esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Variation of the Nusselt Number  
Figure 10 presents the local Nusselt number difference adjacent to the top heated wavy surface employing several \(N\) and \(A\) values. The overall evaluation shows that during both states, local maxima and minima values occur at the concave and the convex portion, respectively. An increment of the Nusselt number remains recognized concerning both cases when increasing the number of waves as well as wave amplitudes. This does observe notably that the heat transfer augmentation through examining the performance of wave amplitude is higher than the variation of the number of waves.  
Figure 10: Variation of local Nusselt number interfaces with the wavy \(W\) for different (**a**) \(N\) and (**b**) \(A\) at \(Ra=10^{6}\), and \(\phi=0.02\).  
Figure 9: Variations of local normalized solid volume fraction interfaces with the horizontal line (\(Y=0.1\)) for different (**a**) \(N\) and (**b**) \(A\) at \(Ra=10^{6}\) and \(\phi=0.02\).  
3-D and 2-D changes regarding average Nusselt number among \(A\) and \(N\) are given in Figure 11. As recognized by this figure, the average Nusselt number increases by boosting the values of both \(A\) and \(N\). For both cases, the length of the wavy surface increased, leading to a greater energy shift of the hot surface toward the cold one. Heat transfer enhancement is more significant for the variation of \(A\) than that of \(N\). Furthermore, average Nusselt number versus Rayleigh number during various \(N\) is also shown in Figure 11c. At lower \(Ra\) values, conductive heat transfer exists dominant than the convective heat transfer. Furthermore, developing the Rayleigh number leads to higher heat transfer due to the strong convection current.  
The variation in the average Nusselt number among volume fractions of nanoparticles of various \(N\) and \(A\) is shown in Figure 12a,b. The volume fraction of \(\phi\) varies of 0 upon 0.04. According to this configuration, the average Nusselt number improves by the growing of \(\phi\) toward both \(N\) and \(A\) variation. Causes higher thermal conductivity regarding nanoparticles, adding more particles improves the thermal energy transport within the two surfaces. More to the point, the variation from \(A\) and \(N\) has the similar effect as seen in Figure 10 concerning various volume fractions of nanoparticles. The outcome of \(A\) toward heat transfer occurs more noticeable than that of \(N\).  
Figure 11: (**a**) 3D-plot of the average Nusselt number with \(N\) and \(A\), (**b**) variation of the average Nusselt number with \(N\) for different \(A\), and (**c**) variation of the average Nusselt number with \(Ra\) for different \(N\) at \(\phi=0.02\).
## 4 Conclusions  
* The hydrological and hydrochemical conditions of the Cuernavaca aquifer were evaluated through the application of water quality indices and statistical techniques. This study provides an approach to the spatial and temporal behavior of an urbanized aquifer and assesses its vulnerability due to population growth.
* This study identified spatial variations between the sampling sites and evidenced the influence of urbanization on groundwater chemistry and quality in the Cuernavaca aquifer. The spatial variation of the chemical elements highlights the vulnerability of the aquifer, especially at P2.
* The time series analysis demonstrated no trend, seasonal, or cyclic patterns in the groundwater quality data. The multivariate statistical analysis showed a high number of correlations between the groundwater parameters. These parameters were grouped based on hierarchical cluster analysis which revealed the main chemical processes controlling groundwater chemistry in the aquifer.
* Most of the parameters (physicochemical and ions) measured in the Cuernavaca aquifer were within the standards allowed by the WHO for irrigation purposes. This situation was confirmed by the water quality indexes since the groundwater of the aquifer was classified as good quality. However, the presence of fecal coliforms, organic matter, and the high content of ions such as Ca\({}^{2+}\), Mg\({}^{2+}\), and HCO\({}_{3}\)\({}^{-}\) is an important situation that must be addressed to reduce the vulnerability of the aquifer.
* This study provides an approach to describe the behavior of the hydrochemical features and water quality of the Cuernavaca aquifer and points out the main driving factors for the deterioration of groundwater quality in an aquifer located in an urban area.  
