esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation on a Case Study
### Interferometer Simulations  
The numerical simulations of the interferometer have been performed by placing three vertical lines of sight, as shown in Figure 3. Three lasers emit beams with a wavelength of 20 \(\upmu\)m and each laser emits 100 rays. Each beam interacts with a 50-50 beam-splitter. Thus, half of the rays are reflected, while the others are transmitted. The transmitted rays interact with the mirrors on the top of the tokamak and enter in the vessel, probe the plasma, exit from the vessel and are reflected by the mirrors placed below the vessel. Then, they pass through the beam mixers and are collected by the detectors. The reflected rays from the beam-splitters represent the \"reference\" beams, used to measure the differential phase. They are reflected by the mixers and collected by the detectors.  
Nine numerical simulations were performed changing the electron density to simulate different plasma conditions, from the absence of the plasma to the flat top condition. The electron density (\(N_{0}\)) goes from 0 to 2 \(\times\) 10\({}^{20}\) m\({}^{-3}\). Figure 4 shows the results of the numerical simulations. The figure on the left represents the ray trajectories from the laser to the detector, where the ray colour shows the calculated phase according to Equation (8). Since the \"reference\" and the \"plasma\" beams run different distances, the interferometer must be calibrated in the absence of plasma (numerical simulation number 1). Thus, knowing the phase difference in the absence of plasma, the phase difference due to the plasma is calculated and the electron density as well, from Equation (10). The figure on the right-top represents the calibrated differential phase for all lines of sight, where the phase for \(N_{0}=0\) m\({}^{-3}\) has been placed equal to zero (calibration). The figure located at the right-bottom shows the true electron density, calculated as the integral on the line of sight of the electron density field, and the electron density calculated by the interferometer, through the phase difference. The comparison clearly shows that the methodologies implemented to model the interferometer in COMSOL work properly.  
Figure 4: Interferometer results.  
Figure 3: Interferometer simulation geometry.
Many efforts have been made to predict the nanocomposites' tensile behavior using micromechanical models [21; 22; 23; 24; 25; 26; 27]. This has become a difficult task due to the nanocomposites' peculiar behavior, depending on the matrix's structure, filler's dispersion, as already stated, the particles' aspect ratio, the filler-filler and filler-matrix interactions etc. Various two-phase or three-phase micromechanical models will be tested in this work for the elastic modulus prediction of the prepared nanocomposites. Depending on the applied model, the filler's and/or the matrix's characteristics are taken into account. The aim of the present work is to examine the effect of the ball-milling process as a premixing step prior to melt-mixing on the crystalline structure and tensile properties of polyethylene of raised temperature resistance/graphene nanoplatelets nanocomposites, which are referred to in this manuscript as SCB-PE nanocomposites. SCB-PE/GNPs nanocomposites were prepared using various filler contents of 0.5, 1, 2.5 and 5 wt.%. Two series of nanocomposites were synthesized, the first nanocomposites via direct melt-mixing and the second set with a premixing ball-milling step and subsequently melt-mixing. X-ray diffraction (XRD) analysis was employed to investigate the filler's loading and the ball-milling effect on the prepared polymers' crystalline structure. Tensile tests were applied on neat SCB-PE and its nanocomposites prepared with and without ball-milling in order to examine their tensile properties. At the same time, various micromechanical models were fitted on the experimental elastic modulus values of the nanocomposites. A fractography study using scanning electron microscopy was also employed to investigate the fracture mechanisms of neat SCB-PE as well as its nanocomposites in any case. Shear tests were conducted to evaluate the shear strength of SCB-PE nanocomposites. To the best of our knowledge, it is worth mentioning that the ball-milling effect on the tensile and shear properties and crystalline structure of polyethylene of raised temperature resistance (SCB-PE)/graphene nanocomposites has not yet been studied. Since this polymer is commonly used in piping systems, its mechanical properties optimization has become an appealing goal.
