esa-sceva/satcom-chunk-collection huggingface dataset:
# An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
## 4 Anti-Interference Method Based on Energy Residual Searching
### Constructing the Energy Residual Function
A larger energy residual indicates that more LFM interference signal components have been effectively removed in the transform domain, while a smaller energy residual suggests that fewer interference components have been removed. By optimizing the fractional order parameters to maximize the energy residual, the optimal fractional Fourier transform can be identified, achieving the maximum suppression of LFM interference signal energy in the transform domain and, thereby, enhancing the GNSS signal's interference resistance. Specifically, the optimal fractional order can be expressed as  
\[\underset{p\in[0,2]}{\arg}\;\max|E_{1}-E_{2}(p)^{2}| \tag{9}\]  
The energy residual function transforms multi-peaked, multi-periodic spectral lines into a one-dimensional single-extreme function, altering the structure of the interference suppression algorithm. This modification reduces the search dimensions and significantly enhances the efficiency of solving for the optimal transform order.
###### Abstract  
This paper proposes two solutions based on angle diversity receivers (ADRs) to mitigate inter-cell interference (ICI) in underground mining visible light communication (VLC) systems, one of them is a novel approach. A realistic VLC system based on two underground mining scenarios, termed as mining roadway and mine working face, is developed and modeled. A channel model based on the direct component in line-of-sight (LoS) and reflections of non-line-of-sight (NLoS) links is considered, as well as thermal and shot noises. The design and mathematical models of a pyramid distribution and a new hemi-dodechedral distribution are addressed in detail. The performances of these approaches, accompanied by signal combining schemes, are evaluated with the baseline of a single photo-diode in reception. Results show that the minimum lighting standards established in both scenarios are met. As expected, the root-mean-square delay spread decreases as the distance between the transmitters and receivers increases. Furthermore, the hemi-dodecahedron ADR in conjunction with the maximum ratio combining (MRC) scheme, presents the best performance in the evaluated VLC system, with a maximum user data rate of 250 Mbps in mining roadway and 120 Mbps in mine working face, received energy per bit/noise power of 32 dB and 23 dB, respectively, when the bit error rate corresponds to \(10^{-4}\), and finally, values of 120 dB in mining roadway and 118 dB in mine working face for signal-to-interference-plus-noise ratio are observed in a cumulative distribution function.  
angle diversity receiver; inter-cell interference; signal combining scheme; underground mining; visible light communication +
Footnote †: journal: Sensors  
article  
[FOOTNO
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 5 Substrates of Ypk1
### Other Potential Targets
Indeed, two of the core reactions of yeast sphingolipid biosynthesis require NADPH as a cofactor: conversion of 3-ketodihydrosphingosine to dihydrosphingosine (catalyzed by Tsc10) and conversion of dihydrosphingosine to phytosphingosine (catalyzed by Sur2). However, in this study, no Ypk1 substrate was identified. Similarly, use of TOR inhibitors implicated TORC2-Ypk1 signaling in regulation of actin filament formation that is somehow required for yeast cell survival in response to low levels of DNA damage [203]. Again, however, no Ypk1 substrate was identified. In contrast, in another study in which a yeast strain was engineered in which TORC2 could be specifically inhibited by another imidazoquinoline-derived TOR inhibitor (NVP-BHS345), it was found using phosphoproteomics that action of the Ypk1 substrates Fpk1 and Fpk2 was required to see the inhibition of endocytosis after acute inhibition of TORC2 [34], in agreement with findings already presented above [26]. Using an alternative way to block TORC2 activity, namely shift of the temperature-sensitive _tor2-21\({}^{ts}\)_ allele, it was found [132] using microarray analysis that approximately half of the genes upregulated following TORC2 inhibition are under the control of the calcineurin-activated transcription factor Crz1 [204]. Likewise, using another chemical genetic tactic, namely when a strain bearing an analog-sensitive allele [205] of Ypk1 (Ypk1-as) was treated with inhibitor, microarray analysis also revealed an increase in Crz1-dependent gene transcription [29], suggesting that TORC2-Ypk1 action somehow leads to downregulation of calcineurin activity. There is evidence that the Ca\({}^{2+}\) needed to activate calcineurin is admitted to the cytosol via the ER- and PM-localized calcium channel Mid1 [206], suggesting that Mid1 function is blocked by Ypk1-dependent regulation. However, this regulation must be indirect because Mid1 itself lacks Ypk1 consensus phospho-acceptor motifs. Of course, given the evidence that TORC2 may regulate Pkc1 under certain stress conditions [207], potential Pkc1 targets might also emerge from studies in which TORC2 function is inhibited. Indeed, in the _avo3 AC TOR1-1_ strain, inhibition by rapamycin causes a G2-M cell cycle arrest [15] similar to what is observed in Pkc1-deficient cells [38]. Pkc1 and its substrates are discussed further below.
