esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Model
### Hybrid FSO/RF System Based on MRC Scheme  
For the MRC scheme, it is assumed that the output SNR of the hybrid FSO/RF system is \(\gamma^{MRC}=\gamma^{FSO}+\gamma^{RF}\). Since the FSO sub-link and the RF sub-link are independent, the PDF of \(\gamma^{MRC}\) can be written as:  
\[f_{\gamma^{MRC}}(\gamma)=\int_{0}^{\gamma}f_{\gamma^{FSO}}(t)f_{\gamma^{RF}}( \gamma-t)dt \tag{5}\]  
By substituting (1) and (3) in (5), it can be rewritten as  
\[f_{\gamma^{MRC}}(\gamma)=A_{1}\sum_{k=1}^{\beta}c_{k}\sum_{i=0}^{\infty}C_{1} \gamma^{D_{1}-1}G_{r+1,3r+1}^{3r,1}\!\left(E_{1}\gamma\left|\begin{array}{c} 1,\kappa_{1}\\ \kappa_{2},1-D_{1}\end{array}\right.\right) \tag{6}\]  
where \(A_{1}=\frac{\xi^{2}A_{m}A}{2^{2}-1^{r-1}}\), \(C_{1}=\frac{(-B_{m})^{j}\Gamma(D_{1})}{j!}\), \(D_{1}=C_{m}(\mu_{m}+i)\), \(E_{1}=\frac{E}{\mu^{FSO}}\).  
The CDF of \(\gamma^{MRC}\) obtained by integrating Equation (6) can be expressed as  
\[F_{\gamma^{MRC}}(\gamma)=A_{1}\sum_{k=1}^{\beta}c_{k}\sum_{i=0}^{\infty}C_{1} \gamma^{D_{1}}G_{r+2,3\gamma+2}^{3\gamma,2}\bigg{(}E_{1}\gamma\bigg{|}\begin{array} []{c}1-D_{1},1,\kappa_{1}\\ \kappa_{2},1-D_{1},-D_{1}\end{array}\bigg{)} \tag{7}\]
# Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
## 1 Introduction
Residential laws are generally characterized by low management (low water, low fertilizer, low moving frequency, and no pest management), and ensuring good quality in these contexts is not simple. For these reasons, autonomous mowers initially were meant exclusively for residential lawns, in which the management aims to satisfy aesthetic and functional needs [17]. These machines are becoming more popular among private customers who have no experience in turf management, but desire to have properly managed turfgrass, together with low time and energy consumption [13]. This study aimed to explore the residential lawn quality of four different moving frequencies using two different autonomous mowers. Such turf composition (a hybrid hermudagrass and a spontaneous species) represents a common association in small-size residential turf in a Mediterranean area, which could give high aesthetic and functional benefits to the customer if managed properly and optimally. This study also evaluated the turfgrass response to different levels of over-trampling, which can be a common issue with fast-growing warm-season turfgrass and robot mowers with random trajectories. All the operative and qualitative parameters considered provide the creation of a case study for long-term automated maintenance for common users of residential green areas, demonstrating the effectiveness of automated moving in promoting the establishment of selected species on the spontaneous ones.
## 3 Experiments and Data Analysis
### Experiment on Measuring the Pose of a Rigid Body with a Camera
#### 3.3.3 Measurement and Analysis of the Position of the Center of Gravity of the Rigid Body  
The DLT method mentioned earlier was used to measure \(R_{C}^{W}\), \(T_{C}^{W}\), \(R_{C}^{B}\), and \(T_{C}^{B}\). The centroid coordinates of the rigid body were \(P^{B}=\begin{bmatrix}0&0&0\end{bmatrix}^{T}\), and the real-time position changes were calculated using Equation (3).  
Figures 9-11 present the three-dimensional coordinate changes of the body's center of mass as measured by the monocular camera at the first position. It is seen that the error in the three-dimensional position of the center of mass of the rigid body increased with the tilt of the rigid body relative to the camera lens. This is because the constraint of the non-coplanar control points on the rigid body decreased, and the imaging quality of the control points deteriorated when the tilt ratio increased. The error in the \(X\)-axis coordinate of the rigid body center was much smaller than the errors in the other two axis coordinates because the middle-frame swinging motion of the rigid body involved rotation around the \(X\)-axis, and all control points had the best \(X\)-axis constraint during the motion.  
