esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 3 The PDL-Involved CVQKD System  
The noise of CVQKD system can be divided into two kinds. One is the scattered noise caused by channel loss, which is incompressible. The other is the excess noise introduced by optical devices, electrical noise of circuits, etc., which can be compressed by improved designs as much as possible. In the PDM-based CVQKD system, several optical characteristics such as chromatic dispersion, polarization mode dispersion, and even nonlinear distortions would cause excess noise, but can be compensated for achieving high-tolerance [30; 31]. However, it is difficult to realize the perfect compensation for PDL due to its nonunitary nature. We will analyze the source of PDL and the effect of PDL on the secret key rate in the PDM-based CVQKD system, and find a way to solve it.
# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 3 Methodology
### Multiview Random Walk
\begin{cases}\frac{1}{q}&v_{j}=v_{t1}\\ \frac{1}{p}&v_{j}\
otin N_{1}^{G}(v_{t1})\text{ and }v_{j}\in N_{1}^{G}(v_{t2}) \,,\\ \frac{1}{r}&v_{j}\in N_{1}^{G}(v_{t1})\text{ and }v_{j}\in N_{1}^{G}(v_{t2}) \end{cases}  \tag{7}\]  
In Equation (7), the hyperparameters \(p\), \(q\), and \(r\) control the weight of the next walk between the different nodes (Figure 4a). When the \(q\) and \(r\) values are large, \(W^{(II)}\) is more inclined to facilitate convergence of the walk process to depth-first traversal on the graph (Figure 4b). When the \(q\) and \(r\) values are small, \(W^{(II)}\) is more inclined to facilitate convergence of the walk process to breadth-first traversal on the graph (Figure 4c). Inspired by this finding, we can model the walk process on multigraph \(G\) via synthesis of the following two parts by setting the parameters \(q\) and \(r\) to appropriate values, i.e., (1) breadth-first traversal on graph \(G^{(P)}\), which extracts topology information of the road network; (2) depth-first traversal on graph \(G^{(M)}\), which extracts human mobility information of the road network. As shown in Figure 4a,b, the resulting sequence of breadth-first traversal can capture more global and local structural features of the road network, while the resulting sequence of depth-first traversal tends to capture path features from origin to destination under road network constraints. Figure 4: Illustration of the adjustment matrix \(W^{(II)}\) in the walk process via hyperparameters \(p\), \(q\), and \(r\): (**a**) The role of these three hyperparameters in determining the next walk destination; (**b**) a walk sequence case in which the \(q\) and \(r\) values are large; (**c**) a walk sequence case in which the \(q\) and \(r\) values are small; (**d**) depth-first traversal can capture path features from origin to destination; (**e**) breadth-first traversal can capture more global and local structural features. Note that since multigraph \(G\) is partitioned into many subgraphs \(\{\hat{G}(1),\hat{G}(2)...,\hat{G}(N_{s})\}\), matrix \(\Pi\) is actually stored and calculated in the form of block matrices \(\{\Pi[\hat{G}(1)],\Pi[\hat{G}(2)]...,\Pi[\hat{G}(N_{s})]\}\), where \(\Pi[\hat{G}(i)]\) denotes the part of matrix \(\Pi\) that contains nodes in subgraph \(\hat{G}(i)\). At each node \(v_{i}\), for \(i\in[1,N]\), in multigraph \(G\), we choose \(v_{i}\) as the start node to generate \(R\) sequences with length \(L\) via the above multiview random walk process, where \(R\) and \(L\) are hyperparameters. The details of the multiview random walk process are outlined in Appendix A. After the multiview random walk process, \(N\times R\times L\) sequences that contain topology and human mobility information are generated.
## 5 Secure Key Agreement and Authentication Protocol for VCC
### Registration Phase  
For message confirmation with VCC and communicating with other vehicles or RSUs, the vehicle must register with the TA. Additionally, RSUs also register through TA to make secure session key with the vehicle. The detailed steps are as following and shown in Figure 5.  
