esa-sceva/satcom-chunk-collection huggingface dataset:
These virtual networks are of the utmost importance for any NFV deployments since they enable isolation between different VNF instances at the network level to operate independently and securely, thus allowing to implement the VNF chaining necessary for the deployment of complex NSes. Virtual networks are necessary for the deployment of NSes in cloud-native environments. However, microservice platforms, like K8s, usually take a hands-off approach towards the connectivity of container networking: the flat networking approach. In this model, all microservices are visible to each other at the network (IP) layer through the use of networking agents deployed over a platform. These communications are especially useful when the applications and workloads must communicate through application-based communications (i.e., using APIs to communicate with one another) since these mechanisms dissociate the network configuration from the application itself. This approach in turn can provide high availability and higher resilience to failures, and its implementation can benefit microservice-based applications in multiple ways (high availability, automatic service discovery, etc.). Unfortunately, there is an important downside that could limit the deployment of NSes in microservice platforms, as this flat networking approach prevents the creation and management of virtual networks, which are necessary to interconnect all the VNFs that compose a NS. Since all microservices are able to \"see\" each other at the network layer, there is no isolation between them. In consequence, the VNF chaining needed cannot be performed. Therefore, it is impossible to effectively deploy NSes in microservice platforms that only implement a flat networking approach towards their connectivity, as they lack the necessary tools to create and manage the required virtual networks used in NFV deployments. In order to address this limitation, this paper presents a networking solution that enables the link-layer connectivity of microservice platforms using software-defined networking (SDN) technology. More concretely, link-layer secure connectivity for microservice platforms (L2S-M) provides a programmable data plane that virtualised workloads can use to connect among each other at the link-layer level, enabling the establishment of point-to-point or multi-point links between workloads in a microservice environment. Furthermore, this paper also explores the potential of L2S-M to provide link-layer communications between workloads located in different clusters if they are managed by a microservice platform, or sites, which can be managed by other virtualisation platforms based on VMs. As a validation use case example, this paper presents a Smart Campus scenario, where L2S-M is deployed to communicate different campuses located in geographically distributed scenarios and managed using distinct virtualisation technologies/orchestration functions, implementing a content delivery network (CDN) service to provide multimedia content in a university environment.
### Article
###### Abstract  
The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects' detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target's contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.  
object detection; feature fusion; attention mechanism; lightweight network; embedded devices +
Footnote †: 2021: 11, 1096. [[https://doi.org/10.3390/app11031096](https://doi.org/10.3390/app11031096)]([https://doi.org/10.3390/app11031096](https://doi.org/10.3390/app11031096))
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 4 The Fundamentals of Clustering
### General Framework
#### 4.1.2 Cluster Head Selection  
Practically, a CH is mainly used for information aggregation and distribution to the SNs; the selection of a CH is significantly crucial for energy usage optimization [29], meaning that efficient CH selection can improve the service life of the network [30]. Energy usage by CHs near the BS is usually higher in cluster-based networks, resulting in hotspot problems. This problem is addressed using unequal clustering algorithms [31]. The conditions that are considered during CH selection mainly include the CH's mobility, communication, and role. The metrics used during CH selection include the distance from the CH to the nodes, the distance between the CH and the BS, the distances between the nodes and the BS, residual node energy, RSSI, node degree, cluster density, node weight, and position metrics. The CH selection communication criteria are determined by the data transfer rate from the cluster members to the CHs and from the CHs to the BS.  
