esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
MEC has become increasingly important in today's technology landscape. With the substantial increase in data produced by IoT networks, the need for computational and storage resources has become more prominent. Addressing the above-mentioned demands for delay-sensitive and computation-intensive tasks is made feasible by deploying MEC in the network. In this paper, we studied the hierarchical architecture from cloud-based networks to edge computing. Then, with the support of the latest literature on the topic, the challenges in how to offload tasks and how to allocate resources to perform tasks in MEC-assisted vehicular networks in an optimal way are investigated. Furthermore, our research extended to exploring the challenges associated with deploying cutting-edge technologies such as SDN and DT within MEC-enabled VNs. These technologies are crucial for making informed decisions, efficiently managing network resources, and exercising precise control over network operations. We recognized that the successful integration of SDN and DTs into MEC environments presents its own challenges. Furthermore, our research encompassed an in-depth analysis of recent developments in leveraging MEC to enable vehicular applications. This included a comprehensive review of the following areas: MEC to enhance collision avoidance systems in VNs; the application of MEC in platooning scenarios; MEC-assisted in tele-operated driving; MEC-powered enhanced video streaming VN applications. Moreover, we also considered how vehicles themselves can contribute to augmenting MEC capabilities. This involved studies on investigating how vehicles can serve as mobile edge nodes, extending the reach and capacity of MEC infrastructure. Our comprehensive study of these topics not only contributed towards complete understanding of MEC's potential in vehicular applications but also provided valuable insights into the practical implementation and optimization of these technologies within the context of modern transportation systems.  
Conceptualization, A.P., G.N. and G.S.; methodology, A.P. and G.N.; investigation, A.P.; writing--original draft preparation, A.P.; writing--review and editing, G.N and G.S.; supervision, G.N. and G.S. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the Italian Ministry of Education and Research (MUR) in the framework of the FoReLab project (Departments of Excellence), by the European Union--NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033--17/06/2022, Spoke 10), and by the Centre for Logistic Systems of the University of Pisa.  
Not applicable.  
The authors declare no conflict of interest.
# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 5 Experiments
### Robust Performance and Analysis of the AEM Network  
Next, the robustness of the proposed network is verified under different SNRs, and experiments are conducted separately relative to all the above test datasets. The inversion rates under different SNRs are shown in Table 4. It is clear that the proposed network exhibits higher inversion rates under both closed- and open-set test datasets.  
Figure 16: Reconstruction experimental diagram of different networks in closed-set test dataset: (**a**) reconstruction of SAE network; (**b**) reconstruction of AEM network.  
Figure 17: Experimental diagram of different networks in open-set test dataset4. (**a**) visualization of DNN network; (**b**) visualization of CNN network; (**c**) visualization of SAE network; (**d**) visualization of AEM network; (**e**) inversion of DNN network; (**f**) inversion of CNN network; (**g**) inversion of SAE network; (**h**) inversion of AEM network.  
Particularly under different unknown operating parameters, the inversion performance of the network is still relatively stable as the SNR decreases gradually. When the SNR is \(-10\) dB, the inversion rate can still reach more than 80%, which proves the anti-noise performance of the proposed AEM network. In addition, in the training stage, the network only learns the data in the environment. In the environment of low SNR without training, the deep RF structure features extracted by the AEM are still unaffected by the unknown low-SNR environment, which further demonstrates that AEM has high robustness and generalization performances.
## 5 Experimental Results
### Characteristics of the Proposed Approach
#### 5.1.1 Evolution of Solutions for \(n_{k}=2\)
From Figure 4d, we can see that the corresponding values for random selection were achieved for \(gen=100\). At this point, the front is not yet fragmented. This might indicate that extreme and middle solutions are advanced further during different periods in the evolution process, and an adaptive selection procedure might be required for _PO-prob_. We aim to investigate this question further in our future work. As in the previous case, _PO-prob_ covers the extreme solutions better, see Figure 5b. However, a large portion of its solutions is dominated by the solutions produced by other algorithms. The difference between _PO-prob_ and _PO-prob_* in this case is less visible. At the same time, using _NSGA-II_ during the last 150 generations in _PO-prob_* does push the resulting Pareto-frontier a bit further. From Figure 5a, we can again see that _NSGA-III_ and _PO-count_ behave very similar to _NSGA-II_. Overall, tournament selection usually favors one objective more than another. These observations were confirmed in a large fraction of our 30 independent runs.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.2 Precise Link Delay Calculation  
Link delay for one-hop transmission depends on the MAC delay to access the channel, the queuing delay due to the limited buffer size of each UAV [58], the propagation delay due to inter-UAV distance, and the processing delay in the UAV processor. Additionally, node density is also an important factor that can affect the delay because high node density increases the competition to access the shared medium and very low node density increases the inter-UAV distance. Thus, routing protocols should jointly consider all the parameters to improve the network delay. In [20], the authors adaptively control the node congestion window according to node density in FANETs to precisely calculate the delay. Furthermore, in [22], the authors use an exponentially weighted moving average to precisely calculate the link delay for each UAV.
