esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 3 Construction of Three-Layer Complex Networks
### The Three-Layer EWE Network Modeling  
The construction algorithm of the three-layer EWE network model is as follows.  
Step 1: The initial conditions of the network are the same as described in the algorithm above.  
Step 2: The single network layers \(\alpha\) and \(\gamma\) are both randomly connected in the intra-layer, while the intermediary layer \(\beta\) uses the construction algorithm of the WS small-world networks. First, it starts from the rule graph, a nearest-neighbor coupled network containing \(N_{\beta}\) nodes is considered, where each node is connected to \(K/2\) nodes of its left and right neighbors, and \(K\) is an even number. And then, the randomized re-link is performed, it sets the re-link probability as \(P_{\beta}\), and each edge in the above regular graph is randomly reconnected with the probability \(P_{\beta}\), and there is no duplicate edge.  
Step 3: The inter-layer is connected using a random link method, and there are no directly connected edges between the first and third layers of the network.  
Figure 1: Three-layer network. (**a**) Three-layer network EEE; (**b**) three-layer network EWE; (**c**) three-layer network EBE.  
The network model is constructed according to the above three-layer EWE network construction method. Figure 1b shows the three-layer EWE network model with the following parameters: \(N_{\alpha}=25\), \(N_{\beta}=20\), \(N_{\gamma}=17\), \(P_{\alpha}=0.2\), \(P_{\beta}=0.8\), \(P_{\gamma}=0.2\), \(P_{\alpha\beta}=0.2\), and \(P_{\beta\gamma}=0.2\).
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 5 Challenges
### Foreground Occlusion  
In complex industrial scenes, the condition of object occlusion appears frequently. Because the target is obscured by other objects, the recognition of the target's features is disturbed. In addition, due to part of the object information being missing, it is difficult to calculate accurate pose results. As a common condition, object occlusion has been studied by many researchers.  
Figure 7: The overall workflow chart.  
[PERSON] et al. [19] proposed a method for representing the pose of each part on the basis of the 2D reprojections of a small set of 3D control points. It was able to predict the 6D pose of the object by predicting the pose of the visible part through the reprojections of 2D control points. Even if the object was only partially visible, the method was able to calculate the 6D pose of the object. If the object had several visible parts, the method was able to combine all the information and obtain more accurate results. Distinct from the method above, [PERSON] et al. [87] used 3D information as input and chose an end-to-end strategy. They proposed a novel network named Point-wise Pose Regression Network (PPR-Net). For each of the points in the point cloud, the network regressed a 6D pose of the object instance that the point belonged to. In the pose space, a clustering method was adopted in order to segment multiple instances from the clutter point cloud, and an instance's pose can be computed by averaging each subsidiary point's pose prediction. Essentially, the method used the information of visible parts to predict the pose of the object. The more parts of an object that could be seen, the more reliable the results obtained. [PERSON] et al. [88] proposed a network which took the point cloud as input and regressed the point-wise unit vectors pointing to the 3D keypoints. Then the vectors were used to generate keypoint hypotheses from which 6D object pose hypotheses were computed. [PERSON] et al. [28] predicted the 2D image locations of the projected vertices of the object's 3D bounding box and used a PnP algorithm to estimate the object's 6D pose. Taking RGB-D images as input, [PERSON] et al. [89] combined holistic and local patches to measure the 6D object pose and obtained high precision and good performance under conditions of foreground occlusion and background clutter.  
In conclusion, the principle of the method for dealing with object occlusion is to use the information of any visible parts to predict the 6D pose of the whole object. Generally, the number of methods to achieve this based on 3D information is greater than the number of those based on 2D information. In addition, among the methods based on 2D information, it is more appropriate to use outer information such as bounding boxes and contours. Because inner information, such as the texture, is occluded by other objects, the extraction of the information is affected to a great extent.
## 4 Conclusions  
The effect of a WC-C coating compared to DLC and uncoated surfaces in a cam-tappet contact is investigated. The analysis is performed using a combination of implicit and explicit models, leading to a time-efficient simulation workflow. The following conclusions can be drawn from this research:  
* The results show an insignificant contribution of EHL film stiffness in the overall stiffness of the contact. The elastic deformation and piezo-viscous effects are present throughout the cycle, supporting the drawn conclusion. The presented workflow can be used to analyse detailed tribological effects in a coated cam-tappet conjunction and in a time-efficient manner.