The nodes communicate directly with the CHs and the CHs communicate directly with the BS; this communication is established in single-hop or multi-hop modes. Another factor considered during CH selection in a WSN is CH mobility, because of the mobility of most WSN applications [32]. Fixing the CH entails that the clusters will remain fixed,  
Figure 2: Clustering architecture in a WSN.  
which improves inter- and intra-cluster network management. By contrast, a mobile CH continually changes the sensors' cluster membership, requiring continuous monitoring of the clusters. The CHs sometimes move within limited distances to improve network efficiency through self-repositioning. The CH serves as a relay for the generated traffic and aggregates the data from the SNs. It can also sometimes serve as an SN when its action is based on the intended targets. CHs are also involved in ensuring load balancing and energy efficiency in the network.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 7 Conclusions  
This study presents a machine learning-based method for predicting the power grid state subjected to heavy-rain hazards. This study used two sets of data: Local weather data and power distribution grid damage data, from January 2008 to March 2018. To understand the heavy-rain event, we investigated the heavy-rain-related outage distribution and analyzed the correlated characteristics between weather  
Figure 6: Study area.  
Figure 7: Comparison between the spatial distribution of actual grid state and forecasted grid state for heavy rain in October 2016.  
and outages. The results show that the effect of wind speed is significant in causing power outages, even under heavy-rain conditions.  
Furthermore, this study proposed a grid state prediction method using a cost-sensitive support vector machine (SVM) model. The penalty and kernel parameters of SVM were optimized using Bayesian optimization. The accuracy of the model was improved by applying a cost-sensitive learning algorithm to the SVM model, which was subsequently used to predict the grid state of a region managed by a branch office. The developed models were evaluated using the G-mean values. The proposed method was verified using the actual data of heavy-rain events that occurred in South Korea. The results show that the SVM model with cost-sensitive effects outperforms the general SVM model.  
The method proposed in this study is useful in predicting the grid state during heavy-rain events. This study is expected to provide insight into heavy-rain events, as well as damage prediction. Moreover, the proposed method can be extended to other disruptive events since it only relies on the simple weather variable provided by the weather forecast system. Moreover, the condition of power components and weather are changing over time. Thus, future study is needed to develop grid state prediction models to generalize and reflect the changes in the grid conditions over time.  
S.O.; writing the original version, methodology, analysis, and interpretation of data, investigation, and conception. J.K.; visualization, analysis, validation. M.C.; sources, data collection. J.J.: conception, supervisory, submitting corresponding and corrections, submit the final approval of the version. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by Korea Ministry of Environment (MOE) as Graduate School specialized in Climate Change. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20171210000210).  
The authors declare no acknowledgments.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Surface Free Energy and Adhesion Parameters
Apart from the hybrid ink systems, high values of interfacial tension were also measured between the substrates and TLC ink. Generally, TLC inks have a tendency to be poorly resistant to abrasion and are therefore demanding to work with. For this reason, TLC inks and hybrid ink systems presented in this research could benefit from a protective coating or other form of mechanical protection or adhesion improvement. Furthermore, the wetting coefficient was not optimal between the papers and in-between the hybrid ink layers. Since all wetting coefficients in the  
Figure 3: Surface free energy components of papers and printed ink layers on: (**a**) UT paper; (**b**) CB paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Adhesion Parameters (m/m\({}^{2}\))**} \\ \hline
**Surfaces of Interaction** & \(\gamma_{12}\) & \(W_{12}\) & \(S_{12}\) \\ \hline UT–TLC & 12.88 & 77.72 & \(-\)12.44 \\ UT–UVF & 4.52 & 62.72 & 18.23 \\ UT–UVFp in TLC & 10.19 & 55.87 & \(-\)4.89 \\ TLC–UVF (on UT) & 13.30 & 66.45 & \(-\)14.79 \\ CB–TLC & 13.10 & 93.96 & \(-\)28.06 \\ CB–UVF & 7.63 & 74.23 & 7.37 \\ CB–UVFp in TLC & 10.11 & 92.40 & \(-\)0.83 \\ TLC–UVF (on CB) & 14.38 & 66.82 & 12.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Adhesion parameters between the papers and printed ink layers. TLC/UVF systems (except TLC-UVF on CB substrate with high wetting coefficient of 12.73 mJ/m2) presented negative values, it can be concluded that the wetting between the layers was generally not complete. This occurrence is generally not favourable, but a negative wetting coefficient in the printing process can decrease the unevenness of the print called mottling, resulting with the improved print quality [43]. It can be concluded that the best adhesion in hybrid ink systems was achieved between the CB paper and UVFp in the TLC hybrid ink. Compared to UVFp in the TLC hybrid, higher interfacial tension and values of the wetting coefficient further from zero between TLC and UVF layers pointed to weaker adhesion in the hybrid systems with UVF ink overlayed on TLC ink.
