esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Apparatus and Analytical Procedures  
The degradation rates of TMP during photolytic and photocatalytic experiments were determined using a Waters 2795 Alliance high performance liquid chromatography (HPLC) system coupled with a 2996 photodiode array (PDA) detector. The Kinetex C18 chromatographic column (Phenomenex, 150 mm \(\times\) 4.6 mm, 5 mm, 100 A) was used for chromatographic separation. Eluent A consisted of MilliQ water containing 0.1% formic acid, while eluent B was acetonitrile containing 0.1% formic acid. The analysis was carried out with isocratic elution and mobile phase composition A:B = 90:10 (\(\
u/v\)). The flow rate was maintained at 1.0 mL/min throughout the analysis with an injection volume of 20 mL. TMP was detected at the wavelength of 273.8 nm.  
Samples from photolytic and photocatalytic degradation experiments analyzed using an Agilent Series 1200 HPLC system (Santa Clara, CA, USA) coupled with an Agilent 6410 triple-quadrupole mass spectrometer equipped with an ESI interface (Santa Clara, CA, USA). Chromatographic separation was performed on a Synergy Hydro-RP Fusion embedded column (100 mm \(\times\) 2.0 mm, particle size 2.5 \(\upmu\)m) (Phenomenex, Torrance, CA, USA) using mobile phase, comprising MilliQ water with 0.1% formic acid as eluent A and acetonitrile with 0.1% formic acid as eluent B. The elution gradient started with 90% of eluent A, which was kept constant for the next 5 min. In the next 10 min, the proportion of eluent was decreased to 70% and was kept for 7 min. In the next 3 min, the proportion of eluent A decreased to 5% and remained for the next 8 min. In the next 1 min, the proportion of eluent A was returned to the initial value (90%) and kept for 10 min. The flow rate of 0.2 mL/min was maintained throughout the analysis. An injection volume of 5 \(\upmu\)L was used in all analyses. The analyses were conducted in positive ion mode under the following conditions: drying gas temperature 350 \({}^{\circ}\)C, drying gas flow 11 L/min, capillary voltage 4.0 kV, and a nebulizer pressure of 35 psi. Instrument control and data acquisition and evaluation were performed with Agilent MassHunter 2003-2007 Data Acquisition for Triple Quad software version B.01.04 (B84) (Santa Clara, CA, USA).  
Total organic carbon (TOC) analyses were performed using a TOC analyzer, type TOC-VCPH (Shimadzu, Kyoto, Japan) via the non-purgeable organic carbon (NPOC) method.  
The global simulated solar radiation was measured using a pyranometer (Kipp & Zonen Co., Delft, The Netherlands), model CMP11, while the UV-A radiation was measured using an RM 21 radiometer equipped with an UV-A Opsytec sensor manufactured by [PERSON] [PERSON] (Ettlingen, Germany).
## 2 Model Development and Simulation Method  
### Physical Model Establishment  
The cooling plate was of the same dimensions as the battery cell, and heat was transferred into the coolant through heat conduction and convection in the plate. Normally, there are three types of channel configurations used in cooling plates: cavity, parallel, and serpentine. The cavity type does not have flowing channels but rather an empty space in the plate. The parallel case is manufactured with many straight internal parallel paths. A serpentine flow-field is designed by forcing the coolant to flow through a single, long, meandering path that travels over the entire active area. Their configurations are demonstrated in Figure 1 and their dimensions are shown in Table 1.  
The intervals of flowing channel and plate edges were all of 5 mm in width and length, but only 1 mm in thickness, for the sake of maximizing flow section area. Here the channel width was kept at 16 mm in parallel and serpentine channels to avoid the influence induced by different channel widths. For the same reason, only one inlet and one outlet were set in the same positions of the models. The 3-D models of the cooling plates were built in SolidWorks and then imported into ANSYS Workbench for simulation.  
Since the objective of this paper was to design an optimized cooling plate channel structure and to study the effect of different structures on the pressure drop and mass maldistribution, the working conditions were simplified so that there was no heat applied on the plate. The other working conditions are also shown in Table 1. At the beginning, two cavities with outlets in different locations were modeled for reference, and then a basic parallel channel and serpentine channel were also studied for comparison with the reference cavity.