## 6 Conclusions  
The simulation framework developed through this research plays a crucial role in addressing the congestion issues in Korean EDs, offering practical solutions to alleviate ongoing problems such as shortages of medical resources. The framework is capable of simulating various scenarios, exploring the applicability in actual ED environments, and, through the integration with real-time data, predicting future congestion and creating optimal operational environments. Moreover, it provides methods to improve the ED environment using machine learning-based resource scheduling policies.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**KTAS**} & **FIFO** & **FIFO** & **SRPT** & **SRPT** & **CR** & **CR** & **Integrated** \\  & & **(Random)** & **(Centroid)** & **(General First)** & **(Senior First)** & **(General First)** & **(Senior First)** & **ML** \\ \hline \multirow{3}{*}{A} & KTAS1 + 2 & 456.85 & 453.25 & 455.38 & 456.32 & 452.22 & 449.69 & 447.80 \\  & KTAS3 & 389.38 & 386.52 & 391.34 & 394.38 & 388.57 & 382.76 & 383.16 \\  & KTAS4 + 5 & 236.73 & 235.35 & 236.70 & 236.37 & 241.85 & 245.18 & 233.75 \\ \hline \multirow{3}{*}{B} & KTAS1 + 2 & 500.69 & 500.02 & 501.35 & 500.15 & 494.27 & 494.58 & 497.03 \\  & KTAS3 & 442.18 & 440.94 & 442.62 & 445.38 & 435.52 & 435.10 & 434.04 \\  & KTAS4 + 5 & 243.94 & 243.55 & 239.15 & 241.91 & 249.92 & 252.07 & 239.69 \\ \hline \multirow{3}{*}{C} & KTAS1 + 2 & 387.22 & 386.94 & 392.24 & 387.50 & 383.26 & 382.20 & 383.99 \\  & KTAS3 & 313.73 & 313.82 & 318.06 & 314.75 & 311.81 & 308.36 & 311.72 \\ \cline{1-1}  & KTAS4 + 5 & 188.90 & 185.52 & 186.74 & 183.75 & 194.18 & 192.98 & 186.44 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Compares of KTAS between resource scheduling policies (min).  
Future research directions involve applying the developed simulation system to actual EDs and building digital twin services through real-time data exchange. Such real-time simulations and data analysis can further enhance the operational efficiency of EDs, ultimately reducing patient waiting times and improving the quality of healthcare services.  
This work was supported by the National Research Foundation of Korea (NRF) (NRF 2021R11A1A01049609). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
Not applicable.  
The data used in this paper are statistical data provided by the Central Emergency Medical Center in Korea. The data used are open data, and our articles contain no data related to personal information. Administrator approval is required to use data. [[https://e-medis.nemc.or.kr/](https://e-medis.nemc.or.kr/)]([https://e-medis.nemc.or.kr/](https://e-medis.nemc.or.kr/)) (accessed on 2 February 2024).  
The author declares no conflicts of interest.
# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 3 Optimization Model
### Particle Swarm Optimization Algorithm  
Initially, the implementation-relevant characteristics of the system model are introduced in depth. For the proposed protocol, we assume a large, dense network and a database that is predominantly static. When deployed within the network, all items are identical in terms of their physical properties, energy capacity, and transmission range. Each network object is aware of its geographical position and home base. The operation is carried through via a PSO algorithm [8; 27].  
