esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing
## 3 Numerical Results
### Full-Array vs. Sub-Array Architecture
Analyzing the SER vs. the angle \(\phi_{2}\), the user separability is diminished due to a loss in angular resolution (see Figure 4(c)). Thus, closely spaced users cannot be separated anymore as compared to the full-array case since the individual PSPs of the users already start to overlap at larger angular distance. Here, eigenspace-based processing reveals again a superior performance than just conventional beamforming. Figure 4: Full-array architecture: Power-space profile and symbol error rate performance. Colors correspond to users: user 1 (), user 2 (), user 3 (). Gray plots () represent results without digital beamspace preprocessing for each user. Results of different sub-array configurations are illustrated in Figure 6 considering various angular positions of user 2. The smaller the number of antennas per sub-array \(N_{\mathsf{rx,sub}}\), the smaller the angular resolution and thus the user separability. At small \(N_{\mathsf{rx,sub}}\), the conventional beamforming suffers from performance variations, which originates from strong inter-user interference caused by non-orthogonal beams. On the contrary, eigenspace post-processing offers a better user separation at all times. Figure 5: Sub-array architecture of \(N_{\mathsf{sub}}=8\) and \(N_{\mathsf{rx,sub}}=16\): Power-space profile and symbol error rate performance. Colors correspond to users: user 1 (), user 2 (), user 3 (). Gray plots represent results without digital beamspace preprocessing for each user.
# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 2 Mechanisms of Sinkhole Formation
The process of sinkhole formation is presented graphically in Figure 2. Certain conditions necessary for the formation of cover-collapse sinkholes have been described [13; 29; 32]. Firstly, the overburden must have enough inherent strength to form a roof, yet should be permeable and erodible. Near-vertical walls or pillars should also be present near the surface to support bridging material that, if failing, results in the sudden formation of cavities [29; 33] (Figure 2B,D). If there is no bridging support or the overburden is too weak to form an arch, subsidence sinkholes will occur instead. Furthermore, a means of transportation of materials, for example from hydrological action, should be present [33]. The water table should consequently be low, since percolating water has a higher erosive potential above the water table [32]. When a void of appropriate size is present, mechanisms that cause the roof to collapse needs to be present. The results will be upwards void migration until the surface is reached [13; 29]. An example of a cover collapse sinkhole in South Africa is provided in Figure 2D. Although sinkholes are generally considered to occur with little prior warning, the appearance of tension cracks, cracks in walls and surface subsidence are often early warning signs of sinkhole formation [8; 20]. An example of tension cracks, developed on the edges of a sinkhole in South Africa, is provided in Figure 3. Such evidence manifesting on the surface before the collapse is the result of a phase of continuous underground erosion events (known as arch collapse) leading to upward cavity migration until the surface is reached [34]. Such signs are known to develop either rapidly, in a matter of hours, or over periods of as long as a couple of months or years prior to sinkhole collapse [34; 35; 36]. The presence of these precursory deformation features suggests that it may be possible to derive early warning indicators of sinkhole formation prior to its ultimate collapse [24]. If a technique for the early detection of small-scale surface deformation prior to collapse of a sinkhole can be developed, the technique can contribute to a sinkhole early warning system. The derived information can inform  
Figure 2: A graphic representation of the mechanisms of sinkhole formation. (**A**) The presence of a sub-surface cavity and a triggering mechanism initiates the process of subsurface erosion; (**B**) Competent roof strata supports the formation of an arch; (**C**) Sudden collapse can lead to damage to infrastructure and pose a health and safety risk; (**D**) An example of a cover collapse sinkhole in South Africa. disaster managers and affected communities of impending sinkhole formation, thereby minimising the potential for injuries and death.
# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 4 Methodology
### Model Training
#### 4.2.7 Performance Metrics
It is important to note that the domain-specific metrics used in this study--**Missed Service Opportunity Rate (MSOR)** and **Excess Capacity Allocation Rate (ECAR)**--are mathematically complementary to conventional classification metrics. Specifically, **MSOR corresponds to the complement of recall**, while **ECAR corresponds to the complement of precision**, as defined below:  
\[\text{MSOR}=\frac{(\text{FN})}{\text{{Actual Service Visits}}}=1-\text{ Recall} \tag{25}\]  
\[\text{ECAR}=\frac{(\text{FP})}{\text{{Predicted Service Visits}}}=1-\text{ Precision} \tag{26}\]  
These formulations offer a more intuitive interpretation in the context of automotive service operations. MSOR reflects the proportion of actual service visits that were not predicted by the model (i.e., missed opportunities), whereas ECAR represents the proportion of predicted visits that did not occur (i.e., wasted capacity). By framing recall and precision in terms of operational risk, these domain-aware indicators help translate model performance into actionable business insights. While these metrics are not standardized across industries, they serve as valuable decision-support indicators for business stakeholders. Incorporating such contextual measures enhances the interpretability and actionability of predictive models in real-world automotive settings.
## 4 Results
### Wave Field Plate with Stringer
#### 4.2.1 Comparison of Wave Propagation of Wave Field Plate with and Without Stringer  
A study was performed with the SLDV to show the influence of the applied omega stringer. Therefore, a B-scan was recorded along the entire plate and perpendicular to the omega stringer. The corresponding time-distance representation is shown in Figure 14 for different frequencies and excitation signals. The chirp signals were pulse-compressed using a matched filter. Moreover, the envelope of the signal was applied before imaging [34]. A logarithmic scale was used to increase the image dynamic.  
The dashed black lines indicate the position of the actuator on the plate (y = 0.25 m); the dashed red lines show the contact area between the stringer and the plate. As soon as the first wave hits the stringer area, a reflection can be detected at any center frequency. A second and third reflection can be detected at the second, respectively the third, impedance discontinuity caused by the bonded stringer. This effect is more pronounced at lower frequencies. This study demonstrates the influence of the omega stringer on the wave propagation. In principle, prediction of frequency-selective reflection and attenuation is challenging, because it depends on the exact boundary conditions of a discontinuity, such as the bonding of a stringer. However, from the study of tapered waveguides, it is known that the change of thickness, e.g., due to the foot of a stringer, can lead to a frequency-selective velocity alteration in the \(S_{0}\)-mode [35] and \(A_{0}\)-mode [36].  
The phenomenon of wave mode conversion at adhesively bonded joints was studied in [37; 38]. A detailed understanding of the geometry and material as well as geometric discontinuities or transitions is of high importance. The SLDV studies performed in this work also reveals wave mode conversion that can be seen in Figure 14f. The \(A_{0}\)-mode and \(S_{0}\)-mode can be identified by their different group velocities, which is represented by the slope of the propagating waves in the B-scan. The group velocity of the wave changes when the faster symmetric mode hits the stringer (40 \(\upmu\)s). This is shown in the change of the wave front in Figure 14f. Beneath the stringer, the formerly \(S_{0}\)-mode burst was converted almost completely to a slower \(A_{0}\)-mode.  
Figure 13: The peak intensity at the damage location \(D_{1}\) increases with damage size.
## 4 Numerical Experiments
The travel time (s) refers to the time when all vehicles in the platoon passed the section (in this paper, we set the end point of the section to a position of 1000 m in the lateral direction). These two indicators mainly reflect the efficiency of the platoon. The velocity deviation (m/s) represents the average value of the corresponding velocity deviation between all following vehicles and the leading vehicle. A larger velocity deviation always means worse safety. The average jerk (m/s\({}^{3}\)) refers to the change rate in vehicle acceleration, which represents the driving comfort degree during the driving process. A smaller average jerk indicates higher driving comfort. We can find that the constant time headway control method has the highest driving comfort, but it is based on the premise of sacrificing a lot of traffic efficiency. On the whole, the G-SPF-based strategy has the best overall performance, especially in terms of improving traffic efficiency. Note that when the velocity of the platoon is higher, the constant time headway strategy will produce a much larger gap distance, which will be unfavorable for traffic efficiency.
