esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Nonlinear Damping on Vibrations of Microbeam
## 5 Conclusions  
In the present paper, we propose a new partial differential equation to represent nonlinear oscillations of the microbeam. The new model considers the small-scale effect and nonlinear terms, which are induced by the axial elongation of the beam and Kelvin-Voigt damping. Here, we solved the model using the Galerkin and multiple scale methods. The results show that nonlinear damping affects the microbeam vibrations in the following three aspects: First, under the non-resonant condition, nonlinear damping scarcely affects the small-amplitude vibrations. However, in the large-amplitude vibrations, nonlinear damping remarkably reduces the amplitudes and suppresses the multi-period vibrations. Second, nonlinear damping slightly decreases the vibration amplitudes when the frequency of the load is less than the modal frequency of the microbeam under the primary resonance condition. Third, when the frequency of the load is greater than the modal frequency under the primary resonance condition, nonlinear damping can significantly change the bifurcation points of the load and strongly affects the vibrations. The present research suggests that the accurate description of microbeam vibrations must consider nonlinear damping.  
Conceptualization, K.H. and W.X.; methodology, K.H. and T.L.; software, T.L. and L.C.; validation, K.H. and L.C.; formal analysis, K.H.; investigation, K.H.; resources, K.H.; writing--original draft preparation, K.H.; writing--review and editing, K.H.; project administration, K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers 11562009 and 12050001).  
Not applicable.  
The data presented in this study are available in the article insert.  
The authors declare no conflict of interest.
## 4 Experimentation and Loss Separation  
### Coils Preparation and Test Setup  
In order to validate the effectiveness of each coil design, five coil samples are manufactured using different magnet wire topologies. For a fair comparison, all the samples have the same number of turns. Additionally, all the samples are connected in series so that the electromagnetic performance is compared under the same current. The five designs are explained as follows:  
* Coil I: Flat copper coil with a single solid strand [Figure 15].
* Coil II: Stranded coil with 10 parallel strands [Figure 16].
* Coil III: Stranded coil with 45 parallel strands [Figure 16].
* Coil IV: Flat lift coil with 45 strands [Figure 17a].
* Coil IV: Flat lift coil with 45 strands (vertical) [Figure 17b].  
The winding losses are measured for different samples using the test setup in Figure 18. It consists mainly of a DC voltage source, an IGBT inverter, V-I sensors, and a data acquisition (DAQ) system. A dSpace MicroLabBox 1202 is used to control the PWM current waveform. Also, an LC filter is used to filter the high-order harmonics in the AC output of the inverter. Moreover, a thermal camera (GTC400) is used to monitor the temperature of the windings. There is also a multi-channel power analyzer (Tektronix PA4000) used to measure the power losses. A scope (Tektronix TDS3054) is used to monitor the waveforms. Finally, all test samples are connected in series with a water-cooled power resistor to limit any excessive current.  
Figure 17: Prototyped flat litz coils with 45 strands: (a) Coil IV: horizontal arrangement with 8 layers \(\times\) 1 conductor. (b) Coil V: vertical arrangement with 4 layers \(\times\) 2 conductors.  
Figure 18: Test platform for loss measurements of the winding samples.
## 3 Internet of Things  
IoT has gained significant attention in the past few years among industrialist, policy makers, personal users, and technologists. The key concept of the Internet of Things (IoT) paradigm is reducing the existing gap between digital and physical worlds. The IoT extends the actual form of the Internet to a network of connected people and objects (things). Within the IoT, objects obtain intelligent behavior as they can collect, exchange data, and make decisions. The data gathering and exchange are guaranteed thanks to microcontrollers, sensors, and software embedded in objects.  
The core of the IoT paradigm lies in the fact that personal devices (e.g., smart phones, tablets) play the role of a gateway that links people, the physical and the digital world (see Figure 2). Thus, IoT allows the existence of the people-to-people, people-to-objects, objects-to-people, and objects-to-objects information exchange paradigms.  
IoT has been extensively applied in a wide-ranging scope; in fact, IoT applications can embrace all human-object activities, such as:  
* Smart home: With the increasing demand for human comfort and safety, many appliances and equipment have come into our households to ensure our wellbeing and comfort. There is a possibility to make our lives more comfortable, more convenient, and much easier: this is achieved through the management of all those appliances. Yet, managing them is a challenge. To surpass this problem, many proposals and solutions based on IoT have been given [10; 11; 12].
