esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Vibration Velocity Results and Analysis
### GPPV and Pipe Peak Vibration Velocity (PPV)  
Under these experimental conditions, the ratio of GPPV to PPVV was between 2 and 3. The test data (\(R\) = 2.7 m) was taken as shown in Table 4. Here, the peak velocity was synthesized by the maximum values of the X-, Y-, and Z-axes. Although the three maximum values were not simultaneous, the time difference was small, and the synthetic maximum value was slightly larger than the true value. The average ratio was 38.6%. Although the ratios fluctuated with the scaled distance, the range was small.  
The attenuation curves with the scaled distance show a good power function relationship, as shown in Figure 14. The attenuation index was larger than those in the related literature. This was because the explosion took place in the soil, the propagation medium was clay, and the location was near the explosive source. There was a strong linear correlation between GPPV and PPVV, as shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{8}{c}{**Peak Velocity (mm-s\({}^{-1}\))**} \\ \cline{2-9}  & **50 (g)** & **75 (g)** & **100 (g)** & **125 (g)** & **150 (g)** & **175 (g)** & **200 (g)** \\ \hline PPVV & 35 & 47 & 44 & 62 & 78 & 83 & 87 \\ GPPV & 90 & 119 & 114 & 152 & 194 & 229 & 247 \\ Ratio & 38.9\% & 39.9\% & 38.7\% & 40.6\% & 40.3\% & 36.1\% & 35.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ground peak particle velocity (GPPV) and pipe peak vibration velocity (PPVV, \(R\) = 2.7 m).  
Figure 14: The attenuation curves of PPVV and GPPV.  
Figure 15: The relationship between GPPV and PPVV.
## 3 System Model and Problem Formulation
### Offloading Cost  
The quality of service (QoS) mainly depends on user-perceived latency and energy consumption. Moreover, considering the precious energy reservation of satellites, we also include the energy consumption of satellites into the cost. Both real tasks and dummy tasks for privacy protection will incur the above cost. The detailed definitions of offloading cost for different locations are given as follows.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Discussion
#### 4.4.2 Evaluation Analysis  
The visual results, as well as the error metrics, were studied to evaluate the performance of the networks. The error metrics provide an objective assessment of the average performance across the entire test set, whereas the visual inspection, which was limited to a few images, cannot be considered to represent the distribution of the entire dataset. In order to obtain a more reliable qualitative evaluation, a larger number of predictions should be inspected and evaluated in a more structured manner by several people.  
As a result, the quantitative results were regarded as more reliable, and a greater weight was placed on them when determining the best network architecture. The quantitative analysis, however, is not without flaws. Quantitative analysis compares the predicted results with ground truth. This means the error metrics only assess how effectively the network predicts the depth values in relation to the given ground truth.
## 2 Optical Uplink Channel Model
### Statistical Properties of Channel Fading Factors
#### 2.1.4 Background Noise  
In deep space optical communication, usually, the optical power received is very small because of the ultra-long link distance. Therefore, background noise can easily impair communication performance. The power of received background noise \(P_{b}\) is [27]  
\[P_{b}=0.25\pi d_{r}^{2}B_{o}\Omega_{F}N_{b}(\lambda), \tag{10}\]  
where \(B_{o}\) is the bandwidth of optical filter, and \(N_{b}(\lambda)\) is the background radiation spectral radiance. Moreover, \(N_{b}(\lambda)\) is expressed as [27]  
\[N_{b}(\lambda)=\frac{2\hbar cR_{f}}{\lambda^{3}\exp\left(\hbar c/\kappa_{c}T_{ b}\lambda\right)-1}, \tag{11}\]  
where \(\hbar\), \(c\), and \(\kappa_{c}\) are the [PERSON]'s constant, light speed, and [PERSON]'s constant, respectively. The parameter \(R_{f}\) is the spectral albedo of a celestial body, defined as the ratio of radiation reflected by the planet (including its atmosphere) to total incident light. In the solar system, the Sun is the luminous body; therefore, the Sun's spectral albedo  
Figure 3: Schematic of AOA fluctuations and Airy pattern.  
value is set to 1. The average spectral albedo of the Earth is \(R_{f}=0.25\). The values of spectral albedo for other important celestial bodies can be found in [27]. It is critical to recognize that other celestial bodies inherently exhibit blackbody radiation even in the absence of solar reflection. Consequently, when such bodies act as independent radiation sources, \(R_{f}\) is equivalently set to unity. In addition, the parameter \(T_{b}\) denotes the average blackbody temperature of a celestial body; for example, the Sun's \(T_{b}=5776\) K and Earth's \(T_{b}=288\) K. When the specifics of a deep space mission are known (e.g., communication range, satellite position), the variables in (11) are nearly fixed, thereby determining the value of background radiation spectral radiance.
