esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 3 Experimental Methods
### Data Acquisition and Processing System  
In the experiment, the rotational angular displacement \(\theta\) was measured by a magnetic-sensitive angular displacement sensor. The measurement method involved connecting the rotating shaft of the sensor with the upper rotary shaft, \(P\), by using shaft-coupling to ensure that they rotated synchronously, as shown in Figure 8b. The sensor could output a voltage signal proportional to the rotational angle, and the instantaneous voltage signal was captured by the data acquisition system, shown in Figure 8c. Finally, MATLAB software was used to process the recorded data. The sampling frequency was set to 50 Hz. Additionally, the oscillation frequency (\(f_{osc}\)) of the FIR system was calculated by the Fast Fourier Transform (FFT) method under continuous oscillation for 60 s [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**L/D** & **\(\mathbf{J_{osc}}\) (kg\(\cdot\)m\({}^{2}\))** & **\(\mathbf{J_{d}}\) (kg\(\cdot\)m\({}^{2}\))** & **\(\mathbf{J^{*}}\)** \\ \hline
4.0 & 1.443 & 0.583 & 2.475 \\
4.5 & 1.602 & 0.733 & 2.186 \\
5.0 & 1.779 & 0.901 & 1.974 \\
5.5 & 1.974 & 1.087 & 1.816 \\
6.0 & 2.188 & 1.290 & 1.696 \\
7.0 & 2.673 & 1.750 & 1.527 \\
8.0 & 3.231 & 2.280 & 1.417 \\
9.0 & 3.865 & 2.881 & 1.342 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parametric details of the experiments in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Symbol** & **Upstream Cylinder** &
\begin{tabular}{c} **Downstream** \\ **Cylinder** \\ \end{tabular} \\ \hline Diameter & \(D\) [m] & 0.1 & 0.1 \\ Height & \(H\) [m] & 0.9 & 0.9 \\ Thickness & \(d\) [m] & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of the circular cylinder.  
Figure 7: Test circular cylinders.
# Tectonic Subsidence and Its Response to Geological Evolution in the Xisha Area, South China Sea
###### Abstract  
The evolution and mechanisms of tectonic subsidence in the Xisha area are poorly investigated, especially the spatiotemporal distribution features and reasons for the variations in tectonic subsidence. In this study, multi-channel seismic data and stratigraphic and lithologic features of wells are used to examine tectonic subsidence in the Xisha area from the Paleogene to Quaternary. The largest tectonic subsidence in the Xisha area is located in the Changchang Depression, with a maximum subsidence of 5.4 km, while the smallest tectonic subsidence is located on the Guangle Uplift and Xisha Uplift, which are close to 1.0 km and 1.5 km, respectively. Two rapid tectonic subsidence phases were mainly in the Oligocene, and from Middle to Late Miocene, with maximum subsidence rates of 0.45 m/k and 0.32 m/k, respectively. Five phases for the tectonic subsidence are proposed since the Paleogene based on our data. (1) The slow subsidence phase during the Eocene (53.5-32 Ma) was due to the transchronicity of the basement in the pro-rifted stage. (2) The rapid subsidence phase was common in the south and north margins of Qiongdongnan Basin, because of the faults triggered by the inherited stretched and thinned of crust in the Oligocene from 32 to 23.3 Ma. (3) The interim phase followed the rapid subsidence phase was in the Early Miocene (23.3-15.5 Ma) and marked the end of the rifted stage. (4) The accelerated rise phase started from the Middle Miocene (15.5 Ma) to the Late Miocene (5.5 Ma), and the reversal of the Red River Fault Zone may be tied to the acceleration of the tectonic subsidence. (5) The transitional phase started in the Pliocene (5.5 Ma) and lasts to the present. As the Red River Fault Zone changed from sinistral to dextral movement, the stress field of the study area has changed. Our results are helpful to better understand the spatiotemporal coupling relationship between tectonic subsidence and regional geological evolution in the Xisha area, South China Sea.  