Conceptualization, J.G.L.; formal analysis, S.A.M.-A.; investigation, R.E.-V. and Y.B.-T.; methodology, J.G.L. and V.B.-T.; project administration, V.B.-T.; supervision, Y.B.-T.; validation, J.G.R.-P.; visualization, S.A.M.-A. and A.Q.-C.; writing--original draft, J.G.L. and Y.B.-T.; writing--review and editing, J.G.R.-P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author.  
The authors kindly acknowledge the National Water Commission for providing data and the Polytechnic University of Morelos for their support.  
The authors declare no conflict of interest.
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 4 Discussion  
### Dual-Indicator Stability Evaluation Model of Unstable Rock Mass  
The collapse of unstable rock masses is a sudden geotechnical phenomenon where jointed slope rock masses gradually separate from the parent rock and undergo abrupt collapse. The stability of unstable rock masses can be assessed by monitoring the bonding state of internal structural planes. By using MEMS sensors to measure NF and RMS-VAR in real time, researchers can track the separation process between unstable rock masses and parent rock, enabling the real-time prediction of collapse timing.  
As the relative bonding length of structural planes decreases, the NF values captured by MEMS sensors exhibit a declining trend, mirroring the reduction in the limit equilibrium safety factor. Simultaneously, the RMS-VAR between unstable rock masses and bedrock, measured via MEMS sensors, reflects changes in the structural plane's relative bonding length and provides earlier indications of stability degradation compared to tilt angle variations.  
Based on the correlated trends of decreasing stability, declining NF, and increasing RMS-VAR, a dynamic stability assessment model (Figure 11) was established [24]. In this model, NF and RMS-VAR values in the upper-left quadrant indicate stable conditions, while migration toward the lower-right quadrant signifies destabilization. These dual indicators--NF and RMS-VAR--overcome the limitations of single-parameter assessments by enabling the real-time monitoring of stability transitions.  
Figure 11: Dual-indicator evaluation model of unstable rock.  
Figure 10: Relation between RMS-VAR and tilt angle.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### Field Measurements
#### 2.2.2. Measurement of Canopy Chlorophyll Content  
At the XTS site, fresh wheat leaf samples were taken from the top of the canopy in a 1 m\({}^{2}\) area for each plot [30], and rapidly placed in a plastic box containing ice for transport to the laboratory. The chlorophyll concentration was determined using a spectrophotometer [45]. The LAI of the sample leaves was measured using the dry-weight method [46]. At the US-Ne2 and US-Ne3 sites, fresh leaves from the soybean plants were collected in six small plots (20 m \(\times\) 20 m) within each site. The leaf pigment was extracted with 80% acetone and LCC was obtained using a spectrophotometer [47]. The LAI of sample leaves was measured using an area meter (Model LI-3100, Li-Cor Inc., Lincoln, NE, USA) [43]. LCC and LAI for six plots were then averaged as site-level values. The total chlorophyll parameter for the CCC was calculated by multiplying the LAI by the LCC. The statistical analyses of the measured wheat and soybean CCCs are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sites** & **Country** & **Lat/Long (\({}^{\circ}\))** & **Crop Species** & **Sampling Periods** & **MERIS Periods** \\ \hline  & & & & 2 April, 10 April,18 April, 6 & \\ XTS & China & 40.18/116.44 & Wheat & & May, 17 May 2002 & - \\  & & & & 14 April, 19 May 2004 & \\  & & & & 13 June–17 September 2002, & \\ US-Ne2 & America & 41.165/\(-\)96.47 & Soybean & & 27 measurement campaigns & 25 June–24 \\  & & & & 29 June–20 September 2004, & September 2004 \\ US-Ne3 & America & 41.18/\(-\)96.44 & Soybean & & 19 June–17 September 2002, & - \\  & & & & 18 measurement campaigns & & \\ \hline \end{tabular}
\end{table}
Table 1: Details of the study sites, including the sampling locations (Lat = latitude; Long = longitude), crop species, sample periods and dates for the MERIS image acquisitions.