## 3 Materials and Methods
### Method Validation
#### 3.4.2 LLOQ and Sensitivity  
Sensitivity was defined as the lowest non-zero standard on the calibration curve (LLOQ). Determined from the calibration curves, LLOQ samples were analyzed in five replicates in three separate runs. The acceptance criteria were: analyte response at LLOQ \(\geq\) five times the analyte response of the blank, accuracy at \(\pm\)20% of nominal concentration and precision \(\leq\)20% (CV%).
## 2 Materials and Methods
### Algal Growth Inhibition Assay  
Culture media differed between the three species: for the _D. tertioleta_ the f/2 medium was prepared by adding to sterilized synthetic seawater (Instant Ocean(r)) a mixture of vitamin (cyanocobalamin + biotin + thiamine) and trace elements [49]. The _P. tricornutum_ medium was prepared by adding sodium silicate (30 g L\({}^{-1}\)) to the _D. tertioleta_ medium. The _R. subcapitata_ was cultivated in [PERSON]'s culture medium [50].  
The stock cultures were prepared by inoculating 0.5-1 mL of microalgae suspension on an Erlenmeyer flask containing 250 mL of the respective medium. The flasks were covered with a porous cotton plug and maintained under illumination (white lamp/red lamp Osram Daylight 2 \(\times\) 36 W plus Osram Gro-Lux lamp 36 W) (8 h light/16 h dark) at 20 \({}^{\circ}\)C. The stock cultures were allowed to grow until the required cell density was reached to prepare a fresh stock culture. To perform the assay, a specimen of the stock culture in log phase growth was diluted, obtaining a cell suspension that contained no more than 1.10\({}^{3}\) cells mL\({}^{-1}\). Since physiological or metabolic defects can be better evaluated following a prolonged period of growth in spiked culture, a diluted inoculum was employed to let the algae grow for quite a long time before reaching the log phase again. The assays were performed not within the usual interval of 72-96 h, but the growth rate of algae was checked at different intervals after starting the test, about 20 \(\pm\) 2 (interval A), 27 \(\pm\) 2 (interval B), and 34 \(\pm\) 2 days (interval C). The 10 mL test tubes were filled with the appropriate medium containing the desired pollutant's final concentration (100, 50, 25, 10, 5, 1, 0.1, and 0.01 mg L\({}^{-1}\)), and then the algal culture inoculum (0.1 mL of the diluted suspension) was added. To prepare the controls, the algal inoculum was simply added into the appropriate medium. The tubes were covered with sterilized porous cotton and gauze and kept under the same conditions (light and temperature) as the stock cultures.  
We evaluated the algal density, after gentle shaking to homogenize the suspension, by measuring the absorbance of the cultures at 684 nm, an indirect method for cell counting also mentioned in an ISO procedure [51]. The overall experiments were replicated on three independent samples.  
The toxic effects were evaluated by calculating the percent of growth inhibition in the samples with respect to the controls according to the absorbance values (Equation (1)).  
\[I\%=\frac{A_{control}-A_{sample}}{A_{control}}\times 100 \tag{1}\]  
where \(A\) is the absorbance at 684 nm.  
The EC\({}_{50}\) values for each compound were calculated according to [PERSON], 1971 [52], by using the average value of the % samples inhibition, since the toxic effects are always relative to the respective controls in each experiment.
