esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental and Numerical Setup
### The Atomizer Design and Test Bench  
The atomizer geometry was derived from a small-sized atomizer studied in our previous work [26], see schematic drawing in Figure 1. Three inlet ports are used to take advantage of the circumferential periodicity of the atomizer. The swirl chamber has a conical converging part to aid manufacturing simplicity. The atomizer is manufactured as ten times scaled copy since the small dimension of the original atomizer does not allow for direct optical measurement. The transparent parts of the atomizer are made from cast polymethyl methacrylate, PMMA, which was ground and polished to achieve transparency. The operating conditions were partially derived from [26] where the original atomizer operated with JET A-1 at \(p_{l}=0.5\) MPa roughly yielding \(Re=1000\), which is at the lower end of the typical operating range. Other operating regimes, see Table 2, covered \(Re=2000\) and \(4000\), which is close to maximal pressure with respect to atomizer body strength. This range of \(Re\) values corresponds to the wide range of \(p_{l}\) from \(0.5\) to \(8\) MPa for the originally sized (10-times smaller) atomizer using JET A-1.  
The operating liquid was p-cymene (1-Methyl-4-(propan-2-yl) benzene). It is one compound, colourless liquid, whose value of refractive index \(n=1.49\) is closely matched to the atomizer body which simplifies the optical measurement and reduces the measurement errors. The physical properties of the p-cymene at room temperature are as follows: surface tension \(\sigma=0.028\) kg/s\({}^{2}\), liquid dynamic viscosity \(\mu_{l}=0.00085\) kg/(m\(\cdot\)s), and liquid density \(\rho_{l}=848\) kg/m\({}^{3}\). The identical test bench as in [25] was used. The uncertainty in pressure sensing was \(0.05\) kPa and the calculated uncertainty of \(C_{D}\) was \(0.25\%\) of the actual value.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(Re\) & \(p_{l}\) & \(\dot{m}_{p}\) & \(v_{p}\) & \(C_{D}\) & \(Fr\) & \(d_{aco}\) & \(d_{acc8}\) \\ \hline
**[–]** & **[kPa]** & **[kg/h]** & **[m/s]** & **[–]** & **[–]** & **[mm]** & **[mm]** \\ \hline \(1000\pm 10\) & 2.1 & 47.5 & 0.206 & 0.420 & 5.0 & 2.82 & 1.55 \\ \hline \(2000\pm 10\) & 8.8 & 94.8 & 0.411 & 0.410 & 9.9 & 2.99 & 1.64 \\ \hline \(4000\pm 10\) & 36.3 & 188.4 & 0.816 & 0.401 & 19.6 & 3.20 & 1.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of operating regimes.  
Figure 1: Atomizer schematic drawing with main dimensions in millimetres.
## 2 Materials and Methods
### Satellite Constellation and RadCalNet Sites
#### 2.1.2 RadCalNet Sites  
In this study, we used four RadCalNet sites (i.e., Baotou, Gobabeb, La Crau, and Railroad Valley) [10]. The calibration product (i.e., BOA reflectance and atmosphere parameters) published by RadCalNet was downloaded and processed from December 2019 to December 2021. Detailed information regarding the four RadCalNet sites is shown in Table 2, and the selected regions of interest (ROIs) in the calibration sites are shown in the yellow boxes in Figure 5.  
Figure 6 shows the BOA reflectance provided by RadCalNet (located on website: [[https://www.radcalnet.org/#](https://www.radcalnet.org/#)]([https://www.radcalnet.org/#](https://www.radcalnet.org/#))!, accessed on 12 July 2022), which was used in this study to simulate the TOA radiance and the atmosphere parameters shown in Table 3. As shown  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Filed** & **Abbr** & **Lon** & **Lat** & **DEM (km)** & **Type** \\ \hline Baotou, China & BTCN & \(109.615^{\circ}\) & \(40.865^{\circ}\) & \(1.301\) & desert \\ Gobabeb, Namibia & GBNA & \(15.120^{\circ}\) & \(-23.600^{\circ}\) & \(0.510\) & desert \\ La Crau, France & LCFR & \(4.860^{\circ}\) & \(43.559^{\circ}\) & \(0.020\) & grassland \\ Railroad Valley, United States & RVUS & \(-115.69^{\circ}\) & \(38.497^{\circ}\) & \(1.435\) & desert \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration site information.  
