esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 ML Applications for RIS-Assisted Communications
### Beamforming Applications
The RIS learns how to optimally interact with the incident signal, given only the channels in the active elements. The authors use the DeepMIMO  
Figure 14: Scheme for DL-based beamforming for RIS-assisted communications systems. Figure 15: General approach of DRL for beamforming on RIS-aided communication systems. dataset to generate channels based on the outdoor ray-tracing scenario. The DL architecture is based on a multilayer perception (MLP) with a variable number of fully connected layers, ReLU activation, and MSE loss function. In [85], the authors developed a DL approach for phase configuration on an RIS-MIMO system. The architecture consisted of two networks. The first DNN was fed with the pilot signals and compared with a least-squares estimator for CSI. In contrast, the second network received shorter pilot sequences as input and predicted the optimum phases and beamforming vectors online. The architecture of the first DNN consisted of three fully connected hidden layers with a standard scalar preprocessing of inputs, Adam optimizer, and ReLU activation function. The second DNN consisted of four fully connected layers with the Adam optimizer and a ReLU activation function. Also, the pilot signals were generated synthetically from channel realizations. Unsupervised learning solutions have also been proposed for beamforming. The research in [86] performs an offline training mechanism to make real-time predictions of RIS phase shifts online, maintaining a desired rate of performance. The proposed
## 3 Influence of Structural Parameters of the Magnetic Circuit on Magnetic Induction Intensity  
### Magnetic Circuit Simulation Pre-Processing  
To ascertain the rationality of the parameter design for the MRD, an investigation was conducted to discern the influence of magnetic circuit component parameters on the operational performance of the MRD. Given the axisymmetric nature of the MRD's magnetic circuit, the intricate three-dimensional magnetic field problem was elegantly simplified and analyzed using the two-dimensional static magnetic field approach in the ANSYS Electronics Desktop 2021 R1 software [19]. The resulting analytical model for the damper's magnetic circuit is presented in Figure 8 and Table 4.  
The magnetic permeability of the material is determined based on the material properties. The magnetic permeability of the air and coil takes the value of 1. Both 20 steel and MRF are nonlinear magnetically conductive materials, and their magnetic permeability changes with the change in the magnetic field strength. The magnetic permeability was defined by importing the B-H curves of MRF and 20 steel according to Figures 5 and 7.  
The structural parameters of the damping channel gap, piston rod, and cylinder barrel, as the main components of the magnetic circuit, have a certain effect on the magnetic induction intensity at each place of the MRD. To visualize and analyze the effect of each parameter on the output damping force of the damper, as indicated by the labeling in Figure 6, this paper selected the distance at the damping channel gap, \(h\), the core radius, \(c\), the piston rod wing radius, \(l\), and the cylinder thickness values, \(t\), as variables, and the distribution of the magnetic induction intensity as an indicator of the effect of the electromagnetic simulation.  
To ascertain the magnetic induction intensity within the operational region of the magnetorheological damper, a line segment was designated along the center of the damping channel as the observation path. Subsequently, the magnetic induction intensity corresponding to each point along this path was extracted, leading to the derivation of a magnetic induction intensity distribution curve. Typically, the average magnetic induction within the damping channel serves as a benchmark for assessing the structural dimensions of the magnetorheological damper. This average magnetic induction within the operational area was computed utilizing Equation (28).  
\[B_{0}=\frac{1}{l}\int_{0}^{1}B(x)d \tag{28}\]  
where \(B_{0}\) denotes the average magnetic induction intensity; \(L\) denotes the effective length of the damping channel.