# The Development and Analysis of a Multistage Spraying Method for Liquids in an Ultrasonic Field
## 4 Results and discussion
### The Dispersion Characteristics of the Formed Droplets  
To confirm the applicability of the proposed spraying technique, the disperse composition of droplets formed by spraying water (settled, tap water, temperature of 23 \({}^{\circ}\)C) was analyzed. The sound pressure level was 182 dB. The water supply capacity was equal to 5 mL/s. The initial size of the droplets injected into the tubular emitter was equal to 2000 \(\upmu\)m. Histograms of the droplet distribution, after the first stage of spraying and at the outlet of the tubular emitter, are shown in Figure 7. Statistical processing of the obtained data was carried out using the Malvern SprayTech laser meter software v3.20. The measurements were carried out in long-term measurement mode with data averaging for 1 s.  
As can be seen from the histograms presented in Figure 7a, a bimodal aerosol consisting of small droplets (the first peak formed by the capillary mechanism of liquid film breakdown) and large droplets (the second peak consisting of droplets formed after the structural breakdown of the liquid film) is generated at the first stage of dispersion of the initial droplets. The median droplet diameter (hereinafter referred to as Sauter's diameter) of the first peak is 89 \(\upmu\)m.  
The largest droplets formed at the first stage are subjected to subsequent stages of breakdown. As a result, the distribution of aerosol droplets at the tubular emitter outlet takes the shape shown in Figure 7b. The median (Sauter) diameter equals 34 \(\upmu\)m. It can be concluded from a comparison of the obtained distributions that the median diameter of the resulting droplets (Figure 7b) is smaller than the median diameter of small droplets after the first breakdown stage (the first peak in Figure 7a). This indicates that coagulation processes have almost no effect on the disperse composition of the formed droplets. This is due to the fact that the ultrasonic frequency used (22.2 kHz) is significantly higher than that required for the coagulation of the formed liquid droplets and does not grant them oscillatory motion [32, 33]. This fact will be the subject of future studies.  
It was established that the droplets formed have a smaller diameter than the same obtained by the known method of ultrasonic spraying of the liquid in a layer at the same frequency of exposure. The proposed multistage spraying technique provides a median (Sauter) diameter of 120 \(\upmu\)m vs. 34 \(\upmu\)m for ultrasonic spraying in a layer of liquid. This provides significantly higher (by more than 10-fold) spraying productivity than in the known ultrasonic spraying techniques [34, 35].
# Quasi-Wireless Capacitive Power Transfer for Wire-Free Robotic Joints +
## 4 Discussion  
A unique, unilateral current flow technique was demonstrated using a single degree of freedom to transfer power over the chassis of a robotic joint. This work explored the use of a matching capacitance in tandem with various tap locations along a QWR for improved efficiency using the single-wire QWiC method. From the efficiency data, it was determined that the tap location and matching capacitance allows one to achieve an improved power transfer scheme with a predictable effect on frequency shift of the QWR. The results from this study can be used to design a system that operates at both a specified frequency and a much higher transfer efficiency.  
This novel quasi-wireless power technique opens a completely new design space, where wiring is eliminated within the chassis and between joints of machines, allowing for highly resilient systems that could have transformative impacts in robotics and their associated applications/industries.  
Future work will explore different matching techniques beyond a single capacitor (such as LCC) in the load branch, along with capacitive methods of impedance transformation that do not require a transformer. Research is ongoing to improve various aspects of the design. Modulation of the RF power along the chassis could be used for communication, similar to [27].  
Conceptualization, T.M., C.S.J., M.P., C.P. and C.W.V.N.; Methodology, T.M., C.S.J. and C.W.V.N.; Software, T.M., J.-C.W. and M.P.; Validation, T.M., C.S.J. and C.W.V.N.; Formal analysis, T.M. and C.W.V.N.; Investigation, T.M., J.-C.W. and C.P.; Resources, C.W.V.N.; Data curation, T.M.; Writing--original draft, T.M.; Writing--review & editing, T.M., M.P., C.W.V.N., C.V. and D.B.; Visualization, J.-C.W.; Supervision, C.W.V.N.; Project administration, C.W.V.N., C.V. and D.B.; Funding acquisition, C.W.V.N. and D.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NASA Marshall Space Flight Center under Cooperative Agreement grant number 80 NSSC20M0186.  
Figure 12: A future design of the robot with multiple QWRs to allow tapping to assist with all joints.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The research described and the resulting data presented herein this document was conducted at Tennessee Technological University with testing performed at Marshall Space Flight Center. Permission was granted by NASA to publish this information.  
The authors declare no conflicts of interest.
