esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Definition and Collection of Available Spatial Data  
In this study, the spatial data necessary for the annual updating of landcover maps were defined and collected according to the following criteria: (1) Annually updatable spatial data allowing temporal tracking of landcover changes; (2) Spatial data amenable to landcover classification according to the existing landcover map guidelines, given that updates are performed on existing landcover maps; (3) Spatial data from large-scale maps suitable for representing small-area landcover items matching the context of actual land use status; (4) Low-cost spatial data. Then the data meeting these criteria were collected. The 2015 high-resolution landcover map was collected by the Environmental Geographic Information Service of the Ministry of Environment as the baseline map for upgrading. A digital topographic map 2.0 (1:5000) of the National Geographic Information Institute was obtained to check the accurate boundaries of objects with impervious surfaces (such as buildings). In order to detect landcover changes by visual inspection and accurately check pervious surfaces existing within an impervious area, we collected ortho-aerial photos with 25 cm spatial resolution from the National Geographic Information Institute. Additionally, a portal site map service was used to check areas occluded by trees, shadows, and high buildings. The collected spatial data were provided free of charge with various map data and visualization information as an open service to the public, and are shown in Table 1.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
###### Abstract  
In this paper, we investigate the coexistence of the 5G communication network with a fixed-satellite service (FSS) in the 3.5 GHz and 26 GHz frequency bands. We analyze a distance protection scheme for the FSS Earth station (ES) and 5G base stations (BS). Furthermore, we define the exclusion and restriction zones to develop a transmit power control scheme based on the Citizens Broadband Radio Service (CBRS). An interactive power control scheme is also devised for the restriction zone and extensively analyzed through simulations. The proposed scheme is examined for practical scenarios such as the rural macrocells (RMa), urban macrocells (UMa), and urban microcells (UMi) as defined by the 3 GPP. The impact of the antenna type is also investigated, and BSs with omnidirectional, \(4\times 4\), \(8\times 8\), and \(16\times 16\) antenna arrays are examined, as defined by 3 GPP, for the 5G networks. The results confirm that 5G systems can coexist with the FSS and provide quantitative insights into the selection of the system parameters, including interference margins, exclusion sizes, and reduction zones, for different scenarios and antenna types.  
5G system; fixed satellite systems; coexistence/sharing scheme; distance protection Article
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 2 Problem Formulation and Solution
### Switch Node Migration Strategy for Controller Load Balancing  
Because the satellite constellation moves periodically, the load of the controller will be out of balance with the change in time slice, so it is necessary to trigger the node migration of the switch in order to achieve load balance among the controllers. A pseudo-code description of the migration strategy is given below as Algorithm 2.  
```
0:\(C\_temp\gets product\_new\_point(C)\)
1:\(controller\_xyz\gets miller\_to\_xyz(S,S_{H})\)
2:\(L\gets calculate\_delay(controller\_xyz,pos\_xyz)\)
3:\(R,CF\gets relation\_satellite\_controller(L,SF)\)
4:\(loss\_temp\gets calculate\_loss(R,L,SF)\)
5:\(probability\gets np.random.rand()\)
6:\(pro\_new\gets calculate\_pro(loss\_temp,loss,t)\)
7:if\(loss\_temp<loss\) or
8:\(pro\_new>probability\)then
9:\(loss\gets loss\_temp\)
10:\(C\gets C\_temp\)
11:endif
12:endfor
13:\(t\gets stepst\)\(t\)
14:\(save(R\_best,C\_best,L,B)\)
15:endwhile
```  
**Algorithm 2** Multi-controller deployment algorithm based on simulated annealing.  
When the relation matrix is updated in the seventh step, the propagation delay between the switch and the controller is less than the maximum propagation delay, and the switch node is not migrated; then, the load of the controller is considered, and a migration strategy is made. Figure 4 shows the flowchart of the switch migration policy.  
