esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Data Preprocess
#### 3.1.1 Data Completion  
HAR datasets are typically acquired using inertial sensors at different body parts. The data at each sampling point are spliced according to the timestep. During the acquisition process, data may be missing at certain sampling timesteps. Although missing data at a single timestep has limited impact on the overall data, it can affect the integrity of the timing data. Therefore, linear interpolation is used to fill in missing values. Let \((x,y)\) represent missing data, where \((x_{0},y_{0})\) represents the previous non-missing data and \((x_{1},y_{1})\) represents the next non-missing data. Since the timestep \(x\) is known, the missing value \(y\) can be obtained by using linear interpolation:  
\[y=y_{0}+(x-x_{0})\frac{y_{1}-y_{0}}{x_{1}-x_{0}} \tag{1}\]
# Laser Beam Welding of CubeSat 1U Structure Parts Obtained by Powder Bed Fusion
## 2 Materials and Methods
at a speed of 600 rpm and a diamond paste with a granulometry of 6 \(\upmu\)m, 3 \(\upmu\)m, and 1 \(\upmu\)m. Final polishing was performed using colloidal silica. To visualize the microstructure, after polishing the samples were etched with Kroll reagent (10 mL of HF, 45 mL of HNO\({}_{3}\), and 45 mL of H\({}_{2}\)O) for 30 s, washed in water, and dried with a jet of compressed air. The etching methodology was based on ASTM E 407-99 [14]. The real piece was compared with the electronic model of the connector using 3D scanning of the part after fabrication. Three-dimensional scanner equipment of the brand GOM model ATOS Triple Scan was used; it offered 16,924 measurement points with a resolution of 20 nm. The results allowed for the determination of the maximum and minimum differences between the real and electronic surfaces of the model in relation to the normal of their surfaces. Roughness analysis was performed using a Leica confocal microscope (DCM3D). A roughness analysis was performed to measure the difference in surface finish between the connectors produced by SLM and EBM. As reported in the literature, there are differences in the surface finish of parts produced by SLM and EBM [15]. The Vickers microhardness test was used to measure the variation in the hardness values of the two samples analyzed in different regions. The Vickers microhardness testswere performed using a Future-Tech microhardness meter, model FM-700. The load used in the tests was a 100 g force (gf), with a load application time of 10 s, and with the aid of a microscope coupled to the microhardness meter, the diagonals of the impression were measured, and the equipment provided toughness values. The samples used for mapping the hardness profile were the same as those used in the microstructural analyses. Therefore, the samples were embedded, sanded, polished, and etched. The samples to be tested were positioned in a microdurometer such that the weld bead was perpendicular to the indentation line. The first indentation was performed on the angle towards the weld bead until it reached the tube, maintaining a spacing between the indentations of 0.05 mm. Tensile strength tests were carried out in the universal testing machine Emic, model DL 100 kN, with a maximum capacity of 10,000 kgf and a bench speed of 1 mm/min, at room temperature.
## 2 Materials and Methods  
### In Vitro Circular Cross-Section Microchannel  
PDMS (SylgardTM 184, Dow Corning, Wiesbaden, Germany) scaffold was prepared by mixing elastomer silicone and curing agent in a 1:10 ratio. The PDMS mixture was then degassed in a vacuum oven (NAPCO, Thermo Scientific, Waltham, MA, USA) to eliminate the bubbles that arose due to the stirring involved with the mixing of the elastomer and curing agent. The mixture was then poured into standard cuvettes (14955125, FisherbrandTM, Pittsburgh, PA, USA). The circular cross-section microchannel was made in this scaffold by inserting a 27G 1-1/2\" needle (BD Sciences, Franklin Lakes, NJ, USA) in the center of the cuvette. The needle was held in the center using tape. The PDMS with the needles in the cuvette was cured overnight at room temperature to obtain the channel mimicking the blood vessel. The PDMS scaffold containing the channel was then released by removing the cuvette. The ends of the channels were cut so that the channel was open-ended to facilitate the exchange of gases during cell seeding. The microchannel mimicked blood vessels with a radius of 450 \(\upmu\)m and a length of 3.5 cm. A picture of the fabricated microchannel is shown in Figure S1. The PDMS scaffold was sterilized in 70% ethanol for 30 min before collagen coating. The high-concentration rat tail type I collagen (354249, Corning, AZ, USA) of 350 \(\upmu\)g/mL was used for coating these channels. The collagen was injected into the channels using a sterile syringe (BD Sciences). The channel was then incubated at 37 \({}^{\circ}\)C for 1 h. The excess collagen was aspirated by drying the channel for two hours in a time hood.  
