esa-sceva/satcom-chunk-collection huggingface dataset:
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 4 Applications
### Image Fusion  
The preliminary results of the tool presented in Section 2 were applied in [2; 10; 11] for image fusion with the goal to enhance optical image visibility using thermal infrared camera data. The context of such research work was focused on facing problems like the one presented in Figure 23c, where in a proximity scenario, the target object, in this case, the TANGO satellite, enters a low visibility condition such as the eclipse phase, where the object is in complete shadow. This is exactly one of the problems described in the introduction in Section 1.  
Figure 23: VIS synthetic images (top) and respective TIR synthetic images (bottom). (**a**) VIS Frame 1. (**b**) VIS Frame 2. (**c**) VIS Frame 3. (**d**) TIR Frame 1. (**e**) TIR Frame 2. (**f**) TIR Frame 3.  
Figure 22: Radiance field \(q_{fc}\)(**left**), infrared black and white image (**right**).  
In this scenario, the thermal image generation pipeline is able to provide the requested thermal images with the same camera attitude of the visible one, thus enabling the fusion of the two. As it is visible from the results reported in Figure 24, the visible image has parts that are in complete shadow and cannot be used by the algorithms to fully detect the shape of the object, thus missing features that can be used as key points for the navigation algorithms. Such regions are completely visible in the thermal infrared image, so fusing the thermal image with the visible one provides a third image that retains the detail of the visible spectrum image (thermal cameras usually have smaller pixel arrays, thus fewer details) but without shadowed regions. In Figure 24, the best fusion methods are reported; for deeper analysis and comparison of different fusion methods, the reader can refer to [2].
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 6 Related Work
The work in [25] analysed the network connectivity problem in a wirelessly-powered battery-less sensor network, which arises from the fact that the nodes are powered intermittently, leading to connectivity problems, and proposed an approach based on dividing time into separate harvesting and communication periods. Similarly to [22; 23], the approach relies on the stable and predictable energy flow from the source. The work in [1] described a federated energy storage concept, where each peripheral, such as radio, sensor or a microcontroller, is assigned a dedicated capacitor for increased reliability so that faulty or misconfigured peripherals do not affect the shared energy storage and thus the operation of the entire system. The platform contains a first-stage capacitor, which powers the microcontroller, an array of peripheral capacitors, and allows for faster charging by setting capacitor sizes for specific peripherals and controlling the charging priority to individual capacitors. In subsequent work, the authors described a reconfigurable federated energy storage, where an engineer can assign charging priorities, capacitor sizes and voltage thresholds in both compile and run-times. The main limitation of federated energy storage is that it requires the first-stage capacitor to be charged to power the microcontroller and the peripheral capacitors. Once the first-stage capacitor is depleted, the microcontroller shuts down, and the energy stored in peripheral capacitors cannot be used and will be slowly dissipated. Similarly to a fixed capacitor design, there is a dilemma in that the first-stage capacitor needs to be small to charge quickly, but large enough to operate an MCU when the node moves to a low energy area, which presents a problem for mobile battery-less applications. In contrast, the proposed hybrid platform is powered by a single capacitor at any moment of time. When the node detects a high energy area, it switches to a bigger capacitor, which enables it to accumulate available energy and use it when the node moves into a low energy area.
# The Size Effect of Shear Bands in Dense Sands--A Discrete Element Analysis
## 4 Explanations of the Size Effect
### Porosity Inside and Outside the Shear Band  
The formation of shear bands is the result of strain localization. To accurately determine the porosity inside and outside the shear band area, measuring circles were placed inside specimens (Figure 10), and the porosity was measured and plotted on a contour map. Thereby, the average porosities inside the shear band and outside the shear band were compared.  
Through the above approach, we obtained the contours of porosity for different isotropic confining stresses at the same axial strain of 10% (Figure 11). The resulting shear bands included both single-type and cross-type. It was seen that the internal porosity of the shear band is relatively high, while the external porosity of the shear band is relatively small. For a lower-level isotropic confining stress, the proportion of shear band area in small specimens is significantly larger than that in large-sized specimens.  
Figure 10: Measuring circles and the contour plot of porosity. (**a**) The original data in measuring circles; (**b**) the contour plot of the porosity.  
