esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Though the anisotropy of the surface structures influences the anisotropy of the CA, with the smaller width of the structures (ripples itself), the influence is smaller [36]. Very small widths of the ripples (-700 nm) in the presented experiment suggest that the influence of the structure anisotropy (LIPSS) to the droplet anisotropy is not significant. Corrugation is responsible for hydrophobicity (wetting). Classical theory demands the size of corrugation to be above micrometer level in order to achieve super-hydrophobicity. If we assume that a multilayer structure can be imagined as an optical cavity, quantum effects cause the rise of super-hydrophobicity [21; 22]. Recently, experimental proof of the impact of nano-corrugation to wetting properties exploiting the Casimir effect has been published [37]. The effect of small corrugations and quantum effects to superhydrophobicity is phenomenologically confirmed. In our work, it has been shown that, for laser generated nano-corrugation, observed wetting properties can be explained only if quantum effects are taken into account. Corrugations of nanoscopic level dramatically affect the Van der Waals interaction energy (through quantum vacuum photon modes) and thus the wetting contact angle is modified. Up to now, to achieve super-hydrophobicity, different coatings and dozens of micro-size structures were described elsewhere [38; 39]. For thin film materials, the wetting angle is nearly insensitive to the chemical nature of the immediate substrate and, by the forming of hyperbolic (plasmonic) metamaterials, the effective refractive index changed as well as the contact angle [30]. Here, the authors are the first, to our knowledge, to report significant change in the wetting properties by subwavelength corrugation. The increase in hydrophobicity caused by fs nano-patterning experimentally confirms the model developed by [PERSON] et al. [21; 22]. Furthermore, the presented study goes beyond the proposed model, since super-hydrophobicity is achieved on the metallic surface (not only the molecular solid). In the end, the possibility of attaining super-hydrophobicity using submicron corrugation by controlling quantum vacuum modes opens large almost unlimited fundamental and technological applications. While the responsibility for the change in the dispersion component is doubtful, the corrugation is responsible for all the changes in the CA. However, the magnitude of the effects is too big for such small corrugations, which can be explained with the abovementioned non-classical approach [21; 22]. Untreated metal surfaces are generally hydrophilic, but in this experiment they changed to highly hydrophobic by the implementation of sub-micrometer-sized corrugation.
# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
###### Abstract
Radar-based sensing allows to measure the vital signs heartbeat and respiration through remote measurement (up to several meters) of small mechanical motions of the body surface. Remote radar-based vital signs sensing has been intensively investigated in the last two decades. The attention has been focused mainly on contactless vital signs monitoring in indoor environments [10; 11; 12; 13; 14; 15; 16; 17; 18]. Considerable research on multi-people vital signs sensing and tracking has been performed by imec [19; 20; 21; 22; 23; 24], which also presented recently a Frequency-Modulated Continuous Wave (FMCW) integrated chip (IC) radar capable of detecting the vital signs up to a distance of 15 m while achieving a low-power consumption record of only 680 uW [24]. Radar has also been used for driver monitoring. In [25], the radar was placed inside the seat and the back of subject was directly in contact with the antennas. The conclusions of this work were that static measurements showed outstanding accuracy in detecting the vital signs while during the dynamic tests (i.e., while driving) the results were not robust enough to be used as input for medical or emergency prevention systems. In this work, a radar sensor, which integrates an accelerometer to measure the vibrations, was placed behind the driver's seat. These two sensor techniques are complementary: capacitive-based sensors can provide a complete ECG waveform which permits the most accurate determination of heart rate variability (HRV), but they only work through a number of clothing layers. On the other hand, radar-based sensors provide true remote sensing, but can only provide heartbeat information, not a complete ECG. Both techniques can provide respiration information. Hence, there is potential for sensor fusion combining these two techniques to improve overall performance under varying conditions. Initial work on sensor fusion of capacitively-coupled ECG and radar in an indoor environment was published by the Authors in [26]. In this work, both techniques are evaluated in real-world driving tests. In addition, signal processing techniques are presented that improve the robustness of these unobtrusive sensors to motion artefacts. For the ccECG, signal quality indicator (SQI) based motion artefact removal algorithms are implemented for this purpose. For the radar signals, a signal processing technique which correlates the radar signal to the accelerometer signal in order to remove the contribution of the vibrations from the vital signs information is presented. This paper is organized as follows. In Section 2, the capacitively-coupled and radar systems and their physical positioning in the car are detailed together with the signal processing techniques. The experimental results are shown in Section 3, while the discussion and the conclusions are drawn in Section 4.
