esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## Particle Deposition Model
### Particle Migration
#### 2.2.2 Migration Force of Particulate Matter in Flow Field  
The migration process of micron-sized particles in the air is closely related to the migration force, such as drag force, gravity force, Basset force, and Saffman force. The force balance equation of the particles can be simplified as follows [31; 32; 33]:  
\[m\frac{dv_{P}}{dt}=G+F_{D}+F_{S}+F_{B} \tag{3}\]  
\[G=\frac{mg_{x}\left(\rho_{P}-\rho_{f}\right)}{\rho_{P}} \tag{4}\]  
\[F_{D}=C_{D}\frac{\pi\rho_{f}\left(v_{f}-v_{P}\right)^{2}{d_{P}}^{2}}{8} \tag{5}\]  
\[F_{S}=1.61\left(\mu\rho_{f}\right)^{\frac{1}{2}}{d_{P}}\left(v_{f}-v_{P}\right) \left|\frac{dv_{P}}{dy}\right|^{\frac{1}{2}} \tag{6}\]  
\[F_{B}=\frac{3}{2}{d_{P}}^{2}\left(\pi\mu\rho_{f}\right)^{\frac{1}{2}}\int_{l_{ 0}}^{t}d\tau\frac{d}{\frac{d}{\tau}\left(v_{f}-v_{P}\right)}{\left(t-\tau \right)^{1/2}} \tag{7}\]  
where \(G\), \(F_{D}\), \(F_{S}\), and \(F_{B}\) represent gravity, drag force, Saffman force, and Basset force respectively, where \(\rho_{f}\) is fluid density, \(\rho_{P}\) is particle density, \(m\) is particle mass, \(C_{D}\) is the drag coefficient, \(d_{P}\) is the particle diameter, \(g_{x}\) is acceleration caused by gravity, \(\mu\) is dynamic viscosity of the fluid, \(v_{P}\) represents particle velocity, and \(v_{f}\) represents the fluid phase velocity.  
The \(C_{D}\) is a parameter determined by the Reynold number Re\({}_{P}\) of particles, which has a great influence on the forces and motion trajectories of particles. According to different experimental data and applicable scopes, many empirical and semi-empirical formulashave been given by the authors' predecessors. Among them, the more widely used is the lift-Gauvin formula, as follows [34]:  
\[C_{D}=\frac{24}{\text{Re}_{P}}\Big{(}1+0.15\text{Re}_{P}{}^{0.687}\Big{)}+\frac{0. 42}{1+4.25\times 10^{4}\text{Re}_{P}{}^{-1.16}} \tag{8}\]  
where \(\text{Re}_{P}\) can be expressed as [34]:  
\[\text{Re}_{P}=\frac{\rho_{f}\Big{|}v_{f}-v_{P}\Big{|}d_{P}}{\mu} \tag{9}\]  
For spherical particles, the ratio of eddy pressure differential force to viscous friction resistance is 1:2 under the condition of \(\text{Re}_{P}\to 0\). When the \(\text{Re}_{P}\) increases to the Newtonian resistance region (\(\text{Re}_{P}>1000\)), the proportion of viscous resistance in the total drag force is very small, and the total drag force is basically determined by the eddy pressure difference force, and the \(C_{D}\) also approaches to a fixed value of 0.44. The object of study in this paper is the particles in gaseous fluid, whose \(\text{Re}_{P}<1000\). The expression of \(C_{D}\) as shown in Formula (10) can be obtained from the Shiller-Naumann relation, and the error is estimated to be between \(-4\%\) and \(5\%\)[35].  
\[C_{D}=\left\{\begin{array}{cc}\frac{24}{\text{Re}_{P}}&,\text{Re}_{P}\leq 1 \\ \frac{24}{\text{Re}_{P}}\Big{(}1+0.15\text{Re}_{P}{}^{0.687}\Big{)}&,1<\text{ Re}_{P}\leq 1000\end{array}\right. \tag{10}\]
## 7 Experimental Research
### Multipath Channel  
The application of algorithm (9) was considered for a channel with multipath wave propagation. We conducted experimental studies on the reception of FTN signals in burst mode in an urban area. In this case, the experiment was carried out repeatedly to obtain the necessary statistics in the presence of traffic and people.  
