esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Nanofluids Behavior in Concentrated Solar Power Collectors with Organic Rankine Cycle
## 2 Mathematical Model
### Nanofluid Heat Transfer Fluid  
The basic fluid in the CPS loop is the thermal _oil_. However, nanofluids have been added to the thermal oil to enhance its thermal properties. [PERSON] et al. [25], [PERSON] [26] and [PERSON] et al. [31] presented equations to calculate the thermophysical and thermodynamic properties of nanofluids such as specific heat, thermal conductivity, viscosity and density, employing the law of mixtures as a function of the volumetric concentration of nanoparticles;  
\[\alpha_{\text{ total}}=\alpha_{\text{ particles}}+\alpha_{\text{ base fluid}} \tag{19}\]where \(\alpha\) represents a particular thermophysical property of the nanofluid under investigation.  
The nanofluid thermal and thermophysical properties, \(\alpha_{\text{total}}\), can be calculated as follows:  
\[\alpha_{\text{ total}}=\alpha_{\text{ base fluid}}+\alpha_{\text{particles}}(\Phi) \tag{20}\]  
where \(\Phi\) represents the nanoparticles volumetric concentration.  
The thermal conductivity is related to thermal diffusivity and density of the nanofluids as follows:  
\[\lambda=\alpha\;\delta\;C_{p} \tag{21}\]  
where \(Cp\) is the specific heat, \(\alpha\) is the thermal diffusivity, \(\lambda\) and \(\rho\) represent the thermal conductivity and density, respectively.  
The specific heat is calculated for nanofluids as follows [25; 26; 31]:  
\[c_{pnf}=\frac{(1-\mathcal{O})(\rho Cp)_{bf}+\mathcal{O}(\rho Cp)_{p}}{(1- \mathcal{O})\rho_{bf}+\mathcal{O}\rho_{p}} \tag{22}\]  
where \"\(\eta\)\" and \"\(bf\) refer to nanofluid and basic fluid, respectively. \(\mathcal{O}\) is the nanofluid particle concentration. \(\rho\) represents the density.  
The density of nanofluids can be written as follows [25; 26; 31]:  
\[\rho_{nf}=\mathcal{O}_{p}\rho_{p}+(1-\mathcal{O})\rho_{bf} \tag{23}\]  
\(\rho_{p}\) represents the density of the nanoparticle.
## 4 Simulation Results
### Performance Comparison
To inspect the effect of the proposed designs on the quality of the communication systems, the bit error rate (BER) performance is compared in Figures 7 and 8 for \(3\times 3\) and \(4\times 4\) MIMO systems, respectively (as convention, we here refer to a MIMO system with \(M_{t}\) transmit antennas and \(M_{r}\) transmit antennas as an \(M_{t}\times M_{r}\) MIMO system). The training SIR is set to 15 dB and the uncertainty parameters are chosen as \(\alpha_{T}=\alpha_{R}=\alpha_{Q}=0.3\). The orthogonal space-time block code in [39] is used to encode the QPSK-modulated symbols and the well-known minimum MSE (MMSE) receiver in [40] is employed to recover the transmitted symbols before detection. For the case of the proposed, nonrobust, and semi-robust training schemes, the imperfect CSI estimated from the training signal is used to implement the MMSE receiver. Additionally, we present the BER performance of the MMSE receiver with the perfect CSI as a benchmark for the BER performance and it is denoted by _\"Perfect CSI\"_ in the figures. The results are averaged over 500 channel realizations, where \(4\times 10^{6}\) symbols are transmitted for each
# Retrofitting of Bridge Slabs for Safety Railing Refurbishment in Italy: A State-of-the-Art Review
## 3 Slab Assessment and Retrofitting Approaches
### Slab Retrofit Approaches  
Reinforced concrete slabs in bridges are considered secondary elements, and most of the research on retrofitting solution is related to primary elements like girders and piers, e.g., [32; 33]. In this case, the focus is the slab strengthening, which can be performed through several techniques based on increasing the amount of reinforcement on the top part of the slab section across the cantilever part [34]. Both the truck wheel load and the crash impact force generate a negative moment that may require additional reinforcement where tensile stresses are present (i.e., at the extrados). Four approaches can be used:  
1. Additional steel rebars in correspondence with barrier posts
2. Additional steel rebars along the whole slab length
3. Near surface mounted (NSM) ordinary steel plates connected through chemical anchors
4. NSM bars, laminates, or textile composites  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Concrete Cores** & **Rebars** \\ \hline
300 m\({}^{2}\) of building floor & 2 & 2 \\
300 m\({}^{2}\) of bridge deck & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum number of samples and investigations to be extracted according to KL3.  