First, the process begins to input the delay from all switches to all controllers, and determines whether there is a switch node that is larger than the maximum propagation delay, and if there is a target controller that is reallocated to minimize the propagation delay; if it does not exist, all switches are judged to be bad. In the next step, according to the relation matrix obtained in the previous step, the load of the controller is counted, and then it is judged whether the controller achieves load balance: one is all the controller load less than 85% of the maximum load, and the other is to find out the variance of all the controller load, to determine whether the variance is less than a parameter (according to the specific experimental settings). If the condition that the controller satisfies the load balance is satisfied, the calculation of the next time slice is finished. If the load balance condition is not satisfied, the switching strategy is started. Firstly, the controller with the largest load is selected as the outgoing controller, and the switch with the largest load is selected as the outgoing switch; then, the minimum load controller is selected as the immigration controller, and then the propagation delay between the switch to be migrated and the immigration controller is determined to be less than the maximum propagation delay, and the switch migration is completed by the cooperation of the out-and-in controller and the in-out controller. Otherwise, the switch is cycled until the load balance is achieved. Finally, the program ends by returning the relational matrix and the controller load.
## 7 Experiments and Results Analysis
### Fitness Analysis of the Path  
The OEABC parameters are set as: \(SN=5\), \(Limit=10\), \(Cycle=50\), and the running process of the algorithm is as follows:  
The first step is to initialize the population. Randomly generate five paths and calculate the fitness of each path. The results are shown in Table 3. Path 4 has the best fitness and is the optimal path.  
The second stage is the worker bee stage. The results are shown in Table 4. It can be seen that paths 1 and 4 are optimized, the path fitness is improved, and the optimal path is path 4.  
The third stage is the onlooker stage. As shown in Table 5, path 3 is optimized and the optimal path is still path 4. Since it is the first operation of the algorithm, it does not meet the requirements of Limit, so the scout phase is not performed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Path** & \(\eta\) & \(C\) & \(E\) & **Fitness** \\ \hline
1 & \(v_{0}\), \(v_{6}\), \(v_{19}\), \(v_{35}\), \(v_{53}\), \(v_{67}\), \(v_{88}\), \(v_{100}\), \(v_{112}\), \(v_{133}\), \(v_{140}\), \(v_{150}\) & 0.63 & 0.61 & 23 & 50.19 \\
2 & \(v_{0}\), \(v_{5}\), \(v_{28}\), \(v_{37}\), \(v_{60}\), \(v_{75}\), \(v_{84}\), \(v_{100}\), \(v_{118}\), \(v_{132}\), \(v_{138}\), \(v_{150}\) & 0.49 & 0.56 & 29 & 45.26 \\
3 & \(v_{0}\), \(v_{13}\), \(v_{28}\), \(v_{35}\), \(v_{49}\), \(v_{71}\), \(v_{81}\), \(v_{97}\), \(v_{107}\), \(v_{123}\), \(v_{136}\), \(v_{150}\) & 0.67 & 0.48 & 31 & 57.43 \\
4 & \(v_{0}\), \(v_{10}\), \(v_{23}\), \(v_{43}\), \(v_{48}\), \(v_{74}\), \(v_{85}\), \(v_{93}\), \(v_{114}\), \(v_{134}\), \(v_{139}\), \(v_{150}\) & 0.69 & 0.43 & 20 & 59.76 \\
5 & \(v_{0}\), \(v_{7}\), \(v_{27}\), \(v_{41}\), \(v_{56}\), \(v_{73}\), \(v_{90}\), \(v_{93}\), \(v_{109}\), \(v_{131}\), \(v_{149}\), \(v_{150}\) & 0.49 & 0.44 & 19 & 49.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The stage of worker bees.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation area: & 100 m \(\times\) 100 m \\ Energy model: & Generic radio energy model \\ Attenuation model: & Two ray \\ Signal transmission range: & 10 m \\ Signal interference range: & 20 m \\ Packet size: & 512 Byte \\ Output queue type: & First-In First-Out (FIFO) \\ Channel capacity: & 1 Mbit/s \\ Cache capacity: & 50 packets \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameter Settings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Path** & \(\eta\) & \(C\) & \(E\) & **Fitness** \\ \hline
1 & \(v_{0}\), \(v_{6}\), \(v_{19}\), \(v_{35}\), \(v_{53}\), \(v_{67}\), \(v_{88}\), \(v_{100}\), \(v_{112}\), \(v_{133}\), \(v_{140}\), \(v_{150}\) & 0.63 & 0.61 & 23 & 50.19 \\
2 & \(v_{0}\), \(v_{5}\), \(v_{28}\), \  
The results of the operations after 50 iterations, are shown in Table 6. It can be seen that the five paths have been optimized, the optimal path has changed from path 4 to path 3, and the fitness has also increased from the initial 58.42 to 71.46.
