esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Simulation of a 5 Degrees of Freedom Rotor Dropping on a Protective Bearing
## 3 Characteristic Analysis of Dynamic Simulation Model of 5-Dof Rotor-Protective Bearing System
### Simulation Analysis of Rotor Speed Fluctuation during Rotor Dropping
It can be seen from the results that after the initial speed of the rotor increases, the speed fluctuation of the rotor during the drop process becomes smoother, but the speed drops faster, and the inner ring speed of the protective bearing also increases, and the inner ring speeds of the left and right bearings increase asynchronously, and finally, the inner rings of the left and right protective bearings keep the same speed. It can be analyzed that when the initial speed of the rotor is very high, cylindrical motion occurs at the inner ring of the protective bearing at the beginning, and then it evolves into conical motion, and finally, it begins to do cylindrical motion again with the decrease in the speed.
## 4 Conclusions  
The PEG/GNPs nanocomposites with various contents of GNPs have been successfully prepared with the objective of investigating the enhanced thermal conductivity and photothermal performance of this nanocomposites. Samples of the PEG/GNPs nanocomposites were synthesized using a temperature-assisted solution blending method. SEM reveal that the GNPs are uniformly dispersed in the PEG matrix and construct a heat conduction way. FT-IR and XRD analysis show that no chemical interaction between PEG and GNPs happen, in addition, the suitable content of GNPs can promote the crystallization of PEG. The DSC results show that the phase change temperature and enthalpy are slightly influenced by GNPs content. In addition, the thermal conductivity of the composites is remarkably enhanced by the addition of GNPs, and adding 2 wt. % GNPs results in 146% increase in thermal conductivity and only 6.3% reduction in the latent heat of the PEG. The photothermal conversion performance of the PEG/GNPs nanocomposites is higher than that of pure PEG, which is ascribed to the nanocomposites possess better thermal conductivity and excellent visible light absorptivity in comparison with the pure PEG. The enhanced thermal conductivity property and photothermal conversion performance make the PEG/GNPs nanocomposites very promising for the application of solar energy conversion and storage.  
Data curation, H.W. and Y.G.; Formal analysis, X.L.; Investigation, H.W.; Methodology, H.W. and H.Z.; Resources, X.M.; Software, H.W.; Supervision, L.H.; Writing--original draft, H.W.; Writing--review & editing, H.W.  
This work was jointly supported by National Natural Science Foundation of China (Grant Nos. 51508063, 51506018), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJ1705145).  
The authors would like to express gratitude to thank the reviewers and editor for kindly giving revising suggestions.  
The authors declare no conflicts of interest.
## 3 Problem Statement and Formulations
### Graphical Formulation  
To graphically formulate the considered problem on a directed network, inland container transportation tasks are re-categorized into _pickup_ and _delivery_ requests depending on whether they provide or require empty containers. IF requests are treated as (empty container) pickup requests since they provide empty containers after unpacking. By contrast, OF and OE requests require empty containers and are therefore classified as (empty container) delivery requests. Regarding the depot, since it can not only supply but also receive empty containers, it can be duplicated and then treated as both _pickup_ and _delivery_ depots. By representing pickup and delivery requests/depots with nodes, we can formulate the problem on a directed network \(G=(N,A)\), where \(N\) represents the set of nodes and \(A\) denotes the set of arcs, as illustrated in Figure 2. Note that \(N=P\cup D\cup\{N_{0}\}\cup\{N_{end}\}\), where \(P\) is the node set for pickup requests/depots, \(D\) for delivery requests/depots, and \(N_{0}\) and \(N_{end}\) are the source and sink nodes, respectively. In what follows, the definitions and properties of nodes and arcs are explained in depth. Table 2 reports notations used for this section.  
\begin{table}
\begin{tabular}{l l} \hline \hline Sets & \\ \hline \(N_{IF}\) & Node collection of IF requests \\ \(N_{OF}\) & Node collection of OF requests \\ \(N_{OE}\) & Node collection of OE requests \\ \(N_{E}^{p}\) & Node collection indicating empty containers that were initially positioned \\  & at the depot \\ \(N_{IF}^{p}\) & Node collection of pickup depots associated with IF requests \\ \(N_{IF}^{D}\) & Node collection of delivery depots associated with IF requests \\ \(P\) & Node collection of pickup requests/depots, \(P=\{N_{IF}\cup N_{E}^{p}\cup N_{IF}^{p}\}\) \\ \(D\) & Node collection of delivery requests/depots, \(D=\{N_{OF}\cup N_{OE}\cup N_{IF}^{p}\}\) \\ \hline \hline Parameters & \\ \hline \(N_{0}\) & The source node \\ \(N_{end}\) & The sink node \\ \(K_{track}\) & Number of trucks \\ \(\rho_{0}\) & The fixed cost associated with dispatching a truck \\ \(\rho_{1}\) & Cost incurred by each unit of travelling time \\ \(e\) & The initial stock of empty containers at the depot, \(e=|N_{E}^{p}|\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.  
