esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Monitoring  
Use of Equations (1) and (2) to assess EAHX performance requires monitoring of outdoor air temperature (to determine \(T_{\text{a0}}\), \(\theta_{\text{rm}}\) and \(T^{\text{U}}_{\text{k}}\) with \(k\in\{\text{rSI}\text{rSI}\}\)); EAHX exit temperature (\(T_{\text{aL}}\)); and airflow rate through the EAHX (\(\dot{V}_{\text{a}}\)). To assess the relevance of latent heat exchanges in the EAHX, outdoor air relative humidity (\(\theta_{\text{a0}}\)) was also monitored. To have an estimate of undisturbed soil temperature (\(T_{\text{soc}}\)) at the EAHX depth, outdoor air temperature was monitored for a whole year and the average annual outdoor air temperature was determined; see [34].  
A weather station [35] was installed at the EAHX location and started collecting outdoor data in late May. The built and functional EAHX was monitored during the summer period from 1 June to 30 September. During this period the EAHX operated in continuous mode, 24 h per day, 7 days per week, at the nominal airflow rate of 8000 m\({}^{3}\)/h (4000 m\({}^{3}\)/h per pipe). Table 3 characterizes the sensors used for data collection.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Outdoor Air (Vaisala Weather Station [35])** \\ \hline Out.air temp.sensor, \(T_{\text{a0}}\) & Duration: 1 year. \\  & Log.frequency: 1 min. \\  & Sensor location: Outdoor, \(\sim\)1.5 m above ground in a unobstructed area. \\  & Range: \(-\)53 to +60 \({}^{\circ}\)C. \\  & Accuracy: \(\pm\)0.3 \({}^{\circ}\)C (at 20 \({}^{\circ}\)C). \\ Out.air rel.hum.sensor, \(\phi_{\text{ao}}\) & Duration: 1 year. \\  & Log.frequency: 1 min. \\  & Sensor location: Outdoor, \(\sim\)1.5 m above ground in a unobstructed area. \\  & Range: 0 to 100\%. \\  & Accuracy: \(\pm\)3\% (from 0 to 90\%); \(\pm\)5\% (from 90 to 100\%). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the sensors used to monitor the performance of the built EAHX.  
Using sensors' characteristics in Table 3 and depicting measurement errors (assumed uncorrelated) as \(\pm\delta(\cdot)\), it is possible to determine errors associated with the temperature gradient and heat transfer equations presented in Section 2.3. For the temperature gradient between air entering and exiting the EAHX, the error is constant and equal to  
\[\pm\sqrt{\delta T_{\text{aL}}\,^{2}+\delta T_{\text{a0}}\,^{2}}\simeq\pm 0.5 \,^{\circ}\text{C}. \tag{13}\]  
For the heat transfer in the EAHX, assuming constant values of \(c_{\text{a}}\) and \(\rho_{\text{a}}\) (see Nomenclature) with \(\delta V=\pm 100\) m\({}^{3}\)/h (=\(\pm 0.0278\) m\({}^{3}\)/s), the measurement error associated with the nominal airflow rate (8000 m\({}^{3}\)/h = 2.22 m\({}^{3}\)/s), and using the maximum temperature gradient \(\Delta T=9\) K, with \(\delta(\Delta T)=\pm 0.5\)\({}^{\circ}\)C, the maximum error is  
\[\pm\rho_{\text{a}}c_{\text{aN}}\sqrt{\Delta T^{2}\delta V^{2}+\dot{V}^{2} \delta(\Delta T)^{2}}\simeq\pm 1400\ \text{W}. \tag{14}\]  
This maximum error applies to both \(Q_{\text{oa}}^{\text{EAHX}}\) and \(Q_{\text{effII}}^{\text{EAHX}}\).
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Multiple Methods to Build Models Based on Vegetation Index  
For the construction of wheat yield estimation models, Multiple Linear Regression, Stepwise Multiple Regression, Principal Component Analysis and Neural Network methods were implemented using the SPSS software. On the other hand, the Random Forest method was implemented using the Python programming language. This combination of tools allowed for a comprehensive analysis and modeling approach to estimate wheat yields.