# Innovative Antibacterial Polymer Coatings
## 2 Antibacterial Coatings
### Anti-Adhesive Coatings
#### 2.3.2 Superhydrophobic Surfaces  
Bacterial adhesion has also been studied for hydrophobic surfaces, but only superhydrophobic surfaces are well known for their ability to prevent contamination. Superhydrophobic surfaces can be defined as surfaces with a high contact angle (greater than 150\({}^{\circ}\)), making them difficult to wet. Generally, superhydrophobicity results from a combination of hierarchical topography and chemical composition, which creates a low-energy surface [57]. Two key factors contribute to the superhydrophobic properties of coatings applied to substrates such as glass, cotton fabrics, and others (Figure 6). These factors include: (a) the creation of a hierarchical rough surface with micro-nano dual roughness and (b) the deposition of a low surface energy material, which can be selected from options such as long-chain fatty acids, thiols, fluorinated silanes, and amines. Hierarchical roughness can be created on the surface of the substrate by incorporating Ag, Cu, ZnO, TiO\({}_{2}\), graphene, and chitosan nanoparticles through various methods. In the next stage, the hierarchical rough surface needs to be modified using a low-energy material, employing various techniques such as layer-by-layer deposition, dip-coating, chemisorption, and self-assembly processes. The trapped air within the rough surface, caused by the micro- and nanostructures, prevents water droplets from making contact with the substrate. This is one of the key factors contributing to the antibacterial activity of superhydrophobic coatings [63].  
The main drawback of superhydrophobic surfaces, which makes them challenging, is their limited long-term application in real-world situations. The micro-/nanoscale  
Figure 6: General scheme for the development of superhydrophobic coatings. Adapted from ref. [63] with permission from NanoWorld Journal.  
features are often not durable, and their degradation typically results in the loss of the surface's superhydrophobicity. Another limitation of these coatings is the complexity of production [57].
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 5 Experimental Results and Analysis  
Synthetic data and real data are used to test the proposed multi-frame blind super-resolution algorithm. Our algorithm is compared to BayesSR [16], SRB [17], DeepSR [29] and FBSR [14]. Simulation results are conducted in MATLAB (R2018b) on a notebook computer with Intel(R) Core (TM) i7-8650U CPU and 16 GB RAM. We first transfer the input RGB LR images to YCbCr color space. The chromatic components Cb and Cr are super-resolved by bicubic interpolation. Our algorithm uses only the Y component containing the luminance or geometry of the image. The final estimated HR image is composed of the processed Y component and the chromatic components.
# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 3 The _Welive_ Dataset  
In this paper, we have used _WELIVE_ (\"Women and Emotion in real LIFe affectiVE computing dataset\"), a multimodal affective computing dataset designed to address Gender-Based Violence (GBV) in real-world scenarios [33].  
_WELIVE_ contains multimodal contextual information about the daily routines of 14 Gender-Based Violence Victims (GBVV), aged 25-59 [34]. These women wore the _bindi_ system for 7-10 days [6], which included a pendant that recorded audio, and a bracelet that measured physiological signals that were sent to a smartphone via a Bluetooth(r) link. Global Positioning System (GPS) was also available but due to its high battery consumption, it was only activated sparsely. Finally, all this information captured was transmitted to a secure and encrypted server [6; 35].  
In addition, a smartphone application was available to report their location and emotions felt at any time of the day. Using this application, users could also send audio notes to verbally explain the situations that had triggered their tagged emotions. These audio notes were the data that we employed in this paper to generate the deepfakes. This application was included for the purpose of providing labels for the fine-tuning and adaptation of the models during a personalization stage and also provided a means of obtaining weak labels to train the ML models.  
Before participating, the volunteers were interviewed by a psychologist specialized in GBV and completed a questionnaire to document their daily routines [33; 36] (as well as a standardized test to assess their mental health status and specifically if they suffered from post-traumatic stress disorder). All the procedures were approved by the ethics and data protection committee of University Carlos III Madrid. Overall, _WELIVE_ aims to investigate the impact of real-world environments on emotions and how these emotional states are reflected in vocal expressions and physiological responses [36]. Analyzing the impact of these real environments on the creation and detection of deepfakes can be essential for legal interventions aimed at enhancing the safety of GBVV.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 5 Targeted Safety Interventions
### Intersection and Segment-Level Crash Analysis  
[PERSON] et al. [77] investigated the characteristics and countermeasures for motor vehicle crashes at stop signs, focusing on four U.S. cities. The study found that stop sign violations--particularly \"rolling\" stops--accounted for roughly 70% of crashes, with younger and older drivers being disproportionately involved.  
[PERSON] et al. [78] proposed a dynamic wavelet-based model to locate and measure high-crash road segments. Their approach improves identification performance by 25-38% when the analyst seeks the worst 10-20% of roadway length, underscoring the value of precise segment delineation for multi-scale safety analysis.  