Together with the development of the deviatoric stress, the evolutions of the porosity inside and outside the shear band are plotted in Figure 12. It is noted that no matter whether inside or outside the shear band, there was a tendency for the porosity to increase first and then decrease. The transition point is about the peak strength point. Such a phenomenon is consistent with the size effect in Section 3. The porosity inside the shear band was finally stabilized, indicating that the critical state was reached.  
Figure 11: Contours of porosity: (**a**) specimen size: S; (**b**) specimen size: M; (**c**) specimen size: L; (**d**) specimen size: XL.  
To explore the size effect further, the relationship between the porosity and isotropic confining stress is plotted in Figure 13, where the porosity inside and outside the shear band area is considered separately. The porosity both inside and outside the shear band decreased with increasing isotropic confining stress. The porosity inside and outside the shear band is consistent under various isotropic confining stress conditions; therefore, the size effect of the specimen deformation characteristics is mainly caused by the difference in the proportion of the shear band area.
## 2 IRMI Method
### Calculation of Equilibrium Condition  
To find the equilibrium conditions, the following steps are repeated until convergence, as shown in Figure 1.  
1. Initial conditions The initial value of the mutual induction coefficients is defined as below. \[b_{F}=b_{R}=0\] (3)
2. Induced FS velocity and induced TSR at FR  
The velocity at the FR is induced not only by the FR but also by the RR as shown above. The induction of the RR, which is not considered in the individual rotor CFD, is calculated by the ADM results. The FS velocity is replaced by the induced velocity as below. \[U_{FI}=U(1-b_{F})\] (4)  
And the TSR is replaced by the induced TSR as below. \[\lambda_{FI}=R_{F}\Omega_{F}/U_{FI}\] (5)
3. Torque and thrust coefficients of FR  
The torque and thrust coefficients of the FR are calculated by the individual rotor CFD results at the induced TSR as following equations. \[C_{QF}=C_{QF}(\lambda_{FI})\] (6)\[C_{TF}=C_{TF}(\lambda_{FI}) \tag{4}\]  
Torques of FR and RR  
The torque of the FR is calculated as below by the torque coefficient in Equation (6) and induced FS velocity in Equation (4).  
\[Q_{F}=\frac{1}{2}\rho{U_{FI}}^{2}S_{F}R_{F}C_{QF} \tag{8}\]  
The torque of the RR is set to be the same value as the FR's as  
\[Q_{R}=Q_{F} \tag{9}\]  
Mutual induction factor and induced FS velocity at RR  
The mutual induction factor from the FR to the RR is calculated by the ADM results and the thrust coefficient of the FR by using the ADM results and the thrust coefficient of the FR as below.  
\[b_{R}=b_{R}(C_{TF}) \tag{10}\]  
The FS velocity is replaced by the induced FS velocity as below at the RR.  
\[U_{RI}=U(1-b_{R}) \tag{11}\]  
The torque coefficient of the RR is calculated as below by the torque in Equation (9) and the induced FS velocity in Equation (11).  
\[C_{QR}=2Q_{R}/\rho{U_{RI}}^{2}S_{R}R_{R} \tag{12}\]  
Induced TSR and angular speed of RR  
The induced TSR is calculated by the individual rotor CFD result as below, by using the torque coefficient of the RR in Equation (12).  
\[\lambda_{RI}=\lambda_{RI}\big{(}C_{QR}\big{)} \tag{13}\]  
And the angular speed is calculated as below using the induced TSR and the induced FS velocity in Equations (13) and (11).  
\[\Omega_{R}=\lambda_{RI}U_{RI}/R_{R} \tag{14}\]  
The thrust coefficient of the RR is calculated as below by the individual rotor CFD results and the induced TSR in Equation (13).  
\[C_{TR}=C_{TR}(\lambda_{RI}) \tag{15}\]  
The mutual induction factor at the FR is calculated by the ADM results as below using the thrust coefficient of the RR in Equation (15).  
\[b_{F}=b_{F}(C_{TR}) \tag{16}\]  
The mutual induction factor is applied for Equation (4) to consider in the induction of the RR tor the induced speed at the FR.  
(10) Convergence judgment  
Repeat the above calculation until the above parameters converge. Once converged, the thrust, torque, and power coefficients of the two rotors are obtained as well as the operation point, and the mutual induction factors.
