esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Physical Process of the Nozzle Starting  
When the test gas enters the nozzle, the nozzle starts, and an incident shock wave is then generated in the nozzle; that is, the starting shock wave as a result of the much higher reservoir region pressure than the initial pressure in the nozzle. The high-temperature and high-pressure gas in the reservoir region accelerates through expansion in the nozzle and produces a series of left-traveling expansion waves in the expansion section. As the cross-sectional area of the nozzle expansion section increases, the starting shock wave is continuously attenuated, resulting in the left-traveling compression waves. A series of compression waves then coalesce into a shock wave, forming the second shock wave. The second shock wave is a left-traveling wave, but due to the high velocity of the gas in the expansion section, it is carried to the nozzle outlet by the airflow [6]. The second shock wave interacts with the boundary layer, bending inward and forming a local low-velocity zone and oblique shock wave on the wall. Only when the low-velocity zone flows out of the nozzle can the flow field at the nozzle outlet be stable, as shown in Figure 1.  
Since the flow in the center of the nozzle is not affected by the low-velocity zone, the flow field reaches stability first, which is called center startup. Then, as the low-velocity zone moves, the flow field gradually becomes stable from the nozzle center to the wall. When the flow field is stable at the entire nozzle outlet, it is called complete startup.  
In the nozzle starting process, the starting shock wave first contacts the model, followed by the second shock wave, and eventually the left-traveling expansion waves. At the starting process, the flow field in the nozzle is not stable due to the influence of waves, and the test data changes sharply, which is greatly different from the design condition [33]. The period from the time when the gas enters the nozzle to the time when the flow field in the center of the outlet reaches stability is defined as the center startup duration, and the time interval between the starting shock wave and the steady flow flowing through the center of the nozzle outlet is defined as the center instability duration. This represents the time period when the model located at the center of the nozzle outlet is hit by the unstable airflow during the starting process, i.e., the period when the experimental data fluctuates violently. Moreso, according to the moment when the flow field reaches stability at the entire nozzle outlet section, the complete startup duration and the instability duration are correspondingly defined. The gas that can be used for the testing in the shock tunnel is the finite amount of gas in the driven section. The longer the nozzle startup duration, the shorter the effective test duration. Furthermore, the longer the duration of the instability of the starting process, the longer the time period of the impact on the experiment data. Therefore, a shorter duration of both startup and instability is beneficial to the experiment.
# Design of Wideband FMCW Radar Transceiver System Using Photonic Elements
## 4 Conclusions  
This letter proposed a FMCW radar with photonic elements, and the signal generation with the use of photonic elements was performed to obtain wideband characteristics. It was shown by field tests that the proposed radar system is capable of detecting a small drone target at 2.5 km distance, as expected through the budget analysis. In addition, ISAR images of a small drone target with RCS of 0.1 m\({}^{2}\) at 0.33 km distance were acquired. ISAR images were obtained by changing the bandwidth from 500 MHz to 2 GHz, and the ability of acquiring high-resolution ISAR images was observed in order to verify the effectiveness of achieving wideband characteristics of the proposed radar system. The results demonstrated that the proposed transceiver is suitable for the radar system to offer high-resolution ISAR images that can detect desired targets.  
Figure 7: Obtained ISAR images of the drone target using the proposed FMCW radar with photonic elements having bandwidths of (**a**) the field test, (**b**) 500 MHz, and (**c**) 2 GHz.  
**Author Contributions:** Conceptualization, S.J. and S.Y.; methodology, Y.B.; software, Y.B.; validation, S.Y.; formal analysis, Y.B.; investigation, S.J.; resources, S.Y.; data curation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.J. and S.Y.; visualization, S.Y.; supervision, S.Y.; project administration, S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Agency for Defense Development Grant funded by the Korean Government.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Data and Methods
### Coda Attenuation Method  
\(Q_{c}\) analysis as a function of frequency was performed by assuming the single isotropic scattering theory [1; 38]. The theoretical decay rate of the coda energy envelope \(A_{th}=A_{th}(r,f_{c},t)\) is given by [39]:  
\[\ln\frac{A_{th}r}{r_{0}\sqrt{K(a)}}=\ln A_{0}-\frac{\pi f_{c}}{Q_{c}(f_{c})}t_ {L} \tag{1}\]  
where \(r\) is the source to receiver distance (km), \(r_{0}\) is the reference distance (km), \(A_{0}\) is a source term, \(f_{c}\) is the central frequency (measured in Hz), \(K(a)=\frac{1}{a}\,\ln\frac{a+1}{a-1}\), \(a=\frac{t_{L}}{t_{1}}\) with \(t_{s}\) the S-wave travel time, and \(t_{L}\) is the time (measured in s) elapsed from the earthquake  
Figure 2: An overview map of the seismic networks (red triangle of OT and blue triangles for IV) and the selected earthquakes (circle dimension is proportional to magnitude, circle color to depth, as indicated in the legend). The map was produced using the QGIS open-source software [36]. For OT and IV stations, please refer to [37] and [32], respectively.  
