esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A practical implementation of flexible antennas with polymer substrates PET, PTFE, Teflon, and PVC polymer substrates operating at 2.25, 4.25, and 7.45 GHz, is presented in this paper and compared with the evaluations of our comprehensive study of polymer-based antennas in [1]. Due to the rapid growth of IoT and subsequent interconnection of various smart devices, the flexibility of these IoT's is an important aspect which, in turn, requires flexible antennas for communication. Therefore, an experimental approach to examine bending capabilities of polymer substrate flexible antennas is presented in each of the different range of frequencies, whereby the effects of bending levels of 27 and 14 mm on the PET, PTFE Teflon, and PVC substrate antennas were analyzed. For the antennas operating in the first range at 2.45 GHz, the PET-based and PVC-based flexible antennas were observed to possess a less than 1% frequency shift towards the lowest components, with the PTFE (Teflon)-based antennas showing the highest impact with a percentage shift of frequency of 1.25% for the 27 mm bend state. With the 14 mm radial curvature, the PET substrate antenna gave the best performance with 0.99% of frequency deviation from its flat status. For the second range, at an operating frequency of 4.25 GHz, the Teflon-based antenna contrastingly possessed the lowest frequency deviation, which was 0.25% and 1.61% for bending levels of 27 and 14 mm, respectively. For the operating frequency of 7.45 GHz, in the third range, the Teflon-based flexible antenna presented almost no impact of bending at 27 mm, and for 14 mm only a 0.29% shift towards the lowest components of the operating frequency. The PVC-based flexible antenna was impacted the most for the highest bend level where, in terms of the impedance mismatch at 14 mm bending, with a 1.97% shift, the PVC substrate antenna was ascertained to be the most efficient for this frequency range. Hence, this successful bending analysis of polymer-based flexible antennas proved their reliability in terms of bending for various stages of curvature, and provided a means to select a suitable polymer substrate for future wearable sensors and antennas with high bendability without significantly distorting the radiation characteristics. Due to their bendability and flexibility characteristics, these polymer-based antennas are advantageous for incorporation in designs for future smart sensors, including applications in the Internet of Things (IoT).  
Conceptualization, M.U.A.K., R.R. and F.T.; methodology, M.U.A.K.; software, M.U.A.K.; validation, R.R., P.I.T. and F.T.; formal analysis, M.U.A.K.; investigation, M.U.A.K. and F.T.; resources, P.I.T.; data curation, M.U.A.K.; writing--original draft preparation, M.U.A.K.; writing--review and editing, R.R., F.T., P.I.T.; visualization, M.U.A.K.; supervision, R.R. and F.T.; project administration, R.R.; funding acquisition, R.R. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Design of a Dual-Path Speech Enhancement Model
## 4 Experimental Setup
### Evaluation Metrics  
In this paper, we employ a comprehensive set of evaluation metrics to assess SE performance from the perspectives of both noise suppression and speech restoration. Specifically, we use seven objective metrics: wideband perceptual evaluation of speech quality (WB-PESQ) [27]; mean opinion score (MOS) predictions for signal distortion (CSIG), background noise distortion (CBAK), and overall quality (COVL) [28]; short-time objective intelligibility (STOI) [29]; segmental signal-to-noise ratio (SNRseg) and word error ratio (WER). Among these, CBAK and SNRseg reflect the model's ability to suppress background noise, while CSIG, STOI, and WER are more indicative of speech content restoration. WB-PESQ and COVL, on the other hand, serve as composite metrics that capture both aspects.  
WB-PESQ evaluates perceptual speech quality by comparing the enhanced speech with a clean reference, with scores typically ranging from 0 (poor) to 4.5 (excellent). CSIG, CBAK, and COVL are MOS-based metrics that range from 1 to 5, where higher scores indicate better preservation of speech, more effective noise reduction, and improved overall quality, respectively. STOI assesses intelligibility based on time-frequency correlations between the clean and enhanced signals and is expressed as a percentage, with higher values (e.g., 100%) representing perfect intelligibility. SNRseg measures the segmental signal-to-noise ratio on a frame-by-frame basis in decibels (dB), where higher values reflect stronger noise suppression. Finally, WER is computed using transcriptions generated by the Whisper model [30], and measures intelligibility by comparing enhanced speech transcriptions against those of the clean reference--lower scores indicate better performance.