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 3 Gait Recognition Using GERF
### Post-Processing  
After transforming the GEI with the optimal GERF, we adopt two sequential processes for further improvement. The first one is Gabor filtering, which has been demonstrated to be effective for gait recognition and Gabor functions-based image decomposition is biologically relevant to image understanding and recognition as reported in [18; 42]. Similar to [42], we use subsampled Gabor features with Gabor functions from five scales and eight orientations. Because the silhouette gait feature provided in the databases is \(88\times 128\), the resolution of the Gabor feature is \(320\times 352\). An example of Global GERF after Gabor filtering is shown in Figure 4. The second one is a spatial metric learning, that is, two-dimensional PCA (2 DPCA) [43] and two-dimensional LDA (2 DLDA). They are used not only for reducing feature dimensions, but also for getting more discriminant features. Similar to [26; 27], they are applied in Gabor feature space for horizontal and vertical direction, respectively.  
Next, the dissimilarity score between the gallery and probe elements of a pair is measured as the Euclidean distance in the 2 DPCA+2 DLDA space. Finally, in verification scenarios, the score is compared with an acceptance threshold to verify whether the pair belongs to the same subject or not. While in identification scenarios, nearest neighbor classifier is used to assign the final identity of the probe according to the dissimilarity scores between the probe and all the galleries.
## Problem Definition
### Parameters Considered in This Study  
The object of the present study is to investigate the buckling of the perforated plates strengthened with FRP under uniaxial compression. The geometric dimensions (such as \(t_{f}\), \(t_{s}\)), and the elastic modulus of FRP \(E_{f}\), the BCs of 4S and 3S1F, the effect of plate aspect ratio \(\beta\) (i.e., \(a/b\)), hole size \(d/b\), hole position \(e_{x}/b\) and \(e_{y}/b\) have been considered:  
* The parameters of geometrical and mechanical properties such as \(t_{f}\), \(t_{s}\), \(E_{f}\), \(E_{s}\) appear in the form \(\omega=E_{f}t_{f}/E_{s}t_{s}=E_{f}n_{f}t_{f0}/(E_{s}t_{s})\). In this equation, \(E_{f}=1.6\times 10^{5}\) MPa, \(2.4\times 10^{5}\) MPa and \(4.4\times 10^{5}\) MPa, \(E_{s}=2.0\times 10^{5}\) MPa, \(n_{f}\) varies between 0 and 3 with an increment of 1, \(t_{f0}=0.167\) mm, \(t_{s}=2\) mm, 3 mm and 4 mm.
* The BCs: 4S and 3S1F.
* Plate aspect ratio \(\beta\): varies between 1 and 4 with increment of 1.
* Hole size \(d/b\): varies between 0 and 0.7 with an increment of 0.1, in some case refines to 0.05.
* Hole position \(e_{x}/b\) and \(e_{y}/b\). \(e_{x}/b\) varies between 0.05 and 0.5\(\beta\) with an increment of 0.0625. \(e_{y}/b\) varies between 0.5 and 0.9 with an increment of 0.025 for BCs of 4S and between 0.05 and 0.95 with  
Figure 2: The common cross sections and deformations under compression. (**a**) Rectangle section; (**b**) L section; (**c**) T section.  
an increment of 0.025 for BCs of 3S1F. In all conditions, the distance between hole's edge and plate's edge is no less than 0.05\(b\).
## 4 Results  
### Fire Detection Capability across Swath  
Comparisons of the minimum, mean, and maximum FRP per fire pixel show that the fire detection capability across swath (or VZA) varies considerably for the MODIS sensor while it is generally consistent for VIIRS I-band. As VZA increases from nadir to the scan edge, the minimum, mean, and maximum FRP from MODIS all show an increasing trend, while those from VIIRS show a very small variation (Figure 4). The VIIRS FRP at the boundaries between aggregation zones shows a triangular zig pattern due to the sharp change of VIIRS I-band pixel size (Figures 1 and 4). Minimum FRP shows that MODIS is able to detect fires with FRP of \(\sim\)4.3 MW (per pixel) at nadir and \(>\) 31.7 MW at the scan edge, whereas VIIRS can sense fires with FRP of \(\sim\)1.3 MW (per pixel) and \(>\) 2.9 MW, respectively. This indicates that the fire detection capability of MODIS decreases by a factor of 7.4 at the scan edge while the capability of VIIRS changes by a factor of 1.5, 1.6 and 2.2 in the three-sample, two-sample, and one-sample aggregation zones. As view angle varies from nadir to the scan edge, the mean FRP increases from \(\sim\)23.7--\(\sim\)130.8 MW for MODIS and \(\sim\)9.4--18.6 MW for VIIRS and the maximum FRP increases from \(\sim\)237.2--\(\sim\)1130.7 MW and \(\sim\)72.4--142.6 MW for MODIS and VIIRS, respectively (Figure 4). The sharp decrease in MODIS fire detection capability at large VZAs is associated with the increase in MODIS pixel size at off nadir (Figure 1); on the contrary, VIIRS fire detection capability varies slightly due to the generally consistent pixel size across swath (Figure 1).  