Figure 5: Registration phase of our proposed protocol.  
**Step 1:**: vehicle \(v_{i}\) chooses identity \(ID_{i}\), password \(PW_{i}\) and random number \(b_{i}\). And vehicle computes \(PE_{i}=h(PW_{i}||b_{i})\) and \(BE_{i}=b_{i}\oplus h(ID_{i}||PW_{i})\). \(v_{i}\) sends the message \(\{ID_{i},PW_{i},PE_{i},BE_{i}\}\) to \(TA\).
**Step 2:**: \(TA\) has master key \(x\) and secret key \(y\). After receiving the registration request message from \(v_{i}\), \(TA\) generates random numbers \(a_{i}\) and \(s_{i}\) for the vehicle. Subsequently, \(TA\) calculates \(AE_{i}=h(ID_{i}||PE_{i})\oplus a_{i}\), \(HD_{i}=h(ID_{i}||PW_{i}||a_{i})\), \(HPW_{i}=h(HD_{i}||PW_{i})\), \(MV_{i}=h(HD_{i}||h(x||y))\), \(A_{i}=HPW_{i}\oplus s_{i}\), \(V_{i}=MV_{i}\oplus s_{i}\) and \(VS_{i}=h(HD_{i}||MV_{i}||s_{i})\). Afterwards, \(TA\) saves \(A_{i},VI_{i},AE_{i},BE_{i}\) and \(VS_{i}\) in the \(OBU_{i}\), and then sends \(OBU_{i}\) to the vehicle through a closed channel.
**Step 3:**: road side unit \(RSU_{i}\) chooses \(ID_{RSU_{i}}\) and random nonce \(a_{j}\) and sends these values to \(TA\) via a closed channel.
**Step 4:**: when \(TA\) receives values from \(RSU_{i}\), \(TA\) calculates \(RA_{j}=h(ID_{RSU_{i}}||a_{j})\) and \(RB_{j}=h(RA_{j}||h(x||y))\). Subsequently, \(TA\) sends the message \(\{RA_{j},RB_{j}\}\) to \(RSU_{j}\) via a secure channel.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### Sub-Components  
INS components have hardware and/or software sub-components. Even though the IMO defines minimum performance standards for the components, sub-components are not defined. Accordingly, manufacturers' products were analyzed to identify sub-components; this means that optional sub-components may be available in the market. The essential sub-components of the INS components are shown in the Appendix A and briefly discussed in the sequel.  
The AIS works in connection with one GPS antenna and one VHF antenna [47; 100; 101]. However, combined antennas are available in the market today [100]. Some administrations require a connection for pilots at the bridge front [47]. The class A type AIS has a pilot input/output port [101]. A plug should be connected to such a port on the bridge by which pilots can connect their Personal Pilot Unit (PPU) [101]. Wi-Fi routers specifically developed for the AIS (e.g., Transas Pilot AIS Wifi Interface) are available in the market to create a Wi-Fi network via pilot plug [102]. By using mobile applications (e.g., Wartsila  
Figure 3: ITU regions [98].  
Pilot PRO), broadcasting data from a Wi-Fi router can be gathered and presented to the user [102].  
The GPS should have the capability to process DGPS signals [60]. The GPS and DGPS combined antennas are available in the market as Furuno GPA-021S, or GPA-023S antennas [103]. Other combinations are also possible; for instance, Multi-GNSS antennas (e.g., combination of GPS and GLONASS) are available in the market, such as Furuno GPA-022S and GPA-023S [103]. Nowadays, Multi-GNSS antennas are commonly installed onboard ships.  
The IMO allows alternative reset functions other than reset buttons for the BNWAS [54]. The alarm may be raised by the crew moving in the bridge, and it is possible to find BNWASs with motion detectors in the market [104].  
Some bridge equipment, such as ECDIS and RADAR require operating systems to work. Different operating systems are used, namely Linux [11], Microsoft Windows Embedded Standard 7 [105], Microsoft Windows Compact Embedded [105], Microsoft Windows 7 Professional [106], Microsoft XP Embedded [107].  