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 1 Introduction
The recent use of high-resolution seismic exploration methods for near-surface active fault characterization is very interesting, as the identification and characterization of active faults are critical to studies of regional seismic hazard [13; 14]. To perform probabilistic seismic hazard analysis, it is fundamental to develop seismic source models, by identifying seismogenic faults and finding the main earthquakes linked to them. Those tasks are crucial and are performed using geological, historical, seismological, and other geophysical data [15]. Seismic source models are used to determine the characteristic magnitudes and frequencies of earthquakes for each seismogenic source. However, historical and instrumental records of seismic activity may not provide a complete picture of the earthquake cycle, since in many areas, these records are typically much shorter than the recurrence times of the largest earthquakes [16]. To address this limitation, the integration of very-high resolution geophysical imaging methods and paleoseismological techniques have become critical tools in the identification of major seismogenic faults that have disrupted the uppermost few meters of the ground (see [PERSON] et al. [17] and references therein). In this review article, I aim to present to non-specialists the methods of seismic exploration that are more valuable for structural studies and active fault characterization without delving into the mathematical background. In doing so, I hope to offer an accessible introduction to those methods to geologists and other professionals who may lack a strong mathematical background but wish to grasp the underlying concepts. For those seeking more in-depth information, I will provide references to the fundamental scholarly literature. Additionally, I will explore recent advancements in seismic reflection imaging, and I will discuss their applications. I will specifically focus the attention on the techniques used to enhance structural features, in particular faults and fractures. For each technique, I will provide a concise overview of the fundamental concepts, reviewing recent developments and showcasing several examples of its significance including active fault characterization.
## 4 Discussion
### Advantages of IST-Cube and Other Potential Uses  
The IST-cube combines spatiotemporal context information with the forest function index, recognizing and utilizing spatial and temporal changes hidden in spatiotemporal data to analyze regularities and explore causes. Furthermore, the results obtained in this study are consistent with those in the literature (Section 4.1). Figures 10 and 13 and Table 2 show that IST-cube detection is reliable and valid.  
Apart from the main aim of developing IST-cube to capture forest dynamics, there are other potential uses. Understanding spatiotemporal changes is important for wetlands [85], which may be fragmented [86]. Nevertheless, the IST-cube can still identify wetland fragments as the same wetland. Thus, the IST-cube may offer a useful method for wetland  
Figure 17: (**a**) Spatial distribution map of the overlap of different types of IST-cubes. (**b**) Different types of IST-cubes overlap areas as a percentage of the total study area.  
change detection. Disturbance refugia are locations that suffer severe or frequent disturbances less than their surroundings and can aid in forest recovery from disturbances [9; 87]. Fortunately, the cube-core in the long-term IST-cube is such an area that is unaffected by changes. Hence, using the IST-cube model to find disturbance refugia and further analyze the type and duration of disturbance refugia is feasible. Spectral traits will change if plants experience stress, processes, disturbances, and resource limitations [88]. Thus, these traits can also change the appearance of the IST-cube. Therefore, the IST-cube model can also be used to assess vegetation health.
## 2 Design of the Harvester  
As shown in Figure 1, the metasurface consisted of a metal ground, a dielectric substrate layer, and a periodic array of specially designed units. The centrally symmetrical circular sectors had the center point of O and a radius of R1. Each unit cell was surrounded by a square, ring-shaped metal via, and a metal harvesting via for connecting the harvesting load was placed near the center at position O'. This via shifted a little from the center point O and was located at the position having the strongest surface current. The polarization insensitivity property was generated from the approximate, centrally symmetrical structure (except for the harvesting via). To effectively conduct and collect the absorbed EM energy, a harvesting load of 50 \(\Omega\) was added between the cell and the ground through the harvesting via. The position of the harvesting via and the value of the harvesting load were adequately optimized to maximize the harvesting efficiency of the metasurface.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **Center** & **Frequency** & **Incidence** & **Efficiency** & **Efficiency at** \\  & **Frequency** & **Deviation** & **Angle Range** & **at 0\({}^{\circ}\)** & **60\({}^{\circ}\)** \\ \hline
[16] & 5.4 GHz & 300 MHz (5.55\%) & 0–60\({}^{\circ}\) & 92\% & 48\% \\
[17] & 5.8 GHz & 75 MHz (1.3\%) & 0–75\({}^{\circ}\) & 88\% & 62\% \\
[18] & 2.7 GHz & 80 MHz (2.96\%) & 0–60\({}^{\circ}\) & 91\% & 54\% \\
[19] & 5.7 GHz & 120 MHz (4.44\%) & 0–60\({}^{\circ}\) & 81\% & 30\% \\
[20] & 3.8 GHz & 100 MHz (2.63\%) & 0–45\({}^{\circ}\) & 90\% & 52\% \\ This work & 5.8 GHz & 11 MHz (0.19\%) & 0–75\({}^{\circ}\) & 91\% & 72\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the frequency deviation and maximum efficiency of metasurfaces with a wide range of incidence angles.  