# The Elasto-Plastic Contact Models of Cylinder-Based and Sphere-Based Fractal Rough Surfaces
## 4 The Total Real Contact Area and Total Contact Load  
### The Cylinder-Based Fractal Surface Is in Contact with a Rigid Flat Surface  
#### 4.1.1 The Modified Fractal Contact Model  
Figure 6 illustrates the contact zone between an equivalent cylinder-based fractal surface and a rigid flat surface. The parameter \(\omega\) is the contact interference for the whole fractal surface, and \(d\) is the distance between the bottom of the base circle and rigid flat surface. \(L_{\rho}\) is the half width of the contact zone. The fractal rough surface includes many asperities for which the frequency indexes range from \(n_{\text{min}}\) to \(n_{\text{max}}\). The interference \(\omega\) ranges from zero to \(\max[\delta_{n_{\text{min}}}\delta_{n_{\text{min}}+1},\ldots\delta_{n},\ldots, \delta_{n_{\text{max}}}]=\delta_{n_{\text{min}}}\), so the sum of interference \(\omega\) and distance \(d\) can be given as  
\[\omega+d=\delta_{n_{\text{min}}} \tag{32}\]The center angle \(\theta\) corresponding to the half width \(L_{\rho}\) can be expressed as [22]  
\[\theta=\arccos\frac{\rho+d}{\rho+\delta_{n_{\rm min}}} \tag{33}\]  
so the half width \(L_{\rho}\) is written as  
\[L_{\rho}=(\rho+d)\ \tan\biggl{(}\arccos\frac{\rho+d}{\rho+\delta_{n_{\rm min}}} \biggr{)} \tag{34}\]  
Because the rough surface profile is the cylinder-based fractal surface, the deformations of different locations along the half width \(L_{\rho}\) are different for a given contact interference \(\omega\). The contact zone is divided into two parts by the \(z\) axis, and the two parts are symmetrical with the \(z\) axis. When the center angle \(\theta\) is uniformly divided into \(N\) subdivisions, the right contact zone with a \(z\) axis corresponding to \(\theta\) is divided into \(N\) subdivisions (Figure 6). The width of the first subdivision of the contact zone equals \((\rho+d)tg(\theta/N)\), and the width of the \(i\)th subdivision of the contact zone equals \((\rho+d)[tg(i\theta/N)-tg((i-1)\theta/N)]\). Because the \(\theta\) is very small, \(tg(\theta/N)\approx\theta/N\) and \(tg(i\theta/N)\approx i\theta/N\). We can obtain the width of each subdivision of the contact zone, which equals \((\rho+d)(\theta/N)\). The distance between the middle at the \(i\)th subdivision of the base circle and \(x\) axis can be given as  
\[d_{iz}=\rho-\rho\cos(\theta_{i})=\rho-\rho\cos\biggl{(}\frac{2i-1}{2N}\theta \biggr{)}\ i=(1,2,\cdots,N) \tag{35}\]  
The cylinder-based fractal rough surface includes many asperities for which the frequency indexes range from \(n_{\rm min}\) to \(n_{\rm max}\), so each subdivision of the contact zone also includes many asperities for which the frequency indexes range from \(n_{\rm min}\) to \(n_{\rm max}\). For the interference \(\omega\) caused by a rigid flat surface, the \(i\)th subdivision interference can be obtained as  
\[\omega^{i}=\delta_{n_{\rm min}}-(\delta_{n_{\rm min}}-\omega+d_{iz})/\cos( \theta_{i}),\ \theta_{i}=\frac{2i-1}{2N}\theta \tag{36}\]  
Figure 6: The contact zone between the cylinder-based fractal rough surface and a rigid flat surface.