###### Abstract
The kriging method is one of most important meta-modeling methods to predict the responses at unknown points from some observed data [37], and the prediction is based on the spatial structure correlation, which makes it quite suitable for _challenge_ 2. In addition, the closed-form formula of the predictor can be derived, which makes the method easy to implement. The kriging method can also be extended to consider random noise at the same point, such as stochastic kriging [38], which meets the demand of the studied problem. One of the main factors that affect the performance of the kriging predictor is the choices of the correlation function, which is usually application-dependent. Thus, several spatial structure correlation functions, i.e., the variogram functions, are developed based on the features of the problem and compared to solve challenge 1. This is one of the main contribution in this paper. The kriging method is one of the most important meta-models for the spatial structure correlation analysis in a random field [39; 40]. Detailed descriptions of existing research on Kriging meta-modeling are provided in review [41]. Little research on WEEE return prediction considering spatial correlation has been conducted, and the literature on this topic is sparse. In contrast to the current methods, in this paper, a new mathematical spatial model for predicting WEEE return in reverse logistics based on Kriging method [42] is developed, considering the spatial correlation of the WEEE return amounts of different recycling sites. The spatial statistics based on Kriging method has been widely applied in many scientific fields such as geography, agrology, and meteorology. This study analyzes the spatial variogram structure considering the spatial correlation based on the Kriging method. Firstly, the WEEE recycling system is described. The second order randomness and the spatial structure of the WEEE return are analyzed. Secondly, the Kriging spatial model of WEEE return prediction is explored, and three variograms including spherical, exponential, and Gaussian variogram for prediction of WEEE return are calculated by the Monte Carlo simulation. Thirdly, a case study based on actual data is presented. The developed spatial prediction model can be used as a novel method and theory analysis tool for the prediction of WEEE return in reverse logistics. The rest of this paper is organized as follows. The spatial model based on Kriging method for WEEE return prediction is developed in Section 2, which considers both the randomness of the return amount and the spatial correlation between two different recycling sites. In Section 3, the simulation experiment and performance analysis are conducted based on the Monte Carlo method. In Section 4, a case study is presented to validate the proposed model based on the actual data. Finally, conclusions are given in Section 5.
# Effective Assessment of Inelastic Torsional Deformation of Plan-Asymmetric Shear Wall Systems
## 3 Inelastic Torsional Response of Plan-Asymmetric Structures
### Inelastic Torsional Response Assessment Procedure  
In this section, we discuss a procedure to assess the inelastic torsional response of plan-asymmetric wall structures. If any wall components yield during the application of lateral force, the linear relationship between the lateral displacement and rotation is no longer valid, and thus it must be modified by considering the reduced stiffness of the wall component that has yielded.  
Figure 3 illustrates a simple model problem of plan-asymmetric wall structures to explain the assessment procedure of the inelastic torsional response. This indicates that the structure has two lateral force resisting walls in the \(x\)- and \(y\)-directions, respectively, and the lateral force is applied in the \(y\)-direction. The two walls in the \(y\)-direction (Walls \(Y_{1}\) and \(Y_{2}\)) have elastic lateral stiffnesses of \(k\) and \(\alpha k\) (\(\alpha>1\)), respectively, and thus Wall \(Y_{2}\) is stiffer than Wall \(Y_{1}\). The two walls in the \(x\)-direction have the same elastic lateral stiffness \(\beta k\). Here, \(\beta\) can be of any value, but it may affect the inelastic behavior of the asymmetric structure after yielding of Walls \(Y_{1}\) and \(Y_{2}\), as will be discussed later. For simplicity, the out-of-plane deformation of the walls is ignored.  
The post yielding behavior of the two lateral force resisting walls \(Y_{1}\) and \(Y_{2}\) is defined as a bilinear function, as shown in Figure 4. They have the same yield displacement (\(u_{y}\)), and their post-yield stiffness is \(\gamma\) times the elastic stiffness (\(K_{y}^{E}\)). In contrast, we assumed that the other two walls in the transverse direction (Walls \(X_{1}\) and \(X_{2}\)) remain in the linear elastic range even after the two lateral force resisting walls yield. The inelastic lateral-torsional response of this structure can be obtained by following the step-by-step procedure described below.  