Figure 6: Example of in situ investigations for (**a**) edge curb detailing, (**b**) slab reinforcement across the external beam, and (**c**) detailed survey of reinforcement reported in (**b**).  
Techniques 1 and 2 differ only in terms of extension of the slab-strengthening intervention and, for this reason, will be described in the same section.  
It is worth noting that when performing safety checks under flexure in the presence of new additional reinforcement, the existing concrete properties must be used. In fact, although a new concrete cover made of high-strength cementitious grout is realized on the slab extrados, the intrados (resisting compression stresses) is made of existing concrete. Therefore, a reliable evaluation of its properties through core drilling and laboratory testing is of paramount importance. In the same way, the existing reinforcement should be considered in the safety checks. In order to corroborate the information gathered through investigations, existing sources of information can be used regarding the analyses of databases including historical concrete and steel testing results, which can help in recognizing the concrete and steel grades used at the time of construction [35; 36].
## 4 Embedded Battery Tile Modal Testing
### Design of Experiment--Actuator Locations  
A critical aspect of the experimental setup is the location of the piezoelectric actuators on the targets. This location must be carefully chosen to obtain an acceptable level of excitation of the first and the second free-free modes of the targets at least. Thus, a preliminary investigation was made. For each target, the set of possible actuator positions was chosen based on the first three mode shapes. All possible configurations were tested with an MSC NASTRAN SOL 111 frequency response model. An analogy between the piezoelectric effect and the thermal effect was exploited [30] to model the excitation of the actuators.  
As an example, the outer skin case is presented (Figure 13). The first three structural modes are identified with a linear modal analysis and free boundary conditions. The eigenfrequencies and the mode shapes are shown, respectively, in Table 2 and Figure 13a.  
Given the mode shape symmetry, two configurations were considered (Figure 13b). For configuration 1, the actuator is on the central, flat portion of the skin, while in configuration 2, the actuator is on the rib of the skin. The SOL 111 analysis was performed on the outer skin model to choose the best experimental configuration. The excitation voltage in this case has a constant amplitude from 0 Hz to 600 Hz. For the results, the nodes depicted in Figure 13c were considered. The results are shown in Figure 14. With configuration 1, it is very difficult to appreciate mode 7, while modes 8 and 9 are much more excited and are thus well visible. The results for configuration 2 instead indicate that all three modes 7-9 are well excited, but to a level which is slightly inferior to the one of modes 8 and 9 for configuration 1. However, configuration 2 is chosen as the best experimental configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mode** & **Frequency** \\
**[-]** & **[Hz]** \\ \hline
7 & 81.407 \\
8 & 104.99 \\
9 & 242.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Outer skin eigenfrequencies for free boundary conditions.  
Figure 12: Fixing system for the outer skin (**a**) and for the inner PCB (**b**). Laservibrometer scan points are shown in red.  
The printed circuit boards have been treated in a similar manner. From a mechanical point of view, PCBs are a laminate composed of several layers of Flame Retardant-4 (FR-4) and copper. The FR-4 is approximated as an isotropic material, although it may have randomly dispersed glass fibers in a matrix of epoxy resin. The lamination for the PCBs is taken from the electronic board project files developed with the Altium Designer (6.0 in 2005) software.  
Figure 13: Design of the experiment to evaluate different actuator locations for the outer skin: (**a**) shapes of modes 7–9 for free–free boundary conditions; (**b**) possible configurations of the piezoelectric actuator; and (**c**) NASTRAN nodes considered for the frequency–response analysis.
## 3 Results and Discussion  
### Characterization of Main Spectral Regions  
#### 3.1.1 Spectral Region 650-1200 cm\({}^{-1}\)  
Within region 650-750 cm\({}^{-1}\), samples from stems of the four species analyzed in this study as well as the cellulose standard presented a broad band with several weak peaks (Figure 4). Previous studies attributed the band from \(\approx\)660 cm\({}^{-1}\) to the out-of-plane bending vibration [3; 53], while the one at 710 cm\({}^{-1}\) was attributed to CH\({}_{2}\) rocking vibration [53].  