## 3 Results
### Biogenic Amines Content  
Tryptamine, 2-phenylethylamine, putrescine, spermidine and spermine were under the detection threshold in all samples. The concentrations of cadaverine, tyramine and histamine are presented in Table 1. High variability was observed within the triplicates.  
Concentrations of the three biogenic amines in the SG started to increase between 7 and 14 days of storage and remained stable until 28 days within a range of 276 \(\pm\) 31 to 404 \(\pm\) 79 mg/kg for cadaverine, 42 \(\pm\) 13 to 65 \(\pm\) 61 mg/kg for tyramine and 56 \(\pm\) 7 to  
Figure 4: Normalized 2D principal component analysis (PCA) representation of the salmon gravax (SG), smoked salmon stored under a modified atmosphere (MAP) and smoked salmon under vacuum package (VP) sensory evolutions based on visual and odor descriptors (**A**) and flavor and texture (**B**). Numbers associated with the sample name correspond to the sampling date in days.  
\(117\pm 104\) mg/kg for histamine. As for the TVBN content, a decrease in biogenic amines was observed after 35 days.  
In the VP CSS, biogenic amines production started after 28 days and also remained more or less stable until day 49. Their respective range values were always lower than in the SG and ranged from 153 \(\pm\) 160 to 219 \(\pm\) 188 mg/kg for cadaverine, 27 \(\pm\) 36 to \(49\pm 78\) mg/kg for tyramine and 11 \(\pm\) 6 to 59 \(\pm\) 45 mg/kg for histamine. Although the mean concentration of histamine was lower than 100 mg/kg (European tolerated concentration in some fish species), some replicates exceeded this limit (SG at day 14, VP CSS at days 35 and 49; data not shown).  
In the MAP CSS, no biogenic amine exceeded 10 mg/kg for the whole storage duration.
## 3 Results and Discussion
### Corrosion Inhibition Mechanism
#### 3.5.2 Corrosion Inhibitors
After all, it could be claimed that the dominant structures of the double-layer compounds are those of iron oxides and hydroxides, whereas into the inner layer of mild steel, hybrid crystalline and amorphous combinations of [Fe,[O\({}_{2}\)],H\({}_{2}\)O,HPO4\({}^{2-}\),PO4\({}^{3-}\)] compounds are commonly solidified, providing a unique composition and behavior for each layer. The investigation of ambient aquatic circumneutral systems of corroding mild steel revealed that a variety of crystalline iron-phosphate products were to be detected, interstitial of the different stoichiometry and oxidation states of iron, during the successive alteration of local conditions, from vivianite (ferrous phosphate), Fe\({}_{3}\)(PO4)\({}_{2}\)-8H\({}_{2}\)O, to strengite (ferric), FePO4-8H\({}_{2}\)O (reactions 16,17) [74; 124; 125]. The final constitution of the converted coating is meant to emerge by the balancing of the competitive processes of ionic precipitation, solid-state transformations, and crystallization, in conjunction with the local reagent concentrations and aeration level. In accordance with the experimental results, we assume that phosphate anions converted successfully onto the steel surface, participating in green rust stabilization, preventing GRI(CI\({}^{-}\)) oxidation [107] and fine-grained crystalline well-ordered structures in the final rust layer. \[\mathrm{H_{3}PO_{4}\to H_{2}PO_{4}{}^{-}+H^{+}\to HPO_{4}{}^{2-}+2H^{+}\to PO_{4}{} ^{3-}+3H^{+}} \tag{16}\]  
\[3 Fe^{2+}+2 PO_{4}{}^{3-}+8H_{2}O\to Fe_{3}(PO_{4})_{2}\cdot 8H_{2}O(s) \tag{17}\]
## 1 Introduction
### State-of-the-Art of Large Antenna Array for Communication or Sensing  
#### 1.1.1 Array Tiling  
By grouping antennas into the so-called \"tiles\" and feeding each tile with one phase shifter, it guarantees a large aperture with a reduced number of phase shifters, reducing the hardware costs. Until now, the investigations on array tiling and the discussions on the resultant performance were mainly for phased array communications [11; 12; 13; 14] or massive MIMO communications [6; 7]. The genetic algorithm [17; 18], iterative convex programming or information-theoretic entropy concepts were used for tiling the array in a (near-)optimal manner. Among these solutions, the authors in Ref. [11] have proposed that two adjacent antennas can be excited by the same phase shifter, which is hence regarded as one domino tile. If the phase centers of these dominoes are distributed with maximum entropy in the aperture, the radiation pattern has a low SLL.
