esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cooperation Architecture and Models
### Service and Service Pool Model  
As discussed above, resource virtualization is a critical aspect for this architecture, which will improve the usage of resources and the mission efficiency of swarm systems. Therefore, various physical resources, covering the platform resource, processing resource, payload resource, and data resources, as shown in Figure 1, are virtualized and encapsulated into services, and all these services are gathered into a service pool. To represent these different resources uniformly, we firstly design a unified service model \(\varepsilon\). This model is defined as a four-tuple \(<id,b\_prop,d\_prop,ins\_para>\), where \(id\) is an identity within the global pool, \(b\_prop\) and \(d\_prop\) are static properties and dynamic properties respectively, and \(ins\_para\) is the set of parameters inherited from a corresponding \(\tau\) for instantiating this service. Concretely, each element is defined as the following. Of course, the definitions of these elements will vary with the types of services. \(b\_prop:=<Cid,typ,Val,MaxV,MaxD,MaxLW,mtbf,bittr>\)  
\(d\_prop:=<Cap,Reli,cV,cD,cLW,cPos,cSta>\)  
\(ins\_para:=<\tau.id,exec\_IF>\). Based on this model, the dynamical efficiency of each service can be fundamentally assessed from several aspects. For example, the availability \(\alpha\), reliability \(\gamma\), and the comprehensive service ability \(\xi\) of \(\varepsilon\) can be evaluated by Formulas (1)-(3), separately. It is clear that \(\alpha\) and \(\gamma\) of \(\varepsilon\) are all closely related to the carrier of \(\varepsilon\), except the platform service. In addition, Formula (3) shows that \(\xi\) is a combinational function of several factors \(x_{i}\) and its weight \(w_{i}\). For example, \(\xi\) of one fighter can be defined as \(f=\ln(S\times P_{\varepsilon}\times K_{m}\times P_{n})+\ln Q\), where \(S\) is the maximum voyage distance, \(P_{\varepsilon}\) is the penetration coefficient, \(K_{m}\) is the long-range weapon coefficient, \(P_{n}\) is the navigation ability, and \(Q\) is the \(MaxLW\)[51]. For specific swarm system, the definition of this function should be different. \[\varepsilon.\alpha=\frac{\varepsilon.mtbf}{\varepsilon.mtbf}+\varepsilon.bttr \times\varepsilon^{\prime}.\alpha\ \ \ where\ \ \varepsilon^{\prime}.id=\varepsilon.Cid \tag{1}\]  
\[\varepsilon.\gamma=\varepsilon.Reli\times\varepsilon^{\prime}.Reli\ \ \ \ where\ \ \varepsilon^{\prime}.id=\varepsilon.Cid \tag{2}\]  
\[\varepsilon.\xi=f(x_{i},w_{i})\ \ \ (i=1,2,...,m) \tag{3}\]  
For one swarm system, there exists a global service pool that is composed of local service pools of individual members within this system. In addition, in each member, a \(\varepsilon\)-Agent is deployed to manage the service pool, interact with the intelligent planner and local event scheduler. Considering the topic of this paper, all details about service, service pool, and \(\varepsilon\)-Agent are not presented here, but the advantage of such model is obvious.
## 2 Materials and Methods
### Databases for Literature Searching  
To conduct the literature search, two major databases were chosen: Web of Science Core Collection (WoS CC) by Clarivate and Scopus by Elsevier. These databases were selected due to their prominence as major repositories of general scientific literature and citation indexes. The search was performed in the first half of March 2023 and yielded 15,798 and 5558 records, respectively, resulting in a combined total of 21,356 studies (Figure 1).
# Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi
## 2 Fundamentals of the Chosen EMS
### Optimization Problems  
Both _Planning_ and _Online Replanning_ tasks are executed solving optimization problems. The considered variables are the battery state of charge (SOC) and the power flows between couples of system devices: loads, renewable generators, battery storage systems, and connection to the public grid.  
In the optimization problem of the _Planning_ stage, the objective function is the end user's cash flow (\(CF\)), which is expressed as:  
\[f_{obj}(\mathbf{x})=\sum_{t=1}^{N}CF_{t} \tag{1}\] \[CF_{t}=\begin{cases}GEPP_{t}\Delta t\,C_{buy}&\text{if }GEPP_{t} \geq 0\\ GEPP_{t}\Delta t\,C_{sell}&\text{otherwise}\end{cases} \tag{2}\]  
where \(C_{sell}\) and \(C_{buy}\) are the prices of sold/purchased energy, respectively; \(\Delta t\) is the duration of each time step; \(t\) is a discrete variable representing the time step index; and \(N\) is the number of time steps in a day.  