* During a complete cycle of the investigated cam-tappet, the contact area is extended under WC-C and DLC coatings due to their lower modulus of elasticity. However, the difference between pressure and film thickness distributions from DLC and WC-C surfaces are negligible.
* The effect of coating layer thickness is less than the effect of material properties of the coating, although it is present and measurable.
* The application of coatings increases the lubricant film thickness by up to 41%. This gain is mainly in the middle section of the cycle under moderate loads.
* The boundary friction is reduced by applying WC-C and DLC coatings. This is due to the increase in the film thickness values. A greater than 95% reduction is achieved by applying WC-C on a steel tappet, which showed a superior performance in this case.
* The viscous friction increases by applying WC-C or DLC, mainly due to the increased contact area. This increase is lower for WC-C, putting this coating in a better position compared with DLC. Overall, WC-C can balance its increased and reduced viscous and boundary frictions, respectively, whereas DLC causes increased friction in overall comparison with the uncoated surface.  
**Author Contributions:** Methodology, P.A., P.S., M.M. and S.R.B.; Software, P.A., P.S., M.M., S.R.B. and G.O.; Investigation, P.A., P.S. and M.M.; Resources, M.M., S.R.B. and G.O.; Writing--original draft, P.A. and P.S.; Writing--review & editing, M.M., S.R.B. and G.O.; Supervision, M.M., S.R.B. and G.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data cannot be shared due to confidentiality.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
An accurate and rapid determination of soil water-soluble nitrogen is conducive to scientific fertilization in precision agriculture. Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive fingerprint with the advantages of simple operation and high detection efficiency. In this paper, partial least squares (PLS), principal components analysis (PCA), and least squares supports vector machine (LS-SVM) were applied to analyze the relationship between soil water-soluble nitrogen concentration and SERS. The results showed that the SERS-enhancing effect based on Opto Trace Raman 202 (OTR 202) was better than that of silver nanosubstrate and gold nanosubstrate. In addition, the prediction accuracy of soil water-soluble nitrogen in PLS was the highest (\(R_{p}^{2}=0.91\), \(\text{RMSE}_{p}=8.76\) mg/L, \(RPD=3.00\)) when the original spectra were preprocessed with first-derivative. Moreover, 1028, 1370, 1436, and 1636 cm\({}^{-1}\) could be determined as characteristic peaks of soil water-soluble nitrogen, the association between soil water-soluble nitrogen concentration and a SERS intensity of 1370 cm\({}^{-1}\) was the highest (\(R_{p}^{2}=0.94\)), and the regression equation was \(y=93.491x+1771.5\). Beyond that, the prediction accuracy of distinguishing between a low soil water-soluble nitrogen concentration (22.7-63.7 mg/L) and a high soil water-soluble nitrogen concentration (70.5-118.3 mg/L) based on PCA and LS-LVM was 86.67%. In conclusion, soil water-soluble nitrogen could be detected rapidly and quantitatively using SERS, which was beneficial to provide a rapid, accurate, and reliable scheme for scientific and precise fertilization.  
Keywords:soil water-soluble nitrogen; Surface-enhanced Raman spectroscopy (SERS); PLS; PCA; LS-SVM; precise fertilization +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Evaluation of Network-Carrying Capacity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Return Period \(P\) (y)** & \begin{tabular}{c} **Average Duration of** \\ **Overflow Node \(t_{n}\) (h)** \\ \end{tabular} & \begin{tabular}{c} \(N_{n}\) **and Percentage of** \\ **Overflow Nodes** \\ \end{tabular} & \
\begin{tabular}{c} **Average Time of** \\ **Overload Conduit T\({}_{p}\) (h)** \\ \end{tabular} &
\begin{tabular}{c} \(N_{p}\) **and Percentage of** \\ **Overload Conduits** \\ \end{tabular} \\ \hline
2 y & 0.24 & 6 (5.56\%) & 0.21 & 8 (7.48\%) \\
5 y & 0.34 & 14 (12.96\%) & 0.32 & 16 (14.95\%) \\
10 y & 0.58 & 27 (25.93\%) & 0.56 & 25 (24.30\%) \\
20 y & 0.73 & 42 (41.67\%) & 0.70 & 38 (37.38\%) \\
50 y & 0.93 & 73 (72.22\%) & 0.89 & 58 (57.94\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Drainage characteristics of networks for different return periods. Figure 6: Trends of numbers and percentages of overflow nodes and overload conduits. (**a**) Number of overflow nodes \(N_{n}\); (**b**) number of overload conduits \(N_{p}\). that the campus drainage system experiences severe overload under large-scale rainstorms, highlighting its limited capacity to manage extreme precipitation events. Figure 7 shows the duration of overflow nodes (\(t_{N}\)) on campus across various return periods. As the rainfall intensity increased, the extent of ponding, waterlogged sections, and affected areas expanded significantly. For a 2-year return period, only intermittent areas with minor water accumulation were observed. However, with sustained precipitation and increasing scale, the number of overflow nodes and instances of ponding and waterlogging grew markedly along Hong Yi Road, Practical Road, West First Road, West Third Road, and North Playground Road. This pattern highlights critical risk points and primary areas requiring waterlogging control.
