esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 5 Literature Review
### IoT and Healthcare Integration  
This section reviews the integration of the IoT in the healthcare sector by analyzing existing studies that discuss its challenges, opportunities, and applications.  
[PERSON] et al. [23] explored the use of blockchain technology in healthcare, specifically in medical records, research, and patient monitoring. The authors discussed challenges, including interoperability, security, and scalability, proposing solutions involving encrypted data pointers and robust software development. The paper points to the blockchain's opportunities and challenges for use in healthcare.  
[PERSON] et al. [22], using Secure Multi-Party Computation (MPC) and homomorphic encryption (HE), proposed a novel approach for secure online healthcare systems. The system enables encrypted data sharing and disease prediction based on symptom similarity using the Random Forest Algorithm. The paper also discusses other data security models and techniques in healthcare.  
A solution based on blockchains to preserve privacy and data integrity in IoT-based healthcare was presented by [PERSON] et al. [24]. The paper presents the risks associated with sensor data collection and proposes a privacy-preserving algorithm with efficient time complexity. The solution ensures data security and patient privacy.  
[PERSON] et al. [25] proposed a blockchain-based monitoring system for the reliable and secure management of personal medical information. The system included a sensor unit, control unit, and smartphone interface, providing real-time data and risk analysis based on biosignals. The paper highlighted the system's improved reliability and security, achieved through blockchain integration.  
[PERSON] et al. [21] provide a comprehensive review of the IoT in healthcare, covering technologies, security, and market opportunities. They propose a new security model, address challenges in transitioning to IoT-based systems, and review various healthcare technologies and services. The paper concludes with open issues and future research directions in IoT healthcare.  
[PERSON] et al. [26] explore the potential of blockchains and the IoT for use in healthcare. The paper discusses their application in drug traceability, remote patient monitoring, and medical record management. Through innovative solutions, they focus on refining healthcare systems and addressing challenges such as interoperability and privacy concerns.  
The issues with integrating blockchains in the IoT were investigated by [PERSON] et al. [27], and they provided recommendations to address these challenges, including authentication, data protection, stability, resistance to attacks, ease of deployment, and self-maintenance. Anonymity, permissionless blockchains, and pseudonymization were highlighted as significant concerns. The paper concludes by addressing unresolved issues and future trends in blockchain-IoT integration.
## 3 Results and Discussion
### UV-Visible Analysis  
Figure 3a presents the UV-visible spectra of POA, PPA, and PPA@TiO\({}_{2}\), POA@TiO\({}_{2}\) nanocomposites, where these materials were dissolved in an NMP solution. The three samples show two peaks: A peak near 298-378 nm, which can be attributed to transitions in the benzenoid structure [35; 36], and a peak around 452-530 nm, which may correspond to quinine-imine group transitions [36; 37]. These results reveal that polymer deprotonation by NH\({}_{4}\)OH forms emeraldine bases [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **PPA@TiO\({}_{2}\)** & **POA@TiO\({}_{2}\)** & **PPA** & **POA** \\ \hline EC (S.cm\({}^{-1}\)) & 0.08 & 0.09 & 0.22 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The EC values of POA, PPA, and PPA@TiO\({}_{2}\), POA@TiO\({}_{2}\) nanocomposites.  
Figure 3: (**a**) UV–Vis spectra of samples; (**b**) TGA analysis of TiO\({}_{2}\), POA, PPA, POA@TiO\({}_{2}\) and PPA@TiO\({}_{2}\) nanocomposites.  
Intense band changes after inserting TiO\({}_{2}\) nanoparticles and a notable redshift of the peaks were observed for pure polymers compared to nanocomposites. The redshift distribution of POA@TiO\({}_{2}\) was larger than PPA@TiO\({}_{2}\) likely due to interchain species, which profoundly impact the conjugated polymers' process [28]. These results are in accord with nanocomposites' decreased conductivity (various transitions are included in Table 3). The continuous variation in the UV-visible peaks' wavelength and intensity confirms the interaction between polymers and nanoparticles.