Figure 4: FT-IR spectra for stems of flax (**a**), velvet leaf (**b**), hemp (**c**) and jute (**d**), showing the regions selected for detailed analysis.  
Figure 3: Spectral pellets, macroscopic aspect of ground samples, microscopic observation of coarse ground samples magnified 64\(\times\) showing fibrous texture for the samples: (**a**) cellulose (standard), (**b**) _Linum usitatssimum_ ’Domnesti’, (**c**) _Cannabis sativa_ ’Succesiv’, (**d**) _Cannabis sativa_ ’Zenti’, (**e**) _Corchorus olitorius_ GZU, (**f**) _Corchorus olitorius_ NTM, (**g**) _Corchorus olitorius_ SZTE, (**h**) _Abutilon theobrnsti_.  
For the cellulose standard, besides the peak at 897 cm\({}^{-1}\), we observed another one at 876 cm\({}^{-1}\) that was not present in the stems of the four species analyzed (Figure 5). The peak situated in this region (\(\approx\)890 cm\({}^{-1}\)) was attributed to glycosidic bonds of monosaccharide particles [3; 53; 54; 55; 56] as well as to the amorphous region of cellulose [40].  
Similarly, in _Abutilon indicium_ lignocellulosic fibers, a peak around 899 cm\({}^{-1}\) was identified and attributed to the vibration of functional group C-O-C [10]. FT-IR investigation of _Gossypium hirsutum_ fiber development revealed that the intensity of bands at 897 cm\({}^{-1}\), besides those at 667 cm\({}^{-1}\), has a relationship with cellulose content [53]. The fact that the signal was stronger for the cellulose standard sample suggests that these are in fact due to cellulose. The signal was the weakest in jute samples and strongest in flax and hemp, out of the four species studied (Figure 5).
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 3 Efficient Mobility Support for Sink Group Utilizing Region Shift
### Preliminaries  
Prior to the design of RSMGP, it is based on the following assumptions in terms of network environment:  
* We assume that the member sinks exist on an infrastructureless sensor field such as disaster areas or battlefields. Thus, member sinks can communicate only via a wireless sensor network.
* The proposed scheme relies on position-based routing. Location update of groups and data delivery to the region are performed by this routing method.
* Each member sink is aware of its own location by global positioning system (GPS) or localization techniques [13].
* Source nodes obtain a location of a sink group by location service schemes [14].  
We define a group region as a circle with a center point and a fixed radius. We refer to this region as a defined group region (DGR). We assume that member sinks in a group have the defined group region information at the initial stage by pre-programmed data or manual operation of a network administrator. Henceforth, movement of the group can be expressed as alterations of a center point. However, sensor nodes and member sinks could not be aware of the exact center point of moved DGR. The mobility support of mobile sink group thus involves the region discovery phase to figure out the (approximate) center point. Intuitively, frequent querying for locations of member sinks is a simple solution, but this behavior causes large communication overheads as mentioned above.  
To eliminate the frequent querying, the region shift mechanism of RSMGP utilizes deviation distance from a group region. The distance could be obtained by the property of circle-line intersection. If a member sink moves out of a group region, a (infinite) line is determined by points of before and after the movement, which is known as a secant line [15]. The secant line may intersect a determined circle as the group region as shown in Figure 2. We use the line segment from the intersection point to the point of after motion as a moving distance of the group. Thus, we need to be aware of the intersection point location. First, the secant line can be formulated as follows:  
\[y=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}x+K, \tag{1}\]  
where \(K\) is obtained by substitution of \(x\) and \(y\) as \((x_{1},y_{1})\) or \((x_{2},y_{2})\). In addition, the circle can be expressed as  
\[(x-C_{x})^{2}+(y-C_{y})^{2}=R^{2}, \tag{2}\]  
\[y=\pm\sqrt{R^{2}-(x-C_{x})^{2}}+C_{y},\]  
where \((C_{x},C_{y})\) is a center point and \(R\) is a radius of the circle. Then, we can obtain the intersection point of the line and circle by the simultaneous equations as follows:  
\[\frac{y_{2}-y_{1}}{x_{2}-x_{1}}t_{x}+K=\pm\sqrt{R^{2}-(t_{x}-C_{x})^{2}}+C_{y}. \tag{3}\]  
Consequently, two \(t_{x}\) values might be derived and the one is determined by the range, \([x_{1},x_{2}]\). The line between \((t_{x},t_{y})\) and \((x_{2},y_{2})\) is used as the deviation distance to shift the center point.