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 2 Methods  
This section presents the proposed DIES method in detail. To address the multi-source interference with wide-band distribution problem under dual-frequency excitation, the DIES method operates through three key technical aspects. First, the IES method based on cyclic spectral analysis is applied to amplify fault-related spectral components. The resultant IES provides the critical basis for subsequent aspects. Furthermore, to eliminate the influence of inherent interference frequencies, a spectrum subtraction method is established. This method utilizes both healthy and faulty signals to derive the subtracted spectrum. Subsequently, an improved adaptive threshold denoising method is applied to further suppress background noise in the subtracted spectrum. Finally, the fault-related information of the inter-shaft bearing could be mined clearly.
# Hilbert-Huang-Transform-Based Seismic Intensity Measures for Rocking Response Assessment
## 2 Methods
### EDP-Limit States  
As long as performance criteria have to be clearly identified, a proper engineering demand parameter (EDP) should describe the earthquake-induced damage to structures qualitatively and quantitatively. In the examined structural system, the absolute maximum developed title angle \(\mid\)\(\theta_{\text{max}}\)\(\mid\) normalized to the critical overturning rotation (\(\alpha\)) should be adopted as an EDP.  
\[\text{EDP}=\frac{\mid\theta_{\text{max}}\mid}{\alpha} \tag{33}\]  
Performance-based earthquake engineering relies on the definition of acceptable performance levels of a structural response for predefined seismic levels. In this context, to perform vulnerability analysis, it is crucial to express capacity limit states (LSs) in terms of an EDP. By adopting a proper EDP, various performance objectives can be easily identified. Similar to [PERSON] et al. [30], the rocking response is distinguished into three performance levels in the present study, as shown in Table 2. The first performance level (LS\({}_{1}\)) corresponds to slight rocking. In this performance level, the examined structural systems benefit from uplift, while the overturning potential is negligible [32]. The second (LS\({}_{2}\)) corresponds to moderate rocking rotations which may lead to local damage due to the impact of the rocking piers on the ground. Finally, the third (LS\({}_{3}\)) corresponds to rocking overturn.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Limit State** & \(\mid\)\(\theta_{\text{max}}\)\(\mid\)\(\mid\)\(\alpha\) & **Performance Level** & **Description** \\ \hline LS\({}_{1}\) & 0.10 & \multicolumn{1}{c}{Slight Rocking} & \multicolumn{1}{c}{Rocking initiation} \\ LS\({}_{2}\) & 0.50 & Moderate Rocking & Local damage due to impact \\ LS\({}_{3}\) & \(\infty\) & Rocking Overturn & Collapse \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adopted performance levels for rocking response.  
Figure 2: Computational scheme of vulnerability analysis: (**a**) time history analyses results, (**b**) PSDM of the finite rocking responses, and (**c**) MLE for overturning probability parameters.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## Bus Depot Alsterdorf
### Electrical Energy Consumption of the Buses  
Hochbahn has been gathering field data based on the operation of already existing electric buses in their fleet. The analysis shows that the energy consumption depends on the ambient temperature. Table 1 gives an overview of energy consumption of different bus types for several temperatures. Further dependencies such as road topology and number of passengers also influence the energy consumption. These factors are not considered in this paper.  