## 2 Materials and Methods
### Fluid-Structure Coupling Construction Method
#### 2.1.3 Adiabatic Process  
The calculation of the air spring chamber temperature is based on the predefined temperature at the reference point, and the temperature of the fluid inside is determined through the conservation of energy during the adiabatic process. This assumption assumes that no heat is transferred into or out of the fluid cavity, except through the fluid exchange defined by the model or through the generator.  
The mass flow rate in the gas spring chamber can be obtained from the conservation of mass, which can be obtained from the following formula.  
\[m=\dot{m}_{in}-\dot{m}_{out} \tag{8}\]  
where \(m\) is the mass of the fluid, \(\dot{m}_{in}\) is the mass flow rate into the fluid cavity, and \(\dot{m}_{out}\) is the mass flow rate of the fluid cavity.  
The first law of thermodynamics is applied to derive the energy equation of the air spring system. By considering only the internal energy of the fluid cavity, the equation describing the energy of the fluid is obtained while disregarding the kinetic and potential energy components.  
\[\frac{d(mE)}{d(t)}=\dot{m}_{in}H_{in}-\dot{m}_{out}H_{out}-\dot{w}-\dot{Q} \tag{9}\]  
where \(\dot{w}\) is the work done by the expansion of the fluid cavity and \(\dot{w}=P\dot{V}\); \(H_{in}\) and \(H_{out}\) are the inflow and outflow times, respectively. \(\dot{Q}\) is the thermal energy flow rate, generated by the heat transfer from the surface of the fluid cavity, and when it is positive, it means that the heat will flow out from the mainstream body cavity, and the unit energy \(E\) is given.  
\[E=E_{I}+\int_{\theta_{I}}^{\theta}C_{V}(T)dT \tag{10}\]where \(E_{I}\) is the initial unit energy at the initial temperature \(\theta_{I}\), \(C_{V}\) is the specific heat of equal volume, and ideal gases are only temperature-dependent. \(H\) is the specific enthalpy, which is defined as  
\[H=H_{I}\int_{\theta_{I}}^{\theta}C_{P}(T)dT \tag{11}\]  
where \(H_{I}\) is the specific enthalpy at the initial temperature \(\theta_{I}\); \(C_{P}\) is the constant pressure specific heat capacity, which is only related to the temperature of the ideal gas, and the parameters such as the pressure, temperature, and density of the gas spring chamber are obtained through the ideal gas law, energy balance, and mass conservation equation.
## 6 The Proposed Clustering Routing Protocol
### CHN Selection Phase  
Selecting appropriate CHNs is of great importance to reduce and balance energy consumption. The CHNs receive the data from the CMNs, fuse the data, and transmit the fused data to the SN. The original LEACH generates CHNs through a probabilistic selection and the residual energy of nodes has not been taken into account. These selected nodes may die too early as a result of their insufficient remaining energy, which affects the balance and efficiency of the network energy. Hence, by taking the residual energy of nodes into consideration, we propose an improved CHN selection scheme as follows:  
\[H_{th}=\left\{\begin{array}{c}\frac{P_{CHN}}{1-P_{CHN}\times(\text{mod}\, \frac{1}{P_{CHN}})}\times\frac{E_{Ins}}{E_{Ins}},\,\,node\in G\\ 0\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,,\,\,\,\,\,\,\,\,\,  
where \(H_{th}\) is the threshold for the node, and \(P_{CHN}\) denotes the percent of CHNs in the network (e.g., \(P_{CHN}\) = 10%), and \(r\) is the current round, and \(E_{res}\) represents the residual energy of the node, and \(E_{up}\) is the average residual energy of all nodes, and \(G\) is the node set where the nodes do not become CHNs in the last \(1/P_{CHN}\) rounds. In this process, every node produces a random number ranging from 0 to 1 and if the number of one node is less than its threshold \(H_{th}\), it turns into a CHN candidate. Then the CHN candidate broadcasts candidate-messages with the residual energy to its neighbor nodes. If one neighbor node that receives the candidate-message is also a CHN candidate, the one with higher residual energy becomes a CHN, which can prevent the geographically close nodes from being CHNs. If one CHN candidate does not receive any candidate-messages from its neighbor nodes for a certain time, it becomes the CHN.
