esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup of Optical Homodyne and Heterodyne Detection  
### Experiment for Heart Rate Detection with Self-Mixing Optical Homodyne Technique  
Thin nano film reflectors are used for reflection of the laser signal. These reflectors are mounted on the wrist and chest wall of the test subject. Due to their negligible mass, thin film reflectors vibrate with the same frequency as that of heart frequency. The thin film reflectors are prepared with gold sputtering and aluminum sputtering techniques. Thin antireflection coating on glass for optical applications are deposited by sputtering. Sputtering is a vapor phase deposition (PVD) method of thin film deposition [33]. This involves ejecting material from source onto a silicon wafer. Optical coatings are deposited in the wavelength regions between visible and far-IR. The used samples were silica substrates with thickness of 1 \(\upmu\)m. The atomic force microscopy (AFM) image of gold-sputtered thin film nano reflector is shown in Figure 8. In order to optimize the quality of the bio-signal, a thin film reflector with an adhesive tape was placed on the chest wall. The thin film reflector increases the S/N ratio of the vibratory signal. Instead of thin film reflectors, 45% hydrating zinc oxide spray or paste on skin can also be used in order to maximize the reflection.  
Figure 8: (**a**) Gold deposition process on silica substrate; (**b**) thin film reflector on wrist position.  
The experiment setup of homodyne detection consisted of a laser diode, a beam splitter, mirrors, and photodetectors. The laser diode used in this setup was semiconductor AlGaInP laser with wavelength 660 nm and power 10 mW. The laser beam is split into two parts by a beam splitter. One beam travels towards the fixed mirror while other beam travels towards thin film reflector, which is fixed on the human wrist or the chest wall. Both beams after reflection from the mirrors combine at the same beam splitter and interference pattern is observed at the photodetector. A tight strip band is fixed near the elbow position so that heart pulse is properly located on the wrist. Figure 9 shows the experimental setup for homodyne detection scheme. Figure 10 shows the response of photodetector for homodyne detection. It clearly shows measurement of vibration on human wrist skin, which indicates heart frequency. The measurement of heart frequency was carried out at the chest wall position with similar results. The laser was placed at approximately 10 cm from the subject's chest wall and wrist position [34]. A low pass filter (cut off frequency \(\leq\) 5 Hz) was placed in series with photo detector for denoising of the optical beat signal. Laser diode with power of 10 mW was used, so that no special safety measures were required. Even with low power level, a working distance of 10 m is feasible.  
Figure 10: \(HR\) detection on wrist position with optical homodyne technique.  
Figure 9: \(HR\) detection with optical homodyne technique.
## 3 Results and Discussion  
### FTIR Analysis  
The FTIR spectra of birch bark and beech bark are very similar, suggesting a similar chemical composition (Figure 1).  
The spectra show bands that are characteristic of lignocellulosic materials. Peaks in the kayarange of 1440-850 cm\({}^{-1}\) are typical for polysaccharides. The peaks at 1028 cm\({}^{-1}\) and 1156 cm\({}^{-1}\) are due to the glycosidic C-O-C bond between the pyranose and hemicellulose rings [47; 48; 49]. The peaks at 1100 cm\({}^{-1}\) and 890 cm\({}^{-1}\) indicate stretching of the saccharide skeletons of the homocellulose rings [50; 51]. Additionally, specific to cellulose and hemicellulose are the peaks at 1440 cm\({}^{-1}\) and 1368 cm\({}^{-1}\), which correspond to the bending vibrations -CH\({}_{2}\), and the peak at 1316 cm\({}^{-1}\), which corresponds to the wave vibrations -CH\({}_{2}\)[52].  
Lignin is characterised by peaks at 1240 cm\({}^{-1}\) and 1510 cm\({}^{-1}\), which are due to the presence of C-O and C=C bonds in the area of the lignin benzene rings [47; 53] as well as the peak at 1604 cm\({}^{-1}\), which is related to the vibrations of these aromatic rings [51]. The content of lignin in the bark biomass is also indicated by the typical peak at 1734 cm\({}^{-1}\), indicating the presence of C=O carbonyl bonds in the structure [54].  