## 3 Simulation and Experimental Measurements
### First Proposed Isomorphic Circuit Simulation and Experimental Results
Figure 5:3 The measured phase noise performance of the VM QSO at the \(\rm V_{\rm o2}\) output terminal. Figure 5:4 The experimental results of the V\({}_{\text{o3}}\) voltage gain when R\({}_{2}\) was varied. \begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**Non-Inverting Bandpass Filter Factor**} \\ \hline Supply voltage (V) & \(\pm 5\) \\ Power dissipation (W, measurement) & 0.22 \\ Central frequency (kHz, design/measurement) & 165.8/161.7 \\ Maximum operating voltage gain (dB, design/measurement) & 28.3/27.86 \\ Measured total harmonic distortion at V\({}_{\text{m}}\) = 120 mV\({}_{\text{PP}}\) (\%) & 1.08 \\ Measured output 1-dB compression point (dBm) & \(-\)11.4 \\ Measured third-order intermodulation distortion at 63 mV\({}_{\text{PP}}\) (dBc) & \(-\)40.1 \\ Measured third-order intercept point (dBm) & \(-\)7.95 \\ Measured phase noise at 30 Hz (dBc/Hz) & \(-\)83.86 \\ Measured spurious-free dynamic range (dB) & 40.79 \\ Figure of merit & 3.07 \(\times\) 10\({}^{6}\) \\ \hline \multicolumn{2}{c}{**Fully Uncoupled Quadrature Oscillator Factor**} \\ \hline Supply voltage (V) & \(\pm 5\) \\ Power dissipation (W, measurement) & 0.22 \\ Number of sinusoidal voltage output used & 3 \\ Oscillation frequency (kHz, design/measurement) & 165.8/162.3 \\ Measured total harmonic distortion (\%) & 0.81 \\ Measured phase noise at 30 Hz (dBc/Hz) & \(-\)40.18 \\ Measured operating oscillation frequency range (kHz) & 82.15–1629 \\ Measured operating amplitude control range & 1.51–24.05 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance parameters of the performance of the first proposed isomorphic circuit. Figure 25: The measured phase difference of quadrature output voltages V\({}_{\text{o1}}\) and V\({}_{\text{o2}}\) versus the tuning FO.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 3 Proposed Methodology
### Step 5: Curvature Estimation Using a DFF Method
Second, it makes it easier to extract 2D patches for the application of the DFF method, which does not need to be oriented in the same direction as the plane defined by the frequency \(f_{e}ta\). Figure 6: Illustration of the geometric and visual interpretation of the 4D phase space transform \((x,y,\xi,\eta)\) of a 2D hologram. The 4D phase space transform represents propagation value along rays that have an orientation determined by \(\xi\) and \(\eta\) and a position determined by \(x\) and \(y\). By fixing the values for a position–frequency pair \((x,\xi)\), the 2D matrix \((y,\eta)\) can be interpreted as the phase space transform of the 1D hologram produced by the intersection points between the oriented 2D plane (whose orientation and position are determined by the selected values of \(\xi\) and \(x\), respectively) and the 3D scene. The ROIs are extracted for each of these 2D phase space representations \((x,\xi)\) as shown in Figure 7, and then the minimal representation lines are extracted from the extracted ROIs using the iterative algorithm. More formally:  
\[\hat{M}_{b}(y[n_{2}])=\mathcal{G}_{\theta}\{\mathbb{(}\mathbb{R}e\{L[n_{2}]\}, \mathbb{I}m\{L[n_{2}]\}\}), \tag{22}\]  
\[\hat{\mathcal{P}}(y[n_{2}])=\{(x,z)\ |\ \mathbb{1}_{score(p,\hat{M}_{b}(y[n_{2}]))=1 }\ \ \ \forall p=(x,z)\in\mathbb{R}^{2}\}, \tag{23}\]  
Figure 5:0  
Finally, a new fixed-size ROI centered on the predicted lines is defined and used for the DFF method using the auto-switch proposed in [8] as follows:  
Figure 5:1  
Figure 5:2  
Figure 5:3  