Figure 8: Comparison of results obtained for the attitude angle \(Q_{x}\) in the medium frame swing mode.  
Figure 11: Centroid Z-coordinate versus time for the first camera position.  
Figure 9: Centroid X-coordinate versus time for the first camera position.  
Figure 10: Centroid Y-coordinate versus time for the first camera position.  
The maximum deviation of the mean absolute error was 0.53 cm, and the maximum deviation of the standard deviation was 0.66 cm, indicating that the measurement process was insensitive to the camera position and that repeated measurements could be made.
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 4 Performance Evaluation
### Simulation Setup  
Figure 4 shows examples of the network topology with different numbers of devices. All devices are deployed in a 100 \(\times\) 100 m\({}^{2}\) region and have the same interference range. We consider a pair of devices (i.e., Tx and Rx devices) per transmission and set the Tx devices to transmit the packets consecutively. Multiple pairs of devices are deployed in the simulation region; one of the pairs is set to the Wi-Fi backscatter devices, and others are set to the legacy Wi-Fi devices. Considering the architecture of Wi-Fi backscatter uplink communication in Section 2, the Tx device of a pair of Wi-Fi backscatter devices should be a Wi-Fi helper. Therefore, we investigate the channel occupancy of Wi-Fi backscatter devices in the simulation by measuring the throughput of the Wi-Fi helper under IEEE 802.11n PHY/MAC environments. The simulation is repeated 30 times. The simulation parameters are listed in Table 1 in detail.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 1 Introduction
According to the simulation results, the proposed EGSD has 46.6% longer lifetime and 31.7% lower overhead compared to the conventional minimum-variance-based spectrum decision (MVSD) scheme. The rest of this paper is organized as follows: we provide some related works in Section 2. In Section 3, we describe the details of the proposed EGSD scheme and the two spectrum selection algorithms: random selection (EGSD-R) and game-theory-based selection (EGSD-G). The performance evaluation of the proposed scheme is described in Section 4, for both spectrum selection algorithms and a representative related work. Finally, we conclude our paper in Section 5.
## 4 Experimental Design and Setup
### Prediction Models
#### 4.2.5 Random Forest  
Random forest is an ensemble model that build a forest of numerous unpruned decision trees from the training dataset. Then, it uses the mode of the classes of the individual trees on the testing dataset to make a prediction. RF integrates tree predictors that depend on the values of a random vector sampled individually, along with the same distribution for the whole trees in the forest. Applying this random selection of features leads to higher error rates than those in AdaBoost [47], but the random forest is better in terms of dealing with noisy data [48]. Moreover, this algorithm performs bagging on features based on majority voting and selects the dependent variables which have thehighest votes [48]. In this study, random forest integrates algorithms of the same types (i.e., decision trees), so it can be classified as a homogeneous ensemble model. RF depends on four parameters: the number of trees to grow, the sub-sample size common to each tree, the tree depth, and the number of variables randomly sampled for splitting [26]. We used the default parameters in Weka, where Weka creates a forest of several decision trees as the base models and initializes this forest to 100 tree instances [49].
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 3 Modeling the Arrival Processes
### Marginal Distribution of the Arrivals
#### 3.1.2 Lognormal Distribution  
Consider a normal (Gaussian) random variable \(\mathcal{N}(\mu,\sigma^{2})\) with mean value \(\mu\) and variance \(\sigma^{2}\). The related Lognormal random variable \(X\) has the density function  
\[f(x)=\frac{1}{x\sigma\sqrt{2\pi}}\exp\left(-\frac{\log x-\mu}{2\sigma^{2}} \right),\qquad x\in\mathbb{R}^{+}, \tag{5}\]  
where the logarithms are natural. Thus \(\log X\sim\mathcal{N}(\mu,\sigma^{2})\). The expected value of \(X\) is \(\exp\left(\mu+\frac{\sigma^{2}}{2}\right)\), and the variance is \(\exp\left(\sigma^{2}-1\right)\exp\left(2\mu+\sigma^{2}\right)\). We will use for our numerical evaluations the quantization \(\lfloor X\rfloor\) as the point arrival process in each slot.
