esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 3 Results and Discussion
### Dependence of the UCL Intensity of Aqueous Solutions of CDs on pH  
The UCL spectra for aqueous solutions of CD-poly and CD-COOH with pH from 2 to 12 were obtained. From them, the dependences of the integrated intensity and position of the maxima of UCL spectra of CDs on the pH of aqueous solutions were calculated (Figure 8). As can be seen, these spectral parameters depend significantly on pH. Moreover, for CDs with polyfunctional and carboxylated surfaces, these dependences differ significantly. The UCL intensity of an aqueous solution of CD-COOH monotonically increases as the pH of the solution increases from 2 to 12, while the UCL intensity of CD-poly increases with the increase in pH from 2 to 5, and then decreases at pH > 5 (Figure 8a).  
Figure 7: Dependences of the UCL integrated intensity of aqueous solutions of CD-poly and CD-COOH (pH = 11.7) on the power of exciting radiation and approximation of experimental data by quadratic dependences.  
The maximum of the UCL spectra of CD-COOH sharply shifts to the short-wavelength region by 20 nm with an increase in the pH value of the solution from 2 to 5, while at further increase in the pH its position practically does not change (Figure 8b). It should be noted that in the pH range from 2 to 5, deprotonation of carboxyl groups on the surface of nanoparticles in water occurs, which significantly affects the state of the electronic levels of surface states [37,44]. The maximum UCL spectra of CD-poly gradually shifts to the short wavelength region by 10 nm with an increase in the pH value of the solution from 2 to 10, and then sharply shifts by 8 nm to the long wavelength region with a further increase in the pH of the solution. It can be assumed that the course of the dependences obtained for CD-poly solutions is also to some extent affected by the processes of deprotonation in the COOH, OH, and NH surface groups in water [37,44].
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 3 Stimuli Dependent Techniques
### Physical Stimuli-Based Self-Assembly of Nanomaterials
#### 3.2.4 Temperature-Induced Self-Assembly
Further, the EM field intensity significantly increases in the nano-junctions between the plasmonic NPs, in typical configurations such as nano-dimers (metal-metal, metal-dielectric, dielectric-dielectric), and this comprises the second generation of hotspots. The plasmonic coupling between the localized surface plasmon resonance (LSPR) of the NP and the SPPs of thin metallic film results in the generation of the third generation of plasmonic hotspots in a typical nanoparticle-on-mirror (NPoM) configuration. In this background, the fourth generation of plasmonic hotspots comprises the hybrid coupling of modes sustained by the nano-assemblies with that of the modes sustained by the metallic thin film, when appropriate phase matching conditions are satisfied. The CSNE demonstrates an effective methodology to integrate the main three sub-domains of plasmonics: (i) metal plasmonics, (ii) dielectric plasmonics, and (iii) graphene plasmonics, as elaborately presented in the related publication [33; 123; 125]. Figure 10i,j presents the overview of the futuristic scope and opportunities rendered by the CSNE technology where altering the shape, size, and material properties of the starting materials would render desirable properties for applications concerning plasmonics, optoelectronics, biosensing, and molecular diagnostics. Figure 9: (**a**) The conceptual schematic of the cryosoret nanoengineering methodology. The reduction in the time required for the synthesis of the cryosorets with decrease in the cooling temperature is also captured. (**b–e**) Multiple TEM images of the AgTiO\({}_{2}\) (rods, R) cryosorets (CS). (**f**) HRTEM image of the AgTiO\({}_{2}\) (R) CS, where the d-spacing characteristic to that of AgNPs (shown in yellow) and TiO\({}_{2}\) (R) (shown in blue) is presented. (**g**) SPCE enhancements realized with different types of nanomaterials in spacer, cavity, and ext. cavity nanointerfaces. (**h**) SPCE, FS, s, and p modes of the sample yielding highest SPCE enhancements shown with the corresponding angularity in Figure (**i**). Adapted with permission from reference [42].