## 3 The Proposed Method and Scenarios
### Initial Authentication Phase
#### 3.4.1 Vehicle and AS Communication Stage  
This phase encompasses communications transmitted from the vehicle to the AS, and vice versa, mirroring steps (a) and (b) depicted in Figure 6. Initially, the vehicle transmits the Registration Acknowledgment (RA) to the AS, containing the \(ID_{v}\), the specific service name that the vehicle intends to utilize (in this instance, the service name pertains to RSU service), \(IP_{v}\), and the stipulated duration for the Ticket Granting Ticket (TGT) (\(Req_{TGT}\)). The \(Req_{TGT}\) serves to restrict the duration, thereby enhancing system security through a finite time constraint. These data will be forwarded to the AS in the Trusted Authentication Server (TAS).  
The messages that are sent by the vehicles to the AS are shown in Equation (3). Equations (4) and (5) show the corresponding response messages. The parenthesis symbol denotes one group of unencrypted messages, while the bracket symbol illustrates the encrypted messages.  
\[RA=(ID_{v}||ID_{s}||IP_{v}||Req_{LT}). \tag{3}\]  
\[AT_{ASV}=\ K_{\text{Yes}}[ID_{TGS}||TS_{ASV}||\quad LT_{TGT}||K_{TGSse}]. \tag{4}\]  
\[TGT=\ K_{TGSs}[(ID_{v}||ID_{TGS}||TS_{TGT}||\quad IP_{v}||LT_{TGT}||K_{TGSse})]. \tag{5}\]  
The Authentication Server (AS) possesses a registry of authorized users alongside their corresponding confidential keys. Verification involves confirming the presence of \(ID_{v}\) and the messages in the aforementioned registry. Upon successful validation, a duplicate of \(K_{Vs}\) is extracted. Subsequently, the AS initiates the creation of an \(AT_{ASIV}\), following which the Ticket Granting Ticket (TGT) is dispatched to the user. \(AT_{ASIV}\) incorporates \(ID_{TGS}\), timestamp \(TS_{ASIV}\), and a designated duration of validity. The TGT includes \(ID_{v}\), \(ID_{TGS}\), \(TS_{TGT}\), \(IP_{v}\), and \(LT_{TGT}\). Encryption of both messages is carried out using \(K_{TGSse}\), a symmetric key generated randomly, intended for the user's decryption of various communications from the Transportation Authority Server (TAS) and Road Side Unit (RSU) as a service server, limited to that specific instance. The Attribute message (\(AT_{VITGS}\)) is encrypted with \(K_{Vs}\), while the TGT undergoes encryption with \(K_{TGSs}\). Subsequently, these two messages are dispatched from the AS to the vehicle.
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 5 Traffic Sign Classification
The CNN for circular traffic signs classified the super class into 20 subclasses, and the CNN for triangular traffic signs classified the super class into 15 subclasses. We believe that further partitioning the subset of the database and classifying it independently, reduces the interference between different classes and improves classification accuracy. To verify the effectiveness of this approach, we designed the third CNN that classified all the traffic signs. We used four orientations and two scales for the SGW filtered image, as the eight-channel input of the three CNNs. The parameters of SGW were the same as that of the detection phase. When our algorithm was used in online applications, the features used in the classification phase were the features extracted from the detection phase, and features extracted twice during detection and classification were avoided. In this way, time was greatly saved, and the method better met the demands of real-time. As the inputs of the CNN must all be the same size, we resized all the images to a size of \(32\times 32\) before being filtered by the SGW. The three CNNs had the same structure, which is shown in Figure 9. The architectures of the three CNNs were almost the same as the expected number of output classes.