# Progress in Construction Robot Path-Planning Algorithms: Review
## 6 Path-Planning Technology in the Future Development of Clustered Construction Robots
### Path-Planning Algorithms in the Future Development Direction of Clustered Construction Robots  
1. Construction robot swarms must be capable of both independent autonomous operation and the ability to form a group for collaborative work when necessary. This requires that the robot path-planning algorithms can respond quickly and are capable of calculating the movement paths of multiple robots simultaneously.  
Additionally, if a robot in the swarm fails and cannot move, the path-planning algorithm needs to quickly remove it from the swarm. The failed robot should then be treated as an obstacle.  
2. Construction robots need to move in changing environments, which requires the path-planning algorithm to have strong adaptability and obstacle-avoidance capabilities. It must ensure that robots within the swarm do not collide with each other while moving and that the entire swarm avoids collisions with static or dynamic obstacles.  
3. Swarm construction robots work not only in planar maps but also in high-dimensional maps, such as in the air, in the ocean, or between high-rise buildings. This necessitates the ability of the path-planning algorithm to plan paths in high-dimensional space. Since different robots have different modes of movement, such as rolling, climbing, and walking, the suitability of the paths also varies. The algorithm must make path selections based on these differences in movement, which poses high demands on the intelligence and learning capabilities of the path-planning algorithm.
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 5 Conclusions  
We propose a parallel dual-branch disentangling network for decouple the global and local blur features. The network decomposes the feature extraction process into two branches. Through a phased training strategy, the network is trained to decouple and analyze global and local blur features. The attention fusion module is used to dynamically guide the reconstruction of restoration image. The feature map extracted by the branch network shows that the proposed dual-branch network can extract complementary features. The experimental results show that, compared with the existing dynamic scene deblurring methods, the proposed method significantly improved the subjective and objective performances, and the running speed has also been accelerated.  
Our work insight that the task-specific branch training brings great promising to disentangle the degradation factors in real-world low-quality images. We provide a potential way to explore a partially interpretable framework for dynamic restore the real blur images.  
**Author Contributions:** Conceptualization, X.L.; methodology, F.Y. and J.H.; software, F.Y.; validation, F.Y. and J.H.; writing--original draft preparation, X.L. and F.Y.; writing--review and editing, F.Y.; funding acquisition, L.Z. and X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work in this paper is supported by the National Natural Science Foundation of China (No.61471013), the Beijing Education Committee Cooperation Beijing Natural Science Foundation (No. KZ 201910005007, No. KZ201810005002).  
**Data Availability Statement:** The GoPro dataset is openly available in [[https://github.com/SeungjunNah/DeepDeblur_release](https://github.com/SeungjunNah/DeepDeblur_release)]([https://github.com/SeungjunNah/DeepDeblur_release](https://github.com/SeungjunNah/DeepDeblur_release)) accessed on 8 November 2017. The representative results of the propose method are openly available in [[https://doi.org/10.6084/m9.figshare.14068721.v1](https://doi.org/10.6084/m9.figshare.14068721.v1)]([https://doi.org/10.6084/m9.figshare.14068721.v1](https://doi.org/10.6084/m9.figshare.14068721.v1)) accessed on 20 February 2021.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 3 Vibration Control of Structures
This study discusses the advantages of passive systems in a seismic design and provides several examples of their successful applications. It also highlights limitations of passive control, considering uncertainties in seismic forces and limit states induced by unexpectedly demanding events, and points towards the need for better practical guidelines in their design and implementation. A comprehensive review on the response control of structures by TMDs is reported by [PERSON] and [PERSON] [22]. They review different configurations of dampers, involving one or more tuned masses, installed at different locations of the structure. They report that the findings in the literature support effectiveness of TMDs in reducing wind and earthquake-induced vibrations of certain types of structures. They also identify potential obstacles, such as robustness and reliability, across different levels of loading, especially those that exceed the yield limit, causing inelastic deformations in the structure. A state-of-the-art review of different types of structural control systems was presented by [PERSON] et al. [53]. Their review includes different control technologies that can be classified as active, semi-active, passive, or hybrid. They conclude that control systems have a huge potential and importance in modern structures. [PERSON] and [PERSON] [54] present a detailed review of semi-active control systems for the seismic protection of structures and conclude that different solutions, such as stiffness control devices, electrorheological dampers, friction control dampers, fluid viscous dampers, etc., have the potential of practical feasibility in full-scale structures. [PERSON] and [PERSON] [55] also report on the state of the art of semi-active technologies for structural vibration control. They report that smart damping devices, such as Magnetorheological (MR) dampers, appear to combine desirable features of both passive and active control solutions, and they offer a viable control solution against wind and earthquake forces. The literature on control of structures against wind or seismic forces is vast. As explained above, the state-of-the-art and recent findings, in the structural control of different kinds, have been presented in many works. As an example, one of the first comprehensive state-of-the-art reviews of structural control systems was published more than two decades ago by [PERSON] et al. [50]. Although performance of different control schemes in a single hazard scenario, such as wind or an earthquake, is well-known and summarized in many works, structural control in multi-hazard scenario is an emerging field of research. While some interesting research has been published in this field, there is a lack of an overview of the state-of-the-art, ongoing progress, and future directions. Most of the literature in this regard is on seismic and/or wind-induced response reduction in bridges, buildings, and wind turbines. These topics are dealt with separately in the following sections.