## 2 Materials and Methods
In the vertical direction, the first measurement line segment (ML1) was placed 1 mm below the weld surface while the last measurement line segment (ML4) was near the root portion located 1 mm above the back surface of the test specimen. The other lines (ML2 and ML3) were evenly distributed vertically between lines ML1 and ML4 at intervals approx. 2.7 mm away from each other. Figure 5 presents an example showing the locations of the EDS-measurement lines in the studied weld cross-sections. Line segments had  
In weldability analysis, dilution is conventionally determined mathematically from the cross-sectional areas of the original joint geometry, melted base metal and weld metal. Dilution ratio is the ratio of the amount of melted base metal to the total amount of fused weld metal [22]. To get a quantitative basis for comparison of the weld cross-sections and to analyse how different groove configurations and wire feeding direction affect the base/filler metal-ratio, the following assessment procedure was used: The weld micrograph cross-sections were converted into CAD-images and the groove geometry superimposed over the original weld cross section. The vertical symmetry axis of the groove was aligned with the centreline of the studied weld cross-section. The cross-sectional areas of the melted base metal, filler metal and weld metal were then defined using area inquiry tools of CAD-software (Autodesk, San Rafael, CA, USA ). An example of the above principle is shown schematically in Figure 6. The figure shows dilution calculation and boundary lines of the defined regions of cross-sectional areas converted from the macrograph of test weld LAH1. Dilution calculation gives a dilution ratio of 79%, which means that the weld metal of test sample LAH1 is estimated to contain 79% base metal and 21% filler metal. Normal practice usually assumes that the proportions of base and filler metal are completely mixed and evenly distributed throughout weld cross-section. In welding of rather thin plate thicknesses in which joint penetration of a single pass weld is around 5 mm or less, this assumption would normally be reasonable but for thick section welding, it may not lead to an appropriate outcome, as will be seen in this work.
# Protein Dynamics in F-like Bacterial Conjugation
## 6 Exclusion Proteins and their Low Structural Dynamicity  
Alongside MPs, yet prior to the formation of the mating pore, exclusion steps occur to prevent redundant plasmid transfer to a recipient that already holds the same element. Sfx proceeds first and is mediated by the OM lipoprotein TraT expressed on the cell surface; if TraT is present in the recipient cell's membrane, then the formation of mating aggregates is inhibited and conjugation will be prevented [105; 142]. If Sfx fails to prevent mating aggregate formation and MPs steps proceed, then Eex is relied upon to avert redundant donor-donor transfer. The IM protein TraS and the periplasmic C-terminal half of TraG are interacting partners in the Eex event; if the potential recipient has the same plasmid, TraG in the donor cell is thought to scan the IM of the recipient cell and interact with the cognate TraS to arrest conjugation [79]. The cells will remain in an aggregate if the colony is highly dense (however they are separable by SDS and shear forces unlike MPs-stabilized aggregates), and if the environment is diffuse the cells will separate and drift apart [105]. If the recipient cell does not have a cognate TraS in its IM, the C-terminus of TraG is predicted to extend to contact TraN in the OM and further the formation of the mating junction [106].  
Exclusion genes have proven to be important for the stability of a conjugative plasmid; F mutants with both _traS_ and _traT_ mutations are unstable and have not been isolated [105; 143]. This is supported by F plasmid-mediated superinfection immunity, which was determined to be mediated by TraT and TraS [144]. Superinfection refers to the process of lethal zygosis, a phenomenon that was seen when an excess of high frequency of recombination (Hfr) F donors were placed in contact with F-recipients; many of the recipient cells died [145]. The presence of TraS however was deemed to be more important for colony survival than TraT as Eex by TraS and TraG also provide a higher exclusion index (EI) than Sfx by TraT [142; 144]. EI is calculated as the frequency of transfer of a plasmid given to a plasmid-free recipient divided by the frequency of transfer to a recipient carrying the same plasmid. For the F-plasmid in _E. coli_, mating was observed at 100-300 times higher frequency when the plasmid was not present in the recipient in comparison to donor-donor exchange; thus, the EI was 100-300 [105]. In comparing EI of donor-recipient exchanges for the F plasmid, _traT_ point mutants resulted in a smaller reduction in mating than the _traS_ point mutants [143]. An increased exclusion activity was seen in the Eex system, which was attributed have an EI 500-1000-fold greater than that associated to the Sfx system, indicating a higher reliance on the Eex system for preventing erroneous donor-donor transfers [11; 79; 142]. The exclusion process performed by these systems is also seen to be gene-dose dependent; when _traS_ and _traT_ were cloned into a multicopy plasmid, the EI increased to 10,000 [105].