Figure 4: Integral imaging principle. (**a**) is the first line of the sensor in a certain spectral band to image object A; (**b**) is with the movement of the satellite, the first line is to image object B, and the second line is to image object A; (**c**) is the image of object A with the movement of the satellite. For the movement of the satellite, the first line images object C, the second line images object B, and the third line images object A, and so on, to achieve eight integral images of the same scene in each line.  
Figure 5: Four RadCalNet sites and corresponding regions of interest.  
in Figure 6, the BOA reflectance in RVUS is very high: it is higher than 0.4 at 700 nm. The LCFR site has the lowest surface reflectance, and the reflectance of the other two fields (i.e., BTCN and GBNA) falls between these values. In terms of the atmospheric parameters, the threshold of AOD was set to 0.2, which means that the corresponding date of AOD below this threshold value was used in the following calibration procedure.
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 2 Oc-Smart
### Algorithm Validation  
The performance of our MLNN algorithms was first evaluated with a synthetic dataset. We randomly selected 90% of the synthetic dataset to train the (\(R_{\rm rs}\)) neural networks, the remaining 10% of the data were used to evaluate the trained neural networks. For the \(R_{\rm rs}\) MLNN the \(R^{2}\) was higher than 0.993 for all visible bands (412, 443, 488, 531, 547, 667, and 678 nm) and the APD was \(<\)5.4%. These results imply that the \(R_{\rm rs}\) MLNN algorithms have learned the radiative transfer inversion process very accurately. In other words, they have learned the spectral relationship between \(L_{rc}(\lambda)\) and \(R_{\rm rs}(\lambda)\). The \(R_{\rm rs}\) MLNN algorithm was also validated by comparing its retrieval performance to SeaDAS NIR, SeaDAS NIR/SWIR, and C2 RCC algorithms for a large set of ocean color data. For water-leaving radiance retrievals, the MLNN algorithm has the best performance, especially in the blue bands. On average, at 412 nm, the MLNN algorithm reduced APD by 13.4%, 9.6%, and 8.8% comparedwith the NIR, NIR/SWIR, and C2 RCC algorithms, respectively. Furthermore, the NIR algorithm produced more than 17% negative \(nL_{w}\) values in blue and red bands and the NIR/SWIR algorithm produced approximately 15% negative \(nL_{w}\) values in those bands. In contrast, our neural network-based MLNN (OC-SMART) algorithm and the C2 RCC algorithm did not produce any negative \(nL_{w}\) values.
## 3 Experimental Program
### Test Setup  
The measurement system was selected according to the following main behavioral features of a specimen that needed to be obtained:  
* Loading force;
* Longitudinal deformations;
* Transverse deformations.  
To conduct the axial compression test, the class 1 testing machine (with a maximum error of 1% FS) and a maximum load of 3000 kN, which was controlled by a PC-unit, was used. The applied load was measured by an external pressure sensor, which had been installed in the oil line of the testing machine.  
Four linear displacement transducers (LDT) with a 50-mm stroke and a high-measure resolution (\(<1\) um) were used to measure the longitudinal deformations. The sensors were located in the specimen's central zone in order to exclude the influence of the edge effect associated with the friction forces occurring at the contact area between the specimen and the testing machine plates. The base of the longitudinal deformation measurement was 15 cm. The upper view of the tested specimen and the measurement devices was given in Figure 1. Figure 2 shows the location of the devices on the specimen's surface development.  
Figure 1: Measurement device position scheme: 1. Cylindrical concrete specimen; 2. LDT devices; 3. Strain-gauge (SG) sensors; A, B, C, and D-sectors.  
Figure 2: Specim surface development: 1. LDT devices; 2. SG sensors; A, B, C and D-sectors (according to Figure 1).  