To mimic the choroidal vasculature of the eye, _Macaca mulatta_ choriorental endothelial cells RF/6A (ATCC(r) CRL-1780 TM) were used. The cells were cultured in a 75 cm\({}^{2}\) flask placed in a cell culture incubator (NuncTM, Thermo Scientific, Waltham, MA, USA) at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). The cells were split in a 1:2 ratio as they reached 80% confluence. Eagle's minimum essential medium (EMEMEM) (ATCC(r) 30-2003 TM) culture media supplemented with 10% fetal bovine serum (ATCC(r) 30-2020 TM) and 100 IU/mL penicillin and 100 \(\upmu\)g/mL streptomycin solution (ATCC(r) 30-2300 TM) was used. Cells used in this study were between passages 4 and 7. The channels coated with 350 \(\upmu\)g/mL collagen were injected with the cells for all the experiments. The cells were allowed to grow completely inside the channels for two days. Channels were kept in a Petri dish with medium for the exchange of nutrients. The growth and morphology of the cells were continuously monitored using an inverted microscope to ensure appropriate cellular morphology.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 3 Methods
### The Architecture of the BP-ARQ Scheme
On the other hand, when the original sub-frame fails and the packet is received via the backup ones, the energy of retransmitting the packet in a new transmission can be saved. Figure 5: An example of the A-MPDU transmission using the backup padding ARQ (BP-ARQ) scheme where W = 64.
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 4 Lane Detection and Tracking
### Lane Line Fitting Based on a Polar Coordinate System
The curvature radius is represented by:  
\[\text{r}=\frac{1}{K}=\left|\frac{\left(\rho^{2}(\theta)+{\rho^{\prime}}^{2}( \theta)\right)^{\frac{3}{2}}}{2{\rho^{\prime}}^{2}(\theta)+\rho^{2}(\theta)- \rho(\theta)\rho^{\prime\prime}(\theta)}\right|, \tag{39}\]  
where \(\beta\) is the inclination of the tangent line at each point below the track, which can be computed by the equation below:  
\[\tan\beta=\frac{dy}{dx}=\frac{d(\rho\sin\theta)}{d(\rho\cos\theta)} \tag{40}\]  
According the relationship between \(\rho\) and \(\theta\), \(k_{t}\), the slope of the tangent line at each point of the lane line is solved. \[k_{t}=\tan\beta=\frac{\rho^{\prime}(\theta)\sin\theta+\rho(\theta)\cos\theta}{ \rho^{\prime}(\theta)\cos\theta-\rho(\theta)\sin\theta} \tag{41}\]  
For simplification, \(\rho\) and \(\theta\) are represented by x and y in the following text. Because the least-squares method has prominent advantages in lane line fitting and high fitting accuracy [26; 27], it can realize the fitting of straight lines and curves. In this section, the specific steps of fitting are explained as follows:  
Firstly, the approximate curve \(y_{i}=\varnothing(x_{i})\) can be obtained by the given lane line data \(\text{P}_{i}(x_{i},y_{i})\). At the same time, to ensure the accuracy of the curve, the bias between the obtained equation and the original data point must be minimized. The bias of the curve on the point \(\text{P  
Therefore, the value of \(A\) can be solved by multiplying the transpose of X, as shown below:  
\[A={{{(X^{T}X)}^{-1}}}{X^{T}Y}, \tag{46}\]  
Then, the least square fitting coefficient of \(A\) can be obtained based on Equation (46). \(A\) is substituted into the polynomial fitting equation to obtain the lane line equation.