Figure 1: A schematic illustration of three distinct request types.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 3 Simulation Analysis and Results
### Model Training
#### 3.1.4 Gated Recurrent Unit (GRU)  
This paper proposed a deep learning model with 5 GRU layers, and techniques to avoid overfitting and underfitting were implemented. The data were structured into 3D for use with the GRU layers, and the model had a Dense output layer with three units as several output features (Figure 8b). The network architecture has an input layer capable of processing sequential data and accommodating numerous features at each time step. The LSTM layers, specifically designed to efficiently process sequential data, extract more complex features by utilizing three layers with progressively decreasing numbers of units.  
Figure 8: Block diagram of LSTM and GRU.  
The network employs LSTM layers, Rectified Linear Unit (ReLU) activation functions, and a concise output layer to produce complex patterns. Deep learning frameworks such as TensorFlow/Keras and PyTorch are employed to build comparable network structures. Factors such as the number of LSTM layers, the number of units in each layer, the choice of activation functions, and the configuration of the output layer are of utmost importance.
## 3 Results
### Melt Viscosity and Chain Termination  
In a first and very simple approach, the melt viscosity of polymers can be determined by MVR (Melt-Volume-Rate) measurements. If Orgasol-IS and DF-PA are measured in a virgin state, the following values are obtained: MVR (DF-PA): approx. 60 g/cm\({}^{3}\) and MVR (Orgasol-IS): approx. 15 g/cm\({}^{3}\) (measurements are performed with: 235 \({}^{\circ}\)C/5 kg). This large difference in viscosity is a clear indication that polymer interdiffusion for Orgasol-IS is reduced, as a lower MVR-value means a higher viscosity. A restrained mixing of molten polymer, and consequently, limited chain entanglement at the layer boundaries of the LS-parts, would be the likely consequence.  
However, there exists a second effect on a molecular level influencing the inter-layer bonding for these polymers. The synthesis of PA12 is technically accomplished by the ring opening polyaddition of lauryl lactam. The principle of the sequence of the reaction is presented in Figure 9 (deprived of stoichiometric correctness). This figure schematically depicts what can be controlled regarding the end-groups during the course of polyaddition.  
If the end-groups are chemically terminated, a polymer with a defined chain length is achieved, resulting in a fixed melt viscosity. On the contrary, if the end-groups are unregulated, it results in a still reactive polymer with chains that can be extended under suitable conditions like those encountered in LS, by the reaction of remaining end groups (post-condensation). For LS-polymers, this means that if end-groups are still active, they can strongly support the process of layer adhesion by post-condensation in the interdiffusion zone of evolving LS-parts. A better layer-adhesion and reduced anisotropy of LS-parts is the result.  
A melt viscosity check (MVR) helps to verify the presence of an active chain end polymer. If DF-PA powder is extracted from the LS-process and subjected to MVR-measurement after a few process loops, the MVR of DF-PA is drastically reduced to values lower than 20 g/cm\({}^{3}\). At the same time, MVR-values of Orgasol-IS remain almost unchanged after several loops. In conclusion, the enhanced isotropy of the DF-PA parts can be explained by two effects:  
* DF-PA starts in a low viscous status, with a high flowability of melt that leads to a good interdiffusion in the boundary-zone of progressing LS-parts.
* In the zone of interdiffusion, post-condensation takes place and gives the parts additional stability in the overlapping zone by covalent bonding.
## 2 Feature Engineering
### Harmonic Voltage Factor  
Likewise, the investigation included the computation of the harmonic voltage factor (HVF), a critical index widely used to evaluate electrical system quality. The mathematical expression for HVF is outlined in (5). As is apparent from (5), the HVF serves as a rigorous indicator for distinguishing the presence of harmonic distortions. Hence, it can be considered as an additional indicator that is highly responsive to anomalies associated with a faulty machine.  
\[\text{HVF}=\sqrt{\frac{V_{5}^{2}}{5}+\frac{V_{7}^{2}}{7}+\frac{V_{11}^{2}}{11 }+\ldots+\frac{V_{n}^{2}}{n}} \tag{5}\]  
In this context, HVF represents the harmonic voltage factor, with \(V_{n}\) denoting the magnitude of harmonic voltage, and '\(n\)' signifying the order of the voltage signals' odd harmonics. This features expansion contributes to a better understanding of the electrical fault's behavior and potential deviations from the norm, enabling a more profound comprehension of the system's complexities. By including an additional three features for the three-phase voltages, the total number of features in this study is increased to sixty-three features.
