esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced FANO Structure Based on Tip-Field-Enhancement Theory
###### Abstract  
High-Q metasurfaces have attracted much interest owing to their potential application in biological sensors. FANO is a type of high-Q factor metasurface. However, it is difficult to achieve large resonant intensity and a high-Q factor at the same time. In this paper, by sharpening the tips of the asymmetrical split-ring FANO structure and letting more charges stack at the tips to enhance tip coupling, the Q factor was significantly improved without sacrificing too much resonant intensity. Simulation results showed that the Q factor increased up to 2.4 times, while the resonant intensity stayed higher than 20 dB, and the experiment results agreed with the simulations. This indicated that the tip-field-enhancement theory can be applied in time-harmonic electromagnetic-fields, and the method proposed here can be used to increase the sensitivity and accuracy of microfluidic sensors. Additionally, other types of research, such as on antenna design, could benefit from this theory.  
metasurface; FANO; tip field enhancement theory; Q factor; resonant intensity +
Footnote †: journal: Applied Soft. 2019, 9, 5009; doi:10.3390/app9235009
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods
### Petrophysical Testing
#### 2.3.3 Seismic P- and S-Wave Velocities  
P-wave velocity was measured by both GTK and SP, S-wave velocity by SP only. All velocities were measured on unloaded specimens in the axial direction. The P-wave velocity measurement at GTK was done using sonar elements with a pulse central frequency of approximately 1 MHz. The saturated specimens were measured submerged at normal temperature and pressure.  
P- and S-wave velocity measurements carried out by SP were conducted according to ASTM International standard D 2845-00 [10]. The system used for velocity measurements was a GCTS ULT-100 ultrasonic pulse generator and sampling device with heavy-duty 76-mm-diameter steel plates with integrated piezoelectric crystals from GCTS. The transducershad a resonant frequency of 200 kHz and included both P- and S-crystals. The pulse used was a 130 V/5 us square wave generated with the ULT-100; see [PERSON] et al. [7] for details.  
By assuming the rock to be a homogeneous isotropic material with a linear elastic response it was possible to determine the [PERSON]'s ratio from the P-and S-wave velocity values as:  
\[v=\Big{(}V_{p}^{2}-2V_{s}^{2}\Big{)}/2\Big{(}V_{p}^{2}-V_{s}^{2}\Big{)}, \tag{1}\]  
where \(v\) is [PERSON]'s ratio, and \(V_{p}\) and \(V_{s}\) are the P- and S-wave velocities, respectively. [PERSON]'s modulus can be calculated as:  
\[E=2\rho V_{s}^{2}\times(1+v). \tag{2}\]
###### Abstract
Next, these logic objects send sensor data which are often fused and integrated into the cloud. The cloud is equipped with machine learning or deep learning prediction models to offer basic services, including water quality prediction models, fish metrics estimation models, and fish food requirements for daily feeding. The basic services provide AI functions to transform unstructured sensor data into structured fish farm information, which is further inputted into the succeeding decision-making processes to establish fish farm data applications. In this system, the fish farm manager uses web interfaces to connect to these data applications for precision aquaculture. Advanced technologies integrating IoT and cloud virtualization techniques facilitate the construction of an AIoT system based on digital twin modeling. Despite this development, the existing industrially available sensors are insufficient to address the issues of low power consumption, high bandwidth networking, waterproofing, and end-computing AI functions. These physical sensors need further improvement before implementing the corresponding logic objects. Figure 1: Schematic view of the proposed AIoT system for intelligent fish farming [8]. Moreover, the knowledge management of conventional fish farming is the core technology for aquaculture transformation using emerging AIoT technologies. In this study, we exploit the effective knowledge management methodology by representing the knowledge of traditional fish farming as a set of digital twins using the modified analytical hierarchical process [9; 10]. Based on the analysis results, we constructed an AIoT prototype system to verify the effectiveness of intelligent fish farming. The remainder of this paper is organized as follows. Section 2 presents the literature reviews of intelligent fish farming. Section 3 presents the research methodology. The empirical results and discussions of our research findings in comparison with previous studies are detailed in Section 4. Finally, the conclusions and future work for the digital transformation based on digital twins are presented in Section 5.