# Transverse Vibration of Functionally Graded Tapered Double Nanobeams Resting on Elastic Foundation
## 1 Introduction
Moreover, [PERSON] [8] applied the nonlocal elasticity theory to analyze the bending, buckling, and vibration of various beams including the Euler-Bernoulli, Timoshenko, Reddy, and Linenson beam. Analytical solutions were obtained, and the effect of the small-scale parameter was shown. [PERSON] and [PERSON] [9] reported on the torsional vibration of tubes made of a bi-directional FG material. It was shown that the frequencies decline as the nonlocal scale effect rises. [PERSON] and [PERSON] [10] presented the vibrations of nanobeams subjected to thermal loads and resting on an elastic foundation. A new finite element technique was used to obtain the frequencies, and it was concluded that the modulus of the foundation, the scale effect, and the thermal loads have a remarkable influence on the frequencies. Besides, [PERSON] and [PERSON] [11] applied [PERSON]'s nonlocal theory to study the vibrations of a double-nanoplate-system connected by a continuous linear springs. The frequencies of the system were found analytically. [PERSON] et al. [12] used [PERSON]'s nonlocal elasticity theory to investigate the natural vibrations of orthotropic composite nano beams. The Poisson effect was included in the study, and the effects of the thickness of the beams, the scale parameter, and the number of the layers on the natural frequencies were studied. Moreover, [PERSON] et al. [13] applied [PERSON]'s elasticity theory to present the scale parameter coefficients for the natural vibrations and buckling behavior of simply supported rectangular plates. Further studies about this topic were examined by other authors [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. In this research, the natural transverse vibrations of AFG tapered double nanobeams embedded in elastic medium are investigated. [PERSON]'s nonlocal elasticity theory is utilized to account for the scale parameter. The equation of the transverse motion is obtained and the Chebyshev spectral collocation method (CSCM) is applied to find the dimensionless frequencies and the corresponding mode shapes.
## 3 Method
### Wavelet Domain Channel Selection Module  
In Section 3.1, we analyzed the advantages of DWT. Inspired by this, we propose the Wavelet Domain Channel Selection Module (WDCSM). FcaNet [17] proposed that GAP represents the lowest frequency component of 2D DCT. Therefore, relying solely on GAP may lead to insufficient information utilization in channel attention. FcaNet incorporates additional frequency components of 2D DCT to address this issue. Furthermore, by analyzing the sub-bands generated by 2D DWT, it is observed that the diagonal high-frequency component (\(D\)) includes diagonal detail features with low information content and is mainly composed of noise signals. Therefore, in the WDCSM, the diagonal high-frequency component (\(D\)) is discarded, and horizontal detail (\(H\)) and vertical detail (\(V\)) are used as additional effective features to generate channel descriptor, as shown in Figure 6.  
Figure 6: Illustration of Wavelet Domain Channel Selection Module.  
Specifically, the proposed WDCSM can be expressed as follows. Firstly, 2D DWT can decompose the original input into four sub-bands, as shown in Equation (5). Then, aggregate the \(\{A,H,V\}\) sub-band into a channel descriptor. It can be formulated as:  
\[d_{c}=\sum_{i}^{\frac{H}{2}}\sum_{j}^{\frac{W}{2}}(A+H+V) \tag{8}\]  
Subsequently, the channel descriptor \(d_{c}\) is fed into a lightweight feed-forward network to fully capture inter-channel dependencies. The WDCSM attention map can be formulated as:  
\[\overset{\sim}{\mathrm{X}}_{i}=\sigma(FC(\delta(FC(d_{c})))) \tag{9}\]  
where \(FC(\cdot)\) refers to fully connected layers, \(\delta(\cdot)\) refers to the RELU function, and \(\sigma(\cdot)\) refers to the sigmoid function. The final output of the WDCSM is:  
\[\overset{\sim}{\mathrm{U}}_{i}=\mathrm{U}_{i}\overset{\sim}{\mathrm{X}}_{i} \tag{10}\]  
where \(i\) refers to the index of the WDCSM branch, the purpose is to multiply the features extracted from the two large-kernel branches by their respective channel selection masks. As shown in Figure 5, we design the Wavelet Domain Selective Kernel module with a dual-branch structure. The Wavelet Domain Channel Selection Module explicitly models the interdependencies between channels of feature maps. Furthermore, it leverages global information to adaptively recalibrate the channel-wise feature responses, enhancing the representation capability of the network.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 5 U-18 Nb as a Proxy for Protactinium
### Melting Simulations of Pa  
The high-\(T\) crystal structure of Pa is bcc; hence, the QMD melting simulations were carried out for this structure. These simulations were performed in the same way as described above for \(\gamma\)-U. The electronic structure of Pa was represented by [Hg] 5f\({}^{2}\) 6p\({}^{6}\) 6d\({}^{1}\) 7s\({}^{2}\) (11 valence electrons). Just as for \(\gamma\)-U, the PBE implementation of GGA was employed, and a 432-atom \((6\times 6\times 6)\) supercell with a single \(\Gamma\)-point was used to avoid potential size effects associated with the mechanical instability of bcc at low \(T\), which is also present in Pa, and its high-\(T\) stabilization.  
