esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduction of Satellite Signature Effect in High-Accuracy Satellite Laser Ranging to Etalon
## 2 Satellite Signature Effect
For the purpose of testing the hypotheses described above, the SLR system in the Changchun observation was used to obtain observation data of Etalon. According to Appendix A, the working process of the SLR system at the Changchun station is as follows: Firstly, the azimuth, altitude, and distance of the target satellite in the station coordinate system are calculated using Etalon's prediction ephemeris. The telescope is guided by the control system to track the target in real time when the elevation angle of Etalon is greater than 20\({}^{\circ}\) relative to the Changchun station. Following the stabilization of the tracking, a laser pulse is emitted to the target satellite. Meanwhile, the laser pulse signal is transmitted by PIN to the constant ratio discriminator in order to record the transmission time. Upon receiving the echo signal from Etalon, a photodiode in the receiving system converts it into an electrical signal. By comparing the difference between the two moments of the event timer, we can determine the distance from the target satellite to the station. Figure 4 indicates the statistical results of the residual distributions for Etalon in January 2021, while the orange curve represents the simulated echo signal based on the retroreflector uneven distribution model. In the case of raw observation data, the full rate residual histogram contains all the reflected signals from each incident direction during a typical period of segment tracking of 20 min. To obtain the average reflected pulse shape, we simulate reflected signals from 2594 incident directions equally distributed around Etalon and sum the displacements of individual pulses. The results presented in Figure 4 are consistent with the observed distribution of residuals, including the long tail. Pearson coefficient between simulated and experimental echo waveforms is 0.92, indicating that the spreading and deformation of the residual segments are caused by the accumulation of satellite signature effects. Figure 3: Distribution of the returned signals from Etalon at different incident angles.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 1 Introduction
This achieves a balance when the synthesized images G(z) are similar to real images from the data distribution, and the discriminator D predicts 0.5 between G(z) and the real images for most inputs. Both the networks of G and D have learned the capacity sufficiently, which is called the Nash equilibrium [10]. Unfortunately, some of the GAN structures are unstable during the training performance, which causes that lead generator to produce some artifacts and nonsensical outputs. Considering that the CNN architecture has a remarkable performance in terms of feature extraction, we take advantage of CNNs to construct our generator, and then utilize a discriminator to test whether the generated images satisfy the SR performance. Quantitative and qualitative evaluations have been demonstrated so that our algorithm not only takes full advantage of the Convolutional Neural Networks (CNNs) to improve the image quality, but it also performs better than previous GAN algorithms for super-resolution tasks. Super-resolution reconstruction is very important for acquiring image information and reconnaissance detection. Existing works have some problems, such as a weak generalization ability and a large space for the mapping function solution. Our framework improves the resolution detail of the image texture, which provides original materials for subsequent image processing. In this paper, we propose Enhanced Generative Adversarial Networks (EGAN) in order to efficiently correct these issues. The main contributions of our network are as follows:  
* Convolutional skip-connections: Some CNN algorithms show significant advantages for SR tasks. Different from cascade networks in typical methods, we design a convolutional skip-connections network based on an end-to-end manner. Note that feature maps from the intermediate layers may contain some latent information. It is crucial for the generative model to generate a plausible-looking image that relies on the convolutional skip-connections. Therefore, our generator can project some high-frequency details onto the synthesized images to fool the discriminator, D.
* Perceptual loss function: Normally, several GAN methods are limited by instability learning during training. We propose a perceptual loss function to penalize the samples in our adversarial network. Considering that the edge details usually play a significant role in image generation, the generator produces synthesized images that are closer to the real images via our loss function. Moreover, our loss function corrects the errors between the real images and the generated images, which improves the accuracy of the discriminator.
# Online Streaming Feature Selection via Conditional Independence
## 2 Related Work
For the above algorithms to discover _Markov blankets_, all features must be made available from the beginning [20]. Meanwhile, the algorithms above cannot accurately find _Markov blankets_, leading to the possibility that error nodes may be mined [22]. More crucially, in the context of streaming features, the features are generated dynamically and arrive one at a time. The feature selection must be done immediately in the course of generating the features. Thus, these algorithms are not suitable for mining the _Markov blankets_ of streaming features. Existing methods greatly relieve the burden of processing highly dimensional datasets. However, in consideration of the above limitations, we propose an efficient framework for online feature selection in a streaming feature space with low redundancy and high relevance. Based on this framework, we develop a novel algorithm called _ConInd_.