## 3 Results and Discussion
### Ignition Delay  
Figure 8 depicts the ignition delay (ID) recorded in several engine configurations, namely, diesel (D100), DP20, DP20H5 (with 5 lpm hydrogen), DP20H10 (with 10 lpm hydrogen), and DP20H15 (with 15 lpm hydrogen). Based on the graphical depiction, it is evident that the ID of the DP20H5, DP20H10, and DP20H15 engines exceeds that of the D100 engine across all tested configurations. The officially specified minimum ID for D100 fuel is recorded as 15.6 degrees. However, the DP20H15 sample has a higher ID value, reaching a maximum of 22.9 degrees at 23.5\({}^{\circ}\) b TDC (before top dead centre). The experimental findings suggest that the diesel fuel blend containing 15 lpm hydrogen had the higher ID while running at maximum capacity, with a recorded value of around 31.8%. This observation may be attributed to the lower cetane number of hydrogen blends. This research investigates the influence of hydrogen supplementation on the variability in cylinder pressure throughout the process of combustion, in contrast to the utilisation of D100 fuel. Significant modifications are seen in diesel fuel containing 10 and 15 lpm hydrogen. The ID demonstrates a decline at various engine configurations with the introduction of hydrogen propellant [34; 35; 36]. Table 1 displays the cetane number values for hydrogen and diesel fuel, which range from 5 to 10 and 48 to 52, respectively.  
Figure 7: MRPR with injection timing and hydrogen blends.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 6 Results and Discussion
### Performance of Packet Loss Rate  
ED-MORFO and existing methodologies were used to compare the performance of packet loss rate (PLR). Figure 9 depicts the existing MCH-EOR [24] and RDSAOA-EECP [25] assessments. Figure 9 illustrates that, when compared to existing approaches, ED-MORFO produces better outcomes. In contrast to the existing MCH-EOR [24] and RDSAOA-EECP [25], there is a decrease in the packet loss rate of ED-MORFO. Table 8 tabulates the comparative analysis of packet loss rate performance. Table 8 shows that the proposed ED-MORFO achieves a better PLR of 0.6% when compared with the existing techniques of MCH-EOR and RDSAOA-EECP, which attain rates of 1.3% and 2.6%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{3}{c}{**Routing Overhead**} \\ \cline{2-4}  & \multirow{2}{*}{**Existing MCH-EOR [24]**} & **Existing** & \multirow{2}{*}{**Proposed ED-MORFO**} \\  & & **RDSAOA-EECP [25]** & \\ \hline
100 & 0.52 & 0.33 & 0.11 \\
200 & 0.64 & 0.41 & 0.17 \\
300 & 0.69 & 0.49 & 0.22 \\
400 & 0.75 & 0.58 & 0.26 \\
500 & 0.82 & 0.67 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performances of routing overhead.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{3}{c}{**Packet Loss Rate (\%)**} \\ \cline{2-4}  & \multirow{2}{*}{**Existing MCH-EOR [24]**} & **Existing** & \multirow{2}{*}{**Proposed ED-MORFO**} \\  & & **RDSAOA-EECP [25]** & \\ \hline
100 & 1.3 & 2.6 & 0.6 \\
200 & 1.9 & 3.5 & 1.1 \\
300 & 2.2 & 5.2 & 1.5 \\
400 & 3.8 & 7.8 & 2.2 \\
500 & 4.5 & 9.2 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performances of packet loss rate.  
Figure 9: Performance of PLR for proposed EDMORFO and Existing MCH-EOR [24], RDSAOA-EECP [25].
## 2 System Model  
A downlink two-tier HCN comprising macrocells and picocells is considered. Macro BSs (MBSs) and pico-BSs (PBSs) are characterized by the corresponding density, transmit power, and target SIR. BSs in the \(k\)-th tier are modeled as a PPP \(\phi_{k}\) (\(k=1\), \(2\)) in the two-dimensional Euclidean plane, whose density, transmit power, and target SIR are denoted as \(\lambda_{k},P_{Tk}\), and \(\gamma_{k}\), respectively. Mobile user equipment (MUE) is modeled by another independent PPP with density \(\lambda_{u}\). As shown in Figure 1, different cells in different tiers could be separate, overlapping, or included.  
