esa-sceva/satcom-chunk-collection huggingface dataset:
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 1 Introduction
Gravity-based clustering is a gravitational search based data clustering algorithm, which finds cluster centers iteratively in order to arrive at the optimal clustering solution [9]. Using the visibility functionalities available in SDDC, the necessary information for the proposed strategy, such as the east-west traffic statistics and the resource utilization of the VMs, can be readily obtained from the data center. Further, SDDC allows VMs to be dynamically moved between different physical servers using the VM migration functionalities. The key contributions of this work are summarized as follows:  
* We propose an approach of analyzing the traffic dependency among the VMs using the PCA for an efficient VM placement strategy in a large scale SDDC hosting a number of VMs. * We propose a new VM placement method using a gravity-based clustering and Hungarian matching methods for reducing the volume of network traffic in the SDDC. * We build an SDDC testbed using open source solutions and verify the efficiency of the proposed VM placement method in real environment. The remainder of this paper is organized as follows. Section 2 presents background and related studies on SDDCs and VM placement. Section 3 describes the system description and problem statement. Section 4 explains our proposed VM placement strategy. Sections 5-7 provide detailed explanations of the performance evaluation. Lastly, the study is concluded in Section 8.
# A Multi-Coupling 3D Frequency-Selective Surface with High Selectivity and Wide Dual Passband
## 3 Fabrication and Measurement
The comparison in Table 3 reveals that the proposed 3D-DBFSS achieves a superior frequency selectivity, with bandwidths of 5.3 GHz for the lower passband and 4.0 GHz for the upper passband, which are sufficient to meet practical application requirements. Moreover, the selection characteristic indices, KL and KU, which quantify the ratio of the \(-3\) dB bandwidth to the \(-20\) dB bandwidth, are 82.8% and 80.2%, respectively. These values are significantly higher compared to those of the existing designs summarized in Table 3, underscoring the enhanced selectivity of the proposed FSS. For instance, the bandwidths of previous designs range from 0.42 GHz to 6.4 GHz for the lower passband and from 1.32 GHz to 17.5 GHz for the upper passband, whereas the selection indices for these designs vary from 11.4% to 69%. These results collectively demonstrate that the proposed 3D-DBFSS offers an effective solution with good angular stability, superior frequency selectivity, and sufficient bandwidth, positioning it as a competitive option for practical applications requiring dual-band bandpass filtering.
# Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis
## 2 CEL Method for One- and Two-Way Fluid-Structure Coupling Validation
### Verification of Round-Rod Dragging Results
As shown in Table 2, when \(Re\) values were between \(10^{3}\) and \(10^{5}\), the simulated drag force using the CEL method was close to the empirical solution, with a relative error of less than 10%. This indicates the reliability of the CEL method for simulating two-way fluid-solid coupling problems. The two examples presented above demonstrate the reliability of the CEL method in calculating both one-way and two-way fluid-structure coupling problems. Next, the CEL method was used to simulate the dynamic responses of SFTs under seismic action.
## 2 Problem Formulation
### Power Grid Frequency Dynamic Model  
We consider the interconnected multi-area power system. Each one of \(n\) areas is connected to each other by tie-line (also called transmission line). As shown in Figure 1, each area equips a turbine generator and DES, such as wind power, solar power, battery, etc. [20].  
