esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### SFNet  
Figure 2 illustrates the main structure of the proposed signed fingermark liveness detection network, which consists of convolutional layers, average pooling layers, and residual blocks. The fingermark image's local features are obtained through convolutional layers. The average pooling layer downsamples the image, removes redundant information, and compresses the number of parameters to prevent overfitting. The enhanced residual structure improves the network's ability to extract high-dimensional features and filter effective features. Batch normalization layers and activation units are added after all of the convolutional layers in the network. This reduces the difference in the distribution of the training data, accelerates data convergence, improves network generalization ability, reduces parameter interdependence, and mitigates possible overfitting. The prediction results are confidently achieved through the classification layer, utilizing the feature maps of the localized blocks to accurately detect the liveness of signed fingermarks.  
Figure 2: Structure of SFNet.  
The rectified linear unit (ReLU) activation function is employed in the network's activation unit to enhance its expressive power. The loss function of the network is represented by Equation (2) and is expressed as cross entropy.  
\[\mathrm{L}=\frac{1}{\mathrm{N}}\sum_{\mathrm{i}}-[y_{\mathrm{i}}\cdot\log( \mathrm{p}_{\mathrm{i}})+(1-y_{\mathrm{i}})\cdot\log(1-\mathrm{p}_{\mathrm{i}})] \tag{2}\]  
where \(y_{i}\) denotes the label of sample \(\mathrm{i}\), \(\mathrm{p}_{\mathrm{i}}\) denotes the probability that sample \(\mathrm{i}\) is predicted to be true, and \(1-\mathrm{p}_{\mathrm{i}}\) is the probability that sample \(\mathrm{i}\) is predicted to be false.  
The residual block in the fingermark liveness detection network combines the residual structure proposed in [19] with the multiscale backbone structure proposed in [20]. This fusion results in a structure that is better suited for the signed fingermark liveness detection network. The improved residual block structure is shown in Figure 3. The features are decomposed into multiple groups of equal-length sub-features equally by the decomposition layer, and the class residual structure is introduced between the multi-scale sub-features to deepen the chunking depth. The multiscale residual structure [20] accurately represents multiscale features at a finer granularity level, ensuring consistency in the depth of sub-features across scales. This improvement has been extended to multiple scales through the class residual structure, resulting in a significantly larger sensory field. The connection layer is successfully reorganized into a single feature and connected with the input feature residuals to form a residual block embedding network. This approach effectively improves the depth and breadth of the network without increasing the number of parameters.
## 3 Results and Discussion
### HPLC Detection of HNS
#### 3.1.2 Determination of Flow Velocity  
The column temperature was set to an ambient room temperature of 25 \({}^{\circ}\)C, with an injection volume of 20 \(\mu\)L. To minimize interference from impurity peaks on the main peak, the sample testing duration was adjusted to 15 min. The mobile phase consisted of a methanol (MeOH) volume fraction of 70% and an ultrapure water volume fraction of 30%. The mobile phase flow rates were tested at 0.4 mL/min, 0.6 mL/min, 0.8 mL/min, and 1.0 mL/min. The results indicated that overall performance, including peak shape, peak broadening, and retention time, was better at flow rates of 0.4 mL/min and 0.6 mL/min. Therefore, additional experiments were conducted at a flow rate of 0.5 mL/min, which revealed minimal differences between 0.5 mL/min and 0.6 mL/min. Optimal results are shown in Figure 3; chromatograms at other flow rates were similar. Compared to the results at a flow rate of 0.4 mL/min shown in Figure 3a,b, the outcomes at 0.5 mL/min presented in Figure 3c,d were notable. They showed shorter retention times for HNS and appropriate spacing between the DMF and HNS peaks. Predominantly, the chromatograms displayed only DMF and HNS peaks under dual-wavelength detection, with no impurity peaks.  