Given the stationary nature of the network model, network performance can be characterized by considering the throughput of a typical MUE located at the represented origin [11]. The typical MUE is called tagged MUE, and its serving BS is called a tagged BS. The distance between the tagged MUE and the tagged BS located at \(x\in\phi_{k}(k=1,2)\) is denoted as \(d_{x}\). The propagation channel is characterized by both path loss and small-scale fading. The received power at the tagged MUE attributed to the transmission from its serving BS is \(P_{Tk}h_{x}d_{x}^{-\alpha}\), where \(\alpha>2\) is the path-loss exponent and \(h_{x}\)-\(\exp(1)\) is a random variable modeling Rayleigh fading. All channel coefficients are assumed to be independent identically distributed (i.i.d). Thus, SINR of the tagged MUE is  
\[SINR(x)=\frac{P_{Tk}h_{x}d_{x}^{-\alpha}}{I_{x}+\sigma^{2}}=\frac{P_{Tk}h_{x}d _{x}^{-\alpha}}{\sum\limits_{k=1}^{2}\sum\limits_{x\in\phi_{k}(x)}P_{Tk}h_{x}d_ {x}^{-\alpha}+\sigma^{2}},x\in\phi_{k} \tag{1}\]where \(I_{x}\) is the cumulative interference from all of the tiers when the tagged MUE is served by the tagged BS located at \(x\), while \(\sigma^{2}\) is the noise power. Typical HCNs are interference-limited; the noise power can be neglected in interference-limited cellular networks [14]. Therefore, in the following analysis, we use SIR instead of SINR. SIR of the tagged MUE can be written as  
\[SIR(x)=\frac{P_{Tx}h_{x}d_{x}^{-\alpha}}{I_{x}}=\frac{P_{Tx}h_{x}d_{x}^{- \alpha}}{\sum\limits_{k=1}^{2}\sum\limits_{x^{\prime}\in\phi_{k}\backslash \left\{x\right\}}P_{Tx}h_{x^{\prime}}d_{x^{\prime}}^{-\alpha}},x\in\phi_{k} \tag{2}\]
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 4 Surface Composition of Phobos
### Speculative Composition of Phobos  
#### 4.3.1 Capture Hypothesis  
If Phobos is a captured asteroid, then the majority of its mass may consist of primitive carbonaceous material, possibly akin to D-type bodies from the outer asteroid belt. Generally, D-type asteroids are believed to contain volatile components; however, these asteroids, significantly distant from the Sun, typically do not exhibit features of molecular water absorption, leading to the possibility that their composition might be anhydrous silicates [4]. In Phobos spectra, no definitive evidence of features indicating molecular water absorption has been detected (e.g., [10; 36; 37]). It is conjectured that the high daytime surface temperature of Phobos and the presence of fragmented, weathered regolith may have desiccated hydrated minerals to depths of several centimeters or more [4].  
[PERSON] et al. [4], based on various models of Phobos' origin, proposed its composition, drawing parallels to different types of meteorites (Table 2). Depending on the analyzed variant of Phobos' origin, the authors indicated the predicted composition, as well as the composition of elements and minerals. A compilation of this information is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Origin Hypothesis** & **Composition Predicted** & **Elemental Abundance** & **Mineral Abundances** \\ \hline Capture of organic- and water-rich outer solar system body & Ultra-primitive composition (Tagish Lake is the best known analog) & High C. High Zn/Mn. High S. Composition possibly unique compared to known meteorites. High C. & Abundant phyllosilicates. Carbonates and organic phases. Anhydrous silicate phases rare. Anhydrous silicate phases rare. Anhydrous silicates plus system body & Anhydrous silicates plus elemental carbon & Mg/Fe ratio \(\sim\)2–4. Bulk composition unlike any meteorite & Abundant amorphous carbon or graphite? \\  & Composition like common meteorites (e.g., ordinary chondrites) & Al/Si \(\sim\)0.05–0.1. Zn/Mn and Al/Mn ratios separate known meteorites. Likely low C. & Pyroxene, olivine probably in range of known meteorites. \\ \end{tabular}
\end{table}
Table 2: The predicted composition of Phobos depending on the type of captured body [4].  
Figure 9: A schematic cross-section through the Stickney crater (from its eastern rim towards its center to its northern rim) with delineated stages of the formation of the observed morphology and distribution of red and blue units: (**a**) stage 1, (**b**) stage 2, and (**c**) stage 3 [12].
