esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recently, there has been increased interest in the relationship between cerebral small vessel disease (CSVD) and circadian rhythm disruption, particularly sleep disturbance. However, the neural mechanism of sleep disturbance in CSVD patients remains poorly understood. The purpose of this study is to explore the gray matter alterations in CSVD patients with and without sleep disturbance. 59 patients with CSVD and 40 healthy controls (HC) were recruited for the present study. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. CSVD patients were categorized into either the good sleepers group (CSVD-GS, \(n=23\)) or the poor sleepers group (CSVD-PS, \(n=36\)) based on PSQI score. Voxel-based morphometry (VBM) analysis was used to assess differences in gray matter volume (GMV) between groups. Multivariate regression analyses were performed to investigate the relationships between sleep quality, GMV, and white matter hyperintensities (WMH). We observed GMV differences between the three groups in the bilateral caudate, right thalamus, bilateral calcarine cortex, left precentral gyrus, right orbitofrontal cortex, left cingulate gyrus, and right sub-syral temporal lobe. Additionally, the CSVD-PS group exhibited decreased GMV in the bilateral calcarine cortex yet increased GMV in the right caudate compared to the CSVD-GS group. In fully adjusted models, GMV of the right caudate and bilateral calcarine cortex was associated with sleep quality in CSVD patients. The present study revealed structural brain alterations in CSVD patients with sleep disturbance. These findings may provide novel insights into the neural mechanisms of sleep disturbance in CSVD.  
c pacs: 20
## 7 Experimental Results  
We now show the full results of our experimental testing. In Figure 13, we show a 100 \(\upmu\)s zoom-in of the corrected signal shown in Figure 12, as well as the simulated baseband behavior. The signal has a temporal periodicity of 98.9 \(\upmu\)s that matches closely with the value expected from Equation (8) of \(T_{p}=100\)\(\upmu\)s.  
For continuity, we restate Equations (4) and (5) in their baseband form below:  
\[\begin{split} F_{bb}(t;R,\theta)&=\bigg{[}\sum_{n= 0}^{N-1}e^{i2\pi f_{n}(t-R_{n}/c)}\bigg{]}\cdot e^{-j2\pi f_{0}t}\\ &=\sum_{n=0}^{N-1}e^{j2\pi\Delta f(t-R_{n}/c)}\cdot e^{-j2\pi f_{ 0}R_{n}/c}\end{split} \tag{22}\]  
and  
\[\begin{split} F_{bb,B}(t;R,\theta)&=B(t)\bigg{[} \sum_{n=0}^{N-1}e^{i2\pi f_{n}(t-R_{n}/c)}\bigg{]}\cdot e^{-j2\pi f_{0}t}\\ &=B(t)\sum_{n=0}^{N-1}e^{i2\pi n\Delta f(t-R_{n}/c)}\cdot e^{-j2 \pi f_{0}R_{n}/c}.\end{split} \tag{23}\]  
Taking the magnitude of Equations (22) and (23) yields  
\[\begin{split}|F_{bb}(t;R,\theta)|&=\bigg{|}\sum_{n= 0}^{N-1}e^{i2\pi n\Delta f(t-R_{n}/c)}\cdot e^{-j2\pi f_{0}R_{n}/c}\bigg{|},\\ |F_{bb,B}(t;R,\theta)|&=\bigg{|}B(t)\sum_{n=0}^{N-1 }e^{i2\pi n\Delta f(t-R_{n}/c)}\cdot e^{-j2\pi f_{0}R_{n}/c}\bigg{|}.\end{split} \tag{24}\]  
Using the identity \(|z_{1}z_{2}|=|z_{1}||z_{2}|\) (where \(z_{1}\) and \(z_{2}\) are complex numbers) and the fact that \(|B(t)|=1\)\(\forall\)\(t\), we see that \(|F_{bb}(t;R,\theta)|\) and \(|F_{bb,B}(t;R,\theta)|\) in Equation (24) should have the same fast-time behavior:  
\[\begin{split}|F_{bb}(t;R,\theta)|&=\bigg{|}\sum_{n= 0}^{N-1}e^{i2\pi n\Delta f(t-R_{n}/c)}\cdot e^{-j2\pi f_{0}R_{n}/c}\bigg{|}\\ |F_{bb,B}(t;R,\theta)|&=\bigg{|}\sum_{n=0}^{N-1}e^{ i2\pi n\Delta f(t-R_{n}/c)}\cdot e^{-j2\pi f_{0}R_{n}/c}\bigg{|}.\end{split} \tag{25}\]  
Figure 12: Captured baseband after receiver synchronization (**left**) and constellation behavior of the modulation region \(b_{1}\leq n\leq b_{2}\) (**right**).  