## 4 Design Procedure of the Metamaterial Unit Cell  
The proposed design was chosen to analyze different unit cell design approaches based on a trial and error method, as shown in Figure 3, and the \(S_{21}\) and \(S_{11}\) plot for the different design approaches is shown in Figure 4a,b. Multiple resonance frequencies require more factors, i.e., the size and thickness of the substrate, width and split gap of the resonating patch, and mutual coupling of the resonator. The variation in capacitance and inductance of the unit cell is responsible for creating a multiple resonance frequency. The unit cell design was initiated with the insertion of a split ring resonator through the split gap of 0.2 mm. Three resonance frequencies were obtained from design 1, the resonance frequencies of reflection coefficients (\(S_{11}\)) are 3.325, 9.05, and 11.5 GHz with the magnitude of \(-27.78\), \(-9.88\), and \(-11.04\) dB, respectively, whilst the resonance frequencies of transmission coefficients (\(S_{21}\)) are 2.05, 7.19, and 9.47 GHz with the magnitude of \(-29.42\), \(-28.92\), and \(-11.25\) dB, respectively. In the next step, a Coptic shape is added to the previous design showing triple resonances. This inclusion improves the inductance value and thus creates a similar resonance with the previous design, but increased the magnitude by slightly changing the first and second resonance frequencies to a lower frequency range. The resonance frequencies of \(S_{11}\) are at 2.995, 7.39, and 15.97 GHz with the magnitude of \(-27.75\), \(-12.15\), and \(-3.57\) dB; the \(S_{21}\) is at 2.02, 6.79, and 12.91 GHz with the magnitude of \(-28.48\), \(-22.41\), and \(-34\) dB, respectively. According to design 3 of Figure 3, the same resonance frequency exists as the earlier design by inserting four cross shapes into design 2, because of only improving the inductive property. The magnitudes of the resonance frequencies of \(S_{11}\) are \(-27.52\), \(-12.06\), and \(-4.05\) dB at 2.98, 7.39, and 15.8 GHz, respectively, whereas, the resonance frequencies of \(S_{21}\) are 2.02, 6.79, and 12.88 GHz with the magnitude of \(-28.97\), \(-22.45\), and \(-34\) dB, respectively. The design is completed by inserting two split gaps in Coptic shape, as shown in Figure 3. The inserting split gap in design 3 creates additional resonance frequency due to generating a capacitance property. According to Figure 4, the resonance frequencies of \(S_{11}\) are 3.085, 8.095, 10.795, and 15.775 GHz with the magnitude of \(-27.03\), \(-13.14\), \(-9.66\), and \(-4.91\) dB; the resonance frequencies of \(S_{21}\) are 2.02, 6.985, 9.985, and 14.425 GHz with the magnitude of \(-29.45\), \(-25.44\), \(-19.05\), and \(-24.45\) dB, respectively. Consequently
## 3 Design Methodology Based on the Common Framework
### Controller Optimization  
As previously discussed, the approach to identifying the optimal Actuator Mass Dampers (AMDs) involves determining the most effective IRC parameters to minimize the peak values of the FRFs associated with nodes N16 and N22 across the frequency range of 0 Hz to 15 Hz. This optimization task was accomplished through the utilization of the built-in MATLAB function _fminsearch_, which employs a derivative-free method to locate a local minimum of an unconstrained multivariable function. The configuration of the optimization is as follows:  
* A variation of \(\pm 25\%\) in the natural frequencies of the system was considered.  
Figure 7: Magnitude (in dB) plot of the identified model (black) with the measured FRF (grey), demonstrating excellent alignment between the actual system and its developed model.  
* The peak values of the FRFs associated with nodes N16 and N22 across the frequency range of 0 Hz to 15 Hz were obtained with the _bode_ MATLAB function.
* In each optimization, the real part of the closest pole to the imaginary axis was calculated. If this value is greater than zero, the value of the magnitude is penalized.