The dielectric substrate had a thickness of 0.8 mm, a dielectric constant of 2.65, and a loss tangent of 0.0012. The top and bottom metal planes were copper foil, with a conductivity of \(5.8\times 10^{7}\) S/m and a thickness of 0.035 mm. The dimensions of the unit cell were as follows: R1 = 5.8 mm, R2 = 1 mm, R' = 0.6 mm, W = 0.3 mm, Dv = 0.6 mm, S = 0.6 mm, H = 0.8 mm, and P = 15 mm.
# Mapping Wild Leek through the Forest Canopy Using a UAV
## 4 Discussion
In this context, satellite imagery such as Landsat 8 and Sentinel-2 can be used to identify areas that may be experiencing change related to wild leek, such as the establishment of new populations or illegal harvesting. If harvesting is known to occur, frequent surveys will be useful to assess the decline. On the other hand, wild leek seeds take four to five years to germinate and become reproductively mature, and detecting a recovery or a natural expansion in the population could therefore take many years. Depending on the changes occurring, such areas could be targeted for field surveys and/or UAV-based wild leek mapping.
###### Abstract  
A novel non-coherent, low-power, area-efficient binary phase-shift keying demodulator for wireless implantable biomedical microsystems is proposed. The received data and synchronized clock signal are detected using a delayed digitized format of the input signal. The proposed technique does not require any kind of oscillator circuit, and due to the synchronization of all circuit signals, the proposed demodulator can work in a wide range of biomedical data telemetry common frequencies in different process/temperature corners. The presented circuit has been designed and post-layout-simulated in a standard 0.18 \(\upmu\)m CMOS technology and occupies \(17\times 27\)\(\upmu\)m\({}^{2}\) of active area. Post-layout simulation results indicate that with a 1.8 V power supply, power consumption of the designed circuit is 8.5 \(\upmu\)W at a data rate of 20 Mbps. The presented demodulation scheme was also implemented on a proof-of-concept circuit board for verifying its functionality.  
implatable biomedical microsystems; data telemetry; low power; high data rate; binary phase-shift keying demodulation Article An Ultra-Low-Power Area-Efficient Non-Coherent Binary Phase-Shift Keying Demodulator for Implantable Biomedical Microsystems
# Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data
## 1 Introduction
Given a sufficiently large training set consisting of pairs of independent and dependent data (in our case that would be the microstructural maps and their corresponding ultrasonic travel time data), DNNs can approximate the non-linear relationship between the two parameter spaces [23]. Although this training process can be computationally expensive and time consuming, once the network has been satisfactorily trained, inversions of unseen data can be conducted in real-time. Unfortunately, the uptake of these methods has been limited to date due to the scarcity of experimental data where the ground truth is known. However, more recently, the use of physics based models to generate training data pairs has become more prevalent in the machine learning literature and offers a convenient way to bypass the extensive data requirements for these methods. This approach has already been used to solve travel time tomography problems in heterogeneous and locally isotropic media with applications in the seismology and medical imaging communities [24; 25; 26; 27; 28]. These methods have exhibited reconstruction accuracy comparable to that achieved using the much more expensive iterative and stochastic sampling methods. In this paper, we present the first ever DNN framework for fast, non linear travel-time tomography of two dimensional, locally anisotropic weld structures. Due to the lack of experimental ultrasonic travel time data arising from scenarios where the weld structure is well characterised, a model based approach to training the network is taken, where the Anisotropic multi-stencils fast marching method (AMSFMM) [4] is used to approximate wave travel times between pairs of ultrasonic transducers (arranged in linear array configurations) through synthetic weld geometries constructed using perturbations of the Ogilvy weld model [29]. The AMSFMM is a simplified physical model of wave propagation, in which only travel times are calculated, and it therefore does not include the underlying physics of multiple scattering, reflection or attenuation. We demonstrate accurate (within 3\({}^{\circ}\)) and rapid inversion (0.04 s) of grain orientations within welds using the DNN framework, even in the case where the modelling approach used to generate the test data is different to that which is used to generate training data; an important proof of concept for model based training of DNNs and a step towards future transition to testing on experimentally obtained data. Three different data acquisition geometries are explored and in the final pulse-echo case, we show how predicted grain orientations can improve the ability to detect and characterise defects using an extension to the total focusing method [30], improving the signal to noise ratio by up to 5.3 dB.
