esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### BGM ROMs  
It is possible to regulate the routing and shifting of intrinsic and extrinsic messages between the routing network, SVNPUs, and SCNPUs by employing a set of ROM blocks  
Figure 5: Block diagram of the proposed stochastic decoder.  
that hold the location and shift values of non-\({}^{\prime}\)-1\({}^{\prime}\) entries of each BGM in a hardware-optimized form: the three ROMs, namely ROM1-location, ROM2-shift, and ROM3-weight. Firstly, the row indices of all non-\({}^{\prime}\)-1\({}^{\prime}\) entries within each block column of BGM are represented by the values in the ROM1-location. Second, ROM2-shift saves the shift value difference between each non-\({}^{\prime}\)-1\({}^{\prime}\) entry in each block column and the previous non-\({}^{\prime}\)-1\({}^{\prime}\) entry in the same block row. This value can be determined at design time and saved as described. Lastly, each block column of the BGM's weight is stored in ROM3-weight. For the proposed architecture, all three ROMs contain \(N_{b}=68\) locations, each with \(W_{v}=30\) values: i.e., \(N_{b}\times W_{v}=2040\) locations.
## 3 FCI-VMD Method Based on BAS
### Weighted Frequency Index  
The AE spectrum of coal failure carries valuable information about the progression of fracture damage, with the main frequency being a crucial feature in the frequency domain. Hence, it is essential to consider the impact of the main frequency on the selection of mode number k during AE signal decomposition.  
After the original signal, \(f(t)\), is decomposed using VMD, the decomposed component \(\chi_{k}(t)\) undergoes the Hilbert transform:  
\[\hat{\chi}_{k}(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{\chi_{k}(\tau)}{t- \tau}d\tau. \tag{15}\]  
The signal is defined as follows:  
\[z(t)=\chi_{k}(t)+j\hat{\chi}_{k}(t)=\lambda(t)e^{i\theta(t)}, \tag{16}\]  
The instantaneous amplitude is as follows:  
\[\lambda(t)=\sqrt{\chi_{k}^{2}(t)+\hat{\chi}_{k}^{2}(t)}. \tag{17}\]  
The instantaneous frequency is as follows:  
\[\rho(t)=\frac{1}{2\pi}\frac{d\theta(t)}{dt}. \tag{18}\]  
The main frequency of the \(m\) mode is \(\rho_{m}\), (\(2<m\leq K\)), and the weighted frequency index, _FCI_, is defined as follows:  
\[FCI=\frac{FI}{C}, \tag{19}\]  
\[FI=|\rho_{m}-\rho_{m+1}|. \tag{20}\]  
\[C=\frac{E[(x-\overline{x})(y-\overline{y})]}{E\left[(x-\overline{x})^{2} \right]E\left[(y-\overline{y})^{2}\right]}. \tag{21}\]  
where \(\rho_{m}\) is the main frequency of the modes and _FI_ represents the main frequency distance between the two modes. x and y represent adjacent modal components.  
From Equations (19)-(21), we can know that a smaller _FI_ suggests a higher likelihood of mode mixing when the main frequencies of two signals are close. Conversely, if _FI_ is too large, further decomposition of the signal is necessary. Hence, finding a suitable _FI_ helps avoid these issues. In this study, the AE frequency range during coal sample failure was primarily distributed between 100 kHz and 300 kHz. The decomposition results were relatively stable when the minimum _FI_ value exceeded 10 kHz, which was chosen as the threshold in this paper. The correlation coefficient, C, can assess the degree of overlap between modes and provide valuable insights. A higher correlation coefficient indicates a greater overlap between modes. Considering the advantages and limitations of both indices, this paper adopts the weighted frequency index as the objective function for optimizing the \(k\) value.