origin time, with \(t_{L}>t_{s}\). The observed energy envelope \(A_{obs}=A_{obs}(r,f_{c},t)\) can be computed from a seismogram \(S(t)\) and its Hilbert's transform \(H(t)\):  
\[A_{obs}(r,f_{c},t)=\sqrt{H^{2}(t)+S^{2}(t)}. \tag{2}\]  
The \(Q_{c}^{-1}\) estimation for the investigated area was obtained by filtering the coda waves with a Butterworth two-poles filter in those frequency bands with a central frequency \(f_{c}\) = 1, 2, 3, 4, 5, 6, 8, 10, 12, 14, and 16 Hz, with a bandwidth ranging inside the interval \([\frac{f_{c}}{\sqrt{2}},f_{c}\sqrt{2}]\). Then, we least-squares fitted the envelope \(A_{obs}\) versus the time using Equation (1), with \(r_{0}\) = 1 km. With this procedure, \(Q_{c}^{-1}\) can be estimated as a function of frequency \(f\) at the assigned lapse time window \(t_{L}\)[10]. In fact, even though the coda envelope decay rate is theoretically independent of both the source-receiver distance and of magnitude, it depends on the time \(t_{L}\) elapsed from the event origin time. In addition, [PERSON] and [PERSON] [1] assumed that scattering is a weak process and is not strong enough to generate secondary waves, once they encounter other scatters (Born approximation). Scattered waves are produced once seismic waves encounter heterogeneities, faults, cracks, or irregular topography [40].  
Generally, the quality factor increases with frequency through a power-law relationship between \(Q_{c}\) and \(f_{c}\), due to [PERSON] and [PERSON] [41]:  
\[Q_{c}=Q_{0}\bigg{(}\frac{f_{c}}{f_{0}}\bigg{)}^{\alpha} \tag{3}\]  
where \(Q_{0}\) is a reference value of the quality factor at \(f_{0}\) = 1 Hz, and \(\alpha\) is the frequency dependence exponent, which is close to 1 and varies from one region to another region, on the basis of the heterogeneity of the medium [38]. Equation (3) indicates that the attenuation of seismic waves with the passage of time (i.e., the distance from the source) varies with frequency.
## 4 Link Budget
### Link Budget under Realistic Atmospheric Conditions
#### 4.2.6 Number of Satellite Revisits  
Until now the link budget accounted for the forest detection using only one lidar profile (_i.e._, one satellite revisit). The number of satellite revisits can be increased to improve the probability of good detection. Considering the forest to be stationary, for \(k\) passes of the satellite over the same forest pixel, the probability (\(P\)) of having at least one good detection is given by:  
Figure 14: Blue bars: distribution\({}^{1}\) of total atmospheric optical thickness \(\tau\) at 1064 nm for one revisit cycle (26 days) of the satellite over the two selected areas: Congo basin (Africa, tropical forest) and Lake Baikal (Russia, boreal forest) with 2 satellite orbits (ISS and ICESat). Black curve: cumulative probability density functions (CPDF) of \(\tau\). The number of the lidar shots following ISS/ICESat orbit inside each selected area is given, as well as the corresponding valid value number of \(\tau\) given by MODIS. \({}^{1}\) The distribution is truncated at 4. (a) ISS—Congo basin area; (b) ICESat—Congo basin area; (c) ICESat—Lake Baikal area.  
\[P=1-(1-p)^{k} \tag{13}\]  
Obviously, the number of required revisits \(k\) changes with \(FOT\) values. Taking the previous examples of observations of tropical forest site with \(FOT=3\), if we want a probability of good detection \(P\geq 0.99\) (an arbitrary choice), we need \(k=4\) or 5 when considering a lidar system onboard the ISS or ICESat, respectively.  