## 2 Methods
### Data Generation Process
#### 2.1.1 Motion Simulator
In the data augmentation process, we translated the midpoint of aortic valves in the range of \(-\)5-5 mm from the center of the images and rotated the aortic valve in the range of \(0^{\circ}\)-360\({}^{\circ}\) around the midpoint of aortic valves, to augment the number of the video stream data. Finally, 8000 video stream data were generated from 80 motion videos, and each motion video corresponds to 100 video stream data. The duration of the video stream data is 305 ms (Figure 2g). Each video stream data contains 320 images of the closed phase, 160 images of the rapid opening phase, and 320 images of the slow closing phase. Here, we did not include motions of the rapid closing phase [30] and only used part of the motion to demonstrate the proposed concept that motion images of aortic valves can be inferred from motion-blurred CT images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Size (mm)** \\ \hline Thickness & 0.42 \(\pm\) 0.12 \\ eH (Effective height) & 9.5 \(\pm\) 1.4 \\ Ad (Aorto-ventricular diameter) & 21.0 \(\pm\) 2.8 \\ Sd (Sinus Valsalva diameter) & 28.5 \(\pm\) 3.5 \\ STJ (Sinotubular junction) & 25.0 \(\pm\) 3.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometric parameters of aortic valves (mean \(\pm\) std). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range for Random Selection** \\ \hline Density (kg/m\({}^{3}\)) & 1000, 1020, 1056, 1060, 1100, 1250 \\ Young’s modulus (Mpa) & 2–8 \\ Poisson ratio & 0.49, 0.4, 0.3, 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties of aortic valves. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **CT Value (HU)** & **Pixel Values** \\ \hline Vessels and aortic valve & 118 & 92 \\ Blood flow & 330 & 255 \\ Background tissue & 52 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CT values and normalized pixel value of each component in the motion image.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 2 VMD Parameter Optimization and Wavelet Noise Reduction
### Optimize VMD Based on RIME
#### 2.2.2 Soft Rime Search  
The process simulates the movement of rime particles in a breezy environment, serving as the exploration phase of the algorithm. Rime particles update their positions by moving toward the vicinity of the soft rime body and coagulating with the particles within it. The position update formula is as follows:  
\[R_{ij}^{new}=R_{best,j}+r_{1}\cdot\cos\theta\cdot\beta\cdot\left(h\cdot \left(Ub_{ij}-Lb_{ij}\right)+Lb_{ij}\right),r_{2}\textless E \tag{5}\]  
\[\theta=\pi\cdot\frac{t}{10\cdot T} \tag{6}\]  
\[E=\sqrt{\left(t/T\right)} \tag{7}\]  
\[\beta=1-\left[\frac{w\cdot t}{T}\right]/w \tag{8}\]  
where \(R_{ij}^{new}\) represents the updated position of rime particles, \(i\) and \(j\) denote the \(j\) rime particles of the \(i\) frost-ice individual; \(R_{best,j}\) represents the \(j^{\text{th}}\) rime particle of the best rime individual in the rime population \(R\); \(r_{1}\) and \(r_{2}\) are random numbers within the range \([-1,1]\); \(\beta\) is an environmental factor; \(w\) is the number of step function segments, with a default value of 5; \(h\) represents adhesion, is a random number within the range [0,1]; \(Ub_{ij}\) and \(Lb_{ij}\) are the upper and lower bounds of the particle diffusion space, respectively. \(E\) is the adhesion coefficient, \(t\) denotes the current iteration number, and \(T\) represents the maximum iteration number of the algorithm.
## 3 CV QKD over Satellite Channels  
The main challenge in the satellite-based communication, and particularly the quantum one, is the extremely strong attenuation levels, which are much higher than the typical loss in the terrestrial fiber and free-space links in which QKD was mostly tested.  
The total level of signal loss in a satellite link can widely vary and depends on the type of satellite and technical specifications of the channel realization. Indeed, in the recent experiment with measurement of the quantum-limited signal from a geostationary satellite using the homodyne detection, the total loss of 69 dB was observed [42] with an aperture of 27 cm. The loss can be reduced to 55 dB once a bigger aperture of 1.5 m is used. Alternatively, the channel loss from a low Earth orbit (LEO) satellite can be substantially smaller and as low as 31 dB for an Alphasat-like satellite at a distance of 500 km. The loss can be reduced to about 20 dB by using larger receiving apertures [42]. Therefore, it is important to assess applicability of CV QKD in various scenarios, mainly resulting in different optical link attenuation levels.