[PERSON] et al. [79] compared traffic safety across French counties with Generalized Linear Models, revealing that differences in safety vary jointly with county and road type. Accounting for sub-county socio-economic factors and road-type mix substantially improves explanatory power.  
[PERSON] and [PERSON] [80] developed a negative-binomial crash-frequency model for 125 two-way stop-controlled intersections. With a product-of-flows formulation and gamma-distributed mean, the model captures a nonlinear rise in crashes with traffic demand, illustrating how such distributions pinpoint hazardous sites.  
These studies demonstrate the complexity of intersection and segment safety analysis, with consistent evidence of nonlinear relationships between traffic volume and crash risk. Research reveals significant methodological diversity, from wavelet-based approaches achieving 25-38% performance improvements to negative-binomial models capturing traffic flow interactions. A critical finding across studies is the importance of spatial and demographic context: both [PERSON] et al.'s [79] county-level analysis and [PERSON] et al.'s age-specific patterns highlight that location and user characteristics substantially influence crash patterns. The dominance of stop sign violations (70% of crashes) in [PERSON] et al.'s findings, combined with [PERSON] and [PERSON]'s traffic demand relationships, suggests that intersection control design must account for both human behavioral patterns and traffic flow characteristics.  
Figure 8: Technology-based safety systems: management and in-vehicle features.  
Figure 9: Factors affecting crash risk including weather and visibility.
# Evaluation of the Aerodynamic Effect of a Smooth Rounded
## 2 Methodology: Wind Tunnel Experimental Tests
### Experimental Setup
The corresponding Reynolds numbers, calculated with a characteristic dimension of 3 m full-scale as required by EN14067-6, are respectively equal to 4 \(\times\) 10\({}^{5}\) and 6.8 \(\times\) 10\({}^{5}\), both higher than the limit value of 2.5 \(\times\) 10\({}^{5}\) required by the European Standard. Reference system for evaluating aerodynamic coefficients is defined according to EN14067-6. The reference system is fixed to the vehicle and its origin coincides with the centre of the vehicle at the level of the top of the rail: the longitudinal axis \(x\) is oriented in the direction of travel, the \(z\) axis is vertical and direct downwards, and the \(y\)-axis is perpendicularly oriented to form a right-handed coordinate system. The reference system for computing aerodynamic coefficients is depicted in the Figure 5, where also the yaw angle \(\beta_{\mathrm{w}}\) of the train with respect to the wind direction is represented. \(\beta_{\mathrm{w}}\) = 0\({}^{\circ}\) when the train is aligned with the wind; \(\beta_{\mathrm{w}}\) is positive if the rotation is clockwise and negative for counter-clockwise rotations. Figure 3: Internal setup for the connection of the dynamometric balance to the train scaled model. Figure 2: Wind tunnel model of ETR1000: (**a**) CAD model; (**b**) scaled model on GVPM wind tunnel. Figure 4: Pressure taps location (quotes in mm). From the EN14067-6, aerodynamic coefficients are defined as follow:  
\[C_{F_{i}}=\frac{F_{i}}{\frac{1}{2}\rho A\overline{U}^{2}}\ \ \ i=\{x,y,z\} \tag{1}\]  
\[C_{M_{i}}=\frac{M_{i}}{\frac{1}{2}\rho Ah\overline{U}^{2}}\ \ \ i=\{x,y,z\} \tag{2}\]  
\[C_{M_{x,lore}}=\frac{M_{x}-F_{z}s}{\
icefrac{{1}}{{2}}\rho Ah\overline{U}^{2}}\ =C_{M_{x}}-C_{F_{z}}\frac{s}{h} \tag{3}\]  
where \(F_{i}\) with \(i=\{x,y,z\}\) is the force component in the European Standard reference system and \(M_{i}\) is the correspondent moment. In Equations (1)-(3), \(\rho\) is the air density, \(\overline{U}^{2}\) is the square of the mean value of wind speed, \(h\) is 3 m (at full-scale), \(A\) is the reference surface area, defined in the standard as 10 m\({}^{2}\) (full-scale) and \(s\) is half of the track gauge (0.75 m for standard gauge according to EN14067-6). The lee-rail rolling moment corresponds to moment with respect to a longitudinal axis set in accordance with the leeward rail. \(C_{Mx,lore}\) is computed from the roll moment coefficient and vertical force coefficient.
