esa-sceva/satcom-chunk-collection huggingface dataset:
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 4 Model Training and Experimental Results
### Zone-Based Risk Assessment
The proposed method was evaluated for Oxford Town Centre dataset, including 7530 frames, and approximately 150,000 people detection and distance estimation. The system was able to perform in a variety of challenges including, occlusion, lighting variations, shades, and partial visibility, and proved a major development in terms of accuracy (99.8%) and speed (24.1 fps) compared to three state-of-the-art techniques. The system performed real-time using a basic GPU platform or a 10 th generation multi-core/multi-thread CPU platform, or higher. We adapted an inverse perspective geometric mapping and SORT tracking algorithm for our application to estimate the inter-people distances, and to track the moving trajectories of the people, infection risk assessment and analysis to the benefit of the health authorities and governments. DeepSOCIAL offered a viewpoint-independent human classification algorithm. Therefore, regardless of the camera angle and position, the outcome of this research is directly applicable for a wider community of researchers, not only in computer vision, AI, and health sectors but also in other industrial applications including pedestrian detection for driver assistance systems, autonomous vehicles, anomaly behaviour detections in public and crowd, surveillance security systems, action recognition in sports, shopping centres, public places; and generally, any applications that human detection falls in the centre of attention. The following are available online at [[http://www.mdpi.com/2076-3417/10/21/7514/s1](http://www.mdpi.com/2076-3417/10/21/7514/s1)]([http://www.mdpi.com/2076-3417/10/21/7514/s1](http://www.mdpi.com/2076-3417/10/21/7514/s1)), we publicly share the DeepSOCIAL materials in our GitHub repository for the benefit of researchers in the field and research reproducibility. Sample videos of the project are also accessible via our YouTube channel and the following links: Link 1, Link 2, Link 3, and Link 4. In this research, the authors [PERSON] (M.R.) and [PERSON] (M.A.) contributed as follows: Conceptualization, M.R. and M.A. ; Methodology, M.R. and M.A. ; Software, M.R. and M.A. ; Validation, M.R. and M.A. ; Formal Analysis, M.R. and M.A. ; Investigation, M.R. and M.A. ; Resources, M.A. ; Data Curation, M.A. ; Writing--Original Draft Preparation, M.R. and M.A. ; Writing--Review and Editing, M.R. ; Visualization, M.R. and M.A. ; Supervision, M.R. ; Project Administration, M.R. All authors have read and agreed to the published version of the manuscript. This research was supported under UAF funding scheme, provided by the University of Leeds, UK. The authors declare no conflict of interest.
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
###### Abstract  
In a heat-assisted magnetic recording, the thermal effect of the head/disk interface has an important influence on the stability of the recording data. In this paper, we will discuss the thermal radiation from the disk, more specifically, the magnetic recording layer, which is at high temperature after laser heating, to the magnetic head, which is at room temperature. The radiative heat flux can be represented by the Poynting vector. In the near-field band, an effective way to obtain the electromagnetic fields is to solve the Maxwell's equations combined with fluctuational electrodynamics. The near-field back radiation between specific head and disk material is calculated by the fluctuation-volume-current method. The radiative heat energy will induce the thermal deformation of the magnetic head, which will be discussed by the simulation, laying the foundation for adjusting and controlling the flying status.  
thermal effect; near-field; HAMR; back radiation +
Footnote †: journal: Physics Letters B
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## System Model  
### Network Model  
A wireless biomedical implant system model is shown in Figure 1 and consists of a series of implanted sensors, on-body relay nodes, and the coordinator [1; 2]. A WBAN deploys a star topology and has only one coordinator. Thanks to the rapid progress of the medical Internet of Things, the coordinator can be connected to the Internet via the gateway and the collected data can be stored on the medical server and prepared for future R&D. In addition, abnormal data can be provided to medical professionals who are able to offer emergency feedback to the patient promptly. Detailed information of the system model is given as follows [1]:  
* Implanted sensor (S): A device that is implanted by surgery inside the human body. A deep tissue implant is positioned at a distance up to 9 cm below the skin.  
* On-body relay node (R): This is considered to be a device that is located on the body surface or up to 2 cm from it. R is usually selected as a wearable computing device and is capable of forwarding the gathered data received from implanted devices to the coordinator.
* Coordinator (D): An access point, placed either on human clothes or close to the human body. A smartphone or other personal digital device is usually selected to act as a coordinator.  