The biggest loads on the depot occur with very low temperatures, which makes this a very interesting scenario for load peak minimization. For this reason, the ambient temperature in this paper is set to \(-15\,^{\circ}\)C.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 4 Realization of ANMO-SDR in SDR-Based CRNs  
### Cooperative Spectrum Sensing  
In this section, we present an implementation of the proposed architecture in CRNs based on a Markov Random Field (MRF) framework adopted from [31]. CRNs are used as a tangible example of a SDR environment, where we can demonstrate the feasibility of ANMO-SDR more explicitly. Specifically, we consider a multi-hop secondary network of CRN users. Each secondary node is characterized by three distinct operational regions, defined by the following corresponding radii:  
* The _sensing radius_\(R_{s}\) that determines the area in which active transmissions of PUs are detected, hence defines the channel availability list of the SU.
* The _transmission radius_\(R_{t}\) that determines the 1-hop communication range of the SU.
* The _interference radius_\(R_{i}\) that defines the area where the busy tone of a SU can be detected, but not necessarily decoded. It depends on the transmission power of busy tones, thus it can be tuned appropriately in order to solve the hidden terminal problem.  
The finite set of \(n  
SUs employ physical spectrum sensing techniques to detect active transmissions of PUs within their sensing radius. Based on that information they define the availability (binary) vector \(\vec{a}_{s}\) which denotes with 1 (or 0) the channels that are available for use (or not). Each secondary node pair communicates through common available channels, transparently to the primary network. Furthermore, a SU collects information from other SUs within its transmission radius and forms the vector \(\vec{n}_{s}=\left(n^{(1)}n^{(2)}\cdots n^{(M)}\right)^{T}\), where \(n^{(m)}\in\{0,1,2\}\) is equal to 0 if the \(m\)th channel is not used and equal to 1 (or 2) if it is used by one (or more than one) user(s). When a secondary node detects a collision, it notifies other SUs about the interference caused within its interference radius. Each SU employs spectrum energy detection in order to detect collisions at its transmission channels and updates accordingly the cost function \(\vec{c}_{s}=\left(c^{(1)}c^{(2)}\cdots c^{(M)}\right)^{T}\) that represents the interference caused at each channel in range greater than \(R_{t}\). A penalty is added to the element \(c^{(m)}\) for every unsuccessful transmission at the corresponding channel.
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 4 Fundamentals of Smart Polymeric Materials: Focus on Magneto-Active Elastomers and Hydrogels
### Metabolism of MAPs  
The application of MAPs in the biomedical field carries the need to study the biocompatibility and degradability of these materials inside the body. The matrix material can be chosen so that its biocompatibility is ensured, and its degradability controlled. Many studies have been carried out to test the biocompatibility of materials, such as (poly)lactic acid, (poly)ethylene glycol or collagen hydrogels [146]. However, regarding the magnetic particles, other factors are to be taken into consideration regarding their biocompatibility, such as structural properties, dosage and chemical composition [147]. For example, some materials are known to be toxic for biological environments and are therefore not suitable for biomedical applications, such as cobalt, nickel, cadmium, zinc and silver, while otherssuch as titanium or iron oxide-based particles are considered relatively safe to cells [147; 148]. In addition, the location of the particles with respect to the cells can also play a role in the biocompatibility of these materials since the internalization of MNPs could invoke a cytotoxic response [149]. Furthermore, the structural properties of the MNPs, such as particle size and shape, may also influence the response of the organism to their presence. Different particle shapes can contribute to their aggregation and coagulation, while their size is a determining factor on their spatial arrangement [147]. In detail, particles smaller than 5.5 nm can be quickly removed through the kidney [150], particles up to 200 nm can be sequestered by phagocytes of the spleen [151], and particles larger than 5 um can be cleared via the lymphatic system [152].
## 3 Numerical Simulations
### Stress Analysis Based on Multi-Field Coupling  
In actual production processes, the components in contact with rubber tires are the upper side plate, tread segment and lower side plate. Particularly, as the primary forming component, the deformation behavior of the tread segment directly affects the molding quality of rubber tires. Therefore, this section focuses on analyzing the stress distribution of the tread segment under the coupled effects of temperature fields (based on fluid temperature) and force fields. The locations of stress measurement points are illustrated in Figure 8.  