where \(d\) is the final depth map, \(N_{z}\) is the interval size, \(FM\) is the focus measure function, \(I_{i}\) is the intensity of the holographic reconstruction performed at the \(i\)-th distance, and \(s\) is the patch size fixed at 17 which showed the best performance in the experiments through hyperparameter tuning. In this final stage, the depth of each point \((x,y)\) in the initial line segment \(\hat{D}(y)[x]\) is refined by the DFF process, which assesses the depth \(i\) for which the focus of the patch \(R_{x,y,i}\) is optimal inside a reconstruction interval of size \(N_{z}+1\) centered around the initial depth estimate \(D(y)[x]\). The obtained results will depend on the choice of the focus measure, the size of the chosen interval, and the obtained result from the iterative method. Figure 5:4 An illustrative example that shows the initially extracted ROI, the refined ROI, and the final estimate produced by the DFF method using a fixed-size ROI of size 31 centered on the refined ROIs at different viewing angles. The extracted ROI can be used to pre-localize the scene in the 3D space. They thus define an appropriate reconstruction interval and contain the pixels on which the focus measurement applies in the reconstruction volume.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The experiments were conducted in three biological replicates and the results were expressed as mean \(\pm\) sd. Data processing, averages, standard deviation values (sd), accumulated Cd loads L\({}_{\text{n}}\), enrichment factor (EF) and translocation factors (TF, LTF) calculations were performed using Microsoft Excel 365. The means were analyzed for significance using one-way analysis of variance, ANOVA (Statistica for Windows v. 13.0, Stat-Soft Inc., Tulsa, OK, USA). Data were checked for normality and homogeneity of variance. Tukey HSD (Honestly Significant Difference) multiple comparisons test was used to check the significance of differences (\(p<0.05\)). When the data did not conform to the normal distribution or failed to pass the variance homogeneity test, non-parametric Kruskal-Wallis Test followed by post-hoc Dunn's test was used to analyze the differences between groups.
## 2 Experimental Study
### Centrifuge Tests and Test Program  
In the experimental study, centrifuge tests were carried out for two different sand densities, loose and dense. Results were obtained for two different ratios of embedment depth, H, and anchor plate width, B. Experiments were carried out in both unreinforced and reinforced conditions. In the experiments carried out for the reinforced case, the uplift capacity values were obtained when the geogrid reinforcement was placed at three different angles just above the anchor plate. In the experiments, the geogrid reinforcement length was chosen as 150 mm, with five times the anchor plate width. The configurations of the geogrid in the tests are shown in Figure 4. Centrifuge test program details are shown in Table 3.  
Test series 1 was carried out within the scope of centrifuge experiments, and the effect of anchor plate embedment depth on the uplift capacity was investigated. In test series 2, we aimed to examine the effect of the density of the sand on the uplift capacity. Test series 3, 4, 5, and 6 include the investigation of the effect of geogrid configuration on the uplift capacity of the anchor plate at different densities of sand and embedding ratios.  
N = 33.3 \(g\) acceleration was applied in the centrifuge setup to ensure that the model strip anchor provides the 1 m width prototype conditions. In this case, the prototype anchor dimensions were brought to: \(B_{P}=N.B_{M}\), \(L_{P}=N.L_{M}\), and \(t_{p}=N.t_{M}\) by applying N times the gravitational acceleration, where B, L, and t are the width, length, and thickness of the anchor plate, respectively. P and M subscripts show the prototype and model, respectively. Thus, prototype uplift force, \(Q_{UP}=N^{2}\) times model uplift force \(Q_{UM}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline D50 (mm) & 0.20 \\ Uniformity coefficient, Cu & 1.50 \\ Relative density of loose sand, D\({}_{\text{r}}\) (\%) & 35 \\ Relative density of dense sand, D\({}_{\text{r}}\) (\%) & 75 \\ Dry unit weight of loose sand, x (kN/m\({}^{3}\)) & 14.5 \\ Dry unit weight of dense sand, y (kN/m\({}^{3}\)) & 16.4 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of sand.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Aperture size (mm) & 2.4 \\ \hline Tensile strength (kN/m\({}^{3}\)) & 30 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of geogrid.