## 3 Experiments  
### SWIMCAT Dataset  
Although there is much research in this area, the public database in this area is very rare. Recently, [PERSON] et al. [16] introduced a database called Singapore Whole-Sky Imaging CATegories (SWIMCAT). The images were captured in Singapore using the Wide Angle High-Resolution Sky Imaging System (WAHRSIS) and a calibrated ground-based whole sky imager [37].  
As specified in Table 3, the dataset has 784 images, which are categorized into five distinct categories. They are clear sky, patterned clouds, thick dark clouds, thick white clouds, and veil clouds. All images have the dimension of \(125\times 125\) pixels. These categories are defined based on the basics of visual characteristics of sky and cloud conditions and consultation with experts from the Singapore Meteorological Services [16; 37]. Figure 4 shows some random images of each class from the SWIMCAT dataset in rows.  
Figure 4: Sample images from the Singapore whole-sky imaging categories (SWIMCAT) dataset.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 4 Results and Discussion
### Results with Colored Noise  
The experiments were also performed in a noisy environment considering Pink noise for variable SNR and RT for both the proposed models as shown in Figures 3-6. For the first proposed model, the SNR values were varied from \(-2\) to 10 dB. The parameter values of window length, NFFT frame length, and RT are considered to 512, 1024, and 100 msec. The results were obtained from different noisy convolutive mixtures generated from the pool of
# Impact of Yeast Selection on Volatile Phenol Levels of Wines Produced from Smoked-Exposed Juice
## 3 Results and Discussion
### Sensory Evaluations  
The results of the triangle tests of the Chenin Blanc wines are listed in Table 4. The judges could distinguish between the smoked and control wines (\(p\leq 0.05\)) despite the levels of volatile phenols being well below their respective ODT values. The ODT of guaiacol is 23 \(\upmu\)g/L and is associated with burnt, smoky, tonaty, phenolic characteristics [13]; 21 \(\upmu\)g/L for 4-methylguaiacol, perceived as sweet-spicy, phenolic and leather [23]; and 62 \(\upmu\)g/L for o-cresol, linked with burnt, smoky, medicinal and tar odors. The panel also observed subtle color differences between the smoked and unsmoked wines. For all the treatments, the panel preferred the control wines that were unsmoked. None of the yeast were effective in masking the negative flavors associated with smoked wines, but the flavor profiles of the wines differed.  
Smoked wines presented smoky, sulfur, vegetative and cooked vegetable characteristics, which are in agreement with the findings of [PERSON] et al. [18; 24]. These authors reported that synergistic interactions between volatile phenols at peri- or sub-threshold levels could be correlated to negative sensory attributes in some wines. [PERSON] et al. [24] also showed that volatile phenols interacted with other volatile compounds, which may have a positive or negative impact the sensory attributes of the wines. Positive characteristics in wine like fruity and sweet attributes can be reduced as a result of the interactions of low levels of phenols, 2-iso-butyl-3-methoxypyrazine and 2,4,6-trichloroanisole [24].  
Even though the yeasts selected in this trial were not effective in masking the negative flavors associated smoked wines, other yeast strains might produce different results. Therefore, more research is needed, where different yeast strains and grape varieties are evaluated and especially, how the volatile phenols are metabolized by different yeast strains.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Load-Sharing Coefficients
#### 4.3.1 LSCs under Rated Condition  
Taking the dynamic LSCs on the left side as an example, the variation curves of the LSCs in the fixed-axes PGT and the differential PGT under the rated condition are shown in Figures 19 and 20, respectively.  
Figure 18: Variances of the planet gear displacements under the different input torques: (**a**) \(x\)-direction; (**b**) \(y\)-direction; (**c**) \(z\)-direction; (**d**) torsional direction around \(z\)-axis.  
From Figures 19 and 20, it can be observed that the variations in the LSCs and the meshing forces were generally consistent, fluctuating at around 1. The fluctuation range for the fixed-axes PGT was between 0.4 and 1.6, while for the differential PGT it is between 0.75 and 1.25. Because of the influence of the geometric relationships and mesh phases, there are differences in the LSCs among the meshing pairs. Since the fixed-axes PGT transmits lower torque compared to the differential PGT, the smaller meshing forces were more susceptible to fluctuations caused by errors. As a result, the fixed-axes PGT exhibited larger fluctuations in the LSCs compared to the differential PGT.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 2 Proposed Model Based on Cascade R-CNN
### Improvement of FPN Model  
#### 2.5.1 Reconstructing FPN  
By analyzing fabric image training data, we found that the span of defect size was considerably large, and some were extremely small or large. Moreover, the feature information of small defects could be easily covered in the top feature map, which complicated the detection of small defects. Therefore, the FPN module was added to improve the detection accuracy of small and multiscale defects.  