# Dissipativity Analysis of Large-Scale Networked Systems
## 4 Numerical Simulations  
Several numerical simulations are employed in this section to demonstrate the efficacy of the strict dissipativity conditions presented in this paper. The simulation experiments are performed on a laptop computer with an Intel(R) Core(TM) i5-3230M CPU @ 2.60 GHz 2.60 GHz and 6 G RAM. In these simulations, we assume that \(m_{ui}=m_{xi}=m_{yi}=m_{zi}=m_{yi}=2\). Furthermore, all the parameters of the subsystem are independent of each other, and the parameters of each subsystem are randomly generated according to a continuous uniform distribution with an interval of [\(-0.9\), \(0.9\)]. The subsystem connection matrix is randomly generated, but there is only one non-zero element 1 in each row and column.  
The conditions in Lemma 4, Theorem 1, and Theorem 3 are used to verify the strict dissipativity of the system. Among them, the conditions in Lemma 4 and Theorem 3 are calculated by the LMI toolbox provided by MATLAB, and the condition in Theorem 1 is calculated by the sparse solvers DSDP. For System \(\Gamma\) introduced in this paper, we generate 10 systems for calculation, and the average value and standard deviation of system dissipativity analysis calculation time are obtained. Tables 1 and 2 give some results when the number of subsystems is among 2 and 45.  
The tables show that the calculation time of the above three methods all increases with the increase of the number of subsystems. When the number of subsystems is 10 or less, the computational efficiency based on Lemma 4 is comparable to that of Theorem 1 and Theorem 3. This is because the dimensionality of the matrix inequality in Theorem 1 is higher than that in Lemma 4, and Theorem 3 requires several inequalities to be verified. With the expansion of the number of subsystems, when the number of subsystems is 20, 30, 40, 45, the ratio of calculation time based on the conditions in Lemma 4 and Theorem 1 is 1.0957, 1.3414, 1.3911, 1.4581. The ratio of the average computation time becomes larger and larger, which means that the computational efficiency of Theorem 1 is improved to some extent. Because the condition in Lemma 4 requires operations such as inversion of high-dimensional matrices. Clearly, Theorem 3 is more computationally efficient than both Lemma 4 and Theorem 1. This is due to the fact that the conditions of Theorem 3 are tested based on individual subsystem parameters, and their computational complexity only increases linearly with the number of subsystems N. In addition, due to the limitation of computer memory, the conditions in Lemma 4 and Theorem 1 may not be calculated, but Theorem 3, which is tested independently for each subsystem, can still be calculated. Therefore, Theorem 3 has more computational advantages in the dissipativity analysis of large-scale networked systems. It should be noted that Theorem 3 is conservative compared to Lemma 4 and Theorem 1.
# Recording a Hologram Transmitted over a Communication Channel on One Sideband
## 2 Experimental Recording of a Rainbow Hologram at the Receiving End of the Communication Channel
For the experimental recording of an SSB hologram in the rainbow hologram technology, we chose a KineMax MASTERING SYSTEM (Polish Holographic Systems, Warsaw, Poland) (Figure 6), in which a complicated set of optical elements was maximally replaced by the computer synthesis of the corresponding diffraction gratings. Figure 5: (a) A scheme for recording of the focused image holograms from different directions, with slit-filter filtering; (b) compilation of 15 angles of the 3D object image. Figure 6: Optical scheme of KineMax: **1** is laser module, 405 nm, 50 mW, with a power supply unit and an electronic shutter, mounted on the optical plate; **2** is a quarter-wave plate; **3** is an objective; **4** is a pinhole aperture; **5** is a regulating mirror; **6** is an immobile mirror; **7** is an objective; **8** is a liquid crystal spatial light modulator (LC-SLM) 1024 \(\times\) 768 pixels, 36.864 mm \(\times\) 27.648 mm, the horizontal and vertical pixel period 36 \(\upmu\)m; **9** is a lens; **10** is an immobile mirror; **11** is a regulating mirror; **12** is a beam splitting polarization cube; **13** is a quarter-wave plate; and **14** is an objective with a low spatial frequency filter. Under objective 14, there is an area-mobile objective table onto which the photoresist is placed. The image of several gratings formed by the LC-SLM (Figure 7) is projected through objective 9 onto the corresponding region of the photoresist. The hologram, as a whole, is recorded by moving the table and projecting a specific calculated set of spatially oriented gratings onto every region; the hologram is composed of a set of holopixels, each containing a region for each of the \(j\) images to be recorded (Figure 8). One can see that the structures obtained through the calculation and composition of the vertical segments with the bands oriented transversely at different angles (Figure 8) are very similar to those obtained in the classical [PERSON]'s scheme (Figure 4). Figure 8: A photograph of a region of the photoresist with the structure of interference fringes formed using the views of the holographic object, as compiled in Figure 5b. Figure 7: The structure of one of the cells of a hologram (holopixel), calculated according to Figure 5b.