_Applied_  
_sciences_  
_Single-Crystalline Si\({}_{1-x}\)Ge\({}_{x}\) (x = 0.5\(\sim\)1) Thin Films_  
**on Si (001) with Low Threading Dislocation Density**  
**Prepared by Low Temperature Molecular**  
**Beam Epitaxy**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1, [PERSON] 1,3,* and [PERSON] 1,***  
Received: 2 April 2019; Accepted: 24 April 2019; Published: 28 April 2019  
**Abstract:** Single-crystalline Si\({}_{1-x}\)Ge\({}_{x}\) thin films on Si (100) with low threading dislocation density (TDD) are highly desired for semiconductor industrials. It is challenging to suppress the TDD since there is a large mismatch (4.2%) between Ge and Si--it typically needs 10\({}^{6}\)-10\({}^{7}\)/cm\({}^{2}\) TDD for strain relaxation, which could, however, cause device leakage under high voltage. Here, we grew Si\({}_{1-x}\)Ge\({}_{x}\) (x = 0.5-1) films on Si (001) by low temperature molecular beam epitaxy (LT-MBE) at 200 \({}^{\circ}\)C, which is much lower than the typical temperature of 450-600 \({}^{\circ}\)C. Encouragingly, the Si\({}_{1-x}\)Ge\({}_{x}\) thin films grown by LT-MBE have shown a dramatically reduced TDD down to the 10\({}^{3}\)-10\({}^{4}\)/cm\({}^{2}\) level. Using transmission electron microscopy (TEM) with atomic resolution, we discovered a non-typical strain relaxation mechanism for epitaxial films grown by LT-MBE. There are multiple-layered structures being introduced along out-of-plane-direction during film growth, effectively relaxing the large strain through local shearing and subsequently leading to an order of magnitude lower TDD. We presented a model for the non-typical strain relaxation mechanism for Si\({}_{1-x}\)Ge\({}_{x}\) films grown on Si (001) by LT-MBE.  
**Keywords:** molecular beam epitaxy; thin film growth; Si\({}_{1-x}\)Ge\({}_{x}\) films on Si (001); TEM characterizations; low threading dislocation density; strain relaxation mechanism
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 6 Performance Study of the UWOC Link
### Clipping Effect on the Link Performance
(For the sake of completeness and following the computational complexity analysis presented in Section 5.4, for the considered LED and SiPM models, \(N_{\text{ch}}\) approximately equals 5, 10, and 20 for data rates of 20, 50, and 100 Mbps and the spectral efficiency of \(\eta\approx 1\,\text{bps}/\text{Hz}\).) Considering a target BER of \(10^{-3}\), we notice a larger link span for ACO and LACO, compared with DCO for relatively small transmit powers, whereas DCO-OFDM offers the largest LBI. On the other hand, for relatively large \(P_{\text{Tx,e}}\), as already noticed in Figure 6, DCO undeniably shows more robustness due to having a lower PAPR and a lower sensitivity to Rx saturation, and in this sense can be considered as a more flexible transmission scheme. LACO provides a larger LBI, as compared with ACO, and more flexibility of setting the transmit power. In fact, ACO and LACO use a low DC bias and are penalized by their higher PAPR for high transmit powers. DCO, on the other hand, uses a larger DC bias and a smaller scaling factor for a given \(P_{\text{Tx,e}}\). As a result, the clipping noise limits its performance for much higher transmit powers. Figure 6: Contrasting clipping effect on DCO-, ACO-, and LACO-OFDM BER performances for \(Z=20\) and 70 m. \(R_{b}=20\) Mbps, \(N=1024\), \(N_{\text{CP}}=9\). Figure 7: Comparison of BER performance versus the link range \(Z\) and the transmit electrical power \(P_{\rm{Tx,e}}\) of the three transmission schemes taking into account the LED DR and signal clipping. \(N=1024\), \(N_{\rm{CP}}=9\), \(20\), and \(28\), for data rates of (**a**) \(20\), (**b**) \(50\), and (**c**) \(100\,\)Mbps, \(\eta\approx 1\,\)bps/Hz.
# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 4 Experiment  
Six different types of parts were scanned with a Zivid One+ Small line-of-sight sensor (Zivid. Oslo Norway) (Disclaimer: Certain commercial equipment, instruments, or software are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by NIST, nor does it imply that the equipment or software identified are necessarily the best available for the purpose), which is based on structured light technology that combines multiple captured images. Figure 8 shows the selection of parts with different geometries, materials, and textures (ranging from black oxide to very shiny alloy steel) that were used in this study.  