We can see BER performance in a case of fading channel in an urban area in Figure 12. The transmission rate of symbols of the channel binary alphabet is equal to \(R=2/T\). There is potential BER performance of BPSK signals. We used an MMSE equalizer for work in fading channels. Figure 12 shows the BER performance without an MMSE equalizer for comparison with the obtained results. The simulation results (Section 5) in the channel with AWGN are also presented here.  
Figure 11: Experimental BER performance for proposed algorithm (9).  
Figure 12: Experimental BER performance for proposed algorithm (9) in multipath channel.  
The following conclusions can be drawn. The energy costs for transmission channels in an urban area are equal to 13.8 dB at \(BER=10^{-3}\) in a case of application of an MMSE equalizer. The energy losses in a multipath channel relative to a channel with AWGN are equal to 3 dB for \(BER=10^{-3}\) for the application of proposed algorithm (9). Energy losses in a multipath channel in relation to the potential BER are equal to 7 dB at \(BER=10^{-3}\) for a symbol transmission rate twice the Nyquist barrier.
## 3 Results and Discussion  
### Penetration Resistance Analysis  
#### 3.1.1 Penetration Resistance of Clay  
The clay stratum was simulated using an ideal elastoplastic Tresca constitutive model to represent undrained conditions. The elastic modulus E was specified as 500-1000 times the undrained shear strength \(s_{u}\)[27], with [PERSON]'s ratio \(\mu=0.49\). The material parameters are fully documented in Table 3.  
Mesh convergence analysis (Figure 12a) confirmed numerical solution convergence when the element size in the spudcan-adjacent refined zone (\(1.5\ L\times 1.5\ L\)) was \(\leq\)0.03 \(L\). Penetration rate sensitivity analysis (Figure 12b) indicated the minimal sensitivity of the CEL simulations to penetration rate variations. The numerical results demonstrated stronger agreement with the centrifuge test data than theoretical methods, with theoretical errors exceeding 10% at a 12 m penetration depth.  
The plastic deformation and stress distribution of the soil at different penetration depths are shown in Figure 13. The plastic zone of the soil was mainly distributed near the bottom of the spudcan and the area around the spudcan hole wall. The corresponding displacement vector diagram of the surrounding soil is shown in Figure 14. Failure mechanism evolution analysis indicated the following: shallow penetration (4 m) induced global shear failure with lateral soil extrusion and vertical cavity formation; intermediate depth (8 m) triggered localized shear and backfill, increasing surface heave by 38%; and deep penetration (12 m+) exhibited a stabilized bearing capacity governed by remolded  
Figure 11: Punch-through modes: (**a**) stiff-over-weak clay; (**b**) sand-over-weak clay.  
strength. Backfilling emerged as the dominant mechanism. No further heaving occurred at the soil surface, as it was no longer affected by localized shear failure around the spudcan. This observation was in full accordance with the localized plastic flow failure assumption proposed by [PERSON] et al. (2018). This process clearly demonstrates the coupled effects between progressive clay softening and cavity development.
# Kinematic Modelling and Motion Analysis of a Humanoid Torso Mechanism
## 2 Materials and Methods
### Mechanical Design of LARMbot's Torso Mechanism  
The LARMbot humanoid robot was developed between 2012 and 2018 [15; 16; 17; 18; 19; 20] at the LARM laboratory of the University of Cassino. It was designed as a low-cost user-friendly humanoid robot for service tasks, and it features parallel mechanisms both in its legs [29] and torso [30] to achieve a high kinematic and dynamic performance.  
As shown in Figure 1, this humanoid robot is characterised by 22 active degrees of freedom (DoFs), a height of 850 mm, and an overall mass of 3.600 kg only, thanks to its lightweight 3D-printed frame [17]. Its torso mechanism, CAUTO (CASsino hUmanoid TOrso), is bioinspired, reproducing Functional Spine Units (FSU). The torso is at the core of the LARMbot performance, as it contributes to both walking balance, general navigation,  
The main structural element of the torso is the compliant backbone, made of commercial flexible shaft couplers that are characterised by a compliant response to bending and a stiffer response to axial torsion, compression, and elongation. As these components connect the rigid vertebrae of the spine, they determine the mobility of the system. The intrinsic stiffness of the backbone also dampens the effect of unexpected wrenches on the system and restores a straight torso position in case of structural failure of the tendons. The configuration of the couplers is actuated by four tendons that determine the pose of the upper torso with respect to the lower torso.
