esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
## 2 Flow Field Signal Acquisition
### Numerical Validation by a PIV Experiment  
A two-dimension PIV experiment was carried out to validate the numerical scheme. PIV is a method of visualizing the flow field based on an optical principle. Tracer particles with reflective properties are placed in the flow field. A laser emits a light source, and a high-speed camera is used to capture the motion path of the tracer particles along with the fluid. The displacement of the particles is calculated by the Fourier algorithm based on two successive frames of images, and the flow field structure is visualized. The experimental set is shown in Figure 3; the length of the water tank was 5.0 m, and its width and height were 1.0 m and 0.8 m, respectively. The water tank was divided into three parts: the accelerating part, the experiment part, and the decelerating part. The length of the experiment part was 3.0 m.  
A sliding rail was used to make the target body move at a uniform speed, and the maximum speed was set at 1 m/s. As shown in Figure 4, the diameter of the target was 0.1 m, and its length was 0.2 m. The support point was located in the middle, and the distance between the center of the target and the bottom was 0.4 m. To ensure the original flow field was at rest, the experiments were carried out one by one, and the time interval between two adjacent experiments was 20 min.  
Figure 2: Simulated results comparing with previous results.  
The two-dimension PIV system was provided by TSI. The model of the layer was YAG200-NWL, with maximum output energy of 200 mJ, and its pulse width was 3-5 ns. The 4 MX-CCD camera was used to capture the image of the flow field at 2048 \(\times\) 2048 pixels. The minimum span frame time of the CCD camera was 200 ns, and its frame rate was 15 frames per second. In this experiment, images were collected in double-frame and double-exposure mode and processed by the mutual algorithm of the system. Some key parameters used in the experiment are shown in Table 1.  
The virtual LLS was set at a distance of 200 mm from the bottom. When the target moved to the middle of the experiment part, the axial velocity component \(v_{x,//}\) of the virtual LLS was captured. The comparison between the experimental and the numerical results is shown in Figure 5. From it, we can see that the results of calculation and testing were in good agreement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling** & **Sample Number** & **Laser Energy/mJ** &
\begin{tabular}{c} **Velocity Vector** \\ **Number** \\ \end{tabular} \\ \hline
100 & 300 & 100 & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some key parameters used in the PIV experiment.  
Figure 4: Schematic diagram of the experimental facility.  
Figure 3: The experimental environment for the PIV experiment.
## 2 Methods and Tools
### Overview of the Ground-Based Geophysical Methods  
Geophysical methods have been instrumental in understanding and mitigating sinkhole hazards. Seismic refraction, a widely used method, measures the velocity of longitudinal waves (Vp) to delineate salt layers. The distinct velocity of salt (2900-4500 m/s) compared to surrounding sediments allows accurate mapping of dissolution fronts and sinkhole-prone zones. This method has been extensively applied along the Dead Sea shores to identify salt boundaries and assess their role in sinkhole formation [39; 40].  
Seismic reflection provides detailed imaging of subsurface structures and faults. Studies employing both 2D and 3D seismic reflection have revealed critical insights into the interactions between faults, salt layers, and sinkholes [41]. More recently, S-wave seismic reflection has been introduced to enhance the resolution of shallow subsurface investigations, particularly in areas with complex geology [42].  
Multichannel analysis of surface waves (MASW) evaluates shear wave velocities (Vs) to estimate the porosity and karstification of salt layers. This method has been effectively used to map underground voids and assess salt layer conditions at depths between 30 and 70 m [43]. Frequency-domain electromagnetic (FDEM) techniques have been used to assess subsurface conductivity and delineate variations in salinity. FDEM is particularly valuable in identifying zones of brackish and saline water, contributing to understanding aquifer aggressiveness and the processes driving sinkhole formation [44]. Transient electromagnetic (TEM) methods complement FDEM by providing resistivity contrasts to map groundwater salinity and delineate salt layer geometry. TEM has proven essential in defining brine interfaces and evaluating subsurface characteristics that influence sinkhole development [45].  