The standard particle swarm optimization starts with a set of random solutions and searches for the optimal solution by updating the generations. There are two learning processes in particle swarm optimization: the cognitive learning process, which is based on the history and past of each particle. The process of social learning is based on the history and past of all particles and is obtained from the exchange of information between all particles in the population. Particles move in the multidimensional search space. While moving, each particle updates its position according to its experience and the experience of neighboring particles, and after receiving information about its best position and the best position of its neighbors, it updates its position based on a mathematical function. Each particle searches for its coordinates in the problem-solving space, which takes the best solution obtained so far (pbest) as the best particle location (gbest). Meanwhile, all particles move toward the best particles. In each iteration, the optimization includes gbest and pbest changes in the velocity of each particle toward the locations. The mathematical model of particle movement is in the form of Equation (1):  
\[\begin{array}{l}|v[t+1]=wv[t]+\rho_{1}r_{1}(\chi_{1}[t]-x[t])+\rho_{2}r_{2}( \chi_{2}[t]-x[t])\\ |x[t+1]=x[t]+v[t+1]\end{array}\, \tag{1}\]  
where the parameters \(w\), \(\rho_{1}\) and \(\rho_{2}\) are real and positive weights that show the effect of inertia. The best cognitive component (the best location of the particles) and the best social component (the best location of the particles) are known when determining the new speed \(v[t+1]\) for a particle. A coefficient \"and\" a \(\mid\)R-dimensional random vector in which each component is a random variable with a uniform distribution between zero and one, the natural number r represents the dimension of the problem search space. The variables \(v[t]\) and \(x[t]\) are respectively the velocity and position vectors of the particle in the \(t\)th iteration. The \(\chi_{1}[t]\) and \(\chi_{2}[t]\) are the components of the best cognitive position and the best social position, respectively [27].
## 2 Modeling and Validation  
### Engine Model  
A DCI11 CI engine produced by Dongfeng motor corporation was employed for the numerical study. Its specifications are listed in Table 1.  
The simulation study was conducted in CONVERGE package. The major models related to the CFD simulation are listed in Table 2. A Kelvin-Helmholtz Rayleigh-Taylor (KH-RT) model was employed to predict the primary and secondary breakups of the fuel droplet. The Frossling correlation method was used to simulate the droplet evaporation process. An RNG (Reynolds-averaged Navier-Stokes) k-\(\varepsilon\) model was employed to simulate turbulent flow. A no-time-counter (NTC) collision model was selected to describe the collision of the fuel droplets. A droplet drag model was established using a dynamic drag model, and the spray wall interaction was modeled using a rebound/slide model.  
In many studies, n-heptane is commonly used as a surrogate for diesel due to its similar ignition characteristics [22; 23]. [PERSON] et al. [24] proposed a skeletal n-heptane mechanism that integrated a CO-C1 submodel, a C2-C3 submodel, and an enhanced low-temperature submodel. This model has been extensively validated under various conditions. Given its reliable performance, it was employed as the basic model in this study. The DMG mainly consisted of hydrogen, CO, and methanol. Its ignition and combustion could be predicted by using the C0-C1 submodel. To predict the emission of soot and NOx, the Hiroyasu-NSC soot model [25] and an extended Zeldovich skeletal model [26] were coupled with Liu'smodel. The combustion of the diesel-DMG mixture was predicted in the SAGE chemistry solver, which was integrated into the CONVERGE package.  
A 45\({}^{\circ}\) sector mesh was selected to reduce the computational cost. The base mesh was set as 4 mm. Additionally, an adaptive mesh refinement (AMR) algorithm was adopted to locally refine the meshing. The combustion chamber computational mesh at the top dead center (TDC) is depicted in Figure 1.
## 5 Experimental Data  
The experimental scenario includes free space detection and through-wall detection. In free space, the result verifies the correctness of using the position of the echo's maximum value as the dividing line. In addition, the validity of the clutter suppression method is demonstrated. In through-wall detection, the experimental result also confirms the correctness of the clutter analysis and the effectiveness of the suppression method. The configuration of the computer is a 64-bit 2.1-GHz Intel Core I7-12700F CPU. All the operations of software are realized by MATLAB (R2021b) codes.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
###### Abstract  
ELIMED has been developed and installed at ELI beamlines as a part of the ELIMALA beamline to transport, monitor, and use laser-driven ion beams suitable for multidisciplinary applications, including biomedical ones. This paper aims to investigate the feasibility to perform radiobiological experiments using laser-accelerated proton beams with intermediate energies (up to 30 MeV). To reach this goal, we simulate a proton source based on experimental data like the ones expected to be available in the first phase of ELIMED commissioning by using the G4-ELIMED application (an application based on the Geant4 toolkit that simulates the full ELIMED beamline). This allows the study of transmission efficiency and the final characteristics of the proton beam at the sample irradiation point. The Energy Selector System is used as an active energy modulator to obtain the desired beam features in a relatively short irradiation time (around 6 min). Furthermore, we demonstrate the capability of the beamline to filter out other ion contaminants, typically co-accelerated in a laser-plasma environment. These results can be considered as a detailed feasibility study for the use of ELIMED for various user applications such as radiobiological experiments with ultrahigh dose rate proton beams.  