# Simplified Calculation Method for Active Anti-Floating of Elliptical Basements by Relief Wells
###### Abstract  
Currently, there are two main types of anti-floating methods for underground structures; one is the passive anti-floating method represented by anti-draft piles, the other is the active anti-floating method which focuses on interceptor-discharge pressure-reducing (IDPR). In the design of an IDPR anti-floating system, the relief well system situated within the cut-off wall serves as the primary drainage channel. The determination of the seepage field distribution within the multi-well system is vital for the overall design. For the seepage field analysis of the IDPR anti-floating multi-well system, currently numerical analysis is usually used, and there is a lack of simplified analysis methods. The simplified analysis methods already available are based on the uniform distribution of wells in circular pits, while the conversion of non-circular pits into circular pits produce large errors, which are not conducive to promoting the use of the method. To address this, we propose a simplified calculation approach suitable for multi-well systems (arbitrary layout) within elliptical pits. The analytical solution of non-uniformly distributed wells in circular pits is deduced through the principle of superposition. Then, the ellipse is mapped into a circle by using conformal mapping. The resistance coefficient method is adopted, and the internal and external seepage fields are connected in series to obtain the total flow rate, as well as the distribution of the seepage field. This is based on the consideration of the permeability of the waterproof curtains and the bypassing seepage. According to the verification of the calculation example, the results of the simplified algorithm are similar to the results of the finite element method, which proves the accuracy of the method; at the same time, when applied to the actual engineering, the obtained calculation results coincide with the measured data, which proves the practicability and reliability of the method. The simplified method can provide an effective way to design an IDPR anti-floating system.  
elliptical pit; anti-floating; multi-well system; cut-off wall; conformal mapping; method of resistance coefficient +
Footnote †: journal: Applied Sciences
## 2 Model Construction and Algorithm Enhancement
### Validation and Evaluation  
To comprehensively evaluate the scientific validity and practical applicability of the canopy-K-means clustering results, a variety of assessment methods were employed. Machine learning models--including logistic regression [25; 26], decision trees [27], Random Forest [28], and XG Boost version 3.0.0 [29]--were used to indirectly verify the rationality and stability of the clustering structure by analyzing the relationships between physical health indicators and student groupings. These models confirmed the robustness of the clustering features and identified key variables that significantly contributed to group differentiation, providing data-driven insights for designing personalized health intervention strategies. Evaluation metrics such as the silhouette coefficient, accuracy, F1 score, and confusion matrices were utilized to assess the cohesion, separation, and classification performance of the clusters, thereby ensuring the reliability and practical relevance of the clustering outcomes.
# Study on Characteristic Strength and Constitutive Model of Red Sandstone under Hydraulic Coupling
## 6 Conclusions  
A series of tests, such as a scanning electron microscope and a hydraulic coupling test, were carried out to analyze the micro- and macromechanical characteristics of weakly cemented red sandstone, and a hydraulic coupling constitutive model was derived. The following conclusions were obtained through experiments and theoretical studies:  
1. The evolution laws of characteristic strength of red sandstone are obtained by the volume strain method. Confining pressure limits crack propagation and enhances strength. With the increase in confining pressure, crack closure strength, crack initiation strength, dilatancy strength, and peak strength increase. The seepage pressure accelerates the crack propagation and weakens the strength, and the effect of seepage pressure is opposite to that of confining pressure.
2. Cretaceous red sandstone has few clay minerals, loose structure, low cohesion, and weak cementation ability. Under the action of seepage pressure, the cement between particles is dissolved, and the cement strength is easily lost. In addition, the water pressure produces expansion tension on the tip of pores and fractures, speeding up the expansion of pores and fractures, resulting in a significant decrease in cohesion, which shows an obvious softening effect.
3. The seepage pressure action term is introduced into the M-C criterion, and the element strength function that can reflect the influence of seepage pressure is obtained by combining the effective stress principle and strain equivalence theory.  
