esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization and Assessment of Nanolubricants and Nanobiolubricants
The ICP-OES analysis reveals distinct trends in elemental concentrations due to the addition of SiO\({}_{2}\) nanoparticles in both nanolubricants and nanobiolubricants. The significant increase in silicon levels correlates with higher concentrations of SiO\({}_{2}\), which is expected and confirms the presence of SiO\({}_{2}\) nanoparticles. This increase is not indicative of contamination but rather reflects the intentional incorporation of SiO\({}_{2}\) nanoparticles, serving as a source of silicon. Sodium levels remain minimal across the samples, with a slight increase observed only at the highest SiO\({}_{2}\) concentration (1.00 wt% SiO\({}_{2}\)). This minimal fluctuation indicates that sodium contamination is negligible and does not significantly impact the overall composition of the lubricants. The analysis of essential additives reveals that the concentrations of calcium, magnesium, and phosphorus remain stable despite the addition of SiO\({}_{2}\) nanoparticles. This stability indicates that the inclusion of SiO\({}_{2}\) does not significantly alter the levels of these critical additives, thereby maintaining the functional integrity of the lubricants. However, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & **Biolubricant (Pure)** & **Nanobiolubricants** & **Nanobiolubricants** & **Nanobiolubricants** & **Nanobiolubricants** \\  & **SAE 10W40** & **0.25\% SiO\({}_{2}\)** & **0.50\% SiO\({}_{2}\)** & **0.75\% SiO\({}_{2}\)** & **1.00\% SiO\({}_{2}\)** \\ \hline Appearance & dark yellow & dark yellow & dark yellow & dark yellow & dark yellow \\ Small & characteristic & characteristic & characteristic & characteristic & characteristic \\ Density at 15 \({}^{\circ}\)C [g cm\({}^{-3}\)] & 0.8719 & 0.8733 & 0.8746 & 0.8758 & 0.877 \\ Viscosity at 40 \({}^{\circ}\)C [cSt] & 87.0 & 87.9 & 88.6 & 89.6 & 90.3 \\ Viscosity at 100 \({}^{\circ}\)C [cSt] & 13.7 & 14 & 14.2 & 14.3 & 14.4 \\ Viscosity Index [–] & 160 & 164 & 165 & 166 & 166 \\ TAN [mg KOH g\({}^{-1}\)] & 1.7 & 2.2 & 2.4 & 2.5 & 2.6 \\ TBN [mg KOH g\({}^{-1}\)] & 9.7 & 9.5 & 9.6 & 9.9 & 9.6 \\ Pollution–silicon [ppm] & 2 & 337 & 702 & 1020 & 1402 \\ Pollution–sodium [ppm] & 0 & 0 & 0 & 0 & 5 \\ Additives–calcium [ppm] & 1672 & 1720 & 1737 & 1720 & 1761 \\ Additives–magnesium [ppm] & 333 & 345 & 353 & 351 & 359 \\ Additives–zinc [ppm] & 693 & 690 & 678 & 668 & 658 \\ Additives–phosphorus [ppm] & 593 & 596 & 594 & 587 & 591 \\ Additives–boron [ppm] & 487 & 472 & 459 & 462 & 476 \\ Additives–molybdenum [ppm] & 74 & 73 & 75 & 73 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Physicochemical Properties of Nanobiolubricants.
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 5 Simulation Results and Analysis  
### Simulation Parameters  
In this section, in order to verify the effectiveness and superiority of the power scheduling scheme based on the TD3 algorithm proposed in this paper in a multi-parks distributed PV grid scenario, comparative experiments are conducted in this paper between this scheme and two other benchmark schemes, the DDPG algorithm and the SAC algorithm. These algorithms are run on a Python-based simulator. The parameters are shown in Table 1.  
In the simulation, we generated the PV cases by referring to some practical scenarios in [25; 26]; however, we did not directly use the original data models. Instead, we changed the PV data to a uniform distribution within a certain range, which can make the model have generalization and robustness. The park electrical load \(E_{i,t}^{load}\) and the photovoltaic power \(E_{i,t}^{pv}\) change over time slot \(t\). The interval of \(E_{i,t}^{load}\) is \([200,1000]\) kW\(\cdot\)h and the interval of \(E_{i,t}^{pv}\) is \([0,1200]\) kW\(\cdot\)h. They are all uniformly distributed and have no memory. When the simulation system starts running, each park would have a randomized value in the interval at the time slot \(t\).
# Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives
## 2 The Mathematical Model of PMSM  
The mathematical model of the PMSM in the \(d-q\) axis rotor reference frame can be expressed as  
\[\left\{\begin{array}{l}L_{d}\frac{d\dot{q}_{d}}{dt}=-R_{s}\dot{q}_{d}+n_{p} \omega L_{q}\dot{q}_{d}+u_{d}\\ L_{d}\frac{d\dot{q}_{d}}{dt}=-R_{s}\dot{q}_{q}-n_{p}\omega L_{d}\dot{q}_{d}-n_{ p}\omega\Phi+u_{q}\\ J\frac{d\omega}{dt}=n_{p}\left[\left(L_{d}-L_{q}\right)\dot{q}_{d}+\Phi\dot{q} _{q}\right]-\tau_{L}-B\omega\end{array}\right. \tag{1}\]  
where \(L_{d}\), \(L_{q}\) are \(d-\)axis and \(q-\)axis stator inductance, \(i_{d}\), \(i_{q}\), \(u_{d}\), \(u_{q}\) are the stator current and voltage in \(d-q\) axis reference frame. \(R_{s}\) is the stator resistance of the motor. \(n_{p}\) is the number of pole pairs. \(\omega\) is the mechanical angular speed of the motor. \(\Phi\) is the rotor flux. \(J\) is the moment of inertia. \(\tau_{L}\) is the load torque. \(B\) is friction coefficient.  
The control objection is to design a speed controller for PMSM, which combines the speed loop and \(q-\)axis current loop together, and the controller can realize the speed tracking. For this purpose,define the state variable, input variable and output variable as \(\boldsymbol{x}=\left[\begin{array}{cc}i_{q}&\omega\end{array}\right]^{T}\), \(u=u_{q}\), \(y=\omega\), respectively. Then the last two formula of Equation (1) can be described as  
\[\left\{\begin{array}{l}\dot{\boldsymbol{x}}=\boldsymbol{f}(\boldsymbol{x})+ \boldsymbol{g_{1}}\left(\boldsymbol{x}\right)u\\ y=\omega=\boldsymbol{g_{2}}\left(\boldsymbol{x}\right)\boldsymbol{x}\end{array}\right. \tag{2}\]  
where \(\boldsymbol{g_{1}}(\boldsymbol{x})=\left[\begin{array}{cc}\frac{1}{L_{q}} &0\end{array}\right]^{T}\), \(\boldsymbol{g_{2}}(\boldsymbol{x})=\left[\begin{array}{cc}0&1\end{array}\right]\), \(\boldsymbol{f}(\boldsymbol{x})=\left[\begin{array}{cc}-R_{e}i_{q}&-\frac{n_{p }\omega L_{q}i_{d}}{L_{q}}-\frac{n_{p}\omega\Phi}{L_{q}}\\ \frac{n_{p}\left[\left(L_{q}-L_{q}\right)i_{d}i_{q}+\Phi i_{q}\right]}{J}-\frac {\gamma_{I}}{J}-\frac{B\omega}{J}\end{array}\right]\)  
The control system diagram of PMSM is shown in Figure 1. The overall control system includes predictive speed controller, extended-state observer for the disturbance, PI controller for the \(d-\)axis current control, inverter, space vector pulse width modulation (SVPWM) and PMSM. As seen from Figure 1, \(i_{d}=0\) control is used in this paper, and the PI controller is adapted in \(d-\)axis current loop and the predictive controller is adapted for the speed control. The whole speed-control algorithm is proposed to realize accurate speed tracking and strong robustness for the disturbances.
## 3 Proposal of Ground Station Direction Detection Method Using Color Markers
### Calculation of Ground Station Direction  
The ground station direction is calculated from the marker coordinates in the detected image. The angle \(\theta\) between the camera lens optical axis and the reference marker is calculated using the center coordinates D of the RCM in the image, the image size I, the image sensor size S, and the focal length L. Equation (7) shows the angle of elevation component and Equation (8) shows the angle of azimuth component in the direction of the ground station. The subscripts \(x\) and \(y\) represent the direction in the image plane. \(x\) is the horizontal direction, and \(y\) is the vertical direction.  
\[\theta=\tan^{-1}\frac{D_{y}\times S_{y}/I_{y}}{L} \tag{7}\]  
Figure 5: Process of detecting RCM from acquired images: (**a**) captured image; (**b**) blob detection; (**c**) marker position prediction process in the image; (**d**) RCM detection.  