Xisha area; tectonic subsidence; South China Sea seafloor spreading; Red River Shear Zone
## 3 Experimental Results
### Dynamic SSSI Effects on S1 Structure
#### 3.2.2 SSSI Effects on Responses of S1 in the Time Domain  
There are several parameters to evaluate structural response in the time-domain. Although peak acceleration (PA) is considered as a simplified parameter to indicate earthquake intensity, arias intensity (I\({}_{\text{a}}\)) is reconcized as more vigorous because it shows total energy of excitation with all recorded data points in the time domain [31; 46]. Arias intensity can be calculated based on the acceleration-time history (\(a_{l}\)), time period (\(dt\)), and acceleration of gravity (\(g\)) using following equation:  
\[\text{I}_{\text{a}}=\frac{\pi}{2g}\int_{0}^{\infty}a_{t}^{2}\;dt \tag{4}\]  
Figure 8 illustrates normalized I\({}_{\text{a}}\) in SSSI testing cases to the reference case (i.e., I\({}_{\text{a}}\)/I\({}_{\text{a,Ref}}\)) in M_Input level for S1-S2 and S1-S3. the average results, measured by two input motions, were plotted with error bars reflecting variations of two input motions. As shown in Figure 8, a reduction in total  
\begin{table}
\begin{tabular}{c|c c c|c c c} \hline \hline \multirow{2}{*}{**APSD\({}_{\text{S1}}\) (\%)**} & \multicolumn{4}{c|}{**Artificial EQ**} & \multicolumn{4}{c}{**Hachinohe EQ**} \\ \cline{2-7}  & **L\_Input** & **M\_Input** & **H\_Input** & **L\_Input** & **M\_Input** & **H\_Input** \\ \hline \multicolumn{7}{c}{S1-S2 case} \\ \hline B-Dist & \(-\)30.30 & \(-\)27.18 & \(-\)18.54 & \(-\)22.90 & \(-\)22.81 & \(-\)17.40 \\
0.5B-Dist & \(-\)9.88 & \(-\)7.20 & \(-\)0.77 & \(-\)13.21 & \(-\)3.31 & \(-\)3.83 \\
0.03B-Dist & \(-\)21.61 & \(-\)8.05 & \(-\)6.97 & \(-\)1.31 & \(-\)1.48 & \(-\)10.16 \\ \hline \multicolumn{7}{c}{S1-S3 case} \\ \hline B-Dist & \(-\)9.17 & \(-\)6.35 & \(-\)2.31 & \(-\)23.72 & \(-\)8.15 & \(-\)19.48 \\
0.5B-Dist & \(-\)5.91 & \(-\)2.01 & \(-\)0.45 & \(-\)15.37 & \(-\)4.61 & \(-\)3.54 \\
0.03B-Dist & \(-\)2.99 & \(-\)0.01 & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage change of total power spectra density (\(\Delta\)PSD) of S1 structure for a series of tests with S2 and S3 structures placed beside.  
response of S1 structure was observed, not only at the foundation, but also at both wall and roof motions, due to appearance of S2 or S3 structure. The most substantial decrement in S1 structure response was experienced in the test of B distance between the two structures, which had normalized a  
Figure 8: Normalized I\({}_{\text{a}}\) of S1 foundation (F), wall (W), and roof (R) in case of interaction with (**a**) S2 structure and (**b**) S3 structure to I\({}_{\text{a}}\) of S1 structure in the reference case. Average value from two input motions was plotted. Error bars indicate variations of two motions.