## 4 Evaluation and Discussion
### Numerical Example
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Point** & **Method** & \(\mathbf{x_{MPP}=\{x_{1},x_{2},x_{3},x_{4},x_{5}\}}\) & **Limit-State Function Value** & **Iteration Times** \\ \hline Case 1 & SMPRA & (1.1726, 1.1726, 1.1726, 1.1726, 1.0017, 1.0017) & \(g_{1}(\mathbf{x})=0.0323\) & 232 \\ \hline \multirow{5}{*}{\begin{tabular}{c} Case2: \\ Test point 1 \\ \end{tabular} } & MU-DBMRA & (1.1933, 1.1620, 1.1625, 1.0036, 0.9931) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\min}=0.0111\) & 258 \\ \cline{2-5}  & \multirow{2}{*}{MCs} & (1.1937, 1.1623, 1.1617, 0.9964, 1.0069) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\max}=0.0431\) & 258 \\ \cline{2-5}  & \multirow{2}{*}{MCs} & (1.2014, 1.1872, 1.1359, 1.0763, 1.0018) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\min}=0.0092\) & 296,000 \\ \cline{2-5}  & & (1.2143, 1.1924, 1.1427, 1.0914, 1.0067) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\max}=0.0513\) & 296,000 \\ \cline{2-5}  & MDF + SQP & (1.2212, 1.1812, 1.0906, 1.0000, 1.0000) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})=0.0429\) & 304 \\ \cline{2-5}  & IDF + SQP & (1.2209, 1.1816, 1.0951, 1.0000, 1.0000) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})=0.0172\) & 276 \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} Case2: \\ Test point 2 \\ \end{tabular} } & MU-DBMRA & (1.1933, 1.1620, 1.1624, 1.0023, 0.9912) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\min}=0.0120\) & 258 \\ \cline{2-5}  & \multirow{2}{*}{MCs} & (1.1936, 1.1622, 1.1618, 0.9977, 1.0088) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\max}=0.0423\) & 258 \\ \cline{2-5}  & \multirow{2}{*}{MCs} & (1.1879, 1.1564, 1.1607, 1.0008, 0.9847) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\min}=0.0021\) & 296,000 \\ \cline{2-5}  & & (1.1952, 1.1693, 1.1684, 1.0042, 1.0106) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\max}=0.0657\) & 296,000 \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Case2: \\ Test point 3 \\ \end{tabular} } & MU-DBMRA & (1.1932, 1.1619, 1.1627, 1.0050, 0.9988) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\min}=0.0143\) & 258 \\ \cline{2-5}  & \multirow{2}{*}{MCs} & (1.1938, 1.1623, 1.1615, 0.9950, 1.0012) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\max}=0.0399\) & 258 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{MCs} & (1.1884, 1.1579, 1.1631, 1.0021, 0.9864) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\min}=0.0074\) & 296,000 \\ \cline{1-1} \cline{2-5}  & & (1.1804, 1.1672, 1.1616, 1.0032, 1.0094) & \(g_{1}(\mathbf{x}\),\(\mathbf{v})_{\max}=0.0362\) & 296,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reliability analysis results of the numerical example.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**tR**} & \multirow{2}{*}{**Compound * **} & \multirow{2}{*}{**CAS**} & \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**KI**} \\ \hline
50 & 14.10 & & & & \\  & & & benzene & & \\
51 & 14.32 & & p-cymene & 99-87-6 & & 1537 \\
52 & 14.56 & & pentadecane & 629-62-9 & & 1556 \\
53 & 14.74 & & 2,4-heptadiene & 4313-03-5 & & 1569 \\
54 & 14.75 & & 1,3-dimethyl-1h-pyrazole & 694-48-4 & & 1570 \\
55 & 14.76 & & 2-ethyl-3-methylcyclopentene & 19780-56-4 & & 1570 \\
56 & 14.81 & & 1-ethyl-cyclohexene & 1453-24-3 & & 1574 \\
57 & 14.81 & & 3-ethyl-1,4-hexadiene & 2080-89-9 & & 1575 \\
58 & 15.17 & benzoic acid & 3782-84-1 & & 1601 \\
59 & 15.28 & & 2-octyl-1-decanol & 45235-48-1 & & 1611 \\
60 & 15.97 & & 2-methyl-pentadecane & 1560-93-6 & & 1667 \\
61 & 16.10 & & tetradecyl vinyl ester carbonic acid & 1000382-54-5 & & 1678 \\