## 6 Conclusions and Future Directions
### Temporal Waveform Synthesis  
Once a very broad spectrum with millions of spectral components is produced, the next challenge will be to synthesize temporal waveforms using pulse-shaping techniques [60,61]. The full optical spectrum (from 200 nm to 10 microns) is much broader than the typical spectra used in pulse-shaping experiments. Because of the large bandwidth of the spectrum, arbitrary temporal waveform synthesis will likely be a challenging task, and there are a number of open questions. To adjust the phases and the amplitudes of components over such a broad spectrum, one approach would be to use piezo-driven moveable metal mirrors [62; 63; 64] and to work with a reflective geometry. These mirrors have a high reflection coefficient over the full optical region, and recent advances in fabrication allow for a large number of individually adjustable pixels. For the infrared region of the spectrum where the wavelength is large, the limited range of piezo-motion might be an issue. To overcome this limitation, an acousto-optic pulse shaper may be used for the infrared wavelengths [65]. A key component of pulse-shaping techniques is a nonlinear detector that allows measurement of the synthesized waveform through correlation studies. This is traditionally a thin nonlinear crystal where the second harmonic signal as a function of time delay reveals the temporal structure. However, for a very broad spectrum, crystals cannot be used; a nonlinear detector with a wide bandwidth is required. The nonlinear processes in noble gases have a sufficiently large bandwidth for this purpose. For example, the experiments of the [PERSON] group used multi-photon ionization and sum frequency generation in Xe atoms to characterize ultrafast pulses [18].
## 4 Discussion
### System Performance and Worker Well-Being as Analysis Metrics  
Once the set of solutions has been narrowed down to those closest to the ideal point and furthest from the nadir point, further evaluation is necessary to ensure the selected configurations meet broader operational goals, particularly under HFE principles. In this study, system performance was represented by the analysis of Green, Yellow, and Red times, which provides insights into how efficiently a layout supports production processes. Green time, which represents value-adding activities, was longer for the solution furthest from the nadir point (Solution 2466), and Red time, which represents non-value-adding activities such as walking, was also then shorter compared with solution 2808, demonstrating a favorable balance between system performance and spatial efficiency. However, the comparison is close and for solution 2808 the objective value of area utilization is lower than for solution 2466.  
The inclusion of these assessments into the decision support framework aligns with Industry 5.0 principles, which emphasize human-centric design in industrial systems. Industry 5.0 aims to balance technological efficiency with human well-being, building on Lean principles that focus on minimizing waste and maximizing value-added activities. However, while Lean emphasizes efficiency and waste reduction, it often focuses primarily on operational metrics. HFE expands this by integrating worker well-being as a central aspect of production design. By evaluating Green, Yellow, and Red times and assessing task-specific strain on workers, this framework incorporates both Lean's focus on efficient workflows and HFE's emphasis on human well-being. This dual focus ensures that the selected configurations not only achieve high production efficiency but also enhance worker health and safety.  
A potential enhancement to this decision support framework could involve the use of knee point detection methods. Knee points are solutions on the Pareto front that represent the most significant trade-offs between competing objectives. Identifying these points could provide decision-makers with an additional perspective, highlighting configurations that offer a balanced compromise without extreme sacrifices in any objective. Integrating knee point detection would enhance the robustness of the selection process and offer a nuanced understanding of trade-offs in complex multi-objective optimization scenarios.