Magnetic resonance sounding (MRS), also known as surface nuclear magnetic resonance (SNMR), is designed to detect water-filled voids and estimate subsurface water content and hydraulic conductivity. It has been a valuable tool for characterizing karstic cavities and assessing their role in sinkhole development [46]. Microgravity surveys detect density anomalies to identify underground voids and estimate the size of caverns, which are often precursors to sinkholes. These methods have been successfully applied in areas such as Nahal Hever South and Ghor Al-Haditha to predict sinkhole formation [47; 48]. Additionally, a combination of microgravity with InSAR was employed at the Lisan Peninsula to monitor ground subsidence and identify hazardous zones linked to underground cavity formation [49].  
Ground-penetrating radar (GPR) focuses on shallow subsurface investigations, detecting density deficits caused by voids or karstification and providing early warnings for potential sinkhole formation [50]. Electric resistivity tomography (ERT) maps resistivity profiles to identify anomalous zones associated with sinkholes. This method has been applied in alluvial fans and other sedimentary formations to detect structural inconsistencies contributing to sinkhole hazards [51].
## 3 Results and Discussion
### Service Life Prediction  
Carbonation spread across concrete exposed to different environments is plotted against exposure time in Figure 6, in keeping with the model set out in Spanish structural concrete code EHE-08 [16]. The dashed horizontal lines show the minimum rated cover for each exposure environment, cement type and design service life. The code defines rated cover as the distance between the outer surface of the reinforcement and the closest concrete surface. In light of the resulting curves, mixes in which 10% GRC or 50% MRA were included separately as well as jointly (R10/50) would be at no risk whatsoever of carbonation-induced reinforcement depassivation in structures with a service life of up to 100 years and design the characteristic strength as listed in Table 4. Mixes with 25% GRC, with or without recycled aggregate, would not be apt for reinforced concrete exposed to carbonation.
## 1 Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points - A Review  
[PERSON],*, [PERSON], [PERSON] and [PERSON]  
* 1 Shenzhen Institute of Advanced Technology, P.R. China
* 2 The University of Southern Queensland, Australia; E-Mails: [EMAIL_ADDRESS]
* 3 The Hong Kong Polytechnic University, Hong Kong; E-mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
* 4 Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-0755-26803534; Fax: +86-0755-26803589  
_Received: 12 January 2009; in revised form: 11 February 2009 / Accepted: 17 February 2009 / Published: 24 February 2009_  
**Abstract:** Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram.  
**Keywords:** Co-registration, InSAR, interferogram, window size, interval of tie points.
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 2 The Mixed-Integer GNSS Model
### The Null and Alternative Hypotheses  
The observation equations of the linear(ized) mixed-integer GNSS model are formulated under the null and alternative hypotheses as  
\[\begin{array}{ll}\mathcal{H}_{0}:&\mathsf{E}(\underline{y})=Aa+Bb\;;\;a \in\mathbb{Z}^{n},b\in\mathbb{R}^{p}\\ \mathcal{H}_{a}:&\mathsf{E}(\underline{y})=Aa+Bb+Cc\;;\;c\in\mathbb{R}^{q} \end{array} \tag{1}\]  
with the observational vector \(\underline{y}\sim\mathcal{N}_{m}(\mathsf{E}(\underline{y}),Q_{yy})\) containing the carrier-phase and pseudorange observables; \(a\in\mathbb{Z}^{n}\) the unknown integer carrier-phase ambiguities; and \(b\in\mathbb{R}^{p}\) the unknown real-valued parameters, e.g., position coordinates, atmosphere parameters, receiver and satellite clock parameters, and instrumental biases [1,2,16]. Under \(\mathcal{H}_{a}\), the mean of \(\underline{y}\) is assumed shifted by \(Cc\), \(\mathsf{E}(\underline{y}|\mathcal{H}_{a})=\mathsf{E}(\underline{y}|\mathcal{H }_{0})+Cc\). Such shifts allow one to model various important GNSS model misspecifications or biases in \(c\). For instance, through the choice of \(C\) in \(Cc\), one may model the presence of one or more outliers in the pseudorange data, cycle-slips in the phase data, the presence of neglected atmospheric effects, or in fact any other systematic effect that one failed to take into account under \(\mathcal{H}_{0}\). Various such examples are given in the GNSS Handbooks [2,3].