As shown in the red box in Figure 9, the FPN model contained two main pathways, i.e., the top-down and lateral pathways. The lateral pathway included 3 \(\times\) 3 and 1 \(\times\) 1 convolution layers. It should be noted that 1 \(\times\) 1 convolutional layers in a network are used to unify input channels from different stages to the same size, while 3 \(\times\) 3 convolutional layers on each merged map are used to reduce the aliasing effect of up-sampling and down-sampling. The top-down pathway and lateral connections are designed to fuse high-level semantic information to low-level maps.  
It is well known that low-level feature maps contain more accurate location information, while high-level feature maps contain more semantic information. To enhance the localization of information at higher levels, a bottom-up path was created in this study to shorten the information path between lower and higher levels, as shown in Figure 10.  
Figure 9: Path augmentation FPN.  
The feature map in level 3 was used repeatedly. It should be noted that this method does not fuse the features of different levels, which is an invalid fusion process. Therefore, we simplified the Path Augmentation FPN [32] module. We removed the feature fusion layer between layers 3 and 4 and deleted the corresponding up-sampling and 3 \(\times\) 3 convolution modules. The improved FPN network structure is shown in Figure 10.
## 4 Results and Discussion
### Effect of the Shape of SSC
Besides, the stresses of both cases are at a relatively low level, indicating that the structural strength of both cases should not be a major concern. Figure 19: Radial deformation of section 4 (mm): (**a**) non-circle case; (**b**) circle case. Figure 20: Mises stress of section 4 (MPa): (**a**) non-circle case; (**b**) circle case. Apart from the issues mentioned above, it should be noted that the stay ring plays an important role as the foundation of the turbine set and one of the main force-transferring structures. Accordingly, the deformation control of the stay ring is of critical importance in the safe and reliable operation of turbine set. Figure 21 shows the displacement of the inner line of the upper stay ring in a horizontal plane. In section 4, the radial displacement is negative in the non-circle case, while it is positive in the circle case. In general, there exists a tendency towards getting elliptical in both cases. However, the deformation in the non-circle case is more severe, and there also exists a slight difference where the displacement is towards the water intake direction in the circle case, while there is approximately an angle of \(15^{\circ}\) in the non-circle case. The results reveal that the deformation of the stay ring needs more attention in the construction period, no matter the shape of the SSC. However, it should not be neglected that the deformation in the non-circle case is more severe than that in the circle case. Figure 22 shows the Mises stress of the stay ring in two cases. Significantly, the stress values and distributions of these two cases are similar, with the peak value of the non-circle case slightly higher. The calculation results in Figure 22 suggest that the shape of the SSC has little effect on the stress level of the stay ring. Overall, the SSC with non-circle sections requires higher standard for construction, and SSCs with both non-circle and circle sections are actually feasible with high-quality construction management. Figure 21: Displacement of the inner line of the upper stay ring (mm): (a) non-circle case; (b) circle case. Figure 22: Mises stress of the stay ring (MPa): (a) non-circle case; (b) circle case.
# Improvements of the xAAL Home Automation System
## 4 The Basic Schema
### Changes in xAAL Schemas
#### 4.2.2 Gathering Types Definitions in a Data Model Section  
With the former versions of xAAL schemas, types of data used by attributes methods and notifications were specified 'in-line'. That is to say, the schema describes the first attribute by giving its name followed by its type definition, then do the same for the next attribute. Then it describes a first method by giving its name and its list of parameters; and for each of these parameters, it gives its name and its type definition.  
As a consequence, a schema may include several copy paste of data type definitions. Typically, attributes definitions were duplicated into the standard get_attributes() method and into the attributes_change() notification. Those copy pastes made the schemas hard to read for a human.  
Therefore, now, xAAL schemas give a type name to each attribute and parameter, and gather type definition into a dedicated section called data model. This data model section is subject to extension mechanisms for attributes, methods, and notifications.
## 5 Analysis of Current Anomaly Detection Models
Consequently, more efficient and effective adaptive anomaly detection methods are required. The attribute/feature correlation can be exploited to improve the efficiency of detection models through data dimension reduction. Few detection models have exploited spatial and temporal correlations of sensor readings [46; 59; 79; 80; 85; 86; 88]. However, incorporating feature correlations with spatial or temporal correlations was not performed before. Such correlation is expected to enhance detection efficiency and effectiveness at the same time.