This number of revisits is important because it will strongly influence the spatio-temporal resolution of the lidar sampling from a spaceborne platform. There is a compromise to find between the revisit cycle and the sampled forest area. An increase of \(k\) induces a larger distance between satellite ground tracks, unless considering longer integration periods, exceeding a month. For a tropical forest that is not to change much during the year, we can consider a sample with a number of revisits spread over one year. The distance between the ground-tracks will be reduced (\(\sim\)50 km). For forests that change with the season, it will be better not to exceed one month and thus to increase the distance between the ground tracks. Hence, there is a trade-off between the temporal resolution, spatial resolution and payload (telescope size, energy). The solution also depends on the technical capabilities.
# Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways
## 3 Materials and Methods
### Trajectory Oscillation  
The characteristic parameters of each car's lateral motion were determined to examine the trajectory oscillation features at the micro level. Specifically, the lateral amplitude (LA), the distance traveled in the oscillation cycle (DTOC), and the mean speed were included. An example of the lateral oscillation of the trajectory is shown in Figure 3. The causes of the lateral offset of the car can be divided into two types: compulsory (caused by lane changing, Situation 1 in Figure 3) and non-compulsory (caused by lane keeping, Situation 2 in Figure 3). However, only the Model 2 conditions were taken into consideration in this study because the characteristics of trajectory oscillation while maintaining the lane are more effective in analyzing the reasonable lane width and the applicable range.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Road Information**} & \multicolumn{4}{c}{**Sampling Information**} \\ \cline{2-7}
**Location ID** & **Lanes** & **Speed Limit** & **Duration** & **Vehicles** & **Cars** & **Trucks** \\  & **(num)** & **(km/h)** & **(hours)** & **(veh)** & **(veh)** & **(veh)** & **(veh)** \\ \hline
1 & 6 & 120 & 11.07 & 85,962 & 69,751 & 16,211 \\
2 & 4 & No Limit & 0.81 & 3074 & 2400 & 674 \\
3 & 6 & 130 & 0.96 & 3747 & 2710 & 1037 \\
4 & 6 & No Limit & 0.93 & 4751 & 3799 & 952 \\
5 & 4 & No Limit & 2.39 & 10,079 & 8192 & 1887 \\
6 & 6 & No Limit & 0.50 & 2903 & 2287 & 616 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information and data collection for each location.  
Figure 2: (**a**) Cross-section of the four-lane divided freeway; (**b**) Cross-section of the six-lane divided freeway.  
The lateral amplitude reflects the degree of vehicle excursion while driving inside the lane, expressed in the driving trajectory as slight fluctuations up and down within a specific range. The value of the \(LA\) is the difference between the vehicle's maximum and minimum lateral position within the lane, as shown in Equation (1).  
\[LA=y_{\text{max}}-y_{\text{min}} \tag{1}\]  
where \(y_{\text{max}}\) and \(y_{\text{min}}\) are the vehicle's maximum and minimum lateral positions in meters.  
The \(DTOC\) is the distance traveled when the vehicle finishes one complete up-and-down oscillation process (i.e., one oscillation cycle). The mean speed is the ratio of the \(DTOC\) to the oscillation time. The two parameters are calculated as follows:  
\[DTOC=|X(y_{\text{max}})-X(y_{\text{min}})| \tag{2}\]  
\[V=\frac{3.6 DTOC}{|F(y_{\text{max}})-F(y_{\text{min}})|}\times Frame \tag{3}\]  
where \(X(y_{\text{max}})\) and \(X(y_{\text{min}})\) are the longitudinal positions corresponding to when the trajectory reaches the maximum and minimum values of the lateral position, \(V\) is the mean speed, \(F(y_{\text{max}})\) and \(F(y_{\text{min}})\) are the corresponding video frames, and \(Frame\) is the frame rate, take 25 fps.
## 2 Materials and Methods
### Cell Viability by MTT Assay  
Cytotoxicity studies were performed following a previously reported procedure using the 4T1 cancer cell line [23]. Cell viability of bare AuNPs and ligand-functionalized AuNPs was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) colorimetric assay. Very briefly, 10\({}^{4}\) cells/well were seeded in a 96-well plate and incubated  
Figure 1: The thiol tridentate ligand used in this study.  
overnight at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). The following day, increasing concentrations of bare AuNPs (50, 50 \(\times\) 10\({}^{-1}\) and 50 \(\times\) 10\({}^{-2}\) ug/mL) and ligand-functionalized AuNPs (50, 50 \(\times\) 10\({}^{-1}\) and 50 \(\times\) 10\({}^{-2}\) ug/mL) were added to the growing cells in 4 replicates. The control group consisted of cells that were not treated with the AuNPs. The following day, RPMI was replaced with RPMI medium containing MTT (1 mg/mL). Cells were further incubated with the MTT solution for 4 h and, after removing it, 100 \(\upmu\)L of isopropanol was added to each well. Finally, absorbance was measured at 560 nm using an ELISA reader. The mean value of the optical density (OD) of the four replicates and the percentage of the OD was calculated with the following equation:  
\[OD\ \%=\frac{mean\ value\ of\ the\ OD\ of\ four\ replicates}{OD\ of\ control}\times 100\]  
Each assay was repeated three times.
