esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Experimental Set-Up
### Experimental Technique  
The properties of surface acoustic waves propagating in these composite structures were studied using a laser based transient grating (TG) method [13; 14; 15]. This technique is an optical method that allows to monitor surface acoustic waves stimulated in thin films with optical short pulses. It is useful for accurately and nondestructively characterizing the high frequency elastic properties.  
In this approach, two laser pulses are spatially and temporally overlapped on the surface of the sample to excite elastic waves with a wave vector determined by the optical interference pattern generated by the crossed excitation pulses (Figure 3). These crossed infrared pulses (wavelength = 1064 nm; 30 ps in duration) heat the surface of samples, which creates thermoelastic stresses that finally relax in the form of two counter-propagating surface acoustic waves with wavelength \(\Lambda\) equal to the interference fringes spacing and frequencies f = \(\Omega/2\pi\). These acoustic waves are then monitored in real time by detecting the green light from a continuous-wave laser (wavelength = 532 nm) diffracted on the surface relief associated with the acoustic disturbances. The detection of elastic waves is made using a heterodyne scheme and fast detection electronics.  
Controlling the angle between the two incident pump beams via adjusting of the focal path allows to tune the wavelength \(\Lambda\) of acoustic wave. The Fourier analysis of experimental data at fixed value  
Figure 3: Optical set-up and laser system for TGM (transient grating method) experiment in reflection geometry. Two convergent lens; M: mirror; DOE: diffractive optical element.  
Figure 2: Scanning electronic microscope picture of GaN NWs on n-doped Si (111) substrate before the encapsulation into a polymer matrix. The NWs are growth at lower temperature than for sample (Figure 1), then leading to a smaller density and the appearance of a parasitic and thick-2D GaN layer located at the NW bottom here. The filling factor is less than 10%.  
of \(\Lambda\) shows distinct frequencies, each corresponding to distinct mode of the structure, as depicted in Figure 4a. Varing \(\Lambda\) and recording the frequencies resonance peaks for each value, allows therefore to map the dispersion diagrams with the step size in wave vector determined by the pattern of diffractive optical element and the magnifying power of optical system.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 2 Time Scale Self-Generated Overall Architecture  
The time scale generation scheme proposed in this paper uses each satellite-borne atomic clock in the navigation constellation as the time-frequency source, and each satellite-borne atomic clock forms two clock groups according to the type of clock through the inter-satellite link: the rubidium atomic clock group and the cesium atomic clock group. The relative clock difference can be calculated by inter-satellite link two-way ranging between satellites. Before the system enters the autonomous operation state, use the historical clock difference data of each atomic clock for five days to calculate the frequency stability, and select the atomic clock with the smallest Allan variance when the sampling interval is 10,000 s as the reference clock. Using the improved time scale algorithm, the time scale TA(Rb) of the rubidium clock group and the time scale TA(Cs) of the cesium clock group are respectively obtained. The frequency stability of two time scales is better than that of the single clock in the respective clock group.  
In the part of time-frequency steer, select TA(Cs) as the control reference, TA(Rb) as the frequency standard to be steered, measure the relative clock difference between the control reference TA(Cs) and the TA(Rb) to be steered, and construct a third-order DPLLcontrol system equivalent to a Kalman filter combined with delay structure. On this basis, combined with the genetic algorithm to design the fitness function to control the adaptive iterative optimization of the system parameters, use the steer reference to obtain the optimal time-frequency steer value, and finally steer TA(Rb) efficiently to obtain the desired optimal comprehensive time scale. The structure diagram is shown in Figure 1.  
Figure 1: Comprehensive time scale generation system architecture diagram.
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
## 4 Discussion
### The CubeSats and the Performance of the Models
The application of a low pass filter (\(3\times 3\) median) to the surface reflectance imagery improves the results of the training of the method (Table 3) as it smooths possible pixel anomalies. The bathymetric zonation of the multiple regressions into two groups (0-10 m and 10-25 m) has been selected to understand the accuracies of the SDB in relation to the requirements of the IHO in the inclusion of bathymetry data in navigation maps and the corresponding ZOC that the depth accuracies fall into [3]. By splitting the analysis intothese two zones, coefficients from the multiple regressions are applied into the respective bathymetric zones, while the final map is the unification of the results from the two zones.