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
## 2 Related Work  
### Shape from Focus  
In SFF methods, images with different focus levels (such that some parts are well focused and the rest of the parts are defocused with some blur) are obtained by translating the object plane at a predetermined step size along the optical axis [11]. By applying a focus measure, the best focused frame for each object point is acquired to find the depth of the object with an unknown surface. The distance of the corresponding object point is computed by using the camera parameters for the frame, and utilizing the lens formula as follows:  
\[\frac{1}{f}=\frac{1}{u}+\frac{1}{v} \tag{1}\]  
where, \(f\) is the focal length, \(u\) and \(v\) are the distances of object and image from the lens, respectively. Figure 2 shows the image formation in the optical lens. The object point at the distance \(u\) is focused to the image point at the distance \(v\).
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### UAV Real-Time Object Detection Evaluation
#### 4.7.3 Latency  
Based on the review results, there are not many studies discussing latency in real-time object detection. In the cloud-computing paradigm, latency mainly refers to the transmission time from the end device to the cloud center. In our database, the edge computing paradigms accounted for 95.8%. In these studies, based on edge computing, they mainly adopted onboard processing, so the latency between edge end and edge server has not been studied much.  
However, one study [88] indicated that offloading the computing to the edge server can reduce the computing delay. The author stated that the total delay consists of the delay in UAV data transmission to the edge server, the UAV computing delay, and the edge server computing delay. The study [80] also considered different communication methods. They compared the transfer time over Wi-Fi and 4G.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Power Control
#### 7.4.1 One Large Network or Multiple Small Networks  
The resource management challenges such as power control are associated directly with the user volume in the network. Power and frequency resources can be managed equally in small networks. However, it has become very hard for a single network to serve all users as the number of users keep growing. To be exact, the performance of networks usually degrades due to the increasing number of antennas, high complexity of decoding, CSI feedback large overhead and precoding metrics. Some of the crucial issues that need to be highlighted are the total number of users that should be allowed in the network, the methods used to allocate the resources in a subnetwork and the ways to come up with a decision, whether it is centralized or distributed.
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 4 Result and Discussion
### Comparison of Prediction Results of Different Models
In contrast, the predictive performance of the PCA-PSO-SVR and PCA-PSO-KNN models was significantly inferior to that of the PCA-PSO-LGBM model, with the \(MAE\) for both roll angle and pitch angle being more than twice as high. In summary, among the predictive tasks for shield tunnelling roll angle and pitch angle, the PCA-PSO-LGBM model proves to be the optimal choice.
## 7 Conclusions  
Properly arranging the transmission paths and units allows the system to provide a wider ratio coverage and higher torque capacity than a CVU, while still possessing continuous ratio variation.  
To develop the complex mechanical structure of the system, a graphical method called the BPD is first proposed. The method is based on the FPG [35]. After simplifying the system elements and the transmission paths, there are only seven basic elements involved in the BPD, and the generic connection among different elements is shown. The system synthesized by the BPD can be sorted into eight categories based on the following three properties concerning the connections among the power sources, the CVU, and the MDOF units.  
1. Direction of power flows through the CVU: One-way manipulated and dual-way manipulated systems. A bidirectional bridge structure comprising the CVU is required in the BPD for a dual-way manipulated system, which can exploit twice the ratio coverage of the CVU.
2. Coupling pattern of the power inputs: Direct-coupled and planetary gear-coupled systems. The power sources of a planetary gear-coupled system are connected to different elements of an MDOF unit.
3. Number of power transmission paths parallel to the CVU: Single-path, bypass-type dual-path, and power-split-type dual-path systems. In the BPD, a loop structure comprising only the CVU is required for a bypass-type dual-path system. For a power-split-type dual-path system, the loop structure should comprise a CVU and an MDOF unit.  
With the aid of the BPD, a hybrid CVT system with dual-way manipulation and power-split-type dual-path features is developed. The required structure of a dual-way manipulated system is a bidirectional bridge structure comprising the CVU. A power-split-type dual-path system must have at least one loop structure comprising a CVU and an MDOF unit. The possible combinations are first listed, and one of them is selected as an example. With proper parameters, the synthesized system can provide a wider ratio coverage by exploiting twice the ratio coverage of the CVU. During all continuously variable ratio modes, the torque transmitted through the CVU is reduced due to the power-splitting principle.  
In the given example, the overall ratio coverage of the synthesized system is 159.5% higher than that of the CVU. The CVU transmits only 74% to 92% of the input power and 74% of the torque load. The ratio variation of the system also remains continuous during mode switching. Therefore, a system with a wider ratio coverage and a higher torque capacity can help increase the energy efficiency of road vehicles.  