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Performance of Proposed Hybrid Model (SE-ResNet-50)
#### 4.4.1 Impact of SE Attention Mechanism  
The significant performance boost can be directly attributed to the integration of SE blocks, which apply channel-wise attention to modulate feature responses based on their relative importance. Unlike traditional convolutional layers that treat all feature channels equally, SE blocks allow the network to attend to channels that encode critical visual cues--such as edge inconsistencies, textural anomalies, and spatial distortions--often introduced by generative models like GANs or diffusion networks [2; 3].  
Through global average pooling, each SE block captures a compact summary of the global context of each feature map, followed by excitation through a pair of fully connected layers. This mechanism enables the model to suppress uninformative or noisy channels while amplifying those that carry meaningful signals indicative of synthetic content.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 4 SXCM and Correlative Microscopy
SIM is a suitable method for imaging under cryogenic conditions because it provides structural information beyond the system's diffraction limits, which causes the volumetric resolution to rise as much as 8-fold. [PERSON] et al. presented, for the first time, a combined SXCM with fluorescent microscopy for the observation and identification of cellular organelles of live hydrated Leydig cells with a nanometric spatial resolution of 90 nm [75]. They applied a high-intensity laser-plasma X-ray source to obtain a single X-ray image. However, the overall system used was extremely large and impossible to apply in biological laboratories. Thus, a compact table-top device for SXCM was presented, with the potential to correlate with fluorescence techniques, as proven by [PERSON] et al. Most of these correlative techniques used a synchrotron, and the samples were cryo-preserved and fluorescently tagged. In turn, the conducted research was only a proof of concept and demonstrated the potential uses, sample preparation protocols, the spatial resolution reached, and troubleshooting. Mammalian cells, tissues, alga, and insects were imaged [73, 74, 75, 76, 77, 78, 79, 80, 81]. As previously mentioned, synchrotron-based X-ray techniques are restricted for many reasons. The compactness of the X-ray source offers biologists modern and much more user-friendly methods for investigating native objects (Figure 5). However, it is still difficult to interpret the SXCM results without comparison with other microscopy techniques. This is why there is a requirement for the compact X-ray source to simplify and disseminate methods, such as SXCM, together with the need to develop correlative microscopy. Figure 5: The anticipated development of SXCM. The development of compact X-ray sources makes SXCM much cheaper, smaller, and easier to use in biological laboratories. The correlation of SXCM with other developing microscopic techniques, such as optical, fluorescence, and super-resolution microscopy, improves the identification of various biological structures and provides additional information on their distribution and functionality.
## 3 _Pbc_ Analysis Method and Result
### Results of PBC Effect Analysis in FCW Installation
#### 3.5.2 Effect on Deceleration  
As shown in Figure 6, the mean value of deceleration was 5.05 m/s\({}^{2}\) for daytime and 5.15 m/s\({}^{2}\) for nighttime without FCW installation. When FCW was installed, the mean deceleration was 4.76 m/s\({}^{2}\) for daytime and 4.68 m/s\({}^{2}\) for nighttime, indicating a decrease in deceleration by 5.7% and 9.1%, respectively. The decrease at nighttime was larger than that of daytime and, in particular, the decrease of drivers aged 60 or above at nighttime was the largest at 10.2%. This is correlated with the cognitive reaction time, and it can be judged that because the cognitive reaction time was reduced, the time margin up to the time of collision also increased, which also affected deceleration. The sample size was 70, and the variances were analyzed as 0.26, 0.27, 0.30, and 0.27 for each scenario.  