## 3 Results and Discussion
### Optimization Design of the Bag Filter
#### 3.2.2 Validation Based on Scaled-Down Simplified Models
As seen in Figure 10c, the velocity at the 130.4 mm deflector (with its bottom aligned with the center axis of the intake channel) is significantly higher, indicating its effectiveness in guiding the airflow. Figure 10: Cont. The velocity distribution contours of three optimized models, each with different deflector lengths (Schemes 1, 2, and 3), were compared. From Figure 9(a), it can be observed that both Scheme 1 and Scheme 3 generate significant vortex phenomena at the bottom of the dust hopper. In contrast, Scheme 2 exhibits a more uniform velocity distribution in this region, reducing the likelihood of secondary dust lifting. However, as shown in Figure 9(b), the outlet airflow distribution in Scheme 2 is more uneven. Furthermore, Figure 9(c) reveals more pronounced scuring at the outer walls and top corners of the filter chamber in Scheme 2. In contrast, Scheme 3 demonstrates a more organized airflow distribution before entering the filter bag area, with a more uniform flow velocity distribution at the outlets of each filter bag. In the present study, the velocity distribution contours for the three optimized models with varying deflector spacings (Schemes 1, 4, and 5) were compared. Notably, the 130.4 mm deflectors in both Scheme 1 and Scheme 5 are positioned identically, while the 130.4 mm deflector in Scheme 4 is placed closer to the inlet. As shown in Figure 9(b), the velocity field distribution patterns at the 130.4 mm deflector location are similar in Schemes 1 and 5. However, in Scheme 4, the airflow impacts the deflector at a higher velocity, resulting in a significant increase in airflow velocity near the side wall. Further observation reveals that the outlet air velocities from the filter bags in Scheme 5 exhibit the greatest variability, while those in Schemes 1 and 4 show less variation. Based on the comparative analysis of the flow field and airflow uniformity evaluation indices for each optimization scheme in the scaled-down simplified models, it can be preliminarily concluded that, for the two designs with different deflector lengths and different deflector spacings, respectively, the flow field distribution patterns of Scheme 3 and Scheme 4 are more reasonable, with relatively better optimization effects. Figure 10: Internal flow field distribution contours of different scaled-down simplified models. (a) Velocity contours at R\({}_{\mathrm{S}}\); (b) velocity contours at Y\({}_{1}\); (c) velocity contours at Y\({}_{2}\).
# Multifunction RF Systems for Naval Platforms
## 3 MFRF System Design Considerations
### Practical Limitations and Trade-Offs
#### 3.2.3 Element-Level vs. Subarray-Level Digitization/Waveform Generation
In the case of subarray-level digitization, a potentially more serious impact on MFRF system performance results from the loss of ability to digitally form multiple simultaneous Rx beams in different directions using signals received from the same set of AESA elements. With digitization at the subarray-level, only one Rx beam can be formed with the elements in a subarray, restricting the maximum possible number of simultaneous independent Rx beams to the total number of designated subarrays on the AESA. This may be problematic for some RF functions like ES and radar that benefit from the use of simultaneous Rx beams to provide rapid, if not instantaneous, coverage of a large surveillance area. Consequently, modern stand-alone naval radar systems are increasingly employing element-level digitization, especially since the cost/benefit trade-off in the case of these narrowband systems has become much more favourable due to the current availability of suitable low-cost ADCs, such as the Texas Instruments ADS1675 at a $20 USD unit price. An alternate approach to subarray-level digitization/waveform generation is a fully connected architecture with hybrid beamforming [41]. This architecture reduces the number of digital receivers and/or DWGs but has greater flexibility than the use of fixed-size subarrays. For example, the use of Butler matrix-based analog beamforming on transmit could allow the beams to be changed dynamically depending on the channel condition and the number of dominant beams.
# Directed Network Disassembly Method Based on Non-Backtracking Matrix
## 6 Conclusions  
An effective disassembly method is proposed for the disassembly of directed networks; the method combines edge module division with network disassembly, using a non-backtracking matrix to construct the function of the minimum number of edges for edge disassembly, and finds the overlapping nodes between edge modules to obtain the approximate solution of the eigenvector of the cost function. Different from the traditional undirected network disassembly method, the DIR method considers the unidirectional relationship between nodes in the directed network, makes full use of the excellent spectral characteristics of the non-backtracking matrix to divide the directed network, ensures the efficiency of disassembly and reduces the impact of disassembly on the overall structure of the network by removing the overlapping nodes between the edge modules during the network disassembly process. By comparing the DIR method with other methods in different artificial directed networks and real directed networks, it is proved that the DIR method is efficient in the network disassembly of directed networks. At the same time, it is also verified that the partition of the edge module applied to the application of non-backtracking operators in the network disassembly leads to a low disturbance of the network structure. The experimental results show that using this method to disassemble  
Figure 8: Curve graph of disassembly cost and module degree Q of real network.  
the network can achieve lower costs and protect the structure information in the directed network to a great extent.  