# InP-Based Foundry PICs for Optical Interconnects
## 3 Building Blocks targeting Hybrid-Integration Applications  
As explained in the introductory section, modules in datacenters will be relying more on hybrid integration to increase link- and channel capacity, while keeping size, power consumption, and cost low. The most relevant parameters for hybrid integration of different PICs into a compact module are: coupling loss, alignment tolerance, polarization dependent loss, and amount of back reflection. All these parameters are very much inter-related to each other by the way that the light is coupled from the InP PIC to the other PIC (here to be named, the interconnect PIC). Figure 7 shows four of the most common hybrid-integration approaches that are reported in the literature for datacenter- and optical-interconnect applications, which we have subdivided in two types: edge coupling, and surface coupling [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. We have been developing three strategies to modify our generic-foundry-service process to enable efficient coupling of light in- and out of the InP PIC for these four hybrid-integration approaches. To achieve this, the following three adaptations are required: deeply-etched waveguide terminations (i.e., etched facets), second-order Bragg gratings to realize grating couplers, and vertical mirrors to steer the light to either the top- or the bottom surface of the PIC.  
Figure 6: Measurement results of the avalanche photodiodes: IV curve including the calculated gain (**a**), responsivity and PDL versus wavelength (**b**), frequency response for various applied voltages (**c**), and linearity for various input powers (**d**).
## 2 Problem Formulation
\begin{split}\ddot{x}_{1}+\xi_{1}\dot{x}_{1}+\xi_{2}\dot{x}_{2}+ \omega_{r}^{2}x_{1}+\frac{N}{\sqrt{1^{5}}\;\dot{\omega}_{1}^{2}}\;x_{1}^{2}+ \frac{2N\theta}{\sqrt{1^{5}}\;\dot{\omega}_{1}^{2}}\;x_{1}x_{2}+\frac{N\theta^ {2}}{\sqrt{1^{5}}\;\dot{\omega}_{1}^{2}}\;x_{2}^{2}+\frac{\theta^{4}}{\dot{ \omega}_{1}^{2}}x_{1}^{3}\\ +\frac{3\theta^{3}}{\dot{\omega}_{1}^{2}}x_{1}^{2}x_{2}+\frac{3 \theta^{2}}{\dot{\omega}_{1}^{2}}x_{1}x_{2}^{2}+\frac{\theta}{\dot{\omega}_{1 }^{2}}x_{2}^{3}\;=-\theta\overline{f}cosf2\tau\end{split}  \tag{30}\]  
and  
\[ \begin{split}\ddot{x}_{2}+\xi_{2}\dot{x}_{1}+\xi_{3}\dot{x}_{2}+ x_{2}+\frac{N\theta}{\sqrt{1^{5}}\;\dot{\omega}_{1}^{2}}\;x_{1}^{2}+\frac{2N \theta^{2}}{\sqrt{1^{5}}\;\dot{\omega}_{1}^{2}}\;x_{1}x_{2}+\frac{N\theta^{ 3}}{\sqrt{1^{3}}\;\dot{\omega}_{1}^{2}}\;x_{2}^{2}+\frac{\theta^{3}}{\dot{ \omega}_{1}^{2}}x_{1}^{3}\\ +\frac{3\theta^{2}}{\dot{\omega}_{1}^{2}}x_{1}^{2}x_{2}+\frac{3 \theta}{\dot{\omega}_{1}^{2}}x_{1}x_{2}^{2}+\frac{\theta}{\dot{\omega}_{1}^{2} }x_{2}^{3}\;=\overline{f},\end{split}  \tag{31}\]  
where  
\[ \begin{split}\xi_{1}=2\zeta_{1}\theta^{2}+2\zeta_{e}\omega_{r}, \;\xi_{2}=-2\zeta_{1}\theta+2\zeta_{e}\omega_{r}\theta,\zeta_{3}=2\zeta_{1}+ 2\zeta_{e}\omega_{r}\theta^{2}.\end{split} \]  
