esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-Controlled Microbots in Biomedical Application: A Review
## 5 Imaging
### Photoacoustic Imaging  
Where conventional optical imaging fails, photoacoustic imaging comes into play. The basic principle of photoacoustic imaging is that impulsive optical energy is absorbed by the tissues and is transformed into sonic waves, which are then detected to provide high-resolution tomographic photographs with decent optical contrasts. A recent study performed deep tissue imaging and targeted in vivo navigation of microbots in the intestine of mice using Photoacoustic Computed Tomography (PACT) [208]. Irradiated NIR light is used to release the drugs once they reaches the targeted region, and the whole process is visualized in real-time in vivo using the PACT imaging system integrated into the micro-robotic system. Current photoacoustic systems operate in serial or parallel configurations with the help of ultrasonic transducers. To reduce the cost and complexity of the imaging by using a single element ultrasonic transducer, the throughput of the imaging also decreases. Deep penetration (48 mm) with a high data acquisition rate (50 Hz) in vivo operation of single impulse panoramic photoacoustic computed tomography (SIP-PACT) has been demonstrated [209; 210; 211; 212; 213]. A 1000-fold less scattering nature of sound compared to light allows it to travel through biological tissues, allowing for more detailed imaging of the system [214]. Recently, a slightly updated version of photoacoustic imaging, known as photoacoustic topography through an ergodic relay (PATER), was introduced [215]. This imaging technique uses a single-element ultrasonic transducer to get a result similar to that of the multi-element one. In that study, high-speed imaging of the propagation of the blood pulse waves inside mice is demonstrated in that study. The rendering of volumetric snapshots of spectrally resolved optoacoustic data in real-time paved the way for a new fifth-dimensional optoacoustic imaging [216]. This technique of imaging has already been demonstrated in both mice and humans. Photoacoustic imaging is favorable for light-actuated microbots, even with a metal coating, which is often done to achieve certain capabilities of robots. Photoacoustic imaging does not interfere with the light-controlled navigation system, and pulsated illumination (\(<\) 10 ns) in the biological medium makes it less invasive for extended operation.
## 2 Background Study on Pipeline Leak Detection Methods  
### Empirical Mode Decomposition (EMD)  
Empirical mode decomposition (EMD) helps to denoise a signal by breaking it down into multiple IMFs and a residual component [51]. Each IMF has a part that oscillates at a single instantaneous frequency. The two primary conditions an IMF must satisfy are as follows:  
1. The extrema and zero crossing counts should be the same or not exceed one.
2. The local maxima defines the envelope's mean value. This envelope is zero at the energy point.  
Algorithm 1 helps to obtain a set of IMFs and a residue. The sifting process continues, treating the \(r_{1}(t)\) as a new signal until the primary conditions are satisfied. The highest frequency oscillations are in the first IMF, the further IMFs contain low frequencies, and the residue contains no frequency component. The original signal can be written with the IMFs and residues, as shown in Equation (1):  
\[S(t)=\sum_{i=1}^{n}I_{1}(t)+r_{n}(t) \tag{1}\]  
EMD is a dynamic data analysis tool that operates by adapting to the local characteristics of data, effectively capturing nonlinear, non-stationary oscillations. EMD functions as a filter bank with a dyadic structure. The only disadvantage of EMD is that the dyadic property cancels out when the data are intermittent.