Figure 7: Flowchart of the proposed procedure.  
A plastic bin was filled with the parts and placed on a table. Sensor was rigidly mounted above the table so the bin was in the sensor's field-of-view (FOV) and the approximate distance between the sensor and the bin center was 0.5 m. The application programming interface (API) of the sensor provided different custom settings available to the user. Data for non-shiny parts were acquired with default settings, while shiny parts were scanned with a few different settings designed specifically for these types of objects. No extra effort was made to find the best possible combination of acquisition parameters that could yield the best 3D point clouds (i.e., with the least number of outliers) as the aim of this study was to demonstrate the performance of the proposed filtering algorithm. Since data post-processing was focused on filtering and no further action was performed (like segmentation of individual parts in a bin and calculation of their 6 DoF poses), robot was not used in these experiments. Each bin filled with one kind of parts was scanned once and for each scan, two files were saved: a color RGB image file and a point cloud in an organized PLY format [36], respectively. In addition, the empty bin was scanned in five different locations on a table within the instrument's FOV. Multiple scanning positions were needed to acquire data covering the entire surface of the bin.  
Figure 8: RGB images of bins filled with six different parts used in the experiments.(**a**) metal spheres; (**b**) M12 zinc nuts; (**c**) M16 metal rods; (**d**) M12 black screws; (**e**) M12 black nuts; (**f**) sprockets.
# Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications
###### Abstract  
The increasing use of social networks such as Facebook, Twitter, and Instagram to share photos, video streaming, and music among friends has generated a huge increase in the amount of data traffic over wireless networks. This social behavior has triggered new communication paradigms such as device-to-device (D2D) and relaying communication schemes, which are both considered as strong drivers for the next fifth-generation (5G) cellular systems. Recently, the social-aware layer and its relationship to and influence on the physical communications layer have gained great attention as emerging focus points. We focus here on the case of relaying communications to pursue the multicast data dissemination to a group of users forming a social community through a relay node, according to the extension of the D2D mode to the case of device-to-many devices. Moreover, in our case, the source selects the device to act as the relay among different users of the multicast group by taking into account both the propagation link conditions and the relay social-trust level with the constraint of minimizing the end-to-end content delivery delay. An optimization procedure is also proposed in order to achieve the best performance. Finally, numerical results are provided to highlight the advantages of considering the impact of social level on the end-to-end delivery delay in the integrated social-physical network in comparison with the classical relay-assisted multicast communications for which the relay social-trust level is not considered.  
multicast; device-to-device communications; Internet of Things; mobile social networks +
Footnote †: journal:  
_Future Internet_ **2017**, 4, 92; doi:10.3390/f9040092
## 4 Materials and Methods
### PL and Time Resolved PL Measurements  
PL and TR-PL measurements were performed by exciting the samples with a solid state pulsed laser (mod. PLP-10, Hamamatsu), providing pulse of about 58 ps, at a wavelength of 400 nm, and at a repetition rate of 1 MHz. The maximum peak power was about 70 mW. The PL was dispersed by an iHR320 (focal length of 0.32 m) Horiba monochromator equipped with a Peltier cooled Hamamatsu photomultiplier (Head-on mod R943-02), operating in single photon counting. Time-correlated single photon counting (TCSPC) technique was used to record TR-PL in the spectral visible range by using an Edinburg Instruments TCC900 TSCPC electronics card. The temporal resolution of the system is about 0.46 ns. In the study of the PL signal stability, the emission signal was also collected in backscattering configuration by an optical fiber (diameter 1 mm) and dispersed by a Triax320 (focal length of 0.32 m) Jobin-Yvon monochromator, coupled to a Peltier cooled CCD in kinetic acquisition mode. PL and TR-PL experiments were carried out in vacuum (V) and wet air (WA) atmosphere,by putting the samples in a chamber connected to a vacuum-pump. The V pressure level (10\({}^{-3}\) mbar) and the recovering of WA condition occurred in less than 5 s, after switching the chamber environment from V to WA or vice versa. The environmental relative humidity was about 50%.
