esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Generation of a Virtual Point Cloud  
This section describes how to generate the virtual point cloud used to train the ANN to solve the problem of coordinate transformation in some reference frames. The scheme of generating a virtual point cloud is shown in Figure 4.  
Figure 4: Generating a virtual point cloud, x1–x3–base points, y1–centroid, z1–z3–mean of the coordinates: \(x\), \(y\) and \(z\).  
Figure 3: Total Free Station (TFS) measurements in a dock on the water, (**a**) total station on the floating dock, (**b**) vessel Navigator XXI–measured object.  
The virtual point cloud was generated on the basis of base points (homologous in two coordinate systems). First, on the basis of the base points (blue in Figure 4) and the geometric center (centroid) of these points (purple in Figure 4), further points are generated (green in Figure 4). The geometric center (centroid) of the base points is calculated as the arithmetic mean of the individual coordinates \(x\), \(y\), and \(z\) (2). Centroid corresponds to the center of geometry (centroid) for an isolated system and unit masses. Then, each of the three points, consisting of at least one point newly generated in the previous step, is used to generate additional points. This operation is repeated until the assumed number of virtual points is reached. Generating virtual point clouds was conducted with the application of the Matlab Software. The method of determining the number of required points is described in Section 2.3: The use of artificial neural networks.  
\[X_{\text{C}}=\frac{\sum_{i=1}^{N}(X_{i})}{3},\ \ Y_{\text{C}}=\frac{\sum_{i=1}^{N}(Y_{ i})}{3},\ \ Z_{\text{C}}=\frac{\sum_{i=1}^{N}(Z_{i})}{3}, \tag{2}\]  
where:  
* \(X_{\text{C}},\ Y_{\text{C}},Z_{\text{C}}\)--coordinates of the centroid (center of mass)  
The applied idea of generating a virtual point cloud is computationally very simple, where the arithmetic mean is used many times. This approach is based on the symmetry of transformations with respect to the displacement. In symmetrical transformations, the angles (symmetry with respect to rotation, shift by angle) and distances (symmetry with respect to the shift translation, time is omitted) are preserved. These symmetries are characteristic of orthogonal transformations, where the invariant of the transformation is the vector length (in real spaces). Therefore, the distances between the points are maintained (for a scale equal to 1) [31; 32; 33]. Such an assumption was made in the discussed case, and it was used to generate virtual points homologous to machine learning (ANN) to solve the problem of coordinate transformation in some reference frames.
# A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect
## 2 Methods  
### The Micro-Doppler Effect  
The micro-Doppler effect was firstly presented in coherent laser systems [40]. When the target or any part of the target has vibrations or rotations in addition to its bulk translation, it might cause additional frequency shifts on the returned signal. This phenomenon is referred to as the micro-Doppler effect [41]. In many early research, the micro-Doppler effect has been used to recognize the moving state of the human body [42; 43]. Figure 1 is a schematic diagram of the micro-Doppler effect. The distance between the target and the ultrasonic transducer is \(R(t)\). The displacement change caused by the target's micro motion is \(x(t)\). \(\lambda\) represents the wavelength of the ultrasonic wave, and the received signal \(r(t)\) can be expressed by:  
\[r(t)=Acos(2\pi f_{c}t-\frac{4\pi R(t)}{\lambda}-\frac{4\pi x(t)}{\lambda}+\phi), \tag{1}\]  
where \(f_{c}\) is the center frequency, \(\phi\) is the initially phase of the transmit signal. Assuming that the distance \(R(t)\) does not change in a short time, the phase change in the received signal \(r(t)\) is caused by the target's micro motion \(x(t)\). In this paper, the center frequency \(f_{c}\) is 300 kHz, and the speed of ultrasound travel in the air is 340 m/s, so the wavelength of transmit signal is 1.13 mm. If the micro motion of target is only 0.5 mm, it will induce 1.77\(\pi\) phase change.
## 4 Results and Discussion
### Case C--Area with Curves and Serpentines  
The example refers to a situation with fixed barriers along the road, although occasional vegetation barriers were present, but they were not vegetated due to the winter season. In this case C, the elevation of the trajectory gradually increased, resulting in a road shape with multiple curves. Figure 13 illustrates how the receivers in this area performed in GNSS positioning.  
From the graphical representation (Figures 13 and 14), it is already clear that the Septentrio AsteRx-U geodetic receiver outperforms the other receivers in the number of detected positions. Interestingly, however, the Xiaomi Mi8 also detected more positions than the two u-boxes. However, the quality of the position determination for the smartphone is significantly worse compared to the u-boxes (Tables 11 and 12, Figure 14).  
