esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In practical applications, track defect detection still faces multiple challenges: firstly, deep networks often have a large number of parameters, making it difficult to balance inference speed and accuracy, especially for real-time detection in resource-constrained environments; secondly, due to the wide variety of defect types and sizes, small targets are often missed, and traditional convolutional networks are prone to losing fine-grained information unless sufficient multi-scale feature fusion is applied; thirdly, track cracks are often narrow and scattered, requiring a larger receptive field and attention mechanism to capture complex textures and long-distance edges. Existing methods still fall short in these areas [7]. To address these challenges, this paper proposes the PerMSCA-YOLO model, built upon the YOLOv8 framework, with a focus on three core issues: \"lightweight real-time detection, multi-scale target enhancement, and precise capture of narrow cracks/complex surface features.\" The specific contributions of this research are summarized as follows:  
1. A lightweight FasterNet backbone network is introduced, reducing the model's parameter count and computational load through operations like grouped and depthwise convolutions, while achieving real-time detection without compromising feature representational power. 2. A multi-scale convolutional attention module (MSCAM) is integrated into the feature extraction phase, combining large receptive fields with channel and spatial attention mechanisms to enhance the recognition ability for defects of varying sizes, particularly small targets and narrow cracks. 3. To address common challenges such as complex textures and noise on track surfaces, perceptual loss is incorporated into the YOLOv8 loss function, aligning detection targets in the deep feature space to further improve the model's robustness in capturing and distinguishing fine defects. These improvements enable the model to achieve more precise detection of various track defects while maintaining high inference speed, thereby meeting the online detection requirements for high-speed and heavy-load railway scenarios and providing robust technical support for the safe operation and maintenance of railways.
## 4 Experiments and Analysis  
### Dataset and Experimental Configure  
We conducted comparative experiments against state-of-the-art methods in 3D object recognition on ModelNet10 and ModelNet40. The ModelNet40 dataset contains 12,311 3D objects distributed across 40 categories, while its subset ModelNet10 includes 4839 objects categorized into 10 classes. Figure 5 illustrates representative images generated through two distinct sequential-view capturing schemes discussed in Section 3.1. In the comparative experiments, the average class accuracy and average instance accuracy were employed as metrics for evaluating the performance of different methods. To ensure a fair comparison, ensemble methods were excluded from the evaluation, our approach was benchmarked against existing methods based on distinct 3D data representations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Layer/Module Name** & **Input Shape** & **Output Shape** \\ \hline
1 & FEM & \(\mathrm{S}\times 3\times\mathrm{H}\times\mathrm{W}\) & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) \\ \hline
2 & SSSA & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) \\ \hline
3 & Dimensional Resetting Layer & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}\times\mathrm{N}\) \\ \hline
4 & TSSA & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}\times\mathrm{N}\) & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}\times\mathrm{N}\) \\ \hline
5 & Dimensional Resetting Layer & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}\times\mathrm{N}\) & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) \\ \hline
6 & SSSA & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/2\times\mathrm{N}/2\) \\ \hline
7 & Dimensional Resetting Layer & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/2\times\mathrm{N}/2\) & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/2\times\mathrm{N}/2\) \\ \hline
8 & TSSA & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/2\times\mathrm{N}/2\) & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/2\times\mathrm{N}/2\) \\ \hline
9 & Dimensional Resetting Layer & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/2\times\mathrm{N}/2\) & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/2\times\mathrm{N}/2\) \\ \hline
10 & SSSA & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/2\times\mathrm{N}/2\) & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/4\times\mathrm{N}/4\) \\ \hline
11 & Dimensional Resetting Layer & \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/4\times\mathrm{N}/4\) & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/4\times\mathrm{N}/4\) \\ \hline
12 & TSSA & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/4\times\mathrm{N}/4\) & \(\mathrm{C}\times\mathrm{S}\times\mathrm{N}/4\times\mathrm{N}/4\) \\ \hline
13 & Classification Head & \(\mathrm{C}\times\mathrm{S}\times\mathrm{H}\times\mathrm{W}\) & Number of Classes \\ \hline \end{tabular}
\end{table}
Table 1: Overview of layer-wise model architecture.