## 2 Materials and Methods  
### Sample Preparation and Characterization  
The MgB4O7:Zn glass series was made from zinc oxide (ZnO), magnesium oxide (MgO) and boron oxide (B2O3) in powder form, with >99% analytical-grade purity (Alfa Aser, Haverhill, MA, USA). The nominal compositions (35-Y)MgO-65B2O3, with \(Y(ZnO)\) = 0.1 \(\leq Y\leq\) 1, were prepared using the melt-quenching technique [23; 24; 25]. The chemical composition of the individual glass samples is shown in Table 1. The detailed preparation process can be found in the literature [26].  
The structural and thermal characteristics of the synthesized samples were observed through X-ray diffraction, field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX) spectroscopy and differential scanning calorimetry (DSC). The amorphous nature of the synthesized samples was confirmed through X-ray diffraction (XRD) analysis using a D8 ADVANCE diffractometer (BRUKER corporation, Billerica, MA, USA). The XRD profiles were collected for powdered glass samples at 20 from 20\({}^{\circ}\) to 80\({}^{\circ}\) at a scanning rate of 0.02\({}^{\circ}\) s\({}^{-1}\). The phase homogeneity and surface morphology of the synthesized samples were analyzed through FESEM and elemental composition was confirmed through EDX spectroscopy using FEI NOVA NANOSEM 450 (FEI, Hillsboro, OR, USA). The thermal stability of synthesized glasses and the glass-forming ability of the proposed mixtures were estimated through DSC using SETSYS Evolution (SETARAM Instrumentation, Caluire-et-Cuire, France). The DSC thermogram was obtained between the temperature range of 50 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min. The temperature accuracy of the SETSYS Evolution is \(\pm\)0.1 \({}^{\circ}\)C [27].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Composition (mol\%)**} \\ \cline{2-4}
**Sample ID** & **B2O3** & MgO & ZnO \\ \cline{2-4}  & **Host** & **Modifier** & **Dopant** \\ \hline S35651 & 65.00 & 34.90 & 0.10 \\ S35652 & 65.00 & 34.80 & 0.20 \\ S35653 & 65.00 & 34.70 & 0.30 \\ S35654 & 65.00 & 34.60 & 0.40 \\ S35655 & 65.00 & 34.50 & 0.50 \\ S35656 & 65.00 & 34.40 & 0.60 \\ S35657 & 65.00 & 34.30 & 0.70 \\ S35658 & 65.00 & 34.20 & 0.80 \\ S35659 & 65.00 & 34.10 & 0.90 \\ S356510 & 65.00 & 34.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of synthesized samples.
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
## 5 Conclusions  
This paper proposed an algorithm based on the kinematic model of a robot to enable self-collision avoidance in real time. The collision model was based on a finite number of discrete spherical bounding volumes with different radii. In addition, the algorithm proposed a sensitivity index for measuring the collisions between spheres. When the bounding spheres collide, two repulsive velocities are generated on the control points, and the motion of the arms are changed accordingly to avoid collisions between the two arms. Simulations and experiments were conducted on a dual-arm robot to verify the effectiveness of the algorithm. The results indicate that the algorithm can effectively avoid self-collision between arms using a simple collision model. The method optimizes the collision model, decreases the computational cost, and increases the reaction speed as well as accuracy. This algorithm can be used in tele-operation and fully autonomous control systems.  
In the future, we aim to devise a strategy/framework that can avoid self-collision and accomplish the desired tasks in concert, similar to human motion. Meanwhile, we aim to develop this strategy to avoid obstacles when external environment changes are detected to deal with more complicated  
Figure 12: Experiment to assess the ability of the dual-arm system to avoid self-collision when the left and right arms had different targets. (**a**) Two arms of the dual-arm robot have different targets; The experiments are illustrated in two views; (**b**) Velocity profile of the end-effector of each arm; (**c**) Trajectory of the end-effector of each arm.  
tasks. In addition, a strategy for self-collision avoidance of multi-arm (\(>\)2) robots is also a direction worth investigating.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/17/5893/s1](http://www.mdpi.com/2076-3417/10/17/5893/s1)]([http://www.mdpi.com/2076-3417/10/17/5893/s1](http://www.mdpi.com/2076-3417/10/17/5893/s1)), Video S1: Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots.  