## 4 Results
The remaining fractions, i.e., oxygenated sesquiterpenes, aromatic compounds, alcohol and aliphatic aldehydes, formed the minor classes, as shown in Table 2, and, with the exception of the latter, they were not affected by treatments. \begin{table}
\begin{tabular}{l c c c c} \hline  & & & **NaCl (mM)** & \\ \hline
**Grouped Compounds** & **0** & **50** & **100** & **150** \\ \hline Monoterpene hydrocarbons & 13.71 \(\pm\) 0.61 \({}^{\text{a}}\) & 12.21 \(\pm\) 0.55 \({}^{\text{ab}}\) & 11.84 \(\pm\) 0.41 \({}^{\text{b}}\) & 10.83 \(\pm\) 0.36 \({}^{\text{b}}\) \\ Oxygenated monoterenes & 41.52 \(\pm\) 0.32 \({}^{\text{a}}\) & 40.82 \(\pm\) 0.25 \({}^{\text{ab}}\) & 39.56 \(\pm\) 0.21 \({}^{\text{b}}\) & 38.95 \(\pm\) 0.1 \({}^{\text{b}}\) \\ Sesquiterpene hydrocarbons & 3.92 \(\pm\) 1.02 \({}^{\text{c}}\) & 5.92 \(\pm\) 1.51 \({}^{\text{b}}\) & 7.45 \(\pm\) 1.78 \({}^{\text{a}}\) & 7.16 \(\pm\) 1.65 \({}^{\text{a}}\) \\ Oxygenated sesquiterpenes & 0.13 \(\pm\) 0.04 \({}^{\text{a}}\) & 0.15 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.12 \(\pm\) 0.23 \({}^{\text{a}}\) & 0.13 \(\pm\) 0.2 \({}^{\text{a}}\) \\ Aromatic compound & 5.23 \(\pm\) 0.24 \({}^{\text{a}}\) & 5.25 \(\pm\) 0.06 \({}^{\text{a}}\) & 6.68 \(\pm\) 0.14 \({}^{\text{a}}\) & 6.05 \(\pm\) 0.16 \({}^{\text{a}}\) \\ Alcohol & 0.23 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.25 \(\pm\) 0.07 \({}^{\text{a}}\) & 0.21 \(\pm\) 0.15 \({}^{\text{a}}\) & 0.26 \(\pm\) 0.20 \({}^{\text{a}}\) \\ Aliphatic aldehydes & 1.35 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.20 \(\pm\) 0.04 \({}^{\text{b}}\) & 1.16 \(\pm\) 0.00 \({}^{\text{b}}\) & 1.13 \(\pm\) 0.02 \({}^{\text{b}}\) \\ \hline \end{tabular} Values followed by different letter are significantly different at \(p<\) 0.05.  
\end{table} 
Table 2: Percentage of the different grouped compounds of _Laurus nobilis_ volatile compound extracted from leaves under salt stress. Data are presented as mean \(\pm\) SE.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 2 Materials and Methods
### Periodic Machine DQA Tests
#### 2.3.4 Variable Aperture Iris(tm) Collimator Fields Size  
The CyberKnife(r) used in this study is equipped with a secondary collimator named Iris(tm) with a computer-adjustable aperture. The Iris(tm) collimator mimics the same 12 openings as the fixed collimators thanks to two offset overlaid tungsten segments that allow a quick adjustment of the aperture. The segments are hexagonal shaped with an offset providing a 12-sided opening. All segments are driven by a single motor and the manufacturer suggests performing a monthly field size check to evaluate reproducibility.  
At the authors' Institute, this is performed with the manufacturer-provided Iris QA Tool, designed to offer a film-based alternative to the use of a water phantom. Before running periodic checks, a baseline measurement was carried out at the time of the collimator installation to determine the reference field sizes by averaging the corresponding values from 4 film shots for each Iris aperture and for the 15 mm fixed collimator used as reference. The Iris QA software determines an optical density (OD) threshold value corresponding to a computed field size for the 15 mm cone that matches its nominal size of 15 mm. This threshold is then used to extract the field size from irradiated films, exploiting two different approaches:  
1. an Equivalent Diameter (ED) calculation: \[\mathit{field\ size}\ =\ \sqrt{4\times\frac{Area}{\pi}}\] (1)
2. a Profile Diameter (PD) calculation where the field size is determined based on the average of 12 profiles taken at equally spaced angles around the centroid of the thresholded image.  
The baseline calculations at the time of the collimator installation for all field sizes over the 4 irradiated films gave a range of variability of 0.4 mm. In the authors' Institute, all Iris constancy QA checks are performed by averaging the ED and PD calculations from two film shots for a given Iris aperture. The result is then compared to the baseline measurement.  