## 4 Numerical Results
In good communication environments (i.e., light fog type, no pointing error, or good case), the difference in ergodic capacity performance between the HD and IM/DD techniques is small, whereas in bad communication environments (i.e., dense fog type, high pointing error, or bad case), the difference in ergodic capacity performance between the HD and IM/DD techniques is large. Therefore, owing to the characteristics of the HD and IM/DD techniques [19], applying the IM/DD technique in normal environments is more appropriate. When communication environments deteriorate, it is more appropriate to apply the HD technique to achieve a high system capacity performance. Note that we can consider ergodic capacity (in bits/sec/Hz, which is the universal unit), by multiplying \(\frac{1}{\ln 2}\) in our derived results.
## 4 Results and Evaluation
### Assessing Soil-Specific Measurements
Although these quantities are  
Figure 9: Soil measurement analysis under controlled laboratory conditions: (**a**) estimation of \(\theta\) by TEROS 10 instrument for loam with EC\({}_{\mathrm{w}}\) of 10 dS/m; (**b**) estimation of \(\theta\) by TEROS 12 for loam with EC\({}_{\mathrm{w}}\) of 6 dS/m; (**c**) estimation of \(\varepsilon_{\mathrm{a}}\) for aqueous solution of increased salinity by TEROS 10 and TEROS 12 instruments; (**d**) performance of TEROS 10, 10 HS, and TEROS 12 in measuring \(\varepsilon_{\mathrm{a}}\) for different soil moisture levels. not of the same type, i.e., they are raw values (in the case of digital instruments) or voltage values in mV (in the case of analog instruments), it can be observed that they follow the manufacturer's output range specifications (per each instrument) and that the way that these values changed over the time followed an almost identical pattern. Figure 10b,c depict (for the same time period as in Figure 10a) the in situ performance of the TEROS 10, TEROS 12, and 5 TE instruments after their original data were post-processed (through Python on the sink/gateway node), i.e., after the application of the manufacturer calibration equations. Figure 10b presents the soil moisture (\(\theta\)) graph as a function of time (in minutes), while Figure 10c depicts the apparent dielectric permittivity (\(\varepsilon_{\mathrm{a}}\)) as a function of time (in minutes). The differences before the soil wetting event can be attributed to the fact that not all the buckets were emptied at the same time over the area of each instrument and to the differences among the instruments in measuring under very dry soil conditions. After the main wetting event, all the curves evolved the same way, with the estimation of both \(\theta\) and \(\varepsilon_{\alpha}\) very close among the participating instruments, with a small exception: the underestimation of \(\varepsilon_{\alpha}\) by the TEROS 10, at low moisture conditions (green curve in Figure 10c). This behavior is also in accordance with recent relevant research outcomes [(39; 40)]. Figure 10: Soil measurement analysis under in situ agri-field conditions: (**a**) time evolution of the original quantities generated by the typical soil instruments hosted by the sensor nodes; (**b**) estimation of the \(\theta\) parameter as a function of time (in minutes); (**c**) estimation of the \(\varepsilon_{\mathrm{a}}\) as a function of time for the same period.
## 5 Discussion
### Object Extraction  
Several object-based classification studies use field boundary information obtained from either high-resolution (meter-scale) commercial satellites or local agricultural administrations [41]. However, the cost of commercial data is high and the accessibility of official cadastral data is low. Sentinel-2 satellites, while only having a spatial resolution of 10 m, are not as fine as commercial satellites. However, high temporal resolutions (5-7 d) make up for the limitations of spatial resolution. The fields identified from super-imposed multi-temporal edge layers are promising from our study. The advantage of this approach is that the open and free access to Sentinel-2 data enables scaling of study areas. The past several years have witnessed an increasing number of object-based rice classification researches using Sentinel data [42; 43; 44]. Among them, the commercial software eCognition [45], providing a number of sophisticated image segmentation algorithms, is often used. However, the fact that it runs on a local computer makes it less suitable to adopt the multi-temporal images approach, since the volume of multi-temporal high-resolution images will overwhelm the PC, especially in the case of a large study region. In contrast, the GEE platform, providing both on-shelf data and computational resources, is more suitable to adopt a multi-temporal images segmentation approach. A multi-temporal image approach solves a primary defect of edge-based image segmentation algorithm, that is, edges between fields can sometimes be weak and false edges within fields may exist in a single image. Besides an edge-based image segmentation approach, there are a few region-based image segmentation approaches on the GEE platform, such as Simple Non-Iterative Clustering (SNIC, [46]). Some researchers have compared edge-based and region-based approaches in recognition of agricultural fields and concluded that the edge-based approach is better because it takes advantage of key characteristics of the agricultural landscape (e.g., ridges or roads surrounding fields) [20].