Figure 12: Sensitivity analysis of parameter \(m\).  
Figure 13: Sensitivity analysis of parameter \(k\).  
* The model can better reflect the stress-strain relationship of red sandstone under the hydraulic coupling action, and has wide application. At the same time, it can degenerate into a conventional damage constitutive model without the action of seepage pressure.  
Conceptualization, X.L. ([PERSON]) and X.H.; methodology, Z.Y.; investigation, X.H.; data curation, X.L. ([PERSON]) and X.W.; writing--original draft preparation, X.L. ([PERSON]); writing--review and editing, X.H.; project administration, X.W.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the graduate research project of Anhui Provincial Department of Education (Y)S20210382), China Scholarship Council (202108340062), Anhui Provincial College of Natural Science Research Key Project (KJ2018A0098), China Postdoctoral Science Foundation (2018M642502), Jiangxi Geological Environment and Underground Space Engineering Research Center (JXDHJ)2021-008), State Key Laboratory of Nuclear Resources and Environment (2022 NRE07), and Engineering Research Center of the Ministry of Education of Mine Underground Engineering (JYBGCZX2022105).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Results
### A Complex Object with an Indented Surface
#### 3.3.2 A Problem with Known Solutions  
It was assumed that the object is immersed in an infinite water domain. The mean density of the water is 1000 kg/m\({}^{3}\), and the speed of sound is 1500 m/s. The wave propagated from the point source was assumed to be spherical:  
\[\varphi(t)=R\varepsilon\Bigg{[}\frac{e^{-ikr_{p}}}{r_{p}}\;e^{i\;(2\;\pi\;f)\;t }\Bigg{]}=R\varepsilon\Bigg{[}\frac{e^{i\omega(t-\frac{r_{p}}{c})}}{r_{p}} \Bigg{]}=R\varepsilon\Bigg{(}\frac{e^{i\cdot\omega t_{s}}}{r_{p}}\Bigg{)}\;\;\; \;(\mathrm{m}^{2}/\mathrm{s}), \tag{15}\]  
where \(r_{p}\) is the distance between the point source and any point on the object surface, \(f\) is the frequency (\(f\) = 50 Hz), and \(\varphi\) is the velocity potential of the wave.  
According to the BEM in Wu [24], the amplitude of the radiating pressure at 50 Hz, computed by using the point source on the indented surface, is shown in Figure 16. The amplitude of the surface velocity at 50 Hz on the indented surface, computed by the point source, is shown in Figure 17.  
The radiating pressure on the indented surface at 50 Hz was also obtained by the present method using the boundary condition computed by the point source, as shown in Figure 13 and plotted in Figure 18. Here, the time shift is unnecessary (\(F\) = 0 in Equation (4)) because the frequency is far from the internal eigenfrequency. A comparison of Figures 16 and 18 indicates that the result computed by the point source is almost the same as that by the present iterating scheme.  
Figure 16: The radiating pressure at 50 Hz computed by the point source on the indented surface.  
Figure 17: The amplitude of the surface velocity at 50 Hz on the indented surface computed by the point source.
## 1 Introduction  
Seismic anisotropy can provide direct and independent information on geodynamics through the directional changes in seismic velocities, which can be related to the mantle flow and/or deformation (e.g., [1, 2]). This information is particularly interesting for the Arabian plate, because its geodynamics are very complex due to the extensional/compressional regimes that exist around this plate [3]. Shear-wave splitting can be used to estimate anisotropy (e.g., [4]), but this technique provides limited constraints on the depth distribution of the anisotropy. This limitation is overcome if the velocity of the shear waves polarized vertically (\(\beta_{\rm V}\)) and horizontally (\(\beta_{\rm H}\)) is calculated versus depth, i.e., determining S-velocity distributions (\(\beta_{\rm V}\), \(\beta_{\rm H}\)) with depth, such as those derived from surface-wave analysis. Thus, the goals of the present study are the determination of the azimuthal variation in surface-wave fundamental-mode phase velocity for the Arabian plate, and the parameter \(\xi=(\beta_{\rm H}/\beta_{\rm V})^{2}\) versus depth, from the Love- and Rayleigh-wave dispersion curves anisotropically inverted ([5, 6]).