]\), defining the neighborhood size around \(x\).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.6 Standardized Benchmarks and Datasets [3; 248]  
Create open-source datasets, simulation platforms, and performance benchmarks to evaluate and compare IMI strategies under standardized B5G scenarios.
# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
## 3 Experiments and Results
### Experimental Results  
This section shows the results of experiments performed to evaluate the selected features and the effect of smoothing on the pose estimation output. For each of the views, the dataset was split into a training set (75%) and a testing set (25%) and fitted on a Random Forest classifier with 250 trees. The summary of the results is shown in Table 4.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Total** & \multicolumn{2}{c}{**Front View**} & \multicolumn{2}{c}{**Side View**} \\  & **Healthy** & **Parkinsonian** & **Healthy** & **Parkinsonian** \\ \hline Number of subjects & 167 & 67 & 48 & 26 & 26 \\ After augmentation & 974 & 314 & 360 & 92 & 208 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subject distribution in the dataset.  
Figure 5: Feature selection based on 5-fold cross-validation scores. Each line represents a single run. The vertical dotted line indicates the number of features selected.  
As shown in Table 4, smoothing shows no effect on the accuracy achieved in the front and side views. However, it results in a slight improvement in the accuracy and F1-scores achieved for all views. We observe that conventional filters such as the Savgol filter used in this study may not be suitable for smoothing human motion time series obtained via pose estimation, since important motion data may be distorted. In addition, using five features (arm steadiness, arm swing, step height, step length, and gait speed) on the front view yields the same results as using all features with smoothing. Similarly, excluding the arm swing symmetry feature on the side view still yields results similar to using all features with smoothing. Lastly, when all views are considered, the arm steadiness, arm swing, step height, step length, and gait speed prove sufficient for differentiating a healthy from a Parkinsonian gait.
## 3 A Model for the Measured Spectral Shape
### The Simple Viscoelastic Model  
The simplest method to account for \(THz\) viscoelastic effects on the spectrum of density fluctuation is to assume a single exponential decay of the memory function:  
\[m_{L}\left(Q,t\right)=\Delta_{2}exp\left(-t/\tau\right) \tag{12}\]  
where the amplitude of the memory function is fixed by the superimposition of sum rules to the spectrum, which eventually leads to:  
\[\omega_{o}^{2}=\frac{\left\langle\omega^{2}\right\rangle}{\left\langle\omega^ {0}\right\rangle}=\frac{k_{B}T}{MS\left(Q\right)}Q^{2} \tag{13a}\] \[\omega_{\infty}^{2}=\frac{\left\langle\omega^{4}\right\rangle}{ \left\langle\omega^{2}\right\rangle}\] (13b) \[\Delta_{2}=\left(c_{\infty}^{2}-c_{0}^{2}\right)Q^{2}=\frac{\left\langle \omega^{4}\right\rangle}{\left\langle\omega^{2}\right\rangle}-\frac{\left\langle \omega^{2}\right\rangle}{S\left(Q\right)} \tag{13c}\]  
here \(\left\langle\omega^{0}\right\rangle=S\left(Q\right)\), while \(k_{B}\) and \(M\) are the Boltzmann constant and atomic mass, respectively;  
Equations (12) and (13a-c) define the so called Debye [91] or simple viscoelastic model [92]. Clearly, this model can hold validity only if: (1) thermal relaxations have a negligible weight on the time decay of memory function, _i.e._, if \(\gamma\approx 1\) and (2) the contribution from fast relaxation (term \(\propto\delta(t)\) in Equation (9)) phenomena is also negligible.  