* Transportation: Several contributions have been suggested based on IoT that aim basically at transforming the transportation systems. The Internet of Things (IoT) is basically transforming the transportation systems: transportation-based IoT systems will optimize the traffic and allow tracking, delivering of merchandise, improving economics, public safety, and the environment [13].
* Environment: IoT can be used to track the variation of some environmental parameters such as temperature, humidity, and light level. The main goal of this task is to define the quality of the surrounding environment in which we are living [14].
* Healthcare: Healthcare represents a field in which IoT can be wildly applied. Medical devices can be considered as smart objects. Healthcare services based on IoT can play a major role in reducing costs, increasing the quality of life, and keeping patients safe and healthy [15].
## 2 Results and Discussion
### Methodology Performance
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Calibration** **(Sample)**} & **Linearity for At** **1.25–313 ng/mL** & **Linearity for Sc** **1.25–313 ng/mL** & **MDL \({}^{\circ}\) (ng/mL)** & **MQL \({}^{\circ}\) (ng/mL)** & **ME \({}^{\circ}\) (\%)** \\  & **(R\({}^{2}\))** & **(R\({}^{2}\))** & **(R\({}^{2}\))** & **At** & **Sc** & **At** & **Sc** & **At** & **Sc** \\ \hline Matrix-matched calibration & \(2.2\times 10^{6}\)x + \(4.1\times 10^{6}\) & \(8.6\times 10^{5}\)x \(-1.1\times 10^{6}\) & 0.04 & 0.05 & 0.14 & 0.18 & 10 & 23 \\ Matrix-matched calibration & \(1.7\times 10^{6}\)x + \(2.9\times 10^{6}\) & \(4.7\times 10^{6}\)x + \(2.0\times 10^{6}\) & 0.02 & 0.05 & 0.06 & 0.18 & \(-\)15 & \(-\)33 \\ (Her-Inf-4) & (0.998) & (0.996) & & & & & & \\ \hline \end{tabular}  
* The linear range corresponds to 0.1–25 ng/mL in the infusion sample according to the validated analytical methodology. \({}^{\circ}\) MDL: method detection limit. \({}^{\circ}\) MQL: method quantification limit. \({}^{\circ}\) Matrix effect (ME) = (slope matrix-matched calibration/slope solvent calibration) \(-1\)) \(\times\) 100. Solvent calibration: \(y\) = \(2.0\times 10^{6}\)x + \(2.1\times 10^{6}\) (R\({}^{2}\) 1.000) for atropine and \(y\) = \(7.0\times 10^{6}\)x \(-1.6\times 10^{6}\) (R\({}^{2}\) 0.999) for scopolamine.  
\end{table} 
Table 1: Analytical performance of the \(\upmu\)SPEed® procedure for atropine (At) and scopolamine (Sc) determination in Tea-B-3 and Her-Inf-4 samples. The methodology used in this work showed good analytical parameters, such as good recovery percentages, as well as other methodologies developed for the determination of atropine and scopolamine in infusion samples [31]. Although the detection limits of this methodology are slightly higher than those proposed by [PERSON] et al. [31], this methodology complies with the limits required by legislation and presents certain advantages, such as rapid application, obtaining purified extracts ready to be analyzed by HPLC-MS/MS in 12 min. In addition, it generates less residue because this methodology uses small amounts of solvents, and its cartridges are reusable at least 80 times.
## 2 Preliminaries
### Data Anonymization
#### 2.1.2 \(k\)-Anonymity  
\(k\)-anonymity is a countermeasure used to prevent uniqueness attacks.  