The ECDIS requires electronic charts to work. Electronic charts of the ECDIS are divided into two types, namely Raster Navigation Chart (RNC); and ENC. The RNC is facsimiled from paper charts, whilst the ENC is created on computers directly [57]. The ENC is widely used onboard ships, compared to RNC. The charts in use should be the latest edition and approved [57]. Accordingly, electronic charts require updates and can be updated through a USB stick, internet connection, or CD-ROM.
## 3 Long-Term Feature Points Localization  
The safe operation of the PCS is achieved using online pantograph and catenary images. Determining the position of the contact point and pantograph horn is the main difficulty faced by this system. The positioning of the pantograph horn and the contact points which are on the pantograph slide can be used to estimate the position of the rigid structure of the pantograph, so as to evaluate the posture of the pantograph body; the positioning of contact points can more intuitively evaluate the contact position between the pantograph and the catenary, and is also an effective factor to further analyze PCS contact state. For the above two positioning process, high precision, high speed, and high robustness are needed.  
High speed processing is a necessary condition to realize continuous observation in train operation, which can meet the demand of providing real-time PCS state; high-precision positioning ensures the accuracy of 3D reconstruction and the effectiveness of state judgment. As the feature points are distributed in the PCS, long-term stable positioning during train operation will be interfered with by various outdoor illumination conditions and similar targets, such as other lines in catenary, which have higher requirements for the robustness of the localization algorithm in the long-term changeable image.  
In this paper, an adaptive method is used to track the contact points, which can keep the continuous robust positioning by adaptive tracking parameters according to the tracking state. In addition, for pantograph catenary horn and arcs, the deep learning method is selected to achieve a simple and robust target detection framework. Thus, it can realize fast, accurate, long-time robust positioning of PCS features, and provide guarantee for subsequent analysis.
## 4 Experimental Results  
### Setup  
In this subsection, the validation of the proposed JD scheme incorporated with TV-AL3 (i.e., JD-TV) in terms of performance and efficiency is presented. A standard set of grayscale CIF [25] and HD resolution [26] video sequences with sizes of 352 \(\times\) 288 and 1024 \(\times\) 678 are selected on which the  
Figure 4: Visual quality analysis for a complete GoP (J = 8) of News video sequence at a subrate of 0.1 using proposed JD-TV framework.  
proposed scheme is applied for evaluation. The selected video sequences are based on different parameters such as # of frames, motion content type (slow, moderate, fast). The details of the video sequences (# frames, motion type) are presented in Table 2. The video sequence indicated as low, moderate, and high contents are based on spatial details plus camera and object movement.  
The experimental setup comprises of the application of proposed JD-TV having various GoP (J) sizes (3, 5, and 8) to validate its efficiency at various levels. The assessment is performed by observing the peak signal to noise ratio (PSNR) at various sampling rates (subrate). Subrate or sampling rate is the number of recorded samples (pixel) per unit distance when converting from an analog signal to digital and is give as S = M\({}^{\text{B}}\)/N. Where, M is the number of CS measurements, N = B \(\times\) B (B = block size).  
Also, the structural similarity index (SSIM) is also recorded as it is anticipated as more precise and consistent (human visual perception) than PSNR. All PSNR and SSIM values presented are the mean of five independent experiments. The values are averaged because the \(\Phi\) is of random nature, and thus, the image quality may vary. A smaller block size of 16 \(\times\) 16 is implemented compared to larger ones (32 \(\times\) 32, 64 \(\times\) 64), as it is observed that larger block size will produce more samples and will require additional time and energy to transmit. Although, the larger block size provides better reconstruction quality but at the expense of complexity. The selection of block size is a tradeoff between reconstruction quality and computational complexity. In this regard, it is not feasible for a battery-powered device to always encode and transmit the captured images at larger block size. In addition, most of the research works focused on low powered application have adopted block size of 16 \(\times\) 16 or 32 \(\times\) 32 as it provides better image quality with less computational complexity. The encoding of all keyframes are performed at a fixed subrate of 0.5 while the non-key frames within a GoP are encoded at various lower subrates (0.05, 0.1, 0.15, 0.2, 0.25, 0.3).