## 3 Results and Discussion  
### Spectral Measurements and Investigation  
The first step of the multivariate calibration was the use of NIR spectra detection to remove variation by SNV.  
The profile of the collected spectra is presented in Figure 2(a), while the spectra normalized by SNV are shown in Figure 2(b).  
The second step of the NIR spectra pre-processing was outlier detection. The outliers were preliminarily found by checking the spectra based on the Global H (GH).  
As shown in Figure 2(b), the spectrum in red, after the SNV, was found to be anomalous and was excluded from the dataset.  
The GH value of the spectrum in red is 5.2, confirming an anomalous relationship between the variables, that could be due to an error in the preparation of the sample or in the collection of the measurement.  
The trend was also investigated with the GH plot from the PCA model with four components obtained from spectra pre-treated with SNV (Figure 4).  
On the basis of the Global H and PCA, the spectrum in red shown in Figure 2(b) was excluded from the dataset of the calibration model.
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
## 2 TEM Horn Antenna Design  
### Adaptor Design  
Figure 1 shows a 3D view of an adaptor which consists of a double-ridged waveguide (DRW), a SMA connector (Gigalane, PAF-S06-000), and a cavity. The DRW has a longer cut-off wavelength and lower characteristic impedance of the dominant mode (TE10 mode) relative to those of conventional waveguides [17]. The SMA connector operated in the TEM mode is used to excite the DRW. Therefore, a cavity model is required for the mode transition from the TEM to the TE10 mode.  
Figure 2 depicts a cross-sectional view of the DRW, which is designed by the transverse resonance method (TRM) considering the operating band of the TE10 mode and the characteristic impedance of the SMA connector [18]. The dimensions of the DRW obtained from the TRM are as follows: \(a=11.6\) mm, \(b=7.78\) mm, \(d=0.78\) mm, and \(s=4.64\) mm.  
Figure 3 shows the simulated attenuation constant and characteristic impedance of the designed DRW. All of the simulation results in this paper are obtained using ANSYS high-frequency structure simulation (HFSS) software. As shown in Figure 3a, the attenuation const  
Figure 1: 3D view of an adaptor with a double-ridged waveguide (DRW).  
modes begin to have zero values starting at 4.7 and 19.3 GHz, respectively. This result indicates that the operating band of the dominant mode is 4.7-19.3 GHz. In Figure 3b, the characteristic impedance of the dominant mode varies from 44.6 to 77.4-\(\Omega\) within the 6-18 GHz band.  
Figure 4 shows the \(yz\)-plane view and simulated S-parameters of the adaptor. In Figure 4a, a rectangular cavity is attached to the DRW to realize the mode transformation between the SMA connector and the DRW. The width and height of the cavity are equal to the dimensions \(a\) and \(b\) of the DRW. The thickness \(t\) is fixed at 1.75 mm to consider the dimensions of the SMA connector. The values of \(l_{s}\), \(l_{c}\), and \(l_{r}\) are obtained through a parametric study. The simulated S-parameters of the adaptor with dimensions of \(a=11.6\) mm, \(b=7.78\) mm, \(d=0.78\) mm, \(s=4.64\) mm, \(l_{s}=3.57\) mm, \(l_{c}=5.72\) mm, and \(l_{r}=5\) mm are described in Figure 4b. The simulation result shows that the reflection and transmission coefficients are less than \(-10\) dB and higher than \(-0.45\) dB, respectively, within the 6-18 GHz band.  
Figure 4: (**a**) \(yz\)-plane view and (**b**) simulated S-parameters of the adaptor.  
Figure 3: Simulated results of the DRW: (**a**) attenuation constant and (**b**) characteristic impedance.  
Figure 2: Cross-sectional view of the DRW.