Figure 3: Model problem of a plan-asymmetric structure.  
Figure 2: Lateral displacement-rotation (D-R) coordinate system.
# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 6 ReID and Spatial-Temporal Multi Object ReID Methods
### Spatial-Temporal Constrained and Multi Object RelD Methods
#### 6.2.1 Deep Learning Metrics for Vehicle ReID
With this in mind, for each query image \(i\) and test image \(j\), a spatial-temporal similarity \(ST(i,j)\) is defined, and with a re-ranking strategy in combination to model the spatial-temporal information with the appearance and plate features. The model was evaluated using the VeRi-776 dataset, showing that the proposed method achieved a 0.277 mAP. The results are much lower when compared with related works. In addition, the use of discriminating features, such as the vehicle identification plate, may cause problems in the usage on public surveillance systems and comply with legislation in place. A unified multi-object tracking (MOT) framework is presented in [86], enabling exploring the full potential of the long-term and short-term cues for handling complex cases in MOT scenes. For better association, a switcher-aware classification (SAC) is proposed, exploring the potential of the identity-switch causer (switcher). Specifically, the proposed method incorporates a single object tracking (SOT) subnet to capture short-term cues, a ReID subnet to extract long-term cues and a switcher-aware classifier to make matching object decisions, using extracted features from the main target and the switcher. The main objective of short-term cues is to help find false negatives, while long-term cues avoid critical mistakes when occlusion occurs, with the SAC learning used to combine multiple cues in an effective way to improve robustness. The SOT subnet and the ReID subnet are trained independently. For the SOT subnet, image pairs of targets are generated according to the GT of the videos, and the pairs are extended to include part of the background according to the training schema of Siamese-RPN. On the other hand, for the ReID subnet, each target is regarded as one class, with the network trained to predict the class of the input target. Extensive model evaluations were performed using the challenging MOT16 benchmarks [87], achieving a CLEAR MOT of 71.2%, proving the effectiveness of the switcher to robustly assign long-term occluded objects to corresponding tracklets. Performance evaluation of vehicle ReID using deep learning metrics is resumed in Table 7.  
Since it is similar to person ReID, several authors explore the Siamese and triplet loss for the ReID task. Because the scheme is similar in what concerns the job itself, the leaned features are very distinct, hampering the use of Siamese and triplet loss, both for person and vehicle ReID in a single unified framework.
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS
### Scenarios  
There are several scenarios that can be considered while envisioning the widespread usage of JCRS. The authors of [10] described communication challenges across several verticals of future communication systems. Numerous scenarios can potentially reuse the existing infrastructure/hardware benefiting from JCRS technology. Here, three such scenarios are discussed, placing a focus on modern system requirements.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 5 Details of Proposed Protocol
### Abnormal Video Recording  
We assume that the in-built camera has the functionalities which can allow the driver to upload a given file or the camera's sensors can decide to upload a particular video after analyzing abnormal movements within the video [49]. The timing and circumstances techniques in which the video should be uploaded are not within the scope of this paper. The vehicles perform the following before uploading the video file captured by the camera:  
1. After recording a video file, \(v_{i}\) composes a message \(V=\{Vfile_{j},KN\}\); uses the access policy \(\omega_{v_{i}}\) retrieved in the credential \(R\)-_cred_ and encrypts the file under the given attribute set \(\omega_{v_{i}}\) as \(SF_{j}\leftarrow\text{ABE.Encrypt}(Vfile_{j},\omega_{j},\mathit{labe.params})\).
2. \(v_{i}\) sends \(C_{4}=Enc_{PK_{RS_{C_{j}}}}\{alias_{i},KN,SF_{j}\}\) to \(RSC_{j}\).
3. \(RSC_{j}\) decrypts \(C_{4}\) and check if \(\{KN\in List_{KN}\}\).
4. If not \(C_{4}\) is discarded. Note that \(KN\) value are similar for all the vehicles which have a similar set of attribute such as \(\omega\). As mentioned before, \(DMV_{j}\) can choose to give a type of attributes to a vehicle based on different criteria such accident record and reckless driving record. The choice of those criteria is beyond the scope of this paper.
