esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Reduction Using Synthetic Microjet Excitation in Supersonic Rectangular Jets
###### Abstract
Large Eddy Simulation (LES) offers an alternative that captures large scales directly while modeling small ones, thus balancing computational cost and accuracy. This approach has become the method of choice for jet aeroacoustic calculations [18]. Shortly after the first LES-based simulation of supersonic jet noise was reported [19], this technique was utilized to study noise generation and propagation. Hybrid methods, such as Detached-Eddy Simulation (DES) and Delayed Detached-Eddy Simulation (DDES), refine LES by allowing more flexibility in simulating flow regions with different turbulence characteristics [20]. Finally, the Reynolds-Averaged Navier-Stokes (RANS) equations, which yield time-averaged solutions, provide a lower-fidelity alternative that, while limited in capturing far-field pressure signals, can be coupled with empirical models to predict noise. After the jet noise sources are predicted using LES-based techniques, several approaches can be used to extend the nonlinear CFD results to the acoustic far field [21]. One idea is the evaluation of [PERSON]'s stress tensors using a volume integral, but this is memory-intensive. The Kirchhoff method [22] was the first method to allow the use of surface integrals and is much more efficient, and it has been applied to the evaluation of jet noise [23]. The classical formulation for the Kirchhoff method requires pressure and its time and normal derivatives. Other Kirchhoff-like methods have been proposed to avoid evaluation of the pressure normal derivatives, such as SIF (Surface-Integral Formulation) in the special case of cylindrical surfaces [24]. [PERSON]'s work was also expanded to consider moving solid boundaries, resulting in the Flowcs Williams-Hawkings (FWH) formulation covering areas such as rotor noise and impinging jet noise [25]. The FWH method requires pressure, velocity, density, and its time derivatives to be collected on a control surface that encloses most of the noise sources. Atfirst, the formulation was proposed for an impermeable control surface coinciding with a radiating solid surface. The method was later extended to include a permeable control surface formulation which improved its versatility and is now the prevailing method to extend near-field CFD results to the acoustic far-field. The placement of the control surface for unsteady data collection and grid refinement play a vital role in the accuracy of acoustic prediction in these methods, and generally requires a parametric convergence study.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 4 Experimental Results and Analysis  
### Experimental Detail  
To demonstrate the suitability of the KAN for ECG signal classification tasks, we conducted comprehensive experimental analyses on the MIT-BIH dataset. Initially, we employed a Multi-Layer Perceptron (MLP) to classify ECG signals, establishing a baseline for evaluating the effectiveness of the KAN. Subsequently, we compared the performance of various models proposed in previous studies on ECG signal classification, highlighting the superiority of the KAN in arrhythmia triage. Furthermore, we evaluated the migration and generalization capabilities of the KAN using the PTB dataset. Finally, we analyzed the convergence properties and clinical application potential of the KAN during the experiments.  
The KAN was trained using a learning rate of 5 \(\times\) 10\({}^{-5}\) and the Adam optimizer [22]. The batch size was set to 64, and the number of epochs was set to 150. The input dimension was 187, the hidden layer size was 128, with output dimensions of 5 for MIT-BIH and 2 for PTB. Other parameters, including the _scale spline_ and _grid eps_, were set to 1.0 and 0.02, respectively. Additionally, SiLU was employed as the activation function, and the _grid range_ was set to [\(-\)1, 1]. All experiments were conducted and evaluated using Python 3.9 and PyTorch 1.12.1, with the experimental hardware comprising an NVIDIA GeForce RTX 4050 Laptop GPU.  
During the experiment, we used repeated random sampling to equalize the heart rhythm categories (N, S, V, F, Q) of the samples, with the _random_state_ set to 42, 123, 124, 125, and 126, respectively. Additionally, the _random_state_ ensures consistency in the results of KAN construction and ECG data splitting, facilitating reproducibility and comparison of the model.
