esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model
### Transport Model
#### 4.2.2 Energy Conservation  
The energy conservation equation is based on the local thermal equilibrium approach, i.e., it is assumed that, in the domains filled with porous media (anode and cathode), the fluid and solid are always in the thermal equilibrium. Therefore, the energy conservation can be written mathematically as:  
\[\rho_{\rm mix}c_{p,\rm mix}\varepsilon\mathbf{v}\mathbf{\cdot}\mathbf{\
abla}\,T=\mathbf{ \
abla}\mathbf{\cdot}\left(k^{\rm eff}\mathbf{\
abla}\,T\right)+\dot{q}_{V}, \tag{19}\]  
where \(c_{p,\rm mix}\) (\(\rm J\,kg^{-1}\,K^{-1}\)) is the constant pressure specific heat of the gas mixture (see [60] for the polynomial formulas), \(k^{\rm eff}\) (\(\rm W\,m^{-1}\,K^{-1}\)) stands for the effective thermal conductivity and \(\dot{q}_{V}\) (\(\rm W\,m^{-3}\)) is the heat source.  
The effective thermal conductivity depends on the microstructure and it is computed as [54]:  
\[k^{\rm eff}=\varepsilon k_{\rm mix}+\left(1-\varepsilon\right),k_{\rm solid}, \tag{20}\]  
where subscripts mix and solid stands for the value for the gas mixture and the solid particles, respectively. The thermal conductivities of the solids are shown in Table 10,  
\[k_{\rm mix}=\sum_{i=1}^{N}\frac{p_{i}k_{i}}{\sum_{j=1}^{N}p_{i}A_{ij}}, \tag{21}\]  
where \(k_{i}\) (\(\rm W\,m^{-1}\,K^{-1}\)) is the thermal conductivity of a pure gas \(i\), calculated using the [PERSON] and [PERSON] data [60]. The function \(A_{ij}\) is calculated using the data of [PERSON] and [PERSON] [61] as well as the [PERSON] and [PERSON] method of estimating the thermal conductivities at low pressure [62; 63]. The heat is produced within the SOFC in three ways:  
* the Joule heat \(q_{V}^{\rm Joule}\), produced because of the resistance for the electronic or ionic charge flow,
* the activation heat \(\dot{q}_{V}^{\rm act}\), the result of the irreversible activation barrier,
* and the water formation heat \(\dot{q}_{V}^{\rm H,O}\), the heat formed when a molecule of water is created.  
All of these contributions to the total heat source are generated in the different elements of the PEN. The summary of the heat sources is shown in Table 11.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Material** & **LSCF (Cathode)** & \
\begin{tabular}{l} **YSZ** \\ **(Electrolyte)** \\ \end{tabular} & **Ni/YSZ (Anode)** &
\begin{tabular}{l} **AISI 304L Steel** \\ **(Interconnector)** \\ \end{tabular} \\ \hline \hline
**thermal** & & & & \\
**conductivity** & \(k\) (\(\rm W\,m^{-1}\,K^{-1}\)) & & & \\ \hline reference & [64] & [65] & [66] & [67] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Thermal conductivities of solid materials.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Species** & \(\rm H_{2}\) & \(\rm H_{2}O\) & \(\rm O_{2}\) & \(\rm N_{2}\) \\ \hline
**Mass Source**\(\ddot{m}\) (\(\rm mol\,m^{-3}\,s^{-1}\)) & \(-\frac{j_{\rm TPB}^{\rmano}}{2F}\) & \(\frac{j_{\rm TPB}^{\rmano}}{2F}\) & \(-\frac{j_{\rm TPB}^{\rm cath}}{4F}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mass sources for Equation (13).
## 2 Materials and Methods
### Case Studies  
Five case studies of existing electricity consuming facilities with different demand profiles were selected. The five cases correspond to a winery, an irrigation pumping in a vineyard, a pig farm, a single-family home and a second home for vacations. From each of them, real data were obtained on their electricity consumption for 2019. Four of the five cases are actually powered by the electricity grid, so the consumption records of their respective remote-managed meters were obtained. The case of irrigation pumping is actually powered by a diesel genset, so its consumption data were obtained from the irrigation history of 2019. All data were obtained on an hourly basis. The basic consumption data of the five cases are shown in Table 1. In summary, although none of the five cases is currently being powered by a standalone PV generation system, their consumption data are representative of their energy demand, and suitable to be used in simulations throughout this study.  