# Bonding Performance of Steel Rebar Coated with Ultra-High-Performance Concrete
## 3 Results and Discussion
\begin{table}
\begin{tabular}{r r r r r r r r} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{
\begin{tabular}{c} \(P_{max}\) \\ **(kN)** \\ \end{tabular} } & \(\tau_{max}\) & \(\tau_{max-mean}\) & \(S_{\text{m}}\) & \(S_{\text{m-mean}}\) & \(\tau^{{}^{\ast}}_{max}\) & **Failure** \\  & & **(MPa)** & **(MPa)** & **(mm)** & **(mm)** & **(MPa)** & **(MPa)** & **Mode** \\ \hline
21-N-1 & 39.71 & 15.68 & 15.40 & 1.64 & 1.63 & 3.26 & PO \\
21-N-2 & 38.30 & 15.12 & 15.40 & 1.62 & 1.63 & 3.14 & PO \\
21-C-1 & 45.24 & 17.87 & 17.76 & 1.79 & 1.74 & 3.71 & PO \\
21-C-2 & 44.70 & 17.65 & 17.65 & 1.68 & & 3.66 & PO \\ \hline PVAOS1 ** & 69.64 & 27.50 & & 0.50 & & 3.55 & PO \\ PVAOS2 ** & 70.40 & 27.80 & 27.27 & 0.80 & 0.60 & 3.59 & PO \\ PVAOS3 ** & 67.10 & 26.50 & & 0.50 & & 3.42 & PO \\ PVAIS1 ** & 71.16 & 28.10 & & 1.30 & & 3.59 & PO \\ PVAIS2 ** & 73.18 & 28.90 & 28.80 & 1.00 & 1.20 & 3.70 & PO \\ PVAIS3 ** & 74.45 & 29.40 & & 1.30 & & 3.76 & PO \\ PVA2S1 ** & 77.74 & 30.70 & & 1.50 & & 4.03 & PO \\ PVA2S1 ** & 76.47 & 30.20 & 30.73 & 1.60 & 1.10 & 3.97 & PO \\ PVA2S1 ** & 79.26 & 31.30 & & 1.20 & & 4.10 & PO \\
21-C-0.2-HSF-1 & 46.39 & 18.32 & & 2.27 & 2.22 & 3.80 & PO \\
21-C-0.2-HSF-2 & 47.60 & 18.80 & 18.56 & 2.16 & & 3.90 & PO \\ \hline
21-C-0.4-HSF-1 & 49.54 & 19.56 & 19.44 & 1.44 & 1.41 & 4.06 & PO \\
21-C-0.4-HSF-2 & 48.90 & 19.31 & & 1.38 & & 4.01 & PO \\
21-C-0.6-HSF-1 & 50.63 & 20.00 & & 1.92 & 1.86 & 4.15 & PO \\
21-C-0.6-HSF-2 & 51.40 & 20.30 & & 1.80 & & 4.21 & PO \\ \hline
21-C-0.8-HSF-1 & 52.86 & 20.88 & 20.75 & 1.52 & 1.49 & 4.33 & PO \\
21-C-0.8-HSF-2 & 52.20 & 20.61 & & 1.47 & & 4.28 & PO \\ \hline
21-C-1.0-HSF-1 & 53.33 & 21.06 & & 1.81 & 1.80 & 4.37 & PO \\
21-C-1.0-HSF-2 & 53.60 & 21.17 & & 1.78 & & 4.40 & PO \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results for pullout test specimens. Even if the maximum load of the specimens with PVA is greater than that of the other specimens because of the difference in concrete strength, the normalized bond strengths (\(\tau^{*}_{max}\)) of the specimens with normal or high-strength concrete will be similar.
# Characterization of the Solution of the Seismic Source Mechanism in Southeastern Sichuan
## 4 Stress Field Inversion
### Mohr Circles  
According to the Mohr-Coulomb damage criterion [62; 63; 64], on an activated fault, the shear traction must exceed the critical value \(\tau_{c}\), which is calculated from the cohesion \(C\), fault friction \(\mu\), compressive normal traction \(\sigma_{\mathrm{n}}\), and pore pressure \(p\) as  
\[\tau_{c}=C+\mu(\sigma_{\mathrm{n}}-p)\]  
In the context of Mohr's circle, the Mohr-Coulomb failure criterion is represented by a straight line with a slope of \(\mu\). A fault surface that extends beyond this line meets the conditions for failure, as defined by the Mohr-Coulomb criterion. [PERSON] et al. [65] introduced the concept of fault instability, where an increase in fault instability signifies that the fault plane is closer to the periphery of the Mohr's circle. Concurrently, an increase in pore pressure \(p\) results in a rightward shift of the Mohr-Coulomb failure line, intersecting a more central region of the Mohr circle. This shift implies that elevated pore pressure diminishes the stress requirements for seismic rupture to occur. Consequently, earthquakes associated with fault surfaces located within the inner region of the Mohr's circle are indicative of the influence of pore pressure on the faulting process.  
Drawing upon the theoretical framework outlined above, we utilized the Mohr's circles derived from the inversion of STRESSINVERSE [66] for various regions in southeastern Sichuan (Figure 8). Our analysis reveals that Regions A and B share similar stress shape ratios and distributions of fault planes. However, Region A is distinguished by a higher concentration of fault planes situated within the Mohr's circle. Through statistical evaluation, we determined that the proportions of fault planes located outside the Mohr-Coulomb failure criterion line are 40% for Region A and 60% for Region B. This finding suggests that Region B exhibits a higher level of fault instability relative to Region A, where the initiation of fault rupture is predominantly influenced by pore pressure.  