## 4 Discussion
SMAP data were processed for the period between September 2016 and August 2017, over the Occitanie region, with one day of temporal resolution. Figure 8 shows the distribution of SMAP soil moisture values as a function of one day of cumulative precipitation obtained from the IMERG-GPM data. In average, SMAP shows an estimation of 15 vol.% following light rain one day before the SMAP acquisition, and about 30 vol.% following 100 mm of cumulative rainfall during one day before the SMAP acquisition. Although the difference in soil moisture between the two events (light rain and heavy rain) is greater for SMAP (about 15 vol.%) than that of S1 (7 vol.%), the results obtained with SMAP at 9 km \(\times\) 9 km and one-day revisit do not also allow the easier detection of heavy rainfall events. However, the potential of the SMAP in detecting heavy rainfall appears to be better than the potential of S1 for rainfall events >150 mm, where SMAP-SSM shows values greater than 30 vol.%. The previous discussion leads to the conclusion that soil moisture, which is obviously correlated with the amount of rainfall, depends also on other water cycle parameters such as evaporation, soil type, and the previous state of soil moisture before the heavy rainfall. Although it is obvious that heavy rainfall will lead to very wet soil, a slight rain over already wet soil will also result in extremely wet soil. A revisit time of several days (case of S1) makes the detection of heavy rainfall more complicated, because an extreme rainfall event that takes place four or five days before the S1 acquisition and is then followed by a significant evaporation will not lead to very high soil moisture values. In addition, radar sensors don't correctly estimate soil moisture for SSM values above 30 to 35 vol.%. [PERSON] et al. [25] demonstrated that the radar signal is stable when the SSM values ranged between 30-35 vol.%, and it decreases beyond this threshold. This eventually leads to an underestimation of SSM beyond this threshold value [26]. Thus, the configurations for having very wet soil are numerous, and do not always depend on the amount of rain that falls. That many environmental parameters affect the value of SSM and the limitation of SAR to estimate the very high SSM values makes the monitoring of extreme rain events very delicate when it is based only on soil moisture maps. Therefore, the analysis carried out in this paper confirms that the use of the S1-SSM maps with a revisit period of six days (at plot scale) or the SMAP-SSM maps with very high temporal resolution of one day (at 9 km \(\times\) 9 km) makes the detection of heavy rainfall not always obvious. Figure 8: Soil Moisture Active Passive (SMAP)-SSM distribution as a function of one day of cumulative precipitation (revisit time of SMAP-SSM = one day).
# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 3 Materials and Methods
### Training and Validation Approach
Adopting this approach ensures that the trained model can effectively differentiate defective components from non-defective ones, thus enhancing its applicability, robustness, and efficiency for real-time quality assurance scenarios. Throughout the training process, both loss and accuracy metrics were continuously monitored for the training and validation sets across all epochs. Model performance was assessed using a comprehensive suite of evaluation metrics. These included overall classification accuracy, along with precision, recall, and F1-score, reported both globally and on a per-class basis. A confusion matrix was employed to visualize misclassification patterns and to illustrate performance distribution across different classes. Beyond classification performance, metrics critical for deployment were also evaluated. These included model size (in megabytes) and inference speed, measured in both frames per second (FPS) and average milliseconds per image. Such deployment-aware metrics are particularly important for assessing real-time feasibility on resource-constrained edge devices. Collectively, these evaluation criteria offer a detailed view of the model's predictive accuracy, generalization capability, and practical viability in industrial settings.
# A Novel SIW Leaky-Wave Antenna for Continuous Beam Scanning from Backward to Forward
## 2 Antenna Design
### Full Structure Array Antenna  
As this antenna operates at frequencies ranging from 14.5 GHz to 22.5 GHz, it is critical that the feeding structure has good impedance matching in this band. To provide good impedance matching, a wideband microstrip-to-SIW transition scheme is used in the feeding section, as shown in Figure 9. This transition was presented in [39]. When compared with the traditional microstrip-to-SIW transition, using a short metallic via can improve impedance matching. Figure 17 shows the microstrip-to-SIW transition, which compares the traditional design with the proposed one. The return loss for the two types of transitions is shown in Figure 18. One can easily see that the proposed microstrip-to-SIW transition significantly improves the impedance matching over the entire frequency range of 14.5 GHz to 22.5 GHz.  