We show this in Figure 14, where the magnitude of an \(N=2\) FDA with \(f_{0}=720\) MHz and \(\Delta f=10\) kHz is overlaid with the magnitude of the BPSK FDA. Also shown in Figure 14 is the correlation of 250 bits from the received bitstream with a length 11 Barker code. The correlation showcases periodic packets with bookended Barker codes, matching the expected behavior of the transmitted packet structure described in Table 1.
## 4 Problem Definition  
Consider the network model presented in Section 3. The goal is to ensure that all GNs are covered and that the data rate requirements are met as much as possible when UAVs are used as relay nodes. We assume that there is a cost associated with each used UAV. Thus, minimizing the number of UAVs is desirable. On the other hand, GNs may have different data rate requirements. The satisfaction  
Figure 1: System model overview.  
of data rate as GN requirements is closely dependent on the channel conditions (e.g., SNR), which also depends on the communication distance, which results from the number and placement of the serving UAV in the network. We intend to deploy as few connected UAVs as possible in suitable locations to enable communication between GNs, while satisfying multiple independent data rate requirements. In some instances, the QoS demands are competitive, i.e., one cannot satisfy them simultaneously. This gives rise to the need for finding solutions that try to balance them. This problem can be modeled meta-heuristically as a multi-objective optimization problem to find the trade-off among non-dominated solutions. In the rest of this section, we define the Multi-Objective Optimization Problem (MOP) and present the formulation of our UAV placement optimization problem as an MOP.
# 3D Shape Generation via Variational Autoencoder with
## 3 Proposed 3D-VAE-SDFRaGAN
### 3D-VAE-SDFRaGAN Framework
#### 3.3.2 SDF-Generator and SDF-Discriminator Networks  
The SDF-generator network (\(G_{sdf}\)) is composed of five transpose convolution layers with channel numbers \(\{512,256,128,64,1\}\) and kernel sizes of \(\{4,4,4,4,4\}\), with strides of \(\{1,2,2,2,2\}\). Between the transpose convolution layers, ReLU and batch normalization layers are utilized, except for the last layer, which applies a Tanh function to map the output into \([-1,1]\). The SDF of a \(64^{3}\) matrix is the output of the generator network(\(SDF_{gen}\)), with values in the range of \([-1,1]\). The triangular mesh surfaces are generated from the SDF matrix using the MCA [45].  
The SDF-discriminator network (\(D_{sdf}\)) is similar to the SDF-generator network and uses leaky ReLU as an activation function. The sigmoid function is applied at the last layer to compress the output to \([0,1]\). It consists of five 3D-convolution layers with channel numbers \(\{64,128,256,512,1\}\) and kernel sizes of \(\{4,4,4,4,4\}\), with strides of \(\{2,2,2,2,1\}\). The loss function for SDFRaGAN is  
\[\begin{split} L_{SDFRaGAN}=&-\mathbb{E}_{sdf_{real }\sim p}[\log(\tilde{D}(sdf_{real}))]-\mathbb{E}_{z}\sim_{Q}[\log(1-\tilde{D}( z))]\\ &-\mathbb{E}_{z_{img}}\sim_{Q}[\log(1-\tilde{D}(z_{img}))]\end{split} \tag{7}\]  
where  
\[\begin{split}\tilde{D}(sdf_{real})=\text{sigmoid}(C(sdf_{real}) -\mathbb{E}_{z\sim Q}C(z,z_{img}))\\ \tilde{D}(z)=\text{sigmoid}\left(C(z)-\mathbb{E}_{sdf_{real} \sim p}C(sdf_{real})\right)\\ \tilde{D}(z_{img})=\text{sigmoid}\left(C(z_{img})-\mathbb{E}_{ sdf_{real}\sim p}C(sdf_{real})\right)\end{split}\]  
The total loss \(L_{total}\) function used in the proposed 3D-VAE-SDFRaGAN framework consists of the sum of three components: reconstruction loss (\(L_{R}\)), a cross-entropy (\(L_{SDFRaGAN}\)), and KL divergence (\(L_{KL}\)) to impose a limit on the distribution of the output of the 2D encoder network:  
\[L_{total}=L_{SDFRaGAN}+\gamma_{1}L_{KL}+\gamma_{2}L_{R} \tag{8}\]  
where \(\gamma_{1}\) and \(\gamma_{2}\) are weights of \(L_{KL}\) and \(L_{R}\), respectively.