## 2 Materials and Methods
### HPLC Analysis of Phenolic Acids and Flavonoids  
The concentration of phenolic compounds (phenolic acids and flavonoids) was determined by the high-performance liquid chromatography (HPLC) method, as previously described by [PERSON] et al. [33], using Shimadzu equipment (USA Manufacturing Inc., Canby, OR, USA). The 100 mg samples of grain material were mixed with 5 mL of 80% methanol, vortexed (using a Micro-Shaker 326M, Premeo, Marki, Poland) and incubated for 10 min at 30 \({}^{\circ}\)C and 5.5 kHz in an ultrasonic bath, then samples were centrifuged (10 min at 6000\(\times\) rpm at 5 \({}^{\circ}\)C). Following a second centrifugation (5 min, 12,000\(\times\) rpm at 0 \({}^{\circ}\)C) of the acquired supernatants, 900 \(\mu\)L of the clear supernatants were transferred to HPLC vials for analysis. The HPLC-set included the following: two LC-20 AD pumps, a CBM-20A controller, a SIL-20 AC column oven, and a UV/Vis SPD-20 AV spectrometer, manufactured by USA Manufacturing Inc. Using the Synergy Fusion-RP, A-80 Phenomenex column (250 \(\times\) 4.60 mm), the phenolic compounds were separated at a flow rate of 1 mL min\({}^{-1}\). For phenolic acids, the wavelength was 250 nm, while for flavonoids, it was 370 nm. The identification of individual compounds was based on Fluka and Sigma-Aldrich (Warsaw, Poland) standards with HPLC purities of 99.00-99.99%. The HPLC chromatograms present  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Months**} & **Average** & \multicolumn{2}{c}{**Long-Term**} & \multirow{2}{*}{**Rainfall (mm)**} & **Long-Term** \\  & **Temperature** & & & **Average** \\  & **(\({}^{\circ}\)C)** & **(1981–2010)** & **(\({}^{\circ}\)C)** & **Rainfall (mm)** \\ \hline September & 14.7 & 13.4 & 17.6 & 60 \\ October & 9.6 & 8.5 & 22.3 & 37 \\ November & 4.7 & 3.2 & 32.4 & 38 \\ December & 0.6 & \(-0.8\) & 63.2 & 37 \\ January & 1.0 & \(-2.1\) & 47.1 & 30 \\ February & 1.1 & \(-1.2\) & 9.9 & 30 \\ March & 5.1 & 2.7 & 54.4 & 35 \\ April & 8.2 & 8.7 & 33.0 & 39 \\ May & 12.6 & 14.5 & 118.3 & 58 \\ June & 16.8 & 17.2 & 32.9 & 65 \\ July & 19.8 & 19.3 & 54.9 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weather conditions, including monthly average air temperature (\({}^{\circ}\)C), total monthly rainfall (mm), and long-term averages (1981–2010), in the season of barley cultivation, at the Osiny experimental station.  
ing time of retention and peaks of the phenolic acids and flavonoids identified in barley grain samples are presented in Figures S1 and S2 (Supplementary Materials).
###### Abstract  
This study presents a novel approach to the increasingly important task of distinguishing AI-generated images from authentic photographs. The detection of such synthetic content is critical for combating deepfake misinformation and ensuring the authenticity of digital media in journalism, forensics, and online platforms. A custom-designed Vision Transformer (ViT) model, termed Patch-Based Vision Transformer for Identifying Synthetic Media (PV-ISM), is introduced. Its performance is benchmarked against innovative transfer learning methods using 60,000 authentic images from the CIFAKE dataset, which is derived from CIFAR-10, along with a corresponding collection of images generated using Stable Diffusion 1.4. PV-ISM incorporates patch extraction, positional encoding, and multiple transformer blocks with attention mechanisms to identify subtle artifacts in synthetic images. Following extensive hyperparameter tuning, an accuracy of 96.60% was achieved, surpassing the performance of ResNet50 transfer learning approaches (93.32%) and other comparable methods reported in the literature. The experimental results demonstrate the model's balanced classification capabilities, exhibiting excellent recall and precision throughout both image categories. The patch-based architecture of Vision Transformers, combined with appropriate data augmentation techniques, proves particularly effective for synthetic image detection while requiring less training time than traditional transfer learning approaches.  