Figure 16: Dispersion diagram of unit cell with longitudinal slot and H-plane Step.  
Figure 17: Microstrip to SIW transition: (a) traditional design; (b) proposed design.  
Figure 15: Dispersion diagram of unit cell with longitudinal slot.
## 5 Results
### Effectiveness and Efficiency Evaluation of the Model
The Local Extend algorithm expands the nodes from inside to outside in descending order of similarity, which can quickly form the optimal solution. Along with the increase in \(k\), the initial feasible community becomes closer to the optimal community. Only a few nodes need to be deleted to build the optimal community. Therefore, the Shrink algorithm performs better than the Local Extend algorithm. At the same time, we note that no matter how the value of \(k\) varies, the TWC model is faster than VAC and ATC. This is because the community score function of TWC only considers the similarity with the query node, while ATC and VAC consider all pairs of nodes, so their calculation is expensive. Since ACQ adopted k-core as the structural cohesion metric, it is faster than ATC and VAC. However, because the index construction takes a long time, ACQ has little difference from the efficiency of TWC. Figure 5: Effectiveness evaluation of different models. (**a**) Change of similarity improve ratio with different \(k\); (**b**) change of similarity improve ratio with different time windows.
## 3 Results
### Sensitivity Study, Intra-, and Inter-Observer Reproducibility  
Figure 4 shows a sensitivity analysis at peak-systole. The relative error in LV cardiac volumes and helicity density did not show significant differences between groups across LV segmentation. There were significant differences for some segmentation for the other parameters, particularly for the velocity magnitude, energy loss, and kinetic energy. The hemodynamic parameters showed a relative error proportional to the dilatation or erosion of the contour in the segmentation. When the segmentation was dilated or eroded 1 pixel or less, the relative error differences, with respect to the original segmentation for volunteers and DCM patients, were: velocity magnitude (9.03%, 6.78%), vorticity magnitude (5.49%, 2.74%), helicity density (12.11%, 13.98%), viscous dissipation (6.31%, 3.34%), energy loss (3.59%, 5.89%), and kinetic energy (7.66%, 6.09%). Similar results were obtained at e-wave and end-diastole, as shown in Figures S2 and S3. Those errors were more significant, particularly when the segmentation was dilated or eroded by more than 1 pixel. Helicity density and energy loss showed greater dependency on the segmentation error.  
Figure 2: Box whisker plots for hemodynamic parameters (**a-f**) in the entire LV cavity of HV and DCM patients groups at peak systole, e-wave, and end-diastole. On each box, the central mark is the median, the bottom and top edges of the box are the 25 th and 75 th percentiles, respectively, and the whiskers extend to the most extreme data points not considered outliers. *,+ Indicates statistically significant differences (_p_ < 0.05).
## 6 ML and the Security of Blockchain
### Quantum Attack Resistance  
Although quantum attacks remain largely theoretical, ML can contribute to the development of quantum-resistant cryptographic methods. ML models can simulate quantum attack scenarios on various cryptographic algorithms, enabling researchers to identify potential vulnerabilities. These insights facilitate the development and optimization of post-quantum cryptographic techniques [112]. ML thus plays a dual role by both simulating advanced attack vectors and assisting in the design of more robust, quantum-resistant security measures.