## 7 Conclusions and Future Work  
The automatic collection of key milestone nodes in the process of aircraft turnaround plays an important role in terms of upgrading the intelligence of airport ground operations. The main purpose of this study was to exploit a framework of the automatic collection of four KMNs in the process of aircraft turnaround based on apron surveillance videos. To achieve this goals, two datasets of imagery captured at Beijing Capital International Airport were firstly established to train, test, and assess the proposed framework and related algorithms. Secondly, to extract spatiotemporal information of the executors of KMNs from the complex background of the airport ground, a preprocessing module seamlessly integrating state-of-the-art detection and tracking algorithms was proposed. Specifically, a detector which uses the CIOU-loss function to improve the positioning accuracy of the object in the YOLOV5 was designed. Moreover, the sub-modules of prediction and association were combined with the detector to improve both the accuracy and real-time performance of the spatiotemporal feature extraction. Thirdly, for the nodes based on the action of a single target (represented by _in-block_ and _off-block_) and the interaction of two targets (represented by _docking_ and _undocking stairs_), two approaches of automatic collection of KMNs were proposed. Experimental results on the two remote stands (No.734 and No.939) of Beijing Capital International Airport showed that the timing error of the proposed framework was well under 60 s, meeting the requirements of the A-CDM system.  
Although we initially assessed the proposed framework and found it to be promising, there is still a lot of room for further development. First, instance segmentation-based methods will be developed to obtain more accurate target bounds. Second, as the datasets is further expanded, deep learning-based action localization methods will be investigated to further improve the robustness and accuracy of the automatic collection of KMNs against complex background on the airport grounds. Moreover, the autonomous selection of the thresholds of Algorithms 2 and 3 is also one of the important tasks in the future work.  
Conceptualization, J.X. and M.D.; methodology, M.D.; software, Z.-Z.Z.; validation, J.X. and F.Z.; formal analysis, Y.-B.X.; writing--original draft preparation, J.X.; writing--review and editing, M.D.; visualization, M.D. and Z.-Z.Z.; supervision, M.D. and X.-H.W.; project administration, M.D. All authors have read and agreed to the published version of the manuscript.  
This work is co-supported by the National Natural Science Foundation of China (No. U2033201). It is also supported by the Opening Project of Civil Aviation Satellite Application Engineering Technology Research Center (RCCASA-2022003) and Innovation Fund of COMAC (GCZX-2022-03).
## 4 Experiments
### Data Augmentation  
The dataset's quality is crucial for achieving satisfactory model performance. However, the quantity of the real dataset Raw used in this paper was insufficient to meet the experimental requirements. Therefore, we performed two rounds of data augmentation on the experimental data. We employed the STR Augmentation method proposed by Google, which covers eight categories of augmentation methods: distortion, geometry, pattern, blur, camera, processing, noise, and weather. Each category includes multiple specific augmentation functions, totaling 38 functions used for augmentation.  