## 3 Results and Discussion  
### Nitrogen Measurement  
In this experiment, we collected 82 corn leaf samples, randomly divided into two data sets: 80% (64) as the training set and the remaining 20% (18) as the test set. Table 2 shows the total statistics of measured LNC values. The LNC data of the calibration dataset ranged from 0.65 to 2.57, with a mean of 1.63 and a standard deviation of 0.43. Similarly, the statistical parameters of the test dataset were 1.12-2.69, 1.69, and 0.4, respectively.
## 2 Data Collection
### Dynamic GNSS Data Collection  
The purpose of the dynamic test is to analyze the characteristics of raw observations in dynamic GNSS-degraded environments and the navigation performance improvement by the use of multi-GNSS and dual-frequency observations. Two predesigned tracks, as shown in Figure 3, were used: track 1 was mostly under an open sky on the playground (red dots) and track 2 was on a road surrounded by dense trees and buildings (white dots), where the GNSS signal was significantly degraded. The test devices are illustrated in Figure 4. A utility cart carrying NovAtel's SPAN GNSS Inertial Navigation System and Xiaomi Mi 8 was used to perform the dynamic test. The NovAtel's SPAN GNSS Inertial Navigation System used in our experiments was an earlier version of the device, which supports GPS and GLONASS. Another reference station, whose coordinates were known, was the choke-ring antenna connected to a NovAtel ProPak 6 on the roof of Wuhan University School of Geodesy building. The distance between the reference station and the utility cart was less than 500 m during the experiment. In track 1, the NovAtel software Inertial Explorer (IE) was used to analyze the SPAN system data by RTK and inertial navigation system (INS) combination and then a reference trajectory was obtained. Considering the distance between the phase center of SPAN GNSS antenna and the Xiaomi Mi 8 smartphone, an arm correction was applied to reduce the reference trajectory to the position of Xiaomi Mi 8. In the GNSS-degraded environment of track 2, GPS signal interruption was serious, and no reliable reference trajectory was available. Alternatively, several reference points on the route, shown as orange points in Figure 3, whose precise coordinates were determined by post double-frequency RTK or surveyed using total station, were used to evaluate positioning accuracy. The Android raw GNSS observations were collected on 18 December 2018 with 1-s sampling interval for approximately 2 h. The cart speed was about 1.0 m/s. The observed L1/E1 and L5/E5 sky plots and availability of parts of the satellites for Xiaomi Mi 8 are shown in Figure 5. Clearly, the characteristic of ADRS is quite different from that in Figure 2. In the dynamic situation, more frequent carrier phase cycle slips exist, and there is no obvious correlation between cycle slips and satellite elevation.  
Figure 2: Sky plots and ADRS of observed Global Navigation Satellite System (GNSS) satellites by Xiaomi Mi 8 in static test. (**a**) is for L1/E1 measurements. (**b**) is for L5/E5 measurements. The short red line is the ADRS field provided by an Android smartphone.  
Figure 4: Utility cart with NovAtel’s SPAN GNSS Inertial Navigation System and Xiaomi Mi 8.  
Figure 5: _Cont_.  
Figure 3: Trajectory of the utility cart obtained by NovAtel’s SPAN GNSS Inertial Navigation System. In track 2, due to the severe GNSS signal occlusion by dense trees and high buildings, fewer GPS + GLONASS) measurements are available. The NovAtel’s SPAN GNSS Inertial Navigation System derived trajectory has an obvious deviation to the reference points.