The objective function in Equation (1) must be minimized satisfying a set of physical and design constraints at each time step, namely:  
1. non-negative value for each variable;
2. power balance at generation node;
3. power balance at load node;
4. maximum contractual grid-exchanged power;
5. maximum battery charging/discharging power;
6. minimum and maximum SOC values;
7. continuity of SOC between consecutive days;
8. evolution of SOC between time steps; and
9. cyclicity of SOC profile between consecutive days.  
The optimization problem of the _Online Replanning_ stage aims at finding the solution that minimizes the maximum deviation between the actual GEPP and the self-committed GEPP (\(\tilde{\gamma}\)). Such a deviation is due to forecasting errors. Hence, the objective function to minimize is:  
\[f_{obj}(\mathbf{x})=\max_{i\leq t\leq N}\lvert\gamma_{t}-GEPP_{t}\rvert \tag{3}\]  
To avoid greedy minimization of instantaneous differences, at each execution, the algorithm decides the variable values for the current time step \(t=i\) (using measurements) and also for the following steps \(t\leq N\) (using forecast values). However, only the reference power values for the current time step (\(t=i\)) are sent to the hardware, whereas the other values (for \(t>i\)) are discarded.  
The constraints for Equation (3) are readily adapted from those of the _Planning_ stage by:  
* changing the time intervals on which the constraints are defined; and
* splitting power balance constraints to account for measured values (at \(t=i\)) and for forecast values (at \(t>i\)).
###### Abstract  
In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster's central point to the base station (BS), which ultimately enhances the network's lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster's configuration and then elects the CH applying an improved K-means clustering algorithm--one of the machine learning methods--integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node's mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection.  
WSN; LEACH; K-means; machine learning; energy efficiency; cluster head Article
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 5 Conclusions  
For the traditional image defogging algorithm model based on a convolutional neural network to capture incomplete global feature information and incomplete defogging, this paper proposed a parallel end-to-end defogging network model with a deep coding and decoding structure. The encoder of the model consists of two branches: a CNN and a vision transformer in parallel, which continuously downsamples the image features and extracts local and global features of the haze images captured at different scales, respectively. The decoder part of the model consists of the vision transformer, which restores the image to its original size by upsampling. To better fuse the local and global features obtained through the encoder and decoder, the feature extraction was finally enhanced using FPN. The comparison results on the uniform, non-uniform haze datasets and real datasets showed that the algorithm in this paper had a better defogging effect and could recover fog-free images better under different haze environments. Future work will continue to optimize the algorithm structure in order to achieve better defogging results.  
Conceptualization, S.L. and Q.Y.; Methodology, S.L. and Q.Y.; Funding acquisition, S.L.; Validation, S.L., Q.Y. and Y.Z.; Writing--original draft preparation, Q.Y.; Writing--review and editing, S.L., Q.Y., Y.Z. and B.L.; Supervision, Y.Z., B.L. and F.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Henan Province Science and Technology Key Project (Grant Nos. 212102210050, 202102210081); the Doctor Foundation of Henan Polytechnic University (Grant No. B2014-35, B2019-50); the Outstanding Young Scientists Program of Beijing Colleges and Universities (Grant No. BJJWZYJH01201910006021); the Open Foundation of the State Key Laboratory of Fluid Power and Mechartronic Systems (Grant No. GZKF-202016); the Sub project of strengthening key basic research projects in the basic plan of the Science and Technology Commission of the Military Commission (Grant No. 2019-JCIQ-ZD-120-13); the Postgraduate education and teaching reform project of Henan Polytechnic University (Grant No. 2021Y04); the Fundamental Research Funds for the Universities of Henan Province (Grant No. NSFRF200403).  
Figure 7: The experimental results of the real outdoor hazy images. (**a**) Hazy image; (**b**) method in [5]; (**c**) method in [6]; (**d**) method in [11]; (**e**) method in [12]; (**f**) method in [15]; (**g**) proposed method.