## Materials and Methods  
### Sampling Site  
The biofilms used for the tests reported in this article were collected at the Domus Aurea in the Palatine Hill, Rome, Italy (Figure 1). A wall in Room 93 was selected for the sampling due to the evident biodeterioration of the surface (white, green and blue-green patinas). The wall in  
Figure 1: **(a)** Map of the remains of the Domus Aurea which consists of a series of buildings separated by gardens, woods and vineards; **(b)** a detail of the area where the study site, Room 93, is located.  
_opus latericium_ is covered with soil which was not completely removed during the excavations. The biofilm was consistently and evenly distributed (Figure 2). Room 93 is adjacent to the Criptoportico and, although not part of the tourist route, it is illuminated by fluorescent lamps. In 2012, the wall was treated with Preventol(r) RI80, which is a solution of quaternary ammonium salts with a broad spectrum of activity (fungi, bacteria and algae). Previous intervention by the restorers consisted in spraying the biocidal product (diluted to 3% with de-ionised water) on the surface and this was effective only after multiple applications with prolonged laying time. However, the recolonization was very fast (data not published).  
Environmental parameters in Room 93 were measured on each sampling trip and during each visit during the in-situ experimental period (about 75 days). Temperature (T \({}^{\circ}\)C) and relative humidity (RH%) were measured using a digital humidity and temperature meter (CEM, model DT-625). Surface temperatures were made using a DT-8833 infrared thermometer (Thermosense, UK) and cross-checked using a Type K thermocouple resistor (Thermosense, model HK-400). Irradiance was measured using a radiometer (model LI-185B; LI-COR Inc., Lincoln, NE, USA) equipped with a quantum sensor (LI-190 SB) to give PPFD in umol photons m\({}^{-2}\) s\({}^{-1}\).
# Preparation and Properties of Poly(imide-siloxane)
## 3 Results and Discussion
### Characterization of PAAs and PIs  
The structures of PAAs and PIs were confirmed by FT-IR spectroscopy (Figure 1). FT-IR spectra of PAA-4 and PI-4 showed absorption bands at 1021 and 1095 cm\({}^{-1}\), respectively, due to Si-O-Si stretching in the structure of PDMS. The absorption band at 1259 cm\({}^{-1}\) was also attributed to the symmetric  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PAA Code a** & \begin{tabular}{c} **Molar Feed Ratio** \\ **(6 FDA:MDA:PDMS)** \\ \end{tabular} & \begin{tabular}{c} **PDMS Content** \\ **(mol\%) b** \\ \end{tabular} & \
\begin{tabular}{c} **\(\overline{M}_{n}\)** \\ **(\(\times\)104 g/mol)** \\ \end{tabular} &
\begin{tabular}{c} **\(\overline{M}_{w}\)** \\ **(\(\times\)104 g/mol)** \\ \end{tabular} & **PDI c** \\ \hline PAA-1 & 1:1:0 & - & 1.03 & 2.32 & 2.2 \\ PAA-2 & 1:0:9:0.1 & 13 & 10.8 & 37.8 & 3.5 \\ PAA-3 & 1:0:7:0.3 & 32 & 10.8 & 18.1 & 1.6 \\ PAA-4 & 1:0:5:0.5 & 49 & 3.73 & 13.2 & 3.5 \\ PAA-5 & 1:0:3:0.7 & 70 & 21.1 & 48.8 & 2.3 \\ PAA-6 & 1:0:1:0.9 & 89 & 16.3 & 50.9 & 3.1 \\ PAA-7 & 1:0:1 & - & 7.92 & 25.1 & 3.2 \\ \hline \hline \end{tabular}  
* PAA: poly(amic acid-siloxane); b Calculated from 1H NMR integrations of PAA copolymers; c Polydispersity index (\(\overline{M}_{w}/\overline{M}_{n}\)).  
