esa-sceva/satcom-chunk-collection huggingface dataset:
# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
## 5 Conclusions  
In this paper, we analyzed the characteristics of traffic noise in seismic records and proposed a combination of the UNet and ACBs to achieve attenuation of traffic noise in seismic or acoustic records. In the real data, high amplitude traffic noise covers the signal and affects the data processing and interpretation. Therefore, traffic noise attenuation is an essential step. However, the traffic noise and the signal significantly challenge noise attenuation work. Unlike traditional denoising methods, the UNet uses training data to extract potential features between data and can realize intelligent denoising without any assumptions and domain transformation. The ACBs use \(3\times 1\), \(1\times 3\), and \(3\times 3\) convolution kernels instead of a \(3\times 3\) square convolution kernel in the UNet, which can be easily integrated into the the UNet framework to improve network feature extraction and detail processing capabilities. Another significant advantage of the method is that the cost lies mainly in network training. The prediction after successful network training is very convenient and does not require any parameter adjustment. The trained network can learn the potential features between the data and identify and attenuate the traffic noise in the data. This method has the potential to identify and separate different data as long as we build rich training samples for the data. Synthetic and real data tests show that the method can effectively attenuate high amplitude traffic noise in seismic records, improve the signal-to-noise ratio of seismic records, and achieve effective utilization of seismic data. At the same time, the method preserves effective signals, especially those with weak energy, while attenuating road traffic noise. Moreover, the method has favorable application foreground.  
Conceptualization, Z.Z. and D.C.; methodology, Z.Z., X.C. and D.C.; validation, X.C., M.C. and S.D.; writing--original draft preparation, Z.Z. and X.C.; writing--review and editing, Z.Z. and X.C.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Hainan Provincial Natural Science Foundation of China, Grand No. 422 MS091, and funded by Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City, Grand No.: 2021 CXLH0018, and funded by Hainan institute Foundation of Zhejiang University.  
Not applicable.  
The data are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & convolution neural networks \\ ACB & asymmetric convolution blocks \\ ACB-UNet & U-Net with asymmetric convolution blocks \\ \end{tabular}
## 2 Methodology
### Design
#### 2.1.2 Boundary Conditions  
Both optimisation and Finite Element Analysis (FEA) require accurate input boundary conditions. Crank assemblies must pass a 100,000-cycle fatigue test, alternating loading of each crank (set at 45\({}^{\circ}\)) with 1800 N applied 65 mm from its pedal-side face (BS EN ISO 4210-8).  
Many studies have developed models to relate crank power to the crank angle. [PERSON] [17] defined crank force to be muscular and non-muscular (gravitational or inertial). [PERSON] et al. [18] used horizontal and vertical components of these forces at 70 rpm to produce a force vector profile for realistic loading. The author has scaled this force vector profile to provide the desired 1800 N at 45\({}^{\circ}\) to comply with the BS EN ISO 4210 testing conditions. The forces obtained for various crank angles from the resulting force vector profile (Figure 2) agree with those found in numerous studies: maximum load crank angle [19], hip and knee angles of 38\({}^{\circ}\)-50\({}^{\circ}\) and 73\({}^{\circ}\)-145\({}^{\circ}\)[20].  
The calculated loads from Figure 2 were applied 65 mm from the outboard crank face. An RBE3 (Rigid Body Element) connected this point to the inside face of the pedal spindle hole. This RBE represents the pedal spindle, transferring applied load to a singular external grid to the hole's face. Loads at 30\({}^{\circ}\) crank angle intervals were applied to ensure realistic loading and acceptable processing time. Lateral loads were not considered, as with a correctly adjusted Q-Factor, these would be zero.  
Figure 2: Force Applied to Crank at Angle.  
All degrees of freedom were constrained by a Single-Point Constraint (SPC) at the centre of the square taper void. Each tapered face was connected to this with an infinitely stiff RBE2 element. This infinitely stiff RBE simulates the connection to a BB axle which cannot undergo translational or rotational displacement. While in reality, rotation is allowed about one axis, this produces an unsolvable model for optimisation and this configuration may thus produce an overly stiff result.