## 1 Introduction
Although several entropy-based methods have been proposed for capacity estimation, the performance of these estimators have not been compared when different voltage datasets are used as input. Considering that the voltage response varies under different test conditions, the influence of voltage datasets needs to be evaluated. In this paper, the hybrid pulse power characterization profile was divided into two single pulses (i.e., one for charging and another for discharging) that were performed on a LiFePO4/C battery cell at three SOC levels. Therefore, six test conditions (TCs) considering the current direction and SOC were studied. Approximate entropy (AE), SE, and multiscale sample entropy (MSE) were chosen as features, and the relationship between them and the battery capacity was established based on a first-order polynomial fitting. The accuracy of these capacity estimation methods under different TCs was compared. The rest of this paper is organized as follows. Section 2 presents the experimental battery aging test and the particularities of the current pulse test applied to the battery. The theory of the AE, SE, and MSE algorithm is introduced in Section 3. Section 4 compares the accuracy of the capacity estimation considering the voltage datasets and the type of entropy. Finally, conclusions are drawn in Section 5.
# Motion Compensation for Long Integration Times and DoA Processing in Passive Radars
## 3. Bistatic Scenarios with Stationary PR and IoO  
In the study case, the PR receiver and the IoO are stationary. The target bistatic Doppler shift depends on the projection of the target speed vector \(\vec{v}_{T}=v_{T}\hat{v}_{T}\) on the bistatic plane, \(\vec{v}_{tg}=v_{tg}\hat{v}_{tg}\), and it is calculated as \(f_{D}(Hz)=(2\cdot v_{tg}/\lambda)\cdot\cos(\psi_{tg})\cdot\cos(\beta_{tg}/2)\), with \(\psi_{tg}\) being defined by the projected target speed vector, \(\hat{v}_{tg}\); the bistatic bisector, \(\beta_{tg}\); and the  
Figure 2. Time parameters for PR data processing.  
target bistatic angle [1] (Figure 3). In this case, the target bistatic delay depends on time \(t_{tg}(t)=t_{T}(t)+t_{R}(t)-t_{L}=(R_{bis}(t)/c)-t_{L}\).  
\(R_{bis}(t)\), according to the Weierstrass approximation principle, can be expanded to the second order (Equation (2)) defining \(R_{bis0}\) as the bistatic range at the reference time, \(v_{bis0}=f_{D0}\lambda\) as the bistatic velocity and Doppler shift at the reference time \(t_{0}\), and \(a_{bis}=f_{Dr}\lambda\) as the bistatic acceleration and Doppler shift rate [28]. For the CPI processing, a relative time \(\tau=t-t_{0}\) is used assuming \(t_{0}=n_{CPI}PRI\) for redefining \(R_{bis}\) as (3).  
\[R_{bis}(t)=R_{bis0}-f_{D0}\lambda(t-t_{0})-\frac{f_{Dr}\lambda(t-t_{0})^{2}}{2} \tag{2}\]  
\[R_{bis}(\tau)=R_{bis0}-f_{D0}\lambda\tau-\frac{f_{Dr}\lambda\tau^{2}}{2} \tag{3}\]  
For a IoO passband signal \(S(\tau)=Re\{\tilde{s}(\tau+t_{L})\exp\left(j2\pi f_{c}(\tau+t_{L})\right)\}\), the reference and surveillance baseband signals are, respectively, expressed in (4) and (5) [35], where:  
* \(f_{c}\) is the carrier frequency.
* \(\tilde{\xi}_{ref}\) and \(\tilde{\xi}_{surv}\) are the complex gain factors for the reference and the surveillance signals, respectively. The propagation effects and clutter and target contributions are considered, but possible reference channel multipaths are discarded.
* \(\tau_{tg}(\tau)=(R_{bis}(\tau)/c)-t_{L}\).
* \(\tau_{tg0}=(R_{bis0}/c)-(L/c)\) is the target bistatic delay at \(\tau=0\) or \(t=t_{0}\).
* \(\phi_{t0}=-j2\pi f_{c}\tau_{tg0}\) is the phase constant associated with \(\tau=0\) or \(t=t_{0}\).  