The peaks at 2924 cm\({}^{-1}\) and 2853 cm\({}^{-1}\) can be traced back to the asymmetric and symmetric tensile vibrations of the -CH\({}_{2}\) groups, which originate from the aliphatic structures of holocellulose and lignin [55; 56]. The broad band at 3500-3000 cm\({}^{-1}\) is due to the stretching of the O-H bond of hydroxyl functional groups present in both saccharides and lignin.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 3 Physical Basis of Multi-Sensor Precipitation Estimation  
### IR-Based Precipitation Estimation  
IR observations are indirectly linked to precipitation. The fundamental physical principle is that precipitation is predominantly associated with deep clouds and, consequently, cold cloud tops. The lower cloud-top temperature serves as an indicator of precipitation occurrence. However, it should be noted that this relationship does not necessarily hold [(76)]. The GOES Precipitation Index (GPI) [(69)] and the GOES Multi-Spectral Rainfall Algorithm (GMSRA) [(70)] represent typical examples of IR-based techniques employed in this context. These IR-based approaches perform relatively favorably when applied to larger spatiotemporal scales [(17)].
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 2 Materials and Methods
### Laboratory Experimental Set-Up  
The measurement experiment of the STS triangulation positioning system was carried out on the terrace of the Faculty of Electrical Engineering of the Bialystok University  
Figure 8: Analog front-end and digital part of the IR receiver.  
Figure 9: Hardware configuration of the IR receiver.  
of Technology. As these were the first experimental tests on the constructed system, it was ensured that there were no obstacles in the working area (and its close vicinity) that could affect the measurement result. It is well known that in such measurement systems, reflections of signals from objects (e.g., walls) interfere with the measurement spectrum; therefore, an error occurs in the determination of the beacon-receiver angles. The beacons \(B_{1}\), \(B_{2}\) and \(B_{3}\) were arranged in an equilateral triangle at positions \(B_{1}\) = (0.0, 0.0) m, \(B_{2}\) = (6.032, 0.0) m, \(B_{3}\) = (3.016, 5.223) m as shown in Figure 10. The IR receiver of the positioning system 1 was placed on a tripod. To enable measurements for different orientations of the receiver disk, it was placed on a servo motor (see 2 in Figure 10). Measurements were therefore taken for a given tripod position and for different orientations of the receiver, which was rotated every 5 degrees. For each given IR receiver position (defined by tripod position and receiver disk orientation), 10 measurements were taken, which gives a total of 10 \(\times\) 360/5 = 720 measurements for the given tripod position. The measurement experiment was performed for the eleven tripod positions marked (from 1 to 11) in Figure 10.  
Based on the experimental measurement data, the following statistical parameters were calculated for each tripod position: average position in x-direction \(\hat{x}\), average position in y-direction \(\hat{y}\); standard deviation \(\sigma_{x}\) of the position in the x-direction; standard deviation \(\sigma_{y}\) of the position in the y-direction; standard deviation \(\sigma_{R}\) of the position \(R=\sqrt{x^{2}+y^{2}}\); and root mean square error of the position \(R\) define as:  
\[\textit{r}mse_{R}=\sqrt{\frac{\sum\limits_{j=1}^{n}\left(x_{real}-x_{j}\right) ^{2}+\left(y_{real}-y_{j}\right))^{2}}{n}} \tag{8}\]  
where \(\left(x_{real},y_{real}\right)\) is the real triod position, \(\left(x_{j},y_{j}\right)\)--measured triod position and \(n\)--number of measurements in a given tripod position (\(n=720\)); standard deviation \(\sigma_{\gamma}\) of the angles \(\gamma_{i}\) defined as:  
\[\sigma_{\gamma}=\sqrt{\frac{\sigma_{\gamma_{1}}^{2}+\sigma_{\gamma_{2}}^{2}+ \sigma_{\gamma_{3}}^{2}}{3}} \tag{9}\]  
Figure 10: Laboratory experimental set-up.  
and root mean square error of the measured angles \(\varphi_{i}\):  
\[rmse_{\varphi}=\frac{1}{2}\sqrt{\frac{rmse_{\gamma_{1}}^{2}+rmse_{\gamma_{2}}^{2 }+rmse_{\gamma_{2}}^{2}}{3}} \tag{10}\]  
The determined parameters are presented in table form in Section 3.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 4 Discussion
### Reference Signal without Noise
 \tag{45}\]  
Therefore, the output SNR \(G_{new}\) in this paper is as follows:  
\[G_{lower}\leq G_{new}\leq G_{upper} \tag{47}\]  
\[G_{lower}\approx 10\log_{10}\frac{\left|a_{1}\right|^{4}(\sum\limits_{l=1}^{ N-1}l)^{2}}{\sum\limits_{l=1}^{N-1}l+2\left|a_{1}\right|^{2}(N-2)\sum\limits_{l=1}^{ N-1}l}\approx 10\log_{10}\frac{\left|a_{1}\right|^{4}(\sum\limits_{l=1}^{ N-1}l)}{1+2\left|a_{1}\right|^{2}(N-2)} \tag{48}\]  
\[G_{upper}\approx 10\log_{10}\frac{\left|a_{1}\right|^{4}(\sum\limits_{l=1}^{ N-1}l)^{2}}{\sum\limits_{l=1}^{N-1}l+2\left|a_{1}\right|^{2}\sum\limits_{l=1}^{ N-1}l}\approx 10\log_{10}\frac{\left|a_{1}\right|^{4}(\sum\limits_{l=1}^{ N-1}l)}{1+2\left|a_{1}\right|^{2}} \tag{49}\]  
where \(G_{lower}\) represents the lower bound of the output SNR, and \(G_{upper}\) represents the upper bound of the output SNR. The simulation results are shown in the Figure 2.  