Our data augmentation strategy is grounded in our prior research [55]. The approach utilized in earlier research involved employing Google's default set of 38 augmentation methods in the initial round of data augmentation. Following that, in the second round, a refined selection was made, consisting of 12 methods, namely Curve, Distort, Stretch, Rotate, Perspective, Shrink, TranslateX, TranslateY, Contrast, Brightness, JpegCompression, and Pixelate. We conducted the initial round of data augmentation on the original dataset, consisting of 7267 scene images. In contrast to previous research methods, we omitted the Grid, VGrid, and HGrid methods, as we observed that these three methods produced entirely black images after augmentation. We opted for the remaining 35 augmentation functions from STRAug, including Curve, Rotate, Perspective, Distort, Stretch, Shrink, TranslateX, TranslateY, Contrast, Brightness, JpegCompression, Pixelate, RectGrid, EllipseGrid, GaussianNoise, ShotNoise, ImpulseNoise, SpeckleNoise, GaussianBlur, DefocusBlur, MotionBlur, GlassBlur, ZoomBlur, Fog, Snow, Frost, Rain, Shadow, Posterize, Solarize, Invert, Equalize, AutoContrast, Sharpness, and Color. This led to the creation of the Aug1 dataset, which encompasses 254,345 scene images. Examples of augmented images are depicted in Figure 4.  
Figure 4: \(Cont\).  
Figure 3: Samples of training and testing datasets.  
Subsequently, we applied the second round of augmentation to the Aug1 dataset, selecting 12 augmentation functions: Curve, Distort, Stretch, Rotate, Perspective, Shrink, TranslateX, TranslateY, Contrast, Brightness, JpegCompression, and Pixelate. This led to the creation of the Aug2 dataset, comprising 3,052,140 scene images.
# A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
## 5 Results and Discussion
### Classification Accuracy  
Although standard object detection datasets (e.g., Microsoft COCO [22]) exhibit volume and a variety of examples, they are not suitable for plate detection as they annotate a set of object categories not include number plates because of privacy reasons. Therefore, we collected more than 160 images for testing our ANPR model from different sources such as Kaggle [23] and Google Web Scraper [24]. Many images in our dataset are in their natural environments because plate detection is highly dependent on contextual information.  
We observed that our model delivers good results even when the car images are captures from different distances from the camera, orientations and illumination conditions. Figure 7 shows some samples of the successful recognition of all plate characters. However, sometimes the model fails to recognize some characters correctly as the model confuses alphanumeric characters with similar structures such as D with O, 6 with 8, K with I, etc. Figure 8 illustrates an example of a character mismatch error.  
Figure 9 depicts the average classification rates of our model with respect to different dataset sizes and accuracy granularities. We found that our model achieves classification accuracies of 94.6%, 96.9%, and 99.4% for recognizing the plate characters with 0, 1, and 2 character(s) mismatch error, respectively. We also calculated the overall classification accuracy of the two ANPR modules: plate detection, character segmentation and recognition. We found that our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide.  
The overall classification accuracy is calculated based on the following formula:  
\[Accuracy=(DSR)\% \tag{5}\]  
where \(D\) is the plate detection rate, \(S\) is the character segmentation rate, and \(R\) is the character recognition rate. \(D\) is calculated as the number of detected plates divided by the total number of plates in our testing dataset. \(S\) is calculated as the ratio of the number of correctly segmented characters to the total number of characters in our dataset. \(R\) is the proportion of correctly classified characters over our dataset.
# Folding Every Point on a Polygon Boundary to a Point
## 2 Preliminaries  
In this section, we provide a formal definition of the problem. We are given a convex polygon \(P\) as an ordered list of vertices \(V(P)=[v_{1},v_{2},\ldots,v_{n}]\) in counterclockwise order. We shall treat the list as a circular list. Given two points \(a\) and \(b\), we refer to a line segment whose ends are \(a\) and \(b\) as a segment \(\overline{ab}\). Equivalently, the polygon \(P\) is also represented as  
Figure 1: The problem of folding every point on the boundary of a polygon \(P\) to a point \(f\). (**a**) Sample creases from points on edge \(\overline{v_{2}v_{3}}\); (**b**) The safe region \(R\).  