Conceptualization, H.L. and J.M.; methodology, H.L.; software, H.L.; validation, J.M. and P.W.; supervision, H.L. and J.M.; formal analysis, J.M. and P.W.; writing--original draft preparation, H.L. and P.W.; funding acquisition, H.L. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant 71871233, Science and Technology Project of Hebei Education Department under Grant ZD2022031, Research on the Development of Social Science of Hebei Province under Grant 20220202181 and Fundamental Research Funds for the Hebei Universities under Grant 2021 YWF08.  
Not appliable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
### Paper Contributions  
The main objective of this position paper is to conduct an investigation of the key issues involved in supporting and verifying timing predictability and security in safety-critical industrial CPSs during their development. In this regard, we pose three main Research Questions (RQs), as depicted in Figure 2 and presented at the end of this subsection. Before answering the research questions, we present a concrete structure of the CPS eco-system providing clear boundaries among various constituting components, as shown in Figure 1. To answer the posed research questions, we first explore the state  
Figure 1: Defining the boundaries of embedded systems and CPSs in the CPS eco-system.  
of the art in verifying and supporting the above-mentioned properties in time- and safety-critical embedded systems (a fundamental component of the CPSs) during the design time, and then identify the level of existing support in a broader context of industrial CPSs. While investigating each research question, we identify the existing solutions that can either address the research question or have the potential for extensions to address the research question. In the latter case, we propose guidelines for possible extensions. In the case a research question is not addressed by existing solutions, we identify the gap in the state of the art. Finally, based on the outcomes of our investigation, we present our position and highlight the opportunities for further research.  
**RQ1**: Are the existing frameworks for the development of safety-critical CPSs expressive enough to specify timing predictability and security requirements on the various components in these systems?  
**RQ2**: Are there any existing techniques that can formally verify the specified timing predictability and security requirements of safety-critical CPSs at the design time?  
**RQ2a**: If the answer to RQ2 is \"yes\", can the identified techniques efficiently support and be integrated to the existing safety analyses?  
**RQ3**: Are there any existing techniques to support timing predictable and secure runtime environment for safety-critical CPSs?  
If the answer to any of the above research questions is \"no\" or \"maybe\", we further investigate the following two questions.  
**RQ (i)**: What is missing from the existing solutions?  
Figure 2: Research questions and expected outcomes contributing to the position and identification of opportunities for further research.  
**RQ (ii)** How can the existing solutions be extended to support the development of time-critical, secure and safety-critical CPSs?
## 4 Results Analysis  
### Establishment of the Standard Curve Model  
Four groups of SBS-modified asphalt standard samples with different content of 2%, 3%, 4%, 5%, and 6% were detected by the infrared spectrum. The standard curve model was established by linear fitting with the characteristic peak area ratio A966/(A966 + A1377) and SBS content as shown in Figure 4.  
The four groups of linear correlation coefficients R\({}^{2}\) are 0.9982, 0.977, 0.9942, and 0.9948. The closer R\({}^{2}\) is to 1, the better the linear correlation. The R\({}^{2}\) of the four groups are all above 0.995, which proves that the curves fit well and meet the test requirements.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 4 Results and Discussion
### Objective Evaluation  
For the first proposed model shown in Figure 1, the SNR values were varied from \(-2\) to \(10\) dB. The NFFT, window size, and RT are considered 1024, 512, and 100 msec, respectively. The obtained results of different input speech mixtures were averaged and the results are provided in Table 1. Table 1 shows that the proposed model shows performance improvement as compared to BSS methods with multivariate super-Gaussian [31], multivariate Student's T [42], and generalized Gaussian [32] source priors for estimated speech signals \(S_{1}\) and \(S_{2}\). Next, the RT parameter was varied from 40 to 200 msec. The window size and NFFT were set to 512 and 1024, respectively. In Table 1, the proposed model shows better results on SNR = 4 dB in comparison to the rest of the SNR values. Therefore, SNR = 4 dB was considered for the RT experiments. From Table 2, it was also concluded that the proposed model shows improvement in comparison to [31; 32; 42]. The first proposed model shows better performance due to its adaptability according to the non-stationary nature of the observed convolutive mixture as it contains low- and high-energy components compared  
in comparison to [31; 32; 42] for the \(\hat{S}_{1}\) and \(\hat{S}_{2}\). Similarly, the parameter RT was varied to evaluate the proposed model robustness. The values for window size, NFFT, and SNR are 512, 1024, and 4 dBs, respectively. The results provided in Table 4 shows performance improvement of the proposed model in comparison to the methods in [31; 32; 42]. The results of the second model conclude that the switching between the two source priors according to the low- and high-energy components in the received convolutive mixture improve the performance from the single source prior BSS models [31; 32; 42].  