As mentioned above, there was a technological eccentricity problem caused by inaccuracy in the applied force transmission. This problem was solved by using four sensors located on the tested specimen's opposite sides (see Figure 1). Such sensor distribution allowed more accurate data to be obtained that revealed the presence of eccentricity in stress distribution. The LDT sensors were attached by originally developed mounts, allowing independent deformation transfer to each sensor.  
Two strain-gauges (SG) with a 60 mm basis, a measure resolution up to 0.1 microstrains, a resistance of 120 Ohm, and a gauge factor of 2.07 were used to measure the transverse deformations. The SG length was chosen based on the requirement of exceeding the coarse aggregate maximum dimensions by at least three times [21]. The general view of the test setup is presented in Figure 3.  
A thin layer of sifted fine grain sand was used to level the upper surface of the cylindrical specimens. It also allowed for a reduction in the friction effect between the testing machine plate and the specimens.
## 2 Antenna Design
### Antenna Parametric Study
#### 2.2.2 Parametric Study on the PIN Diode Equivalent Model  
In this section, a parametric study on the values of the equivalent model is performed to understand their effects on the S11 response of the antenna. The equivalent model of the PIN diode consists of an inductor, in series with a resistor for the ON state, as shown in Figure 3a, and an inductor in series with a capacitor in parallel with a resistor in the OFF state, as shown in Figure 3b [23; 24].  
Figure 6b shows the S11 response for different values of the equivalent parallel capacitor of the PIN diode with Rp = 15 K\(\Omega\) pF, L1 = 0 nH, C1 = 0 pF, L3 = 17 nH, L4 = 2 nH, l\({}_{1}\) = 14.3 mm and l\({}_{2}\) = 23 mm. We see that the parallel capacitor has a direct impact on the resonance frequency (868 MHz) and a slight impact on the lower frequency (401 MHz). When the capacitor value increases, the resonant frequencies shift to lower frequencies. Figure 6a shows the input reflection coefficient response at different value of the reverse parallel resistor Rp with Cp = 0 pF, L1 = 0 nH, C1 = 0 pF, L3 = 17 nH, L4 = 2 nH, l\({}_{1}\) = 14.3 mm and l\({}_{2}\) = 23 mm. The results show that a high equivalent resistor of the PIN diode in the OFF state helps to obtain a good impedance matching for both lower and higher frequencies, Figure 6a.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 4 Health Monitoring Techniques for Wind Farms
### Health Monitoring System of the Tower and Foundation  
The problems and threats that the support pillars of the wind turbine face are pretty different from the ones that the blades face. The major reason is that the pillar has to bear a specific weight on the same place for a long time. It has to be robust to continue to carry the maximum weight without faltering, because even though its monitoring may  
Figure 15: SCADA and CMS systems common description and specifications of use.  
require a different method, its safety is as important as that of the blade. If there is even a small imbalance in the support or a change in its stress-bearing capacity, it could lead to a serious flaw in the calculations and subsequently the wind energy productions. In addition, monitoring of the blades will be affected by a fault in the support.  
The wind turbines that are used in off-shore applications were developed much later, and due to that reason, their monitoring systems are much more effective. This is because with older projects, the structural designs and aerodynamic properties could not be optimized to fit in a proper monitoring system. However, due to being built later with the idea of the advanced monitoring system already in the works, their design is good and accommodating. The SCADA system has been utilized, but it is not very effective when it comes to a decreased sampling frequency [182, 183].  
For overcoming this problem, the wireless network sensors were utilized that had the range of 50 to 150 Hz in three intervals for sampling purposes, which allowed total offline modal analysis of the towers. The SESHMS is a comparatively more economical system that was released as a further development. They have established small programmable Object-Oriented Technology using software to wirelessly transfer data to neighboring receiver nodes. It smartly used the DSL system for the study of dynamic data of displacement, temperature, acceleration, and wind speed during the same time period for even better monitoring and estimation of lifetime of the tower and foundation [184, 185, 186, 187, 188].  