## 3 Results and Discussion
### Surface Band Structure  
The topological surface band structures of CsPbBr\({}_{3}\) and CsPbI\({}_{3}\) from the supercell structure illustrated in Figure 1b, namely one PbBr\({}_{2}\)- (PbI\({}_{2}\)-) and CsBr- (CsI-) terminated slabs, are depicted in Figure 7a,e, respectively, and they exhibited a Dirac cone near the Fermi level at the M points of the surface Brillouin zones with the respect to the natural behavior. The polar surface band structures of CsPbBr\({}_{3}\) and CsPbI\({}_{3}\) come from the center Pb atom moved out-of-plane at approximately 0.08 A, as shown in Figure 7b,f, respectively. This indicates that the surface conduction band is separated into positive and negative surfaces in terms of energy or achieves a ferroelectric TI. The Dirac cone split because of the break in the time-reversal symmetry, suggesting the presence of a Rashba band-splitting mechanism caused by the electric polarization from the Pb displacement. The topological surface band structures of CsPbBr\({}_{3}\) and CsPbI\({}_{3}\) from the supercell structure are illustrated in Figure 1c and two PbBr\({}_{2}\)- and PbI\({}_{2}\)- terminated slabs are depicted in Figure 7c,g, respectively. The PbBr\({}_{2}\) (PbI\({}_{2}\)) surface did not appear to contribute to Dirac corn-like energy dispersion, but the CsBr (CsI) surface did. Furthermore, the polar surface band structures of CsPbBr\({}_{3}\) and CsPbI\({}_{3}\) with PbBr\({}_{2}\)- and PbI\({}_{2}\)- terminated surfaces on both sides are depicted in Figure 7d,h, respectively, showing that PbBr\({}_{2}\) (PbI\({}_{2}\)) surfaces change from not contributing topological surface conduction bands to contributing such bands under the effect of polarization.
## 4 Microbial Adhesion
### Control of Bacterial Adhesion
#### 4.5.2 Contact Killing Coatings  
Im immobilizing antibacterial biocides onto the surface platform by covalent bonding creates a contact killing surface (or so-called contact active surface). However, contact killing surfaces do not have any bacterial adhesive resistance, instead of eliminating bacteria that encounter the surface. This is facilitated by either physical damage or by nonoxidative stress [75]. There are multiple forms of contact killing coatings that are defined by the type of antibacterial agent attached to the surface and the proposed mechanisms of action in which the bacteria are eliminated. The most commonly reported as contact active antibacterial agents include quaternary ammonium compounds (QACs), such as alkyl pyridiniums and quaternary poly (2-(dimethylamino) ethyl methacrylate), quaternary phosphonium (QPs), and N-chloramines [75]. Green reports that QACs are often used as an antimicrobial agent (AMA) for contact killing coatings and it denotes the distinction between QACs based on silanes (Si-QAC) and QACs based on polymers [76]. Furthermore, a wide range of molecules has been tested as AMA, including guidelines, enzymes, chitosan, peptides, and other peptidomimetics [76].  
Tiller et al. examined the first bacterial elimination mechanism [77]. According to them, the long polymeric strings that were attached to the microbial agents enable penetrating a hole in the membrane of a gram-positive bacterium and with this eventually kill the cell. However, recent studies cite that the length of the polymer chain does not influence the antibacterial impact of the coating. Kugler et al. proposed the second mechanism [78]. According to them, the exchange of ions is responsible for the bacterial killing due to the destabilization and the loss of natural ions of bacteria.  
Bieser and Tiller et al. proposed the most recent mechanism, which is called the phospholipid sponge effect [79]. They state that the antimicrobial action occurs by the selective adhesion of negatively charged phospholipids in the bacterial cell membrane onto the cationic surface. A recent study that was performed by Gao et al. confirmed the existence of the phospho-lipid sponge effect [80].  
They reported a negative connection between the film cross-linking density and negatively charged phospholipid, whereas the neutral phospholipid exhibited no correlation supporting the action of anionic phospholipid suction that was proposed in the sponge effect. The three mechanisms described above are all based on contact killing QACs coatings, immobilizing either silanes, or polymers as antibacterial agents. The last mechanism is only based on N-chloramines coatings. This mechanism is believed to be revolved around the active chlorine transfer. Nevertheless, further examination of the antibacterial mechanisms of N-chloramine modified surfaces is required [81].