* X = fminsearch (FUN, X0) was configured as follows: FUN is the magnitude obtained with _bode_, and the variable to optimize is X, being \(\gamma_{1}\), \(\gamma_{2}\), \(d_{1}\), and \(d_{2}\). The initial conditions were \(\gamma_{1}=\gamma_{2}=d_{1}=d_{2}=1\), which guaranteed the stability of the closed-loop system. The resulting controller is as follows (see (6)):
* \(K_{1}(s)=187.3/(s+0.10)\);
* \(K_{2}(s)=88.47/(s+0.61)\).
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AGV & Automated guided vehicle \\ AoA & Angle of arrival \\ API & Application programming interface \\ BLE & Bluetooth low-energy \\ BW & Bandwidth \\ CSI & Channel state information \\ CSS & Chirp spread spectrum \\ DOP & Dilution of precision \\ e.i.r.p. & Effective isotropic radiated power \\ ER & Emergency response \\ ETSI & European Telecommunications Standards Institute \\ EUREF & Regional Reference Frame Sub-Commission for Europe \\ FCC & Federal Communications Commission \\ FR & First responder \\ GDOP & Geometric dilution of precision \\ GNSS & Global navigation satellite system \\ GPS & Global positioning system \\ HF & High-frequency \\ IEEE & Institute of Electrical and Electronics Engineers \\ IFAFRI & International Forum to Advance First Responder Innovation \\ IMU & Inertial measurement unit \\ IoT & Internet of Things \\ IPS & Indoor positioning system \\ LAES & Location-tracking application for emergency and disaster situations \\ LF & Low-frequency \\ LOS & Line-of-sight \\ LR-WPAN & Low-rate wireless personal area network \\ MAC & Medium access control (layer) \\ MCU & Microcontroller unit \\ NLOS & Non-line-of-sight \\ NTRIP & Networked transport of RTCM via internet protocol \\ PDoA & Phase difference of arrival \\ PHY & Physical (layer) \\ PoA & Phase of arrival \\ PRF & Pulse repetition frequency \\ REST & Representational state transfer \\ RF & Radio frequency \\ RFID & Radio-frequency identification \\ RSSI & Received signal strength indicator \\ RTCM & Real-time correction messages \\ RTK & Real-time kinematic \\ RTOS & Real-time operating system \\ RTT & Round-trip time \\ SS-TWR & Single-sided two-way ranging \\ \end{tabular}
## 4 Distribution Method for Observation Scheduling in the AEOS Constellation
### Target Inclusion Phase  
In this phase, each AEOS \(s\) adds some target \(g\in G\backslash\mathbf{a}_{s}\) into its sequence \(\mathbf{a}_{s}\) by comparing \(Q_{sg}\) with \(g^{\prime}\)s marginal significance \(q_{s}{*}(\mathbf{a}_{s}\oplus g)\) computed based on its sequence \(\mathbf{a}_{s}\). Specifically, we first determine whether the following Inequality (20) is satisfied or not.  
\[\max_{g\in G}\{q_{s}^{*}(\mathbf{a}_{s}\oplus g)-Q_{sg}\}>0 \tag{20}\]  
If (20) holds, we select target \(g^{\prime}=\operatorname*{argmax}_{g\in G}[q_{s}{*}(\mathbf{a}_{s}\oplus g)-Q _{sg}]\) whose removal leads to the maximum increase in Y(\(\mathbf{a}\)) and let \(p_{f}=\operatorname*{argmax}_{p\in[1,\ 2,\ \text{$\mid$}s\mid+1]}(F( \mathbf{a}_{s}\oplus g^{\prime})-F(\mathbf{a}_{s})]\) be the position of \(\mathbf{a}_{s}\) to insert. Then, we add \(g^{\prime}\) into the \(p_{f}\)-position of \(\mathbf{a}_{s}\) and update \(Z_{gg^{\prime}}\) such that \(Z_{gg^{\prime}}=s\). Meanwhile, we update the marginal significance \(q_{s}{*}(\mathbf{a}_{s}\oplus g)\) for each target \(g\in G\backslash\mathbf{a}_{s}\). We repeat the above process until (20) is not met.  