Figure 2: Comparison of the settings of the problem considered in this paper with that of [4]. (**a**) Corner folding in [4]; (**b**) Boundary folding in our paper.  
a list of edges \(E(P)=[e_{1},e_{2},\ldots,e_{n}]\), where \(e_{i}=\overline{v_{i}v_{i+1}}\) is a line segment joining \(v_{i}\) and \(v_{i+1}\). We denote its boundary as \(\delta P\), i.e., \(\delta P=\bigcup E(P)\).  
We are also given a point \(f\) strictly inside \(P\). Consider a point \(u\in\delta P\) on edge \(e_{i}=\overline{v_{i}v_{i+1}}\). Folding \(u\) onto \(f\) results in a straight line \(L\), referred to as a _crease_ line, which passes through the middle point on the line segment \(\overline{uf}\) and is also orthogonal to \(\overline{uf}\). We are interested in the set of crease lines when folding every point on \(e_{i}\) onto \(f\). The envelope of this family of crease lines corresponds to a parabola, whose focus is \(f\) and directrix is a line resulting from extending the segment \(e_{i}\) to a line. We refer to this parabola as \(p_{i}\). See Figure 3.  
To see this, let line \(L^{\prime}\) be erected perpendicularly to the directrix at point \(u\), and let \(u^{\prime}=L\cap L^{\prime}\). We have \(|\overline{u^{\prime}f}|=|\overline{u^{\prime}u}|\), which indeed obeys the definition of parabolas. In other words, \(u^{\prime}\) is a point on a parabola's curve. Furthermore, for every point \(v\in L^{\prime}\) such that \(|\overline{vf}|<|\overline{vu}|\), the point \(v\) is \"safe\" from other creases produced by fold-and-unfold of every other point from this directrix, which is a line extension of the segment \(e_{i}\).  
Since \(p_{i}\) divides the plane into two parabolic half-planes, we are interested in the region containing \(f\). We define a half-space \(H(p_{i})\) to be the half-plane containing \(f\). More formally, \(H(p_{i})\) contains all points \(v\) such that \(|\overline{vf}|\leq|\overline{vu}|\), where \(u\) is an orthogonal projection of \(v\) on the line extension of \(e_{i}\).  
Our goal is to find:  
\[R=\bigcap_{i=1}^{n}H(p_{i}),\]  
defined to be the _safe region_. When focusing on point \(f\), we sometimes refer to the safe region with respect to point \(f\) as \(R_{f}\).  
We describe the algorithm in Section 3. Later in this section, we state relevant geometry facts.
# Effect of PAS-LuxR Family Regulators on the Secondary Metabolism of _Streptomyces_
## 5 Conclusions  
PAS-LuxR family regulators are a class of regulators that have recently been discovered, and most of them exist in the biosynthetic pathway of PKS type I products. They regulate the transcription of the target gene by binding to the characteristic 5'-CTVGGGGAWWTCCCBAG-3' sequence on the promoters of target genes. Except for PteF, which indirectly affects the morphological differentiation, all other regulators are pathway-specific. When playing regulatory roles, PAS-LuxR family regulators need to cooperate with other family regulators, mainly including the SARP-LAL-PAS-LuxR vertical regulatory system and LAL-PAS-LuxR network regulatory system. In the regulatory system, the PAS-LuxR regulators locate in the core position and have the potential role of corresponding extracellular environment changes, which increase the complexity and accuracy of the regulatory system to ensure the normal metabolism of _Streptomyces_. The PAS-LuxR family regulator has a similar structure and function that can realize cross-strain inter-replacement. Although the regulatory mechanism of PAS-LuxR family regulator has been widely studied, PAS-LuxR family regulators TmRIV and NysRIV play independent regulatory functions in _S. aloyroscopic_, which is rare, and the precise regulatory mechanism is not clear. Therefore, the regulators of this family need to be further studied. The precise regulation of TmRIV and NysRIV is the stability factor that maintains _S. aloyroscopic_ metabolism. Further studies on the independent regulation of PAS-LuxR family regulators in _Streptomyces_ would contribute to a deeper understanding of polyene macrolides biosynthesis of regulation, and more comprehensive understanding of the precise regulation of gene expression in _Streptomyces_. This study also provides a theoretical foundation for the use of precise regulation to synthesis of new antibiotics by synthetic biology means in the efficiently and directly. Lastly, it may play a positive role in promoting the research and development of new antibiotics.  