As in case B, the u-boxes were able to determine many more fixed solutions in GNSS mode; however, as in the other cases, this was not true for the Septentrio AsteRx-U. The majority of all the available solutions were achieved with a deviation of the distances from the (measured) reference of less than 1 m for the Septentrio and 84.0%, 79.0% and 79.4% of the solutions for ZED-F9P, ZED-F9R, and Xiaomi Mi8, respectively. At the 30 cm threshold, the Septentrio was superior to the other receivers, as 80.0% of the solutions were less than 30 cm and 37.9% of the solutions were less than 10 cm. The GNSS solutions were significantly better for all the receivers with respect to the GPS.  
Figure 12: Percentage of solutions for Case B for (**a**) GPS solutions; (**b**) GNSS solutions. Each bar in a group shows: available solutions (1 st bar), percentage of fixed solutions (2 nd bar), deviation at horizontal distances (3 rd bar, where light blue is for thresholds less than 10 cm, green is for thresholds less than 30 cm, and brown is for thresholds less than 1 m), and 4 th bar shows deviation at spatial distances (yellow, green, and dark red for 10 cm, 30 cm, and 1 m thresholds, respectively).
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 5 Computational Complexity Analysis  
The complexity of the aforementioned frequency-domain FxLMS algorithms is summarized in Table 9. We assume that one \(2L\)-point real FFT can be realized using one \(L\)-point complex FFT plus additional operations, requiring \(2L{\rm log}_{2}(L)+4L\) real multiplications [4]. The peak and average multiplications per sample required for the frequency-domain ANC algorithms involved are presented in Figure 6, where we use \(L=256\) and \(P_{\rm s}=2\). The average complexity of all the frequency-domain algorithms is lower than that of the FxLMS algorithm, which shows the complexity advantage of the  
Figure 5: Proposed delayless algorithm.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Operation** & **Complexity** \\ \hline (1) Compute the frequency-domain reference matrix \(\mathbf{X}_{0}(k)\) using (8) & One FFT \\ (2) Compute the filtered reference signal vector \(\mathbf{v}(k)\) using (11) & One FFT and \(4L{P_{\rm s}}\) multiplications \\ (3) Compute the FFT of \(\mathbf{v}_{0}(k)\) using (15) & One FFT \\ (4) Compute the frequency-domain cancelling matrix \(\mathbf{Y}_{0}(k)\) using (45) & One FFT \\ (5) Estimate the control signal at the error microphone \(\hat{\mathbf{z}}(k)\) using (44) & One FFT and \(4L{P_{\rm s}}\) multiplications \\ (6) Estimate the disturbance signal vector \(\hat{\mathbf{d}}(k)\) using (30) & 0 \\ (7) Compute the pseudo frequency-domain error vector \(\hat{\mathbf{E}}(k)\) using (46) & Two FFTs and \(4L{P_{\rm w}}\) multiplications \\ (8) Update \(\mathbf{\Lambda}(k)\) using (17) & 4L multiplications \\ (9) Update the weight vector using (47) & \(2P_{\rm w}\) FFTs and \(6L{P_{\rm w}}\) multiplications \\ (10) Compute \(\mathbf{b}(k+1)\) using (42) & One FFT and \(4L({P_{\rm w}}-1)\) multiplications \\ (11) Compute the cancelling signal \(\mathbf{y}(k+1)\) using (40) & \(2L^{2}\) multiplications \\ \hline \hline \end{tabular} Note: Steps (1)–(10) should be completed between \(kL\) and \(kL+L\); Average and peak multiplications: \((8+2P_{\rm w})A/L+14P_{\rm w}+8P_{\rm s}+2L\).  
\end{table}
Table 8: Proposed delayless algorithm.  
frequency-domain adaptive algorithms. The FPBFxLMS I in [28; 37] and [PERSON]'s delayless algorithm in [44] both have high peak complexity and hence are unrealizable in real-time systems. The proposed delayless method achieves load-balanced implementation and is only slightly more complex than [PERSON]'s delayless algorithm in [46].
# In Vitro Synovial Membrane 3D Model Developed by Volumetric Extrusion Bioprinting
## 3 Results  
### Bioink Characterization  
The three GelMA formulations were characterized by performing swelling and rheological analyses that provided useful information for 3D printing. Swelling ability is crucial to test hydrogels' ability to absorb/hold large amounts of fluids. As shown from the experimental data (Figure 1A), the swelling degree increases significantly, raising GelMA's concentration in solution from 7.5% GelMA to 12.5 % GelMA. It is important to note that the differences in swelling ratio are statistically significant. This behavior was expected and can be related to the lower hydrogel concentration in solution, which raises network expansion and, thus, the water absorption. Moreover, the rheological characterization performed in terms of frequency sweeps attested a measurement of GelMA samples elastic modulus at 37 \({}^{\circ}\)C (Figure 1B).  