# Towards DC Energy Efficient Homes
## 3 Factors for DC Installations
### Generalization of Electric Vehicles
Figure 1: EU-expected evolution of EV sales under current regulations. (Data collected from [18] on 26 May 2021). The charging levels include two AC 1-phase levels (up to 2.3 kW and 7.4 kW), one AC 3-phase level (normally up to 22 kW), and one DC level (from 50 to 175 kW). Nevertheless, if there are several EVs connected to the same charging station, there is a point bias with a consequent reduction in charging speeds [46]. This fact is something that has to be taken into account when assessing energy efficiency in houses with two or more EVs. Fast charging is used for a power transfer higher than 22 kW, and can be carried out in either AC or DC. However, with DC, the charging point is in direct contact with the car battery, and the conversion from AC to DC is not performed inside the EV. The voltage levels of the EV batteries range from 300 to 400 V and the average seems to be around 350 V [47]. The charging efficiency depends on the state of charge and weather, and it may be reduced from 12 to 36% due to transmission and heat losses [48]. Smart charging addresses external control of the charging process of the EV [49; 50; 51]. It has to facilitate the reliability of the supply while meeting mobility requirements. It should be noted that G2V takes energy availability into account in order to establish periods of charging as well as their speed or whether or not to store energy when there is overgeneration. The opposite process exists as well, V2G, when the battery is used to provide power back to the grid during periods of low energy production.
# Integrated Information in Relational Quantum Dynamics (RQD)
## 2 Integrated Information Measure Definition  
Before formalizing integrated information, we briefly review the quantum Jensen-Shannon divergence (\(D^{Q}_{\mathrm{JS}}\)). Given two quantum states (density operators) \(\rho\) and \(\sigma\) on the same Hilbert space, \(D^{Q}_{\mathrm{JS}}(\rho\|\sigma)\) is defined as the quantum analog of the classical Jensen-Shannon divergence, which itself is a symmetrized and smoothed version of Kullback-Leibler divergence. One convenient expression is  
\[D^{Q}_{\mathrm{JS}}(\rho\|\sigma)=\left[S\Big{(}\frac{\rho+\sigma}{2}\Big{)}- \frac{1}{2}S(\rho)-\frac{1}{2}S(\sigma)\right], \tag{2}\]  
where \(S(\rho)=-\mathrm{Tr}(\rho\log\rho)\) is the von Neumann entropy [13]. This quantity is symmetric (\(D^{Q}_{\mathrm{JS}}(\rho\|\sigma)=D^{Q}_{\mathrm{JS}}(\sigma\|\rho)\)) and bounded between \(0\) and \(\ln 2\). In particular, \(D^{Q}_{\mathrm{JS}}(\rho\|\sigma)=0\) if and only if \(\rho=\sigma\), and the maximum \(D^{Q}_{\mathrm{JS}}=\ln 2\) is attained for perfectly distinguishable orthogonal states. We will not require the detailed form of \(D^{Q}_{\mathrm{JS}}\) beyond these properties; what is crucial for us is that it behaves as a _distance measure_ on the space of states.  
**Definition 1** (Quantum Integrated Information).: _For a quantum state \(\rho\) composed of \(n\) subsystems, the integrated information \(\Phi(\rho)\) is defined as the minimum quantum Jensen-Shannon divergence between \(\rho\) and any product state on a partition of the subsystems. In formula_  
\[\Phi(\rho)\ =\ \min_{\{P_{1},\ldots,P_{k}\}}\ D^{Q}_{\mathrm{JS}}\!\Big{(} \rho\ \Big{\|}\ \rho_{P_{1}}\otimes\cdots\otimes\rho_{P_{k}}\Big{)}\, \tag{3}\]  
_where the minimum is taken over all possible ways of partitioning the set of subsystems \(1,2,\ldots,n\) into disjoint blocks \(P_{1},\ldots,P_{k}\), and \(\rho_{P_{i}}=\mathrm{Tr}_{\bar{P}_{i}}(\rho)\) denotes the reduced state on block \(P_{i}\). By convention, we consider only nontrivial partitions with at least two blocks (so if \(\rho\) is a total product state, \(\Phi(\rho)=0\) is achieved when each subsystem is isolated in its own block)._  
This definition captures the degree of holism or irreducibility in \(\rho\). If \(\rho\) factorizes neatly into two or more independent components, then \(\Phi(\rho)\) will be small (zero if an exact factorization exists). Conversely, if no accurate factorization exists (all partitions yield significant divergence), \(\Phi(\rho)\) is large, indicating that the state's information cannot be localized to separate parts without a significant loss. In practice, one might compute \(\Phi(\rho)\) by evaluating\(D_{\text{JS}}^{Q}(\rho\|\rho_{P_{1}}\otimes\cdots\otimes\rho_{P_{k}})\) for each candidate partition and taking the minimum. While the number of partitions grows quickly with \(n\), our results below (especially Theorem 3) will sharply reduce the search space.