In the statistical test, as shown in Table 6, the effect of reduced deceleration with FCW installation was observed both in the daytime and nighttime.
# On the Micromixing Behavior of a Spinning Disk Reactor for Metallic Cu Nanoparticles Production
## 2 Materials and Methods
### Methods  
The adopted method in this work to produce the copper nanoparticles relies on the use of an SDR that allows the operation in wet continuous conditions. Compared to other technologies available to nanoparticle production, the SDR has the advantage to exhibit the required and specific micromixing conditions in the framework of a low energy-consuming, continuous operation, allowing to obtain a high quantity of nanoparticles of specific PSD. This characteristic appears to be compatible to industrial practice [12].  
The SDR consists of an external reactor vessel hosting a rotating disc of 8.5 cm in diameter, on which surface the mixing of the poured reactants and the consequent reaction will take place. The scheme of the adopted SDR is reported in Figure 1. The rotation speed of the disk, \(\varpi\), is one of the most important operating parameters that directly affects the micromixing conditions and was changed to values between 500 rpm and 1500 rpm [13]. Together with the reactants flow rate and feeding point number and position, it is possible to obtain the nanoparticles of specific narrow, unimodal PSD. For all experiments, the used injectors were of 0.2 cm in diameter and positioned 2 cm above the disk surface [14]. The temperature of the solutions was kept constant at 80 \({}^{\circ}\)C. The product was continuously removed from the outlet at the bottom of the reactor which was maintained at room temperature.  
The use of the SDR is aimed to promote the mixing among the reagents with low contact times in order to maximize the product concentration with high nucleation rates and very low particle growth. The efficiency of the precipitation reaction depends on the mixing within the system. If the mixing is not adequate, the reagent streams penetrate the aqueous bulk formed on the disk, creating three segregated phases. Due to this stratification, the reaction takes place without the optimal pH, leading to the formation of bigger and unstable particles with a higher tendency to agglomerate. Therefore, the optimal conditions are obtained with a rapid mixing of the streams, low contact times of the reagents, high nucleation rate, and low supersaturation available for the particle growth. All these aspects are favored by a high rotational velocity of the disk in the overall film thickness [8].
## 3 DAC Integral/Differential Nonlinearities--New Normalized Error Vector Metrics
### Conventional INL Review (in Our Notation)
Noting that the c-th INL sample is evaluated in as the random vector \(\delta_{\text{nom}}l_{c}\) divided by the random variable \(I_{u}\equiv\overline{\
abla\mathbf{J}}\), and that both \(\delta_{\text{nom}}l_{c}\) and \(I_{u}\) are Gaussian (dependent) RVs, the evaluation of the statistics of the INL samples turns out rather difficult in the case of stochastic constellation deviations stemming from electronic mismatch errors, since the INL samples are the ratios of two Gaussian RVs; thus, they are not Gaussian themselves. Note: an evaluation of the distribution of the ratio of two Gaussian RVs calls for advanced statistical analytic tools, e.g., the \"Brownian Bridge\" [8; 9]. In Section 3.5, we introduce a simpler yet highly useful variant of the INL, namely the IEV, bypassing the need to work out the distribution of the ratio of two Gaussian RVs. It is useful to solve the INL defining Equation (281) for \(\mathbf{J}\), expressing the constellation vector and its levels in terms of the INL vector, \(\mathbf{INL}\):  
\[\mathbf{J}=\mathbf{J}^{\text{nom}}+\mathbf{INL}\cdot I_{u} \tag{283}\]  
or component-wise,  
\[J_{c}=J_{c}^{\text{nom}}+\text{INL}_{c}\cdot I_{u}\underset{\
eq 2}{\equiv}J_{1}+(c-1)I_{u}+ \text{INL}_{c}I_{u}=J_{1}+(c-1+\text{INL}_{c})I_{u} \tag{284}\]  
where for \(\#2\), the second equality in (278) was substituted for \(J_{c}^{\text{nom}}\). To recapitulate,  
\[J_{c}=J_{1}+(c-1+\text{INL}_{c})I_{u} \tag{285}\]  
indicating that the constellation levels are determined by initial condition \(J_{1}\) along with the \(\mathbf{INL}\) vector.