\[\tilde{s}_{ref}(\tau)=\tilde{\xi}_{ref}\cdot\tilde{s}(\tau) \tag{4}\]  
\[\begin{split}\tilde{s}_{surv}(\tau)=\zeta_{surv}\cdot\tilde{s}( \tau-\tau_{tg}(\tau))\cdot\exp\left(-j2\pi f_{c}\tau_{tg}(\tau)\right)\\ =&\zeta_{surv}\cdot\tilde{s}\big{(}\tau-\tau_{tg}( \tau)\big{)}\exp\left(j2\pi f_{c}\big{(}\frac{f_{D0}}{f_{c}}\tau+\frac{f_{Dr} }{2f_{c}}\tau^{2}\big{)}+\phi_{t0}\right)\\ =&\zeta_{surv}\cdot\tilde{s}\big{(}t-t_{tg}(\tau) \big{)}\exp\left(j2\pi\big{(}f_{D0}\tau+\frac{f_{Dr}\tau^{2}}{2}\big{)}+\phi_ {t0}\right)\end{split} \tag{5}\]
# O-Band Grating Couplers Using Silicon Nitride Structures
## 3 Simulation Results
The maximum coupling efficiency is obtained at 2.6 \(\upmu\)m, alongside 7.5 degrees. Area 2 is located where h is between 3.65 and 4.35 \(\upmu\)m, \(\theta\) is in the same range as the first area, and the maximum coupling efficiency is obtained at 4 \(\upmu\)m, alongside 7.5 degrees. These results show a wide tolerance range of h with 400 nm and 350 for area 1 and area 2, respectively. Figure 10 presents the coupling efficiency in relation to the wavelength in the O-band spectrum, specifically considering TE mode polarizations. The wavelength tolerance range spans from 1270 to 1360 nm, which is notably suitable for achieving a coupling efficiency of over 24%. Hence, our suggested GC device can readily manage the laser drift phenomenon, which can happen due to the heating process of the laser that can shift the wavelength around 0.08 nm/\({}^{\circ}\)C.  
Figure 8: Electric field distribution inside the SMF over the x-z plane. BR is another noteworthy facet of the GC. It can potentially pose substantial risks to the signal transmission because it can lead to undesired disturbances in the laser, which can reduce the signal integrity. To determine the BR power in our model, two monitors (green color in Figure 11a) were positioned. One was placed at the input of the GC waveguide to capture the entirety of the light that was reflecting from the GC region, as depicted in Figure 11a. Another one was located at the Si background substrate to collect all the diffraction light coming back from the rectangular teeth areas of the GC, as shown in Figure 11a. The FDTD BR results show that 31.5% of the input light comes back into the Si substrate layer, and a minor BR light of 0.015% is reflected into the input waveguide section, as shown in Figure 11b. Hence, integrating a mirror located at the bottom before the Si substrate area can dramatically improve the light coupling from 28% to around 50% and above. However, this solution is not recommended because of the high cost of fabrication, alongside a larger footprint size. The BR coming to the input signal can be handled easily by integrating a Si\({}_{3}\)N\({}_{4}\) adiabatic waveguide taper, which can reduce it to 40 dB BR [43]. Figure 10: Variation of coupling efficiency with wavelength. Figure 2:0 Optimizations of the SMF location from the GC. Table 1 presents an extensive comparison of Si\({}_{3}\)N\({}_{4}\) on an insulator-based chip-to-fiber GC, involving essential factors such as Si\({}_{3}\)N\({}_{4}\) GC technology, coupling efficiency, wavelength, complexity of fabrication, and fabrication costs. From this table, it can be seen that only a GC with a complex configuration alongside a high cost can obtain a higher coupling efficiency ranging from 38% to 56%. Our proposed device shows superior characteristics, such as a low cost with a simple configuration that is easy to fabricate, and can also be operated in the O-band spectrum. Thus, our GC is the most suitable GC to use for testing signals for PICs working in data center applications.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 3 Small Delay Defects vs. Hidden Delay Defects  
### Small Delay Defects  
A small delay defect [15; 16] is a type of delay defect that makes the chip fail during at-speed operation. The total path delay may go beyond the clock period when the small delays get propagated through the long paths. These delay defects will be huge enough to fail the circuit during the at-speed operation, but it will escape the traditional TDF model-based tests. The TDF model assumes that only large delay defects are localized in a single node, so whatever transition passes through this node will get delayed past the clock period. Hence, the TDF-based ATPG tool will try to propagate the fault effect through the easiest and shortest path possible to reduce the run time, as shown in Figure 5a. Here Path 1 and Path 2 are short paths through which the fault can be propagated, but the longest path is the third path mentioned as the Longest path in Figure 5a. If the TDF ATPG tool tries to propagate the fault effect through Path 1 or Path 2, then this will leave the small delay fault undetected. But this delay can cause the circuit to fail during at-speed functional operation.