\[\psi=\tan^{-1}\frac{D_{x}\times S_{x}/I_{x}}{L} \tag{8}\]
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 4 Experiments
### Main Results  
We will submit the results to KITTI's online testing server, as shown in Tables 1 and 2. Overall, our method significantly improves the detection accuracy of bicycle categories. In terms of 3D object detection, the average detection accuracy (\(AP_{3D}\)) of the easy, moderate, and hard level cyclist categories are 80.17%, 65.74%, and 58.32%, respectively. In terms of BEV object detection, the average detection accuracy (\(AP_{BEV}\)) of the easy, moderate, and hard level level cyclist category are 82.58%, 69.61%, and 62.47%, respectively. We compared the test results with several classic 3D object-detection methods based on LiDAR point clouds. Compared with the SECOND network, our method has improved the accuracy of 3D object detection at a moderate level in three categories by 3.49%, 10.10%, and 3.47%, respectively, while the accuracy of BEV object detection has improved by 0.26%, 5.66%, and 3.38%, respectively. Compared with the PV-RCNN method, our method has improved 3D object-detection accuracy by 1.57%, 2.03%, and 0.67%, and BEV detection accuracy by 0.09%, 0.72%, and 0.06% in the cyclist category. This discovery indicates that our algorithm supplements some of the feature information required to detect cyclist category objects on the basis of the original method. It can be noted that our method performs poorly in the detection tasks of the car and the pedestrian. We will discuss this problem later. Table 1 also shows a comparison of the average detection runtime between different algorithms. Because our network has introduced more modules and parameters, the number of detection frames has decreased to 14.2 Hz, but it can basically meet actual usage requirements. We set 40 recall locations and IoU threshold values of 0.7 (for car) and 0.5 (for cyclist) and calculated the AP of 3D object detection of the network on the val set of the KITTI dataset for further comparison. Table 3 shows the specific calculation results: our method has a detection accuracy of 84.31%, 74.57%, and 59.39% at the moderate level for three categories, performing well on detection tasks in the cyclist and pedestrian, but not well on detection in the car. According to the results in Table 3, our method is comprehensively superior to SECOND, and the accuracy of moderate level detection for the three categories improve by 2.70%, 7.83% and 8.25%. At the three levels of the cyclist category, our method is 2.01%, 1.93%, and 1.87% higher than PV-RCNN's AP, 1.48%, 1.29%, and 0.97% higher than the original Voxel R-CNN, with an obvious improvement effect. At three levels of pedestrian category, our method is 2.49%, 1.13% and 0.50% lower than the original PV
## 2 Experimental Method and Results  
The approach proposed in this paper to assess the reduced error of the linear location technique of AE sources was implemented using a steel plate as the simplest isotropic object for control. The methodology of computational and experimental studies consisted of the following stages.  
At the initial stage, a series of laboratory experiments (Section 2) was carried out to estimate the accuracy of the 1D linear location technique. An electronic simulator was installed on the surface of a steel sample. It was applied to imitate an AE source presence. Based on the results of experimental studies, the reduced error of the 1D linear location technique was calculated. The shapes and spectra of the AE signals were also recorded.  
Then, a finite element model was developed (Section 3) to simulate the generation and registration processes of acoustic signals by AETs. To increase the reliability of the FE model, the frequency characteristics of the modeled AE signals correlated with spectra parameters of AE signals were recorded experimentally. The developed AET models were used to assess the reduced error of the 1D linear location technique of AE sources by means of modeling.  
The efficiency of this approach was confirmed by comparing experimental and modeling calculations of the reduced error, as well as by the correlation assessment of the obtained dependences (Section 4).
# RSS Indoor Localization Based on a Single Access Point
## 2 Related Work
### Mitigating Multipath and Polarization Effects  
Research on polarization and multipaths referring to indoor localization techniques tends to focus on the minimization of their influence on the received signal, instead on how to use polarization toimprove the localization process. In this scope, authors of [24] presented a method for decreasing errors of TOA-based indoor positioning systems, based on directional antennas with small side lobes.  
Authors of [25] investigated the effects of polarization on the accuracy of an indoor location tracking system, and established an experimental model that includes parameters which take into account environmental effects. Based on their observations, they concluded that the accuracy of the location estimation is mainly dependent on the accuracy of the range measurements and the antenna polarization angle, which influence RSS, and thus, range accuracy.  