In \(k\)-anonymity, features are divided into quasi-identifiers and sensitive information, and the same quasi-identifier is modified such that it does not become less than \(k\) users. Table 2 shows anonymized data that has been \(k\)-anonymized (\(k=4\)) using quasi-identifiers, e.g., zip code, age, and nationality.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Productivity Monitoring
Similarly, [PERSON] and [PERSON] [150] compared four machine learning algorithms RF, decision trees (DT), SVR, and Lasso regression (LassoR) and found that RF and DT outperformed the other methods in predicting crop yield. [PERSON] [151] observed the same results in barley yield prediction, and [PERSON] et al. [152] noted the superior performance of RF in predicting Irish potato and maize yields. Additionally, [PERSON] et al. [153] reported successful use of RF for mango yield estimation, while [PERSON] et al. [154] achieved excellent results in wheat yield prediction. On the other hand, some studies have shown that other machine learning algorithms can also outperform in yield forecasting. For example, [PERSON] et al. [144] demonstrated that SVR models, using soil properties and NDVI as predictors, outperformed linear regression and K-nearest neighbor (KNN) algorithms in predicting potato tuber yield. [PERSON] et al. [155] showed that random forest, Gaussian process regression and support vector machine models are robust for predicting winter wheat yield using a combination of climate data, vegetation indices and soil information. In addition to RF and SVR, artificial neural networks (ANNs) represent a commonly employed machine learning technique for predicting crop yield, particularly suitable for modeling complex nonlinear relationships [145; 156]. [PERSON] et al. [157] demonstrated that the XGBoost model achieved the best performance in predicting the crop yield of Argania sinosa, followed by GBDT, RF, and ANN. Moreover, [PERSON] et al. [158] utilized the XGBoost model for annual crop yield prediction in Malaysia, achieving an R-squared value of 0.98 and outperforming current models. Overall, the choice of algorithm for crop yield prediction is influenced by various factors, including training time, model structure, cost, and the handling of incomplete data. Each algorithm has its strengths and can be chosen based on the specific requirements of the prediction task. Figure 4: Commonly used algorithms for crop yield estimation. Future research should focus on integrating multiple remote sensing indices and machine learning algorithms to enhance the robustness and applicability of crop yield predictions across diverse agricultural landscapes. Standardized protocols for data collection, preprocessing, and model calibration are essential to ensure consistency and comparability of results. Furthermore, leveraging the spatial and temporal resolution capabilities of remote sensing technologies can provide real-time insights into crop health, growth dynamics, and environmental conditions. Collaborative efforts among researchers, agronomists, and policymakers are crucial to implement these advanced techniques effectively in precision agriculture. By optimizing crop management practices based on accurate remote sensing data, stakeholders can mitigate risks associated with climate variability and contribute to global food security.
## 3 Results and Discussion
### Effect of Temperature
#### 3.4.1 Estimate of the Size \(R\) from the Star Overlap Concentration  
At the critical overlap concentration \(c{{}^{\star}}\), the stars are packed in contact with each other. Their center-to-center average distance is therefore equal to \(2R\), where \(R\) is their (geometrical) radius. \(R\) can also be considered as the mean end-to-end distance of each arm. The precise value of \(c{{}^{\star}}\) is known from the change in the variation of \(q_{2}{{}^{\star}}\) with \(c\). Having checked the different models of packing in Figure 3b, we can use directly the relation \(q_{1}{{}^{\star}}=2\pi/2R\) at \(c=c{{}^{\star}}\) to determine the radius of the stars. The obtained  
Figure 4: Scattering functions of the same NaPSS star solution (S100-S91 in D\({}_{2}\)O at \(c=0.480\) mol\(\cdot\) L\({}^{-1}\)\(-\)\(\phi=5.34\%\); \(c>c{{}^{\star}}=0.075\) mol\(\cdot\) L\({}^{-1}\)\(-\)\(\phi{{}^{\star}}=0.83\%\)) at two distinct temperatures 25 \({}^{\circ}\)C (circles) and 65 \({}^{\circ}\)C (crosses).  
values of \(R\) for the three quite different arm degrees of polymerization \(N_{\rm a}\), 36, 96, and 280 are plotted in Figure 5.  
These values of \(R\) are compared with the ones for the \"parent\" PS stars, before sulfonation, in good solvent (their related scattering functions can be found in Ref. [5]). In the latter case \(R\) varies as \(N^{3/5}\), where \(3/5\) is approximately the exponent for the self-avoiding random walk, or a chain in good solvent. For PE stars, the three points \(R(N_{\rm a})\) are aligned in log-log plot, with a slope clearly equal to one, suggesting strongly elongated arms. This is in agreement with the linear scaling law (\(R\propto N_{\rm a}\)) predicted by the scaling theories [14; 46; 47], and with simulations [10; 11; 12; 13]. The front factor of the scaling law here is close to 1.41 A per repeat unit that indicates a rather extended average conformation for the arms.