## 3 Numerical Validation
### FSI with Forced Motion  
#### 3.1.1 Forced Pitching of Airfoil  
The pitching NACA0012 airfoil in subsonic flow is considered to test the performance of ALE-LBFS. The airfoil is forced to pitch around its pivot axis sinusoidally, on which occasion the angle of attack is modified with the specified motion function depending on time as  
\[\alpha(t)=\alpha_{m}+\alpha_{o}\sin(\omega t) \tag{51}\]  
where \(\alpha_{m}\) means the averaged angle of attack and \(\alpha_{o}\) is the oscillation amplitude.  
\[k_{c}=\frac{\omega c}{2U_{so}} \tag{52}\]  
Figure 7: Flowchart of the solution process for the FSI problem.  
\(\omega\) is the angular frequency, along with airfoil chord \(c\) and free-stream velocity \(U_{\infty}\). Detailed conditions for NACA 0012 are listed in Table 1. Among them, \(a=0.25\) indicates that the pivot axis lies a quarter chord away from the leading edge of the airfoil.  
Compared with numerical results obtained by [PERSON] [51], the performance of ALE-LBFS is validated by the forced pithing of the NACA 0012 airfoil. Figure 8 offers a comparison of \(C_{L}\) and \(C_{\rm m}\) with numerical and experimental results. Its aerodynamic coefficients vary over a pitch cycle and coincide with experimental results. Figure 9 plots various snapshots of pressure coefficients distributed on the surface of NACA 0012. They correspond to four phase angles in one pitch cycle, namely, \(\phi=45^{\circ},90^{\circ},135^{\circ},180^{\circ}\). Calculated by Equation (51), their angles of attack are equal to \(\alpha=4.59^{\circ},5.30^{\circ},4.59^{\circ},2.89^{\circ}\), respectively. The four subplots indicate that ALE-LBFS preserves satisfactory performance for simulating the forced pitch of the NACA 0012 airfoil in moving meshes.
# Multidimensional Study on Users' Evaluation of the KRAKEN Personal Data Sharing Platform
###### Abstract  
Background: Recent advances in the design of blockchain-based personal data sharing platforms bring the benefit of empowering users with more control and privacy-preserving measures in sharing data products. However, so far very little is known about users' intentions to adopt such platforms for providing or consuming data products. Objective: This study aims to investigate users' main expectations, preferences, and concerns regarding the adoption of blockchain-based personal data sharing platforms in the health and education domains. Methods: Fifteen participants were involved in a multidimensional evaluation of a prototyped release of the KRAKEN blockchain-based data sharing platform and asked to assess it in the health or education pilot domains. Data collected during online group interviews with participants were analyzed by applying the micro interlocutor technique to provide a descriptive overview of participant responses. Results: Participants showed a marginal acceptance of the prototype usability, asking for some improvements of the user experience and for a more transparent presentation of the platform security and privacy preserving capabilities. Participants expressed interest in using the platform as data providers and consumers as well as setting privacy policies for sharing data products with third parties, including the possibility of revoking access to data. Conclusions: Blockchain-based data sharing platforms are more likely to engage target users when technical design is informed by a deeper knowledge of their needs, expectations, and relevant concerns.  
data sharing platform; blockchain; users' adoption; privacy-preserving systems; user-centered design; focus-group interviews +
Footnote †: journal: Applied Sciences
# A Methodology Linking Tamping Processes and Railway Track Behaviour
## 4 Proposed Methodology  
Although it is commonly accepted that the compactness of the track ballast is crucially important for the quality and longevity of railway tracks (Section 3.2), there is no study known to the authors providing a direct relation between the two topics. Bottom-up approaches such as the water-filling method [40, 43] or seismic method [39] are restricted to the local testing zone; scaling those experiments to larger track sections or even entire railway lines is not feasible. Furthermore, experiments such as those introduced in Section 3.2 limit the scope to the ballast compactness and do not consider the subsequent behaviour of the track geometry. Top-down approaches, i.e., using the track behaviour as indication for the quality of the ballast compaction, are quite vague. The track geometry is influenced by many factors, which (likely) have a larger impact on the geometry quality and long-term stability of the tracks. Among the relevant factors are type of superstructure, line speed, traffic load, alignment, and weather conditions [11, 46].  