## 2 Graphene and Its Derivatives: Graphene Oxide and Reduced Graphene Oxide
### Electronic Properties of Graphene, GO, and rGO  
The intrinsic features of graphene serve to strongly support mechanical, thermal, electronic, magnetic, and optical applications. In particular, charge carriers, electrons, in the graphene behave like massless Dirac fermions, which directly affect the linear energy dispersion relation [3]. However, the zero bandgap in graphene makes it unsuitable for FETs (Figure 3a,b). Thus, opening the bandgap in graphene [36] is an urgent issue, and great efforts have been made regarding this topic over the past decade [37; 38; 39].  
GO is an electronically hybrid-type material containing both conducting \(\pi\)-states from graphitic sp\({}^{2}\) hybridized carbons and a large energy gap between the \(\sigma\)-states of its sp\({}^{3}\)-bonded carbons [40]. That is, with a low degree of oxidation, the GO is a semiconducting material, and with a full degree of oxidation, GO is an insulating material [41].
## 3 Numerical Simulation and Results Analysis
### Boundary Condition  
The inlet boundary condition was the velocity-inlet, assuming that the inlet gas-liquid two-phase was uniformly mixed. The primary phase was set as water and the secondary phase as air. The secondary phase volume fraction was the inlet gas volume fraction. The outlet boundary condition is defined as the pressure outlet, and the reference pressure is the default pressure. In the numerical simulation, the no-slip boundary condition was applied to the flow boundary, because the velocity of fluid on  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Turbulence Models** & **Applicability** \\ \hline Standard _k-\(\varepsilon\)_ model & A simplified multiphase model, which is suitable for gas–liquid two-phase flow with low gas phase fraction. There is only one set of momentum equations, and the continuity equation can be solved for gas–liquid two-phase flow. \\ \hline RNG _k-\(\varepsilon\)_ model & The model can deal with the turbulent flow problem with a low Reynolds number. The influence of the vortex in the turbulent flow is considered in the RNG \(\kappa\)-\(\varepsilon\) model, it has higher accuracy than the standard _k-\(\varepsilon\)_ model. \\ \hline SST _k-w_ & The model considers the effect of shear stress on turbulent flow, can deal with free flow in the near-wall region and is often used for flows with large eddies. It has high accuracy and reliability. \\ \hline \hline \end{tabular}
\end{table}
Table 6: The applicability of different turbulence models. SST: shear stress transport.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Multiphase Models** & **Applicability** \\ \hline Mixture & A simplified multiphase model, which is suitable for gas–liquid two-phase flow with low gas phase fraction. There is only one set of momentum equations, and the continuity equation can be solved for gas–liquid two-phase flow. \\ \hline Eulerian & The model establishes the momentum equation and continuity equation for each phase, which can describe the two-phase flow well and has a high accuracy of calculation. \\ \hline VOF & The model is suitable for two-phase flow with obvious stratification and is used to deal with the problem of multiphase flow without interpenetration. \\ \hline \hline \end{tabular}
\end{table}
Table 5: The applicability of different multiphase models.  
the surface of wall is zero. The no-slip boundary condition is generally adopted in the viscous flow and the analysis of large-scale flow [41]. For example, if the scale is less the \(10^{-6}\) order of magnitude, the slip boundary condition can be adopted in the flow. The standard wall function method was applied to complement the turbulence model near the boundary. The physical quantities on the wall are connected with those in the turbulent core, it has the advantages of less computation and better accuracy [42], so the flow field near the wall can be calculated.