deep learning; Vision Transformers; image classification; AI-generated images; attention mechanism; transfer learning Article
# Approximate Networking for Universal Internet Access
## 1 Introduction
### What Is Approximate Computing?  
Broadly speaking, approximate computing leverages the capability of many computing systems and applications to tolerate some loss of quality and optimality by trading off \"precision\" for \"efficiency\" (where efficiency can be in the terms of increased performance or reduced costs in terms of energy consumed or system cost/area). Approximate computing systems are able to optimize the efficiency of systems by relaxing the commonly applied notion of exact (numerical or Boolean) equivalence between the specification and implementation at multiple layers of the hardware and software stacks (see Figure 1 for a depiction). The use of approximate computing is motivated by the following factors: (1) modern \"big data\" applications are based on noisy real-world data; (2) many computing applications (e.g., recommendation and web search) do not have a single golden answer; (3) the perceptual limitations of users mean that some approximations may not even be noticed; and (4) many applications exist--e.g., images, video, and sound--where minor errors and approximations can be tolerated by different users. Some recent case studies for applying approximate computing to video processing [7], signal processing [8] and communication systems [9] have shown early feasibility. The research in the field of approximate computing has been led by seminal contributions from the industry players such as Intel [10], IBM [11; 12], and Microsoft [13], as well as several research groups from academia [5; 6; 14; 15; 16].
# Current State of the Gold Mining Waste from the Ores of the Ursk Deposit (Western Siberia, Russia)
###### Abstract  
The article presents the original results of the investigation of sulfide-bearing mine tailings dumps (Ursk, Western Siberia, Russia), the adjacent territory, and acid mine drainage flows. The novelty of this study is related to integration of geophysical and geochemical data. The geoelectrical structure of the dump and the drainage valley was determined using the electrical resistivity tomography (ERT) method. Magnetic anomalies above the surface of the site were identified using ground magnetic surveys. The orthophotomap and a digital elevation model were obtained on the basis of aerial photography from an unmanned aircraft. The model of thermal imaging allows us to identify a number of temperature anomalies in the upper part of the dump slope and filtration zone in the drainage valley, caused by exothermic reactions. The digital relief model was constructed comprising three zones: (1) oxidation, (2) leaching, and (3) mixing and dilution. The oxidation zone is marked by low electrical resistivity (1-10 \(\Omega\)-m), low pH values of the drainage waters (pH 3.61), low values of the modulus of the magnetic induction vector, and heating by +5 \({}^{\circ}\)C compared to the solid dump material that is not actively exposed to water. The oxidative conditions and low pH values favor the dilution of the Fe\({}^{2+}\) compounds in the drainage solutions, which also contain Cu (3000 \(\mu\)g/L), Pb (1200 \(\mu\)g/L), As (1300 \(\mu\)g/L), and Hg (34 \(\mu\)g/L). The zone of the primary geochemical barrier is formed within the first 400 m, where the major proportion of dissolved elements if deposited. The second iron-aluminum sorption geochemical barrier is formed at a distance of 1000 m at the mixing zone with the Ur River. The stable geochemistry of surface waters, formed due to the inflow of drainage waters into the Ur River and further into the reservoir, persists at a distance of up to 7.7 km from the dump.  
Keywords:mine tailings; metals; metalloids; electrical resistivity tomography; drainage system; river; pollution +
Footnote †: journal: Applied
# Decision-Making Approach Based on Neutrosophic Rough Information
## 1 Introduction  
The concept of a neutrosophic set, which generalizes fuzzy sets [1] and intuitionistic fuzzy sets [2], was proposed by [PERSON] [3] in 1998, and it is defined as a set about the degree of truth, indeterminacy, and falsity. A neutrosophic set \(A\) in a universal set \(X\) is characterized independently by a truth-membership function (\(T_{A}(x)\)), an indeterminacy-membership function (\(I_{A}(x)\)), and a falsity-membership function (\(F_{A}(x)\)). To apply neutrosophic sets in real-life problems more conveniently, [PERSON] [3] and [PERSON] et al., [4] defined single-valued neutrosophic sets which take the value from the subset of [0, 1].  