For details on the derivation of the modal equations, the reader is referred to Appendix A. It can be observed from Equations (30) and (31) that the modal equations exhibit quadratic and cubic nonlinearities. The system parameters can be tuned such that there exists a 2:1 internal resonance between the modal coordinates. This demands that \(\omega_{r}\) be set equal to \(1/2\), which means that the electric frequency, \(\omega_{e}\), is half the effective mechanical frequency, \(\hat{\omega}_{1}\), as per Equation (29). The material properties of the beam material and the PZT patch are given in Table 1. The numerical value of the PZT capacitance is calculated as follows:  
\[ \begin{split} C_{p}&=\frac{2e^{T}e^{\alpha}L_{p}b_{ p}}{t_{p}}\\ \end{split} =193.16\;\text{nF}. \tag{32}\]  
The piezoelectric coupling coefficient \(K_{c}\) is defined as [37]  
\[ \begin{split} K_{c}&=\int_{0}^{L_{p}/L}2E_{p}d_{31 }b_{p}\frac{\left(t+t_{p}\right)}{2}\phi_{1}^{\prime\prime}(\xi)\Delta H\;d\xi,\end{split}  \tag{33}\]  
where \(\Delta H\;=\;H(\xi-\xi_{1})-H(\xi-\xi_{2})\) is the Heaviside function, and \(E_{p}\) is the elastic modulus for the piezoelectric material. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Material**} \\ \cline{2-3}  & **Structure** & **PZT-P (ZrTi) O\({}_{3}\) C-82** \\ \hline Length (mm) & 260 & 100 \\ \hline Width (mm) & 15 & 15 \\ \hline Thickness (mm) & 1 & 0.5 \\ \hline Elastic modulus (GPa) & 210 & – \\ \hline Density (kg/ m\({}^{3}\)) & 7850 & – \\ \hline Permittivity & – & 3650 \\ \hline Piezoelectric coefficient (C/N) & – & 266 \(\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material characteristics of the structure and the piezoelectric patch [38].
## 2 Materials and Methods
### Physicochemical Characterization of Honey Samples  
Physicochemical parameters were analyzed according to \"The Official Methods of Analysis of Association of Official Analytical Chemists\", [40] and \"The Harmonized Methods of the European Honey Commission\" [41].  
The honey samples were homogenized by stirring thoroughly.  
HMF was determined by HPLC-UV using as the isocratic mobile phase water/methanol (90:10 \(v/v\)) with a flow rate of 1.0 mL/min. The column used was a ZORBAX (Eclipse Plus C18, 4.6 \(\times\) 150 mm, 5 \(\upmu\)m particle size), from Agilent (Agilent Technologies, Santa Clara, CA, USA), and the detector was set to 285 nm [42].  
Water activity was measured using an electronic dew-point water activity meter (25 \(\pm\) 0.2 \({}^{\circ}\)C) (Aqualab Series 4 mod. TE Decagon Devices, Pullman, WA, USA), with a temperature-controlled system [43].  
Electrical conductivity was determined at 20 \({}^{\circ}\)C by conductivity (Crison Basic 30, Crison Instrument, ITALY). Results were expressed in microSiemens per centimeter (\(\upmu\)S/cm).  
Moisture content was analyzed at 20 \({}^{\circ}\)C with an Abbe type refractometer (PDT/001 Atago, Bellevue, Washington, USA), equipped with a thermometer having a graduation of 0.5 \({}^{\circ}\)C and a resolution of 2 \(\times\) 10\({}^{-4}\) units of refraction index (RI). Results were reported as percentages obtained from the Chat table by using the Wedmore method [44].  