62 & 16.54 & & hexadecane & 544-76-3 & & 1715 \\
63 & 16.74 & & 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde & 432-25-7 & & 1731 \\
64 & 16.79 & & beta-cyclocitral & 432-26-7 & & 1738 \\
65 & 16.84 & & & & 1743 \\
66 & 17.05 & & sarcocapidine & 87069-33-8 & & 1762 \\
67 & 17.68 & & 3-methylbut-3-enyl undecyl & & 1000348-91-0 & & 1819 \\  & & & ester fumaric acid & 1000348-91-4 & & 1819 \\
68 & 17.68 & & & 1000348-91-4 & & 1819 \\
69 & 17.68 & & octadecanoic acid & 57-11-4 & & 1819 \\
70 & 18.22 & & 2-methylene cyclobutanone & 17714-43-1 & & 1868 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **tR** **(min)** & **Compound *** & **CAS** & **Structure** & **KI** \\ \hline
71 & 18.31 & naphthalene & 91-20-3 & 1876 \\
72 & 18.57 & isobutyl tetradecyl ether & 1000406-32-7 & 1899 \\
73 & 18.72 & isopropyl tetradecyl ether & 1000406-34-0 & 1913 \\
74 & 19.13 & 2,4-dimethylbenzaldehyde & 15764-16-6 & 1955 \\
75 & 19.13 & benzaldehyde & 53951-50-1 & 1955 \\
76 & 19.18 & 3,5-dimethylbenzaldehyde & 5779-95-3 & 1960 \\
77 & 19.34 & geranyl acetone isomer 1 & 689-67-8 & 1976 \\
78 & 19.35 & geranyl acetone isomer 2 & 105-87-3 & 1977 \\
79 & 19.64 & benzyl alcohol & 100-51-6 & 2006 \\
80 & 19.72 & cis-2-(1-pentenyl)furan & 70424-13-4 & 2013 \\
81 & 19.93 & tricyclo[4,4,0,0(2,8)]decane & 49700-59-6 & 2034 \\
82 & 19.93 & neophytadiene & 504-96-1 & 2034 \\
83 & 19.93 & 5-methyleneacethydro-1H- & 1000152-00-6 & 2034 \\
84 & 19.93 & bicyclo[3,3,1]non-2-en-9-one & 4844-11-5 & 2035 \\
85 & 20.24 & cyclohexanol & 55000-30-1 & 2066 \\
86 & 20.35 & 3-buten-2-one & 79-77-6 & 2077 \\
87 & 20.92 & \(\beta\)-ionone & 23267-57-4 & 2134 \\
88 & 20.97 & phenol & 108-95-2 & 2141 \\
89 & 21.04 & [(2-methylpropyl)thio]-benzene & 13307-61-4 & 2148 \\
90 & 21.05 & 2-isopropyl-5,5- & 1000191-19-3 & 2149 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 5 Inversion Test of an Ideal Model
### One-Dimensional Three-Layer Model Test
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Layer Thickness** \\ **Threshold** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Resistivity** \\ **Ratio** \\ \end{tabular} } & \multicolumn{6}{c}{**D-MSE**} \\ \cline{3-8}  & & & \(\lambda_{1}\) & \(\lambda_{2}\) & \(\lambda_{3}\) & \(\lambda_{4}\) & \(\lambda_{5}\) & \(\lambda_{6}\) & \(\lambda_{7}\) \\  & & **(2\(\times\)10\({}^{-3}\))** & **(6\(\times\)10\({}^{-4}\))** & **(2\(\times\)10\({}^{-4}\))** & **(6\(\times\)10\({}^{-5}\))** & **(2\(\times\)10\({}^{-5}\))** & **(6\(\times\)10\({}^{-6}\))** & **(2\(\times\)10\({}^{-6}\))** \\ \hline \multirow{2}{*}{J\({}_{1}\)} & \(\sigma_{r}=5\) & 0.1930 & 0.0833 & 0.0553 & 0.1052 & 0.1642 & 0.1264 & 0.4442 \\  & \(\sigma_{r}=40\) & 0.2471 & 0.3109 & 0.2045 & 0.1874 & 0.1219 & 0.1096 & 0.2647 \\ \hline \multirow{2}{*}{J\({}_{2}\)} & \(\sigma_{r}=5\) & 0.2020 & 0.0704 & 0.1149 & 0.1442 & 0.2062 & 0.2385 & 0.6767 \\  & \(\sigma_{r}=40\) & 0.2784 & 0.1601 & 0.1952 & 0.1683 & 0.0673 & 0.1547 & 0.1881 \\ \hline \multirow{2}{*}{J\({}_{3}\)} & \(\sigma_{r}=5\) & 0.2078 & 0.0937 & 0.0833 & 0.1079 & 0.1310 & 0.2041 & 0.3827 \\  & \(\sigma_{r}=40\) & 0.2543 & 0.2178 & 0.1727 & 0.1614 & 0.1325 & 0.1863 & 0.2603 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean square error of inversion curve of three-layer H-type model under different layer thickness thresholds, regularization factors, and resistivity ratios. Figure 8: Inversion results for different conductivity ratios of the intermediate layer and the background layer when \(\sigma_{r}=5\). Layer thickness threshold: (**a**) J\({}_{1}\), (**b**) J\({}_{2}\), and (**c**) J\({}_{3}\). Figure 7: Inversion results for different conductivity ratios of the intermediate layer and the background layer when \(\sigma_{r}=40\). Layer thickness threshold: (**a**) J\({}_{1}\), (**b**) J\({}_{2}\), and (**c**) J\({}_{3}\). H-type model at the corresponding depth and are generally consistent with the actual model's resistivity distribution.