###### Abstract  
This paper proposes a novel approach to simultaneously identifying multiple critical parameters in a wireless power transfer (WPT) system, such as the resonant frequency, mutual inductance, and load resistance, solely from the primary side. The key is to adopt a primary-side-switch-controlled capacitor (SCC) to ensure that the imaginary part of the input impedance is only caused by the secondary-side reflected impedance at three predesigned frequencies. The DSP controller then samples and processes the primary voltage and current using a gradient descent algorithm to derive the above parameters. After the identification, the SCC adjusts its equivalent capacitance based on the secondary-side practical resonant frequency to ensure a zero-phase angle (ZPA), thereby significantly improving the compatibility of the WPT system with unknown receivers. Compared to the previous frequency-sweeping method, the proposed approach is simpler and more suitable for deployment on the controller. Finally, experimental results demonstrate that the identification error of mutual inductance and resonant frequency are within 7.5% and 2.68%, respectively.  
wireless power transfer; switch-controlled capacitor; parameter identification; zero-phase angle
# Role of Functional Monomers upon the Properties of Bisphenol A Molecularly Imprinted Silica Films
## 3 Results and Discussion
### Thermal Stability of BPA-MIP and NIP Films  
Thermogravictic analysis, performed under nitrogen gas at a 10 \({}^{\circ}\)C/min heating rate, indicated similar decomposition trends for the two types of sol-gel films (Figure 5). The thermal degradations of BPA-MIP\({}_{\text{DAMOT}}\) and BPA-MIP\({}_{\text{MPTES}}\) and their non-imprinted NIP\({}_{\text{DAMOT}}\) and NIP\({}_{\text{MPTES}}\) revealed that all films were thermally stable. Three main degradation steps were observed for all the samples. For DAMO-T-based films, a higher mass loss was observed between 50 and 150 \({}^{\circ}\)C and it was attributed to the evaporation of intrinsic water, hence confirming the FTIR spectroscopy results where significant amount of water was retained in the structure of DAMO-T-based film.  
Figure 5: Thermogravimetric curves (TG) and their derivatives (DTG) for (**a**) BPA-MIP\({}_{\text{DAMOT}}\) and NIP\({}_{\text{DAMOT}}\) (**b**) BPA-MIP\({}_{\text{MPTES}}\) and NIP\({}_{\text{MPTES}}\) films.  
The first degradation step was recorded in the 200-300 \({}^{\circ}\)C temperature range, with maximums registered at 273 \({}^{\circ}\)C and 223 \({}^{\circ}\)C for BPA-MIP\({}_{\text{DAMOT}}\) and BPA-MIP\({}_{\text{MPTTES}}\), respectively, and attributed to BPA degradation according to the thermogram of BPA provided in Figure S4 (Tmax = 268 \({}^{\circ}\)C). The shift to lower or higher maximum decomposition temperatures is not unusual and this observation can help explain two interesting phenomena that may occur during imprinting in soft and rigid sol-gel networks [21; 36; 41]. For instance, in the case of BPA-MIP\({}_{\text{MPTTES}}\) films (Figure 5b), the low stability of BPA indicated that interactions between the template and the functional SH groups of the monomer, potentially weaken the (C-CH\({}_{3}\)) bonds from BPA which led to the fragmentation of the molecule. On the other hand, the BPA imprinted in the BPA-MIP\({}_{\text{DAMOT}}\) film (Figure 5a) degraded at higher temperatures due to the inherent rigidity of the sol-gel network, given by the addition of TEOS. It should also be mentioned that a small mass loss was also registered for the NIP based on DAMO-T in this same region and may be linked with the loss of amino fragments from DAMO-T.  
The second degradation stage occurred between 300 and 500 \({}^{\circ}\)C and was generally attributed with the total degradation/fragmentation of the pendant groups (i.e., methyl/ethyl fragment types, amino-ethyl/methyl, mercaptopropyl). The degradation of the polymer backbone occurred in the last degradation step, between 500-800 \({}^{\circ}\)C, [34]. It should be underlined that for both BPA-MIPs the residues were up to 57 wt.% and slightly lower compared to their corresponding NIIPs, up to 61 wt.%, confirming a similar behavior between them but also their good thermal stability considering their structural differences.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 2 Related Work
### Attention Mechanisms  
In the field of computer vision, the attention mechanism is widely used. The attention mechanism is similar to the working mechanism of the human visual system, which makes the network pay more attention to the important features and ignore the unnecessary ones by weighting the input features.  