Figure 3: _Cont._  
Figure 2: Presents high-performance liquid chromatography chromatograms of DMF and HNS at dual wavelengths using different mobile phases, ACN volume fraction of 60% (**a**) 226 nm (**b**) 271 nm; MeOH volume fraction of 60% (**c**) 226 nm (**d**) 271 nm; MeOH volume fraction of 70% (**e**) 226 nm (**f**) 271 nm.
## 4 Analysis of Microstructure Changes after the Welding Process
### High-Temperature Heat-Affected Zone and Fusion Zone  
In the high-temperature heat-affected zone and fusion zone, as a result of high temperatures (above \(A_{1}\)), recrystallized austenite is formed, which during the rapid cooling after the welding process  
Figure 5: Microstructure of 0.82C–1.66 Si–2.05 Mn–0.22 Cr–0.36 Mo–1.06 Ni steel. (**a**) Initial microstructure; (**b**) film-like morphology of the microstructure of the simulated low-energy welding process; (**c**) degraded microstructure for the simulated high-energy welding process; (**d**) area of blocky austenite for the high-energy welding process. Images produced via SEM [21].  
Figure 6: Bright field image of 0.82C–1.66 Si–2.05 Mn–0.22 Cr–0.36 Mo–1.06 Ni steel. (**a**) Base material; (**b**) low-energy welding process; (**c**) high-energy welding process. Images produced via TEM [21].  
is transformed into brittle martensite. This is the main reason for the cold cracking of these steels [19]. Thus, the following analysis includes welds after regeneration aimed at reconstructing the structure of the base material.  
In the fusion zone, the columnar solidified structure characteristics for welding processes were identified. The visible structure in the inter-dendritic areas differs from the structure of the base material due to the segregation of carbon, chromium, and manganese [20]. In References [14; 20], it was found that unstable blocky austenite occurs in the inter-dendritic areas. It was also determined that in the FZ zone, in addition to the coarse inter-dendritic structure, there is also a typical fine nanobainite structure and blocky austenite [20]. However, the volume of austenite in the weld is lower than in the base material [20], which can be explained by the reduction of the carbon content during welding [41] and, thus, by the increased rate of bainite transformation.  
The presence of less stable, coarse blocky austenite in both the FZ and HHAZ is the reason for a significant reduction of impact toughness in relation to the base material [15; 42; 43]. On the other hand, blocky austenite increases elongation due to the TRIP effect [6]. A more preferred morphology is stable film-like austenite because it increases strength and fracture toughness. For this reason, a large amount of blocky retained austenite in the FZ and HHAZ is undesirable. The temperature of regeneration can affect the amount of retained austenite. It is known that increasing the temperature of bainitic transformation increases the amount of retained austenite [44; 45], in which case the welded joints may increase the volume of unstable blocky austenite [20].
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
###### Abstract  
Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings.  
deep strike-slip faults; seismic attribute fusion; fault detection; craton basin; seismic processing +
Footnote †: journal: Applied sciences
## 5 Conclusions  
In this study, we have realized a fundamental prototype of an IBFD wireless communication system with a three-stage SI cancellation including antenna, analog, and digital processing functionalities. Notably, within the digital processing domain, we adopt FIR-type AF-based SI cancellation with the LMS adaptive algorithm, which is a computationally efficient and readily implementable AF technique.  
To verify the performance of our proposed IBFD prototype system, we conducted a series of experiments. The experimental findings reveal that the antenna isolation and SI cancellation components in AC can effectively suppress SI power by an impressive about 30 dB each. Furthermore, upon assessing the EVM and BER of the demodulated signal following the application of AF-based digital SI cancellation, it becomes evident that this digital SI cancellation methodology also delivers a substantial 30 dB SI cancellation performance boost.  
Furthermore, these results show the remarkable effectiveness of our proposed IBFD system, by its capacity to attain a BER in the order of \(10^{-3}\) when the SNR is larger than 22 dB, which significantly validates the effectiveness and practicality of the proposed IBFD system design.  