Monte Carlo simulations; Geant4; laser-accelerated ion beams +
Footnote †: journal: Physics Letters B
## 4 Conclusions
When the ion concentration is increased, quantum efficiencies decrease but the absorption near 800 nm together with the cross relaxation effect enhancing the 1860 nm band increase, which suggests that 0.25 or 0.5 mol % samples are potential candidates as hosts in laser applications for bulk as well as fiber applications. For the TeNbTm sample, we obtained average emission cross sections of 2.62 \(\pm\) 0.02 \(\times\) 10\({}^{-21}\) and 5.63 \(\pm\) 0.02 \(\times\) 10\({}^{-21}\) cm\({}^{2}\) for the 1460-nm and 1860-nm bands, respectively. For the sample containing zinc as a glass modifier (TeZnTm), the average emission cross sections were determined to be 2.78 \(\pm\) 0.39 \(\times\) 10\({}^{-21}\) and 6.33 \(\pm\) 0.39 \(\times\) 10\({}^{-21}\) cm\({}^{2}\) for the 1460-nm and 1860-nm bands, respectively. These results compare well with values reported for other tellurite hosts [9; 39] and it can also be pointed out that for the TeZnTm sample, we obtained higher stimulated emission cross sections, especially for the 1860-nm emission band. Among the hosts we analyzed, TeZnTm glass stands as a good candidate for the development of 2-\(\upmu\)m lasers with high emission cross sections near 1860 nm. [PERSON] and [PERSON] performed the spectroscopic studies, consisting of absorption measurements, emission measurements, and data analysis by using the Judd-Ofelt and McCumber theory. [PERSON] prepared the manuscript, which was edited by [PERSON], and [PERSON], [PERSON] and [PERSON] designed and supervised the spectroscopic studies. [PERSON] built the laser used during the experiments and prepared the codes for data analysis. [PERSON] prepared the computer-controlled emission measurement system and the related data fitting programs. [PERSON] and [PERSON] fabricated the glasses under investigation and contributed to the analysis of the experimental data. [PERSON] also measured the preliminary absorption spectra. The authors declare no conflicts of interest.
# Incentive Price-Based Demand Response in Active Distribution Grids
## 4 Proposed IDR Method for Control of the BESS
### Distributed Optimization Based on Incentive Prices  
The optimization problems of the LVGC and BESS controllers defined in (13) and (14) respectively can be solved by the below iterative distributed algorithm. The proof of the following can be found in [18] and it is omitted here.  
Update of power setpoints by each flexible resource are done by the below expression, where \(k\) denotes the iteration number.  
\[p_{fj}(k+1)=\left[p_{fj}(k)-\varepsilon_{1}\Big{(}\partial f_{fj}(p_{fj}(k))+ \alpha_{fj}(k)\Big{)}\right]_{p_{f}}\quad\forall j\in\mathcal{N}_{f}\] (15a) Update of dual variables of the node voltages are made according to the below equation.  
\[\mathbf{\mu}_{v\min}(k+1)=\left[\mathbf{\mu}_{v\min}(k)+\varepsilon_{2}( \mathbf{v}_{\min}-\mathbf{v}(k)-\phi\mathbf{\mu}_{v\min}(k))\right]_{+} \tag{15b}\]  
\[\mathbf{\mu}_{v\max}(k+1)=\left[\mathbf{\mu}_{v\max}(k)+\varepsilon_{2}( \mathbf{v}(k)-\mathbf{v}_{\max}-\phi\mathbf{\mu}_{\max}(k))\right]_{+} \tag{15c}\]where \([\quad]_{+}\) denotes the projection operation onto the set \(\mathbb{R}_{+}^{N}\), \(\phi>0\) is a regularization factor, and \(\varepsilon_{1},\varepsilon_{2}>0\) are predefined parameters to prescribe the step sizes for the dual variables update. The node voltages are updated with the latest value of flexible powers from BESS as follows  
\[\mathbf{v}(k+1)=\mathbf{v}_{s}+\mathbf{B}_{vp}(\mathbf{C}_{f}\mathbf{p}_{f}(k )-\mathbf{p}_{d})-\mathbf{B}_{vq}\mathbf{q}_{d} \tag{15d}\]  
The incentive price of flexible powers from BESS are updated from the values of updated dual variables.  