###### Abstract  
Green communications have witnessed significant attention being paid to the next generation of wireless systems research and development. This is due to growing use of sensor- and battery-oriented smart wireless devices. The related literature in green communications for next-generation wireless systems majorly relies on transmission and sensing power management, but lacks a fault-tolerant centric approach. In this context, this paper presents a fault-tolerant and reliable green communications framework for next-generation wireless systems (FRGNWS). Firstly, maximum node-disjoint routes from all source nodes to the base station are identified based on the hybrid adapted grey wolf sine cosine optimizer. Secondly, a fault-tolerant and reliable route is selected from the maximum disjoint routes for each sensor node to the base station based on the hybrid adapted grey wolf whale optimizer. The performance of our proposed green communications framework is assessed by simulation experiments considering a realistic implementation scenario and different metrics. Simulation results clearly validate the efficacy of the proposed green communications framework as compared to the state-of-the-art techniques.  
green communication; wireless sensor network; energy harvesting; whale optimization; grey wolf optimization; sine cosine optimization Article
## 3 Methodology
### Control Logic Model Development  
As previously mentioned, the control logic model contains two sub models: the pre-validated models of the technology providers function blocks and the model of the end user's custom code. The standard industry practice when programming motion control is to use preconfigured function blocks supplied by a technology provider [51]. With this work being a proof of concept, there are currently no technology providers who supply Simulink models which perform the functionality of their equipment function blocks.  
Therefore, to prove the feasibility of this work, a model, representing the behaviour of the Rockwell Automation MAM status bits, was created in StateFlow to model this equipment function block. The state logic of the function block model (MAM model) is displayed in Figure 7, which represents the behaviour presented in Table 2 [50].  
Figure 6: (**a**) CAD model of System in Solidworks®: (**b**) SimscapeTM Multibody Block diagram in Simulink®.  
Figure 7: State Logic of the MAM model (function block model).  
The model of the custom code is presented in Figure 8. This model is based on the ISA-88 Procedural Model for batch control, which divides the operating cycle of equipment into a series of states [52]. Each state describes the equipment actions in response to various commands. In this model, the _running state_ contains an equipment phase _\"pos\"_ which contains the logic required to interact with the MAM model and control the position of the servo motor.  
This logic would move the servo motor through five position commands with a two second pause between each position. The system was initialised with the servo motor positioned at 0\({}^{\circ}\) and the arbitrary position commands for the logic were 90\({}^{\circ}\), 180\({}^{\circ}\), 270\({}^{\circ}\), 360\({}^{\circ}\) and back to 0\({}^{\circ}\). An example of the StateFlow logic for setting the position value to 90\({}^{\circ}\) is provided in Figure 9.  
\begin{table}
\begin{tabular}{l} The CB bit stays set until the rung makes a false-to-true transition. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Motion Axis Move Status Bit Behaviour from [50].  
Figure 8: State machine model for custom code.  
The objective of this work was to show that the control logic developed in the MBD environment could interact with standard PLC programming functions to perform an operation. It should be noted here that, although 0\({}^{\circ}\) and 360\({}^{\circ}\) physically occupy the same position in terms of rotation, in the PLC control environment they were two unique position commands, and the behavior of the motor reacted differently to each one. For example, if the motor was to move from 180\({}^{\circ}\) to 0\({}^{\circ}\) it would rotate counterclockwise until it reached 0\({}^{\circ}\), but if it was to move from 180\({}^{\circ}\) to 360\({}^{\circ}\) it would rotate clockwise until it reached 360\({}^{\circ}\).
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 4 Experiments
### Ablation Study  
To understand the individual contributions of each core component to the detection performance, a further comparative analysis was conducted for the proposed model and its variants. These variants included one without the subgraph preprocessing stage, with two versions: (1) without embedding enhancement (denoted as 'w/o e.e.'), and (2) without embedding enhancement and extra node-subgraph view (denoted as 'w/o e.e and e.v.'). Another variant excluded the graph structure learning decoder and adopted the GCN-based decoder instead (denoted as 'w/o gsl'), and the final variant eliminated the neighborhood selection process (denoted as 'w/o n.s.') in the anomaly scoring stage. As shown in Table 5, all components in the model contribute to the identification performance, and notably, the subgraph preprocessing stage emerges as the most critical component, as its removal from the model results in the sharpest decline in AUC scores. In addition, as illustrated in the table, the version without both the embedding enhancement and the extra view exhibits a further drop in performance compared to the version with only the embedding enhancement stage removed. This result highlights the effectiveness of the introduced node-subgraph view in learning informative structural relations. Additionally, the neighborhood selection module consistently enhances the AUC scores across nearly all datasets. This suggests that aligning the reconstructed graph structure with the original  
Figure 4: Parameter analysis for datasets with respect to embedding dimensions.  
graph structure in terms of node degrees for final anomaly scoring after training can indeed provide more informative information for anomaly detection. The graph structure learning technique applied in the structure reconstruction decoder yields a slight improvement in AUC scores across nearly all datasets. This result underscores the importance of focusing more attention on learning structural relationships between nodes to improve anomaly detection performance. One noticeable finding is that for the ACM dataset, the subgraph preprocessing stage provides the most contribution to the detection performance as there is only slight fluctuation observed when the graph structure module or neighbor selection module is removed.