## 3 Results
### Sem/EDs  
The morphological analysis performed via scanning electron microscopy (SEM) allowed for the detailed observation of surface features across all POSCA-coloured areas, both before and after artificial ageing (Figure 8). The non-aged surfaces displayed homogeneous  
Figure 6: Contact angle values for untreated (pre-coating) and coated chromatic areas. Measurements were recorded prior to and following artificial ageing. The dispersion of errors is shown by the swarm plots superimposed on the boxplots. Squares indicate statistical outliers. Static contact angle images are included in Supplementary Materials.  
Figure 7: Peel force (N/cm) for different coatings (CS, CS/CNC1.5, CS/CNC3, CS/CNC6) before and after ageing.  
layers, with limited roughness and well-adhered pigment films. In contrast, the aged areas showed more pronounced micro-roughness, slight textural discontinuities, and, in some cases, particularly blue and light blue, incipient cracking or shrinkage phenomena. The accompanying EDS mapping (Figures S19-S33) supported these observations, revealing the elemental distribution across each sample. Titanium was consistently detected across all colour areas, consistent with its known use as a white pigment base (TiO\({}_{2}\)) in acrylic formulations. In contrast, silicon (Si) and aluminium (Al) appeared more prominently in coloured layers than in the background substrate, confirming their attribution to the pigment composition of the Posca inks. This aligns with the elemental contrast previously noted between the unpainted banknote--characterised by higher aluminium and lower silicon content--and the painted areas. In some aged samples (e.g., green, red), the presence of additional elements such as copper (Cu), chlorine (Cl), or sulphur (S) was detected, possibly associated with specific pigment compositions or degradation products.  
Figure 8: Top-view SEM images of chromatic areas treated with biopolymer coatings, acquired before (**a**) and after (**b**) artificial ageing. Imaging conditions: magnification 100–200\(\times\); accelerating voltage (HV): 30 kV; working distance (WD): 13–15 mm; view field: 1–2 mm; detector: SE + BSE.  
Notably, the green ink showed localised peaks of Cl and Cu after ageing, potentially indicating a copper phthalocyanine-based formulation. Post-ageing images did not reveal dramatic surface disruptions, suggesting that while photochemical degradation may occur at the molecular level (as seen in FTIR results), the physical integrity of the film remains generally stable.
## 3 Design Methodology of the Ideal Class-E PA with FDI  
The design flow of the proposed methodology is shown in Figure 4. In step 1, the designer defines the input parameters, the maximum ratings, and the optimization goals. The maximum ratings can be defined based on the manufacturer's data sheets [19], the input parameters can be defined from the Class-E PA design specifications, and the optimization goals can be defined to maximize or minimize circuit variables (e.g., maximize the \(P_{out}\)). In steps 2 and 3, the designer must find the extended design set that involves the mathematical expressions for the unknown circuit values, maximum ratings, and optimization goals. In step 4, the designer must plot the search space (i.e., all possible values of the design variables subject to ZVS and ZVDS operation as a function of \(D\) and/or \(q\)) in a particular range of the \(D\) and \(q\) variables, if they are unknown. Once the designer explored the search space and found the optimum combination of \(D\) and \(q\), the circuit components of the PA must be calculated in step 5, and the circuit operation must be verified in step 6. If all of the specifications are met, the design process is finalized. Otherwise, a new search of \(D\) or \(q\) values can be made to achieve it.  