# Photodynamic Properties of CdSe/CdS Quantum Dots in Intracellular Media
## 1 Introduction
With this method, the physicochemical properties of the nanoparticles, such as their size, shape, core material, and surface functionalization have a strong impact on cellular interaction, including uptake. Our work uses endocytosis with the assistance of polyethylene glycol (PEG) that enhances the penetration capacity of the nanoparticles, allowing the nanoparticles to trespass into the intracellular cytoplasm. Although single particle injection into single selected cells is not possible through endocytosis, this method is simple, economical, and sufficient for specific applications such as when investigating the photodynamic properties of nanoparticles in intracellular media. Monitoring the uptake of nanoparticles into cells is important to ensure that the nanoparticles have indeed been injected into the cells. Surface-enhanced Raman spectroscopy (SERS) is a well-known method for this purpose. SERS is a non-destructive and multiplexed technique for live intracellular imaging [31]. Although SERS overcomes the limits inherent in traditional cell imaging techniques, some issues remain. Firstly, it requires high-power lasers or long exposure times to be detected because of the weak scattering from biomolecules. Furthermore, cells usually exhibit a complicated Raman spectrum and interfering background from endogenous biomolecules. This makes target detection and data interpretation very demanding [32]. Fluorescence spectroscopy provides a stronger signal compared to SERS, although it could also have a low signal-to-noise ratio. Previously, Forster Resonant Energy Transfer (FRET) using plasmonic nanostructures has been reported for single molecule detection [33]. The key features of this technique include the reduction of detection volume and improved signal-to-noise ratio. However, the selective functionalization at the specific positions where the plasmonic nanostructure is present is still challenging and not well understood [33]. In this paper, the photodynamic properties of CdSe/CdS QDs in intracellular media are presented. CdSe is among the widely used QDs core due to its size-tunable and stable fluorescence in the visible to NIR regions, the wide availability of precursors, and the well-defined technology of crystal growth [22]. Capping the CdSe core with a CdS shell to produce a CdSe/CdS core/shell QDs offer improved photoluminescence quantum efficiency and red-shifted photoluminescence spectra, making them bright and robust materials for bioimaging [2]. Using fluorescence and time-resolved fluorescence spectroscopy, we demonstrate the interaction between CdSe/CdS QDs and intracellular media and show that QDs were successfully seeded into Jurkat cells.
## 3 Results
### Experiment (1): Noise for Single Strip Measurements  
The first experiment analyses the noise of the system using planar surfaces in the study area. The data sets utilized for this are the four repetitions of the single strip measurements. The objects used for the noise analyzes are the six tables shown in Figure 3, which are extracted from the four data sets. For noise evaluation, the residuals with respect to a plane fit are taken, as described in [43]. For the plane estimation, a RANSAC algorithm is first used for plane identification and extraction of the corresponding 3D points \([x_{e},y_{e},z_{e}]^{T}\). Afterwards, the individual plane can be described with equation  
\[n_{x}\cdot\mathbf{x_{e}}+n_{y}\cdot\mathbf{y_{e}}+n_{z}\cdot\mathbf{z_{e}}-p=0 \tag{2}\]  
in the three dimensional space. The parameters for the estimation using a Gauss-Helmert model are the orthogonal distance \(p\) between origin and plane, and the components of the unit normal vector \(n_{x},n_{y}\) and \(n_{z}\)[44]. Since the tables are placed horizontally and directly under the flight path, it can be assumed that the angle of incidence for all measurements is close to 90\({}^{\circ}\) and deviations due to different incidence angles are therefore negligible. Based on this assumption, the residuals to the plane in z-direction \(\mathbf{v}_{z_{i}}\) obtained from the adjustment are used for the estimation of the range precision \(\sigma_{d}\) with  
\[\sigma_{d}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\mathbf{v}_{z_{i}}^{2}}\quad, \tag{3}\]  
where \(n\) defines the number of points referring to the plane. An example of the distribution of residuals with respect to the plane fit is shown in Figure 5(a) for a table from the first data set.  
Considering all data sets for the single strip measurements, the range precision is estimated for all tables included in each data set and plotted summarized by a histogram in Figure 6b. A total of 48 planes were used for noise evaluation with a mean number of points per plane fit of 1327. The resulting mean range precision for the 10 m flight height is 0.35 cm, which is within the manufacturer's specification of 1.0 cm [41]. Furthermore, this result describes the impact of scanner precision on the subsequent quality analysis, which is comparatively negligible taking the uncertainty due to trajectory estimation into account. The absolute accuracy of the laser scanner cannot be analyzed with this approach because a systematic offset cannot be separated from errors due to trajectory estimation. For investigations that consider the absolute accuracy of the laser scanner, the sensor should be analyzed without the UAV, while the uncertainties due to trajectory estimation have no influence.