## 5 Simulation Verification  
To validate the accuracy of our proposed single-end ranging method, a simulation model of a 10 kV small current grounding system using MATLAB/Simulink is built, with the topology illustrated in Figure 5. The model incorporates voltage traveling wave measuring devices positioned at section M, with black solid lines representing cable lines and gray solid lines indicating overhead lines. The system contains five main feeders (from top to bottom: main feeder \(l_{1}\), \(\ldots\), main feeder \(l_{5}\)). The main feeder \(l_{5}\) is an overhead line-cable hybrid structure. The length of each line is marked in Figure 5. The line parameter table is shown in Table 1. The sampling frequency of the system is 1 MHz, and the total simulation time is 0.5 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**line Type** & **Phase Sequence** & **\(R\)/\(\Omega\)** & **L/mH** & **C/\(\mu\)F** \\ \hline \multirow{2}{*}{overhead line} & positive sequence & 0.17 & 1.21 & 0.09 \\  & zero sequence & 0.23 & 5.48 & 0.05 \\ \multirow{2}{*}{cable line} & positive sequence & 0.11 & 0.52 & 0.29 \\  & zero sequence & 0.34 & 1.54 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Line parameter table.  
Figure 5: Simulation model of 10 kV small current grounding system.  
Despite the complex topology of small current grounding systems, their typical length remains relatively modest, generally not exceeding 10-20 km. Drawing from reference [22], which establishes a monotonic relationship between zero-mode detection wave velocity and fault distance, the zero-mode wave velocity can be considered to be constant in the small current grounding system. Consequently, the constant parameter model (5) is employed to calculate both line-mode and zero-mode wave velocities. Using the parameters specified in Table 1, the wave velocities for both overhead lines and cable lines are computed according to Formulas (26) and (27).  
\[\begin{cases}v_{0}=0.5846\times 10^{5}\text{km}/\text{s}\\ v_{1}=0.8143\times 10^{5}\text{km}/\text{s}\end{cases} \tag{26}\]  
\[\begin{cases}v_{h0}=1.7452\times 10^{5}\text{km}/\text{s}\\ v_{h1}=2.9212\times 10^{5}\text{km}/\text{s}\end{cases} \tag{27}\]
## 2 Theory and Methodology
### Leja Interpolation Nodes  
As pointed out in Sections 2.1 and 2.2, the univariate interpolation nodes employed in Lagrange interpolation schemes must satisfy certain requirements. First, for the interpolation to become more accurate with increasing levels, the Lebesgue constant associated with the interpolation nodes must increase at a rate lower than the decrease in the polynomial approximation error [6,16]. Second,the choice of nodes must result in accurate quadrature schemes, similar to Equation (8), to be used in the computation of statistics. Third, interpolation nodes which form nested grids can be used to derive hierarchical interpolation schemes, which result in reduced computations per added interpolation level. Additionally, as discussed in Section 2.4, sparse interpolations depend crucially on hierarchical univariate interpolation rules. Finally and most importantly in the context of this work, the interpolation must be constructed with respect to arbitrary weight functions, i.e., input PDFs. All requirements are addressed here by using weighted Leja sequences [30], discussed in the following.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
###### Abstract
The results show that the proposed Gabor-based model achieves an average spectrogram accuracy of 74% in single-speaker scenarios and demonstrates excellent vocabulary reconstruction ability with an average accuracy of 81%. Moreover, the experiments also demonstrate that the proposed GaborFea2 Speech model can maintain robust speech reconstruction capability in multi-speaker scenarios, with 72%, 68%, 71%, 65%, and 61% accuracy for different multi-speaker models. This paper makes several contributions. Firstly, a detailed review of state-of-the-art research is conducted within the visual speech reconstruction domain. Secondly, using our proposed Gabor-based feature extraction method, two advanced lightweight speech reconstruction models are introduced: GaborCNN2 Speech and GaborFea2 Speech. Thirdly, comprehensive testing across single-speaker, multi-speaker, and vocabulary contexts demonstrates that our proposed GaborCNN2 Speech model offers enhanced performance compared to traditional end-to-end Convolutional Neural Network (CNN) models, presenting a combination of superior results, lightweight design, and rapid processing. Moreover, our innovative GaborFea2 Speech model capitalizes on three-dimensional lip features, achieving lightweight and rapid system attributes and excelling in single-speaker and vocabulary reconstruction tasks. Most significantly, it achieves multi-speaker speech reconstruction without the need for supplemental information. The remainder of this paper is presented as follows. Section 2 presents a comprehensive literature review of the speech reconstruction field. Section 3 introduces the Gabor feature extraction technique. Section 4 introduces our novel Gabor-based speech reconstruction models. The experimental methodology is provided in Section 5, with results in Section 6 and discussion in Section 7. Finally, Section 8 concludes the paper and provides future research directions.