The communication paths between the \(i\)th implanted sensor and the \(j\)th on-body relay node are represented as \(S_{i}R_{j}\), where \(i\in\{1,2,\ldots n\}\) and \(j\in\{c,\omega\}\). It is worth noting that the implanted sensor node ID is assigned by the coordinator and \(j\) is the location of the on-body relay node on the human body, in accordance with [23], and \(c\) and \(\omega\) mean the chest and wrist, respectively. Similarly, the paths between the on-body relay node and the coordinator are denoted \(R_{j}D\) and the links between the implanted sensor and the coordinator are represented as \(S_{i}D\).
## 4 Simulation Research on the Temperature Distribution of the Arrester
### Electothermal Coupling Calculation Method
#### 4.3.1 Governing Equations and Boundary Conditions  
Due to the short duration of the overvoltage, the calculation of the temperature field in this paper is mainly based on the transient heat transfer differential equation, coupled with the electric field governing equation.  
Figure 11: Volt-ampere characteristics of the arrester.  
The governing equation of the electric field in the whole solution domain is as follows:  
\[\begin{array}{c}\
abla\cdot J=Q_{j,\sigma}\\ J=\sigma E+\frac{\partial D}{\partial t}+J_{e}\\ E=-\
abla V\end{array} \tag{2}\]  
where \(\sigma\) is the electrical conductivity, \(D\) is the electric displacement, \(J\) is the current density, \(J_{e}\) is the external injection current density, \(Q_{j,\
u}\) is the voltage source, \(E\) is the electric intensity and \(V\) is the electric potential.  
The external boundary condition of the arrester is:  
\[n\cdot J=0 \tag{3}\]  
where \(n\) is the interface normal vector and \(J\) denotes the current density.  
The internal boundary condition of the arrester is:  
\[n_{2}\cdot(J_{1}-J_{2})=0 \tag{4}\]  
where \(n_{2}\) is the interface normal vector and \(J_{1}\) and \(J_{2}\) are the current densities on both sides of the interface.  
The heat transfer governing equation and boundary conditions are as follows:  
According to the heat transfer theory, the governing equation of the transient heat transfer of the arrester is:  
\[\begin{array}{c}\rho C_{p}\frac{\partial T}{\partial t}+\rho C_{p}v\cdot \
abla T+\
abla\cdot q=Q+q_{0}+Q_{\text{ted}}\\ q=-k\
abla T\end{array} \tag{5}\]  
where \(\rho\) is the density of the material, \(C_{p}\) is the heat capacity of the material, \(T\) is the temperature gradient, \(v\) is the velocity vector, \(q\) is the rate of heat conduction, \(k\) is the thermal conductivity of the material, \(Q_{\text{ted}}\) is the increase in heat of the heat source, \(q_{0}\) is the convective heat flux, \(Q\) is the heating power per unit volume and its value is the dot product of \(E\) and \(J\) in the governing equation of the electric field.  
The boundary conditions of the temperature field are as follows:  
\[\begin{array}{c}-n\cdot q=q_{0}\\ q_{0}=h(T_{ext}-T)\end{array} \tag{6}\]  
where \(n\) is the interface normal vector, \(h\) is the heat transfer coefficient of the external surface of the arrester, \(T_{ext}\) is the external boundary temperature and \(T\) is the internal boundary temperature. According to relevant literature [25], the comprehensive heat transfer coefficient of the silicon rubber outer surface of the arrester is generally set as 4-10 W/(m\({}^{2}\cdot\)K). When there is no or little air flow, the convective heat transfer coefficient is between 4-10 W/(m\({}^{2}\cdot\)K). Therefore, the heat transfer coefficient is taken as 10 W/(m\({}^{2}\cdot\)K) in the simulation.