Measurements of pH were performed with a pH meter (Orion420 A) in a solution containing 10 g of each honey sample in 75 mL of Milli-Q grade water, by using the White method [45].  
\begin{table}
\begin{tabular}{c c c} \hline
**Floral Origin** & **Geographical Origin** & **Number of Samples** \\ \hline \multirow{6}{*}{Honeydew} & Abruzzo & 1 \\  & Aosta Valley & 2 \\  & Apulia & 2 \\  & Emilia Romagna & 1 \\  & Tuscany & 2 \\  & Trentino Alto Adige & 2 \\ \hline \multirow{6}{*}{Multifiloral} & Apulia & 4 \\  & Campania & 2 \\  & Calabria & 2 \\  & Emilia Romagna & 2 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The diastase activity was assessed by Phadebas tablets as recommended by the International Honey Commission [41]. Phadebas Honey Diastase Tests (\(\copyright\), Sweden) were used according to the manufacturer instructions--5 mL of 1% honey solution in 0.1 M acetate buffer (pH 5.2) was prepared and transferred to a test tube; after incubation at 40 \({}^{\circ}\)C for 5 min, the Phadebas tablet was added. Then the sample was reincubated for 30 min at 40 \({}^{\circ}\)C. Finally, 1 mL of 0.5 M NaOH was added to stop the reaction. The absorbance was measured at 620 nm, after samples filtration (filter paper), with BioMate 160 UV-Visible Spectrophotometer (Thermo Scientific, Waltham, MA, USA).  
All analyses were performed in triplicate.
## 3 Working Frequency of the Bending Transducer  
Firstly, we used finite element method (Ansys software, ANSYS Inc, Canonsburg, PA, USA) to gain the resonance frequency of the bending transducer by modal analysis. Figure 3 gives the main dimensions of the FEM model. The material of the beam is aluminum alloy with mass density of 2810 kg/m\({}^{3}\), Young modulus of 7.2 \(\times\) 10\({}^{10}\) N/m\({}^{2}\) and Poisson ratio of 0.33. The material of the ceramic is PZT-4, whose physical parameters are:  
\[d= \begin{bmatrix}0&0&0&0&5&0\\ 0&0&0&5&0&0\\ -1.6&-1.6&3.3&0&0&0\end{bmatrix}\times 10^{-10}\text{C/N} \tag{1}\] \[c^{\varepsilon}= \begin{bmatrix}15&8.4&6.8&0&0&0\\ 8.4&15&6.8&0&0&0\\ 6.8&6.8&12.9&0&0&0\\ 0&0&0&3.3&0&0\\ 0&0&0&0&2.8&0\\ 0&0&0&0&0&2.8\end{bmatrix}\times 10^{10}\text{N/m}^{2}\] (2) \[\varepsilon^{T}= \begin{bmatrix}8.1&0&0\\ 0&8.1&0\\ 0&0&6.7\end{bmatrix}\times 10^{-9}\text{F/m} \tag{3}\]  
The two first bending modes have the same resonance frequency as the symmetrical structure. During the modal analysis, calculations were accomplished under two different boundary conditions: electrical short-circuit and electrical open. Zero voltages were applied on the PZT plates in the electrical short-circuit calculation, while no voltage was set in the electrical open simulation. These two boundary conditions lead to different resonance frequencies of the bending modes, which are the series resonance frequency \(f_{s}\) and the parallel resonance frequency \(f_{p}\), separately.  
Figure 4: Parameter sensitivities of the resonance frequencies and electromechanical coupling factor.  
Figure 3: Main dimensions of the bending transducer (unit: mm).  
The length of the horn \(L_{1}\), the length of the metal beam \(L_{2}\), and the width of the metal beam \(A\) were changed in the FEM model to obtain their effects on the series and parallel resonance frequencies. Figure 4 shows the parameter sensitivities of the resonance frequencies and electromechanical coupling factor, in which the electromechanical coupling factor \(k\) was calculated by the following equation:  
\[k=\sqrt{\frac{f_{p}^{2}-f_{s}^{2}}{f_{p}^{2}}} \tag{4}\]  
The calculation results indicate that the resonances frequencies decrease linearly with the lengths of the metal beam and the horn, but increase with the width of the metal beam. The increase of the length of the horn results in the obvious fall of the electromechanical coupling factor, and the width of the metal beam. However, the length of the metal beam shows a minute and nonlinear effect on the electromechanical coupling factor. The distinctive merit of the proposed bending transducer is that its dimensions can be adjusted freely, but always has two identical first bending frequencies. For the size decrease requirement, it is better to decrease the width of the beam in order to maximize efficiency.