The proposed system implementation prototype is a universal proposal that can be applied in most wireless systems like cellular systems. In the future, we are planning to implement the proposal in this study on the 5G New Radio (5 GNR) platform as our next step.  
Conceptualization, W.-S.L.; methodology, W.-S.L., O.Z., K.L. and T.M.; software, W.-S.L. and O.Z.; validation, W.-S.L., O.Z., K.L. and T.M.; formal analysis, W.-S.L. and O.Z.; investigation, W.-S.L. and O.Z.; data curation, W.-S.L.; writing--original draft, W.-S.L.; writing--review and editing, W.-S.L., O.Z., K.L., H.K. and T.M.; visualization, W.-S.L.; supervision, T.M.; project administration, H.K. and T.M.; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript.  
A part of this research has been conducted under the contract \"R&D for the realization of high-precision radio wave emulator in cyberspace\" (JPJ000254) made with the Ministry of Internal Affairs and Communications of Japan.  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} Adaptive Filter & AF \\ Analog Circuit & AC \\ Base Station & BS \\ Bit Error Rate & BER \\ Coupler & CPL \\ Device-to-Device & D2D \\ Divider & DIV \\ Downlink & DL \\ Error Vector Magnitude & EVM \\ Fixed Impulse Response & FIR \\ Full-duplex & FD \\ In-band Full-duplex & IBFD \\ Inout-output & I/O \\ Intermediate Frequency & IF \\ Key Performance Indicator & KPI \\ Least Mean Square & LMS \\ \end{tabular}
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 2 Materials and Methods
### Hyperspectral Images Collection  
In this study, hyperspectral images were acquired using a Headwall Hyperspec MV.X instrument at NBL Imaging System Ltd. (Guangzhou, China). The push-broom line-scan hyperspectral system was composed of a 12-bit CMOS detector, a conveyor motivated by a motor with a speed controller, a uniform illumination unit, a computer, and an image collection software. The spectral sampling interval was 1.75 nm/pixel, and the number of spatial pixels was 1024. The protection level was IP67, and the onboard hardware included 8 GB RAM and 128 GB SSD. All the collected hyperspectral images contained 301 channels with a spectral resolution of 2 nm.  
Prior to the images capture, walnut samples were gently rubbed with soft tissues to remove any residues of dust or soil possibly present. The walnut samples were then neatly placed on a dark non-reflective conveyor first. After that, hyperspectral images will be captured with the movement of a platform from left to right. In order to eliminate the noise generated by the system and the influence of surrounding environmental factors such as humidity, white and black reference images collection was conducted before the hyperspectral images were captured. A white reference image (\(W\)) was acquired using a white Teflon with the reflectivity close to 100%. Dark reference image (close to 0% reflectance, \(D\)) was obtained by completely closing camera lens with its own opaque cap. After raw hyperspectral images were collected, the calibrated hyperspectral image (C) was calculated using the following equation:  
\[C=(R\ -\ D)/(W\ -\ D)\times 100\% \tag{1}\]  
where \(C\) and \(R\) indicate the calibrated and raw hyperspectral image, respectively, \(D\) denotes the dark reference image, and \(W\) represents the white reference image. The subsequent analyses were conducted based on the calibrated hyperspectral image, that is, the above \(C\).
## 3 Geotechnical Guidelines and Practices for the Design of Integral Bridges
### UK Practice  
In the UK, the SSI published PD 6694-1:2020 Recommendations for the design of structures subjected to traffic loading BS EN 1997-1:2004+A1:2013 [25]. BS EN, the British counterpart of European Standard (EN), is released by the SSI. Since EN 1997-1 [44] does not have specific design requirements for integral bridges, it refers to an earlier UK Highway Agency design manual BA 42/96 [45] to design integral bridges. The design recommendations in BA 42/96 [45] did not incorporate the response of backfill soil behind bridge abutments subjected to cyclic thermal movements. Moreover, information contained in BA 42/96 [45] has not always been correctly interpreted by practitioners. Meanwhile, a number of studies, for example [12,14,46,47], and [48], also found that repeated cycles of thermal movement have resulted in an increase in soil pressure. PD 6694-1 [25] updates the information in BA 42/96 taking into consideration of the effects of soil-structure interactions in alignment with the findings reported by these researchers.  