## 3 Results
However, at T\({}_{\text{GM}}\), group comparison regarding lightness revealed no statistical differences between the tested biomaterials either when in contact with saline solution (\(p=0.065\)) or blood (\(p=0.102\)), which also applies to a* and b* values in both conditions (saline: a* \(p=0.072\); b* \(p=0.355\); blood: a* \(p=0.148\); b* \(p=0.060\)). When in contact with saline solution, both MTA and Biodentine exhibited statistically significant color alteration over time (\(p=0.001\) and \(p=0.012\), respectively). In the case of blood contamination, MTA kept presenting statistically significant color variation (\(p=0.001\)) alongside Biodentine (\(p=0.009\)), and PCM (\(p=0.006\)). No statistically significant differences were detected regarding \(\Delta\)E of TotalFill groups over time, either in contact with saline solution (\(p=0.102\)) or blood (\(p=0.086\)). At 6 months all CSC groups presented perceptible color changes (\(\Delta\)E \(>3.3\)). Statistical differences are found considering the factor material (F(8.45, 92.90) = 5.635; \(p<0.001\)), the factor treatment (F(2.11, 92.90) = 5.04; \(p=0.007\)) or both material and treatment (F(8.45, 92.90) = 3.31; \(p=0.002\)). The graphs on Figure 4 show the mean \(\Delta\)E over time, considering materials (Figure 4a) and treatment (Figure 4b) variation. Figure 4: Color variation (\(\Delta\)E) over time considering only the (**a**) materials (mineral trioxide aggregate (MTA), Biodentine, TotalFill, and PCM) and (**b**) treatment (Saline and Blood) variation.
## 3 Results  
### Extraction Yield of SF and SSF Extracts in Different Solvents  
In this study, 95% ethanol, 95% methanol, and hot water extracts of SF and SSF were prepared, and the extraction yield of SF and SSF in the different solvents ranged between 8-25% and 2-19%, respectively (Table 1). The highest yield was obtained for water extracts for both SF and SSF at 25.0% and 19.0%, respectively. Compared to methanol extract, the yield of water extract was 3.9-fold higher for SF and 6.6-fold higher for SSF.  
[PERSON] et al. [10] reported that the content of carbohydrates in SF was higher than the other components (40-62%, dry weight). [PERSON] et al. [25] found that monosaccharides, proteins, and minerals in brown algae are highly polar and thus easily extracted in water. In addition, [PERSON] et al. [40] examined 26 marine algae species, including _Sargassum asperfiolium_, _Sargassum angustifolium_, and _Sargassum boseanum_, in different extraction solvents (50% ethanol, absolute ethanol, and water) and demonstrated a higher yield for water extracts, wherein proteins and the soluble and polar components were eluted in abundance. Thus, the higher yield of SF and SSF water extracts compared to the other two solvent extracts may be attributed to the high dissolution level of large amounts of carbohydrates in SF. The findings suggest that hot water extraction is more suitable to ensure an efficient yield for SF and SSF.
# Floating Step-Down Converter with a Novel Lossless Snubber
## 5 Efficiency Measurement  
Figure 18 shows the block diagram of the proposed circuit for efficiency measurement. Firstly, a current-sensing resistor (shunt) is connected to the input and output current paths, and then a digital meter (Fluke 179, Fluke, Everett, WA, USA) is used to measure the voltage across the current-sensing resistor in order to measure the input and output currents, and meanwhile, the input and output voltages are measured using a digital meter to obtain the input power \(P_{in}\) and the output power \(P_{o}\). Eventually, the input and output powers are used to calculate the efficiency of the actual circuit operation.  
From Figure 19, it can be seen that the efficiency is above about 95% all over the load range with the proposed snubber used. From this figure, it also can be seen that when the proposed circuit is operated without the proposed snubber, it has a better efficiency of  
Figure 16: Measured waveforms at 20% load: (1) \(v_{ggi}\) (2) \(v_{Lo}\).  
Figure 17: Measured waveforms at 20% load: (1) \(v_{ggi}\) (2) \(V_{o}\); (3) \(I_{o}\).  
Figure 18: Efficiency measurement block diagram.  
about 90% at 100% load, but for the hard switching, the efficiency is not high, especially at 20% load, where the efficiency is only about 69%.
## 4 System modeling
### Partial least square regression (PLSR)
#### 4.1.7 Radial basis function neural network (RBFNN)  
RBFNN is another widely used neural network model. The basic concept of RBFNN is a radial function-based interpolation process, seeking a function \(f\big{(}\mathbf{x}_{i}\big{)}\!=\!\mathbf{y}_{i}\) to create a map from sample set \(\{\mathbf{x}_{i}\}\) to their corresponding outputs \(\{\mathbf{y}_{i}\}\). The typical structure of RBFN is also a three-layer forward structure. The hidden layer performs a nonlinear transformation to transform the input space into a high dimensional transitional space through radial basis functions. Gaussian kernel functions are a typical choice of radial basis functions.  