## 2 Materials and Methods
### Scanning Electron Microscopy (SEM)  
After each storage period, three samples of each group were sectioned along the sagittal plane, at the center of each cylinder using a saw microtome ([PERSON], Le Locle, Switzerland). Subsequently, the resin-dentin interfaces of the specimens were etched using 37% phosphoric acid for 10 s, rinsed for 10 s with distilled water, and immersed in a 2.5% NaOCl solution for 3 min [20]. The specimens were finally rinsed with distilled water and dehydrated in a graded series of ethanol solutions. These were then mounted on aluminum SEM stubs, and sputter-coated with gold-palladium alloys (20/80) using a sputtering device ([PERSON], Technics, CA, USA). The adhesive layer thickness and length of the resin tags (10 measurement for each section) were analyzed using a Quanta 250 FEG scanning electron microscope (FEI Company, Eindhoven, The Netherlands) functioning with a 10 kV acceleration voltage of the electrons.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adhesive** & **Composition** & **Application** \\ \hline \hline \multirow{4}{*}{\begin{tabular}{c} Iperbond Max—‘IPM” \\ (Itena Clinical, Paris, France) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} 10-MDP, 4-META, methylacrylates, photo-initiators, ethanol, water, fumed silica \\ \end{tabular} } & - & Apply (20 s). \\  & & & Wait until the solvent \\  & & & had completely \\  & & photo-initiators, ethanol, water, & vapored (20 s) \\  & & fumed silica & - \\  & & & Dry (5 s) \\  & & & Light cure (10 s). \\ \hline \multirow{4}{*}{\begin{tabular}{c} Iperbond Ultra— \\ “IPU” (Itena Clinical, Paris, France) \\ \end{tabular} } & Triethylenglycol dimetracylate, acrylate polyester urethane aliphatic, bonding acetators, metacylate hydroxyleth, photo-starters, acetone, ethanol, nanoparticles. & - & Apply + rub (20 s). \\  & & Methylen glycol (5 s) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Ibond Universal—‘IB” \\ (Kulzer, ZA \\ Courtaboeeuf, France) \\ \end{tabular} } & \begin{tabular}{c} Methacrylate monomers (UDMA), \\ Hydrophilic monomers (4-META), \\ glutaraldehyde, photo-initiators, \\ stabilizers, acetone, and water \\ \end{tabular} & - & Apply + rub (20 s). \\ \hline \multirow{4}{*}{\begin{tabular}{c} FuturaBond M+—‘FB” \\ (Voco GmbH, \\ Cuxhaven, Germany) \\ \end{tabular} } & \begin{tabular}{c} HEMA, BIS-GMA, ethanol, Acidie \\ adhesive monomer (10-MDP), UDMA, \\ catalyst ethanol, water, initiators, and catalysts \\ \end{tabular} & - & Apply + rub (20 s). \\  & & & Dry (5 s) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Scotchbond \\ Universal—‘SU’ (3M, \\ St. Paul, MN, USA) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 10-MDP, HEMA, dimetachacrylate resins, \\ Vitrebond copolymer, filler, ethanol, water, initiators, silane \\ \end{tabular} } & - & Apply + rub (20 s). \\  & & & Dry (5 s) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adhesive system: chemical composition, application process, and manufacturing [3; 17; 18; 19].
## 4 Problem Formulation and Algorithm Design  
### Problem Formulation  
We aim to maximize the achievable secrecy rate by optimizing digital beamformer \(\mathbf{v}_{1},\mathbf{v}_{2}\) and holographic beamformer \(\left\{M_{x,y}\right\}\), subject to the maximum power constraint at BS. The problem can be formulated as  
\[\begin{split}(\mathrm{P0}):\max_{\mathbf{v}_{1},\mathbf{v}_{2}, \mathbf{m}}&\quad\left\{R_{u}-\max_{k}R_{e_{k}}\right\}\\ \text{s.t.}&\mathbf{v}_{1}^{H}\mathbf{v}_{1}+\mathbf{ v}_{2}^{H}\mathbf{v}_{2}\leq P_{\max},\\ & 0\leq M_{x,y}\leq 1.\end{split} \tag{9}\]  
Since both \(R_{u}\) and \(R_{e_{k}}\) are non-concave functions as well as the coupled optimization variables, \((\mathrm{P0})\) is difficult to solve, but we find that when one of \(\mathbf{v}_{1},\mathbf{v}_{2}\) or \(\mathbf{m}\) is fixed, it will be easy to solve, so we propose an optimization method based on an alternating algorithm. By alternately optimizing one of \(\mathbf{v}_{1},\mathbf{v}_{2}\) or \(\mathbf{m}\) while the other is fixed at each iteration, convergence is finally achieved through multiple iterations. Therefore, in order to effectively solve \((\mathrm{P0})\), we decompose it into two subproblems, i.e., digital beamforming to optimize \(\mathbf{v}_{1},\mathbf{v}_{2}\) and holographic beamforming to optimize \(\mathbf{m}\). By iteratively solving these two subproblems, we can eventually solve \((\mathrm{P0})\) sub-optimally. Different from the traditional phase-controlled beamforming design, \((\mathrm{P0})\) is a complex-domain optimization problem subject to nontraditional real-domain constraints. The traditional algorithm is no longer applicable here. For the optimization problem of \(\mathbf{m}\), we use the traversal approach to approximate solving. In the next two subsections, we will solve these two subproblems.