Figure 22 presents a comparison of the output SNR between two algorithms. When the cross phase of the differential signal is completely correlated, the output SNR of the algorithm obtains the minimum value, that is, the theoretical lower bound in Figure 22. When the cross term of the differential signal is completely uncorrelated, the output SNR of the algorithm reaches the maximum value, that is, the theoretical upper bound in Figure 22. As can be seen from Figure 22, the theoretical lower bound of the output SNR  
of the algorithm in this paper is lower than that of the traditional R-D algorithm, but the theoretical upper bound is much higher than that of the traditional algorithm. However, the cross item is the corresponding point multiplication of the reference signal and noise with different delays. The cross term cannot be completely correlated, and there must be a certain phase difference. Therefore, the lower bound of the output SNR for this algorithm cannot be achieved through practical processing.
## 5 Crack Detection Method Based on CS
### Reconstruction of Sparse Signals  
Problem \(P0\) is a standard sparse recovery problem. For the selection of sparse recovery algorithms, the Orthogonal Matching Pursuit (OMP) algorithm is employed. According to Equation (12), given \(\mathbf{G}(\mathbf{\alpha})\) and \(\mathbf{d}\), the goal is to recover \(\mathbf{t}\) from these values. It is evident that we need to fully utilize the sparsity of \(\mathbf{u}\). Based on the knowledge of linear algebra, \(\mathbf{d}\) can be represented as a linear combination of the column vectors of matrix \(\mathbf{G}(\mathbf{\alpha})\), where \(\mathbf{t}\) acts as a weight for the column vectors of \(\mathbf{G}(\mathbf{\alpha})\). As \(\mathbf{t}\) is sparse, only a few column vectors in \(\mathbf{G}(\mathbf{\alpha})\) significantly contribute to \(\mathbf{d}\). The objective is to identify the column vectors that have a significant impact on \(\mathbf{d}\). Simultaneously, based on the positions of the column vectors in \(\mathbf{G}(\mathbf{\alpha})\), the positions of the non-zero elements in \(\mathbf{t}\) can be determined. This is the fundamental application of the OMP algorithm in the crack estimation. Summarizing the pseudocode for the OMP algorithm to recover \(\mathbf{t}\), the specific algorithm steps are as Algorithm 1 below:  
```
Input: Measurement values of pressure difference \(\mathbf{d}\).
1. Set algorithm parameters, such as \(l\) and the setting of \(\mathrm{d}_{\mathrm{th}}\)
2. Initialize the index set \(\mathcal{S}=\varnothing\) and the set of spatial samples \(\mathcal{A}=\{1,\ldots,I\}\)
3. Set the measured value \(\mathbf{d}_{\mathrm{z}}=\mathbf{d}\)
4. Loop through the following steps until \(\parallel\mathbf{d}_{\mathrm{z}}\parallel_{\mathrm{z}}\leq\mathrm{d}_{ \mathrm{th}}\)
5. Calculate \(v_{i}=\frac{(\varphi(\mathbf{d}_{\mathrm{z}}))^{\mathrm{th}}\mathbf{d}_{ \mathrm{z}}}{\|\varphi(\mathbf{d}_{\mathrm{z}})\|_{\mathrm{z}}},\forall i \in\mathcal{A}\smallsetminus\mathcal{S}\)
6. Find \(i^{\star}\) such that \(i^{\star}=arg_{\mathrm{z}}\max_{\mathbf{d}\in\mathcal{A}\smallsetminus\mathcal{S }}\|v_{i}\|_{\mathrm{z}}\)
7. Gain the index set \(\mathcal{S}\) as \(\mathcal{S}=i^{\star}\cup\mathcal{S}\)
8. Update the estimated value \(\mathbf{t}\) as \(\mathbf{t}=\left(\mathbf{G}(\mathbf{\alpha})\right)^{\mathrm{t}}\mathbf{d}\), where \(\left(\mathbf{G}(\mathbf{\alpha})\right)^{\mathrm{t}}\) is the pseudo-inverse of \(\mathbf{G}(\mathbf{\alpha})\)
9. Calculation result of \(\mathbf{d}_{\mathrm{z}}=\mathbf{d}-\mathbf{G}(\mathbf{\alpha}_{\mathcal{S}}) \mathbf{\hat{t}}\) Output: Estimated value \(\mathbf{t}\)