# Low-Thrust Nonlinear Orbit Control for Very Low Lunar Orbits
## 3 Nonlinear Orbit Control
### Feedback Control Law
\begin{cases}x_{7}|\mathbf{b}+\mathbf{d}|,&\text{if}\ \ x_{7}|\mathbf{b}+\mathbf{d}| \leq u_{Tmax}\\ 0,&\text{if}\ x_{7}|\mathbf{b}+\mathbf{d}|>u_{Tmax}\ \text{and}\ \mathbf{b}^{T}\mathbf{b}+\mathbf{b}^{T}\mathbf{d}<0\\ u_{Tmax},&\text{if}\ x_{7}|\mathbf{b}+\mathbf{d}|>u_{Tmax}\ \text{and}\ \mathbf{b}^{T}\mathbf{b}+\mathbf{b}^{T}\mathbf{d}\geq 0. \end{cases}  \tag{45}\]  
The attracting set contains all the dynamical states that fulfill \(\dot{V}=0\), that is, if all the components of \(\mathbf{b}\) are zero regardless of any choice of the gain coefficients \((k_{1},k_{2},k_{3})\). Thus, the attracting set must be investigated in order to guarantee asymptotic stability via the Lyapunov theorem. Equations (46)-(48) show the analytical expression of the components of vector \(\mathbf{b}\), denoted with \((b_{1},b_{2},b_{3})\),  
\[b_{1}=-2k_{2}\Psi_{2}\sqrt{\frac{x_{1}}{\mu_{m}}}\Big{[}(x_{3}-x_{3d})\cos x_ {6}-(x_{2}-x_{2d})\sin x_{6}\Big{]}, \tag{46}\]  
\[b_{2}=4k_{1}\Psi_{1}\frac{x_{1}-x_{1d}}{\eta}\sqrt{\frac{x_{1}^{3}}{\mu_{m}}}+\]  
\[2k_{2}\sqrt{\frac{x_{1}}{\mu_{m}}}\frac{\Psi_{2}}{\eta}\Big{(}(x_{2}-x_{2d})[x _{2}+(1+\eta)\cos x_{6}]+(x_{3}-x_{3d})[x_{3}+(1+\eta)\sin x_{6}]\Big{)}, \tag{47}\]  
\[b_{3}=2k_{2}\sqrt{\frac{x_{1}}{\mu_{m}}}\frac{\Psi_{2}}{\eta}(x_{5}\cos x_{6}- x_{4}\sin x_{6})\Big{[}(x_{2}-x_{2d})x_{3}-(x_{3}-x_{3d})x_{2}\Big{]}\]  
\[+k_{3}\Psi_{3}\sqrt{\frac{x_{1}}{\mu_{m}}}\frac{1+x_{4}^{2}+x_{5}^{2}}{\eta} \Big{[}(x_{4}-x_{4d})\cos x_{6}+(x_{5}-x_{5d})\sin x_{6}\Big{]}. \tag{48}\]  
First of all, it is straightforward to recognize that if \(x_{1}=0\) (rectilinear trajectories), \(b_{1}=b_{2}=b_{3}=0\). If \(x_{1}=0\), then \(b_{1}=0\) if either \(\Psi_{2}=0\) or  
\[(x_{3}-x_{3d})\cos x_{6}-(x_{2}-x_{2d})\sin x_{6}=0. \tag{49}\]  
The latter relation is satisfied regardless of \(x_{6}\) (which is time-varying also along the desired orbit) only if \(x_{2}=x_{2d}\) and \(x_{3}=x_{3d}\), which is equivalent to \(\Psi_{2}=0\). This implies that also \(b_{2}=0\) if and only if \(\Psi_{1}=0\). Finally, \(b_{3}=0\) regardless of \(x_{6}\) if either \(\Psi_{3}=0\) or \(x_{4}=x_{4d}\) and \(x_{5}=x_{5d}\), which is, however, equivalent to \(\Psi_{3}=0\). To sum up, the attracting set includes the following subsets:  
* \(x_{1}=0\);
* \(\Psi_{1}=\Psi_{2}=\Psi_{3}=0\) (target set). However, as the condition \(\mathbf{b}=0\) implies \(\mathbf{b}=0\), it is possible to rule out subset 1. Indeed, we have  
\[\dot{b}_{i}=\frac{\partial b_{i}}{\partial\mathbf{x}}\dot{\mathbf{x}}+\frac{\partial b_ {i}}{\partial x_{6}}\dot{x}_{6}+\sum_{j=1}^{5}\frac{\partial b_{i}}{\partial x _{jd}}\dot{x}_{jd}=\frac{\partial b_{i}}{\partial\mathbf{x}}\mathbf{G}\mathbf{a}+\frac{ \partial b_{i}}{\partial x_{6}}\dot{x}_{6}+\sum_{j=1}^{5}\frac{\partial b_{i} }{\partial x_{jd}}\dot{x}_{jd}, \tag{50}\]  
for \(i=1,2,3\).