\[\varphi\Big{(}\mathbf{x},\mathbf{c}_{j}\,\big{)}\!=\exp(-\left|\!\mathbf{x}- \mathbf{c}_{j}\right|\!\right|^{2}/\sigma_{j}^{2}) \tag{9}\]  
where \(\mathbf{x}\) is a learning sample vector, \(\mathbf{c}_{j}\) is the radial basis vector of the \(j\)th hidden node, \(\sigma_{j}\) is the Gaussian width of the \(j\)th hidden node. With the weights connecting the output node and the hidden nodes, the output layer produces a linear summation of all the hidden layer outputs. RBFNN can avoid long training time, performing a one-step learning strategy.  
RBFNN was successfully applied for bacteria classification [67], yielding a 98% classification accuracy for six bacteria species. RBFNN was also used to develop sensors for odor classification [68] and fragrance discrimination [69].  
The major hindrance of building up an RBFNN model is to estimate the radial basis vectors and their corresponding Gaussian widths. A conventional strategy to determine the radial basis vector is to use K-means algorithm to group training samples into a number of clusters and then use the cluster center vectors as the radial basis vectors [70]. However the possible local optima of K-means algorithm could make the cluster centers unable to correctly reflect the data space distribution. In the early 1990's, orthogonal least squares (OLS) was introduced to construct RBFNN, using a Gram-Schmidt scheme to select regressors (i.e., radial basis vectors) from the training data [71]. Genetic algorithms were also applied to determine which training samples could be used as radial basis vectors by minimizing the training error [72], but the searching process may take a very long time and can be stuck in local optima. Integrating RBFNN with multivariate analysis methods is another effective strategy for radial basis vector construction [73]. In the RBF-PLSR approach, all the training samples are used as radial basis vectors and then PLSR is applied for the linear regression between the hidden and output layers. This method statistically solves the construction of radial basis vectors and has been used for mass spectrometry calibration [74] and GC/MS based bacterial classification [41].
## 4 Case Study and Results
### Comparative Analysis
#### 4.3.5 Generalization and Outlook  
Although this study focuses on the Belfort public transportation system, the methodology is transferable to any structured urban area with fixed-line vehicles (e.g., metro, tram, van circuits). The modular energy model can accommodate the following:  
* Different UAV types or propulsion technologies;
* Variable altitude or weather profiles;
* Multimodal vehicle couplings (bus + van, train + drone).  
This generalization potential positions the model as a valuable tool for future integrated mobility systems, where drones cooperate with public infrastructure to improve delivery efficiency. These findings lay the groundwork for the conclusions presented in Section 5.
## 2 Materials and Methods  
### Sample Preparation  
We evaluated two polymer classes, including two acrylic and one cellulose nitrate lacquer. Paraloid\({}^{\text{\textregistered}}\) B-72 (PB-72) (CTS, Vicenza, Italy) is a copolymer of methyl acrylate and ethyl methacrylate widely used in conservation as an adhesive, consolidation, and protective layer [27; 28; 29; 30; 31]. Paraloid\({}^{\text{\textregistered}}\) B-44 (PB-44) (CTS, Italy) is the commercial name for methyl methacrylate and ethyl acrylate copolymer, part of the Incalac\({}^{\textregistered}\) coating formulation [29]. Having a higher glass transition temperature (Tg) compared to PB-72, PB-44 is preferable for application in places where the temperature is higher than 40 \({}^{\circ}\)C [30].  
Zapon\({}^{\text{\textregistered}}\) (Lechler, Italy), on the other hand, is a lacquer containing cellulose nitrate polymer soluble in different solvents (e.g., amyl acetate, butyl acetate, and propyl acetate or ethanol, ethyl acetate, and ethyl glycol) that conservators employ as a varnish, adhesive, and consolidation [32; 33].  