It is important to highlight that the design solution is an ideal approximation, which uses assumptions, such as ideal components. When the ZVS and ZVDS conditions are not achieved after the design implementation, either by the effect of non-ideal components or by approximation to commercial values, a tuning process can be performed. Commonly, this process fits the point of ZVS and ZVDS in order to accomplish the soft-switching operation by the tuning of the component values [21; 28; 29].
## 4 Biosensors in the Field of Healthcare
### Wireless Power Transfer to Medical Implants
#### 4.3.3 Far-Field WPT  
Far-field (FF) WPT (Figure 8F) is a method of power transmission over long distances, typically through RF electromagnetic waves or microwaves [151]. In FF-WPT, radiation is the key method of energy transmission from a transmitter to a further away receiver, often within the range of metres. However, the attenuation of human body tissues offers a significant obstacle to the adoption of this technology. On the contrary, NF-WPT continues to offer low efficiency in power transmission as the distance between transmitter and receiver coils increases, while FF-WPT enables the transmission of power in the form of radiation over distances within several metres. Nonetheless, FF-WPT efficiency is limited by the fact that transmission over long distances results in a loss of power density due to the dissipating electromagnetic field in space [152]. In addition, body tissues and fluids cause attenuation to electromagnetic waves, and thereby, this reduces the power received by an implantable device. For tissue safety, the Federal Communication Commission (FCC) imposes rules on how much power can be absorbed by the body. These exposure measures emerged to protect biological tissues from any potential damage that may be caused due to the overheating of powering an implantable device [153].
## 4 Results and Discussion
### Trapped Mode with Fluid  
In order to investigate the influences of the flow, the trapped modes around the cavity were also calculated when the duct was filled with fluid. All the trapped modes in this subsection were captured with cavity length be 0.15. The influence of the Mach number on the trapped mode is revealed in the following.  
Figure 5 shows different trapped modes for the case of Mach number = 0.3. Figure 5a-c depict the pressure distribution in the cavity at trapped modes \(f=3.26\), \(f=4.08\) and \(f=5.67\), respectively.  
Figure 4: The evolution of the frequency of the first trapped mode with cavity length.  
Figure 3: Normalized acoustic pressure distribution around the cavity at the frequency of the embedded trapped mode (\(f=3.187\)).  
Figure 6 shows that, for the second and third modes, the trapped-mode frequency increased as the Mach number increased. However, the frequency of the first trapped mode did not change much with the increase in Mach number. When M = 0.1, the velocity of the fluid was relatively small, so the change in the vortex generated near the cavity had tiny influences on the frequency of the trapped modes. With the increase in Mach number, the frequency of the trapped mode changed greatly due to the vortex generated by the high-speed fluid.  
Variation in the trapped-mode frequency for the duct filled with fluid was the opposite of that without fluid.This also resulted from the interaction between the acoustic and fluid fields. When the Mach number was higher, the fluid flowed faster, and the interaction was more violent. Thus, a relatively stable state of the acoustic and fluid fields was achieved at a higher frequency, which means that the trapped-mode frequency was greater.  
Figure 5: Resonances for the Neumann problem: (**a**) first trapped mode: \(f\) = 3.26; (**b**) second trapped mode: \(f\) = 4.08; (**c**) embedded trapped mode: \(f\) = 5.67 (only the pressure field in the cavity is shown).