## 4 Results
### Comparative Analysis of Mesh Quality  
We use TetGen's default commands to generate the mesh for comparison. The comparative result of mesh quality generated by proposed method and TetGen is as follows. The mesh generated by the bubble vertices has a smaller proportion of mesh with a larger aspect ratio, and the peak value is 1.5\(\sim\)2. The peak value of the mesh generated by the TetGen is 3\(\sim\)4 in Figure 10. The distribution of bubble mesh about face angle is more concentrated between 40\({}^{\circ}\)\(\sim\)70\({}^{\circ}\), and the mesh generated by TetGen is distributed between 30\({}^{\circ}\)\(\sim\)80\({}^{\circ}\) in Figure 11. The triangle elements indicate the bubble mesh is of higher quality. The distribution of the dihedral angle of the bubble vertices is concentrated on 40\({}^{\circ}\)\(\sim\)70\({}^{\circ}\) and TetGen's is 50\({}^{\circ}\)\(\sim\)80\({}^{\circ}\) in Figure 12. From the above three comparison results, the distribution of the face angle is more concentrated on 50\({}^{\circ}\)\(\sim\)70\({}^{\circ}\) and the dihedral angle is focused on 80\({}^{\circ}\)\(\sim\)110\({}^{\circ}\). The aspect ratio is smaller which suggests the quality of mesh by proposed method is better.  
Figure 10: Statistics of the mesh quality of aspect ratio for final geological model.  
Figure 9: Statistics of the mesh quality of dihedral angle
# An Efficient and Accurate Adaptive Time-Stepping Method for the Landau-Lifshitz Equation
## 2 Numerical Method  
### Discretization  
We shall discretize the given domain \(\Omega=(L_{x},\ R_{x})\) as \(\Omega_{h}=\{x_{i}|x_{i}=L_{x}+(i-0.5)h,\ i=1,\ldots,N_{x}\}\), where \(h=(R_{x}-L_{x})/N_{x}\) is a space step size and \(N_{x}\) is the number of grid points; see Figure 1.  
We simply denote a numerical solution as  
\[\mathbf{m}_{i}^{n}=\mathbf{m}(x_{i},t^{n})=(u_{i}^{n},\ v_{i}^{n},\ w_{i}^{n}) =(u(x_{i},t^{n}),\ v(x_{i},t^{n}),\ w(x_{i},t^{n})),\]  
where \(t^{n}=t^{n-1}+\Delta t^{n}\), for \(n\geq 1\), \(\Delta t^{n}\) is the nonuniform time step size, and \(t^{0}=0\). The visualization of \(\mathbf{m}_{i}^{n}\) is shown in Figure 2.  
Figure 1: Cell centered computational domain grid.  
Figure 2: Schematic illustration of \(\mathbf{m}(x_{i},t^{n})\).  
Similarly, \(\mathbf{f}_{i}^{n}=\mathbf{f}(x_{i},t^{n})\). The explicit Euler scheme is given as  
\[\frac{\mathbf{m}_{i}^{n+1}-\mathbf{m}_{i}^{n}}{\Delta t^{n+1}}=-\mathbf{m}_{i}^{ n}\times\Delta_{h}\mathbf{m}_{i}^{n}+\mathbf{f}_{i}^{n},\ \ 1\leq i\leq N_{x}\ \text{and}\ 0\leq n\leq N_{t}. \tag{2}\]  
where the discrete Laplacian is defined as \(\Delta_{h}\mathbf{m}_{i}^{n}=(\mathbf{m}_{i+1}^{n}-2\mathbf{m}_{i}^{n}+ \mathbf{m}_{i-1}^{n})/h^{2}\), and \(N_{t}\) is the number of iterations. The periodic boundary condition (\(\mathbf{m}_{0}^{n}=\mathbf{m}_{N_{x}}^{n}\) and \(\mathbf{m}_{N_{x}+1}^{n}=\mathbf{m}_{1}^{n}\)) is applied for \(n\geq 0\). That is,  
\[\Delta_{h}\mathbf{m}_{1}^{n} = \frac{\mathbf{m}_{2}^{n}-2\mathbf{m}_{1}^{n}+\mathbf{m}_{0}^{n}} {h^{2}}=\frac{\mathbf{m}_{2}^{n}-2\mathbf{m}_{1}^{n}+\mathbf{m}_{N_{x}}^{n}} {h^{2}},\] \[\Delta_{h}\mathbf{m}_{N_{x}}^{n} = \frac{\mathbf{m}_{N_{x}+1}^{n}-2\mathbf{m}_{N_{x}}^{n}+\mathbf{m }_{N_{x}-1}^{n}}{h^{2}}=\frac{\mathbf{m}_{1}^{n}-2\mathbf{m}_{N_{x}}^{n}+ \mathbf{m}_{N_{x}-1}^{n}}{h^{2}}.\]  
Define the discrete energy function [31] as follows:  
\[E(\mathbf{m}^{n}) = h\sum_{i=1}^{N_{x}}\lvert\