The geographic locations (Figure 2) of the five cases are at a similar latitude and not very distant, all of them in the continental Mediterranean climate zone of Spain, so their profiles of solar resource and temperature are similar (Table 1). The solar resource and temperature data, on an hourly basis, were obtained from PVGIS [39].  
Figure 1: Graph of the methodology used in the study.  
Figure 3 shows the graphs of demand throughout the year, for each of the five cases. Case 1 (winery) presents a seasonal demand with its maximum in October (the harvest season). Case 2 (irrigation pumping) presents demand only from June to September (the irrigation season). The stability throughout this season is due to the fact that the pumping was activated the same number of hours each day, except at the beginning and end of the season, with a smaller number of hours. Case 3 (pig farm) shows a stable demand throughout the year. Case 4 (single-family home) presents a seasonal demand, greater during the cold season, due to the use of electric heating. Case 5 (second home) presents a very low demand, except for some weekends and holiday periods when it was inhabited.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Winery** & \
\begin{tabular}{c} **Irrigation** \\ **Pumping** \\ \end{tabular} & **Pig Farm** &
\begin{tabular}{c} **Single-Family** \\ **Home** \\ \end{tabular} & **Second Home** \\ \hline Annual consumption (kWh) & 53,345 & 64,125 & 216,446 & 5036 & 799 \\ Maximum power \({}^{1}\) (kW) & 41,073 & 45,000 & 59,000 & 3644 & 2474 \\ Annual irradiation \({}^{2}\) (kWh/m\({}^{2}\)) & 1633 & 1656 & 1734 & 1703 & 1674 \\ Average annual temperature (\({}^{\circ}\)C) & 10.9 & 10.4 & 13.7 & 13.5 & 11.3 \\ \hline \hline \end{tabular}  
\({}^{1}\) One-hour interval. \({}^{2}\) On horizontal plane.  
\end{table}
Table 1: Main characteristics of the five case studies.  
Figure 2: Location of the five case studies.
## 2 Materials and Methods
### Technological Processing  
#### 2.2.1 Pretreatments  
Blanching, soaking, and ultrasound were used as pretreatments. In all treatments, the material-to-water ratio was constant and amounted to 1:4 [24]. The experiments were performed in two repetitions for each pretreatment.  
Blanching  
Conventional heat treatment was conducted by immersing the material in a container with tap water at a temperature of 90 \({}^{\circ}\)C. After 3 min, the material was placed on a sieve and cooled for 15 s in a stream of cold water [25]. A paper towel was used to remove excess water from the beets. However, blanching caused weight loss, which was related to the transfer of water-soluble ingredients to the medium in which the process took place. The weight loss during blanching was statistically significant (\(p<0.05\)) and amounted to 7.5% in relation to the material not subjected to pretreatment.  
Soaking was performed in distilled water at room temperature (21 \(\pm\) 1 \({}^{\circ}\)C) for durations of 10, 20, and 30 min. After the process, the material was dried on filter paper. This process was performed to compare it with the effect of ultrasound treatment, which was also conducted in water surroundings. The use of soaking caused an increase in weight in samples due to the penetration of water into the raw material. It was observed that in the case of soaked tissue, with increasing soaking time, there was a smaller increase in weight.
# The Leaky-Wave Perspective for Array-Fed Fabry-Perot Cavity and Bull's-Eye Antennas
###### Abstract  
Two-dimensional leaky-wave antennas (LWAs) are a class of planar, traveling-wave radiators with attractive features of a low profile, ease of feeding, frequency reconfigurability of the radiation pattern, and polarization agility. Their use in conjunction with array feeders has been the subject of various investigations in recent decades, thanks to the additional degrees of freedom provided by the presence of multiple independent sources. Here, we provide a review of some of the most recent and promising array-fed two-dimensional (2-D) LWAs, selecting a couple of the most significant structures in application, namely Fabry-Perot cavity antennas and _bull's-eye_ antennas, and discussing some of their recently proposed advanced features.  
leaky-wave antennas; planar antennas; phased arrays; Fabry-Perot cavity antennas; _bull's eye_ antenna; orbital angular momentum; polarization reconfiguration; pattern reconfigurability; near-field focusing
## 5 Results
### Case 3: Maximizing Decentralization and Scalability  
Given \(D=D_{max}=1\) and scalability \((C=1)\), the goal is to achieve maximum security \((S(f)=1)\) for the largest possible adversary fraction \(f\). Consensus mechanisms need to be simplified to maximize decentralization and scalability. For instance, using protocols with lower communication overhead, such as the Nakamoto consensus [34] with reduced confirmation times, can help decrease latency and increase throughput. Furthermore, full node participation is required, meaning that all nodes participate equally in the consensus process.  