In light of the potential uncertainties associated with inversion results due to limited sampling data, we elected not to interpret the characteristics of the Mohr's circle for Region C at this juncture. We plan to pursue a more comprehensive analysis in this domain once a sufficient dataset has been assembled for further investigation.  
Figure 8: Mohr circles for (**a**) Region A, (**b**) Region B, and (**c**) Region C. (**a**) Mohr circle for Region A with \(R=0.7\). (**b**) Mohr circle for Region B with \(R=0.7\). (**c**) Mohr circle for Region C with \(R=0.5\). The blue crosses indicate the fault planes, and the black lines denote the Mohr–Coulomb failure criterion. The Mohr–Coulomb failure criterion line is specified with a friction coefficient \(\mu=0.5\). The circles represent the distributions of stress states, where each Mohr circle corresponds to the deviatoric stress tensor derived from focal mechanisms.
## 8 Evaluation of Feasibility and Applicability of the Reactive Protection Concept
### Scenario Use Cases  
From the aforementioned scenario, three use cases are derived. In each of these three use cases, the same security policy is violated.  
The security policy supported by the Reactive Protection System prototype, as discussed in Section 7, refers to the surpassing of the maximum number of failed login attempts allowed (e.g., User fails to log in to the PLC multiple times). In order to trigger the analysis performed by the prototypical Reactive Protection System (i.e., Figure 3), the CEF generator application located in the  
Figure 6: Emulation of an industrial scenario of a remote maintenance application Figure 5. LAN: Local Area Network.  
PC sends a CEF message. This CEF message contains information that allows the Reactive Protection System to identify the security policy being violated.  
Once the Reactive Protection System identifies that a- and which security policy has been violated, it verifies whether a reactive action is possible to counteract this policy violation. For this type of policy violation, the Reactive Protection System supports the termination of active VPN connections. The execution or rejection of such action depends on the operational policies. The following use cases present different configuration alternatives for these operational policies, which result in different outcomes for the Reactive Protection System. For the purpose of this evaluation, a VPN connection between \(LAN\)\(A\) and \(LAN\)\(B\) is initiated.  
**Use Case 1:** In this use case, an operational policy exists that defines that communication between two parties must always be available. These parties are defined through their IP addresses. These IP addresses are identified as members of the opened VPN connection and hence the reactive action is not executed.  
**Use Case 2:** This use case is similar to Use Case 1. However, in this use case, the operational policy indicates the availability for communication for a specific subnetwork and not specific IP addresses. During validation of the operational policies, it is identified that the IP address of one of the parties of the VPN connection is located in this subnetwork and hence the reactive action is not executed.  
**Use Case 3:** In this use case, none of the aforementioned policies exist and hence the reactive action is carried out. This means that the active VPN connection is closed. If the lack of operational policies was intentional and does reflect the operational requirements of the automation system, then the termination of the VPN connection does not influence the operation of the underlying system. However, if the neglect of operational policies was non-intentional, this could increase an availability issue in the system that could potentially generate a fault in the IAS.
## 6 Testing and Measuring Performance: Analysis of PSO, GWO, and Hybrid PSO-GWO Methods
### Results
#### 6.4.2 Traffic Movement Precision  
The bar graph below shows the average traffic flow achieved by each optimization method.  
The average traffic values for each method are as follows: PSO 0.330601, GWO 0.69259, and hybrid PSO-GWO 0.925173. The hybrid approach shows greater accuracy, demonstrating its greater ability to improve traffic light scheduling. Figure 4 shows the precision with which traffic moves when each of the three proposed methods is implemented.  
Figure 3: Fitness values across iterations for the three proposed methods.
# Dynamic Analysis of a Delta Parallel Robot with Flexible Links and Joint Clearances
## 3 Results and Discussions
### Scenario 7  
In this scenario, the cumulative effects of the three factors (friction, flexibility and clearance) on the kinematic and dynamic behavior of the Delta robot are not considered. ADAMS simulations that consider the simultaneous action of those three factors resulted in values of kinematic deviations that are approximately the same values for displacement and speed (Figure 20). However, for acceleration, smaller values are emphasized due to the better numerical integration in ADAMS compared to summing the deviations generated individually by each factor (Figure 20c).  