The PD 6694-1 [25] recommends two methods of analysis for the design of integral abutments: limit equilibrium approach and SSI analysis. It states that the limit equilibrium approach is applicable for the design of the integral abutments satisfying the following criteria:  
* the characteristic thermal movement of the end of the deck is less than or equal to 40 mm;
* the skew angle does not exceed 30\({}^{\circ}\).  
Here, the characteristic thermal movement is defined as the total thermal movement of the end of the deck from its maximum contraction position to its maximum expansion position, based on the maximum and minimum uniform bridge temperature for a 50-year return period. It further states that the affected depth of soil due to the abutment movement can be identified without SSI analysis for the following abutments:  
foundations (see Figure 3a) and the underside of the base slab for rotationally flexible foundations (see Figure 3b), and  
* \(C\) depends on Young's modulus of the subgrade, \(E\);
* \(C\) is 20 for foundations on flexible soils (\(E\leq 100\) MPa);
* \(C\) is 66 for foundations on rock or soils (\(E\geq 1000\) MPa), and \(C\) may be calculated by linear interpolation for values of \(E\) between 100 and 1000 MPa.  
\(d^{\prime}_{d}\) may conservatively be taken as \(0.5d^{\prime}_{d}\) for abutment with rotationally rigid foundation (see Figure 3a), and \(0.7d^{\prime}_{d}\) for abutments with pinned walls or rotationally flexible foundations (see Figure 3b). Furthermore, it may be determined using a realistic model of the abutment considering the effects of earth pressure applied to it.  
For the limit equilibrium approach, the design value of total thermal movement (\(d^{\prime}_{d}\)) is taken from the characteristic minimum temperature position, i.e., \(d^{\prime}_{d}=0.5d_{K}(1+\varphi\gamma_{Q})\) where \(d_{K}\) is the characteristics value of the movement range, and \(\gamma_{Q}\) and \(\varphi\) are partial factors for thermal actions (BS EN 1990:2002+A1, 6.3.1).
## 2 Related Work  
### Multi-View 3D Reconstruction  
Recent years have seen significant advancements in multi-view 3D object reconstruction tasks according to deep learning techniques, of which transformer-based models are particularly effective [1]. By establishing features from multiple objects, traditional methods, like SfM [2] and SLAM [3], have been frequently employed to reconstruct 3D scenes. For multi-view tasks, deep learning algorithms, such as RNNs and CNNs, can improve feature extraction and fusion. While RNN-based methods 3D-R2N2 [5] and LSM [4] are inefficient and computationally expensive, CNN-based methods, like 3D-DensityNet [8], use maximum pooling but lack view connectivity. The CNN approaches such as Attsets [6], GARNet [10], and Pix2 Vox [7] make use of attention mechanisms to preserve the relationship between different views, but these mechanisms are often simple; as a result, they cannot model complex multi-view relationships. Sequential transformer approaches, such as EVoIT [12] and LegoFormer [15], utilize CNN backbone models to compress tokens across views, yet this reduction reduces the expressiveness of single-view representations. As a result, transformer approaches, such as EVoIT [12] and LegoFormer [15], use the CNN backbone to compress the number of tokens per view, but this compression reduces the number of views represented by individual views. On the other hand, the cross-view processing of the 3D-RETR [14] model operates on isolated views with transformers until the feature proposition combines features but falls short in preserving essential inter-view dependencies essential to multi-view 3D reconstruction. UMIFormer [13] and LRGT [11] attempt to address these challenges while decoupling feature extraction within and between views; adding additional modules to infer relationships between views increases the computational complexity of the model. However, these methods struggle to handle complex and large-scale environments, especially when feature matching is ambiguous or computationally efficient, which is a limiting factor. Despite promising results, these methods fail to capture strong inter-view relationships because they handle all image tokens equally for each view.  