## 3 Methodology  
### Long Short-Term Memory Networks  
Time series data are data collected at different points in time. These data reflect the state or extent of changes of a certain factor, phenomenon, and so on over time. LSTM are a kind of neural network used for processing sequence data. Typically, a neural network contains an input layer, one or several hidden layers and an output layer [63]. The output is controlled by the activation function, and the layers are connected by weights. The activation function is determined in advance. The underlying neural network only establishes a weighted connection between the layers, and the biggest difference of the LSTM is that the right connection is also established between neurons in the layer. A typical LSTM schematic is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Time Resolution** & **Data Source** \\ \hline Albedo VIS & \(0.05^{\circ}\) & 16 days & GLASS \\ Albedo NIR & \(0.05^{\circ}\) & 16 days & GLASS \\ Albedo SHORT & \(0.05^{\circ}\) & 16 days & GLASS \\ ET & \(0.05^{\circ}\) & 16 days & GLASS \\ FAPAR & \(0.05^{\circ}\) & 16 days & GLASS \\ GPP & \(0.05^{\circ}\) & 16 days & GLASS \\ LAI & \(0.05^{\circ}\) & 16 days & GLASS \\ NDVI & \(0.05^{\circ}\) & 16 days & AVHRR \\ Nighttime-Lights & \(0.05^{\circ}\) & & DMSP-OLS \\ MERRA2 & \(0.5^{\circ}\) & 16 days & MERRA \\ DEM & \(0.05^{\circ}\) & 16 days & \\ Longitude & \(0.05^{\circ}\) & & \\ Latitude & \(0.05^{\circ}\) & & \\ Land cover & \(0.05^{\circ}\) & & CNLUCC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction to data used in this study.  
The sigmoid activation function [64] is represented by \(\delta\) in Figure 2, and the calculation expressions of the three new gates, the hidden layer output \(h^{t}\) and the state update \(C^{t}\) are as follows:  
\[f^{t}=\ \delta\ \Big{(}W_{f}\times\left[x^{t},\ h^{t-1}\right]\,+b_{f}\Big{)}, \tag{1}\]  
\[i^{t}=\ \delta\ (W_{i}\times\left[x^{t},\ h^{t-1}\right]\,+b_{i}), \tag{2}\]  
\[o^{t}=\ \delta\ (W_{o}\times\left[x^{t},\ h^{t-1}\right]\,+b_{o}), \tag{3}\]  
\[C^{t}=\ \tan h\ (W_{c}\times\left[x^{t},\ h^{t-1}\right]\ +b_{c})\ +\ f^{t}\times C^{t-1}, \tag{4}\]  
\[h^{t}=\ o^{t}\times\ \tan\ h\ \Big{(}C^{t}\Big{)}, \tag{5}\]  
It can be seen that the inputs of these three doors are \(x^{t}\) and \(h^{t-1}\), and each door has its own weight and skew. These parameters are tuned as the training process continues and play a role in the calculation of state updates and hidden layer output values.
## 2 Materials and Methods  
### The Test Bench for Drop Size Measurement  
The test bench used for spray drop size measurement is fully described in [41; 42], here summarized for completeness. The design was based on the ISO 5682-1 standard [40], exploiting the liquid immersion method to measure droplet size [26; 27]. The hydraulic circuit was designed to reproduce the circuit installed on commercial sprayers. A two-diaphrag pump (AR 30, Annovi Reverberi, Reggio Emilia, Italy), driven at 56 rad/s (540 rpm) by a 2.2 kW AC motor with a gearbox, was used to provide the required pressure at the nozzle under test. The fluid in excess was recirculated into the 70 L main tank by means of a manual pressure regulator. Pressure near the nozzle and liquid flow rate were measured in real time by a pressure transmitter (Series 22 S, Keller Italy Srl, Milano, Italy) and a flow rate meter (SF800-6, Swissflow BV, The Netherlands). The two sensor signals were acquired by two Advantech Ethernet modules (ADAM6024 and ADAM6060). An ASCII UDP based protocol was used for communication with host PC, where a specifically designed user interface was running.  