Figure 3: Cross-meridian width of a MODIS swath gap along latitude (the gray dash line indicates the equator).
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 6 Discussion
Due to a shortage of participants for audio collection we could only get maximum 85 audio records per class and total 6229 audio files. This dataset is smaller for the requirements of the CNN model training and validation process. However, the previous studies [10; 20] on CA audios the authors utilized between 2k-4k audio files to investigate DLNN models. Thus, data augmentation helped us in obtaining sufficiently large dataset. Furthermore, in this paper, we have focused on data collection in single geographic region (95%) from Saudi Arabia, which has a native Arabic speaking population. It would be of interest to evaluate this approach on data from non-native speakers as well as natives from other Arab countries. In the near future, we intended to improve our work by obtaining data from other Arabic-speaking groups to see the generalization of our approach in a wider context. We planned to expand our current work to participants from different nationalities as well as non-native speakers and age groups to explore their pronunciation similarity and differences from native speakers, areas of improvements to facilitate development of Arabic learning tools and applications. The analysis will be made on duration, variability, and overlapping attributes among CA learners. In addition, we also aim to quantify the standard duration of pronunciation of both short and long vowels in the classical Arabic language.
# Durability Evaluation Study for Crumb Rubber-Asphalt Pavement
## 1 Introduction
### Combined Effects of Low Temperature, Water, and Traffic Load  
Rubber crumbs are mainly used as an additive for roads that are exposed to winter ice and snow under a seasonally frozen working environment [36]; therefore, the crumb rubber-asphalt pavement inevitably encounters low temperature, ice-water, and traffic load conditions in combination. Under low-temperature conditions, the crumb rubber-asphalt mixture is in the elastic-plastic state, and a drop in temperature probably leads to its cracking; such conditions are generally accompanied by snow and icy weather. When the road structure is frozen, the asphalt still deforms under the action of the traffic load due to the high elasticity of the rubber crumbs in the mixture, and the melted ice water enters the pavement, where it reduces the degree of bonding between the aggregate and asphalt. These combined problems of moisture entering the mixture due to the decrease in the viscosity of asphalt at low temperatures and the water-induced reduction of adhesion of the asphalt with the aggregate are then exacerbated by flow-scouring inside the mixture due to the hydrodynamic pressure generated by traffic load.  
In summary, the combined effects of (a) low temperature, (b) meltwater and (c) traffic load have a major impact on the road performance of crumb rubber-asphalt pavement. Therefore, it is of great importance to study the durability of crumb rubber-asphalt mixture under the coupling of these three factors. In this study, the durability of crumb rubber-asphalt pavement was evaluated in detail based on the actual working environment, and a corresponding comprehensive evaluation method was proposed.
## 2 Measuring Systems, Materials and Methods
### Methodology
#### 2.1.1 Feature Extraction
Attribute identification and selection require balancing the need to minimize the number of features (reduction) and effectively represent the waveform shape. The characteristic points method can minimize or remove the need for attribute reduction. The technique involves the arbitrary selection of features that vary with changes in the studied object (e.g., extrema, inflection points of the waveform, coordinates of these points, or offset). The technique requires some initial knowledge, which can be gathered from association rules, statistical and mathematical analyses, simulations, or a general understanding of the process. The schematic process of attribute extraction is presented in Figure 4.  