### Remote Sensing Estimation of Gross Primary Productivity and Light Use Efficiency
#### 2.4.3 Calculation of Light Use Efficiency and Stress Scalars  
\(\text{LUE}_{\text{in situ}}\) and \(\text{LUE}_{\text{max in situ}}\) corresponding to Landsat overpass dates were calculated by inversion of the GPP (Equation (1)) and CASA equations (Equation (2)) [8; 10]. Daily \(\text{GPP}_{\text{in situ}}\) and incoming shortwave radiation were available from FLUXNET datasets for each site. Other parameters were kept consistent with the PySEBAL script [12; 23] due to its specificity to cropland and its similarity to other GPP estimates for cropland including GYMEE [1]. The variable, fPAR, was calculated on Landsat images as:  
\[fPAR=-0.161+1.257\times NDVI, \tag{3}\]  
conditional on the Normalised Difference Vegetation Index (\(NDVI\)) being \(\geq\)0.125, below which \(fPAR\) was set as 0. This definition of \(fPAR\) has been widely applied to cropland applications since [PERSON] and [PERSON] [48] aggregated coefficient and intercept values from several cropland experiments, including the work of [PERSON] et al. [49] on maize and soybean.  
_PAR_ was calculated as:  
\[PAR=Rs\times 0.48\times 0.0864, \tag{4}\]  
where \(PAR\) is 48% of incoming shortwave radiation (\(Rs\)) [48] and 0.0864 converts \(\text{W}/\text{m}^{2}\) to \(\text{MJ}/\text{m}^{2}/\text{day}\).  
Temperature stress (\(TS\)) was calculated based on [PERSON] [50; 51] and [PERSON] [52] as:  
\[TS=\frac{(T-T_{l})\times(T_{h}-T)^{l}c}{(K_{t}-T_{l})\times(T_{h}-K_{t})^{l}c} \tag{5}\]  
where \(T\) is the average daily temperature and \(Jc\) is the Jarvis coefficient, calculated as:  
\[Jc=\frac{T_{h}-K_{t}}{K_{t}-T_{l}}, \tag{6}\]  
where \(Th\), \(Kt\), and \(Tl\) are the upper limit, optimum value, and lower limit of temperatures (\({}^{\circ}\)C) for stomatal conductance, respectively. These constants were made consistent with the PySEBAL script [12; 23] Constants used in Equations (5) and (6) are presented in Table 2.  
Vapor stress (\(VS\)) was calculated based on [PERSON] et al. [55], and Fuchs and Stanghellini [56], as:  
\[VS=0.88-0.183\times log(E_{sat}-E_{act}), \tag{7}\]  
where \(E_{sat}\) and \(E_{act}\) are the saturated and actual vapor pressure (kPa), respectively.  
Soil moisture stress (\(SMS\)) was calculated as the ratio of actual (\(T_{act}\)) to potential (\(T_{pot}\)) 24 h transpiration [57]:  
\[SMS=T_{act}/T_{pot}, \tag{8}\]  
where \(T_{act}\) and \(T_{pot}\) were taken from PySEBAL output maps and masked according to Section 2.4.1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constant** & **Definition** & **Source** \\ \hline \(Tl\) & 0 \({}^{\circ}\)C & [53; 54] \\ \(Kt\) & 23 \({}^{\circ}\)C & [53; 54] \\ \(Th\) & 35 \({}^{\circ}\)C & [53; 54] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constant values used for calculation of temperature stress in the estimation of \(\text{GPP}_{\text{CASA}}\).
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 3 Proposed Method: Cross-Interaction-Based Video Stabilization
### Implementation
#### 3.2.2 Stage 2: Video Stabilization  
Previous methods have attempted to implicitly learn frame motion in color videos through learning-based video stabilization. The learning video stabilization using optical flow [8] method proposes a novel neural network that infers per-pixel warp fields for video stabilization from the optical flow fields of input videos. This technique relies on the optical flow for motion analysis and directly learns stabilization, proposing a pipeline for motion inpainting and warp-field smoothing, offering resilience to moving objects, occlusion, and optical flow inaccuracies. The first step involves preprocessing through computing an affine transformation to eliminate large motions. It then generates masks indicating accurate areas of optical flow for each frame and employs the first five principal components suggested by the PCA flow for inpainting inaccuracies. The absence of masking and inpainting would lead to motion discontinuity and distortion. The stabilization network, as the second step, takes the inpainted optical flow field as network input, generating per-pixel warp fields for frame motion compensation. The third step, postprocessing, considers the potential issues of local discontinuities at valid/invalid boundaries of the optical flow, ensuring warp field continuity and replacing raw warp fields with resulting low-frequency fits.  
The output, utilizing a low-frequency warp field for warping the input video, results in a rectangularly cropped image. While this technique achieves full-frame image generation and high stability, it has the drawback of a significant distortion. We prioritized video completion using this technique as the initial step.