Figure 1: (**A**) swelling in distilled water of GelMA formulations at three different concentrations. The swelling experiments were performed at least three times. Statistical analysis was performed with two-ways ANOVA *** \(p\) < 0.001, *** \(p\) < 0.0001). (**B**) elastic modulus (E) of the GelMA hydrogels. The experiments were performed at least three times, on samples derived from different batches.  
In addition, the hydrogel morphologies were studied on the three GelMA formulations by FESEM. The FESEM images (Figure 2) demonstrated a porous structure similar in all the gelatin-based samples, as expected. A porosity of GelMA hydrogels with different diameters on the microscale was observed. The pores were shaped like a pocket, and they were not separated one from the other but interconnected. A slight difference in pore dimensions can be shown, increasing the gelatin concentration in solution.
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 4 Evacuation Simulation Model
### Mobile Agent  
100 agents (evacuees) are randomly located in this building before the emergency. Each agent is capable of simulating an evacuee carrying a mobile device equipped with sensors. Each mobile device saves a local version of the graph of the building, which will be used to compute the shortest path independently during evacuations. The local graph can be updated by:  
* Identify whether the edge being visited by this evacuee is blocked (e.g., by temperature, smoke, or visibility, or by the fact that the owner turns around and seeks alternative paths [67]), and save this updated awareness in their memory.
* Communicate ad-hoc with devices that have come close (e.g., 3 m in the simulation), and exchange their individual awareness of the environment. These other devices can be other evacuees' mobile devices, or static information depositories.
* Fuse the received awareness of another agent or depository with its own awareness. The merging operation ensures that among duplicates only the awareness with the more recent time stamp will be retained.  
During evacuations, each mobile device computes a shortest path locally using A* algorithm, and provides evacuation guidance to the evacuee. The evacuee is supposed to follow the path suggested by the mobile device unless the evacuee finds the edge in front has been blocked. The mobile device senses this situation, allocates the blocked edge with a weight of infinity, and recalculates a new shortest path.  
In the simulation, each device has been initialized with awareness of the complete original graph of this building. Such awareness is valid until a disaster strikes and the emergency evacuation is initiated. During the evacuation, the individual agents' awareness will be updated by continuously sensing the environment and exchanging knowledge with peer agents and depositories.  
Figure 3: Virtual sensors for emergency simulation. Each black dot represents a virtual sensor. The detection range of a virtual sensor is a circular area. Each red point represents a node in the route graph.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 5 Signal Processing Framework
### Radar Sensing Performance  
To study how modulated signals affect the radar sensing performance, we assume there is only one tag in the view. The radar sensing performance is related to the offset frequency estimation performance. We apply a two-step frequency estimation; First, apply FFT to get a coarse frequency estimation. Second, a phase-locked loop is applied to further track the frequency and phase offset. Here, we use a BPSK signal with a symbol rate of 5 Gbaud, and SNR of 20 dB. As Figure 9 shows, when the signature code has 8 or 16 bits, there is around a 10 Hz to 20 Hz estimation error. When the signature code has 32 bits, the estimation error is lower than 0.5 Hz, which is similar to no data modulation case. As a result, the SNR is the key factor in frequency estimation.
## 5 Active Coating and its Control Algorithm
### Notes about the Stability of an Active System with Microphones  
The description above was made under the assumption that active coating forms desired DNOV on the surface \(S_{V}\) sufficiently quickly, precisely and safely. Now we consider possible instability channels, created by microphones. Together these channels must cover all frequencies. Only in this case their blocking could guarantee system's stability. There the three channels can generate instability via microphones.  
1. Active coating generates the acoustical field in the range (3) and this field penetrates via microphones. This channel of instability is blocked by the vector-microphone, which ensures the feedback coefficient has a value of less than 1.  
2. Active coating generates the sound field outside of the range (3) too. On these frequencies the directional pattern of vector-microphone has too many of narrow petals, but does not ensure the desired integral (in body angle \(\pm\,2\pi\)) and anisotropy with respect to the vector \(\,\mathbf{w}_{i}\,\) of incident wave, if \(\,h_{i}\omega/c_{w}>>1\,\), for instance. Besides this, it is impossible to form the desired DNOV by pistons of finite dimension \(\,L_{V}\,\). To block this channel of instability we use low pass filter with time constant \(\,\tau_{Q}\,\) (under the condition \(\,D_{V}<<c_{w}\tau_{Q}<<2\pi\,/\,\chi_{\max}\,\)) on the output of each microphone without inertial distortions of desired signals and inserting of only delay \(\,\tau_{Q}\,\). Control system takes into account this delay without problems with targeting of incident wave with condition \(\,\tau_{Q}<D_{VP}\,/\,c_{w}\,\).  