The impact on security, however, is significant. By reducing the number of consensus rounds, fewer confirmation blocks \((n)\) are used to achieve finality. The attack probability \(P_{attack}\) for a blockchain can be represented as follows:  
\[P_{attack}=\sum_{k=n}^{\infty}\binom{k}{n}a^{n}(1-\alpha)^{k-n} \tag{53}\]  
where \(\alpha\) is the adversary's fraction of hashing power. With smaller \(n\), \(P_{attack}\) increases, making the network more vulnerable. This increased vulnerability makes it easier for adversariesto conduct double-spend attacks or reverse transactions. Additionally, the network may fail to reach consensus due to insufficient validation, leading to consensus failure.  
If we take the example of a PoW blockchain, the probability that an adversary with fraction \(\alpha\) of the total hashing power can successfully perform a double-spend attack after \(n\) confirmations is approximated by the following.  
\[P_{attack}(n,\alpha)=\left(\frac{\alpha}{1-\alpha}\right)^{n} \tag{54}\]  
Therefore:  
\[S(f)=1-P_{attack}(n,\alpha)=1-\left(\frac{\alpha}{1-\alpha}\right)^{n} \tag{55}\]  
For \(n=1\):  
\[P_{attack}(1,0.1)=\left(\frac{0.1}{0.9}\right)^{1}\approx 0.111 \tag{56}\]  
Security Level:  
\[S(f)=1-0.111=0.889 \tag{57}\]  
For \(n=6\):  
\[P_{attack}(6,0.1)=\left(\frac{0.1}{0.9}\right)^{6}\approx 1.7\times 10^{-7} \tag{58}\]  
Security Level:  
\[S(f)=1-1.7\times 10^{-7}=0.99999983 \tag{59}\]  
We remark that reducing \(n\) from 6 to 1 increases \(P_{attack}\) by several orders of magnitude, significantly decreasing security.  
As consensus requirements are loosened, the effective \(f_{max}\) decreases, which means that the system cannot tolerate the intended adversary fraction \((f<f_{max})\).  
In conclusion, achieving \(D=D_{max}\) and \(C=1\) necessitates compromises in security protocols, reducing \(S(f)\) below acceptable levels. Therefore, maximizing decentralization and scalability leads to decreased security, resulting in a contradiction.
## 3 Act
### APC and ARC
#### 3.4.1 Apc  
Figure 7 shows the block schematic of the APC. The DWT and RLC are similar to the ones conventionally used, and ADPCM is a reduced version used for sensed data compression. The encoder of the ADPCM consists of a difference signal computation, adaptive quantizer, inverse adaptive quantizer, quantizer scale factor adaptation, and signal reconstructor. The difference signal computer subtracts the reconstructed signal from the original data. The subtraction of two successive data packets that have similar values reduces the range of data values. The range of values is reduced again by the adaptive quantizer. The quantized data is encoded by RLC and then inserted into the network layer. The quantized data are also inversely quantized by an inverse adaptive quantizer and reconstructed into the signal by the signal reconstructor. The number of generated packets is varied by the adaptive quantizer, which is controlled by the quantizer scale factor adaptation. The quantizer scale factor adapter increases the quantizer step size in proportion to the strength of congestion. Further, the decoder of the ADPCM consists of an inverse adaptive quantizer, signal reconstructor, and quantizer scale factor adapter.  
Figure 7: Block schematic of APC.
# Cross-Domain Access Control Model in Industrial IoT Environment
## 3 Distributed Access Control Model Based on a Master-Slave Chain
### Design of Access Control Processes
#### 3.4.2 Identity Registration  
For effective access control, the domain devices must first register their identities. For example, the specific process for authentication of device \(D_{1}^{A}\) in \(dom_{A}\) is as follows:  
Step 1: \(D_{1}^{A}\) generates a public-private key pair \((PK_{Ai,1},SK_{Ai,1})\) and then sends a registration request \(req\) to \(IMS\). With this request, \(D_{1}^{A}\) completes the request phase of identity registration.  
\[IMS\gets D_{1}^{A}: \tag{6}\] \[req=Enc_{MSP}(PK_{A,1},t,ID_{A,1},sig_{A,1}\left(\begin{array}{ c}PK_{A,1},t\\ ID_{A,1}\end{array}\right))\]  
where \(t\) denotes the timestamp of the message \(req\), and \(ID_{A,1}\) denotes the \(ID\) of the device \(D_{1}^{A}\). \(sig_{A,1}\) denotes the digital signature of \(D_{1}^{A}\). \(Enc_{MSP}\) denotes the elliptic curve encryption for encryption with the public key of \(IMS\).  