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### The Overall Flowchart of The Proposed Algorithm
#### 3.1.1 Hierarchical Feature of Convolution Layer  
The hierarchical features of deep neural networks play an important role in visual object tracking, which can enhance the robustness and accuracy of visual tracking. Existing tracking methods based on deep learning [33; 34; 39] find that the deep features of the convolutional layer encode the semantic information of targets, which is invariant to the appearance change. But, when the convolution layer is getting deeper, the resolution of the image is lower and it is more difficult to estimate the location of the target. In contrast, features from shallow convolutional layers capture more fine-grained spatial details and locate the target accurately but not robust to appearance changes. Our aim is to make full use of  
Figure 1: The overall framework of the proposed visual tracking algorithm.  
the semantic information features of deep convolutional layers to solve large appearance changes and to utilize the features of shallow layers to locate the target precisely and prevent tracking drift.  
We make use of the convolution features of a CNN (VGGNet [8]) to encode target appearance. For visual tracking, the result we want is to more accurately locate the target and know where it is located. However, the semantic information has a great effect on the object after the appearance changes and can estimate the approximate position of the tracking object. Therefore, the target can be positioned more accurately. In our work, the feature extractor we use is pre-trained VGGNet-16 [8] and we select the third, fourth and fifth layer of convolutional layers to represent target objects (Figure 1). The characteristic of conv5 layer handles serious appearance changes but does not accurately locate the target due to low resolution. On the contrary, the characteristic of conv4 and conv3 layers can capture more space details and help better locate the object. The peculiarity is used for image segmentation and detailed localization using CNNs [40]. If we can make clever use of hierarchical feature of convolution layer, it will be very helpful for our experiments in the future.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 2 Micromegas Fabrication Techniques
### Standard Micromegas  
The first Micromegas detectors were built with a fine woven or electroformed mesh separated from the anode. The mesh is stretched on a frame, which is then mechanically attached to the anode. The precise gap is obtained by using adequate insulating spacers (pillars) printed on top of the anode plane by lithography of a photo-resistive film. The most significant advantage of this technology is the possibility of replacing the mesh in the case of damage or detector studies. The main drawbacks are the technical complications in maintaining a uniform gap over a large area and the inability to produce curve-shaped detectors, normally used in High Energy Physics (HEP) to cover high solid angles around a particle collision interaction point.  
Figure 2: Microscopic pictures of (**a**) woven inox mesh and (**b**) electroformed “thinmesh”. Both Micromegas are produced in the bulk technique, and a pillar is shown in the center of the picture.  
This technology is still in use even for large scale detectors, as the frames can be built in large sizes and can secure a uniform stretching over the whole surface. One example of larger area coverage is the ATLAS New Small Wheel (ATLAS NSW) detector (see Section 4). The mesh of the detector tiles is stretched up to a surface of 3 m\({}^{2}\)[15; 16].
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 2 Proposed Methodology
### Condition Monitoring
#### 2.1.1 Feature Extraction  
The purpose of feature extraction is to acquire more reasonable feature information that could improve the accuracy of performance assessment compared with the original signal. In this work, the vibration signal is used as the original information; then we select the feature extraction method based on the research [38]. These features are studied and proven to be monotonically related to the whole degradation procedure. The details of the features set is shown in Table 1.  
As shown in Table 1, there are 14 features, six of them are related-similarity (RS) features including one time-domain RS feature and five frequency-domain RS features. The RS feature represents the similarity between the current features and the benchmark data, which are expressed as follows:  
\[\text{RS}_{\text{t}}=\frac{|\sum_{i=1}^{k}(f_{0}^{i}-\bar{f}_{0})(f_{t}^{i}- \bar{f}_{t})|}{\sqrt{\sum_{i=1}^{k}(f_{0}^{i}-\bar{f}_{0})^{2}\sum_{i=1}^{k}(f_{ t}^{i}-\bar{f}_{t})^{2}}} \tag{1}\]  
where \(f_{t}\) is the features of current data, \(f_{0}\) is the feature from the initial time, \(k\) is the number of features, \(\bar{f}_{0}\) and \(\bar{f}_{t}\) are the mean value of their features, respectively.  