In this paper, we propose a novel methodology which intends to create a relation between the two topics of ballast compaction and track behaviour. Our approach requires measurement data from two different sources: Tamping machine measurements provide the basis to analyse each tamping process individually and derive an indicator for the achieved ballast compaction (Section 4.1) and ballast condition. Track recording vehicle data enable analyses of the initial quality and the behaviour of the track section where said tamping machine is operated (Section 4.2). Subsequently, both data sets need to be carefully joined, so that each tamping process can be assigned the geometric quality and deterioration rate developing at its location (Section 4.3).
# Vibration Properties of Dual-Rotor Systems under Base Excitation, Mass Unbalance and Gravity
## 2 Modeling for a Dual-Rotor System under Base Motions
### Modeling for the Disks, Unbalance Force and Linear Bearings  
The modeling for the disks, unbalance force and linear bearings is similar to that in [1] and is quite simplified in this paper. Three base translations (\(x_{b}\), \(y_{b}\) and \(z_{b}\)) and three base rotations (pitching angle \(\alpha\), rolling angle \(\beta\) and yawing angle \(\gamma\)) are shown in Figure 2a. Two transverse displacements (\(x_{d}\) and \(z_{d}\)) and two angular displacements (\(\theta_{d}\) and \(\psi_{d}\)) relative to the base for the disk are unknown and remain to be solved as shown in Figure 2b. Firstly, the kinetic energy \(T_{d}\) and the gravitational potential energy \(U_{dg}\) of the disk and the kinetic energy \(T_{u}\) of the unbalanced mass are obtained as  
\[T_{d}=\frac{1}{2}m_{d}\mathbf{V_{d}}^{\mathrm{T}}\mathbf{V_{d}}+\frac{1}{2} \boldsymbol{\omega_{d}}^{\mathrm{T}}\mathbf{I_{d}}\boldsymbol{\omega_{d}} \tag{1}\]  
\[U_{dg}=m_{d}gw_{dg} \tag{2}\]  
\[T_{u}=\frac{1}{2}m_{u}\mathbf{V_{u}}^{\mathrm{T}}\mathbf{V_{u}} \tag{3}\]  
where \(\mathbf{I_{d}}=\mathrm{diag}\left([ \begin{array}{cc}I_{dx}&I_{dy}&I_{dz} \end{array} ]\right)\); \(m_{d}\) and \(\mathbf{I_{d}}\) are the mass and mass-moment matrix of inertia of the disk; \(\mathbf{V_{d}}\) and \(\boldsymbol{\omega_{d}}\) are the absolute translational velocity and absolute angular velocity of the disk, respectively; \(g\) is the gravitational acceleration; \(w_{dg}\) is the absolute vertical displacement of the disk; \(m_{u}\) denotes the unbalanced mass; \(\mathbf{V_{u}}\) is the absolute linear velocity of the unbalanced mass; and the superscript T is the matrix transposition symbol. Secondly, the Lagrange principle is applied to achieve the final equations of motion (EOMs) of the disk as following  
\[\mathbf{m_{d}}\ddot{\mathbf{q}}_{\mathrm{d}}+(\Omega_{r}\mathbf{g_{d0}}+ \mathbf{c_{d}})\dot{\mathbf{q}}_{\mathrm{d}}+\mathbf{k_{d}}\mathbf{q_{d}}= \mathbf{f_{dirr}}+\mathbf{f_{dll}}+\mathbf{f_{dan}}+\mathbf{f_{dg}} \tag{4}\]  
where \(\mathbf{q_{d}}\) is the general displacement vector of the disk; \(\mathbf{m_{d}}\) and \(\mathbf{g_{d0}}\) are the conventional mass and gyroscopic matrices of the disk, respectively; \(\Omega_{r}\) is \(\Omega_{LP}\) or \(\Omega_{HP}\) corresponding to the low-pressure or high-pressure rotor; \(\mathbf{c_{d}}\) is the additional damping matrix of the disk due to base rotations; \(\mathbf{f_{dirr}}\), \(\mathbf{f_{dan}}\) and \(\mathbf{f_{dg}}\) are external forces induced by base linear motions, base angular motions and gravity, respectively; and \(\mathbf{f_{dll}}\) is an additional external force due  
Figure 1: A dual-rotor system on a moving base with various coordinate systems. to both the base rotations and the position vector from the origin of the frame \(F_{b}\) to the center of the left bearing.