Figure 19: Driving torque deviations in Scenario 6 (red solid line) and the additive effect of clearance and elasticity factors (blue dashed line) for (**a**) arm A; (**b**) arm B and (**c**) arm C.  
Figure 18: Motion deviations in Scenario 6 (red solid line) and the additive effect of clearance and elasticity factors (blue dashed line) for (**a**) displacement; (**b**) velocity and (**c**) acceleration.  
The maximum deviation of the driving torques shows the same type of harmonic variation as in the scenarios where the elasticity factor is considered. It reaches \(\sim\)120 N\(\cdot\)m vs. \(\sim\)20 N\(\cdot\)m (in the additive case) for _TA_ (Figure 21a), \(\sim\)80 N\(\cdot\)m vs. \(\sim\)10 N\(\cdot\)m for _TB_ (Figure 21b) and \(\sim\)70 N\(\cdot\)m vs. \(\sim\)10 N\(\cdot\)m for _TC_ (Figure 21c).
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Internal/Subsurface Defects
#### 3.2.5 Laser Testing Method (LT)  
Another NDT method that can be used for the detection of defects in steel bridges is the laser testing method. This method is in the experimental stage and information on its applicability is very limited. This method employs Lamb wave initiation by pulsed laser generating a laser impact on the component (Figure 22) [57]. The flaw is detected by a standing Lamb wave produced by the impact and recorded by the photorefractive interferometer. [PERSON] et al., in 2015, inspected and determined the condition ratings of a bridge component by employing quantified surface damage from a 3D laser image (point cloud image). They used two techniques for the inspection: Graph-based Surface Damage Detection and Surface Normal-based Surface Damage Detection. Graph-based Surface Damage Detection was implemented to detect the surface defect, including bent members, ruptures, and points of discontinuity; Surface Normal-based Surface Damage Detection was used to detect corrosion and cracks. It was concluded that the laser scanners could detect defects and flaws and can thus be employed to aid visual inspection [58; 59].  
Figure 21: Example of radiographic testing of a defective component and its results gathered through the X-ray film [53].  
Figure 22: LT method (Schematic layout) [28].
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 4 Modified Identity-Based Encryption Scheme
### Parameters, Security Analysis, And Efficiency Analysis  
In the aforementioned improved IBE scheme, we introduce a collision-resistant hash function \(H:Z_{q}^{*}\to Z_{q}^{n}\) and reconstruct the public key \(\mathbf{A}_{id}^{{}^{\prime}}\), thereby modifying the relationship between the identity public key and the identity vector. Since our extended method eliminates the need for the eigenvector approach to transform the IBE scheme into the IBFHE scheme, the identity private key vector no longer requires its first component to be 1. Instead, we redefine the identity key as \((-1,\mathbf{sk}_{id})\). This change allows the approximation error from the approximate trapdoor in our improved IBE scheme to partially cancel out the decryption noise, effectively reducing the scheme's overall noise. The noise size of the modified IBE scheme is given as follows:  
\[||\beta_{IBE}||=\left|\left|\left|\left<\mathbf{sk}_{id}^{T},\mathbf{e} \right>-\mathbf{e}_{app}\mathbf{y}\right|\right|\leq||\mathbf{sk}_{id}\mathbf{e} ||-||\mathbf{e}_{app}\mathbf{y}||.\]  
Clearly, the noise in the modified IBE scheme is smaller than that in the basic IBE scheme, enabling the modified scheme to perform correct decryption. The reduced noise allows the MIBFHE scheme in Section 5 to support more homomorphic operations. Other parameter choices remain the same as those in the IBE scheme presented in Section 3.
###### Abstract  
Signals from global navigation satellite systems (GNSS) can be utilized as signals of opportunity in remote sensing applications. Geophysical properties of the earth surface can be detected and monitored by processing the back-scattered GNSS signals from the ground. In the literature, several airborne GNSS-based passive radar experiments have been successfully demonstrated. With the advancements in small unmanned aerial vehicles (UAVs) and their applications for environmental monitoring, we want to investigate whether GNSS-based passive radar can provide valuable geospatial information from such platforms. Low-cost GNSS reflectometry sensors, developed using commercial of the shelf components, can be mounted onboard UAVs and flown to sense environmental parameters. This paper presents the results of a preliminary study to investigate the feasibility of utilizing data collected by UAV-based GNSS-R sensors to detect surface water for a potential application in supporting flood monitoring operations. The study was conducted in the area surrounding the Avigliana lakes in Northern Italy. The results show the possibility of detecting small water surfaces with few tens of meters resolution, and estimating the area of the lake surface with 92% accuracy. Furthermore, it is proved through simulations that the use of multi-GNSS increases this accuracy to about 99%.  