Conceptualization, H.C. and H.Z.; methodology, N.Z.; software, Y.D.; validation, N.Z., Y.D. and H.C.; formal analysis, H.Z.; investigation, Y.D.; resources, H.C.; data curation, Y.D.; writing--original draft preparation, N.Z.; writing--review and editing, H.C.; visualization, N.Z.; supervision, H.Z.; project administration, Y.D.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Department of Science and Technology of Jilin province, grant number YDZJ202201 ZYTS440, the National Natural Science Foundation of China, grant number 81903528, the Program of Jilin Provincial Development and Reform Commission, grant number 2022C043, the Jilin Provincial Department of Education, grant number JJKH2021238 KJ, and the Major Scientific Research Foundation of Jilin Institute of Chemical Technology, grant numbers 2020017.  
Not applicable.  
The authors declare no conflict of interest.
# All-Fiber Hyperparametric Generation Based on a Monolithic Fiber Fabry-Perot Microresonator
## 2 Experiment Setup and FFP Microresonator  
The experimental setup is shown in Figure 1a. The light from a C-band cw tunable pump laser (CTL1550, Toptica, Graefelfing, Germany) goes through an in-line fiber polarization controller and then is amplified in an EDFA. A fiber isolator is applied subsequently to prevent feedback light. Then,  
Figure 1: (**a**) Experimental setup. CW: continuous-wave; EDFA: erbium-doped fiber amplifier; SMF: single-mode fiber. The pump source, polarization controller, isolator, and Kerr microresonator are all fiber-based, resulting in low-loss fiber-to-fiber connection. (**b**) A picture of the 148-mm-long fiber Fabry–Perot (FFP) microresonator. Fiber ends are mounted in ceramic ferrules, finely polished, and coated with dielectric mirror to form a high-Q microresonator.  
a short section of HNLF with a ceramic ferrule is fused with the output port of the isolator as an input coupler for the FFP microresonator. The splicing loss can be controlled to be less than 2 dB. The length of HNLF was carefully tailored to be few centimeters to avoid nonlinear effects. Due to the identical HNLF used for the input coupler and FFP microresonator, the insertion loss is as low as 0.5 dB (or 10%), ensuring high pump energy utilization. Finally, the output light of the FFP microresonator is coupled out by spliced HNLF-SMF (single-mode fiber) for the following measurement and analysis.  
Our FFP microresonator was fabricated with a 148-mm-long HNLF (Zero-Slope, OFS, Norcross, GA, USA) (Figure 1b) with near-zero dispersion at 1550 nm and an effective mode area of 12.4 \(\upmu\)m\({}^{2}\). Both ends of the HNLF were mounted in ceramic ferrules and finely polished. The Fabry-Perot cavity was formed by the dielectric coating on both fiber ends with reflectivity of 99.5% at 1550 nm. The quality factor of the FFP microresonator was characterized by scanning the frequency of the tunable laser over the resonances of the FFP microresonator. As shown in Figure 2a, the free spectral range (FSR) of our FFP microresonator was measured to be 667 MHz, which matches well with the 148 mm cavity length. The linewidth of the resonance was fitted to be 2.4 MHz, as shown in Figure 2b. For a high-Q microresonator, the quality factor can be calculated as \(Q=\frac{\
u_{0}}{\Delta\
u}\), where \(\
u_{0}\) is the center frequency of the resonant peak and \(\Delta\
u\) is the linewidth of the resonance. Thus, our FFP microresonator has a Q value of 8.1\(\times\)10\({}^{7}\) at 1550 nm. Similar designs of such FFP microresonators have been applied for Brillouin-enhanced Kerr comb generation [5] and pulse-driven temporal soliton generation [19]. However, neither of these designs could achieve a cw-pumped broadband spectrum as we demonstrate here, which is essential from a practical applications point of view.