For the first time-domain RS feature, there are 11 traditional features, including mean, RMS, kurtosis, skewness, peak-to-peak, variance, entropy, crest factor, wave factor, impulse factor, and margin factor. The frequency-domain features are extracted from the FFT (fast Fourier transform) spectrum so that the features of the whole spectrum and four subset spectrum are produced. F7 to F14 are time-frequency-domain features, which are extracted from the outcome of WP (wavelet package) transform at the three-level decomposition.  
Figure 2: The overview of proposed predictive maintenance architecture.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 5 Conclusions  
In this paper, we proposed a color consistency processing framework for HY-1C images of Antarctica based on a statistical filter and color transfer method. The proposed method eliminated the uneven lighting in the images and reduced the difference in brightness and tone of the different parts. To extract image features and restore consistent images, the novel filter and the artificial lighting information calculation algorithm were proposed. We designed reasonable subjective and quantitative evaluation experiments, and the results verified the effectiveness of the proposed method. The results of proposed method had good color consistency both in the single image and the mosaic image. The results of the Wallis method were also compared, to further show the superiority of the proposed method. The proposed method could also be extended to the Antarctic images of other satellites, which will be beneficial for the image mosaic preprocessing in other regions.  
Conceptualization, Z.L., C.Z., and L.C.; data curation, C.Z.; formal analysis, C.Z. and L.C.; methodology, Z.L. and H.Z.; project administration, L.C.; resources, Z.L.; software, H.Z.; supervision, Z.L. and C.Z.; validation, T.Z. and J.L.; visualization, T.Z. and J.L.; writing, original draft, H.Z.; writing, review and editing, L.C. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (No, 2018 YFB10046 and No. 2017 YFB0504202), the Fundamental Research Funds for the Central Universities under Grant No. 2042018 kf0229, and National Natural Science Foundation of China under Grant No. 41671441 and No. 41776200.  
The authors declare no conflict of interest.
## 2 Structure Description
### Bridge Scheme with CFRP Cable  
When replacing steel stay cables, either the equal-stiffness principle or the equal-strength principle can be adopted. This approach effectively mitigates the geometric nonlinearity induced by the sag effect of cables. To fully utilize the high-strength properties of CFRP materials, this study adopted the equal-strength replacement principle:  
\[A_{C}=[\sigma]_{S}\cdot A_{S}/[\sigma]_{C} \tag{1}\]  
where \(A_{C}\) and \(A_{S}\) represent the cross-sectional areas of the CFRP and steel cables, respectively, and \([\sigma]_{C}\) and \([\sigma]_{S}\) denote the allowable stress values of the CFRP and steel cables. The safety factor for steel cables was set to 2.5, while that for CFRP cables was 3 [19; 20].  
After replacement, the cross-sectional area of the CFRP cables was approximately 1.32 times that of the steel cables. Figure 3 compares the cross-sectional area and diameter of stay cables between the two schemes. The ratios of cross-sectional equivalent elastic [21] modulus to original values under sag effects for both cable types are shown in Figure 4. As illustrated in Figure 4, the equivalent elastic modulus reduction of steel cables was significantly influenced by the cable length, horizontal inclination, and initial cable force. The minimum ratio occurred at the mid-span J1 cable, reaching 0.58, indicating a reduction of approximately 42% in elastic modulus under dead load. In contrast, the CFRP cables exhibited excellent performance, with their elastic modulus nearly unaffected by sag effects. The ratio of equivalent elastic modulus to the original value for the entire bridge remained close to 1.0, and, even at the longest cable J1, the ratio was 0.982, corresponding to a mere 2% reduction.