Squeeze-and-Excitation (SE) networks [28] emphasize valid feature channels and suppress invalid feature channels by establishing relationships between the channels of input features and the weighting each channel. ECANet [29] replaces the two fully connected layers in SE with a 1 \(\times\) 1 convolution, which not only avoids dimensionality reduction, but also achieves cross-channel interaction with fewer parameters. The above two attention mechanisms only consider the interactions between channels, and do not consider the spatial dimensionality of the features. CBAM [30] and DANet [31] use both the channel attention mechanism and the spatial attention mechanism. CBAM uses the channel attention mechanism and the spatial attention mechanism serially, while DANet uses the channel attention mechanism and the spatial attention mechanism in parallel. These two attention mechanisms greatly enhance the effective information of the input features, and the model performance is greatly improved, but their number of parameters also increases substantially compared to the channel attention mechanism. Therefore, [PERSON] et al. [12] address this problem by proposing the Shuffle Attention module, which uses Shuffle Units to effectively combine channel attention and spatial attention. Specifically, the SA module first groups the input features into multiple sub-features by channel dimension; then, for each sub-feature, a Shuffle Unit is used to compute the feature dependencies in the channel and spatial dimensions; finally, all the sub-features are concatenated in the channel dimension, and channel shuffle is used to achieve information interaction between the different sub-features. The SA module is efficient and effective. Therefore, we chose to use the SA mechanism to build the GRFEF module.
###### Abstract
Thhe autonomous navigation approach is proposed, using the Deep Reinforcement Learning algorithm combined with the following sensory fusion strategies: Interactive Model [7] and Late Fusion [8; 9]. Sensory fusion of images is performed and obtained by an RGB-D camera with non-visual navigation sensors, both coupled to the AMR. Sensory fusion, which uses artificial neural networks, generates the unified control signal processing for agent training via reinforcement learning, specifically using the DDQN algorithm (Double Deep Q-Networks). The security module is a separate trained algorithm, specifically using supervised learning and embedded in the sensor fusion architecture. Its objective is to avoid robot collisions when the navigation environment becomes dynamic. The main hypothesis of this article is based on the need for precision in certain navigation missions. It is assumed that these missions cannot be solved only with sensors linked to vision, even with the use of efficient control techniques, such as reinforcement learning. That is, non-visual sensors would increase the perception capacity of AMR through multi-modal fusion techniques. For this, the impact of two modern fusion techniques on mission development is evaluated. The justification for the elaboration of this work is the incipience in the technical literature of sensory fusion methods allied to reinforcement learning for autonomous navigation purposes. Most of the methods are applied in isolated tasks, such as the classification of objects in the scene and identification of routes and pedestrians, among others. In addition, the work uses recent methods that were not previously applied to the robotics area, such as the Iterative method that originated in the medical area for classifying diseases. The article is organized as follows: Section 2 presents related works, and Section 3 presents the proposed problem, as well as the implementation methodology. Section 4 contains the results. Finally, Section 5 concludes the current study and presents future studies.
## 4 Results  
The study proposes an integrated framework (FS-ConvLSTM) and applies it to generate hourly spatial precipitation estimation through spatiotemporally merging data from rain gauge and GPM (IMERG V06) observations in the Jainxi basin of China from 2006 to 2018. The relevant results and findings are displayed in the following sub-sections: quality assessment on GPM (Section 4.1), accuracy evaluation of different models (Section 4.2), and model performance in typical rainfall events (Section 4.3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Rain Gauge Observation**} & \multicolumn{2}{c}{**Satellite Product**} \\ \cline{2-3}  & **Yes** & **No** \\ \hline Yes & TP & FN \\ No & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of TP, FP, FN, and TN.