We obtained a single melting point of bcc-Pa with a lattice constant of 3.225 A (a density of 22.8756 \(\approx\) 22.9 g/cm\({}^{3}\)). The corresponding supercell was subject to a set of initial temperatures (\(T_{\rm init}\)) separated by an increment of 625 K and run with QMD in the \(NVE\) ensemble, for a total of up to 10,000 time steps of 2 fs each, i.e., up to 20 ps, to determine the corresponding values of \(P_{m}\) and \(T_{m}\).  
Figures 4 and 5 demonstrate the time evolution of, respectively, \(T\) and \(P\) during the QMD runs with three initial temperatures, \(T_{0}\). The intermediate run with \(T_{0}\) = 11,250 K is the melting run during which the system melts, and simultaneously, its temperature decreases from \(\sim\)5800 K for a superheated solid down to \(\sim\)4800 K for a liquid, and its pressure increases from \(\sim\)96.8 GPa for a superheated solid up to \(\sim\)97.3 K for a liquid.  
Our QMD simulations result in the melting point of bcc-Pa of (\(P\) in GPa, \(T\) in K) (97.3, 4790). Since the increment in \(T_{0}\) in our simulations is 625 K, the error bar of \(T_{m}\) is 312.5 K (the error bar of \(P\) is of order 0.1 GPa).  
At \(P=97.3\) GPa, the melting curve of U-18 Nb, Equation (19), gives 4435.8 K, within 7.5% of the QMD value of 4790 K. However, if the error bars are taken into account, the model value of 4435.8 K is within 1% of 4477.5 K at the lower error bar, which Figure 3 clearly demonstrates. We therefore conclude that U-18 Nb can indeed be considered as a reliable proxy for Pa because its melting curve is a good approximation for that of Pa; in fact, it represents the lower bound on the melting curve of Pa. Most likely, U-18 Nb can be used to approximate other physical properties of Pa such as its equation of state, principal Hugoniot, etc.
# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 3 Proposed work
### Quantum Cat Swarm Optimization  
Quantum cat swarm optimization (QCSO) is inspired by the behavior of cats and involves search and tracking modes. Cats spend most of their time in search mode, in which they rest or observe the environment. When they identify a target, they start to track it and then attack. These two modes are used in QCSO, seeking to simulate the behavior of cats. These modes guide the optimization process in such a way that they mimic the different strategies used by cats in real life [28; 29]. In search mode, there are four main components: (1) self-position consideration (SPC), (2) seeking a memory pool (SMP), (3) seeking a range of selected dimensions (SRD), and (4) calculating the number of dimensions to change. Here, SMP defines the memory for each cat, indicating the points pursued by a cat. From the selected dimensions, the mutual ration is calculated based on the SMP; then, the number of dimensions required for node selection is determined using SRD. SPC enables us to check whether the selected nodes are correct or whether additional iterations are needed [17]. The flowchart for quantum cat swarm optimization (QCSO) for 5G spectrogram feature extraction outlines a systematic process for selecting the most relevant time-frequency features from a 5G signal spectrogram, which is shown in Algorithm 1. The process begins  
Figure 1: Two-level of DWT decomposition for Signal/Image Feature Extraction. by converting the raw 5G signal into a spectrogram using the short-time Fourier transform (STFT) to extract the spectral characteristics of the signal \(X\). The extracted spectrogram features are then used to initialize a quantum cat population, where each cat represents a potential feature subset. The optimization process consists of two modes: seeking mode (search mode) and tracing mode (tracking mode). In seeking mode, multiple copies of each cat's feature selection vector are generated, and their feature dimensions are randomly adjusted within a defined seeking range of selected dimensions (SRD). The best candidate solution is selected based on a fitness function, which ensures that the chosen features enhance classification accuracy. In tracing mode, the velocity and position of each cat are updated based on the best global feature subset found so far, refining the selection process. The algorithm iterates until convergence, ultimately returning the optimal feature subset, which can be used for efficient 5G signal classification, modulation recognition, or anomaly detection. This approach ensures robust feature selection by balancing exploration and exploitation, leveraging quantum principles for improved optimization performance.
# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 4 Experimental Results and Analysis
### Comparative Experiment on Attention Mechanisms  
In order to improve the detection accuracy of the model, an attention mechanism was added to the original YOLOv8n for comparative experiments. We compared the current mainstream attention mechanisms that combine spatial and channel attention (CBAM), squeeze and excitation (SE), channel attention (CA), and the dual attention mechanism used in this paper, which are suitable for object detection tasks. The specific experimental results are shown in Table 2. According to Table 2, the DA mechanism proposed in this study significantly improved the detection accuracy, with the highest \(mAP\emptyset\) 0.5, at 94.013%, while \(mAP\emptyset\) 0.5:0.95 was the highest at 60.251% (calculation can be found in Formula (10)).
## 4 Implementation of the LHM-N Model  
### Implementation Overview  
The implementation is conducted in MATLAB. Reliable results are achieved in modeling and simulations that are based on discrete events simulations (DES). For instance, an autoregressive integrated moving average (ARIMA) based on the DES method was used to generate a reliable result with minimal error in smart framing [46]. Further, soft computing optimization based on the DES approach was used for the modeling and simulation analysis for a reliable mechanized operation solution [47]. Hence, the LHM-N model is based on DES. The illustrative phase of the LHM-N model implementation is shown in Figure 4. In the simulation, a random binary bit stream was created. The bit stream is then encoded into a convolutional code by the TCM encoder. The coded bit is then mapped to a 16-QAM modulator, which modulates the coded bit streams. A Rayleigh fading channel is then added to the modulated data in order to simulate a multipath fading channel. Then, the MRC combines the data received by the two receivers, which the simulation prepares for demodulation and decoding by the Viterbi decoder. Finally, the simulation compares the decoded information with the original bits streams in order to compute the bit error rate (BER).
## 4 Model Applied to Polyp Segmentation
### Visual Comparison  
To further evaluate the effectiveness and robustness of our proposed FuzzyTransNet method, this paper conducted visual comparative experiments on representative images from both polyp and skin lesion datasets. Given the frequent oversight of polyps in routine colonoscopy examinations, with miss rates ranging from 14% to 30%, we selected two highly representative polyp samples closely adhering to tissue surfaces in Figure 7. In the original images, the color of polyps is often indistinct from that of the surrounding tissues. By observing the results in the second row, it can be stated that the segmentation outcomes of our proposed algorithm exhibit greater similarity to the ground truth compared to the other algorithms. Moreover, we employed the GradCAM (gradient-weighted class activation mapping) [37] visualization method to intuitively discern where each model concentrates its attention in lesion areas, particularly for polyps that may dangle or adhere closely to tissue surfaces. In Figure 8, it is evident that FuzzyTransNet focuses more comprehensively on lesion regions compared to the other models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Index** & **Backbones** & **Sequential** & **Parallel** & **Fusion** & **Kvasir** & **ClinicDB** \\ \hline F.1 & R50 + DeiT-S & ✓ & - & - & 0.909 & 0.919 \\ F.2 & R50 + Swin V2 & ✓ & - & - & 0.911 & 0.925 \\ F.3 & R34 + DeiT-S & ✓ & - & - & 0.918 & 0.934 \\ F.4 & R34 + DeiT-S & - & ✓ & ✓ & 0.921 & 0.936 \\ F.5 & R50 + Swin V2 & - & ✓ & ✓ & **0.927** & **0.942** \\ \hline \end{tabular}
\end{table}
Table 7: Ablation study on parallel-in-branch design. R34: ResNet34; R50: ResNet50; Swin V2: Swin Transformer V2; DeiT-S: DeiT-Small.  
Figure 7: Comparison of representative polyp segmentation results. (**a**) Original image. (**b**) Results of U-Net. (**c**) Results of U-Net++. (**d**) Results of TransFuse. (**e**) Results of **our method**. (**f**) Ground truth. (**g**) Segmentation results generated by **our method** (grayscale).