abla\mathbf{m}_{i}^{n}\rvert^{2}=h \sum_{i=1}^{N_{x}}\Bigl{\lvert}\frac{\mathbf{m}_{i+1}^{n}-\mathbf{m}_{i}^{n} }{h}\Bigr{\rvert}^{2}\] \[= \frac{1}{h}\sum_{i=1}^{N_{x}}\Bigl{[}(u_{i+1}^{n}-u_{i}^{n})^{2}+ (v_{i+1}^{n}-v_{i}^{n})^{2}+(w_{i+1}^{n}-w_{i}^{n})^{2}\Bigr{]}.\]  
In particular, the energy function is conserved when the forcing term \(\mathbf{f}=0\)[23].
## 3 Methodology  
In dense scenes, pedestrians are typically small, occluded, and appear in varying poses and lighting conditions, leading to common detection failures such as false positivesand missed detections. These failures stem from two major limitations: (1) limited spatial adaptability--standard convolutional backbones struggle to capture deformable and irregular pedestrian shapes, especially in occluded scenarios, due to their fixed geometric receptive fields; and (2) inefficient small-object feature fusion--shallow features often lack semantic richness, while deeper features lack spatial detail, making it difficult to localize small objects in cluttered environments. To mitigate these challenges, we propose an enhanced dense pedestrian detection model based on YOLOv8, which integrates multi-scale feature fusion and linear spatial attention mechanisms, with the architecture illustrated in Figure 1. The original C2f module is replaced with a C2f-DP2N (deformable pyramid convolution network) module. By stacking multiple deformable convolution layers, this design expands the network's receptive field and captures richer multi-scale pedestrian features, particularly critical for resolving small-scale target extraction in crowded scenes. A multi-scale linear spatial attention (MLSA) module is introduced to dynamically enhance visible features of occluded pedestrians while suppressing background interference. This module prioritizes discriminative spatial regions across scales, improving robustness in complex environments. A dedicated 160 \(\times\) 160 small-target detection head is added to amplify sensitivity to diminutive pedestrians, addressing the prevalent issue of missed detections for low-resolution targets in dense scenarios. A novel loss is proposed to accelerate model convergence and refine bounding box regression. This hybrid loss combines the advantages of distribution-aware focal loss and adaptive IoU metrics, ensuring precise localization under occlusion and scale variation.  
Figure 1: Network architecture diagram. In the backbone component, C2f-D2 CN module is proposed to replace the original C2f module. In the neck layer, a multi-scale linear spatial attention (MLS_Attention) module is designed to enhance the visible features of occluded pedestrians while suppressing interference from complex backgrounds. In the head layer, a small-scale pedestrian detection layer with a 160 \(\times\) 160 feature map is added to enhance the model’s ability to detect small-scale pedestrians.
# Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks
## 5 Stability Analysis  
The Structural Similarity (SSIM) [35] is rescued to objectively evaluate the difference in quality between the images reconstructed using the MLCNN deep learning methods and the original label images. Although the mean squared error and the peak signal-to-noise ratio have been widely used because of their ease of use and their well-defined physical meaning, these two functions do not match human visual perception. The SSIM in Equation (4) provides a more effective objective criterion by comprehensively evaluating image brightness, contrast, and structure.  