Conceptualization, M.L. and T.W. methodology, M.L.; software, M.L.; validation, M.L. and Z.W.; formal analysis, M.L. and T.W.; investigation, M.L. and T.W.; resources, T.W., C.Y. and Y.D.; data curation, M.L. and T.W.; writing--original draft preparation, M.L.; writing--review and editing, M.L., T.W. and H.L.; visualization, M.L. and H.L.; supervision, T.W., C.Y. and Y.D.; project administration, T.W., C.Y.; funding acquisition, T.W., C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China (Grant Nos. 2016 YFE0206200) and LiaoNing Revitalization Talents Program (XLYC1807018).  
The authors would like to thank [PERSON] and [PERSON] for their help in developing the control system of the upper-body humanoid dual-arm robot, and would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for their support in designing and testing the robot.  
The authors declare no conflict of interest.
## 2 Background Study
### Raindrop Size Distribution  
Raindrop size distribution is used to generate synthetic rainfall rates using SST techniques [71; 72] and the spatial distribution of rainfall rates. In [73], a raindrop size-distribution-based rain attenuation model was proposed. In the literature, different techniques are proposed to measure the raindrop size; two-dimensional video disdrometer [74], momentum disdrometer [75], and X-band polarimetric radar [76] are available, but there exist accuracy issues among these sensor devices as some of the devices were tested in [77]. Real measured accuracy comparison of rain distribution measured by the weather data and the DSD-based data was proposed in [78]. Raindrop size also affects the polarization property because propagating the radio wave through the rain creates depolarization of the propagated signal [79]. The drop size is one of the factors that affect the polarization. In the literature there are various types of raindrop size distribution, such as the normalized gamma model [80], seasonal variations in raindrop size [81], clouds and raindrop size distribution [82], vertical raindrop size distribution [83], exponential distribution [84], gamma distribution [85], log-normal distribution [86], and Weibull raindrop-size distribution [87].
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Delay Optimized Protocols
#### 11.2.1 Throughput and PAR  
RE-AEDG has maximum throughput both in optimized and non optimized versions. In RE-AEDG, presence of GWs at both sides of AUV and sink trajectories highly influence the number of packets that successfully reach sink in a given time interval. In RE-AEDG, AUV comes in the near vicinity of the nodes causing the GWs to be selected nearer the MNs. As a result, the number of hops and the required re-transmissions decreases significantly. In addition to that shorter communication routes also lower the probability of data collision and causes the loss of sensitive information; as shown in Figures 7 and 8.  
In the figures, AEDG shows an intermediate state PAR due to the fact that it does not involve cooperative routing mechanism in its transmission process. Hence, it has lowered number of packets received at sink and PAR as compare to RE-AEDG. Whereas; for U-LVRP random movement of AUVs demand the nodes to dynamically define their communication paths. Therefore, a relatively higher packet drop rate is observed for this scheme as compare to the other protocols.  
Furthermore, the implication of \(d^{th}\) is also visible in the optimized version of the schemes in terms of their lowered PAR and the number of packets being received at sink. \(d^{th}\) reduces the size of eligible  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Network field & 500 m\({}^{2}\) \\ Nodes deployed & 650 \\ Number of GWs & 100 \\ Number of MNs & 550 \\ Per node initial energy of members & 100 J \\ Per node initial energy of GWs & 115 J \\ Transmission range & 70 m \\ Packet size & 50 bytes \\ Number of AUVs for U-LVRP & 2 \\ Number of AUVs for AEDG and RE-AEDG & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation specifications for RE-AEDG.  
neighbourhood set for sender nodes and sometime causes them to stand alone in network specifically when the network becomes sparse.