In [26], another approach is presented where researchers investigated potential accuracy improvements in the RSS indoor localization process, through the introduction of directional antennas in the radio network infrastructure. The position and orientation of the directional antenna was carefully selected in order to decrease the correlation levels of the RSS fingerprints that form the _radiomap_.  
The research community also proposed sophisticated and specialized RF designs for enabling spatial re-usability, as well as polarization diversity to mitigate multipath propagation. Authors of [27; 28] designed a switched beam array optimized for 2.45 GHz wireless indoor applications. The proposed antenna appears to support 2D target localization using measurements from a single anchor node, achieving an average localization error of 1.7 m.  
Polarization Scenarios were also investigated through ultra wide-band (UWB) fingerprinting [29]. Comparisons between vertical and horizontal polarization cases at a frequency range from 3 GHz to 11 GHz suggested that horizontal polarization provides greater accuracy than vertical polarization.  
The usage of linear and circular polarized antennas for indoor RSS positioning techniques was proposed in [30]. In this work, it was shown that the utilization of linear and circular polarized antennas, instead of only linear polarized antennas, decreases the standard deviation of the received power and enhances the effective range.  
All the aforementioned work focuses on mitigating multipath and polarization effects, rather than utilizing their inherent properties for the benefit of improving RSS localization accuracy. To the authors' best knowledge, no previous research has investigated how polarization can be utilized to provide low correlation fingerprints.
## 5 Conclusions  
This study proposes the NBEER scheme to extend a network's lifetime while reducing its energy consumption. Collaborative schemes and clustering mechanisms prolong the lifecycle of a network and maximize PDR, E2 ED, total energy consumption, stability period, and number of packets received, thereby reducing the network's total energy consumption. This is particularly important and advantageous when dealing with time- and delay-sensitive applications. However, when communicating over a single path, channel quality variations may cause routing and path selection issues. Collaboration-free transmission protocols use channel estimation to improve the quality of packets received by the destination node. To forward packets to other NHNs efficiently, it is necessary to select an NHN scheme that takes into consideration the burstiness of the link and the distance between nearby NHNs. The ultimate destination is the BS. By alternating the depth thresholds, the number of applicable NHNs increases, thereby mitigating the loss of critical data in time-sensitive applications. Single-hop and multi-hop transmission methods have been utilized to mitigate conflicts in situations where path loss is a concern and to maximize the network's lifetime. Not only do optimal weight calculation and collaboration feature aid in balancing a network's load, but they can also substantially extend the network's period of stability.  
Our study contributes to the development of energy-efficient routing techniques in UWSNs, offering potential for more sustainable and effective underwater communication networks. NBEER's neighbor-based approach and cooperative strategies address critical challenges, paving the way for improved performance and longevity in UWSNs.  
In the future, it may be possible to develop a mathematical model that will enable us to reduce our energy consumption even further. New energy consumption techniques, like converting a power supply to solar energy using multiple parameters, such as depth, energy, data traffic, and the number of neighboring nodes, are utilized to optimize the UWSN data forwarding strategy.  
Conceptualization, S.M.S.; Data curation, A.H., M.A.K. and R.N.; Formal analysis, Z.S.; Funding acquisition, Z.S.; Investigation, K.Z.; Methodology, S.M.S.; Project administration, Z.S. and I.U.; Resources, A.H. and R.N.; Software, S.M.S.; Supervision, Z.S.; Validation, K.Z.; Visualization, I.U. and Y.Y.G.; Writing--original draft, S.M.S.; Writing--review and editing, I.U. All authors have read and agreed to the published version of the manuscript.  
Key R&D Projects in Shaanxi Province (2022)IbGS3-08); The Fundamental Research Funds for the Central Universities, CHD (Grant No.: 300102242901).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** All the authors declare no conflict of interest.  
Figure 11: Total received packets analysis.
## 1 Introduction
Based on the entire frequency domain loss model, this paper proposes an efficiency optimization control strategy named maximum efficiency per torque (MEPT) control which allows optimizing both the fundamental loss and harmonic loss through the injection of the optimal direct-axis current according to the operating speed and the load conditions. In particular, a golden section method is used in order to acquire, in a fast and simple way, the optimal solution which is suitable for all kinds of PMSMs. The MEPT control is designed to improve the efficiency of PMSMs in the steady-state condition, which is the major opportunity for energy savings. The theoretical analysis and experimental tests on a specific PMSM drive employing the proposed MEPT control have shown that the efficiency performance is enhanced in each operation condition and the dynamic performance is maintained in comparison to the PMSM drive equipped with a traditional \(i_{d}=0\) control.