## 3 Results and Discussion  
### Breakthrough Curves  
The breakthrough curve for the removal of BRed dye by a column loaded with a packed bed of residual _Bacillus sp_. biomass immobilized in alginate, represented in axial coordinates, showing dye concentration of effluent (\(C_{t}\), mg/L), or relative dye concentration of effluent (\(C_{t}\)/\(C_{0}\)) versus biosorption time (\(t\), min), is presented in Figure 2. It is known that the shapes of curves offer preliminary information on the nature of a studied biosorption process as well as the loading behavior of the dye in the biosorbent packed bed of the tested column. Additionally, some characteristic parameters of the biosorption process are presented in Table 2, which were determined from the slope and intercepts of the breakthrough curves in Figure 2.  
From Figure 2, it can be observed that an increase in flow rate (\(F_{v}\)) leads to a decrease in breakthrough time (\(t_{b}\)), which suggests that biosorbent saturation is achieved much faster. This finding is also confirmed by the value of the penetration time (\(t_{b}\)) (Table 2), which is reduced with the increase of the treated effluent flow rate value, and also by the volume of the treated effluent, until the penetration point (breakthrough), which decreases from 909 mL (\(F_{v}\) = 2.9 mL/min) to 575 mL (\(F_{v}\) = 4.5 mL/min). A similar behavior is recorded for saturation time (\(t_{s}\)) and saturation volume (\(V_{s}\)), respectively; thus, the saturation time (\(t_{s}\)) is reduced from 55 min (\(F_{v}\) = 2.9 mL/min) to 16.5 min (\(F_{v}\) = 7.3 mL/min), and the breakthrough volume (\(V_{b}\)) decreases from 20.3 mL (\(F_{v}\) = 2.9 mL/min) to 10.95 mL (\(F_{v}\) = 7.3 mL/min).  
All obtained data suggest that the biosorption of the BRed dye onto the granular biosorbent based on residual _Bacillus_ sp. biomass immobilized on the alginate placed in a vertical column reactor depends on the flow rate (\(F_{v}\)), the height of the biosorbent fixed/packed bed (h) and the dye concentration (\(C_{t}\)) in the effluent passing through the fixed bed column.
## 4 Results and Discussion
### Sample Code for Accuracy Assessment
#### 4.3.3 Landscape Level
The findings underscore the urgent need to integrate these metrics into local policy frameworks to develop targeted interventions, such as reforestation programs, sustainable agriculture practices, and the creation of ecological corridors to restore connectivity. Managing these changes effectively is essential to preserving the natural landscape and ensuring the long-term sustainability of natural resources in the Sonipat district. As discussed in the Methodology, the 30 m (Landsat) and 10 m (Sentinel-2) raster data were harmonized to a consistent 30 m resolution, ensuring compatibility for calculating shape and edge metrics across years, specifically from 2011 to 2021. This resampling process minimizes discrepancies in spatial scale, allowing direct comparison over time. Although downscaling to a coarser resolution may cause minor edge blurring, this harmonization was essential for accurately analyzing landscape metrics without bias from differing resolutions. Figure 26 demonstrates that this approach preserved the integrity of calculated metrics, enabling the attribution of changes observed in 2021 primarily to actual landscape transformation rather than to resolution discrepancies. The landscape's spatial and structural changes, particularly how fragmentation and patch metrics, reflect the ecological shifts within the study area. For example, the increase in edge density and decrease in patch size across cropland areas from 2011 to 2021 indicate a rise in landscape fragmentation. This fragmentation can disrupt habitat connectivity, reduce biodiversity, and create barriers to species movement, ultimately leading to ecological degradation. Furthermore, the metrics highlight a decline in spatial heterogeneity, especially in regions where urban expansion and infrastructure developments are prevalent. This loss of heterogeneity reflects reduced resilience in the landscape, as smaller, more  
Figure 27: Area and perimeter changes in the class of cropland & urban and Built-up from 2011 to 2021.  
