esa-sceva/satcom-chunk-collection huggingface dataset:
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 4 Results and Analysis
### Different Scenarios Comparison  
This paper performs detection and recognition of kiwifruit for different scenes to verify the effectiveness of the improved network model. In order to visually compare the detection effect, we used the original image annotation for comparison (Figure 10). In the close-up scenes, the kiwifruit surface is evenly illuminated and detection is less difficult; in the distant scenes, the kiwifruit targets are small and dense, and the recognition accuracy of the images is lower; in the cloudy days, the light is weaker, the kiwifruit has dark shadow areas, and the features of the target fruit become blurred. The YOLOv4 model algorithm had missed detection in all cases of occlusion, dense, and insufficient illumination, and some small target fruits were not recognized, while the GhostNet-YOLOv4 detection effect was closer to the original image annotation. Compared with the YOLOv4 detection algorithm, the GhostNet-YOLOv4 model had better detection results and can effectively identify both large and small target fruits under the conditions of overcast, fruit overlap, shading and density. The improved algorithm in this study is not only applicable to images with uniform light on sunny days, but also gets better recognition results for images under low light conditions on cloudy days. The improved GhostNet-YOLOv4 network has a larger perceptual field and is more capable of recognizing kiwifruit in natural environments.
## 4 Network Communication
### Channel Characterization  
Accurate channel characterization can improve the quality of applications, such as estimating delay and reducing path loss. In recent years, another key BSN physical layer research area is the channel characteristics of various bands. The IEEE802.15.6 working group has put forward the fundamental content about channels and their characterization. There are three common channel characterization methods:  
* Measurement-based methods. [PERSON] _et al._, have measured the narrow band transmission channel, and analyzed the channel characteristics including the average path spreading gain, large-scale attenuation, and small-scale attenuation. Results have shown that movement, location, and environmental factors can cause path losses. Movements of the human body will result in shadows [30].
* Simulation-based methods. Reference [117] makes 3D scene simulation of signal propagation in the channel by means of Finite-Difference Time-Domain (FDTD). It employs a leap frog algorithm to alternately calculate the electric field and magnetic field in the space-domain  
Figure 3: BSN frequency bands.  
and simulate the electromagnetic field changes by time-domain updating. This simulation model is of great applied value, according to the comparison between simulation results and measured data.
* Combination with simulation and actual measurement method. Reference [118] has provided a path loss model which can be used to evaluate the energy performance in single-hop and multi-hop networks. The sensor nodes deployed around human body can measure the sent and received data among different parts of human body, then import the collected data into a 3D model of human body, and numerically study path losses. Finally, the path loss parameters and time-domain channel characteristics can be obtained.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 3 Routing Protocols in Connected CubeSat network
### Discussion  
Single-layer and multi-layer architectures and their related routing protocols have both been discussed throughout this section. These routing protocols have mainly been proposed for conventional satellites that were featured with high communication capability and reliability. Most of these protocols have assumed that the continuous connectivity between satellites can be established and traditional TCP/IP can be modified and used in such environments. The single-layer networking architecture and its associated routing protocols can generally provide better performance when compared with a single satellite, particularly in terms of maximising the connection window with the ground stations, increasing spatial and temporal data collection, and connecting two or more points on the ground through satellites. However, the existing single-layer architecture routing protocols cannot meet CubeSats networking survivability requirements. The alternative concept of routing in a multi-layered satellite network demonstrates itself as being very beneficial for resource-scarce networks when compared with the typical single-layer architecture. Having satellites that function as a backbone support system, with suitable specifications in high layers of a multi-layered system, allows for better networking capabilities by reducing the intermittent connectivity among the lower layer satellites. Nevertheless, the highly dynamic environment that is created by the mobility pattern of a large number of CubeSats remains challenging, especially with the restriction of limited resources that are available for these satellites, such as radio range, bandwidth, and the high failure rates, which result in intermittent connectivity. Accordingly, traditional TCP/IP based protocols are not reliable, as if a node cannot find a route towards the packet's destination the packet will be dropped, which in turn leads to a lower delivery ratio. Hence, in the next section, DTN routing protocols that tackle the problem of dis-connectivity in a network will be considered.