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Mobility Models
#### 3.1.2 The Random Direction Model  
The RD [2] model is a variant of the RWP model. RD was proposed in order to overcome a certain limitation of RWP. In fact, in RWP, the nodes tend to cluster around the center of the region and move away from the boundaries. This creates a non-uniform spatial node distribution and density wave problem stemming from the fact that the distribution of the movement angles is not uniform in RWP, as shown in [24, 27, 35]. In order to solve this, RD works as follows: instead of picking a random position, like in RWP, a mobile node (MN) randomly (uniform distribution) chooses a direction in which to move. It moves in that direction until it hits a boundary of the simulation region. It then stops for a time before choosing another direction. Similar to RWP, if the pause time is set to zero, the mobile node never pauses until the simulation is over. As we can see in Figure 2, the direction changes occur only at the boundaries. In [36], the authors claimed that RD results in less fluctuation in node density than RWP.  
Figure 1: Trajectory of a UAV under the random waypoint (RWP) model.  
Figure 2: Trajectory of a UAV under the random direction (RD) model.
## 4 Discussion
### Method Specific AVCs
#### 4.1.2 Correlation and B&A Analysis  
All joint angles present a very high correlation for the two methods. A high correlation is required to validate a method--though a high correlation does not necessarily imply that the examined method is a good substitution for the golden standard. [PERSON] and [PERSON] point out that a high correlation essentially implies that the sample is widespread, considering (i) the measuring interval affects r and r2 without affecting the agreement and (ii) a change in the scale of measurement significantly affects the agreement without affecting the correlation analysis [20]. The B&A analysis is performed to evaluate the bias between the two methods. It reveals one significant outlier for the ankle located at 300\({}^{\circ}\), confirming the results of the Spm1D analysis. The ankle's overestimation in the pushing phase and the underestimation in the pulling phase is not found back in the B&A analysis as these values still fall between the margins of 1.96 times the standard deviation.
## 4 Plasma Interaction with Surfaces
### Physical Bombardment of Surface  
Physical bombardment or ablation describes the mechanism underlying the removal of surface layers through mechanical kinetic bombardment. Such bombardment is conducted through the use of electrons and especially heavy ions, which are used to dislodge particles from the surface through kinetic interaction [69]. This is also known as \"Physical Plasma\", where the plasma interaction occurs through physical interactions, rather than chemical reactions. Interaction with targets occurs through inelastic collisions brought about by the acceleration of argon toward the target through electric fields [70].  
Through this physical process, plasmas can also be used to deposit layers on surfaces, thus altering the physical or chemical properties of the surface itself. The radicals that are used must therefore be selected well to ensure that such does not occur in lead frame plasma cleaning systems [69; 71].
## 3 Results
### Transbuccal Drug Delivery Systems in PD
#### 3.2.6. Rotigotine  
One of the major problems associated with PD disease is dysphagia. Therefore, release through the mucosa is advantageous for patient compliance. A study aiming to synthesize mucoadhesive hyaluronic acid (HA) containing rotigotine for the treatment of PD has been conducted. The polysaccharide HA was chemically modified with the ethyl ester of the thiol ligand cysteine through the formation of an amide bond (HAC). HAC was assessed in terms of stability, cytotoxicity, increased permeation, controlled drug release and mucoadhesion [74]. The conclusion of this in vitro study is that HAC could be a keystone to release through the buccal mucosa in the treatment of PD.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 5 Results
### Static Inversion
#### 5.1.3 Relative Weights of Ascending Versus Descending Interferograms  
Without any weighting other than the data uncertainties (inversions 01, 02a, 02b, 03 and 05 in Table 1), the descending LOS always gave a better fit than the ascending LOS. The inversion algorithm searches for models that minimize the misfit and this minimization in the L2-sense favors a fit to the greatest displacement. There are two reasons for this (i) because our implementation of the quadtree subsampling does not account for the surface represented by the data, larger gradients and larger amplitudes of displacement lead to more points and thus have a higher weighting and (ii) because of the larger amplitudes at these points. This is demonstrated by the computation of the reference misfit with a null deformation model (Table 3). Considering only the InSAR data, the sum of the reference misfit on each interferogram leads to a total reference misfit of \(\chi^{2i}_{ref}=3070\), while the reference misfits for all ascending interferograms and all descending interferograms are 970 and 2100, respectively. Thus, although subsampled interferograms have a balanced number of points, the descending interferograms account for 2/3 of the total misfit. This higher weight simply results from the displacement pattern and amplitude and from the projection of this displacement along the LOS (Figure 8).  