```  
**Algorithm 1**The OMP-Based Sparse Signal Recovery
## 3 Results
The regression analysis considering the biomass, soil nutrients, and amount of earthworms is shown in Table 10. In terms of F. macrophylla, the amount of biomass added in the first cycle was between 1 and 2 kg plant\({}^{-1}\), showing a relatively high (\(-0.64\)) and high (\(-0.81\)) inverse relationships among the Zn and Mg with the number of earthworms, respectively. In the second cycle (1 to 4 kg kg plant\({}^{-1}\)) and third cycle (2 kg plant\({}^{-1}\)), B showed a slight correlation (0.59 and 0.58 respectively). Turning to _G. septum_, where 6 to 7 kg plant\({}^{-1}\) was added in the first cycle, a relatively high correlation (0.79) was observed between the Zn with the number of earthworms. A high inverse correlation (\(-0.83\)) was shown in the second cycle (13 to 16 kg plant\({}^{-1}\)) between the Mg with the number of earthworms; while in the third cycle (8 to 9 kg plant\({}^{-1}\)), B showed a strong correlation (0.92) between B with the number of earthworms. \begin{table}
\begin{tabular}{c c c} \hline
**Agroforestry System** & **Season** & **Amount of Earthworms (m\({}^{2}\))** \\ \hline _F. macrophylla ***_ & & 143.89 a \\ _G. septum ***_ & & 109.33 ab \\ _G. septum *_ & & 106.78 bc \\ _F. macrophylla + G. septum *_ & & 98.44 bcd \\ _F. macrophylla *_ & & 85,00 bcde \\ _F. macrophylla *_ & & 73.67 cde \\ _Monoculture ***_ & & 70.44 de \\ _Monoculture *_ & & 60.00 e \\ \hline _F. macrophylla ***_ & & 91.67 a \\ _G. septum *_ & & 70.67 ab \\ _F. macrophylla + G. septum *_ & & 64.22 ab \\ _F. macrophylla + G. septum *_ & Dry season & 62.44 ab \\ _F. macrophylla + G. septum *_ & & 54.89 bc \\ _F. macrophylla *_ & & 54.78 bc \\ Monoculture ** & & 47.33 bc \\ Monoculture * & & 29.78 c \\ \hline \end{tabular} \({}^{*}\) 50% fertilization; ** 100% fertilization; *** without fertilization. Different letters indicate significant differences (\(p<0.05\)) using the ANOVA one-way analysis followed by Tukey’s test.  
\end{table} 
Table 8: Amount of earthworms determined in the agroforestry systems in the two seasons. \begin{table}
\begin{tabular}{c c c c} \hline
**Season** & **Nutrient** & **Estimate** & **Probability** \\ \hline \multirow{4}{*}{Rainy season} & Mn & 2.11 & 1.75\({}^{-5}\) ** \\  & P & \(-\)2.61 & 0.07 NS \\  & B & 89.44 & 0.17 NS \\ \hline \multirow{4}{*}{Dry season} & Cu & \(-\)2.61 & 0.001 ** \\  & B & 164.12 & 0.019 * \\ \cline{1-1}  & Ca & \(-\)8.58 & 0.005 ** \\ \cline{1-1}  & Zn & 3.65 & 0.034 * \\ \cline{1-1}  & N & \(-\)48.53 & 0.131 NS \\ \hline \end{tabular}
\end{table} 
Table 9: Results of the stepwise regression algorithms to identify the nutrients that contributed to the amount of earthworms.
# Power Management Strategy for an Autonomous DC Microgrid
## 2 Autonomous DC Microgrid Configuration and Component Description
### Diesel Generator Electric Modelling  
As mentioned in Section 1, the DG used in this study is supposed to be at a constant speed with its speed regulator system, which ensures the frequency of 50 Hz. It is mechanically coupled to a single-phase brushless synchronous generator, which has a closed loop automatic voltage regulator to keep AC voltage at 230 V and frequency at 50 Hz. These two control systems are assembled and installed inside the DG to provide a relatively fixed AC output voltage, which is independent of output current or load impedance. The DG can therefore be viewed as an independent voltage source. When the voltage amplitude \(\vartheta_{DG}\) and frequency \(f_{DG}\) satisfy Equation (1), a switch is closed to output the DG power into the bus of the DC microgrid.  