At the end of this phase, we update the significance list \(Q_{s}\) by setting \(Q_{sg}=q_{s}(\mathbf{a}_{s}\ominus g)\) for each target \(g\). The whole target inclusion phase can be expressed in the following Algorithm 4.  
```
1:AECS \(s\), target sequence \(\mathbf{a}_{s}\), \(\mathbf{Q}_{s}\) and \(\mathbf{Z}_{s}\);
2:Input: New sequence \(\mathbf{a}_{s}\), updated lists \(\mathbf{Q}_{s}\) and \(\mathbf{Z}_{s}\);
3:while (1)
4:for each \(g\in G\)
5: Set \(q_{s}{*}(\mathbf{a}_{s}\oplus g)=0\);
6:if\(g\in\mathbf{a}_{s}\)
7:\(q_{s}{*}(\mathbf{a}_{s}\oplus g)=-\infty\);
8:else
9:for each \(p\in[1,2,\ \text{$\mid$}\mathbf{a}_{s}\mid+1]\)
10: Compute \(F(\mathbf{a}_{s}\oplus p_{g})\);
11:if\(F(\mathbf{a}_{s}\oplus p_{g})-F(\mathbf{a}_{s})>q_{s}{*}(\mathbf{a}_{s} \oplus g)\);
12: Let \(q_{s}{*}(\mathbf{a}_{s}\oplus g)=F(\mathbf{a}_{s}\oplus p_{g})-F(\mathbf{a}_{s})\);
13:end
14:end
15:end //compute the marginal significance values of all targets
16:if\(\max_{g\in G}[q_{s}{*}(\mathbf{a}_{s}\oplus g)-Q_{sg}]>0\)
17: Let \(g^{\prime}=\operatorname*{argmax}_{g\in G}[q_{s}{*}(\mathbf{a}_{s}\oplus g)-Q _{sg}]\);
18: Let \(p_{f}=\operatorname*{argmax}_{p\in[1,\ 2,\ \text{$\mid$}s\mid+1]}(F( \mathbf{a}_{s}\oplus p_{g}^{\prime})-F(\mathbf{a}_{s})]\);
19: Insert \(g^{\prime}\) into the \(p_{f}\)-position of \(\mathbf{a}_{s}\);
20: Let \(Z_{gg^{\prime}}=s\);
21:else
22: break;
23:end
24: Update \(\mathbf{Q}_{s}\) such that \(\mathbf{Q}_{sg}=q_{s}(\mathbf{a}_{s}\ominus g)\);
25: Output \(\mathbf{a}_{s}\), \(\mathbf{Q}_{s}\), \(\mathbf{Z}_{s}\)
```  
**Algorithm 4** Task inclusion phase running on each satellite.
## 5 An Illustrative Example
### Result Analysis  
Table 6 and Figure 8 show that the sequence sorted according to GCE was always \(1>5>4>6>3>2\) while the sequence sorted due to TOPSIS was always \(1>4>6>5>3>2\). The result means that enterprise 4 is bad in terms of shape but good in terms of distance since it performed well on criterion 3 but badly on criteria 1 and 2. Enterprise 5 is bad in terms of distance, but good in terms of shape since it performed slightly better than average on most criteria, which is similar to enterprise 1.  
The sequence sorted according to FGT is slightly related to parameter \(p\). When \(p=0\), the sequence was \(1>4>5>6>2>3\). when \(p>0\), the sequence was \(1>4>5>6>3>2\). Enterprise 2 has slight advantages on distance, since it performed barely satisfactorily on criteria 4 and 5. However, its performance was instable. When the factor of shape is taken into consideration, enterprise 3 will be better than enterprise 2. Figure 8 clearly shows that the comprehensive evaluation value of enterprise 1 (\(CS_{1}\)) obviously decreased, \(CS_{4}\) slightly decreased, \(CS_{2}\), \(CS_{3}\), and \(CS_{5}\) obviously increased, and \(CS_{6}\) slightly increased with the increase of parameter \(p\), i.e., the increase consideration of shape.  