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 3 Polymer-Inorganic Coordinated Hybrids: Complexation-Induced Properties
### Therapeutics, Sensing, and Polyelectrolytes
The binding of the Cu\({}^{2+}\) ions with the PEI-capped nanoparticles caused a new absorption peak to emerge in the UV/Vis spectrum and changed the solution color, which was attributed to the formation of the Cu-PEI complex. To validate the sensor's performance, the authors tested it on real water samples, including wastewater from a local steel works and river water. The PEI/Au NP sensor provided accurate Cu\({}^{2+}\) concentration measurements, demonstrating its potential for practical applications in environmental monitoring. Figure 7: **(a)** Schematic representation of [Cu(phen)(L\(-\)tyr)BPEI]ClO\({}_{4}\). **(b)** Morphological analysis of MCF\(-\)7 cancer cells stained with acridine orange and ethidium bromide after treatment with [Cu(phen)(L\(-\)tyr)BPEI]ClO\({}_{4}\). The images show a time\(-\)dependent increase in apoptosis, indicated by chromatin condensation and cell shrinkage. Scale bar: 35 μm [120]. **(c)** Schematic illustration for fabrication of PEI/Au NPs for colorimetric detection of Cu\({}^{2+}\)[121]. Adapted with permission from Elsevier [120; 121]. Polyacrylic acid (PAA) chains offer numerous carboxylate groups, which are commonly used as polyelectrolytes. [PERSON] et al. (2018) observed how polyacrylic acid (PAA) interacts with lanthanide ions to form polymer-metal complexes, focusing on the impact of pH and the ionization behavior of PAA. The researchers found that the binding mechanism is highly dependent on the pH of the solution. At low pH levels, the carboxylate groups of PAA are not fully ionized, resulting in minimal interaction with lanthanide ions. As the pH increases, more carboxylate groups become deprotonated, enhancing their negative charge and ability to coordinate with the positively charged metal ions. This increased ionization facilitates stronger and more stable complex formation, which becomes visually apparent through the precipitation observed at higher pH values.
###### Abstract  
Power to gas (P2G) process chains have tremendous potential to enhance energy systems because of the capability of solar energy to convert solar radiation into electrical energy as well as the increasing use of specific gases as a means to store the resulting energy. Utilizing sunlight, photovoltaic systems are capable of producing useful gases such as hydrogen (H\({}_{2}\)) and methane (CH\({}_{4}\)). These gases are utilized in gas grids, transportation, and heavy industry. In employing a sunlight-derived gas, H\({}_{2}\) production, by water disbanding, needs to be cost-effective with tremendous adaptability. New powerful solar to gas conversion system modules have been successfully carried out in the University of Miyazaki, Japan. These systems contain DC/DC converters and electrolyzer sets linked in parallel with efficient three concentrator photovoltaics (CPV). The performance of the solar to methane conversion process and power consumption analysis will be the focus of the current research. Efficiencies of 97.6% of CO\({}_{2}\) to CH\({}_{4}\) conversion and 13.8% for solar to methane on a clear sunny day were obtained by utilizing highly efficient CPV modules connected with multiple converters, electrochemical cells, and reactors fixed with Ni-based catalysts.  
concentrator photovoltaic; DC/DC converter; hydrogen generation; Ni-based catalyst; proton exchange membrane electrolysis; solar to gas; Sabatier process; water electrolysis +
Footnote †: journal: Applied Physics Letters
## 3 Proposed Methodology  
### The Exact Solution and the Supervised Approach  
The Lagrangian function to maximize is [30]  
\[L(P_{l}(h_{l}),v)=\sum_{l=1}^{L}\mathrm{E}[C_{l}(P_{l}(h_{l}),\,h_{l})]-v \left(\sum_{l=1}^{L}\mathrm{E}[P_{l}(h_{l})]-P_{av}\right) \tag{5}\]  
where \(v\) is the dual variable.  