It also contains a load frequency controller (LFC) and tie-line bias controller (TBC) for frequency synchronization. Even if the synchronization measures regulate the frequency, an auxiliary control offered by DES may be necessary to enhance the system performance, when the power system encounters a severe disturbance, such as system fault, and sudden large load drop. Considering the auxiliary control, the dynamic model of area \(i\) can be formulated as a discrete linear difference equation [24]:  
\[x_{i}(k+1)=A_{i}x_{i}(k)+B_{i}u_{i}(k)+\sum_{j\in N_{p}(i)}B_{ji}x_{j}(k)+E_{i }w_{i}(k), \tag{1}\]  
where \(i\in N^{+}\cap[1,n]\), \(x_{i}=\left[\begin{array}{cc}\Delta f_{i}&\Delta P_{mi}&\Delta P_{vi}&\Delta P _{tie-i}&\int ACE_{i}\end{array}\right]^{T}\) is area state. \(\Delta f_{i}\) is the deviation related to synchronized frequency; \(\Delta P_{mi}\) is the mechanical power deviation of generator; \(\Delta P_{vi}\) is the valve position deviation of turbine; \(\Delta P_{tie-i}\) is the deviation of tie-line power injection from other physical neighbored areas; \(ACE_{i}\) is the \(ACE\) signal of area \(i\) and \(ACE_{i}=\alpha_{i}\Delta f_{i}+\Delta P_{tie-i}\). \(u_{i}\) as the auxiliary control output of DES for frequency regulation is the sum of all the powers generated from power-electronic interfaced DES; \(w_{i}\) is the disturbance caused by model error or other time-varying factors; \(A_{i}\) is the system transition matrix; \(B_{i}\) and \(B_{ji}\) are the gains of control effect and other physical neighbored systems; \(E_{i}\) is the disturbance gain. \(N_{p}(i)\) denotes the physical neighbored areas of area \(i\)In this linear model, loads are assumed to be constant because the variation of loads is slow relative to the dynamic frequency regulation. Therefore, \(A_{i}\), \(B_{i}\), and \(B_{ji}\) can be modeled in time invariant [24]. The system is linear time invariant (LTI) as well as an NCS. The DES controller of area \(i\) for frequency regulation are written as  
\[u_{i}=\varphi_{i}(x_{i},x_{N_{c}(i)}), \tag{2}\]  
where the time stage \(k\) is neglected. \(N_{c}(i)\) denotes the cyber connected areas of area \(i\). The controller calculates DES control outputs by the received state \(x_{i}\) and \(x_{j}\) as well as \(j\in N_{c}(i)\) from local and remote areas, respectively. The control objective is to mitigate the frequency deviation and reduce the overall costs defined in Section 3, which consider the least quadratic of state \(x_{i}\) and the control output \(u_{i}\).
## 2 Results and Discussion
### General workflow
#### 2.1.6 Classification and Prediction  
The performance of the fittest GA model is used to predict validation sample sets. The fittest GA model for data predicted class memberships of validation samples with high sensitivity and specificity using nearest centroid analysis, confirming that the frequent components generated by GA are truly discriminative components and capable of predicting unknown samples. To make it clear, the sensitivity of the prediction for a given class k is defined as the proportion of samples in k that are correctly classified, whereas the specificity for a given class k is defined as the number of true negatives divided by the sum of true negatives and false positives.
# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 3 The Face-Mask Label Dataset (FMLD)
### Available Annotations  
To make FMLD useful for experimentation with face-mask detection models and facilitate detailed analyses, we annotate all faces (that come with bounding boxes) with additional labels. Specifically, we add the following labels to all images considered in FMLD--in addition to the annotations related to face-mask presence and placement:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Degree**} & \multicolumn{4}{c}{**Occlusion Type (MAFA Labels)**} \\ \cline{2-6}  & \multicolumn{1}{c}{**1–Simple**} & \multicolumn{1}{c}{**2-Complex**} & \multicolumn{1}{c}{**3-Human Body**} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{2} & \multirow{2}{*}{2-Complex} & \multirow{2}{*}{3-Human Body} \\ \cline{3-4} \cline{6-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of masked faces from the MAFA [18] dataset with different occlusion types and with different number of occluded face parts (i.e., degrees of occlusion).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Donor Dataset**} & \multirow{2}{*}{**Purpose**} & \multirow{2}{*}{**\#Images**} & \multirow{2}{*}{**\#Faces**} & \multicolumn{4}{c}{**Labels \({}^{\ddagger}\)**} \\ \cline{4-7}  & & & & \multicolumn{4}{c}{**with Mask**} \\ \cline{4-7}  & & & & **Correctly Worn** & **Incorrectly Worn** & **without Mask** \\ \hline \multirow{2}{*}{MAFA} & Training & 25,876 & 29,452 & 24,603 & 1204 & 3645 \\  & Testing & 4935 & 7342 & 4929 & 324 & 2089 \\ \hline \multirow{2}{*}{Wider Face} & Training & 8906 & 20,932 & 0 & 0 & 20,932 \\  & Testing & 2217 & 5346 & 0 & 0 & 5346 \\ \hline \multirow{2}{*}{FMLD} & Training & 34,782 & 50,384 & 24,603 & 1204 & 24,577 \\  & Testing & 7152 & 12,688 & 4929 & 324 & 7435 \\ \cline{1-1}  & Totals & 41,934 & 63,072 & 29,532 & 1528 & 32,012 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Labels correspond to faces, not images.  