With myQA(r) SRS the estimate of the diameter was carried out by orthogonally delivering onto the detector array the Iris(tm) beams corresponding to the 12 apertures(SAD = 800 mm). The data collected for each aperture were analyzed using a homemade MATLAB ([[https://it.mathworks.com/products/matlab.html](https://it.mathworks.com/products/matlab.html)]([https://it.mathworks.com/products/matlab.html](https://it.mathworks.com/products/matlab.html))) (accessed on 4 April 2022) script through the following steps:  
1. Identification of the center of the dose distribution by searching the maximum signal of the image;
2. Radial sampling of the distribution with an angular step = 1\({}^{\circ}\) in a range between 0 and 360\({}^{\circ}\);
3. Calculation of the Full Width at Half Maximum (FWHM) for each radial profile (Figure 2);
4. Measure of the field size obtained as average of the FWHMs.  
The beam delivery and analysis procedures were repeated 3 times for each field size.
## 3 Position Tracking Controller Design
### Desired State Design  
From the PMSM dynamics (2), the desired PMSM dynamics for the desired position \(\theta_{r}\) can be obtained as  
\[\dot{\theta}_{r}= \omega_{r} \tag{5}\] \[\dot{\omega}_{r}= -\frac{3}{2}\frac{p\Phi}{J}i_{\alpha},\sin(p\theta_{r})+\frac{3}{2 }\frac{p\[PERSON]}{J}i_{\beta_{r}}\cos(p\theta_{r})-\frac{B}{J}\omega_{r}-\frac{ \tau_{L}}{J}\] \[i_{\alpha_{r}}= -\frac{R}{L}i_{\alpha_{r}}+\frac{p\[PERSON]}{L}\omega_{r}\sin(p\theta _{r})+\frac{1}{L}v_{\alpha_{r}}\] \[\dot{i}_{\beta_{r}}= -\frac{R}{L}i_{\beta_{r}}-\frac{p\[PERSON]}{L}\omega_{r}\cos(p\theta_ {r})+\frac{1}{L}v_{\beta_{r}}\]  
where \(\omega_{r}\) is the velocity, and \(i_{\alpha_{r}}\), \(i_{\beta_{r}}\), \(v_{\alpha_{r}}\), and \(v_{\beta_{r}}\) are the desired phase currents and voltages, respectively. In \(\omega_{r}\) dynamics of (5), the term \(-\frac{3}{2}\frac{p\Phi}{J}i_{\alpha_{r}},\sin(p\theta_{r})+\frac{3}{2}\frac{p \Phi}{J}i_{\beta_{r}}\cos(p\theta_{r})\) corresponds to the desired torque \(\tau_{r}\), which is yet to be defined. To obtain \(\tau_{r}=-\frac{3}{2}\frac{p\[PERSON]}{J}i_{\alpha_{r}}\sin(p\theta_{r})+\frac{3}{2 }\frac{p\[PERSON]}{J}i_{\beta_{r}}\cos(p\theta_{r})\), the desired currents \(i_{\alpha_{r}}\) and \(i_{\beta_{r}}\) are derived as  
\[i_{\alpha_{r}} =-\frac{2\tau_{r}}{3p\[PERSON]}\sin(p\theta_{r}) \tag{6}\] \[i_{\beta_{r}} =\frac{2\tau_{r}}{3p\[PERSON]}\cos(p\theta_{r}).\]  
Substituting (6) into \(\omega_{r}\) dynamics of (5), the dynamics of \(\omega_{r}\) becomes  
\[\dot{\omega}_{r}= \frac{3}{2}\frac{p\Phi}{J}\tau_{r}-\frac{B}{J}\omega_{r}-\frac{ \tau_{L}}{J}. \tag{7}\]  
Figure 1: Block diagram of the general control method using DQ transformation.  