We studied nine transparent polymer disks (Table 1) of 13 mm (\(\pm\)1 mm) in diameter obtained by depositing 35 \(\upmu\)L of the polymer, dissolved in butyl acetate at different concentrations, over a KBr pellet. After one week of curing, the KBr substrate was dissolved in distilled water, and the polymer disks were left to dry for 24 h under laboratory conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Polymer** & **Chemical Composition** & **Refractive Index** & \(\mathbf{T_{g}}\) (\({}^{\circ}\)\(\mathbf{C}\)) & **Sample Code** & \begin{tabular}{c} **Concentration in Butyl** \\ **Acetate (W \%)** \\ \end{tabular} \\ \hline Paraloid\({}^{\text{\textregistered}}\) B-72 & \begin{tabular}{c} methyl acrylate/ethyl \\ methacrylate copolymer \\ \end{tabular} & 1.49 [34] & 40 & \begin{tabular}{c} PB-72\({}_{30}\) \\ PB-72\({}_{25}\) \\ PB-72\({}_{10}\) \\ \end{tabular} & 30 \\ \hline Paraloid\({}^{\text{\textregistered}}\) B-44 & \begin{tabular}{c} methyl methacrylate/ethyl \\ acrylate copolymer \\ \end{tabular} & 1.48 [35] & 60 & \
\begin{tabular}{c} PB-44\({}_{45}\) \\ PB-44\({}_{10}\) \\ \end{tabular} & 25 \\ \hline Zapon\({}^{\text{\textregistered}}\) & cellulose nitrate (Lacquer 30\% v) & 1.54 [28] & 100 &
\begin{tabular}{c} Zapon\({}_{70}\) \\ Zapon\({}_{40}\) \\ Zapon\({}_{20}\) \\ \end{tabular} & 70 (poly. conc. = -21\% v) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the samples studied in this work.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 3 System Model and Problem Formulation
### System Model
In this context, network entropy can also be considered a measure of the ability of the network topology to distribute the traffic load evenly among all the active links and, hence, among all the stations (MBSs and SCs) that compose the network. \[S_{G}=-\sum_{e\in E}Pr(e)\log_{2}Pr(e) \tag{2}\]  
\[Pr(e)=\frac{f(e)}{\sum_{e\in E}f(e)} \tag{3}\]  
In Equation (3), \(f:E\rightarrow\mathbb{N}\) is a function that counts the shortest paths that include link \(e\) and \(\sum_{e\in E}f(e)\) is a normalization constant. Algorithm 1 shows the pseudocode to compute the value of function \(f\) for a given \(e\in E\). The call to \(ShortestPath(G,a,b)\) (seeline 4) returns a shortest path connecting two stations using a given shortest path algorithm. We assume that the implementation of the shortest path algorithm breaks ties arbitrarily but consistently and, hence, that it always computes the same shortest path for a given source-destination pair. ```
0: A network \(G=(V,E)\), an edge \(e\in E\). Output The value of \(f(e)\). 1:\(f(e)\gets 0\)
2:\(AllSourceDest=\{(a,b)\mid a\
eq b\ \&\ a,b\in V\}\)
3:for all\((a,b)\in AllSourceDest\)do
4:\(P\gets ShortestPath(G,a,b)\)
5:if\(e\in P\)then
6:\(f(e)\gets f(e)+1\)
7:endif
8:endfor
9:return\(f(e)\);
```  
**Algorithm 1**\(f(e)\)
## 5 Verification
### Verification Process
#### 5.2.1 Verification of PV Storage Virtual Synchronous Generator System
Afterward, the generator mode is taken as an example to observe the influence of different inertia on the system frequency. Select the three typical values of moment of inertia in the stable interval of small signal analysis and implement the inertia matching. The values of other parameters are the same as values in Table 1.  
As can be seen from Figure 22, before the abrupt change of load, the PV storage system output 10 kW at a frequency of 50 Hz. After the load is abruptly changed, the delivered power decreases from 10 kW to 6 kW and the frequency increases from 50 Hz to 50.4 Hz. When \(J_{1}~{}=~{}0.1\) kg\(\cdot\)m\({}^{2}\), the transient process is the shortest/ When \(J_{1}~{}=~{}1.9\) kg\(\cdot\)m\({}^{2}\), the transient process is the longest. When \(J_{1}~{}=~{}0.9\) kg\(\cdot\)m\({}^{2}\), the transient process is between \(0.1\) kg\(\cdot\)m\({}^{2}\) and \(1.9\) kg\(\cdot\)m\({}^{2}\). The larger the moment of inertia, the longer the transient process and vice versa, which is consistent with the inertia of the synchronous generator. Therefore, the VSG technology in the new control strategy can introduce the inertia characteristic of the synchronous generator into the control of the new energy inverter so that the output power and the frequency of the power grid are more gentle and stable. In addition, by setting the moment of inertia, the optimal control of the response process can be flexibly implemented as needed. Figure 22: The influence of different inertia on the system frequency.