GNSS; GPS; reflectometry; GNSS-R; passive radar; UAVs; geospatial data; flood monitoring; signals of opportunity +
Footnote †: journal: Applied Sciences  
_Article_  
UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study
## 1 Introduction
Pulsed laser ablation in liquid (PLAL) is a \"green\" method to synthesize nanostructures starting from bulk targets by laser irradiation. While the main mechanism of nanostructure or nanoparticle production is quite complex, the physical procedure is a simple matter of exposing a bulk target immersed in a solvent to a pulsed laser [34]. The ablation characteristics, such as pulse width and irradiation time, laser wavelength, ablation fluence (energy per unit area), as well as solvent characteristics, such as viscosity, dipole moment, and refractive index, determine the morphology and characteristics of the produced nanostructures [35; 36]. The prime advantage of this technique is the procurement of surfactant free, well-dispersed nanostructures in the surrounding liquid without any toxic chemical reactants or waste [37]. The energy from the pulsed laser source is concentrated onto the target surface immersed in the liquid medium. Photon absorption, heating, and thermal evaporation are the main mechanisms for nanosecond-pulsed lasers in the process. As is well known, a plasma is generated where the laser energy is focused, which contains evaporated atoms, molecules, and radicals from the target. This plasma is contained by the surrounding fluid medium which causes the formation of a cavitation bubble. The extremely high temperature and pressure of the cavitation bubble cause its expansion and then collapse, regularly triggering the release of nanostructures into the surrounding medium [34]. In addition to the laser characteristics, such as wavelength, pulse energy, pulse duration, and repetition rate, the diverse characteristics of the liquid medium, such as, viscosity,heat capacity, refractive index, dielectric constant, among others, are also important in determining the size and morphology of the produced nanostructures. PLAL was successfully applied to obtain surfactant-free, stable nanofluids of metals, magnetic materials, semiconductors, and ceramics. Based on our previous research, we can successfully synthesize nanofluids of metals, metal oxides, ceramics, and semiconductors. Furthermore, thin films, which were deposited from their respective nanofluid suspensions using spin-coating and dip-coating methods, were produced from these semiconductor nanofluids [38; 39; 40; 41]. As for sunflower, soybean, and corn oils, past studies have described how heat is dissipated and how their tribological performance can be improved. Therefore, this experimental study shows the effects on Ag-nanostructures by a pulsed laser ablation technique dispersed within vegetable lubricants to improve their tribological and heat-transfer characteristics, i.e., wear resistance, coefficient of friction (COF), load-carrying capacity, and thermal conductivity.
## 3 Datasets and Evaluation Metrics
### HDR Image Quality Assessment
#### 3.2.1 HDR Metrics  
This subsection explores metrics aimed exclusively at HDR images and videos. Although they are still less common given the much greater volume of LDR images in the field of computer vision, they are developed to deal with the greater range of luminance and the particularities of visual perception in HDR content. It is important to highlight that metrics based on complex human visual system (HVS) models, such as the VDP family and HDR-VQM, do not rely on a single, simple mathematical formula. Instead, they implement multi-stage computational models simulating visual perception pathways. Their outputs are often visibility maps, which can then be pooled into a single quality score. For the complete detailed mathematical formulations, refer to the original publications cited for each metric.  
#### 3.2.1 HDR Metrics  
This subsection explores metrics aimed exclusively at HDR images and videos. Although they are still less common given the much greater volume of LDR images in the field of computer vision, they are developed to deal with the greater range of luminance and the particularities of visual perception in HDR content. It is important to highlight that metrics based on complex human visual system (HVS) models, such as the VDP family and HDR-VQM, do not rely on a single, simple mathematical formula. Instead, they implement multi-stage computational models simulating visual perception pathways. Their outputs are often visibility maps, which can then be pooled into a single quality score. For the complete detailed mathematical formulations, refer to the original publications cited for each metric.