When the mold with its base constrained is subjected solely to the temperature field, thermal stress comprises thermal expansion stress and temperature gradient-induced stress. Upon applying molding forces, the total stress acting on the mold comprises both thermally induced stress (from the temperature field) and mechanically induced stress (from external molding forces). These stresses superimpose, and the total stress \(\sigma_{total}\) can be expressed as their summation [4]:  
\[\sigma_{total}=\sigma_{thermal}+\sigma_{mechanical} \tag{11}\]  
The combined effect of thermal stress and mechanical stress exerts a more significant influence on the tread segment [17; 18]. Figure 9 illustrates the stress cloud maps of the upper and lower surfaces of the tread segment under identical temperature loading but varying molding forces. Due to the constrained base of the mold, thermal stress is generated under temperature loading, typically manifesting as compressive stress. However, since the constraint is not entirely rigid, a coexistence of tensile and compressive stresses occurs. When molding forces are applied, the total stress gradually decreases as the molding  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **1\#** & **2\#** & **3\#** & **4\#** & **5\#** & **6\#** & **7\#** \\ \hline
4000 s & 164.1 & 158.8 & 168.8 & 164.3 & 150.6 & 144.9 & 153.4 \\
9000 s & 179.1 & 178.9 & 179.4 & 179.1 & 178.3 & 178.0 & 178.5 \\
14,400 s & 179.9 & 179.8 & 179.9 & 179.9 & 179.8 & 179.8 & 179.8 \\ \hline \end{tabular}
\end{table}
Table 2: The temperature values at each measurement point corresponding to the aforementioned time points. (Unit: \({}^{\circ}\)C).  
Figure 8: Schematic diagram of stress measurement points on the surface of the tread segment.  
force increases. Within the range of 70 T to 100 T, each 10 T increase in molding force reduces the total stress by approximately 0.5 MPa. However, this effect diminishes as the force increases, as shown in the figure. Based on the stress analysis results across the four working conditions, it is evident that temperature loading has a greater impact on the mold's stress distribution than mechanical stress.
## 2 Materials and Methods
### Preparation of pDNA-NPs  
Folic acid functionalized PLGA NPs loaded with pVAX1-NH36 (pDNA-NPs) were prepared by using a double emulsification technique followed by solvent evaporation [4; 45; 46; 47]. Three different theoretical pVAX1-NH36 loadings (TDL)--ratio of initial amount of pVAX1-NH36 used in the process with respect to initial amount of polymers--were used in the preparations. Briefly, a mixture with 1 mL of pVAX1-NH36 in aqueous phase (0.2%, 0.5% and 0.8% TDL) and polymers were dissolved in 4 mL of DCM (25 mg of PLGA and 25 mg of PLGA-PEG-FA) and emulsified using an ice bath with a Qsonica 500 sonicator (Qsonica LLC, Newtown, CT, USA) operating at 26.5 \(\upmu\)m of amplitude for 1 min. Then, 8 mL of 3% PVA were added and a second emulsification at 90 \(\upmu\)m of amplitude in an ice bath was performed for 1 min. Organic solvent was then evaporated under magnetic stirring at room temperature overnight. Next, a 2-min centrifugation cycle at 2000 rpm (376\(\times\)\(g\)) using a Sigma 3-30 KS centrifuge (Sigma Laborzentrifugen GmbH, Osterode am Harz, Germany) was employed and supernatant was collected. Supernatant was washed by three centrifugation cycles at 19,000 rpm (33,902\(\times\)\(g\)) for 10 min, discarding supernatant and resuspending pellet pDNA-NPs in 8 mL of deionized  
Figure 1: Schematic of the synthesis of the polymer poly-dl-lactic-co-glycolic (PLGA) polyethylene glycol (PEG) folic acid (PLGA-PEG-FA). First, PLGA carboxylic acid is activated and coupled to a primary amine of PEG (PLGA-PEG). Then, a carboxylic acid from FA is activated and conjugated to the primary amine of PLGA-PEG to finally obtain PLGA-PEG-FA.  
water. On the final centrifugation cycle, pDNA-NPs were resuspended in 4 mL of deionized water and freeze-dried in lyophilized freezeone 4.5 (Laboconco, Kansas City, MO, USA). All experiments were in triplicate.