## 3 Results
### Implementation Details  
All experiments were conducted using MMSegmentation, a PyTorch-based semantic segmentation toolbox from MMIab, which provides diverse mainstream models and supports various dataset formats. The experimental environment included CUDA 11.3, PyTorch 1.12.0, Python 3.9, and a single NVIDIA GeForce RTX 4090 GPU with 24 GB of memory. Our method uses the AdamW optimizer [37] with an initial learning rate of 0.00006. For the first 1500 iterations, we used LinearLR to gradually increase the learning rate, followed by PolyLR [38], which progressively decreases the learning rate over 80k iterations. The batch size is set to 4, and validation is performed every 4k iterations to ensure a balance between training and validation.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 6 Conclusions  
We investigate and optimize the energy efficiency of the uplink of a massive MIMO system with circularly distributed antennas. The energy efficiency is tightly approximated by a closed-form expression. To maximize the energy efficiency, the power allocation, the total transmit power, and the number of antennas are jointly optimized and determined. Given the number of antennas and the total transmit power, the optimal power allocation coefficients are derived in closed-form. Given the number of antennas, the total transmit power and the maximized EE are verified functions w.r.t. the number of antennas. Then, the NP-hard problem can be solved by one-dimension search.  
This work was supported by the Chongqing Science and Technology Plan Project (cstc2016 jcyjA0516).  
[PERSON] created the main ideas and wrote the paper; [PERSON] and [PERSON] critically reviewed and revised the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Materials  
The _R. rugosa_ samples were kindly provided by the Division of Marine Bioscience, Korea Maritime and Ocean University (Busan, Korea). Briefly, the crude extract was initially fractionated by dissolving it in CH\({}_{2}\)Cl\({}_{2}\) and adding H\({}_{2}\)O. The CH\({}_{2}\)Cl\({}_{2}\) layer was then separated between n-hexane and 85% aq. MeOH while the aqueous layer was fractionated using n-BuOH and H\({}_{2}\)O to obtain different solvent fractions of _R. rugosa_ crude extract. Dulbecco's Modified Eagle Medium (DMEM), fetal bovine serum (FBS), L-glutamine solution, antibiotic solution (penicillin and streptomycin), phosphate-buffered saline (PBS), RIPA reagent, and QIAzol lysis reagent were obtained from Thermo Fisher Scientific (Waltham, MA, USA). L-3,4-dihydroxyphenylalanine(L-DOPA), 5-hydroxy-2-hydroxymethyl-4H-pyranone (kojic acid), \(\alpha\)-melanocyte-stimulating hormone (\(\alpha\)-MSH), and (3-4-5-Dimethylthiazol-2-yl)-2-5-diphenyltetrazol bromide (MTT) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The primers used in the present experiment were synthesized by Bioneer (Daejeon, Korea). Antibodies recognizing tyrosinase (sc-20035), TRP-1(sc-166857), TRP-2 (sc-166716), and \(\beta\)-actin (sc-47778) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA),and MITF (#978000), PKA (#4781), p-PKA (#9196), CREB (#9104), p-CREB (#9198), goat anti-rabbit IgG, HRP (#7076), and goat anti-mouse IgG, HRP (#7074) were obtained from Cell Signaling Technology (Danvers, MA, USA).
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 3 Methods
### Inter-Frame Vehicle Association
The linear combination of the two allows for a more robust description of the inter-frame vehicle matching similarity. The matching degree construction equation is given as Equation (10):  
\[C_{ij}=\beta C_{diou}(i,j)+\varepsilon C_{top}(i,j), \tag{10}\]  
where \(C_{diou}\) is the IOU of the two; \(C_{top}=cos(\alpha)\) is the cosine of the directions of the target prediction and detection frames; \(\alpha\) is the angle between the \(t\) detection frame relative to the \(t+1\) detection and prediction frames, as shown in Figure 5; and \(\beta\) and \(\varepsilon\) are weighting factors that sum to 1.