This investigation applies the equal intervals in the domain of amplitude method as the first step. Each waveform's amplitude is divided into equal intervals, with each interval accounting for 5% of the amplitude. Consequently, sections are designated as, e.g., \(U_{30}\) for 30%, \(U_{60}\) for 60%, and \(U_{\text{max}}\) for 100% of the maximum amplitude (Figure 5). Subsequently, the points corresponding to the \(U\) values are marked on the graph (point \(a\) for \(U_{30}\), \(b\) for \(U_{60}\), \(\epsilon\) for \(U_{\text{max}}\)) and projected onto the abscissa axis. This led to the creation of new variables: \(X_{30}\), \(X_{60}\), and \(X_{\text{max}}\). These variables are next used to determine the shape attributes \(d_{30}\), \(d_{60}\), and \(d_{\text{x}}\). It can be noted that for each value marked on the ordinate axis (except \(U_{\text{max}}\)), two corresponding values can be read on the abscissa axis: one smaller than \(X_{\text{max}}\) (\(a\) and \(b\)) and one larger than \(X_{\text{max}}\) (\(a^{\prime}\) and \(b^{\prime}\)). The attribute \(d_{\text{x}}\), representing the distance between the coordinate of the zero value (\(X_{0}\)) and the maximum value (\(X_{\text{max}}\)) is calculated to address this issue. Subsequently, \(X_{\text{max}}\) is used as the reference point for the other shape attributes instead of \(X_{0}\). Furthermore, parameters such as amplitude and offset are established. Figure 6 illustrates the definitions of these terms. Figure 4: Attribute extraction process (equal dividing and characteristic points).
# A Performance Evaluation for Software Defined Networks with P4
## 8 Conclusions  
The above study has answered the research questions given at the beginning of this paper. We conclude that the application of _SDN+P4_ has enabled networks to improve their performance for different topologies and types of traffic. The results from our experiments show an improvement for, for example, delay and packet loss, which have reduced significantly with _SDN+P4_, whilst throughput has increased for all case studies. _SDN+P4_ has improved performance in the networks through the use of parallel processing in P4, which has complemented the standardised SDN architecture. The results for all case studies indicate that _SDN+P4_ is a promising alternative to _SDN+OvS_, providing a resilient approach for future networking.  
Through a combination of SDN and P4 (control plane programmability with data plane programmability), we have established that it is possible to provide an improved service to clients using _SDN+P4_ rather than with _SDN+OvS_. Across all case studies, parallel processing in P4 has provided an increase in available queues for processing traffic with the utilisation of a flexible parser. With the evolution of the internet and the heterogeneity of the applications being used, _SDN+P4_ will, we believe, provide an improved service to all use cases for 5G and beyond. Based on the results collated from this study, we implemented an _SDN+P4_ environment in our 5G testbed paper [76]. The programmable switch was developed using P4, incorporating parsers, match-action pipelines, and control logic capable of processing Ethernet and IPv4 headers, which we deployed on an Intel10 GbE x520 NIC, enabling high-throughput and low-latency packet forwarding, with data plane programmability. A Software Defined Radio USRP x310 [77] was integrated in the testbed to provide radio access, with the host system configured to manage flow rules and policies, facilitating control plane programmability. Future research in this area will involve the application of machine learning algorithms on the SDN+P4 platform for intrusion detection.  
Conceptualisation, O.A.F., H.X., and J.S.; methodology, O.A.F., H.X., and J.S.; software, O.A.F.; validation, O.A.F., H.X., and J.S.; formal analysis, O.A.F., H.X., and J.S.; investigation, O.A.F., H.X., and J.S.; resources, O.A.F., H.X., and J.S.; data curation, O.A.F., H.X., and J.S.; writing--original draft preparation, O.A.F., H.X., and J.S.; writing--review and editing, H.X., J.S., and X.C.; visualisation, O.A.F., H.X., and J.S.; supervision, H.X., J.S., and X.C.; project administration, O.A.F., H.X., and J.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. The authors declare no conflicts of interest.
# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 5 Insight to When to Amalgamate DC-AI with MC-AI
### Limited Availability of Representative Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ML Algorithms**} & \multicolumn{2}{c}{\(\boldsymbol{\mathcal{A}}\) **Results under Three Different Settings (Mostly DC-AI)**} \\ \cline{2-4}  & \(\boldsymbol{\kappa}\) & \(\boldsymbol{\beta}\) & \(\gamma\) \\ \hline Decision tree & 0.8127\(\sim\)0.8411 & 0.8103\(\sim\)0.8513 & \(\sim\)89.11 \\ SVM & 0.8386\(\sim\)0.8701 & 0.9161\(\sim\)0.9271 & \(\sim\)94.65 \\ Random forest & 0.8802\(\sim\)0.9198 & 0.8909\(\sim\)0.9209 & \(\sim\)99.83 (\(\approx\)100\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(\mathcal{A}\) comparisons from using the DC-AI approach (\(\alpha\) = (data augmentation), \(\beta\) = (hyperparameter tuning), and \(\gamma\) = (augmented data and reduced hyperparameters)). \(T_{n}>F_{n}\). Figure 9 presents the structure of the confusion matrices obtained via the DC-AI approach (e.g., the data quality was significantly improved before building ML models). From the results, it can be noticed that \(F_{p}\) and \(F_{n}\) were much lower compared to the MC-AI approach, which did not improve the data quality. In addition, the minority class did not have a higher misclassification. These results fortify the significance of the DC-AI approach in practical scenarios from the perspective of higher accuracy, as well as the balance in the confusion matrices. These results highlight the importance of DC-AI and prove that DC-AI has abilities to yield better results, leading to the better generalization and learning ability of ML classifiers. These results validate the fact that DC-AI is the better choice over MC-AI in safety-critical applications. Next, we demonstrate the effects of DC-AI on the sampling quality. We chose ten samples for analysis and comparison purposes. When the sample size was 0.8 (80%), the maximum difference between classes 0 and 1 in DC-AI was 1212, which is very low compared to MC-AI. From these results, it can be observed that gaps in the number of records of the two stroke categories were small compared to MC-AI, and the sampling quality was good. These results verify the superiority of DC-AI compared to MC-AI in terms of sampling quality. The analysis presented above underscores that the DC-AI approach is imperative in most cases, especially when the underlying data have poor quality. DC-AI has yielded better results in three different aspects. The experimental analysis and comparison prove the essence of the DC-AI approach in practical scenarios, particularly when the data are imbalanced or limited to train AI models.
# Propagation Characteristics of High-Power Vortex Laguerre-Gaussian Laser Beams in Plasma
###### Abstract  
The propagation characteristics of high-power laser beams in plasma is an important research topic and has many potential applications in fields such as laser machining, laser-driven accelerators and laser-driven inertial confined fusion. The dynamic evolution of high-power Laguerre-Gaussian (LG) beams in plasma is numerically investigated by using the finite-difference time-domain (FDTD) method based on the nonlinear Drude model, with both plasma frequency and collision frequency modulated by the light intensity of laser beam. The numerical algorithms and implementation techniques of FDTD method are presented for numerically simulating the nonlinear permittivity model of plasma and generating the LG beams with predefined parameters. The simulation results show that the plasma has different field modulation effects on the two exemplified LG beams with different cross-sectional patterns. The self-focusing and stochastic absorption phenomena of high-power laser beam in plasma are also demonstrated. This research also provides a new means for the field modulation of laser beams by plasma.  
plasma; high-power laser beams; ponderomotive nonlinearity; FDTD method +
Footnote †: journal: Applied Physics Letters