\end{table}
Table 3: Overall FMLD statistics. Images annotated for FMLD were taken from the MAFA and Wider Face datasets and partitioned into three classes (correctly worn masks, incorrectly worn masks and without masks) and later equipped with additional labels.  
* _Gender information_: Each face bounding box is annotated with information on whether a male (6433 faces) or a female subject (6255 faces) is shown. The complete dataset is approximately gender balanced as shown in the corresponding pie chart in Figure 3.
* _Pose information_: Pose is annotated at a coarse level with respect whether faces are frontal or in profile view. As illustrated by the pie chart in the middle of Figure 3, most faces (9245) are frontal, and 3443 faces are shown in profile.
* _Ethnicity_: Three categories are considered to describe ethnic origin, i.e., Caucasian, Asian, and African. FMLD is dominated by faces of Asian origin (6962), followed by Caucasian (5131) and African (595) faces. The label distribution across ethnicity is shown in the last pie chart of Figure 3.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
## 3 Results of the Analysis
### Frequency Splitting for IMT and DTT
For these plans, an initial optimization of the parameters of individual stations was also performed to verify the potential possibility of achieving satisfactory coverage in the modified channel system under the conditions of compatible operation of all networks and a detailed analysis of potential interference between multiplexes. The analyses were conducted using the IT tools of the National Institute of Telecommunications (NIT), which are employed for the analysis and practical and commercial planning of the DVB-T/T2 network by the NIT. The maps each time depict the interference-limited coverages, which take into account all incoming interference--both the network's own and by other SFN networks (interferences inside and outside the network). An example result for multiplex 1 (MUX1) and multiplex 2 (MUX2) coverage based on the developed channel assignments is shown in Figures 3 and 4. The multiplex MUX1 achieves a population coverage of 95.71%, and MUX2 achieves a population coverage of 96,16%; both over 95%, which is adequate for the requirements of the broadcasters and the DTT network operator. Similar good results were attained for the other multiplexes (MUX3-MUX6). Based on the detailed coverage analyses of all six nationwide multiplexes, the feasibility of implementing nationwide networks that meet the relevant coverage requirements (i.e., a minimum of 95% of the country's population) was confirmed for all six multiplexes. This confirms the potential possibility of restricting the radio spectrum for terrestrial television to the range of 470-606 MHz, thereby making the remaining part (606-694 MHz) available for IMT 5G/6G systems.
###### Abstract  
In buildings, multi-generation systems are a promising technology that can replace discrete traditional energy production methods. A multi-generation system makes it possible to efficiently produce electricity, cooling, heating, and freshwater simultaneously. This study involved the numerical analysis of a modified proposed novel solar-driven multi-generation system (MGS-II) integrated with the Organic Rankine Cycle (ORC), Humidification-Dehumidification Desalination System (HDH), and Desiccant Cooling System (DCS) by using heat recovery and thermal energy storage (TES) units. In addition, a comparison study with the basic multi-generation system (MGS-I) is performed. The proposed system is designed to supply electricity, air conditioning, domestic heating, and fresh water to small/medium-sized buildings. How operating conditions affect system productivity and performance metrics have been investigated. The results show that the proposed multi-generation system (MGS-II) can produce electrical power, space cooling, domestic heating and fresh water while maintaining comfortable conditions inside the conditioned space. Moreover, the MCS-II outperforms the MGS-I system, and the maximum MGS-II system productivity; electricity production (\(W^{\star}_{net}\)), freshwater (\(m^{\star}_{freshi}\)), space cooling (\(Q^{\star}_{cooling}\)), and domestic heating (\(Q^{\star}_{heating}\)) are 102.3 kW, 141.5 kg/h, 20.77 kW, and 225 kW, respectively. In addition, the highest total gained output ratio (TGOR), specific total gained energy (STG), and specific total gained energy equivalent price (STGP) of the MGS-II system are 0.6303, 3.824 kWh/m\({}^{2}\), and 0.149 USD/m\({}^{2}\), respectively. The accepted ranges of comfortable space-supplied air conditions (temperature and humidity) are 15.5-18.2 \({}^{\circ}\)C and 9.2-12.00 gv/kga for both systems, MGS-I and MGS-II. Finally, the current system (MGS-II) has the maximum of the system's performance indicators and productivity (TGOR and \(m_{freshi}\)) compared with the other reported systems.  