isolated patches provide fewer resources and habitat diversity than larger, contiguous ecosystems. Consequently, our metrics offer insights into how land use changes impact ecological stability, suggesting a trend toward increased vulnerability to environmental pressures within the study area.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 5 Protocol Implementation  
A prototype implementation has been developed in order to verify if it is compliant with the capability of a real WSN. Since in a real implementation on low cost devices many issues can compromise the activity of the nodes, a key establishment is considered completed when 95% of the possible pairwise keys have been established. The adopted public cryptosystem is the ECC. According to the National Institute of Standards and Technology (NIST, www.nist.gov) guidelines, ECC provides the same level of security of AES with 128-bit keys, by using 256-bit keys. The adopted WSN platform was composed of 10 Tmote sky devices with TinyOS [28]. The developed code was written in language [29]. The nodes are programmed to avoid a new handshake before the end of the current one. A timeout is used to abort erroneous too long handshakes. In this way, the nodes do not require to save the data required by several simultaneous handshakes.  
In order to analyze the performance of PCAT, the key establishment has been executed on networks with different levels of density (i.e., from 2 to 10 neighbors per node), and each test has been iterated 10 times. The time required to complete the key establishment is shown in Figure 9. The chart reports the time required for the establishment if the nodes are deployed simultaneously and if one node is added to a previously deployed node. In the latter case, the quantity of messages is strongly lower, therefore there are less collisions among messages and the time required to complete the key establishment is limited.  
Figure 8: Best schemes for mobile wireless sensor networks (WSNs) and for static WSN with the assumption that a node can be compromised during the initialization phase.  
Figure 9: Time required to complete the key establishment according to the density.
## 2 Higher-Order Sliding Mode Regulation for Boost Converter Output  
As observed in Figure 1, controlling the converter consists of controlling two converters that have the same parameters and work in the same manner. They are both boost converters generating a DC-biased sinusoidal signal. For this reason, the main focus of the problem is controlling a boost converter and forcing it to produce a sinusoidal DC-biased signal.  
This chapter introduces the mathematical model of the DC-DC boost converter, as well as the formulation of the problem. Then, the higher-order sliding mode output regulation technique is used to design a controller for the formulated problem.
## 3 Pose Estimation for Multi-Marker Systems
### Invalid Marker Detection  
Invalid markers within the marker array were detected based on the spatial consistency verification process. The spatial consistency verification process evaluates the geometric relationships between detected markers to identify inconsistent measurements.  
Figure 2 shows the special relationship between the camera, the _i-th_ marker, and the representative marker. In Figure 2a,b, \(T_{cm,i}\) and \(T_{iR}\) are the measured pose of the _i-th_ marker relative to the camera and the known geometric relationship between the _i-th_ marker and the representative marker. In Figure 2c, \(T_{cR,i}\) denotes the homogeneous transformation matrix representing the pose of the representative marker as calculated through the _i-th_ marker.  
Figure 1: Overview of pose estimation using multiple ArUco markers.  
The homogeneous transformation matrix for each _i-th_ marker was transformed so that it could be represented relative to the coordinate system of the representative marker using the relationship shown in Figure 2.  
\[T_{cR,i}=T_{cm,i}T_{iR} \tag{7}\]  
The rotation matrix in the transformed results from Equation (7) was converted to the rotation vector of three columns. Then, the rotation vector differences between pairs of markers were computed:  
\[diff_{i,j}=\left|r_{cR,i}-r_{cR,j}\right| \tag{8}\]  
In Equation (8), \(diff_{i,j}\) denotes the magnitude of the rotation vector difference. \(r_{cR,i}\) and \(r_{cR,j}\) are rotation vectors extracted from \(T_{cR,i}\) and \(T_{cR,j}\), respectively. Indexes \(i\) and \(j\) represent different markers within the marker array.  
A marker was identified as invalid if its rotation measurements consistently deviated from other markers beyond a predetermined threshold. For each marker pair of \(i\) and \(j\), the developed algorithm recorded cases where \(diff_{i,j}\) exceeded the threshold. The marker with the highest frequency recorded was flagged as invalid. The measurements of the invalid marker were excluded from subsequent pose estimation calculations.