From the results of the two proposed models, it is clear that the first model (Figure 1) performs better than the second model (Figure 2). In the first model, the estimated source speech signals are first extracted from the observed noisy convolutive mixture. Then, the noise in the separated speech signal is suppressed individually, leading to a better performance. On the other hand, the second model performs de-noising first, suppressing the noise in the estimated source signal mixed and considering the noisy convolutive mixture, resulting in performance degradation.
## 6 Challenges and Future Directions
### Future Directions  
Based on the above discussion, future fatigue research in hydrogen environments can be focus on the following aspects:  
1. Real-time monitoring and model-driven closed-loop integration: AI is being actively applied in industrial scenarios. To leverage its rapid prediction capabilities, real-time measurement data should be integrated with physics-informed machine learning models for real-time crack growth prediction. An intelligent monitoring and early-warning system should be established to identify and mitigate crack propagation risks through continuous data acquisition and analysis. This is essential for maximising component performance and ensuring structural safety.
2. Multiscale digital twin modelling: microscale properties have a significant impact on fatigue behaviour. Advanced techniques such as density functional theory, crystal plasticity finite element method, and phase-field modelling should be incorporated to capture hydrogen transport and cyclic slip localization effects. This will enable the development of efficient multiscale fatigue simulation frameworks under hydrogen environments, facilitating accurate prediction of crack growth behaviour and the influence of various microstructural mechanisms.
* Standardised database for high-temperature and high-pressure hydrogen environments: The establishment of standardised databases is critical for the development and deployment of hydrogen-related equipment. A unified dataset on hydrogen-assisted fatigue under elevated temperature and pressure conditions should be developed, along with practical guidelines and empirical correlations. Such databases and standards will support material selection, component design, and lifecycle management.
* Uncertainty quantification and structural integrity assessment: Throughout the full lifecycle of hydrogen energy infrastructure, uncertainties exist in material properties, geometric dimensions, and external loading conditions. Understanding how these uncertainties impact structural integrity is crucial. Moreover, in pipelines, storage tanks, and wind-hydrogen integrated systems, risk-based classification and maintenance optimisation can provide decision support for safe and efficient hydrogen infrastructure deployment.
# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 3 VB-Net
### VoxNet Feature Extractor  
The first step of the proposed system is converting raw point clouds into \(d\times d\times d\) voxels. Assuming there are \(N\) points in a point cloud, the point cloud dataset is defined as \(P\) = {\(p_{1}\), \(p_{2}\),..., \(p_{i}\),..., \(p_{N}\)}, where \(p_{i}\) is the \(i\)-th point in the point cloud, \(I\) \(\in\) [1, \(N\)]. Each \(p_{i}\) contains three coordinates \(x\), \(y\), \(z\), denoted by \(p_{i}\) = (\(x_{i}\), \(y_{i}\), \(z_{i}\)), \(p_{i}\)\(\in\)P, and the definition of the voxel space \(V\) = {\(v_{l,a,b}\) = 0 | \(l\), \(a\), \(b\) \(\in\) [1, \(d\)], \(l\), \(a\), \(b\) \(\in\) N\({}^{+}\)}. Points in \(P\) are scaled into the range between 0 and 1 before voxelization. Three equations, \(l=x_{i}\times d\), \(a=d-y_{i}/d\), are employed to calculate to which voxel point \(p_{i}\) belongs, and then increase the value of \(v_{\rm{la,b}}\) by one.  
We employ a feature extractor that is similar to VoxNet [26] as shown in Figure 1, because the BLS cannot process voxel data directly. Conv3d (\(f,e,s\)) represents 3D convolution with \(f\) filters, kernel size \(e\), and stride \(s\). Pooling (\(p\)) means a max pooling layer with pool size \(p\). The activation function for Conv3d is ReLU. The outputs of the flatten layer are the features applied by object classification tasks.  
The feature extractor also needs a pretraining process to extract more suitable features from input voxel data. In order to obtain better training results, we only keep the last fully connected layer in the original VoxNet [26], as shown in Figure 2. Variable \(c\) is equal to the number of categories in the dataset. Consequently, the feature extractor is trained as a deep learning network.