Footnote †: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 4 Performance Evaluation
### Results and Discussion
#### 4.2.4 Routing Overhead  
This section mainly analyzes the performance of the routing overhead of the four different algorithms. The change law of routing overhead with simulation time length is introduced. Additionally, the effect of the number of nodes on the routing overhead is studied.  
Figure 8 shows the routing overhead of the RATP algorithm, the CHBPR algorithm, the GPCR algorithm, and the Greedy algorithm at different simulation times. It can be seen from the figure that  
Figure 6: End-to-end delay vs number of nodes.  
Figure 7: End-to-end delay vs number of nodes.  
as the simulation time increases, the routing overhead of the four algorithms presents an unstable state. The RATP algorithm selects the next-hop data forwarding node by analyzing the node moving position. When the information is forwarded to the next-hop node, it can sense the location information of other nodes in advance and forward the information to the appropriate node. Therefore, path discovery time is reduced, routing overhead is reduced, and route cost changes are more stable than other algorithms.  
Figure 9 shows the routing overhead of the RATP algorithm, CHBPR algorithm, GPCR algorithm, and Greedy algorithm under different node numbers. It can be seen from the figure that as the number of nodes increases, the routing overhead of the four algorithms increases gradually. This is because when the number of nodes increases, the path propagation fails more frequently. As a result, the number of nodes passing through the message increases relatively and the path is found to be relatively slow. Since the RATP algorithm can further select the information forwarding path by analyzing the location information of the node, the algorithm can obtain other mobile node location information in advance and select an appropriate node to forward the information, so the routing cost is lower than other algorithms.  
Figure 8: Routing overhead vs number of nodes.  
Figure 9: Routing overhead vs. number of nodes.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 4 Experimental Tests of the Monitoring System
### Synchronous Reception of Broadcast Measurement Commands  
This test is aimed at checking if several Smart Measuring Nodes take the measurements simultaneously. For that purpose, an oscilloscope was used to check that they receive the broadcast measurement command frame synchronously. Namely, three Smart Measuring Nodes were used. The duration of the tests was 5 min. Several distances were used, ranging from 0 to 15 m. Broadcast measurement frames were sent periodically every 60 ms. Results are shown at Table 3. It can be appreciated how the jitter among the three Smart Measuring Nodes was below 1 ms for all the experimental tests.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 4 Experimental Setup
DPD: The training is enabled/ disabled by DPD. E: The method of time synchronisation (TS) is necessary so that Tx and Rx waveforms are in tight synchronization. F: Required for checking DPD inputs before transmitting to VSGs. CNN has been picked for DPD training. Table 3 shows the CNN settings that result in optimised NN performance. The parameters that constitute the design of the planned convolutional neural network are listed in Table 3. A trial and error test is used to pick the parameters [18]. The table's last part computes the NN's expressions in terms of its coefficients to determine its complexity.
###### Abstract  
The effects of shape memory alloy (SMA) wires on the damage behavior of glass fibers/epoxy resin composite laminates for the case of low-velocity impact are investigated experimentally and numerically. In this work, the low-velocity impact tests of SMAs/glass fibers/epoxy resin composite laminates are carried out. The elastic-plastic theory was adopted to simulate the mechanical behavior of SMA during the loading stage. The three-dimensional (3D) Hashin failure criterion is adopted in Abaqus/Explicit to model the damage initiation of composite laminates. The cohesive damage model is introduced to control the interface element and model the delamination failure. Moreover, the impact damage mechanisms of composite laminates are analyzed based on the experimental and numerical simulation results. These results show that the numerical results obtained in the present study have a reasonably good agreement with the experimental results. In addition, it is also found that impact damages are mainly caused by matrix cracks and delamination with no perforation for the case of 32-J impact energy, and impact damages are mainly caused by fibers breakage with perforation for the case of 64-J impact energy.  