## 2 Literature Review
### Demand Management Considering Congestion
To meet these challenges, we propose another demand management tool, the dynamic plan control method, which can dynamically control open and closed plans for potential customers in each period to relieve mobile network congestion. Moreover, we innovatively apply it in the framework of connection-speed-restriction pricing, which is increasingly popular now that 4G and 5G are available.
## 4 Experimental Results  
In this section, we present the empirical results and our analysis. Tables 3 and 5 present, respectively, averages for Hypervolume and IGD+. For each problem, the algorithms results were submitted to a Kruskal-Wallis statistical test where the following hypothesis was answered:  
**Hypothesis 1**.: _Considering a quality indicator (Hypervolume or IGD+), is a given algorithm output equivalent to the algorithm that has the best output?_  
In these tables, we highlighted (in grey) the algorithm with the best average output; moreover, considering the statistic test outcomes, we represented in bold those algorithms whose outputs are equivalent to the best one.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Problems** & **Param** & **IBEA** & **GDE3** & **mIBEA** & **NSGA-II** & **SPEA2** & **HHCF** & **HHLA** & **HHRL** & **MOABHH** \\ \hline \multirow{9}{*}{All} & Crossover type & SBX & - & SBX & SBX & SBX & SBX & SBX & SBX \\  & Crossover prob. & 0.9 & - & 0.9 & 0.9 & 0.9 & 0.9 & 0.9 & 0.9 & 0.9 \\  & Crossover dist. & 20 & - & 20 & 20 & 20 & 20 & 20 & 20 \\  & Mutation type & Poly & Poly & Poly & Poly & Poly & Poly & Poly & Poly & Poly \\  & Mutation prob. & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) & \(\frac{1}{\mathrm{errs}}\) \\  & Mutation dist. & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\ \cline{2-10}  & DE type & - & \(\mathit{rand}/1/\mathit{bin}\) & - & - & \(\mathit{rand}/1/\mathit{bin}\) & \(\mathit{rand}/1/\mathit{bin}\) & \(\mathit{rand}/1/\mathit{bin}\) & \(\mathit{rand}/1/\mathit{bin}\) \\  & DE CR & - & 0.2 & - & - & - & 0.2 & 0.2 & 0.2 & 0.2 \\  & DE F & - & 0.2 & - & - & - & 0.2 & 0.2 & 0.2 & 0.2 \\  & DE K & - & 0.5 & - & - & - & 0.5 & 0.5 & 0.5 & 0.5 \\  & HHCF a & - & - & - & - & - & 30 & - & - & - \\  & Decision Points & - & - & - & - & - & - & 25 & 25 & - \\  & HHRL/HHLA \(m\) & - & - & - & - & - & - & 2 & 2 & - \\  & HHRL/HHLA \(a\) & - & - & - & - & - & - & 0.1 & 0.1 & - \\  & HHRL/HHLA \(\beta\) & - & - & - & - & - & - & 3 & 3 & - \\ \hline \multirow{9}{*}{P01-P16} & Generations & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 & 1000 \\  & Pop Size & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & HHCF \(g\) & - & - & - & - & - & 25 & - & - & - \\ \cline{1-1}  & HHRL/HHLA \(g\) & - & - & - & - & - & 25 & - & - & - \\ \cline{1-1}  & MOABHH \(g\) & - & - & - & - & - & - & - & 50 \\ \cline{1-1}  & MOABHH \(\beta\) & - & - & - & - & - & - & - & - & 1 \\ \hline \multirow{9}{*}{P17-P18} & Generations & 50 & 50 & 50 & 50 & 50 & 50 & 50 & 50 & 50 \\ \cline{1-1}  & Pop Size & 30 & 30 & 30 & 30 & 30 & 30 & 30 & 30 & 30 \\ \cline{1-1}  & HHCF \(g\) & - & - & - & - & - & 1 & - & - & - \\ \cline{1-1}  & HHRL/HHLA \(g\) & - & - & - & - & - & 1 & - & - & - \\ \cline{1-1}  & MOABHH \(g\) & - & - & - & - & - & - & - & 1 \\ \cline{1-1}  & MOABHH \(\beta\) & - & - & - & - & - & - & - & - & 0.5 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters used in experiments.
# Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
## 2 Background and Literature Review  
Previous studies [5; 6; 7] applied a modified SEIR compartmental model to predict the COVID-19 epidemic dynamics. This study incorporates a basic SEIR model [8] to describe the dynamic process of epidemic propagation. Given a population that contains a group of individuals, a compartment SEIR model includes four states at time \(t\): susceptible (\(S(t)\)), exposed (\(E(t)\)), infected (\(I(t)\)), and recovered (\(R(t)\)), as shown in Figure 1. Individuals in the susceptible state do not have the disease at time \(t\) but may become infected if they come in contact with an infectious person. The infected state includes individuals with a positive COVID-19 test result at time \(t\) who may infect a susceptible individual if they come in contact with each other. Individuals in the recovered state have recovered from COVID-19 and are no longer contagious at time \(t\). Recovered individuals return to the susceptible state. The rates of change of the above-mentioned states are represented by a set of coupled ordinary differential equations (ODEs), as shown in Equations (1)-(4). \[\frac{dS(t)}{dt}=\xi\cdot S(t)-\alpha\cdot S(t)\cdot E(t)-\delta_{S}\cdot S(t)+ \theta\cdot R(t) \tag{1}\]  
\[\frac{dE(t)}{dt}=\alpha\cdot S(t)\cdot E(t)-\beta\cdot E(t)-\delta_{E}\cdot E(t) \tag{2}\]  
\[\frac{dl(t)}{dt}=\beta\cdot E(t)-\gamma\cdot I(t)-\delta_{I}\cdot I(t)-\mu \cdot I(t) \tag{3}\]  
\[\frac{dR(t)}{dt}=\gamma\cdot I(t)-\theta\cdot R(t)-\delta_{R}\cdot R(t) \tag{4}\]  
Table 1 summarizes the characteristics and applications of COVID-19-related existing research studies. Figure 1: Compartment model of susceptible–exposed–infected–recovered (SEIR) disease transmission.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 2 Principle of the Converter
### Modulation of the Proposed Topology  
A unified modulation method is proposed for the two modes in Figure 4. The phase shift between the same half bridge switches is \(180^{\circ}\). The phase shift between the upper or lower arm switches is \(180^{\circ}\) too. The two lower arm switches of \(Q_{2}\) and \(Q_{4}\) have the same duty cycle of 0.5. The two upper arm switches of \(Q_{1}\) and \(Q_{3}\) have the same duty cycle, which is denoted as \(D\). The adjustment range of \(D\) is 0 to 1 in ideal condition. The switching sequences of switch \(Q_{1}\) and \(Q_{3}\) determines the sequence of switch \(Q_{0}\). The circuit operates in buck mode as \(D\) is less than 0.5. The circuit operates in boost as \(D\) is more than 0.5.  
There are current continuous mode (boost-CCM) and current discontinuous mode (boost-DCM) according to input inductor current \(i_{Lin}\) in boost mode. Boost-CCM mode is divided into boost-CCM1 and boost-CCM2 according to the leakage current of the  
Figure 3: Equivalent circuit of the proposed isolated buck–boost topology.  
transformer. In buck mode, there are current continuous mode buck-CCM, current discontinuous modes buck-DCM1 and buck-DCM2 according to the leakage inductor current \(i_{Llk}\).  
Usually, the input inductor is designed as current continuous mode. The leakage inductor of HFT is small to reduce the voltage on the leakage inductor. The leakage inductor current is usually discontinuous. Therefore, boost-CCM2 and buck-DCM2 are commonly used modes. Figure 3(a,b) are the switching sequence diagram in boost-CCM2 mode and buck-DCM2 mode, respectively.
# Radio Channel Scattering in a 28 GHz Small Cell at a Bus Stop: Characterization and Modelling
## 4 Conclusions  
We have built and used a wideband radio channel sounder to characterize the radio channel for a small cell at 28 GHz. This is a very fast sounder, capable of performing the measurement of the channel impulse response every 4.55 \(\upmu\)s.  
The small cell was deployed at a bus stop, and the impulse response was measured as different vehicles passed by. We have characterized the radio channel by determining its characteristic functions. We have shown that the multipath contributions group into clusters that present decreasing power with the delay. The power decay from the main components in the clusters follows a linear trend. Looking at the contributions within each cluster, decreasing power with the delay is also found.  
Despite the DS being confined by the directional antenna used at the transmitter, we have shown that its spectrum varies with time as vehicles pass by the bus stop, reaching Doppler shifts that range from almost 0 Hz to a few kHz. The channel cannot be considered stationary, as the vehicle passed the bus stop. We conclude that a non-stationary DS model is needed, and that it should account for the Doppler spread variation with time by using a time-dependent spread factor value.  