\[\left\{\begin{array}{l}310\;\mathrm{V}<\vartheta_{DG}<340\;\mathrm{V}\\ 48\;\mathrm{Hz}<f_{DG}<52\;\mathrm{Hz}\end{array}\right. \tag{1}\]
## 5 Experimental Results and Discussion  
To evaluate the performance of our proposed hybrid recommendation-based approach, we selected two baseline methods: the ICA [31] and ODMA [7]. These algorithms were chosen because they address similar Fog SPPs, focusing on similar objectives, such as minimizing service delay and maximizing Fog resource utilization. Furthermore, they represent two widely used classes of optimization techniques, meta-heuristics (ICA) and heuristics (ODMA), allowing for a well-rounded comparison. Although other methods, such as genetic algorithms and deep reinforcement learning, have been explored in more recent studies, we focus on algorithms that align closely with our problem formulation, objective functions, and simulator settings.
## 2 Ground Motion Database  
For the analyses performed in this study, we only considered the sites for which both ground motion recordings from significant earthquakes and shear wave velocity profiles were available simultaneously. The list of sites from Romania and the Republic of Moldova is presented in Table 1. The estimated average shear wave velocity values in the top 30 m of soil deposits (Vs,30) were taken based on the topographic slope method of [PERSON] [17]. The computed Vs30 values were obtained based on shear wave velocity profiles collected during various research projects (JICA [18], BIGSEES [19], etc.) or from research contracts with the industry. The Vs30 values for the sites located within Prahova county were retrieved from [20]. The Vs30 value for Bucharest represented a mean value obtained from more than 40 shear wave velocity profiles [21]. The Vs30 for Chisinau (a site in the Republic of Moldova) was based on the information provided in [22]. Table 1 also shows the local geological conditions based on the geological map of Romania [23]. For almost all sites, with the exception of Barloc, only ground motion recordings from Vrancea intermediate-depth earthquakes were available. The ground motion database consists of recordings obtained during the most important Vrancea intermediate-depth earthquakes from the past 45 years, namely the 4 March 1977 event (moment magnitude M\({}_{\rm W}\) = 7.4 and focal depth h = 94 km), August 30, 1986 (M\({}_{\rm W}\) = 7.1, h = 131 km) and 30 May 1990 (M\({}_{\rm W}\) = 6.9, h = 91 km). In the case of the Barloc station, the ground motions recordedduring a M\({}_{W}\) 5.5 crustal earthquake (focal depth h = 9 km) from 1991 were employed. The moment magnitudes and focal depths of the considered seismic events were taken from the ROMPLUS earthquake catalogue [24]. It can be observed from Table 1 that all analysed sites can be assigned as having site classes B, C or F according to the Eurocode 8 draft revision [1]. None of the sites can be considered as having rock conditions. The engineering bedrock is defined according to the Eurocode 8 draft revision [1] as having a shear wave velocity of at least 800 m/s. The positions of the 15 analysed sites and the epicentres of the earthquakes for which ground motion recordings were available are illustrated in Figure 1. It can be observed that most of the sites are situated in the southern and eastern parts of Romania, where the influence of the Vrancea intermediate-depth seismic source is dominant. The mean and the maximum acceleration response spectra for each site class category, defined using the criteria from the Eurocode 8 draft revision [1], are shown in Figure 2. All response spectra were computed considering a damping ratio of 5%. The significant intermediate- and long-period spectral amplifications occurring for class F sites are noteworthy. A total of 59 ground motion recordings from the four abovementioned seismic events were used in the analysis.
## 5 Numerical Simulation and Experimental Verification  
### Simulation Experimental Platform  
To validate the stability and effectiveness of the proposed algorithm, a human lung model is established as the research object, and a 16-electrode EIT simulation model of the human thorax is constructed. The 16 measurement electrodes are uniformly distributed on the surface of the thorax, and the forward problem is solved using the COMSOL Multiphysics 5.6 software, resulting in 2993 mesh elements, as shown in Figure 7. Based on the established model, 120 sets of valid boundary impedance data can be obtained for a specific lung condition using the 16 measurement electrodes. These data are input into MATLAB software to achieve image reconstruction. The reconstructed image is then compared with the ground truth image to quantitatively evaluate the imaging performance. The simulations in this study were conducted on a PC equipped with an Intel Core 2 E8200 2.66 GHz CPU (Intel Corporation, Santa Clara, CA, USA), and 8 GB of RAM. This configuration meets the computational cost requirements for simulations in COMSOL Multiphysics 5.6.