###### Abstract  
Groundwater from alluvial aquifers is a critical source of water supply for rural agriculture, particularly in semi-arid and arid regions. Effective management of these aquifers requires an understanding of the factors that influence their water resources. In this study, we present a case study of the Tata Watershed in southeastern Morocco, where the economy is heavily dependent on agriculture and relies exclusively on groundwater. We demonstrate the importance of integrating geological, hydrogeological, and geophysical methods to characterize the aquifer and evaluate groundwater productivity. Analysis of 64 data wells tapping into the aquifer revealed significant disparities in flow yields, ranging from 0.05 to 15.50 L per second. The highest yields were found between depths of 12 and 43 m, which correspond to the alluvium and the altered and fractured part of its substrate. The maximum alluvial thickness of 57 m was determined using geo-electrical prospecting. A piezometric map was created to define the recharge zones, which correspond to the lateral contributions of the bordering Georgian lisentones, and infiltration of both rain and surface water along the Tata wad. Since 1987, there has been a continuous drop in groundwater level, which can be attributed to the increase in irrigated areas following financial incentives provided by the Morocco government to the agricultural sector. A proposal has been made for the construction of a recharge dam to enable the recharge of the alluvial aquifer. This development is expected to serve a dual purpose by mitigating the deleterious impacts of flooding and facilitating the gradual water infiltration of the alluvial aquifer. This case study provides insights into the hydrodynamics of the aquifer and establishes a simplified model of its functioning. These findings have important implications for the management of alluvial aquifers in similar regions.  
hydrogeology; alluvial aquifer; productivity; geo-electrical; piezometric; recharge dam; Tata Watershed; Morocco +
Footnote †: journal: Applied Sciences  
02023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Methodology for the Assessment of Multi-Hazard Risk in Urban Homogeneous Zones
## 2 Methodology  
The methodology used in this research relies on the combination of the Multi-Criteria Analysis/Multi-Criteria Decision-Making (MCA/MCDM) and Geographic Information System (GIS) to evaluate and visualize this risk assessment in this particular area. The advantage of GIS is in its ability to visualize spatial data (Figure 2) and enhance the spatial decision-making in the risk assessment [25; 26; 27].  
Figure 1: The City of Kastela: (**a**) Historical center of Kastel Kambelovac [21]; (**b**) Coastal flooding events in the City of Kastela [22; 23].  
Furthermore, GIS supports the usage of the multi-scale approach and different criteria sets: the vulnerability analysis can be made for each building, the whole settlement, the settlement's municipality, and for the whole region. The multi-scale approach was discussed in the already-mentioned paper by [PERSON] et al. (2019), but greater contribution was given by [PERSON] et al. (2019) that presented multi-level geospatial modeling of vulnerability indicators from building level to country level. Another example of a multi-level and multi-criteria approach is presented in Figure 3.  