\end{table}
Table 1: Molar feed ratios and molecular weights of PAAs.  
deformation of the -CH\({}_{3}\) group in -Si(CH\({}_{3}\))\({}_{2}\)-, and the absorption band at 803 cm\({}^{-1}\) was assigned to the Si-C vibration [11; 41; 43; 44; 45]. The FT-IR spectrum of PAA-4 showed bands at 1721 cm\({}^{-1}\) (carboxyl) and 1660 cm\({}^{-1}\) (amide) owing to C=O stretching, and at 1545 cm\({}^{-1}\) owing to C-N stretching (amide), suggesting the formation of PAA (Figure 1a) [11; 34; 46]. PI-4 exhibited absorption bands at 1785 cm\({}^{-1}\) owing to imide C=O asymmetric stretching, 1726 cm\({}^{-1}\) owing to imide C=O symmetric stretching, and 1375 cm\({}^{-1}\) owing to imide C-N stretching (Figure 1b) [11; 34; 46; 47; 48]. FT-IR spectra of the other PAAs and PIs are shown in Figures S2-S7.
# Analysis of the Mode Shapes of Kaplan Runners
## 5 Experimental Modal Analysis of a Kaplan Runner
### Runner Mode Shapes  
At each hit point, excitation and response signals are recorded in the time domain. In order to calculate the FRF, the acquired signals have to be converted to the frequency domain. To process these signals, exponential windows of 8 s are used. The spectrums are calculated by using the fast Fourier transform (FFT), with a frequency resolution of 0.125 Hz in every window. The spectrums obtained are averaged and a final averaged spectrum is calculated for each excitation and response signal. Then, the FRF of every point is calculated directly from these processed signals. An experimental mesh which represents the location of every hit point with respect to the others is built-in. Each of the obtained FRF is assigned to each degree of freedom of the experimental mesh shown in Figure 5.  
Since the mode shapes of interest are the ones corresponding to the runner, the FRF are analysed from 0 to 100 Hz. Finally, by the peak-picking method, the natural frequencies and the mode shapes of the runner are extracted. An FRF and the mode shapes extracted at each peak, with their respective natural frequencies, are shown in Figure 6.  
Figure 5: Experimental mesh and degrees of freedom (red points).  
Figure 6: Frequency response function of the Kaplan runner and the mode shapes extracted: (**a**). 31.625 Hz, (**b**). 35.875 Hz, (**c**). 36.625 Hz, (**d**). 39.375 Hz, (**e**). 45.625 Hz, (**f**). 49.625 Hz, (**g**). 50.375 Hz, (**h**). 54.125 Hz, (**i**). 68.750 Hz, (**j**). 87.625 Hz, (**k**). 91.500 Hz.  
In the shown spectrum, it is possible to see that the mode shapes are dominated by three main blade motions. In the range of 30 to 42 Hz, the mode shapes are dominated by the bending motion of the blades (1B). Then, from 42 to 70 Hz the mode shapes are dominated by the torsional motion of the blades (1T). Finally, the mode shapes between 80 and 100 Hz are dominated by the double torsional motion of the blades (2T). Although in some mode shapes such as Figure 6a,e,f,g, one blade has more amplitude than the other blade, the general motion of all the blades describes a global mode shape. These modes can be classified as shown in Table 18. As seen in this table, not all the mode shapes extracted by FEM are detected in the experimental modal analysis. This can be related to structural damping, which is not considered in the theoretical model. The effect of the damping is that some mode shapes, theoretically extracted, can be merged when their natural frequencies are not well separated.  
Finally, the global mode shapes experimentally detected match the FEM model in the mode shapes (1,0), (0,0)1B, (2,0)1B, (2,0)1T and (3,0)1T-1. Therefore, the numerical analysis has been validated with the experimental data.  
It is worth saying that a global mode shape that was detected in the EMA was not extracted from the FEM analysis. Following the previously explained criteria, this mode was classified as (3,0)1T.
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 4 Application Analysis
### Comparative Analysis of the Denoised Results
In addition, the linear correlation coefficient between the denoised and original observed signals in Table 3 indicated that the correlation between the signal denoised by the H-Wavelet method and the original signal was the weakest; the correlation between the denoised signal using the wavelet-EMD method and the original signal was the strongest, which also reflected the good denoising performance of the wavelet-EMD method. The spectral values of the signals in the horizontal and vertical observation sequences denoised by the four methods were calculated to compare the methods' denoising performance further. The results are shown in Figure 10.  