The nozzle under test was anchored to a mobile platform, capable of translating along two 3 m long rails while spraying above the target plane at a distance of 0.5 m. The motion was handled by a motor control unit (MDC1460 model, Roboteq Inc., Scottsdale, AZ, USA), capable of controlling position, speed, and acceleration profiles.  
The software user interface, running on a Windows 10 PC, was designed to allow the management of all the aspects of the spray trials, including data save in a text Comma Separated Value (CSV) file. A schematic view of the whole system is reported in Figure 1.  
Sprayed droplets were captured by Petri dishes containing silicone oil as immersion liquid positioned on the target plane and then photographed in situ by a high resolution (4000 \(\times\) 6000 pixel) Digital Single-Lens Reflex (DSLR) camera (Nikon D5500, Nikon Corporation, Tokyo, Japan), equipped with a macro lens (Nikon Micro Nikkor AF-S 60 mm f/2.8 G ED, Nikon Corporation, Tokyo, Japan) and an electronic flash (Neewer 48 Macro LED Ring Flash, Shenzen, Guangdong, China). Images, saved as high-quality JPEG files, were then analyzed by means of an image processing software, capable of detecting and measuring the size of all the drops.
# A Novel Love Wave Mode Sensor Waveguide Layer with Microphononic Crystals
## 2 Materials and Methods
### Eigen Mode Analysis  
For the ideal two-dimensional piezoelectric PnCs, according to the Bloch periodic theorem, it can be simplified into the single-cell model, as shown in Figure 3a. For the calculation of eigen modes, the calculated wave vector can be limited in its first Brillouin region (as shown in Figure 3b) for discrete scanning.  
Figure 2: Schematic diagram of a Love wave mode waveguide layer with microphononic crystals.  
The finite element method was used to analyze the energy band structure, namely, the eigenvalue character. The simplified model was adopted, and a single cell was extracted as the PnC period, as shown in Figure 3a. In the model, periodic boundary conditions (PBC) were adopted on both sides of the X and Y directions, and a perfect matching layer (PML) was adopted on the bottom as an absorptive boundary. Between the two materials, a continuum boundary was used on their edges; namely, the stress, displacement and potential are continuous.  
In the piezoelectric substrate, the relationships, strain vs. stress and displacement vs. electric field are considered linear; thus, the coupled constitutive equations in piezoelectric substrate can be expressed as follows:  
\[D_{\mathrm{i}}=e_{\mathrm{ikl}}S_{\mathrm{kl}}+\varepsilon_{\mathrm{ik}}^{ \mathrm{S  
The discrete eigenvalue solution equation can be expressed as follows:  
\[\left[\begin{array}{cc}K_{uu}-\omega^{2}M_{uu}&K_{u\varphi}\\ K_{qu}&K_{\varphi\varphi}\end{array}\right]\left(\begin{array}{c}u\\ \varphi\end{array}\right)=\left(\begin{array}{c}0\\ 0\end{array}\right) \tag{4}\]  
For Equation (4), the finite element method was used to scan different wave vectors by parameterization (discrete wave vectors on the three edges of the first Brillouin) to solve the eigenvalue. Therefore, the band structure and mode property of the mixed modes in two-dimensional piezoelectric phononic crystals can be obtained.  
With the above method, in this paper, the eigen modes of Al/SiO\({}_{2}\)/36\({}^{\circ}\) LiTaO\({}_{3}\) phononic crystals were analyzed. Figure 4 illustrates the band structure and typical mode of the PnCs. Multiple curves consisted of discrete points for eigen frequencies at each wave vector. The \(X\)-axis is the wave vector scanning the area of the first Brillouin region, which can be on behalf of the whole band structure according to the Bloch Periodic Theorem. The \(Y\)-axis is the eigen frequencies for each wave vector. The results show that in the low-frequency stage below 200 MHz, there is nearly no band gap. This means that most of the wave can be transmitted in this region with little attenuation.
# Low-Frequency Communication Based on Rydberg-Atom Receiver
## Abbreviations  
The following abbreviations are used in this manuscript:  
LNA Low Noise Amplifier EIT-AT Electromagnetically Induced Transparency-Autler-Townes BPSK Binary Phase-Shift Keying OOK On-Off Keying 2 FSK Two-Frequency Shift Keying SNR Signal-to-Noise Ratio PRBS Pseudo-Random Binary Sequence EVM Error Vector Magnitude BER Bit Error Rate PDH Pound-Drever-Hall ITU International Telecommunication Union LFSR Linear Feedback Shift Register IQ In-phase and Quadrature PSNR Peak Signal-to-Noise Ratio RF Radio Frequency