Step 2: \(IMS\) receives a registration request \(req\) from the device and needs to further verify the message reliability to ensure the trustworthiness of the \(D_{1}^{A}\) identity through authentication. The formula is as follows:  
\[\left\{\begin{array}{c}(PK_{A,1},t,ID_{A1},sig_{A,1}\left(\begin{array}{ c}PK_{A,1},t\\ ID_{A,1}\end{array}\right))=Dec_{IMS}(req)\\ h=H(PK_{A,1},t,ID_{A,1})\\ h^{\prime}=Dec_{A,1}(sig_{A,1})\end{array}\right. \tag{7}\]  
Step 3: \(IMS\) first decrypts req using its own private key to obtain \((PK_{Ai,1},t,ID_{A1},sig_{Ai,1})\), and then it calculates \(h\). Then it decrypts the signature \(sig_{Ai,1}\) using the public key of \(D_{1}^{A}\) to obtain \(h^{\prime}\), and finally it determines whether \(h\) and \(h^{\prime}\) are equal. If they are equal, the authentication passes.  
Step 4: After the authentication is passed, \(IMS\) generates the certificate of identity \(DC_{A1}\) as follows. With this certificate, \(D_{1}^{A}\) can then perform effective cross-domain or intra-domain access control.  
\[IMS\to D_{1}^{A}: \tag{8}\] \[DC_{A,1}=(PK_{A,1},t,ID_{A,1},Attrs_{A1},sig_{IMS}\left(\begin{array}[] {c}PK_{A,1},t,ID_{A,1}\\Attrs_{A,1},Rold\end{array}\right))\]  
where \(sig_{IMS}\) denotes the digital signature encrypted by the private key of the identity management server. \(Attrs_{A,1}\) denotes the set of attributes of \(D_{1}^{A}\); \(Rold\) denotes the role identity of \(D_{1}^{A}\).  
The identity management server stores the generated identity certificate \(DC_{A1}\) for authentication before access control is performed.
# A VHF Circularly Polarized Turnstile Antenna with Balun for Meteor Radar
## 2 Antenna Topology and Simulation Analysis
In the view of the fact that the phase difference of the feeding ports is kept stable at 90\({}^{\circ}\) by the simulator, the dual-feed turnstile antenna performs well in circular polarization. Additionally, we can see from Figure 5c that the axial ratio (AR) below 3 dB is from \(-39^{\circ}\) to \(39^{\circ}\) within the band of interest. It is worth noticing that the phase difference between the two ports directly influences the polarization characteristics. This indicates that a feeding network must be designed to ensure good feed. To achieve the equal amplitude and 90\({}^{\circ}\) phase difference for the 2 ports feeding of the antenna, a Wilkinson power divider with a 90\({}^{\circ}\) phase shifter is introduced. The schematic diagram of the feeding network is shown in Figure 6.
# Implementation of Atomically Thick Graphene and Its Derivatives in Electromagnetic Absorbers
## 2 Graphene-Based Quarter-Wavelength Absorbers
### Graphene and Its Derivatives-Based Dallenbach Absorbers
The length of scale bar is 20 \(\mu\)m; (**e**) the schematic of GF Dallenbach absorber. The measured electrical properties of GF-30, GF-60 and GF-90: (**f**) the real part of complex permittivity; (**g**) the loss tangent. The measured return loss (RL) spectra of GF-30, GF-60 and GF-90 for (**h**) 2–18 GHz; (i) 26.5–40 GHz and (**j**) 75–100 GHz, reprinted with permission from Ref. [48]. where \(\mu_{1}\) and \(\epsilon_{1}\) are the complex relative permeability and permittivity of absorption layer, and \(\mu_{2}\) and \(\epsilon_{2}\) are the complex relative permeability and permittivity of matching layer. \(d_{1}\) and \(d_{2}\) are the thicknesses of absorption and matching layers, respectively. The corresponding RL is calculated with Equations (6) and (9). A Peak absorption of \(-52.2\) dB is achieved at 10 GHz for the proposed bilayer Dallenbach absorber and the effective absorption bandwidth is as broad as 10 GHz (6.4-16.4 GHz) for an overall material thickness of 4 mm (\(d_{1}=1.4\) mm and \(d_{2}=2.6\) mm). Table 1 summarizes the performance of graphene or its derivatives based Dallenbach absorbers as well as the corresponding absorber thickness. It is noted that the RL of most devices are calculated utilizing the measured \(\epsilon_{r}\) and/or \(\mu_{r}\) rather than achieved from an actual S-parameter measurement. Hence, the correction of the calculated RL heavily depends on the accuracy of the extracted \(\epsilon_{r}\) and/or \(\mu_{r}\). What is worse, as the input impedance of Dallenbach absorber given by Equation (10) is derived for homogeneous material above the metallic layer, the homogeneity of composites can also affect the RL and substituting the measured effective \(\epsilon_{r}\) and/or \(\mu_{r}\) into Equation (10) may not correctly predict the input impedance of the absorber. Last but not least, the effect