If the statistical models \(\mathrm{f}_{h_{l}}(h_{l})\) are available, the average channel capacity is then [11; 12]:  
\[\mathrm{E}[C_{l}(P_{l}(h_{l}),\,h_{l})]=\int\limits_{0}^{\infty}\log_{2}\left( 1+\frac{P_{l}\left(h_{l}\right)\left|h_{l}\right|^{2}}{N_{0}}\right)\mathrm{f} _{h_{l}}(h_{l})\mathrm{d}h_{l} \tag{6}\]  
and the average transmitted power is then [11; 12]:  
\[\mathrm{E}[P_{l}(h_{l})]=\int\limits_{0}^{\infty}P_{l}\left(h_{l}\right)\mathrm{ f}_{h_{l}}(h_{l})\mathrm{d}h_{l} \tag{7}\]  
The PA problem (3) and (4) is then convex [30]. The instantaneous optimal PA for the \(i\)th channel is given in [11; 12]:  
\[P_{l}^{*}(h_{l})=\min\left\{P_{s},\,\max\left(\frac{1}{v^{*}}-\frac{N_{0}}{h_{ l}},0\right)\right\} \tag{8}\]  
where \(v^{*}\) is the optimal dual multiplier, and \(N_{0}\) is the optical noise variance.  
The \(v^{*}\) is evaluated numerically from (4), (8) as follows:  
\[\sum_{l=1}^{L}\int\limits_{0}^{\infty}P_{l}^{*}\left(h_{l}\right)\mathrm{f}_{ h_{l}}(h_{l})\mathrm{d}h_{l}=P_{av} \tag{9}\]  
or via an iterative algorithm, e.g., sub-gradient method with decreasing step size \(\eta\) as below [30]:  
\[v^{k+1} =\left[v^{k}-\eta^{k}\left(P_{av}-\mathrm{E}\left[\sum_{l=1}^{L}P_{l}(h_ {l})\right]\right)\right]_{+} \tag{10}\] \[\eta^{k+1} =\eta^{k}/(k+1)\]  
Note that the analytical expression (8) is valid as long as the \(N_{0}\) is constant and not a function of \(P_{l}(h_{l})\). Also, (8) requires precise \(h_{l}\), and (9) demands precise channel PDF knowledge to calculate \(v^{*}\). In practice, it is very hard to assume accurate model information, CSI values, and constant optical noise.  
Supervised learning is a process of providing labeled input datasets to the learning model to make predictions (regression) or decisions (classification) based on the labeled samples. In a regression problem, the output node is continuous, ranging theoretically from negative infinity to positive infinity. The mean square error (MSE) is the most common loss function to employ. From (8) and (10), the exact solution is provided (no need for a brute-force or exhaustive search), which can be utilized as ground truth to train a DNN to predict the optimal power allocation. Non-linear regression fitting or a multi-layer perceptron model may be implemented. However, this supervised approach is model-based.
## 5 Conclusions and Recommendations  
E-roads can have an important role in fostering the adoption of electric mobility. Since transportation is a complex system, the impact of an infrastructure such as an e-road has to be included in a system level LCA which encompasses all the axes of electric mobility - the vehicle; the energy carrier and infrastructure - and their mutual interconnections, as visually represented in Figure 1. Nevertheless, a complete LCA of an e-road was still missing and the aim of the study was to perform such an analysis.  
The study has involved performing an LCA of 1 km of e-road, equipped with DWPT technology. A detailed inventory of the WPT components was included in the analysis due to the availability of the data gathered at the test site in Susa. The construction phase was modelled according to the instructions provided by TRL and the durability of the asphalt pavement and WPT components was the result of a finite element analysis that had already been performed in the Fabric project.  
The results of the LCIA show the predominance of the WPT component production in the construction phase. They account for more the 30% of the share in all the impact categories, even though they are less than 1% of the total components in weight. Maintenance is the phase with the highest impact due to the structural features of the e-road. There is high uncertainty about both the durability of pavements and WPT components in e-roads. These values require further refinement when more data from e-road monitoring are available.  
A comparison with 1 km of a traditional road has been performed. The results show that the impacts of the e-road are almost double those of the traditional road. However, these figures should be considered simply as a reference. In fact, e-roads have a greater impact than traditional roads, but they also fulfil an additional function to a t-road, thus making a comparison unfair if it is limited to the declared unit (km of t-road versus km of e-road). As mentioned in the methodological section, these impacts should be considered from a larger perspective that also takes into account the mutual effect of the infrastructure with the transportation system. This analysis represents a first step in the evaluation of the dynamic charging solution as a whole.  
[PERSON], [PERSON] and [PERSON] conceived the paper. [PERSON] collected the data for the LCI and wrote the paper. [PERSON] and [PERSON] revised the paper.  
The research leading to these results received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 605405 (FABRIC).  
We would like to thank all the FABRIC participants for their contributions.  
The authors declare no conflict of interest.