## 3 Construction of Immersive Simulation Platform
### Kinematics Modeling
#### 3.2.2 Takeoff Model of Carrier Aircraft  
In the process of the aircraft leaving the ground to the safe altitude, the ground coordinate system is selected as the reference coordinate system, and the kinetic equation of the aircraft mass center is as follows.  
\[\left\{\begin{array}{l}\frac{dv_{x}}{dt}=\frac{1}{m}\sum F_{x}\\ \frac{dv_{y}}{dt}=\frac{1}{m}\sum F_{y}\\ \frac{dv_{z}}{dt}=\frac{1}{m}\sum F_{z}\end{array}\right. \tag{11}\]  
where \(\overset{\rightarrow}{v}=\left[v_{x},v_{y},v_{z}\right]^{T}\) is the aircraft ground velocity vector, \(\overset{\rightarrow}{F}=\left[\sum F_{x},\sum F_{y},\sum F_{y}\right]^{T}\) is the aircraft subjected to the ground force vector. The airframe coordinate system is chosen as the reference coordinate system. Taking into account the symmetry of the airframe in the \(XOZ\) plane relative to this coordinate system, the dynamic equations governing the aircraft's rotation around each axis of the airframe coordinate system are as follows:  
\[\left\{\begin{array}{l}\sum M_{x}=I_{x}\frac{d\omega_{x}}{dt}-I_{zx}\Big{(} \omega_{x}\omega_{y}+\frac{d\omega_{z}}{dt}\Big{)}+(I_{z}-I_{x})\omega_{y} \omega_{z}\\ \sum M_{y}=I_{y}\frac{d\omega_{y}}{dt}-I_{yz}\Big{(}\omega_{x}\omega_{y}+\frac {d\omega_{z}}{dt}\Big{)}+(I_{x}-I_{z})\omega_{z}\omega_{y}\\ \sum M_{z}=I_{z}\frac{d\omega_{z}}{dt}-I_{xy}\Big{(}\omega_{y}\omega_{z}+\frac {d\omega_{z}}{dt}\Big{)}+\big{(}I_{y}-I_{x}\big{)}\omega_{x}\omega_{y}\end{array}\right. \tag{12}\]  
where \(\left[\omega_{x},\omega_{y},\omega_{z}\right]^{T}\) is the vector of the angular velocity of the aircraft rotating around the coordinate axis, and \(\left[\sum M_{x},\sum M_{y},\sum M_{y}\right]^{T}\) is the vector of the ground conjunction moment of the aircraft subjected to the external force relative to each coordinate axis.  
\[\left\{\begin{array}{l}\dot{\gamma}=\omega_{x}-\tan\theta\big{(}\omega_{y} \cos\gamma-\omega_{z}\sin\gamma\big{)}\\ \dot{\psi}=\frac{1}{\cos\theta}\left(\omega_{y}\cos\gamma-\omega_{z}\sin \gamma\right)\\ \dot{\theta}=\omega_{y}\cos\gamma+\omega_{z}\sin\gamma\end{array}\right. \tag{13}\]  
The body axis system is selected as the reference coordinate system, and the velocities \(\left(\dot{x}_{d},\dot{y}_{d},\dot{z}_{d}\right)\) of the center of mass relative to each axis of the earth axis system are:  
\[\left\{\begin{array}{l}\dot{x}_{d}=v_{x}\cos\psi\cos\theta+v_{y}(\sin\psi \sin\gamma-\cos\psi\sin\theta\cos\gamma)+v_{z}(\sin\psi\cos\gamma+\cos\psi\sin \theta\sin\gamma)\\ \dot{y}_{d}=v_{x}\sin\theta+v_{y}\cos\theta\cos\gamma-v_{z}\cos\theta\sin \gamma\\ \dot{z}_{d}=-v_{x}\sin\psi\cos\theta+