From the desired PMSM dynamics (5), the desired voltages \(v_{\alpha_{r}}\), and \(v_{\beta_{r}}\) are obtained as  
\[\begin{split} v_{\alpha_{r}}&=Ri_{\alpha_{r}}-p\Phi \omega_{r}\sin(p\theta_{r})+L\dot{i}_{\alpha_{r}}\\ v_{\beta_{r}}&=Ri_{\beta_{r}}+p\Phi\omega_{r}\cos(p \theta_{r})+L\dot{i}_{\beta_{r}}.\end{split} \tag{8}\]
## 1 Introduction
There are several control strategies for the planar \(n\)R underactuated robot, which center on the structural characteristics and the controllability of the planar 2R underactuated robots. For the system with the passive last joint, the original system is reduced to a planar virtual Pendubot [42], and the iterative steering method is designed to stabilize the whole system. For the planar 4R underactuated robot, [PERSON] [53] divides the original system into three planar virtual Acrobot-like systems to achieve the stable control of the original system. For the planar 3R underactuated robot with the passive first joint, [PERSON] [54] presents a switch control strategy by utilizing the planar Acrobot and its holonomic constraints. For the planar 3R underactuated robot with the passive middle joint, [PERSON] [46] proposes a control method by using the characteristics of the virtual Pendubot and the virtual Acrobot. According to analyses, the current control methods of the planar R-type underactuated robots are only suitable for these special systems. However, there are three aspects that are not considered in the existing methods: (1) the methods are unsuitable in some cases, mainly because each system has different characteristics; (2) the case in which the beginning velocity is nonzero is rarely considered; and (3) the disturbances are typically not taken into account. Hence, we present a general control method for the planar R-type underactuated robot, which can be applied to the stable control of the planar underactuated system in a weightless environment such as space and deep sea. The control program for the actuated link includes two stages. In the first stage, we design the trajectory from the states of the actuated link and establish the controller to span the planned trajectory to realize the stable control of the actuated link. In the second stage, the trajectory with adjustable parameters is planned for the actuated link. Meanwhile, the adjustable parameters are calculated with a differential evolution algorithm [55,56] and the underactuated constraints. Then, the controller spans the planned trajectory to realize the objectives of the actuated link and underactuated link. The proposed methods were employed for a planar 2R underactuated robot, and the performance was verified through simulations. The significant contributions of this work are follows:  
1. A general control method is proposed for a planar 2R underactuated robot. 2. This general control method can realize stable control when the initial velocity is nonzero for the planar 2R underactuated robot. 3. The proposed method improves the interference immunity of the planar R-type underactuated robot. 4. The proposed control method for the planar 2R underactuated robot may be widely applied to the other planar R-type underactuated robots with different degrees of freedom.
# The Performance of LiF:Mg-Ti for Proton Dosimetry within the Framework of the MoVe IT Project
###### Abstract  
Proton therapy represents a technologically advanced method for delivery of radiation treatments to tumors. The determination of the biological effectiveness is one of the objectives of the MoVe IT (Modeling and Verification for Ion Beam Treatment Planning) project of the National Institute for Nuclear Physics (INFN) CSN5. The aim of the present work, which is part of the project, was to evaluate the performance of the thermoluminescent dosimeters (TLDs-100) for dose verification in the proton beam line. Four irradiation experiments were performed in the experimental room at the Trento Proton Therapy Center, where a 150 MeV monoenergetic proton beam is available. A total of 80 TLDs were used. The TLDs were arranged in one or two rows and accommodated in a specially designed water-equivalent phantom. In the experimental setup, the beam enters orthogonally to the dosimeters and is distributed along the proton beam profile, while the irradiation delivers doses of 0.8 Gy or 1.5 Gy in the Bragg peak. For each irradiation stage, the depth-dose curve was determined by the TLD readings. The results showed the good performance of the TLDs-100, proving their reliability for dose recordings in future radiobiological experiments planned within the MoVe IT context.  