Rough set theory was proposed by [PERSON] [5] in 1982. Rough set theory is useful to study the intelligence systems containing incomplete, uncertain or inexact information. The lower and upper approximation operators of rough sets are used for managing hidden information in a system. Therefore, many hybrid models have been built such as soft rough sets, rough fuzzy sets, fuzzy rough sets, soft fuzzy rough sets, neutrosophic rough sets and rough neutrosophic sets for handling uncertainty and incomplete information effectively. [PERSON] and [PERSON] [6] introduced the notions of rough fuzzy sets and fuzzy rough sets. [PERSON] and [PERSON] [7] have studied different decision-making methods. [PERSON] and [PERSON] [8] presented operations on fuzzy graphs. [PERSON] et al., [9; 10; 11; 12] considered several new concepts of neutrosophic graphs with applications. Rough fuzzy digraphs with applications are presented in [13; 14]. To get the extended notion of neutrosophic sets and rough sets, many attempts have been made. [PERSON] et al., [15] introduced the concept of rough neutrosophic sets. [PERSON] et al., [16] proposed single valued neutrosophic rough sets by combining single valued neutrosophic sets and rough sets, and established an algorithm for the decision-making problem based on single valued neutrosophic rough sets on two universes. [PERSON] et al., [17]and [PERSON] et al., (2018) introduced rough neutrosophic digraphs, in which they have approximated neutrosophic set under the influence of a crisp equivalence relation. In this research article, we apply another hybrid set model, neutrosophic rough, to graph theory. We deal with regular neutrosophic rough digraphs and then solve the decision-making problem by using our proposed hybrid model.  
Our paper is organized as follows: Firstly, we develop the notion of neutrosophic rough digraphs and present some numerical examples. Secondly, we explore basic properties of neutrosophic rough digraphs. In particular, we investigate the regularity of neutrosophic rough digraphs. We describe novel applications of our proposed hybrid decision-making method. To compare the two notions, rough neutrosophic digraphs and neutrosophic rough digraphs, we give a comparison analysis. Finally, we end the paper by concluding remarks.
## 1 Introduction
They concluded that the pseudo-potential model is a promising tool for liquid-gas systems, but may not be the optimal solution for the simulation of immiscible flows. Also, it produces high spurious velocities and thick interfaces for immiscible flows [20]. [PERSON] et al. [21] used the pseudo-potential model to simulate two-phase flows in a two-dimensional channel with different viscosity ratios. A viscous-fingering-like phenomenon was observed for viscosity ratios of up to 10, and the simulations become unstable for larger viscosity ratios. [PERSON] et al. [22] studied the spontaneous imbibition of water into a water-wet micro-model which was initially saturated with oil. It was verified that the maximum allowable viscosity ratio is four. The free energy model and colour gradient model show similar capabilities in modelling the flow of binary fluids with high viscosity contrast, but the latter produces a relatively thinner interface than the former one. In addition, the colour gradient model is able to control the interfacial diffusion and adjust the interfacial tension and viscosity independently to facilitate the numerical investigation. The colour gradient LBM was first proposed by [PERSON] and [PERSON] [13], and has been widely used for the simulation of immiscible fluids [9; 23; 24; 25; 26]. To obtain an accurate and detailed understanding of the pore-scale mechanisms within porous media, [PERSON] et al. [25] proposed a new algorithm for imposing the contact angle on the solid surface in the colour gradient LBM. The simulation results were validated against the experimental observations of [PERSON] et al. [27]. However, these simulations only dealt with the simplified porous media, where the solid grains are regular circles and rectangles. In realistic porous media, the pore structures are extremely complex and irregular, which allow us to further assess the capability of numerical model. We apply the colour gradient LBM for dynamic displacement of immiscible fluids in a complex porous flow structure extracted from a realistic Berea sandstone sample, and study the effects of the viscosity ratio, interfacial tension, and contact angle on displacement process.