3. Reactive acoustical field (near field), created by active coating in the range (3), penetrates the microphones too. This field can't be reduced by a vector microphone. However this field is exponentially decreasing as \(\,\gamma=\exp\!\!\left[-D_{VP}\sqrt{\left(2\pi\,/\,L_{V}\right)^{2}-\chi_{\max }^{2}}\,\right]\,\) at the distance \(\,D_{VP}\,\) from coating. So at not very large distance \(\,D_{VP}\,\) we can get \(\,\gamma<\!<\!1\,\) by decreasing \(L_{V}\).
## 3 Results
### Dose-Averaged Lineal Energy and Quality Factor  
Figure 2 presents \(y_{D}\) values for a liquid water sphere of 1 \(\upmu\)m diameter over the proton energy range of 1-250 MeV. Proton beams with initial energy between 50 and 250 MeV are increasingly used in cancer radiotherapy. For space applications, protons in the Van Allen belts reach up to 400 MeV. After these protons interact with spacecraft shielding, they slow down. Those capable of penetrating within the spacecraft reach the astronauts with energies well below 300 MeV. Moreover, protons in the energy range below 30 MeV make up the Bragg peak region where the \(\overline{Q}\) (and the carcinogenic risk) becomes maximum. The calculated values are from the different analytical models examined in the present work (X94, Xcom, XO), as well as published MC data by Geant4-DNA [60] and TEPC simulations  
\begin{table}
\begin{tabular}{c c c c} \hline
**Physical Parameters** & **Liquid Water** & **Vapor Water** & **Reference** \\ \hline I value (keV) & 0.078 & 0.071 & Liquid: ICRU Report 90 [5] \\  & 11.0 (d = 3 \(\upmu\)m) & 11.1 (d = 3 \(\upmu\)m) & Vapor: ICRU Report16 [59] \\ \(\Delta\)-cut off electron energy (keV) & 5.62 (d = 1 \(\upmu\)m) & 5.93 (d = 1 \(\upmu\)m) & Liquid: [PERSON] et al. (2016) [57] \\  & 1.37 (d = 0.1 \(\upmu\)m) & 1.50 (d = 0.1 \(\upmu\)m) & Vapor: [PERSON] et al. (2008) [58] \\  & 0.184 (d = 0.01 \(\upmu\)m) & 0.288 (d = 0.01 \(\upmu\)m) & \\ \(\delta\)2-energy-weighted mean & Protons: & Protons: & Liquid: Our fit \\ energy deposited inside the & A = 0.195, B = 0.610 & A = 0.190, B = 0.620 & Vapor: [PERSON] et al. (1994) [48] \\  & Electrons: & Electrons: & Vapor: [PERSON] et al. (1994) [48] \\  & A = 0.121, B = 0.577 & A = 0.114, B = 0.591 & \\ \hline \end{tabular}
\end{table}
Table 1: Physical model parameters used in the present work for liquid and vapor water (both at unit density).  
by the PITS99-KURBUC [46] codes. For better visualisation, the \(y_{D}\) values in the energy range of 1-100 MeV are also presented logarithmically in the inset of Figure 2. Note that the available Geant4-DNA and TEPC simulation data are limited up to 100 MeV.
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 3 Results
### Raw Data and Data Elaboration  
AFM was used to perform force-distance curves, as reported in Figure 3. The cantilevers used in the analyses were calibrated on silicon slabs before the tests on the samples. To evaluate the elastic modulus, the Sneddon model was used [38]. The equation used is the following:  
\[\mathrm{F}=\frac{2}{\pi}\tan\alpha\frac{\mathrm{E_{s}}}{(1-\mathrm{v_{s}^{2}} )}\delta^{2} \tag{3}\]  
where \(\mathrm{F}\) is the applied force, \(\alpha\) is the half-opening of the tip, considered conical in the ending part, \(\mathrm{E_{s}}\) and \(\mathrm{v_{s}}\) are the elastic modulus and the Poisson's ratio of the sample (assumed equal to 0.35), respectively, and \(\delta\) is the indentation depth, as previously described in other studies [9].  
The force was calculated as follows:  
\[\mathrm{F}=\mathrm{k_{C}}Z_{\mathrm{C}} \tag{4}\]  
where \(\mathrm{k_{C}}\) is the cantilever spring constant and \(Z_{\mathrm{c}}\) is the cantilever deflection.  