## 3 Methodologies and Research Framework
### Data Reduction and Representation Learning  
Data reduction refers to reducing high-dimensional data into low-dimensional data to deal with several issues such as the curse of dimensionality and selection of optimal boundary [17]. Furthermore, data reduction is required due to both practical and theoretical reasons, e.g., removal of irrelevant features, the perspective of computation and machine learning, the perspective of probability and statistics, etc. [18]. Dimension reduction can be implemented by a variety of strategies for diverse applications. In [19], three well-known data reduction strategies, namely dimensionality reduction, clustering, and sampling, were discussed. Dimensionality reduction is related to reducing the number of random variables, and feature selection and feature extraction are the representative methodologies of dimensionality reduction. Clustering groups objects into similar groups, and sampling is used to determine if a small set of samples represents the entire dataset [19]. Among the described methodologies of data reduction above, we picked feature extraction belonging to dimensionality reduction at our discretion. Feature extraction in conjunction with the HOG descriptor is further discussed in Section 3.3.  
We also examined several studies to see which dimensionality reduction methodologies were used in which areas. In [20, 21], Principal Component Analysis (PCA) was employed to reduce data dimensions. Note that PCA is one of the state of the art techniques to reduce an unlabeled high-dimensional input dataset for the purpose of feature extraction. However, the authors of [20, 21, 22],  
Figure 5: Example of the extracted images.  
Figure 6: Data visualization of the vehicle and non-vehicle images.  
interestingly, adopted the Restricted Boltzmann Machine (RBM) model for representation learning, also known as feature learning. The reason Deep Neural Networks (DNNs) need to be used for representation learning is that they contain their own way of expressing data, which converts input data into a numerical form that is easy to recognize. Especially, DNNs can learn from data by parameterizing data representation methods [23]. However, we decided not to adopt one of the DNN models for representation learning because it would be too much to run two or more deep learning models on limited hardware specifications.
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 4 Discussion
### Limitations and Uncertainties  
Benefiting from the long-term and continuous observation capability of both the EC system and the Tower Platform Automated spectral observation system, we were able to explore the SIF-GPP relationship in alpine meadow ecosystems at the seasonal and daily scale as well as the response of their relationship to changes in environmental factor levels. However, several limitations and uncertainties still exist here. Firstly, there is a problem of footprint matching and spatial representativeness [65; 66]. The spectral and flux observation are often prone to the problem of footprint mismatch [67]. At this research site, the EC system and the tower-based automatic spectral observation system were positioned on the same tower, and the spectral observation coverage was included in the flux observation coverage. The underlying surface of the AR station is flat and homogeneous, so the footprint mismatch problem was expected to have little impact on this study. However, given that only one alpine meadow ecosystem observation station at the AR site was involved, whether the conclusions obtained in this study are applicable to all alpine meadow ecosystems needs further study. Secondly, there is the problem of observation geometry. The probe of our tower-based platform automatic spectroscopic observation system was placed heading southwest. We have removed the observation data with solar azimuth angle greater than 80\({}^{\circ}\) in order to ensure the data quality. However, the change of solar azimuth angle through the day still lead to the appearance of shadows in the observation field of view, which would certainly introduce errors to the inversion of canopy SIF. The effect of solar observation geometry should be considered in future studies. Thirdly, we merely conducted observational studies on canopy-scale SIF and GPP in alpine meadows at AR stations, without involving the measurement of canopy structural and physiological parameters in alpine meadows, which should be included in future studies to enable us to analyze the relationship between SIF and GPP more comprehensively from a physiological perspective. Finally, recent studies have highlighted the excellent performance of \(\text{NIR}_{\text{V}}\) in tracking GPP [68; 69], but \(\text{NIR}_{\text{V}}\) reflects more information on vegetation canopy structure [70], while SIF is directly related to vegetation photosynthetic processes [16]. In the future study, we will investigate the superior and inferior performance of NIRV and SIF in tracking GPP in alpine meadow ecosystems.