\[\text{SSIM}\ (x,y)=(2\times\mu_{x}\mu_{y}+c_{1})\times(2\times\sigma_{xy}+c_{2})/({ \mu_{x}}^{2}+{\mu_{y}}^{2}+c_{1})/({\sigma_{x}}^{2}+{\sigma_{y}}^{2}+c_{2}) \tag{4}\]  
where \(x\) and \(y\) are the two normalized images to be calculated; \(\mu_{x}\) and \(\mu_{y}\) are the mean values of the two images; \(\alpha_{x}\) and \(\sigma_{y}\) are the standard deviations; \(\alpha_{xy}\) is the covariance of the two images of \(x\) and \(y\); and \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are two constants with \(k_{1}=0.01\), \(k_{2}=0.03\), and \(L=255\) for an 8-bit binary image. The value of the SSIM is between 0 and 1. The closer the SSIM value is to 1, the higher the image similarity. When the two images are identical, the structural similarity is 1. In order to evaluate the quality of the generated images more objectively, the intensity value for all pixels in images are normalized to [0, 1], and the MATLAB software is used to calculate each group of images.  
Figure 10a shows the structural similarity curve between the simulated reproduction of the phase map generated by the MLCNN network and the target light field. Among the 1000 test images, each 100 is a group corresponding to the handwritten numbers \"0-9\". It is obvious that the digital \"1\" has a higher SSIM value due to the simpler structure. Although there are some fluctuations in the quality of the reconstructed image, it can still give a high-quality phase map. The boxplot can better reflect the distribution characteristics of the data. The reconstructed images are grouped by corresponding numbers, and the drawn boxplot is shown in Figure 10b. Among these, the box size reflects the data distribution between 25% and 75% after the data are arranged in ascending order. Between the upper and lower bounds are data within 1.5 times the interquartile range (IQR). Only a small part of the data are outliers, and the overall structural similarity of the images is higher than 0.998. Therefore, the quality of the holograms generated by the MLCNN network is stable.  
Figure 9: Comparison of reproduction results. (**a**) Original image; (**b**) MLCNN reconstruction; (**c**) U-net reconstruction; and (**d**) GS algorithm reconstruction.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 4 A Filter Design Example  
To provide a further understanding of the filter design process, we present a design example of a fourth (\(N=4\)) order spherical microphone array of \(R_{Q}=0.16\) m, designed to record the time domain spherical harmonic coefficients within the spatial region enclosed by the array with a desired frequency band of \([20,1360]\) Hz. Let \(F_{s}=48,000\) Hz and \(c=343\) m/s. Before we apply the proposed method to recording signals, we first analyze the influence of several steps in designing the proposed filter \(\rho_{n}(v,R_{Q})\).
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 5 Conclusions & Future Works  
The current low utilization rate of solar energy and the high cost of PV power generation systems that convert solar energy into electrical energy is still restricting the use of solar energy, thus limiting the development of vehicles with renewable energy, e.g., the endurance of cars powered by PV roofs, etc. In order to sufficiently improve the conversion efficiency of solar energy, it is always vital to maintain the maximum power point (MPP) output of PV cells.  
Therefore, this paper proposes a wavelet-improved fuzzy logic (W-IFL) MPP control model to help ease the battery capacity constraints of electric vehicles, and the simulation results verify its high control performance between the widely used MPPT methods.  
As compared to similar methods, W-IFL can, accordingly, obtain the following improvements. The wavelet predictor saves the trouble of artificially selecting the number of hidden layers and reduces error oscillation by 50.0%. The prediction success rate reaches 85%-90 %, and the training efficiency and prediction accuracy are improved by 85.7% and 67.7%, respectively. Moreover, the disturbance at the MPP in the IFL controller is eliminated. The MPP capture time is reduced to 0.05 s, with an optimization degree of 55.3%, and the controlling ability is improved by 62.3%, which is significantly better than the existing models.  
This work was carried out preliminarily to obtain the present results. However, problems such as practical use or the lack of comprehensive constraints still exist. For example, the PV cell in this paper is simulated based on the mathematical model, which may lead to the different outputs of actual loads. Therefore, as the closure of this study, one recommendation for further research is to combine actual loads and other parts to achieve unified control. Another research direction is collecting sample data with more features, e.g., humidity, etc., to ensure the higher accuracy of the ANN training.  
Conceptualization, M.C.; writing--original draft preparation, S.J.; methodology & validation, M.H., S.J. and M.H. contributed equally. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (Grant number 22 dfx08).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to follow-up studies.  
The authors declare no conflict of interest.