Regarding the risk assessment, it is usually made for a particular assessment area. These areas need to be defined by mutual spatial characteristics or by some already defined urban entity.  
Figure 3: Vulnerability analysis at different scales with multi-criteria approach.  
Figure 2: An example of multiple-risk map for the Ferrara province (2015).  
The first and simplest approach is to use some administrative areas as assessment areas: settlements, municipalities, provinces, counties, etc. This approach was used in the combined seismic and flood risk assessment for municipalities in the province of Ferrara [15].  
The second approach is the definition of assessment areas as \"working units\" by using a grid of blocks. The working unit is the geographical entity in which the calculations will be computed, hereby controlling the geographical resolution of the study. The definition of the working unit depends strongly on two factors: the geographical unit in which the original data is expressed and the scale of the study. For instance, in the urban-scale seismic risk study for city Almeria, a 200 m squared grid was considered appropriate to cover the entire city of Almeria, totaling an amount of 400 equal cells or working units [27].  
The third approach is to define assessment areas as \"homogeneous zones\", which are generated by intersecting relevant thematic layers in the assessment area. The intersection of the defined number of layers becomes an assessment area (zone). This approach has been used in this research for Croatian settlement Kastel Kambelovac, a part of Kastela City.
## 4 Conclusions
### Nomenclature  
\begin{tabular}{l l} \(a\) & receiver aperture side length, m \\ \(A\) & surface area, m\({}^{2}\) \\ \(c_{2}\) & constant used in linear equation \\ \(c_{p}\) & constant pressure specific heat, J/kgK \\ \(d\) & receiver tube diameter, m \\ \(D\) & diameter, m \\ \(F_{n\text{-}j}\) & view factor between surface n and surface j \\ \(f_{r}\) & friction factor \\ \(g\) & gravitational constant, m/s\({}^{2}\) \\ Gr & Grashof number \\ \(h\) & convection heat transfer coefficient, W/m\({}^{2}\)K \\ \(h_{mc}\) & cavity depth, m \\ \(h^{*}\) & enthalpy, kJ/kg \\ \(I_{sun}\) & solar irradiance, W/m\({}^{2}\) \\ \(\dot{l}\) & irreversibility rate, W \\ \(k\) & thermal conductivity, W/mK \\ \(m_{2}\) & slope of linear equation \\ \(\dot{m}\) & system mass flow rate, mL/s \\ \(N\) & number of tube sections \\ \(N_{RC}\) & radiation-conduction number \\ \(Nu\) & Nusselt number \\ ORC & organic Rankine cycle \\ \(p\) & pressure, Pa \\ Pr & Prandtl number \\ \(\dot{Q}\) & heat transfer rate, W \\ \(\dot{Q}_{net}\) & net heat transfer rate, W \\ \(\dot{Q}\) & rate of available solar heat at receiver cavity, W \\ \(\dot{Q}_{loss}\) & heat loss rate from the cavity receiver, W \\ \(\dot{Q}_{solar}\) & rate of available solar heat at dish concentrator, W \\ \(R\) & thermal resistance, K/W \\ Ra & Raleigh number \\ Re & Reynolds number \\ \(t\) & thickness, m \\ \(T\) & temperature, K \\ \(T_{R}\) & temperature ratio \\ TIT & turbine inlet temperature, K \\ \(V\) & volumetric flow rate (mL/s) \\ \(V_{wind}\) & wind speed, m/s \\ \(\dot{W}\) & power, W \\ \(\Delta T\) & temperature difference \\
**Greek symbols** \\ \(\phi\) & volume fraction \\ \(\dot{\beta}\) & volume expansion coefficient, 1/K \\ \(\beta\) & nanolayer-thickness to nanoparticle-diameter ratio \\ \(\delta\) & error \\ \(\delta\) & kinematic viscosity of the fluid, m\({}^{2}\)/s \\ \(\sigma\) & Stefan-Boltzmann constant, W/m\({}^{2}\)K\({}^{4}\) \\ \(\varepsilon\) & emissivity \\ \(\eta\) & efficiency \\ \(\theta\) & cavity inclination angle, \({}^{\circ}\) \\ \(\mu\) & dynamic viscosity, Pa.s \\ \(\rho\) & density, kg/m\({}^{3}\) \\ \end{tabular}