We have shown the requirements that a complete scattering model, accounting for both the delay and Doppler spreads, should meet for this radio channel. The delay spread should reflect the contribution clustering. The Doppler spread model for each delayed component could be the same and even have the same parameter values, but should account for the non-stationary nature of the channel. We propose an empirical model for the DS, where the non-stationary behavior of the channel can be reflected by varying the model parameter over time.  
Conceptualization, M.G.S.; methodology, M.G.S.; software, A.S.V.; formal analysis, M.G.S.; investigation, M.G.S., A.S.V. and I.E.; resources, I.E.; data curation, M.G.S.; writing--original draft preparation, M.G.S. and I.E.; supervision, M.G.S.; project administration, M.G.S.; funding acquisition, M.G.S. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Doppler spectrum for the maximum frequency spread reached in components with relative delays \(\tau_{\mathrm{i}}=0\) ns, \(\tau_{\mathrm{i}}=240\) ns and \(\tau_{\mathrm{i}}=444\) ns, and the fitted model from (7).  
**Funding:** This research was funded by the Spanish Government, Ministerio de Economia y Competitividad, Secretaria de Estado de Investigacion, Desarrollo e Innovacion, grant number TEC 2017-85529-C3-3-R; Xunta de Galicia, project ED431C 2019/26; atlantTlic Research Center and the European Regional Development Fund (ERDF).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 2 Numerical Modelling
### Convergence and Validation  
A standard two-plate Whipple shield was used to validate the methodology and models in this research. To achieve this, the numerical results from the standard Whipple shield (SWS) were compared to experimental observations by [PERSON] et al. [9], where all model parameters were kept the same to allow for a direct comparison. The SWS simulation setup is shown in Figure 6. A 5-mm aluminium projectile was fired with a velocity of 6500 m/s at a Whipple shield with a front plate of thickness 1 mm and a back plate thickness of 2.5 mm. Figure 7 shows the progression of the simulation in approximately 2-\(\mu\)s intervals.  
\begin{table}
\begin{tabular}{l l r r} \hline \hline
**Constitutive Equation** & **Material Parameter** & **AL2024-T3** & **Ti-6 Al-4V** \\ \hline \multirow{6}{*}{Johnson–Cook} & \(A\) (MPa) & 167 & 862 \\  & \(B\) (MPa) & 684 & 331 \\  & \(n\) & 0.551 & 0.34 \\  & \(C\) & 0.001 & 0.012 \\  & \(m\) & 0.859 & 0.8 \\ \hline \multirow{6}{*}{Johnson–Cook (damage)} & \(D_{1}\) & 0.112 & \(-\)0.09 \\  & \(D_{2}\) & 0.123 & 0.25 \\ \cline{1-1}  & \(D_{3}\) & 1.5 & \(-\)0.5 \\ \cline{1-1}  & \(D_{4}\) & 0.007 & 0.014 \\ \cline{1-1}  & \(D_{5}\) & 0 & 3.87 \\ \hline \multirow{6}{*}{Mie–Grüneisen (EoS)} & \(G\) & 5240 & 5130 \\  & \(\gamma_{0}\) & 1.97 & 1.23 \\ \cline{1-1}  & \(S_{1}\) & 1.400 & 1.028 \\ \cline{1-1}  & \(a\) & 0.48 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Johnson–Cook constitutive and damage models, and Mie–Grüneisen equation of state parameters for AL2024-T3 (debris particle, shield and honeycomb core) and Ti-6 Al-4V (honeycomb core) [21; 22].  
Figure 6: Standard Whipple shield configuration.
## 3 Proposed SPMIPv6 Protocol
### Application Scenarios of IP-WSN based on SPMIPv6
#### 3.3.3 Healthcare  
SPMIPv6 based IP-WSNs are envisioned to be heavily used in healthcare environments. Although hospital scenarios can be handled differently, IP-WSN provides great potential to ease the development of new services by getting rid of cumbersome wires and simplifying patient care in hospitals and for home care as the World is rapidly graying. The worldwide population of elderly people over age 65 is expected to be more than double from 357 million to 761 million by 2025 [16]. The speed with which this age-structural change is taking place implies an urgent need for solutions that will relieve the mounting pressure on our health-care systems as well as support a better quality of life and quality of care for our aged.