Figure 8: Projection of surface displacement along the LOS for satellite radar acquisition for ascending and descending orbits. The left-hand plots indicate expected displacements in the ascending and descending LOS for an inflation and an eastward flank slip. The right-hand plot shows the expected sign of the LOS projection (considered as positive when directed toward the satellite ) in the ascending and descending orbits for vectors joining a ground point to the circle.  
To counteract the influence of the weighting, we normalized the data by their reference misfit \(\chi^{2}_{ref}\). This weighting is equivalent to a normalization of the covariance matrix. Without this normalization, each dataset has uncertainties simply estimated from the data [57]. By altering this normalization factor, we used the values coming from the data uncertainties to evaluate the relative weighting of the data from a single dataset. However, we artificially considered that each dataset was equally likely. Results from inversions 03, 04a and 04b processed with this new weighting (Table 1) show an increase of the misfit on ascending data by a few percent, confirming that this weighting is efficient. Doing so, the calculated geometry remains the same and the inversion process is not able to improve the fit for the ascending data, even at the expense of the fit on the descending data.
# Wind Source Localization System Based on a Palm-Sized Quadcopter
## 3 Development of a Wind Source Localization Quadcopter System
### Arrangement of Wind Direction Sensor and Performance Verification Experiment  
The wind direction sensor is placed at the center of the quadcopter. However, the height at which the wind direction sensor should be installed is unknown; therefore, the height at which the wind direction sensor should be installed must be determined. Hence, as shown in Figure 2a, we verified the mounting of the wind direction sensor at distances of 50 and 100 mm from the quadcopter propellers to assess its ability to measure environmental winds. The experiment was conducted with the quadcopter fixed to a monopod, and measurements were taken 3 m from the wind source. The wind speed at the quadcopter setup location was measured using a commercially available anemometer (testo 405i, Testo SE & Co. KGaA, Titisee-Neustadt, Germany), which was calibrated beforehand and was low at approximately 0.1 m/s.  
Wind direction was measured for 60 s at each sensor height during the hovering state with rotating propellers, and the results of the average wind direction and standard deviation are illustrated in Figure 2b. The vertical and horizontal axes of Figure 2b represent measured wind directions and true wind directions, respectively. The black circles in Figure 2b represent ideal values, while blue and red colors denote conditions for lower (50 mm) and higher (100 mm) sensor heights, respectively. Figure 2b indicates that the sensor height of 50 mm results in less error from the true wind direction across all angular conditions.  
Figure 1: An autonomouspalm-sized quadcopter system for wind source localization. (**a**) Overview of the constructed quadcopter system. (**b**) Series of information processing flows ranging from detecting wind to controlling the quadcopter. (**c**) Specifications of the TinyPICO.  
Based on these findings, the wind direction sensor was installed at a height of 50 mm to detect environmental wind direction information in the proposed system. To further verify the detection of more detailed wind direction information with this sensor arrangement, measurements were conducted in increments of 22.5\({}^{\circ}\) from 0 to 360\({}^{\circ}\). Additionally, experiments were conducted under conditions of propeller rotation switched on and off to assess the extent of wake influence, maintaining consistent wind source distance and speed conditions as in Figure 2c. Figure 2c illustrates the average wind direction and standard deviation measured over 60 s at each wind direction, with the vertical and horizontal axes representing measured and true wind directions, respectively. The black lines in Figure 2c denote ideal conditions, while blue and red data points represent experiment results with propellers on and off, respectively. The results indicate that the system can detect wind source directions accurately with minimal influence from propeller rotation, even with fine variations in wind source orientation.