Conceptually, the simple viscoelastic description stems from the idea that, upon crossing the viscoelastic transition, the sound velocity \(c_{S}\), changes from \(c_{0}=\omega_{0}/Q=\sqrt{B/\rho}\) with B being the bulk modulus--as appropriate for a liquid--to \(c_{\infty}=\omega_{\infty}/Q=\sqrt{M/\rho}\) with \(M=B+4/3G\) and \(G\) being, respectively, the elastic and the shear modulus--as appropriate for solids. The superimposition of sum rules links the finite \(Q\) generalization of \(B\) and \(M\) to the second and fourth normalized moments, respectively. In a way the viscoelastic model through the sum rules' fulfillment interpolates between the low \(Q\), viscous, response and the high \(Q\) elastic one. Specifically, one side, Equation (13a) extends to intermediate \(Q\)s the macroscopic compressibility result [55]\(c_{T}=\sqrt{K_{B}T/MS\left(0\right)}\) on the other side, the high \(Q\) link between \(M\) and normalized fourth moment, [1] is here extended down to the same \(Q\)s values.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 2 Signal Model and Signal Preprocessing  
### Signal Model  
The single-input, single-output (SISO) system under consideration in this article can be written as follows [15]:  
\[y(l)=Ae^{j(2\pi f_{0}TI+\theta_{l})}\cdot\sum_{n=-\infty}^{\infty}x(n)h(IT-nT+t _{0}T)+g(l) \tag{1}\]  
Here, \(x(n)\) is the complex baseband symbol sequence transmitted by the transmitter modulated in a certain way, \(A\) is an amplitude factor, \(h(\cdot)\) represents channel effects, \(T\) is the symbol duration, \(t_{0}\) is the timing error, \(f_{0}\) is the frequency offset, \(\theta_{l}\) is the phase offset, and \(g(l)\) denotes complex additive white Gaussian noise (AWGN). It is possible to store the received signal in a discrete IQ form with a sampling length of L to facilitate data processing and modulation recognition, represented as  
\[Y=\left[\begin{array}{c}y_{i}\\ y_{q}\end{array}\right]=\left[\begin{array}{c}\Re\{y[1]\},\cdots,\Re\{y[L] \}\\ \Im\{y[1]\},\cdots,\Im\{y[L]\}\end{array}\right] \tag{2}\]  
where \(y_{i}\) and \(y_{q}\) are the in-phase and quadrature components, respectively. Modulation recognition involves identifying the modulation mode of the transmitted signal \(x(n)\) from \(Y\), even though the signal's structural features may have been distorted. It is also possible to process the incoming signal further and transform it into different signal representations, including eye diagrams, spectral graphs, and higher-order cumulants. For different signal representation methods, it is necessary to design distinct modulation recognition models.
# An Integrated Metabolomics Study of Glucosinolate Metabolism in Different Brassicaceae Genera
## 2 Results
### Three Examples of Distinguished Metabolite Profiles across Specific Pathways  
To highlight how such large datasets can be used to better understand glucosinolate metabolism in Brassicaceae, we further studied three vegetables from different growing environments to illustrate the differences in metabolic profiles across glucosinolate pathways. Figure 4 shows the three different metabolic pathways of three Brassica plants investigated that are selected from the heat map presented in Supplementary Materials Figure S3. As can be seen, the glucosinolate metabolite profiles differed significantly in the pathways among broccoli that grows above ground, watercress that grows in damp soil, and radial that grows below ground.  
Figure 3: Chart of pathways involving the relevant amino acids, glucosinolates, and isothiyanates in the 12 vegetables studied. The solid arrow represents the direct step from the upstream metabolites to downstream metabolites, and the dashed arrow represents multiple steps from the upstream to downstream metabolites. Glucosinolates in orange box are quantified with pure standards, glucosinolates in green box are identified by comparison with MS/MS features in the database METLIN ([[http://metlin.scripps.edu/](http://metlin.scripps.edu/)]([http://metlin.scripps.edu/](http://metlin.scripps.edu/))), and glucosinolates in grey box are not detected in this study. GLS: glucosinolate, ITC: isothiyanate.  
When looking at the metabolic profiles across the biosynthetic pathway for the glucosinolate glucobrassicin and its breakdown product neogluc crosslink, both metabolites are found at higher abundances in broccoli than in watercress or radish. In watercress, a higher abundance of metabolites is detected across the gluconasturtiin pathway and in radish across the gluceorucin and glucoraphasatin pathway. These observations suggest that the pathway from tryptophan to glucobrassicin and then to neoglucobrassicin, is most active in broccoli, whereas the pathway from phenylalanine to gluconasturtiin and phenethyl isothiamate appears to be most active in watercress. On the other hand, the pathway from methionine to glucoraphenin and 4-methylthio-3-butenyl-isothiocyanate is most active in radish, among the three species.
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 5 Conclusions  
In this paper, we proposed a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain. The preprocessing of frequency compression is employed to reduce the computational complexities of the CC acquisition algorithm, which maps the incoming signal into different sub-carrier frequency bins, and the mapped signals are then overlapped with the same code phase, which can reduce the number of bins that should be searched. The simulation results show that when the Doppler frequency offset is greater than 52 kHz, the detection probability of the proposed algorithm is better than that of the only code-domain compression acquisition algorithms, and the computational complexity is reduced by 50% at the same time. In addition, compared with the traditional PMF-FFT and CC acquisition algorithms, the detection probability of the proposed CFC algorithm is insensitive to the Doppler frequency offsets in highly dynamic environments. In the future, the measurement matrix \(\mathbf{\Phi}\) could be optimized and the reconstruction method could be explored to further improve the acquisition performance. Moreover, the impacts of multipath and non-Gaussian noise on compression acquisition are also very interesting research topics.  