## 3 Dynamic Compressive Mechanical Properties of Sandstone after Corrosion by Strong Alkali Solution
### Dynamic Compressive Strength and Dynamic Peak Strain Variation
The \(\varepsilon_{d}\) value after 14 d of corrosion increased by 8.79% compared with that after 7 d, while the \(\varepsilon_{d}\) value after 28 d of corrosion only increased by 5.54% compared with that after 14 d of corrosion, that is, the increase was most significant when the corrosion was 7 d-14 d, and the growth rate of the dynamic peak strain decreased when the corrosion was 14 d-28 d.  
From the analysis of the water-rock chemical reaction and sandstone microstructure, the whole reaction process can be divided into two stages. The first stage is the initial stage of reaction, in which the water-rock chemical reaction rate is faster, and the reaction phenomenon is significant; at a micro level, the OH\({}^{-}\) ions in the strong alkali solution react with part of the components in the sandstone specimen, and the internal micro porosity expands violently, and the porosity increases sharply, which makes the original dense structure of the rock become relatively loose and fragile gradually. At a macro level, it shows a sharp deterioration of the dynamic compressive strength and a rapid increase in the dynamic peak strain. The second stage is the late stage of reaction, when the reaction between OH\({}^{-}\) ions and part of the components of sandstone specimens is close to equilibrium, the chemical reaction rate of water-rock starts to slow down, and the rate of increase in sandstone porosity gradually decreases, and the decrease in the dynamic compressive strength and the increase in the dynamic peak strain also tends to level off.
## 4 Results and Discussion
### First Application: Tilt and Sidelodes Control
#### 4.2.2 Analysis of the Simulations
Additionally, GA-MMC is designed to meet many applications, including simultaneous optimization objectives. Therefore, \(K_{M}=1\) was adopted, but it is noteworthy that, for \(K_{M}=0.05\), GA-MMC provided RSLL \(>37.0\) dB, which is higher than that calculated by the deterministic Dolph-Tschebyscheff method (RSLL = 36.1 dB).
###### Abstract
On the whole, this reveals that, for a single radiating element patch antenna, getting a bandwidth of higher than 10% with a radiation efficiency of more than 90% is very complex, and it depends on the feeding technique and the floating patch construction design. All of the former examples make use of different techniques that include air in the improvement of the performance indexes, whether as a cavity-backed [18] or additional layer, such as in the air-gap technique [13], the suspended patch technique [21], or the inverted patch technique [19]. However, in none of these is the patch really isolated as a completely dielectricless floating element only surrounded by air, which is the new approach used in this novel proposal. In order to solve the above challenges, this work deals with the aim of designing a microstrip patch antenna with an air substrate in the simplest possible way (complex antenna designs are not of interest to the rapidly growing wireless industries [8]). It needs to be able to cope with a high gain, radiation efficiency, low loss, and wide bandwidth, which are the common performance requirements of current radiating elements, while maintaining its compact size, light weight, ease of manufacture, and easy integration with other electronic devices [22]. Therefore, in order to cope with these requirements, this work proposes a real dielectrics floating patch in which the dielectric is removed by using air (\(\epsilon_{r_{air}}=1\)) as the separation between the floating patch and the feeding. Next, the patch shape is chosen to be the simplest for manufacturing, i.e., a rectangle (as this shape minimizes cuts in the base material and can be completed with maximum accuracy). Lastly, and most importantly, the patch feeding is adjusted through a pin rod (probe) to the microstrip feeding line, taking advantage of the through-wire technique equivalently used in [23; 24] for feeding an empty substrate-integrated waveguide. This new feeding technique is used for the first time to feed a patch antenna; it has the same advantages as the coaxial feed, together with low losses, a perfect adaptation to the microstrip line, and a straight and very simple way of connecting to the patch with just a direct insertion point to be tin-soldered (as this is just a monoconductor pin rod). This pin rod will serve, at the same time, as the fastener of the patch to fix its floating position, which therefore is easier with regard to the manufacturing process. The proper combination of these pieces allows for the achievement of an extremely good-performance patch antenna fulfilling all of the desired requirements, and, under a new design approach characterized by a completely dielectricless patch antenna, uses the new through-wire technique to feed and hold the real floating patch and make it through a simple structure with a very quick and easy manufacturing procedure.