Conceptualization, H.-Y.K.; Methodology, H.-Y.K.; Project administration, T.L.; Supervision, T.L.; Writing-original draft, H.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
This study has been supported by Giant Lion Know-How Co., Ltd., which is gratefully acknowledged by the authors.  
The authors declare no conflict of interest.
## 3 Materials and Methods  
Despite the huge body of work in the field of eye CAD systems, there is a gap in handling and classifying more than one retinal disease at once (direction six above), in particular distinguishing between cases of DR, glaucoma, and normal conditions in fundus images. This could be due to the scarcity of publicly available fundus images, especially ones with both glaucoma and DR. It could also be due to the fine and subtle differences between the intensity values of different pathological patterns (pixels) in the images, as shown in Figure 1. The present work is intended to bridge this gap.  
It should be noted that both normal and DR fundus images are macula centered, so FOS and HOS features are applied for characterizing the pathological pattern of DR. On the other hand, glaucoma fundus images are OD centered (K  
In the beginning, the RGB image is processed in two steps. The first step is to transform the RGB image into its essential R, G, and B channels. The second step is to identify and crop the OD region using the given center pixel location and the OD's diameter. The cropped OD region is converted into gray-scale.  
Next, three different feature sets, FOS, HOS, and HOG, are then used for constructing three different sets of features that can distinguish between the pathological and normal patterns for each disease. The first set, FOS, consists of four features: mean, variance, skewness, and kurtosis while HOS features are 14 textural features: angular second moment (ASM), contrasts, correlation, sum of squares (variance), inverse difference moment (homogeneity), sum average, sum entropy, sum variance, entropy, difference variance, difference entropy, information measure of correlation 1, information measure of correlation 2, and auto-correlation.  
The third set, HOG, comprises 81 features. Both FOS and HOS extractors are used to gather the statistical texture information for the entire retinal R, G, and B image, whereas HOG is employed to detect intensity changes in the gray-scale cropped OD region image. At the end of feature extraction, a feature vector with 429 distinct features is constructed.  
In the next stage, a feature selection technique based on modified genetic algorithms is applied to reduce the feature space, which positively affects the final system performance.  
In the last stage, four classifiers, decision tree (DT), k-nearest neighbor (kNN), naive Bayes (NB), and linear discriminant analysis (LDA) are used to make the diagnosis decision.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 5 Discussion
### Remaining Challenges  
The effectiveness and robustness of the proposed algorithm have been effectively demonstrated in previous experiments and discussions. This provides a new way for unsu  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{5}{c}{**Images**} \\ \cline{2-7}  & **Image 1** & **Image 2** & **Image 3** & **Image 4** & **Image 5** & **Image 6** \\ \hline FCM & 1.11 & 1.28 & 1.72 & 4.84 & 2.21 & 7.61 \\ FCM\_S & 0.04 & 0.04 & 0.04 & 0.20 & 0.06 & 0.08 \\ FGFCM & 0.04 & 0.04 & 0.13 & 0.13 & 0.03 & 0.07 \\ RFLICM & 0.13 & 0.24 & 0.19 & 0.85 & 0.12 & 0.29 \\ GKWFLICM & 2.94 & 8.16 & 9.09 & 5.97 & 0.42 & 1.87 \\ MSHLICM & 3724.40 & 6032.45 & 16,026.48 & 17,318.53 & 6080.06 & 4792.17 \\ VT-HMRF-FCM & 316.56 & 326.59 & 1386.42 & 1042.08 & 627.88 & 574.41 \\ Proposed FVT-FCM & 198.37 & 220.23 & 623.47 & 687.90 & 343.45 & 226.73 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Execution time of different algorithms in remote sensing images; (sec).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Time Complexity** & **Algorithms** & **Time Complexity** \\ \hline FCM & \(O(nkt)\) & GKWFLICM & \(O(nket)\) \\ FCM\_S & \(O(nket)\) & MSHLICM & \(O(nket+nke^{2}t)\) \\ FGFCM & \(O(N\_{i}kt)\) & VT-HMRF-FCM & \(O(nmkt)\) \\ RFLICM & \(O(ne+nket)\) & Proposed FVT-FCM & \(O(nmkt)\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Execution time of different algorithms in remote sensing images; (sec).  
pervised classification that can balance noise resistance and boundary fitting. However, it still faces many challenges, such as the following: (1) How to extend its application to large regions. Due to the direct correlation between time complexity and pixel count, large region images inevitably bring more computation time and memory. Thus, we recommend clipping the large image into a series of small images first, and then combining parallel computing for classification. It can maintain classification accuracy while ensuring classification efficiency. (2) How to better distinguish targets with complex scenes. At present, only spectral and spatial information are used in this paper. It can achieve better classification of most general images. To better describe complex scenes, texture information, contextual information, semantic information, etc., will be further considered to establish model constraints.