of incident angle is rarely investigated for the graphene Dallenbach absorbers. As most of them operate at microwave frequencies aiming to reduce the radar cross section for military applications, it is highly desired to achieve effective wave absorption in a wide incident angle. Therefore, further experimental explorations are required to validate the absorption performance of the graphene Dallenbach absorbers. Figure 4: The SEM images of (**a**) SiOC ceramic and (**b**) G/SiOC ceramic; (**c**) the calculated RL spectra of Dallenbach absorber with G/SiOC synthesized under various amounts of graphene oxide. The measured (**d**) real and (**e**) imaginary parts of complex permittivity under various temperatures, reprinted with permission from Ref. [49].
# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
###### Abstract  
Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol's ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments.  
OppNets; fuzzy logic; DTN; routing protocol; vehicular networks; epidemic; MaxProp +
Footnote †: journal: Information  
Article
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 4 SDAG Model and HSDFG Scheduling
### HSDFG Scheduling
#### 4.3.4 Overall Algorithm  
Algorithm 3 describes the overall process of the LSEFT scheduling algorithm.  
```
1:queue \(F\), set of cores \(M\)
2:scheduling result
3:Create empty queue \(D\)
4:\(cl(c)=0\), \(c\in C\)
5:Compute level value using Algorithm 1
6:repeat
7: Put instance \(\alpha\) with maximum level into \(D\)
8: Pick instance \(\alpha_{i}\) from D using Equation 18)
9: Assign instance \(\alpha_{i}\) to core \(c_{i}\) using Equation 19)
10:\(cl(c_{i})\gets cl(c_{i})+et(\alpha_{i})\)
11:until queue \(F\) is empty
```  
**Algorithm 3**LSEFT Algorithm  
The LSEFT algorithm uses a combination of level values and a short-job-first policy to select instance. In Algorithm 3, the instances with the largest level value are first selected from queue \(F\) each time and these instances are added to the to-be-scheduled queue \(D\). Afterwards, the instance with the smallest execution time is selected from the to-be-scheduled queue \(D\). If there are many instances with the same minimum execution time, the instance that enters the to-be-scheduled queue \(D\) earliest is selected first. Finally this instance is assigned to the processor with the minimum finish time, and if many processors have the same minimum finish time, the algorithm will select the one with the smallest processor ID.  
For the scheduling problem of a given SDFG, the SDFG is first converted into an HSDFG using Algorithm 1, where the data structure of the converted HSDFG is a cross-linked table. Then, using Algorithm 2, the priority of each instance in the HSDFG is calculated, and finally, using Algorithm 3, a suitable processor is selected for each instance to execute based on the priority of each instance to optimise the objective function.
# The Synergy of Renewable Energy and Desalination:
###### Abstract  
Water is one of the most valuable and essential resources for human life, yet its scarcity has become a pressing global issue exacerbated by climate change and population growth. To address the increasing demand for water driven by urbanization, industrial expansion, tourism, and agricultural needs, many countries are turning to desalination as a viable solution. This study investigates the integration of renewable energy sources (RES) with desalination technologies to enhance both sustainability and efficiency. A comprehensive review of major desalination methods has been conducted, with a particular focus on the application of solar and wind energy. Additionally, the challenges associated with renewable energy-powered desalination, including the need for effective energy storage systems and the inherent volatility of power supply, were explored. Our findings indicate that coupling renewable energy with desalination not only significantly reduces carbon emissions but also enhances the sustainability of water supply systems. The study also emphasizes the importance of emerging technologies, such as hybrid energy storage systems (HESS) and machine learning (ML), in optimizing RES powered desalination processes. Ultimately, this study aims to guide future research and development initiatives, promoting the global adoption of desalination systems powered by renewable energy.  
renewable energy sources; water scarcity; desalination technology; sustainability +
Footnote †: journal: Applied sciences  
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