## 5 On-Orbit Mode to Support Intercalibration with Radiometric Traceability
### Simultaneous Nadir Overpass (SNO) Cross Intercalibration Transfer Mode (GEO-LEO or LEO-LEO)  
With the reference satellite LIBRA and the targeted satellite flying in different orbital planes, the crossing area of the ground tracks is chosen as the intercalibration area. Differences in observation times and viewing angle have to meet certain requirements.  
As depicted in Figure 9, LIBRA flies into the intercalibration area at time \(\mathrm{t_{C1}}\) and point C1, and exits the area at time \(\mathrm{t_{C3}}\) and point C3. When LIBRA observes within the intercalibration area, the attitude is adjusted such that LIBRA and the targeted satellite view the same area on the ground. The observation time differences \(\Delta\mathrm{t}=\mathrm{t_{C}}-\mathrm{t_{F}}\) shall be small enough to meet transfer accuracy requirements.  
Figure 8: Quasi-synchronous intercalibration transfer mode by orbital maneuvers.
## 3 Data and Method  
### RS Images Processes  
Based on the MSS (Multispectral Scanner) images with 80 m spatial resolution in the late 1970s (June, July and August of 1976 and 1977), TM (Thematic Mapper) images with 30 m spatial resolution in the early 1990s (July, August and September of 1990, 1991, and 1992) and in 2004 (June, July, August and September of 2003, 2004, and 2005), and HJ-1 images (mini-satellite constellation for environment and disaster monitoring) with 30 m spatial resolution in 2012 (July, August and September of 2012), the LULC datasets (1:100,000 scale) were developed for the TRHR for the four above-mentioned time periods. After a series of image processing steps such as radiometric calibration, atmospheric correction, single band extraction, false color composition, geometric correction, histogram equalization, images mosaic, and segmentation, human-computer interactive interpretation, which could increase the overall classification accuracy by approximately 10% [14], was conducted to construct the LULCdatasets based on the Chinese LULC classification system proposed by [PERSON] _et al._[28; 29], which was supplemented by field investigation data, a grassland map (1:1,000,000 scale), a vegetation map (1:1,000,000 scale), a topographic map (1:100,000 scale), _etc._ After the classification accuracy assessment and modification, the boundaries of the headwaters of the Yangtze River and Yellow River were used as masks to extract the LULC data for the comparative analysis. The workflow of this integration is displayed in Figure 2.  
In order to facilitate statistical analysis, we adjusted and combined the LULC types into a total of 9 types, according to the ecological characteristic of each LULC type (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types in this Study** & **Chinese LULC Classification System [28; 29]** & **Description** \\ \hline forest & 21 forest land 23 sparse forest land 24 other forest & Arbor, including timber forest, commercial forest, protection forest and others \\ \hline shrub & 22 shrub & Scrub with the height lower than 2 m and shrub \\ \hline high-coverage grassland & 31 high-coverage grassland & Grassland with the coverage higher than 50\% \\ \hline medium-coverage grassland & 32 medium-coverage grassland & Grassland with the coverage between 20\% and 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: LULC classification scheme.  
Figure 2: The technical process of acquiring LULC dataset.
# Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms
## 2 Method
### Processing of the Object Function  
The typical method to process the object function is the random phase (RP) method, in which a random phase function is multiplied to an object function, giving a new function,  
\[O_{RP}^{n}(x,y)=O(x,y)\exp[j\phi_{RP}^{n}(x,y)], \tag{6}\]  
where \(\phi_{RP}^{n}(x,y)\) is a random value in the range \((-\pi,\ \pi)\). Because there will be numerous distributions of the random phase, it is easy to generate \(N\) different random phase functions for \(N\) binary CGHs. A variation of the RP method is the modified iterative Fresnel algorithm (MIFA), in which the object field and the binary hologram are iteratively obtained with each other [23; 24]. The object-amplitude constraint and the binary-amplitude constraint are applied on the retrieved object and the hologram, respectively. Therefore, each iteration in MIFA needs one forward propagation and one backward propagation. After multiple iterations, the object can be regarded as being modulated by an optimized phase mask, and the phase distribution of the object still looks random.  
The second method of object processing is localized random down-sampling (LRDS). The down-sampled object function is expressed as  
\[O_{LRDS}^{m}(x,y)=O(x,y)M_{LRDS}^{m}(x,y), \tag{7}\]  
where \(M_{LRDS}^{m}(x,y)\) is a binary mask with the same size as the object function. The mask \(M_{LRDS}^{m}(x,y)\) is segmented into many tiles with size \(k\times k\) pixels. For each tile, one pixel is randomly selected as unity (on), and the others are zero (off). Therefore, the down-sampling rate of the mask is \(1/k^{2}\), and many LRDS masks with different sampling positions can be generated. Based on LRDS, there is an advanced method called adaptive intensity accumulation (AIA) [18]. In the AIA method, the object will not only be down-sampled but will also be modified by an adaptive binary mask. The \(n\)-th adaptive mask is determined from the reconstruction of previous \(n\) CGHs to enhance the image quality. Therefore, every CGH for the AIA method needs one forward propagation and one backward propagation.
## Appendix A Artificial Neural Network  
Artificial neural network (ANN) is designed to mimic the working ways of the biological brain. One of the concepts widely recognized regarding ANN is its capability to map and approximate complex functions relationships and the natural propensity to utilize massively distributed processing capacity to store experimental knowledge and makes its use. The common structure of ANN consists of some basic components, namely input layer, weights (connection strength), output layer, and activation function (for hidden and output layer). Each input neurons are multiplied with adjustable weights and summed up with extra value known as bias which is then fed through a nonlinear activation function iteratively to predict results as shown in Figure 11. The transfer function can be given as (\(f\)) = WX + b and the output of the network can be calculated by Equation (A1a).  
\[o_{j}=\varphi(f)=\varphi(WX+b)\] (A1a) where, \[\varphi\] is the activation function, W is weight matrix, b is bias, and X is input data, examples of commonly used activation function are Sigmoid, Tanh, ReLU, and linear (described in Equation (A1b-e)).  
\[linear(n)=n\] (A1b)  
\[\text{Sigmoid}=\phi\ z=11+e-z\] (A1c)  
\[ReLu=\max(0,z)\] (A1d)  
\[Tanh\ (z)=\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}\] (A1e)There are several combinations to train a neural network on a given data set and many possible numbers of hyperparameters such as number of neurons in a hidden layer, number of hidden layers, activation function, learning rate, etc., hence, determining the optimal network configuration is a challenging task. In the present work, back propagation algorithm was used to lower the error between the predicted and actual datasets, and the network is optimized for hyperparameters manually.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 3 Nonlinear Chaotic Grey Wolf Optimization (NCGWO) Algorithm