## 2 Results
### SmCCD4 Regulates Tanshinone and Phenolic Acid Biosynthesis in S. miltiorrhiza  
To investigate the role of _SmCCD4_ in plant growth, we overexpressed _SmCCD4_ in the _S. miltiorrhiza_ WT strain under the control of the _CaMV35S_ promoter. Due to the significantly higher expression level of _SmCCD4_ in the aboveground part of _S. miltiorrhiza_ compared with the roots, subsequent experimental materials were selected from the aboveground  
Figure 3: RT-qPCR analysis of _SmCCD4_ gene expression level. (**A**) Expression level of _SmCCD4_ in different organs (X-axis: root, stem, leaf, and flower) of _S. miltiorrhiza_. (**B,C**) Expression level of _SmCCD4_ under ABA induction and PEG drought stress. Data were normalized to _SmACT_ gene. All data represent means \(\pm\) SDs of three independent experiments. Statistical significance was assessed with Student’s _t_-test (***\(p<0.001\); ***\(p<0.01\); *\(p<0.05\)). The X-axis represents the treatment time of ABA and PEG.  
part of plants. The concentrations of four types of tanshinones in the OE strains were significantly higher than those in the WT (Figure 4A). Among them, tanshinone I increased by 3.5 times in OE-6, tanshinone IIA increased by 6.0 times in OE-4, dihydrotranshinone increased by 2.5 times in OE-6, and cryptotanshinone increased by 5.9 times in OE-6. Compared with the WT, the content of rosmarinic acid increased by 1.4 and 1.5 times in OE-4 and OE-7 respectively, while the content of salvianolic acid in the OE strains showed no significant difference (Figure 4B). The transcription levels of seven genes, _SmDXS1_, _SmHMGR1_, _SmGPPS_, _SmGGPPS_, _SmCPS1_, _SmKSL1_, and _SmCYP6 AH1_, selected from the tanshinone biosynthesis pathway were evaluated by using RT-qPCR (Figure 5A). Except for _SmGPPS1_ and _SmKSL1_, which showed no significant difference in expression levels among different strains, the other genes were highly expressed in the OE strains, especially _SmCPS1_. On the contrary, the expression level of _SmCYP6 AH1_ in the OE strains was suppressed. Seven genes, _SmPAL1_, _SmC4H1_, _Sm4 CL1_, _SmTATT_, _SmHPPR1_, _SmRAS1_, and _SmCYP98A14_, were selected from the phenolic acid biosynthesis pathway (Figure 5B). The expression levels of _SmPAL1_ in the phenylpropanolic metabolic pathway, _SmHPPR_ in the tyrosine metabolic pathway, and _SmRAS_ in two key downstream pathways were increased compared with the WT. The two genes _Sm4 CL1_ and _SmTATT1_ were strongly suppressed in the OE strains.  
Figure 4: Detection of tanshinone and phenolic acid contents in WT and overexpression strains. (**A–D**) Concentration of tanshinone compounds: tanshinone I (**A**), tanshinone IIA (**B**), hydrotranshinone (**C**), and cryptotanshinone (**D**). (**E**,**F**) Concentration of phenolic acid compounds: salvianolic acid B (**E**) and rosmarinic acid (**F**). All data represent means \(\pm\) SDs of three independent experiments. Statistical significance was assessed with Student’s \(t\)-test (***\(p<0.001\); **\(p<0.01\); \({}^{*}p<0.05\)).
## 3 Results and Discussion
### Characterization of Sorbent Material
#### 3.6.2 Fourier Transform Infrared Spectrometer (FTIR)  
The FTIR spectrum of treated bleaching clay (Figure 6a) showed a strong feature at 1049 cm\({}^{-1}\) because of Si-O group stretching mode, while the features obtained at 470 and 790 cm\({}^{-1}\) are due to the deformation mode of Si-O-Si, as well as Al-O-Si, respectively [50; 51; 52]. The peak of the stretching mode of OH group was observed at 3433 cm\({}^{-1}\). The spectrum of AHIBC is demonstrated in Figure 6b; new bands observed between 1200-1430 cm\({}^{-1}\) attributed to C-H bending mode. Features observed at 2800-2950 cm\({}^{-1}\) were attributed to C-H stretching mode, and the strong band between 3550-3210 cm\({}^{-1}\) could be assigned by stretching mode of OH. The bands of secondary amine (-NH) were obtained at 3210 cm\({}^{-1}\). The stretching mode of C-N group for aromatic amine in 3-amino-5-hydroxypyrazole appeared at 1461 and 1150 cm\({}^{-1}\).  