# Zebrafish Larval Melanophores Respond to Electromagnetic Fields Exposure
## 4 Discussion
### Extremely Low-Frequency Magnetic and Low-Frequency Electromagnetic Fields  
Zebrafish embryos/early larvae exposed to the very-low-frequency (0.2 Hz, 40 mT) magnetic field did not show increased mortality but showed a delay in development, and therefore, larvae appeared slightly shorter than the controls. Exposure had limited effects on motion behavior (with three-quarters of the larvae showing normal movements and the remaining one-quarter showing hypo-neuromuscular activity). On the other side, zebrafish embryos/larvae exposed to a low-frequency (270 kHz, 470 uT) magnetic field showed--the only case in our study--increased mortality and a delay in development with respect to control. All larvae exposed to low-frequency magnetic fields showed normal movement. Nevertheless, some evident deformities may occur in terms of spinal curvatures. Notably, our findings correlate well with the literature dealing with low-frequency magnetic fields (<300 Hz). Among these, those of natural or artificial origin, such as solar activity, geomagnetic fields, residential electric equipment, nearby power, and overhead high-voltage transmission lines and domestic installations, are included. [PERSON] et al. [15] report that exposure to a 50 Hz magnetic field at 1.0 mT delays the progress of zebrafish embryo hatching. Furthermore, exposure to magnetic fields at a lower intensity of 0.2 mT results in a delay in hatching, decrease in heart rate, and increase in apoptosis [17]. In this case, more evident effects on zebrafish pigmentation were observed only in the low-frequency group, supporting the hypothesis of alterations in zebrafish development. Absence or delay in pigmentation is, in fact, one of the markers of zebrafish stress and/or toxicity, according to Test Guideline 236 (TG236) [12]. Notably, in the work by [PERSON] et al. [18], embryos were exposed to a 50 Hz sinusoidal magnetic field with intensities of 30, 100, 200, 400, and 800 mT for 96 h. Continuous exposure caused delayed hatching and decreased heart rate at the early developmental stages of zebrafish embryos, whereas no significant differences in embryo mortality and abnormality were observed. Moreover, acridine orange staining assays showed notable signals of apoptosis mainly in the ventral fin and spinal column, and the transcription of apoptosis-related genes was significantly upregulated in exposed embryos.
## 2 Materials and Methods
### WRF-Chem Model
#### 2.3.1 Dust Schemes in WRF-Chem
In the AFWA scheme, the dust flux H caused by the saltation process on the bare soil surface is obtained from the relation introduced by [PERSON] [64] as follows:  
\[\mathrm{H}\Big{(}\mathrm{D}_{\mathrm{P}}\Big{)}=\mathrm{C}\,\frac{\mathrm{P}_ {\mathrm{a}}}{\mathrm{g}}\mathrm{u}_{\mathrm{s}}^{3}\Big{(}1+\frac{\mathrm{u} _{\mathrm{+t}}}{\mathrm{u}_{\mathrm{s}}}\Big{)}\Bigg{(}1-\frac{\mathrm{u}_{ \mathrm{+t}}^{2}}{\mathrm{u}_{\mathrm{+}}^{2}}\Bigg{)} \tag{5}\]  
where C is an empirical constant (0.129), \(\mathrm{\rho}_{\mathrm{a}}\) is the density of air parcel, (g/cm\({}^{3}\)), g is the acceleration due to gravity (cm/s\({}^{2}\)), u\({}_{\mathrm{+}}\) and u\({}_{\mathrm{+t}}\) (in units of cm/s) are, respectively, the friction velocity and the threshold friction velocity. The friction velocity is considered instead of 10 m horizontal wind speed as in the GOCART scheme. In this scheme, the vertical mass flux of dust is calculated based on Marticorena and Bergametti [62] with a correction coefficient. \[\mathrm{F}_{\mathrm{bulk}}=\mathrm{H}\alpha\,\times\,\mathrm{S}\,\,\,\alpha=10 ^{0.134(\%\mathrm{clay})-6} \tag{6}\]  
where \(\alpha\) is the saltation ratio which increases proportionally to the clay percent of the soil and S is the Dust Source function. Additionally, this equation is only valid for 0% \(<\) %(clay) \(<\) 20%.