Figure 2: Height 50 \(\times\) 50 (\(\upmu\)m) image of surface morphology acquired by AFM in tapping mode for PHA thin films sample (**left**) and its line profile (**right**).  
The cantilever spring constant was calculated using the Sader method, that for rectangular cantilevers defines \(\mathrm{k_{c}}\) as follows [39]:  
\[\mathrm{k_{C}}=0.1906\ \rho\ \mathrm{b^{2}\ L\ Q\ \Gamma_{i}(\omega_{R})\ ( \omega_{R})^{2}} \tag{5}\]  
where \(\rho\) is the density of the fluid surrounding the cantilever, \(\mathrm{b}\) and \(\mathrm{L}\) are the cantilever width and length, respectively, \(\omega_{R}\) and \(\mathrm{Q}\) are the radial resonant frequency and quality factor in a fluid of the fundamental flexural mode, respectively, and \(\Gamma_{i}(\omega_{R})\) is the imaginary part of the dimensionless hydrodynamic function evaluated at resonant frequency [40]. The quality factor describes the relative width of the resonance peak, and it is equal to the resonant frequency divided by the half-width of the square amplitude resonance peak.  
The fitting of the curves was performed with OriginPro 8.5 software. The quality of the fitting was evaluated in terms of the coefficient of correlation \(\mathrm{R^{2}}\). The adjusted \(\mathrm{R^{2}}\) coefficient was considered a valid measure for the reliability of interpolation, and it is defined by the following equation:  
\[\mathrm{adjusted\ R^{2}}=-\frac{\sum_{i=1}^{n}\frac{(y_{i}-\hat{y})^{2}}{(n-k)}}{ \sum_{i=1}^{n}\frac{(y_{i}-\hat{y})^{2}}{(n-1)}} \tag{6}\]  
where \(y_{i}\) and \(\hat{y}\) are the original measured data values and modeled values, respectively, \(\hat{y}\) is the mean of the observed data, \(\mathrm{n}\) is the number of dataset points and \(\mathrm{k}\) is the number of parameters calculated by the interpolation. Fitting was selected to have adjusted \(\mathrm{R^{2}}\) higher than 0.990.  
Every sample had a total of eight indentations at different points, leading to eight force-distance curves, from which the elastic modulus was extrapolated. Figure 3 shows an example of such fittings.
## 6 Challenges and Future Perspectives
Another obstacle is that the utilization of these byproducts can elevate the energy consumption and waste stream, causing a negative environmental impact [35; 114]. On the other hand, for the effective application of avocado extracts in pharmaceuticals, food, and cosmetic products, the stabilization and delivery must be studied and tested. The most complex aspect of microencapsulation is choosing the wall material, which will directly influence the performance of the capsules. The coating material must be bio-degradable, resistant to the intestinal tract, provide protection of the bioactive molecules against extreme conditions, non-reactive, tasteless and cost-effective to be suitable for use in the food industry [115]. The added stabilization protocol of the extract involves extra investments that must be carefully considered. The economic analysis of a particular microencapsulation process should reflect on the encapsulation or stabilization technology chosen, the coating material chosen, and the complexity of the procedure [116]. For example, a food-safe gelatin coacervation process requires the drying of the wet capsules for easy handling. Considering a weight of 550 kg of core material to be processed, the cost of raw wall materials, including processing aids, is approximately USD 2391.00, and labor cost, including processing, formulation and sieving and drying cost, is USD 3330.00, amounting to a total cost of USD 5721.00. This would place the cost per kg of processed core and the cost per liter of processed product at 10.40 and 9.27 USD, respectively [116] ([PERSON], 2014). Although the implementation of these technologies presents an added cost, the technique has widespread effects across the health, food and beauty industry, with a market value of nearly USD 12.15 billion in 2023, and an expected growth of USD 36.34 billion by 2032 [117]. As for regulations, the U. S. Food and Drug Administration in their Code of Federal Regulations, Title 21, Part 172, Sec. 172.230 outlines the safe use of microcapsules in food, specifying the allowed components, usage restrictions and labeling requirements [118]. Another residue that should be further studied is the large volumes of wastewater generated with the production of avocado oil. [PERSON] et al. produced powder rich in phenolic compounds and antioxidants from avocado wastewater through spray drying methods [119]. The results demonstrated the possible applications of the powder in the food industry as they were effective in preventing lipid oxidation in pork sausages. Anotherniche to be exploited is avocado leaves. ALs have higher contents of total phenols and flavonoids, as well as a higher radical scavenging ability than peel, seed, and pulp [120].