5. Otherwise, \(RSC_{j}\) forwards the file securely to neighboring \(RSCs\).
6. \(RSC_{j}\) generates \(C_{5}^{{}^{\prime}}=\delta_{RSC_{j}}=\text{CertS.Sign}(SF_{j},\mathit{sk}_{ RSC_{j}})\) and broadcast \(C_{5}=Enc_{RK}(C_{5}^{{}^{\prime}})\) within its coverage area. After receiving the beacons, \(DV\)s performs the following:
7. \(DV_{j}\) decrypts \(C_{5}=Enc_{RK}(C_{5}^{{}^{\prime}})\) using the area shared key of \(RK\). Note that only \(DV\)s assigned to work within \(RSC_{j}\) coverage can decrypt the \(C_{5}\).
8. \(DV_{j}\) runs \(\text{CertS.Verify}(SF_{j},\mathit{RSC_{j}},\mathit{pk}_{RS_{C_{j}}},\delta_{ RSC_{j}})\).
9. \(DV_{j}\) runs \(\text{ABE.Decrypt}(SF_{j},\bar{D},\mathit{labe.params})\) to get the original file of \(Vfile_{j}\).
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 5 Conclusions  
In this paper, MBDQN is proposed as the first reinforcement learning solution for the satellite resource allocation problem based on the transparent transponder. By introducing TL-Branch and RP-Branch into the network, the discriminative features of the resource pool and the task list are accurately extracted. Based on the structure of DQN, a multi-type action-value function, including priority and task index number, is selected through Value-Branch, which considerably improves the performance of average occupancy percentage and time consumption. Finally, comprehensive experiments demonstrated that our proposed MBDQN performed better than both traditional greedy methods and heuristic methods on both the generated zero-waste task dataset and non-zero waste task dataset. These experimental results highlight the effectiveness and reliability of MBDQN in solving satellite resource allocation problems.  
Conceptualization, M.J., N.M., W.Z. and W.S.; methodology, W.Z. and W.S.; software and validation, N.M. and W.S.; formal analysis, N.M. and W.S.; investigation, W.S.; resources, M.J.; data curation, N.M.; writing--original draft preparation, W.Z. and W.S.; writing--review and editing, N.M. and M.J.; visualization, W.S.; supervision, M.J.; project administration, N.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China No. 62231012, Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001.  
Not applicable.  
The data presented in this research are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
## 1 Introduction
[16] applied Lp shrinkage [17] to the reconstruction of tomographic images to obtain an effect superior to the L1-norm constraint. [PERSON] et al. [18] applied an Lp-pseudo-norm constraint to the sparse time-frequency representation of the seismic signal spectrum to obtain a time-frequency spectrum with higher time-frequency resolution. [PERSON] et al. [19] proposed a model combining an Lp-pseudo-norm fidelity term and OGS regularization, which uses neighborhood structure similarity of image gradients for image denoising. Considering the superiority of Lp-pseudo-norm and TGV, here, we employ Lp-pseudo-norm to describe the sparsity of impulse noise and adopt the TGV regularization term to fit the image sparsity. On the one hand, the Lp-pseudo-norm constraint increases the degrees of freedom of the fidelity term and better represents the sparsity of the impulse noise. In this way, the deblurring process is more robust to noise pollution. On the other hand, the TGV regularization term simultaneously puts constraints on the information of the first-order and second-order gradients of the image, thereby effectively suppressing the staircase effect. To solve the problem, the alternating direction method of multipliers (ADMM) [20; 21] is employed to decompose the complex problem into a number of sub-problems that are relatively easy to solve. At the same time, to improve the efficiency of the algorithm, it is assumed here that the image satisfies a periodic boundary condition. Following a fast deconvolution method [3; 22; 23], fast Fourier transform (FFT) is employed based on the convolution law to transform image differences in the time domain to differences in the frequency domain to avoid large matrix multiplication operations. In numerical experiments, the proposed new method is compared with FTVd, TGV, and OGSTV in terms of the following objective indicators: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), signal-to-noise ratio (SNR), and computation time. The rest of this paper is organized as follows: Section 2 presents prerequisite knowledge related to the algorithms used in this study, including the modeling of the TGV regularization term and the Lpnorm. Section 3 firstly presents the model proposed in this study and then elaborates on an efficient method for solving the model in the ADMM framework. Results of the numerical experiment are presented in Section 4, and conclusions are given in Section 5.