## 3 Solution Procedure
### FGM Layer
By dividing the FGM layer into \(N_{f}\) fictitious thin layers with a negligible variation of the material properties through their thickness, the \(\overline{G}_{f}\) matrix is converted to a constant matrix for each of these fictitious layers and the following solution to Equation (21) is derived for the \(k^{th}\) layer, with \(k=1,2,\ldots,N_{f}\):  
\[\left[e^{-\overline{G}_{k}\overline{z}_{k-1}\overline{\delta}_{f_{0k}}}+\int_ {\overline{z}_{k-1}}^{\overline{z}_{k}}e^{-\overline{G}_{k}\eta}\overline{B}_ {f_{k}}(\eta)\overline{T}_{f}(\eta)d\eta\right],\ \overline{z}_{k-1}\leq \overline{z}\leq\overline{z}_{k} \tag{23}\]  
in which  
\[\overline{z}_{k-1}=\overline{h}_{p}+\frac{(k-1)\overline{h}_{f}}{N_{f}},\ \overline{z}_{k}=\overline{h}_{p}+\frac{k\overline{h}_{f}}{N_{f}},\ \overline{\delta}_{f_{0k}}=\overline{\delta}_{f_{k}}(\overline{z}=\overline{ z}_{k-1})\]  
Computing Equation (23) at the upper side of the \(k\)-th layer with \(z=\overline{z}_{k}\) finds  
\[\overline{\delta}_{f_{k}}(\overline{z}_{k})=M_{k}\overline{\delta}_{f_{0k}}+ I_{k} \tag{24}\]  
where \(M_{k}=e^{(\overline{G}_{f_{k}}\overline{z}_{k}\overline{f}_{f})}\) and \(I_{k}=e^{\overline{G}_{f_{k}}\overline{z}_{k}}f_{\overline{z}_{k-1}}^{ \overline{z}_{k}}e^{-\overline{G}_{k}\eta}\overline{B}_{f_{k}}(\eta)\overline{ T}_{f}(\eta)d\eta\), relating the state vector at the upper side of the layer with the one at the lower side of the layer. From the continuity of displacements and transverse normal and shear stresses at the interface of each of two adjacent fictitious layers, it is possible to determine the relation between the state vector at the top, \(\overline{\delta}_{f_{(((p_{p}+h_{f})/h))}}\), and bottom, \(\overline{\delta}_{f_{0}}\), surfaces of the FGM layer:  
\[\overline{\delta}_{f_{(((p_{p}+h_{f})/h))}}=M_{f}\overline{\delta}_{f_{0}}+I \tag{25}\]  
where \(M_{k}=\prod_{k=N_{f}}^{1}e^{(\overline{G}_{f_{k}}\overline{z}_{f})}\) and \(I=\sum\limits_{i=1}^{N_{f}-1}\left(\prod\limits_{k=i+1}^{N_{f}}M_{k}I_{i} \right)+I_{N_{f}}\).
## 3 Methodology  
The method proposed in this paper is illustrated in Figure 4 and consists of three main components: tightly coupled pose compensation, Lidar point cloud mapping, and parking space automatic annotation. First, the collected Lidar data is combined with IMU data for pose compensation. Next, the Lidar odometry and keyframes are calculated to provide initial values for the compensated pose, constructing a three-dimensional point cloud map of the parking scene based on the polarized Lidar odometry and historical keyframes. Finally, the regions of interest (ROI) after filtering out the ground points are extracted from the three-dimensional point cloud map to generate a grid map. This grid map is then used to identify obstacle vehicles, detect free spaces, and mark available parking spaces. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols/Variables** & **Definition** \\ \hline \(i\) & The Lidar frame at time \(i\), that is, the Lidar frame sequence number. \\ \(n\) & The Lidar historical key frame number represents the map node number in the mapping, which increases over time. The node number is associated with the vehicle status number. \\ \(\mathbf{T}_{n,n-1}^{\text{IMU}}\) & The pose-transformation matrix from adjacent nodes \(n-1\) to \(n\) is obtained by IMU measurement, \\ \(\mathbf{T}=\begin{bmatrix}\mathbf{R}&\mathbf{t}\\ 0^{\text{T}}&1\end{bmatrix}\in\text{SE}(3)\). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of symbols and variables. Figure 4: Flow chart of parking space automatic recognition method.