shape memory alloy; composite laminates; low-velocity impact; finite element analysis; damage mechanism Article
# Influence of Multiple Factors on the Wettability and Surface Free Energy of Leaf Surface
## 2 Materials and Methods
### Experimental Methods
#### 2.3.3 Surface Free Energy Evaluation  
In order to quantify the surface properties of solid, the Owens-Wendt-Rabel-Kaelble (OWRK) method could be used to calculate the solid SFE by measuring several contact angles between test liquid and solid surface [34]. Generally, the solid SFE of each phase is divided into polar component \(\gamma^{p}\) and dispersive component \(\gamma^{d}\). The relationship is as follows:  
\[\gamma_{sl}=\gamma_{l}+\gamma_{s}-2\Big{(}\gamma_{l}^{d}\gamma_{s}^{d}\Big{)} ^{1/2}-2\Big{(}\gamma_{l}^{p}\gamma_{s}^{p}\Big{)}^{1/2} \tag{1}\]  
where \(\gamma_{l}^{d}\) and \(\gamma_{s}^{d}\) are the dispersive component of free energy of liquid and solid, respectively, \(\gamma_{l}^{p}\) and \(\gamma_{s}^{p}\) are the polar component of free energy of liquid and solid, respectively.  
Besides this, [PERSON]'s equation, also known as the wetting equation, is the description of the relationship between interfacial tension of solid-air interface, solid-liquid interface, liquid-air interface, and contact angle [35]. When the liquid contacts the solid surface in the gaseous phase, the relationship among contact angle \(\theta\), liquid surface tension \(\gamma_{l}\), SFE between solid-liquid phase \(\gamma_{sl}\) and solid SFE \(\gamma_{s}\) is expressed as:  
\[\gamma_{s}=\gamma_{sl}+\gamma_{l}\cdot\cos\theta. \tag{2}\]  
Substituting Equation (2) into Equation (1) gives:  
\[\gamma_{l}\cdot(1+\cos\theta)=2\Big{(}\gamma_{l}^{d}\gamma_{s}^{d}\Big{)}^{1 /2}+2\Big{(}\gamma_{l}^{p}\gamma_{s}^{p}\Big{)}^{1/2}. \tag{3}\]  
Therefore, when the contact angle between two or more kinds of liquid on the same solid surface (known \(\gamma_{l}\), \(\gamma_{l}^{d}\), \(\gamma_{l}^{p}\)) is obtained, the OWRK method can be used to calculate the solid SFE as well as its corresponding polar and dispersive components [13].
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 4 Architectures
### Adaptive Operations  
This control architecture is also designed for persistent connectivity scenarios since it relies on SDN-enabled networks and NFV, like the coordinated control case. This architecture assumes that the MANO is an evolved system tailored for NTN with improved autonomy and intelligence. This orchestration framework, named NTN-MANO, can efficiently adapt NetOps to SatOps constraints.  
Figure 10 presents the adaptive operations control architecture for persistent connectivity use cases like the one presented in the previous architecture. As can be seen, the CMS does not coordinate both SatOps and NetOps in this architecture. NetOps is directly coordinated by the NTN-MANO. However, the SatOps and NetOps are adapted to each other using the interface between the CMS and the NTN-MANO. The control flow using this architecture starts by the MNO providing NetOps intent rules to the NTN-MANO using the OSS/BSS interface. Then, the NTN-MANO communicates these intent rules to  
Figure 8: Persistent connectivity scenario featuring a LEO satellite megaconstellation.  
Figure 9: Control diagram using coordinated operations for a persistent connectivity constellation. The CMS coordinates both SatOps and NetOps through separate but synchronized channels. SatOps control manages satellite OM and GS operations using traditional satellite interfaces. NetOps control operates through MANO systems that interface with onboard SDN controllers and VIM. The CMS translates network operator intent into both satellite operational plans and MANO orchestration commands, ensuring coordinated execution across both domains.  
the CMS using its shared interface. The CMS generates the plan to execute the NetOps intent as best as possible, while staying aligned with the SNO SatOps criteria. The CMS then communicates the SatOps to the NTN-MANO using the shared interface. Finally, the NTN-MANO generates the NetOps considering the SatOps.  
The execution of the operations is the same as in last architecture. The CMS commands the platform OMs and the ground control, while the NTN-MANO commands the SDN controller and the VIM onboard the satellite as part of its payload. In this architecture, the CMS receives feedback from the ground control and satellite platform through telemetry, and the NTN-MANO receives feedback directly from the payload's SDN controller.