The spectrum in Figure 6c illustrates that the sorption of Y(III) on the AHIBC is more likely attributable to the chemical attraction between their groups and cationic metal ions. The deprotonation of both hydroxyl and amino groups takes place at pH above 6.0, and they have negative charges that bind the positive metal ions in solution. This confirms that the mechanism of sorption of yttrium cations is an ionic exchange mechanism between the cationic metals ions, and the groups of the AHIBC and treated bleaching clay (silanol and aluminol groups) that have negative charges. After adsorption, the features at 3390 and 3210 cm\({}^{-1}\) disappeared, while the features at 1461 and 1150 cm\({}^{-1}\) (amine group) and 1620 cm\({}^{-1}\) (phenolic group) had shifted towards the blue shift. Yttrium ions are sorbed to  
Figure 5: XRD spectra of (a) treated bleaching clay and (b) AHIBC.  
the surface of the AHIBC, and some bonds are created between the Y(III) and the functional groups of the adsorbent, as shown by the decrease in intensity and also shift of absorption peaks [53]. 3-amino-5-hydroxyprazole forms a chelate with Y(III).
## 1 Introduction
In general, the addition of RCA decreases the flowability, reduces the mechanical properties and increases the permeability properties [18; 19; 20; 21; 22]. It has been shown that the flow properties of SCC can be maintained even if all natural aggregates are replaced by RCA [23; 24; 25; 26]. The research conducted has shown that RCA-SCC can be used for structural reinforced concrete and that the incorporation of RCA into SCC is ecologically and economically feasible [9; 27; 28; 29]. When designing structural concrete elements, the long-term structural deformation due to shrinkage and creep must be considered. The incorporation of RCA in concrete increases both shrinkage and creep, which is due to the porosity and reduced stiffness of the mortar adhering to the aggregates [30; 31; 32]. To date, there are only a minimal number of studies addressing the shrinkage [23; 33; 34] and creep [35; 36] of SCC with RCA, and it should be emphasised that no research has yet fully investigated the deformation properties of this material. This paper presents the results of testing the mechanical (compressive strength, static Young's modulus) and deformation (shrinkage, creep) properties of SCC with RCA obtained by crushing precast concrete elements. The tests were carried out with nine concrete mixtures. The influence of the water/cement ratio (\(w/c\)), the type of cement matrix, the amount of recycled aggregate and the cement replacement by fly ash were investigated. The knowledge gained can serve as input for the calibration and verification of existing models for predicting creep behaviour and contribute to an increased application of RCA in the construction industry.
## 2 Transforming Wave Polarization Using Arrays of Passive Radiators
### FSS Unit Cells Potentially Usable as Polarization Transformers  
Three types of unit cells (Figure 4a-c) made on a 1-mm-thick FR4 plate consisting of a single copper layer and a dielectric substrate were analyzed. The unit cells shown in Figure 4 consist of passive \(\lambda/2\) dipoles resonating at 12 GHz. The study was carried out for horizontal, vertical, and cross radiators to observe whether a circular polarization could be obtained. The excitation was applied through a Floquet port. Simulations were performed for different widths of the radiators varying between 1.5 mm and 3 mm with a step of 0.5 mm. The frequency-selective structures were rotated at an angle of 45\({}^{\circ}\) with respect to the horn aperture axis. To use such a structure to generate a circular polarization, one component of the electric field should pass without the phase changing through the FSS, whereas the other component should be out of phase by 90\({}^{\circ}\).  
Figure 3: Components of the incident and reradiated electric field.  
Figure 5a-c shows the phase difference between the two orthogonal \(E\)-field components. It can be noted that a phase difference of \(90\pm 10^{\circ}\) is obtained over the frequency band of interest.  
Figure 4: Unit cell: front and radiation pattern in \(xOy\) plane for radiators with “\(-\)”, “ \(|\) ” and “+” shapes.  
Figure 5: Phase of the two orthogonal electric field components as a function of frequency (grey line—\(y\) component; black line—\(x\) component).