### NCGWO Algorithm Steps  
Figure 5 shows the NCGWO implementation steps. Initially, the initial population is generated, and parameters are defined for the fitness value calculation, where the best values are stored as \(\alpha\), \(\beta\), and \(\delta\). Subsequently, the positions are updated, which ultimately leads to the output of the optimal solution \(X_{\alpha}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **NCGWO** & **GWO** \\ \hline \hline Number 1 & Logistic-tent double mapping generating & Randomly generated \\  & initial populations & initial populations \\ \hline Formula & \(X_{n+1}=\left\{\begin{array}{ll}\left[rx_{n}(1-x_{n})+\frac{(4-t)}{2}x_{n} \right]\text{mod}1,\text{if}x_{n}<0.5\\ \left[rx_{n}(1-x_{n})+\frac{(4-t)(1-x_{n})}{2}\right]\text{mod}1,\text{if}x_{n} \geq 0.5\end{array}\right.\) & \(X_{n+1}=rand(X_{n})\) \\ \hline Reason 1 & The GWO algorithm suffers from a lack of diversity in the initial population due to the random generation of individuals. This limitation hampers the algorithm’s search flexibility. To address this, logistic-tent dual chaotic mapping methods are employed to enhance the initial population’s diversity, optimizing the global search process and increasing the algorithm’s search flexibility. \\ \hline Number 2 & Nonlinear variable speed convergence factor & Linear convergence factor \\ \hline Formula & \(a=He^{-(t^{2}/2M^{2})}-(H-2)\) & \(a=2-2\left(\frac{t}{t_{\text{max}}}\right)\) \\ \hline Reason 2 & The \(a\) plays an important role in balancing the exploration and exploitation of the candidate’s individual search. The search process of the GWO algorithm is nonlinear and complicated; the linear convergence factor cannot balance the global and local search ability. The better performance would be obtained if there was a nonlinear variable speed convergence factor. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the GWO and NCGWO improvement points.  
Figure 4: Convergence coefficients.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 3 Proposed Method
### Method Overview  
The structure of the crowd flow prediction algorithm proposed in this paper is shown in Figure 2a. The algorithm can be decomposed into five steps: (1) spatio-temporal graph construction, where each node in the DSTG is represented by the OD flow of that node since the dependence between regions is changing dynamically over time; (2) feature modeling including spatio-temporal dependence on urban area crowd flow data and also point-of-interest (POI) and holiday information; (3) construction of dynamic graph convolutional layers, where the DSTG's temporal and spatial characteristics are extracted using a continuous DGCN module, and a layer of convolutional layers is then added to scale the features and create output results with the required dimensions; (4) temporal correlation extraction, where in order to obtain the temporal correlation within consecutive  
Figure 2: (**a**) Overview of the proposed model Res-DGCN; (**b**) Spatial Attention Module; (**c**) Improved conditional convolution module.  
time slices, we enhance the connection of neighboring time slices by aggregating the temporal features at the previous time slice of the prediction result through the SCondConv module; (5) model training, considering that the prediction structure is affected by the complex city-region function as well as the spatio-temporal dependence. To train both tasks at the same time, we employ the Huber loss function in conjunction with the cross-entropy loss function.  
Among these five parts, three parts are improvements made based on the original model (3D-DGCN) (shown in the dashed boxes in Figure 2a), namely the SA module, SCondConv module, and loss function. They are further described in this section.
# Anti-Collision System for Small Civil Aircraft
###### Abstract  
This paper presents the results of the research in the field of anti-collision systems for small civil aircraft, which are not dependent on secondary radars and satellite navigation systems. The aviation communication network was used to design the anti-collision system. The simulation results manifested that the anti-collision system precision depended on the errors of synchronization of the aviation communication network. The precision of the anti-collision system is also influenced by the errors of the coordinates of individual aviation communication network users, based on which the system identifies its own position. The results of the simulation show that the dispersion of the positioning error \(\sigma^{2}\)\(\mathrm{\SIUnitSymbolMicro p}\) by the ACS system varied in the range of 1.94 m\({}^{2}\) to 503.23 m\({}^{2}\). The simulation results confirm that the designed anti-collision system is operational in establishing its position against other FOs, with the distance from the given FOs being 50.0 km maximum. The main contribution of this paper are derived algorithms for the operation of an anti-collision system for small civil aircraft, in addition to the design of movement trajectory models of five flying objects which operate within the aviation communication network. The advantage of the anti-collision system is that it is independent of satnav systems and secondary radars. A significant advantage is the low cost of this system.  
anti-collision system; flying object; accuracy; aviation communication network +
Footnote †: journal: Applied