The comparative analysis of the spectral values of the denoised signals from the four methods in Figure 10,b indicated that the main frequencies of the observed sequences filtered by the four methods were prominent, which meant that these methods played a certain role in noise inhibition. However, the noise spectrum power of the denoised signal via the H-Wavelet and S-Wavelet methods was relatively large, and the frequency of the noise still filled the entire frequency band. The noise spectrum power of the denoised signal via the I-Wavelet method was reduced to a certain extent, and the noise spectrum power was the smallest in the case of the wavelet-EMD method. Noise was suppressed well in the high-frequency band greater than 2 Hz, indicating that the method effectively removed the interference of noise. Thus, the denoised data obtained by the wavelet-EMD method served as the input data of the Hilbert spectrum analysis in this study to extract the first natural frequency of the bridge structure.
## 1 Introduction
(1) This article reasonably divided the whole PHM system of MSAE into four functions: HCM, FD, HP, and MD;  
(2) An exhaustive review was given on the PHM technology by analyzing and summarizing a large number of references to provide a perspective for researchers and essential guidance for designers and O&M engineers;  
(3) Various technologies and applications of PHM in the marine field were summarized;  
(4) In order to meet the needs of intelligent ships for PHM, five new health management technologies were proposed, omnidirectional condition perception, DIaK integrated coding technology, treatment of uncertainty problems, proactive perception, and engineering self-healing and immune system. This paper is organized as follows. Section 2 discusses the HCM. Section 3 summarizes the existing FD technology in the marine field and focuses on the application of intelligent machine learning diagnosis technology. In Section 4, the HP is discussed, including processes and methods. Section 5 concentrates on the maintenance strategies and reviews the commonly used approaches and metrics. Section 6 discusses the future challenges of PHM as well as opportunities for MSAE. Conclusions are drawn in Section 7.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 3 Diffuse-Field Equalisation  
### Diffuse-Field Response Calculation  
The diffuse-field response of the SH binaural Ambisonic decoder \(D_{k}^{SH}\) can be calculated from a sum of the RMS of the SH channels, a process referred to as numerical integration. However, in this study an alternative approach was taken through spatial sampling of the sphere (which was found to give equivalent results), so that further developments could be explored, such as directional bias in the diffuse-field response for localised spectral improvements [30].  
The Ambisonic diffuse-field response \(A_{\text{diff}}\), calculated from the RMS of \(Q\) measurements, was approximated (separately for each ear) by:  
\[A_{\text{diff}}=\sqrt{\frac{1}{Q}\sum_{q=1}^{Q}\Omega_{q}{A_{q}}^{2}}, \tag{9}\]  
where \(A_{q}\) is a binaural Ambisonic render and \(\Omega_{q}\) is the relative solid angle weight of the virtual sound source position, which is calculated as the area subtended by its position on the sphere.  
In order to determine the optimal quadrature method and number of points necessary to produce an adequate approximate diffuse-field response, four quadrature methods were investigated with varying number of points by rendering approximate diffuse-field responses.  
Four quadrature methods were investigated for the distribution of points on a sphere: the Lebedev grid [19], Icosahedron division [31], Fibonacci spiral [32] and spherical T-design [33]. Voronoi sphere plots of the quadrature methods with a similar number of points are shown in Figure 3 to compare the regularity of the quadrature methods. The plots show T-design quadrature produces the highest regularity of the four methods. Simulated diffuse-field responses using the four quadrature methods  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(M\) & **1** & **2** & **3** & **4** & **5** \\ \hline \(f\) (Hz) & 743 & 1346 & 1960 & 2595 & 3230 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dual-band decode crossover frequencies producing 20% integrated D-error for Ambisonic orders 1 to 5, according to [23].  
differed by up to \(\pm\) 1.5 dB at 1 kHz, however implementation of solid angle weighting brought the variation to below \(\pm\) 0.1 dB. Therefore, providing solid angle weighting is implemented, the quadrature method need not be highly regular.  
The minimum number of measurements necessary to calculate a sufficient approximation of the diffuse-field was investigated by rendering diffuse-field responses with a varying number of measurements. The number of measurements ranged from \(Q=L\) to \(Q=1\times 10^{5}\) using Fibonacci quadrature, for the three tested orders of Ambisonics, and validated through comparison to the numerical integration method. With solid angle weighting implementation, variation in calculated diffuse-field response was as little as \(\pm 1\times 10^{-4}\) dB at 20 kHz when \(Q>4L\), which will be perceptually negligible. Therefore 240 pt. T-design quadrature was used for the remainder of the study to ensure minimal error between the numerical integration method.