## 2 Problem Formulation
### Block Sparse Representation of UWA Channel  
For a UWA channel with block sparsity, block sparse recovery algorithm is capable of further improving the estimation performance by exploiting the intra-block correlation [23; 30]. The block sparse UWA channel can be treated as a concatenation of \(\zeta\) blocks with a block size of \(d\); hence, the channel \(\mathbf{h}\) can be written as:  
\[\mathbf{h}=[\mathbf{h}^{T}[1],\mathbf{h}^{T}[2],\cdots,\mathbf{h}^{T}[\zeta] ]^{T}, \tag{4}\]  
where:  
\[\mathbf{h}[i]=[h((i-1)d+1),h((i-1)d+2),\cdots,h(id)]^{T},\,1\leq i\leq\zeta \tag{5}\]  
denotes the \(i\)-th sub-block channel of the channel \(\mathbf{h}\), and \(N=\zeta d\). Similarly, the sub-matrix \(\mathbf{X}[i]\in\mathbb{C}^{M\times d}\) consists of the columns of \(\mathbf{X}\) with indices \((i-1)d+1\) to \(id\) for \(1\leq i\leq\eta\). Next, we define the block sparsity \(\kappa\) of the channel \(\mathbf{h}\) as:  
\[\kappa=||\mathbf{h}||_{2,0}=|\text{supp}(\mathbf{h}[i])|,\,1\leq i\leq\zeta. \tag{6}\]  
Thus, the optimization problem of the block sparse channel estimation can be expressed by [9]:  
\[\operatorname*{arg\,min}_{\mathbf{h}}\left(||\mathbf{h}||_{2,0}\right)\quad s.t.\quad||\mathbf{y}-\mathbf{Xh}||_{2}^{2}\leq\epsilon, \tag{7}\]  
where \(\epsilon\) is the noise factor. In order to effectively recover the block sparse UWA channel \(\mathbf{h}\) from (7), the BOMP algorithm is commonly applied to exploit block sparsity of UWA channel. In particular, when \(d=1\), BOMP gives the same results as OMP.
## 2 Description of the Numerical Tools
### The Particle Mesh Scheme  
The Particle Mesh (PM) scheme solves Equation (10) using a fast Fourier transformation (FFT) on a uniform Cartesian grid. Even though FFT methods significantly reduce the cost of the solving Poisson equations, having a finite grid, means that boundary terms must be calculated at the outer boundaries of the grids. Because of its convolution form, the corresponding cost is high. In order to avoid such a cost penalization the James-Lackner algorithm [16] is utilized.  
The first step in the Particle Mesh scheme is to transform the flow information carried by the particles into a continuous distribution. To this end, the particle quantities are projected on the PM grid using \(1D\) interpolation functions (see (11)). Then, the Poisson equations are solved and the velocity at the PM grid nodes is obtained through finite differences. Similarly the entire right hand side of (9) is calculated at the grid nodes. The final step is to back interpolate the information from the grid notes to particle position, using the interpolation operator (13). Finally, the Lagrangian evolution Equation (9) are integrated in time using a 4 th order Runge-Kutta scheme.  
\[q_{i,j,k}\equiv Proj_{PM}(q_{p};V_{p})=\frac{\sum_{p}q_{p}V_{p}W(\vec{x}_{i,j,k }-\vec{Z}_{p})}{\sum_{p}V_{p}W(\vec{x}_{i,j,k}-\vec{Z}_{p})} \tag{11}\]  
where \(x_{i,j,k}\) denoted the position of the \(i,j,k\) grid node,  
\[W(\vec{r})=W_{1}(r_{x}/h)W_{1}(r_{y}/h)W_{1}(r_{z}/h) \tag{12}\]  
\(\vec{r}=(r_{x},r_{y},r_{z})\), \(h\) denotes the grid spacing and \(W_{1}\) the \(1D\) interpolation function used. Concerning the present work a 4 th order interpolation function is used which conserves the moments up to 3 rd order [29].  
\[q_{p}\equiv Interp(q_{i,j,k})=\sum_{i,j,k}q_{i,j,k}W(\vec{x}_{i,j,k}-\vec{Z}_{ p}) \tag{13}\]  
A well-known problem in particle methods is that as the particles loose their regularity, accuracy deteriorates. A way to mitigate this inherent problem of particle methods is remeshing. Remeshing consists of repositioning the vortex blob periodically on the PM grid nodes. In the present work re-meshing takes place at each time-step.