## 2 Materials and Methods
### Evaluation Metrics  
Prediction accuracy in a regression model unlike a classification model demonstrates how close the predicted value is to the ground truth. A perfect model would achieve a prediction error of 0. To quantitatively assess the uncertainty of models trained across ten-fold cross-validation experiments using both the full and reduced feature sets, mean absolute error (MAE) was employed. MAE quantifies the average absolute difference between predicted and observed growth rates of AAAs. The standard deviation (SD) of  
Figure 3: Schematic overview of the MLP used throughout this study. ‘\(h\)’ represents the number of hidden units in each layer, ‘dr’ represents intermediate dropout layers. All ‘dr’ applied a rate of 0.5 (50%). The features pass through seven fully connected dense layers of decreasing size: 256, 128, 64, 32, 16, 8, and 1 neuron in the output layer. Hidden layers use ReLU activations and are followed by ‘dr’ with a rate of 0.5. Each dense layer incorporates L2 reg (keras.regularisers.l2(0.01)). The final output node applies a sigmoid activation to produce a continuous prediction of AAA growth rate. Weight init is ‘he_normal’ for all dense layers. The model employs RMSprop (lr = 1e\(-\)2) and Huber loss (delta = 1.0). Performance is evaluated using MAE (from keras.metrics).  
MAE across folds was calculated to evaluate prediction consistency, while the standard error of the mean (SEM) was computed to quantify the precision of the average MAE. In addition, violin plots of the errors for the predicted growth rates were generated for each test sample, relative to the ground truth. These plots were created from predictions of all models with dropout during inference. Specifically, these plots summarise the variation in growth rate predictions for each test sample (due to model uncertainty), across a total of 10,000 evaluations (i.e., 1000 independent predictions from 10 models trained across the cross-validation experiments).
## 3 Results and Discussion
### HA-MVD Process Facing Walnut Shell Breaking
#### 3.2.2 Unit Time Drying Rate  
The DR analysis results are important for tracking moisture transfer. As shown in Table 5, the DR ranged from 0.141 to 0.229%/min. The response surfaces of each variable to DR are shown in Figures 12A and 13A. According to Figure 12A, there is an interaction between the HA temperature and the microwave power. The DR increases with an increase in the HA temperature at low levels of microwave power, while the DR increases and then decreases slightly when microwave power changes from medium to high levels. This is likely due to the fact that the walnuts with the higher IMC were dried at the higher HA temperature, and to the increased humidity difference between the walnuts and their surroundings, which allowed water to migrate outward more rapidly [15; 37]. In addition, high temperatures increase the kinetic energy and activity of water molecules in walnuts, thus accelerating the migration of water [16]. However, excessive HA temperatures and microwave power may lead to structural shrinkage and surface hardening of the shell, which will prevent water migration cross-layer within the shell [37; 38; 39].  
Figure 12: Response surface and contour plots for DR (**A**), DQ (**B**), MSE (**C**) and \(\Delta\)E\({}^{\bullet}\) (**D**) (at constant MC of the transition point (24%) and MV thermostatic temperature (50 \({}^{\circ}\)C)).  
Figure 13: _Cont._Meanwhile, it can be found that the DR of walnuts dried at higher microwave powers (600-700 W) is faster compared to those dried at lower microwave powers (500-600 W) (Figure 12A). According to the principle of microwave drying, internal heating enables microwaves to directly penetrate the walnut shell, thereby forming a heat source within the walnut based on the aggregation effect that in turn produces a high vapor pressure, which is convenient for the transfer and diffusion of moisture from the kernel to the shell [40,41]. Therefore, high microwave power may result in a more pronounced enhancing effect and cause more increases in the DR of walnuts than low microwave power [42]. Similar results have been noticed for the drying of treebinth, with increasing microwave power increasing DRs [33].  
It can be seen from Figure 13A that there is an interaction between the MC of the transition point and the MV thermostatic temperature. The DR will increase when both the MV thermostatic temperature and the MC of the transition point increase. Meanwhile, it was found that the DR of walnuts dried at high MV thermostatic temperatures (50-60 \({}^{\circ}\)C) was higher compared to those dried at lower MV thermostatic temperatures (40-50 \({}^{\circ}\)C). Similar results have been noticed for the drying of amaranth seeds [43]. In addition, the larger the MC of the transition point, the earlier the MVD stage and the greater the DR is. This can be attributed to the fact that MVD is more efficient, compared to HAD [30].