The result shows that the proposed method can reflect both the difference of shape and distance of objects, which overcomes the one-sidedness of the GCE method and TOPSIS method.
## 2 Materials and Methods
### Physical Characterization of LIP and Plasma Longitudinal Expansion
#### 2.2.1 Plasma Temperature and Electron Density  
Plasma temperature and electron density were calculated using an iterative fitting procedure designed for the analysis of high-resolved UV-ViS LIBS spectra. The emission line intensity profile functions intended for the optimization are summarized in our previous paper [16]. Both electron and heavy particle number densities, together with plasma temperature, were fitted onto the function \(I_{ij}=I_{ij}(N_{\mathrm{S}},N_{\mathrm{e}},T)\) of a particular transition intensity \(I_{ij}\) governed by individual species abundances \(N_{\mathrm{S}}\). Each particular line was modeled by a pseudo-Voigt profile function with a Lorentzian line width directly proportional to the total number density of free electrons. Moreover, we assumed the Boltzmann energy distribution to hold in local thermodynamic equilibrium (LTE) plasma and exploited the particular distribution functions in their nonlinearized form. A fully synthetic spectrum was consequently depicted as a sum of individual line profile functions and fundamental plasma physics parameters (\(T,N_{S},N_{\mathrm{e}}\)), optimized onto a fitted experimental record.  
The physical justification for such a model is dealt with in detail elsewhere; see [16; 18] and references therein. The nonlinear optimization procedures were adopted to prevent possible spectral data corruption, which may arise in such warm, dense ablation plasma governed by nontrivial charge transfer phenomena.
## 2 Materials and Methods
### Ground Measurements  
Hyperspectral reflectance signatures of rice canopy and soil background were measured respectively using a spectroradiometer (FieldSpec HandHeld 2, ASD, Boulder, CO, USA) and a white Spectralon reference panel (Labsphere, North Sutton, NH, USA). Canopy reflectance was measured on each plot three times. The average spectral reflectance was used to verify Cubert hyperspectral camera measurements.  
The leaf gravimetric water content was measured using a destructive method. Three samples of whole rice leave (including leaf blade and sheath, and the stem under the sheath) were taken in each plot on 19 November 2018. Each sample was weighed fresh, cut into small pieces, and heated in an oven at 70 \({}^{\circ}\)C for 24 hours in a laboratory. The leaf water mass was determined as the difference between the fresh and the dry leaf mass. The leaf gravimetric water content was defined as the ratio of leaf water mass to fresh mass (g/g). Leaf nutrient content was also determined in the laboratory using the total digestion method on leaf samples. The macro-elements of nitrogen (N), phosphorous (P), and potassium (K) concentration are determined as a percentage (%) of the dry matter. One soil sample was collected from each plot. The total nitrogen concentration in each sample was tested using a CN 628 Dumas analyzer (LECO, St. Joseph, MI, USA) in the laboratory in National Agricultural Technology Institute (INTA), Costa Rica.  
An automatic meteorological station (Vaisala WT520, Vaisala, Finland) was installed at a height of 1.5 m above the ground to continuously monitor precipitation, wind speed and direction, air temperature, relative humidity, and atmospheric pressure (Figure 1). Each plot was instrumented with two in situ sensors at 15 cm below the surface (Figure 1), including one sensor (Decagon GS3, METER Group, Pullman, WA, USA) measuring volumetric soil water content, soil electrical conductivity, and soil temperature, and another sensor (Decagon MPS6, METER Group, USA) measuring soil matric potential and soil temperature. The sensors were connected to a data logger (CR1000, Campbell Scientific, Logan, UT, USA) to collect the data at 30-minute intervals from 18 July to 21 November 2018.