Conceptualization, [PERSON] proposed the main idea, finished the draft manuscript; [PERSON], [PERSON] conceived of the experiments and drew the figures and tables; methodology, [PERSON]; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] the paper; [PERSON]; [PERSON]; [PERSON] and [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chinese Academy of Sciences Youth Innovation Promotion Association, grant numbers Y202068, Shanghai Industrial Collaborative Innovation Project, grant numbers No.2021-CYXT2-KJ03, Wenzhou Major Scientific and Technological Innovation Projects of  
Figure 9: Algorithm complexities with different \(FCR\) and \(N\): (**a**) \(FCR\) varies from 2 to 10; (**b**) \(N\) varies from 256 to 4096.  
China, grant numbers ZG2021029, Scientific Research Project of Zhejiang Provincial Department of Education, grant numbers Y202146796, Design and Key Technology of Electro-Optic Switching for Satellite Internet Project, grant numbers KJ002-02, National Natural Science Foundation of China, grant numbers U21A20443.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
A theoretical model based on the GSVATM for RC rectangular beams was proposed to predict the full behavior of RC flanged beams under torsion, namely T- and L-shaped beams. The calculation procedure to consider the influence of the flanges was presented. The predictions from the theoretical model were compared with the experimental results of some reference RC flanged beams. From the obtained results, the following conclusions can be stated:  
* it was found that the GSVATM with subdivision II best predicts the full behavior of the reference RC flanged beams under torsion, mainly for the ultimate state. This observation confirms the results from previous studies;
* very good predictions were observed for the torsional strength, although the corresponding twist was overestimated for most of the reference beams;
* the proposed theoretical model does not predict well the response of most of the reference beams for low loading levels, namely the transition zone from the uncracked to the cracked state. It was observed that the cracking torque is, in general, highly overestimated;
* the previous observation shows that the used procedure to subdivide the cross section is not fully appropriate for low loading levels and must be refined.  
[PERSON] worked the theoretical model, developed the solution algorithm, calculated the reference beams, analyzed the data, wrote and reviewed the paper.  
This work was financed by Portuguese national funds through FCT--Foundation for Science and Technology, IP, within the research unit C-MADE, Centre of Materials and Building Technologies (CIVE-Central Covilha-4082), University of Beira Interior, Portugal.  
The author would like to thank [PERSON] for his support in this study.  
The author declares no conflict of interest.
## 2 Background and Related Work
and [PERSON] et al., have begun to address this gap by integrating machine learning and reinforcement learning into digital twin systems for smart greenhouse management and predictive synchronization [21; 22]. However, these efforts are still in their early stages and often focus on specific functionalities or simulation layers, lacking full integration with robotic platforms capable of executing tasks autonomously within real greenhouse environments. Building on these initial advances, this work proposes DigiHHoriRobot, a novel digital twin architecture that unifies the virtual and physical representations of crops, greenhouse infrastructure, and robotic systems. The proposed system advances the state of the art by embedding prediction, task optimization, semantic modeling, and autonomous execution within a modular, IoT-enabled framework. By integrating perception, simulation, control, and decision-making, DigiHHoriRobot transcends monitoring-centric approaches, enabling real-time self-adaptation in dynamic horticultural environments.
## 1 Introduction
### Contribution  
(1) In order to meet the measurement accuracy requirements of vehicle state in tracking control, based on the extended Kalman filter theory, a vehicle state estimator is designed by fusing the information of onboard inertial measurement unit (IMU) and global positioning system (GPS), and the fusion estimation of vehicle state is realised. Based on the coordinate transformation theory, the local coordinate system of the road curve is defined. The road potential field function is also designed, and the potential field compatible with the curve road environment is constructed.  
(2) According to the longitudinal and lateral multi-target tracking control requirements of the autonomous vehicle, the longitudinal and lateral error dimensionality reduction in path tracking is designed based on the 3 DOF vehicle dynamics model and curved road coordinate system. On this basis, a model predictive tracking controller for longitudinal and lateral multi-objective control of autonomous vehicles is designed.  
(3) According to the online application requirements of MPC algorithm, based on the convex optimisation and online active set theory, combined with an open-source solver