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 3 Model
### Multi-Scale Convolution  
One simple observation is that a vehicle closer to the camera appears larger that one of the same actual size yet further from the camera. Put differently, the vehicle size on camera is not necessarily in proportion with its actual size. With our goal of estimating vehicle speed in mind, it is important to consider the differences of vehicle size on camera due to their different distances from the camera. Different distances from the camera give rise to different scales for vehicles, which makes determining the size of convolution kernel difficult. Our way of tackling this issue is to input the convolution kernels of different sizes to the first layers separately of the network, of which the outputs are concatenated to be the inputs of the next layer. The information with different scales is considered in multiple convolution kernels, making the network adaptive. Figure 4 shows our multi-scale CNN. The box on the left indicates the input of the model. The three boxes in the middle respectively represent the three different convolutional layers of convolutional kernels as \(5\times 7\times 7\), \(3\times 5\times 5\) and \(1\times 3\times 3\). The large cuboid on the right represents that the resulting concatenated output, which is used as the input of the next layer.  
Figure 5 illustrates our final model. 'Multi-scale conv' refers to the multi-scale CNN we propose. On the basis of the IR(2+1)D50, we build non-local blocks into the second and third residual modules. The input of the model includes RGB images and optical flow images. In practice, the video stream can be directly decoded by OpenCV; the corresponding optical flow snippet can be extracted by the traditional optical flow algorithm or the deep learning method. Specifically, the optical flow used in this paper is obtained by dense optical flow [35]. After obtaining the RGB and optical flow frames, the data need to be standardized and resized to a fixed size. Finally, the RGB images and optical flow images can be stacked together as the input of the network.  
Figure 4: Multi-scale CNN.  
Figure 5: Model Architecture.
## 2 Small-Sample Adaptive Residual Life Prediction Model Based on Feature Matching Preprocessor-LSTM
### Failure Rate Model of Mechanical Product Life Characteristics  
Failure rate is one of the important metrics of mechanical product life. It can visually reflect the possibility of failure of a component or system at a certain moment, and the failure rate is used as an important parameter for product reliability analysis, service life evaluation, risk assessment, maintenance decision making, etc. Therefore, the failure rate of the equipment is used as a matching criterion.  
In the framework of using time as a life metric, the failure rate can be described by Equation (1), which is the probability of failure per unit time after the product works up to time \(t\).  
\[H(t)=\lim_{\Delta t\to 0}\frac{P(t<T<t+\Delta t/T>t)}{\Delta t} \tag{1}\]  
in the formula  
\[\begin{array}{c}P(t<T\leq t+\Delta t/T>t)=\frac{P(t<T\leq t+\Delta t)}{P(T>t)} =\frac{P(t+\Delta t)-F(t)}{R(t)}\\ H(t)=\lim_{\Delta t\to 0}\frac{F(t+\Delta t)-F(t)}{\Delta t}\cdot\frac{1}{R(t)}= \frac{F^{\prime}(t)}{R(t)}=\frac{f(t)}{R(t)}\end{array} \tag{2}\]  
It is assumed that the mechanical product whose lifetime obeys the Weibull distribution. Its two-parameter Weibull distribution [24] is shown in Equation (3).  
\[f(t)=\left(m/\eta\right)\left(t/\eta\right)^{m-1}e-\left(t/\eta\right)^{m}>0;t\geq 0 \tag{3}\]  
where, \(m\) is the shape parameter; \(\eta\) is the characteristic life.  
The expression for the reliability of its product is  
\[R(t)=1-F(t)=e^{-(t/\eta)m};t\geq 0 \tag{4}\]  
in the formula  
\[\begin{array}{ll}F(t)&=\int_{-\infty}^{t}f(t)dt\\ &=\int_{-\infty}^{t}\left(m/\eta\right)\left(t/\eta\right)^{m-1}e^{-(t/\eta)m} dt\\ &=1-e^{-(t/\eta)m}\end{array} \tag{5}\]  
Figure 1: The overall framework of the model.  
Then the failure rate can be expressed as  
\[H(t)=\frac{f(t)}{R(t)}=(m/\eta)(t/\eta)^{m-1};t\geq 0 \tag{6}\]