TLD-100; proton beam radiation; dose verification; proton dosimetry +
Footnote †: journal: Medical Physics  
Article
## 2 Materials and Methods
### Ionograms
While a measure of uncertainty is provided by ARTIST confidence scores [30; 31], it is not clear how these confidence scores map to an uncertainty in electron density as a function of altitude. ARTIST-5 has the ability to provide electron density error bars after error histograms are calculated, but these errors are more associated with the difference between automatically and manually scaled ionograms [32], which does not directly apply to this study which uses solely manually scaled ionograms. Some general electron density uncertainties are provided in the literature for ionogram inversion at E-region altitudes. The authors of [29] provide an \(foE\) uncertainty of \(\pm 0.3\) MHz (\(\sim\)\(1.1\times 10^{9}\) m\({}^{-3}\)), which is lower than the 100 km uncertainty from the GNSS-RO estimates using the bottom-up approach. However, as noted in [33], the assumption of a parabolic shape for E-region Chapman layers rapidly becomes poorer at distances larger than half the scale height away from the E-region peak. This means that the ionogram EDP estimates for this study are most accurate near \(foE\), and have larger uncertainties at lower  
Figure 2: Local solar time histograms for each site. The hand-scaling process naturally filtered out nighttime profiles, and the wide range at Gakona is due to the length of summer days at high latitudes. altitudes. Interestingly, this is the exact opposite trend in uncertainty that is observed for the GNSS-RO EDP estimates.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 2 System Model  
We assume a classic three-node secure communication model, in which the transmitter ([PERSON]) transmits private information to the legitimate receiver ([PERSON]) with the risk of being passively eavesdropped by an illegal eavesdropper ([PERSON]). We assume that [PERSON] is in a state of high mobility and that [PERSON] and [PERSON] are static. All nodes are equipped with a single antenna. Let us assume that each communication link has \(L\) propagation paths. The channel response of the high-mobility channels can be given by  
\[h(\tau,\
u)=\sum_{p=1}^{L}h_{p}\delta\big{(}\tau-\tau_{p}\big{)}\delta\big{(} \
u-\
u_{p}\big{)}, \tag{1}\]  
where \(\tau_{p}=\frac{l_{p}}{M\triangle f}\) and \(\
u_{p}=\frac{k_{p}}{NT}\) denote the delay and the Doppler shift, respectively, and \(h_{p}\) is the Rayleigh fading.  
To overcome the Doppler effect in a high-mobility scenario, [PERSON] transmits signals to [PERSON] based on the OTFS scheme. Specifically, the signal in the DD domain is denoted by \(x[k,l]\), where \(k\in\{0,\cdots,N-1\}\). \(N\) represents the number of Doppler bins, \(l\in\{0,\cdots,M-1\}\), and \(M\) represents the number of Delay bins. Firstly, the symbols \(x[k,l]\) are preprocessed by using the Inverse Symplectic Finite Fourier Transform (ISFFT), thereby deriving the TF domain signal as  
\[X[n,m]=\frac{1}{\sqrt{NM}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}x[k,l]e^{l2\pi\left( \frac{\#k}{N}-\frac{\#l}{M}\right)}, \tag{2}\]  
where \(n\in\{0,\cdots,N-1\}\) and \(m\in\{0,\cdots,M-1\}\). Then, by applying the Heisenberg transform, the signal \(X[n,m]\) is converted into the time domain signal. Assume that the sending and receiving pulses satisfy the bi-orthogonal property. After applying the Wigner transform, the signal in the TF domain received by [PERSON] can be expressed as  
\[Y[n,m]=H[n,m]X[n,m]+W[n,m], \tag{3}\]  
where \(W[n,m]\) is the complex Gaussian distributed noise in the TF domain, and \(H[n,m]=\iint h(\tau,\
u)e^{l2\tau\
u nT}e^{-l2\pi\left(\
u+mAf\right)\tau}d\tau d\
u\). By using the Symplectic Finite Fourier Transform (SFFT), the signal \(Y[n,m]\) in the TF domain is transformed into the signal \(y[k,l]\) in the DD domain:  
\[y[k,l]=\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}Y[n,m]e^{-j2\pi\left( \frac{\#k}{N}-\frac{ml}{M}\right)}. \tag{4}\]