## 3 Results and Discussion
### CsFAMAPIBr\({}_{3}\) vs. MAPb\({}_{3}\)-Based Devices on SnO\({}_{2}\) Layer
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ESC/Perovskite** & **Aging Days** & **Sweep** & **Jsc [mA/cm2]** & **VOC [V]** & **FF** & \begin{tabular}{c} **PCE [\%]** \\ **(Champion Cell)** \\ \end{tabular} \\ \hline \multirow{4}{*}{SnO2/MAPbI3} & \multirow{2}{*}{Day 1} & Fwd & 21.2 \(\pm\) 0.8 & 1.072 \(\pm\) 0.007 & 0.56 \(\pm\) 0.02 & \begin{tabular}{c} 12.7 \(\pm\) 0.6 \\ **(13.6)** \\ \end{tabular} \\  & & Rev & 20.5 \(\pm\) 0.9 & 1.076 \(\pm\) 0.006 & 0.64 \(\pm\) 0.01 & \begin{tabular}{c} 14.2 \(\pm\) 0.6 \\ **(15.0)** \\ \end{tabular} \\  & \multirow{2}{*}{Week 4} & Fwd & 21.2 \(\pm\) 0.9 & 1.058 \(\pm\) 0.013 & 0.64 \(\pm\) 0.02 & \
\begin{tabular}{c} 14.2 \(\pm\) 0.8 \\ **(15.5)** \\ \end{tabular} \\  & & Rev & 21.0 \(\pm\) 0.9 & 1.065 \(\pm\) 0.007 & 0.71 \(\pm\) 0.02 &
\begin{tabular}{c} 16.0 \(\pm\) 1.1 \\ **(17.7)** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the photovoltaic parameters for MAPbI3 on SnO2 measured in fresh (1 day) and aged (28 days) conditions. Figure 5: JV curves measured at 1 Sun conditions (100 mW/cm2, AM 1.5G) in forward (dashed line) and reverse (solid line) scan for fresh and aged samples of MAPbI3 deposited on SnO2 (**A**) and VOC stability over a period of time for fresh and aged MAPbI3 samples (**B**). ruled out the possibility of this being related to any changes in the bulk of the perovskite, it could be associated with the use of SnO\({}_{2}\) as ESC. The higher conductivity and smaller surface area of SnO\({}_{2}\) compared to the use of mesoporous TiO\({}_{2}\) can have a positive effect on how ions move from this interface. However, as MAPbI\({}_{3}\) JV curves did not improve, we continue observing the negative spike at very early timescales after the laser pulse. Ionic accumulation at the interfaces was described as the major influence for the great capacitance values reported for perovskites [9; 35]. Moreover, recently, it was described that it is iodine defects the main responsible for such ionic accumulation. Additionally, these defects present a lower formation energy and higher defect density for MAPbI\({}_{3}\) compared to the triple cation composition [36]. Therefore, the higher ionic density at the interfaces of MAPbI\({}_{3}\) devices leads to higher capacitance values, increased carrier recombination, and finally, a higher degree of hysteresis in the devices. All the ionic components might contribute to the ionic capacitance observed. As well, it has been previously suggested that MA\({}^{+}\) is the main responsible for the hysteresis observed in the JV curves. However, its ionic mobility is very slow compared to iodide and its vacancies, which may suggest that we cannot see any effect on the TROTR, but it is still observable in JV hysteresis [17; 37; 38].