In latest trend is the smart wireless networks, which have different modules for identification, authentication, and evaluation. This provides information to detect the extent and location of the damage very precisely. The foundation and tower mostly act in the same way, and therefore, it has been observed that the newer technology develops a monitoring system to be able to synchronously detect the conditions of both the components. [189, 190, 191, 192]. These discussed systems aim to determine the integrity of the tower and foundation of the wind turbine, as shown in Figure 15.
## 3 The Application of a Linear Array for the Identification of LE and TE Noise Source
### LE and TE Noise Sources Identification  
The NACA65(12)-10 blade LE and TE noise are tested with various inflow velocities at an angle of attack of 0 degrees. The flow speeds are of 20-70 m/s, and the corresponding Reynolds numbers are 200,000-700,000. Figure 8(a) shows the beamforming results based on the Clean-SC method. It can be seen that very \"clean\" noise source identification is obtained with the present array method. The wind tunnel jet noise source (WT noise), and the LE and TE noise sources are all well identified. In addition, Clean-PSF algorithm was also employed to localize the LE and TE noise sources, and the results were compared with that from the Clean-SC algorithm. As shown in Figure 8(b), the results obtained by the two algorithms are slightly different.  
Figure 8: Microphone array and the test setup.  
Figure 10 is a comparison of the sound source identification results between Clean-PSF algorithm and Clean-SC algorithm. It can be observed that the results with Clean-PSF algorithm have more side-lobes compared to that of the Clean-SC algorithm. There are two obvious side-lobes at \(x=-0.32\) m and \(x=-0.18\) m near the source of wind tunnel in the results with Clean-PSF algorithm. The side-lobes might be due to the fact that the jet noise is a distributed noise source. Further detailed comparison of Figure 10 shows that the noise level of the sound source identified by Clean-PSF algorithm is about 2 dB higher than that identified by Clean-SC algorithm.  
Figure 10: Comparison of the sound source identification results between Clean-PSF algorithm and Clean-SC algorithm.  
Figure 9: Noise maps of the blade test for U = 40 m/s.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### Off-Chain Data Storage Methods
#### 2.2.1 Centralized Data Storage Systems  
Centralized systems refer to data storage methods where information is maintained on traditional servers and cloud infrastructures. These systems offer high availability, reliability, and scalability. However, their primary drawback is dependence on a central authority.  
* _Cloud Storage Systems_: Cloud systems facilitate the storage of data in large-scale data centers, typically managed by a centralized authority. Leading cloud providers such as Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure are key examples of this infrastructure. These systems provide fast access, high performance, and seamless integration with a broad range of services. They excel in reliability and scalability. However, dependence on a central entity limits data control, leading to higher costs and external security risks. Additionally, a centralized structure introduces a single point of failure, posing a risk to data continuity and security [82].  
Despite the advantages of centralized systems, blockchain technology, which is built on the principle of decentralization, is shifting toward distributed data storage solutions to ensure enhanced security, transparency, and autonomy.
## 3 Optimization Model
#### Constraints of FC  
For the smooth operation of the FC, the rate of change in its output power is subjected to ramp rate constrains.  
\[P_{FC,i}-P_{FC,i-1}<\Delta P_{FC_{up}} \tag{18}\]  
\[P_{FC,i-1}-P_{FC,i}<\Delta P_{FC_{dn}} \tag{19}\]  
Similarly, FC output is also bound by the minimum and maximum limits of power generation.  
\[P_{FC_{min}}<P_{FC,i}<P_{FC_{max}} \tag{20}\]  
where  
\(\Delta P_{FC_{up}},\Delta P_{FC_{dn}}\) FC ramp up, ramp down rates  
\(P_{FC_{min}},P_{FC_{max}}\) FC minimum, maximum power limit
# Reliability-Based View Synthesis for Free Viewpoint Video
###### Abstract  
View synthesis is a crucial technique for free viewpoint video and multi-view video coding because of its capability to render an unlimited number of virtual viewpoints from adjacent captured texture images and corresponding depth maps. The accuracy of depth maps is very important to the rendering quality, since depth image-based rendering (DIBR) is the most widely used technology among synthesis algorithms. There are some issues due to the fact that stereo depth estimation is error-prone. In addition, filling occlusions is another challenge in producing desirable synthesized images. In this paper, we propose a reliability-based view synthesis framework. A depth refinement method is used to check the reliability of depth values and refine some of the unreliable pixels, and an adaptive background modeling algorithm is utilized to construct a background image aiming to fill the remaining empty regions after a proposed weighted blending process. Finally, the proposed approach is implemented and tested on test video sequences,  
The geometric misalignment between foreground objects and background regions causes boundary noise and wrapping position displacement. The projected float results lead to rounding errors, which further cause pinholes, cracks, and blurs. In addition to these artifacts, a critical problem also arises during the generation of virtual images, since the regions covered by foreground objects in the reference views may be disoccluded in the virtual viewpoints, and these areas will appear as large holes in the virtual view, also referred to as disocclusions.  