Unlike multi-view fusion methods, 3D Gauss [21] and NeRF [22] focus on improving 3D reconstruction efficiency and detail. Three-dimensional Gauss uses Gaussian splatting for better scalability and uncertainty handling in large scenes. NeRF [22] generates detailed 3D models from sparse 2D images, capturing complex light interactions and volumetric scenes, and it has been extended to handle dynamic scenes to improve efficiency and accuracy in 3D reconstruction.
## 6 Conclusions  
An underwater acoustic communication channel can be classified as WSS or non-WSS on the basis of analysis of a baseband, complex value PRBS signal received by the UAC system receiver. If the received signal can be modelled as a WSS stochastic process, then its in-phase and quadrature components have similar autocorrelation functions, and its cross-correlation function is symmetric. The correlation coefficients of these components are being proposed as indicators for assessment of wide-sense stationarity of the UAC channel. Two algorithms of WSS assessment have been compared in this paper. One of them is used to calculate the correlation coefficients on the basis of the TVIR of the channel. The second one skips the TVIR estimation stage, and thus allows the channel to be evaluated with fewer computational operations. Analysis of the results of PRBS signal transmission in the UAC channel under simulation conditions and during an inland water experiment showed that although the two algorithms lead to slightly different values of correlation coefficients, the results of classification of the measured channel as WSS/non-WSS are the same in both cases.  
The author declares no conflict of interest.
## 4 Results and Discussion
### Radiation Pattern (3D)  
The three-dimensional (3D) radiation plots for the total radiated power for all the antenna elements at 3.5 GHz and 5.1 GHz are depicted in Figure 9. The 3D patterns are drawn under the condition when one port is excited while the other ports are terminated with 50 \(\Omega\) load impedance. As seen from Figure 9 at 3.5 GHz, the maximum gain is achieved for all the ports 1, 2, 3 and 4 with the value of 5.56 dBi, which are placed in the vicinity of the cleared ground region at the corners of the back plane. Likewise, as seen in Figure 9b at 5.1 GHz, the slightly lower gain values around 4.63 dBi are obtained since the size of the antenna elements becomes relatively smaller as compared to the guided wavelength \(\lambda\)g.
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 3 A Method of 3D Point Cloud Generation by Multi-Sensor Fusion  
To solve the coordinate calculation of 3D point cloud in non-GNSS scenarios, we propose a 3D point cloud generation method based on multi-sensor fusion. In the track mobile laser measurement system (TMLS), to solve the calibration of the relative position relationship of the scanner, IMU and carrier space, we defined a coordinate system fixed on the sensor integrated frame, which was called the car body coordinate system. Firstly, the method calculated the displacement vector from the control point to the center of the car body according to the calibration parameters using the cloud coordinates of the control  
Figure 1: RTS smoothing algorithm flow.  
point in the car body coordinate system and the attitude angle of the inertial navigation system at the same time. Next was the calculation of the coordinates of the center point of the same section track according to the coordinates of the control point and the displacement vector. When the track inspection trolley was placed on the track, the center point of the track coincided with the center of the car body. We used the original attitude angle and speed data collected by the IMU and odometer to calculate the center trajectory coordinates of the car body and compare the track center point calculated by the control point with the error of the calculated trajectory point at the same time as the observation update in the Kalman filter. The odometer speed and IMU pitch were corrected using the optimal estimates obtained after filtering and smoothing.  
After the attitude error was corrected, the displacement parameters from the control point to the track center point of the same section were changed. The corrected attitude angle was used to recalculate the displacement vector and track center, and the calculated error was compared with the original data to perform secondary filtering. Figure 2 shows the overall algorithm flow chart.