\[\alpha_{f}(k+1)=\mathbf{R}(\mu_{v\max}(k+1)-\mu_{v\min}(k+1)) \tag{15e}\]  
The steps for the iterative algorithm to calculate incentive price signals is provided below.  
As shown in Algorithm 1, the LVGC and BESS controllers exchange information (incentive price signals and dual variables) until the iterations converge within a specified tolerance.  
```
Initialize the variables: BESS active powers \(\mathbf{p}_{f}\), dual variables \(\mu_{\min}\) and \(\mu_{\max}\) and the incentive prices for BESS active powers \(\alpha_{f}\). for{m = 1,2, \(\cdots\) (repeat until convergence)} do
1:[BESS Controller]: Receive the incentive price vector \(\alpha_{f}\) and compute the power \(p_{f}\) and send it to the network controller based on the gradient of its cost function as per (15a).
2:[LVGC]: Receive the vector \(\mathbf{p}_{f}\) from all BESS. Update the dual variables \(\mu_{\min}\) and \(\mu_{\max}\) based on (15b) and (15c) respectively.
3:[LVGC]: Update the node voltages based on the latest values of BESS powers using (15d) and compute the new incentive prices using (15e). Send the updated \(\alpha_{f}\) to all DERs. endfor
```  
**Algorithm 1** Distributed iterative algorithm to compute incentive prices [18]
## 2 Materials and Methods
### Solid Solutions  
Practically, HYDCEM invokes functions in PHREEQC, and two of these are important to consider when comparing predictions in blended cements. The first of these are solid solutions where several compounds in the cement hydrates form non-ideal solid solutions with each other. The hydrogarnets, for example, show partial solid solution between the hydrogrossular (C\({}_{3}\)AH\({}_{6}\)) typical of Portland cements and a siliceous hydrogarnet (C\({}_{3}\)AS\({}_{0.24}\)H\({}_{5.52}\)) seen as a single phase. More siliceous hydrogarnets, as found in blended slag and fly ash cements, span a miscibility gap, where two hydrogarnets co-exist between the range C\({}_{3}\)AS\({}_{0.5}\)H\({}_{5.0}\) and C\({}_{3}\)A\({}_{0.76}\)H\({}_{4.48}\). As the system becomes more siliceous than this, a single hydrogarnet phase with variable composition becomes stable. Two phases with simple stoichiometries (C\({}_{3}\)ASH\({}_{4}\) and C\({}_{3}\)AS\({}_{2}\)H\({}_{2}\)) are known in this range. The solid solution series end member (grossular garnet, C\({}_{3}\)AS\({}_{3}\)) cannot be formed at atmospheric pressure but is seen as a natural, metamorphic mineral. In addition, a solid solution series exists between andradite garnet (C\({}_{3}\)FS\({}_{3}\)) and hydro-andradite (C\({}_{3}\)FH\({}_{6}\)), but there is little evidence of extensive solid solutions with aluminium bearing hydrogarnets at atmospheric pressure and temperature.  
The major solid solution in cement hydrates is, of course, the gel phase. At high Ca:Si ratios, (>1.5), C-S-H is a relatively simple stoichiometry, forming a solid which dissolves incongruently on leaching, such that the calcium-rich structural units are more soluble in water than are the silicate units. As the Ca:Si ratio of the gel falls (such as in blended Portland-slag or Portland-fly ash cements), the gel phase incorporates increasing amounts of aluminium, and the term '_C-A-S-H_' is widely used to describe these compounds. Conceptually, the C-S-H '_line_' on the triangular Ca-Al-Si hydrate phase diagram becomes the C-A-S-H '_triangle_' broadening as the Ca:Si ratio decreases.