## 2 Materials and Methods
### Anti- and Mutagenicity Tests  
Anti- and mutagenicity tests were assessed in _S. typhimurium_ strains using the Ames test [17]. We utilized the TA98 strain for detecting frame-shift mutations and the TA100 strain for detecting base-pair substitutions with and without metabolic activation (S9 mix) through direct plate incorporation. Positive controls included 4-nitroquinoline-1-oxide (4 NQO; 1.0 \(\upmu\)g/plate) and sodium azide (SA; 0.5 \(\upmu\)g/plate) for direct mutagens (without S9 mix) and 2-aminoanthracene (2 AA; 0.5 \(\upmu\)g/plate) for indirect mutagenesis (with S9 mix). The S9 mix (protein content: 23.6 mg/mL) was purchased from Moltox Co. (11-05L2; Boone, NC, USA); it was isolated from the liver of a Sprague Dawley rat (7-week-old, male) induced by phenobarbital and 5,6-benzo-flavone. DMSO was used as the negative control. The highest test sample concentration was 5000 \(\upmu\)g/plate. We mixed 2 mL of top agar, 100 \(\upmu\)L of DMSO-dissolved test sample at various concentrations, 100 \(\upmu\)L of cultured strains (1-2 \(\times\) 10\({}^{9}\) CFU/mL), and 500 \(\upmu\)L of S9 mix or 0.2 M sodium phosphate buffer in a tube at 45 \({}^{\circ}\)C, vortexed it briefly, and spread it on minimal-glucose agar plates. After 48 h of incubation at 37 \({}^{\circ}\)C, the revert colonies were counted. The antimutagenic activity was quantified as the percent reduction in the mutagenic effects, determined by the following equation:  
\[\text{Inhibition (\%)}=\text{(M $-$ B)}/\text{(M $-$ A)}\times 100 \tag{1}\]  
where M represents the count of revertant colonies induced by the mutagen, A denotes the count of revertant colonies occurring spontaneously, and B signifies the count of revertant colonies observed when both the test sample and mutagen were introduced to the test strains.
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 4 Proposed Work  
The amelioration proposed will make the OLSR routing protocol suitable for vehicle networks in an urban environment. The objective is to route the data step-by-step by taking into account the urban structure streets. Our proposition has two features:  
1. An improved greedy approach \"GFA\" to route packets between two junctions in order to collect the information of this segment delimited by source and candidate intersections, as shown in Figure 3 composed of several intersections A, B, C, D, and E.
2. Select the intersection to reach the destination.  
Figure 3: Mechanism of intersection selection executed by G-OLSR.  
Therefore, the protocol starts by collecting information of two segments delimited by source intersection and the two candidate intersections A and C. Using this collected information, the vehicle determines all candidate intersections to select the right one. Then, owing to the collected information using GFA, we provide a score for each intersection. Therefore, we apply [PERSON]'s algorithm in order to identify the way in which to reach the destination.  
Contrary to other protocols, the source vehicle elects the right intersection sequence to reach the destination.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Denoising Block  
Since there is considerable noise that blurs the underwater image, we designed two noise blocks for the encoder and decoder to filter out noise and boost the image, respectively. The denoising blocks for the two stages have similar structures, as shown in Figure 4. Both consist of an attention mechanism, which first calculates the attention weights along the channel axis. Then, the original features are multiplied by the attention coefficient to obtain the features after attention.  
\[Atten(F)=Mul(F,Con(AvgPool(F))), \tag{6}\]  
where \(AvgPool()\) refers to a pooling operation that reduces the number of channels to 1 while keeping the width and height. \(Con()\) is the convolution operation, and \(Mul(_{\cdot})\) is the multiplication operation between two inputs. Following attention, the encoder denoising block utilizes two convolution layers and a skip connection to refine the feature further. The mathematical representation of the denoising block in the encoder can be defined as follows:  
\[DB_{en}(F)=Con_{\times 2}(Atten(F))+F. \tag{7}\]  
In decoders, the denoising block receives the feature map from the encoder via skip connections. Inspired by the SOS algorithm [44] for boosting image denoising, we first added the feature map of the encoder to that of the decoder, which strengthens the signal. Then, a convolution operation was utilized to denoise the strengthening feature. Finally, we subtracted the previous feature from the restored outcome. The process is formulated as follows:  
\[\begin{split} DB_{de}(F,E^{n-K+1})=Diff(Decon(Atten(F)),X),\\ X=Con(Decon(Atten(F))+E^{n-K+1}),\end{split} \tag{8}\]  
where \(E^{n-K+1}\) is the feature of the corresponding encoder layers with skip connections. \(n\) is the total number of encoders/decoders, and \(K\) is the index of the current decoder.
## 3 System Design
### System Architecture
The sensors can include critical flight data such as vehicle speed, rates, and accelerations via air data and/or inertial systems. Sensors can also perform pressure, temperature, voltage, or other critical measurements to support real-time performance or prognostics of future performance. Vehicle sensors continue to evolve and develop based upon new technology and needs.