In this paper, we propose a novel synthesis framework using two reference viewpoints. This method first determines whether the pixel-wise depth map is reliable or unreliable, then refines some of those unreliable depth values. Second, an adaptive background modeling method is employed to construct background information, aiming to fill the remaining empty regions after a proposed weighted blending. In addition, the proposed adaptive median filter and depth map processing method (DMPM) show satisfactory performance on reduction of noise and other unwanted effects while the texture images remain sharp and clear. This improvement comes from their focus on processing the target pixels instead of handling every pixel in the intermediate texture images. The paper is organized as follows. The related classical algorithms are discussed in Section 2. In Section 3, the proposed framework is described in detail. The experimental results and conclusions are provided in Sections 4 and 5, respectively.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
###### Abstract  
Real-time monitoring of torque in a rotating shaft is not easy to implement with technologies such as optic fiber sensors or strain gages. Surface acoustic wave (SAW) sensors are wireless and passive and can be used to monitor strain in moving parts. Commercial solutions (sensors, antennas and interrogation unit) can easily be purchased from some companies; however, they are not customized and may not meet the specificity of the measurements. In order to evaluate the adequacy of commercial off-the-shelf (COTS) solutions, temperature and strain sensors fabricated by SENSeOR (Besancon, France) were mounted on a load cell. The sensors were calibrated using a thermal chamber and a universal testing machine. The load cell was then assembled together with a steel shaft that rotated at different speeds inside an oven. The commercial antennas were replaced with an RF (radio frequency) coupler and the sensors were interrogated with the commercial interrogation unit. The influence of rotation in the accuracy on the measurements, as well as the adequacy of the sensors structure, was evaluated. It can be concluded that SAW sensors can be used to measure temperature or torque in a rotating environment; however, some customization of the components is required in order to overcome the limitations posed by COTS sensing solutions.  
SAW sensor; rotating shaft; torque measurement +
Footnote †: journal: Sensors
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 3 Method
### Constrained Inverse Differential Kinematics
#### 3.2.1 Joint Limit Avoidance  
We start considering the simple case where we want to constraint a 1-DoF joint within its position limits \(p_{min}\leq p\leq p_{max}\), and for which we define also a maximum and minimum velocity \(\dot{p}_{min}\leq\dot{p}\leq\dot{p}_{max}\). In order to write all the constraints as velocity constraints, the idea is to limit the joint velocity as the position gets closer to its limits. This behaviour can be modelled using the hyperbolic tangent function, and defining the velocity limits as:  
\[\tanh(k_{g}^{min}(p-p_{min}))\dot{p}_{min}\leq\dot{p}\leq\tanh(k_{g}^{max}(p_{ max}-p))\dot{p}_{max}. \tag{12}\]  
where \(k_{g}^{min}\) and \(k_{g}^{max}\) are positive gains regulating the slope of the hyperbolic tangent, and \(\tanh(\cdot)\) indicates the _hyperbolic tangent_ function. When the joint is far from its limit, the argument of \(\tanh(.)\) is positive and far from zero, hence the hyperbolic tangent is \(\approx 1\) and Equation (12) simply express the velocity limits. Instead, when the joint approaches the limits, the hyperbolic function goes to zero bounding the joint velocity to be positive, when approaching the lower joint limit, and negative when approaching the upper joint limit.