esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Handling Massive IoT Traffic in 5G Networks  
[PERSON]  
1 Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25120, Pakistan; [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (T.K.); [EMAIL_ADDRESS] (A.K.)  
[PERSON]  
2 Communication Networks, University of Bremen, 28359 Bremen, Germany; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25120, Pakistan; [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (T.K.); [EMAIL_ADDRESS] (A.K.)  
[PERSON]  
1 Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25120, Pakistan; [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (T.K.); [EMAIL_ADDRESS] (A.K.)  
[PERSON]  
3 Department of Computer Science, University of Engineering and Technology Peshawar (Jalozai Campus), Nowshera 24240, Pakistan; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS]; Tel.: +92-91-922-22  
high spectral efficiency in air interface requires intelligent use of scarce radio resources. Regular human-based broadband traffic in LTE-A can efficiently utilize radio resources with transmission of large messages via a single resource block of frequency. However, these resource blocks may be underutilized while serving narrowband IoT applications due to the transmission of small-sized messages with transmission gaps. As per 3 GPP specifications, each device must use at least one resource block for transmission of a message without sharing the block with other devices.  
Instead of allowing IoT devices to access the Base Station (BS) directly, a Relay Node (RN) could be used to accumulate data packets transmitted by IoT devices. RN is deployable without adding infrastructure for backhaul connection and can be connected wirelessly to the BS. This work investigates a framework for aggregating IoT data from a group of IoT devices and then multiplexing the traffic at RN before sending to the BS. Thus, rather than demanding frequency resources from the BS for singular IoT devices, resource requests are executed by RN for groups of IoT devices collectively. BS would consider a resource demand for aggregated data as a solitary request. The effectiveness of the method is established with the help of an analytical model.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 4 Simulation and Results
### Simulation Setup  
In the scenarios, nine agents are designed as a virtual linkage which consists of two virtual links (See Figure 6) and move around a circle with a radius of 1 m in 10 s. The states of \(\chi=\left(l_{i},{}^{vj}p_{i}^{ini}\right)\) for each agent is predefined with Equations (9) and (10).  
\[Virtual linkage=\left\{\begin{array}{l}virtuallink1=\{agent1,agent2,agent3,agent4,agent5\}\\ virtuallink2=\{agent5,agent6,agent7,agent8,agent9\}\end{array}\right. \tag{9}\]  
Recall that \({}^{vj}p_{i}\) is the representation of the \(i\)th point in the \(j\)th virtual link coordinate frame. The nine agents are initialized with:  
\[\begin{array}{l}{}^{v1}p_{1}^{ini}=[0,0],{}^{v1}p_{2}^{ini}=[0.2,0],{}^{v1}p_ {3}^{ini}=[0.4,0],{}^{v1}p_{4}^{ini}=[0.6,0],{}^{v1}p_{5}^{ini}=[1,0]\\ {}^{v2}p_{5}^{ini}=[0,0],{}^{v2}p_{6}^{ini}=[0.2,0],{}^{v2}p_{7}^{ini}=[0.4,0],{ }^{v2}p_{8}^{ini}=[0.6,0],{}^{v2}p_{9}^{ini}=[1,0]\end{array} \tag{10}\]  
Meanwhile, each agent has an instantiation of virtual linkage states \(\xi_{i}=(r_{i},q_{i},\Theta_{i},\lambda_{i})\) and is initialized as:  
\[\xi_{i}=randn(6,1) \tag{11}\]  
Figure 5: Geometry definition of the virtual linkage.  
Moreover, there does not exist leader selection for each virtual link. Figure 7 shows the communication topologies used for these two simulations. Notice that apart from agent 2, each agent only needs to exchange \(\xi_{i}\) and its own position \({}^{w}p_{i}\) with its two neighbors.  
Initially, the nine agents are required to align in a line formation with a uniform distance of 0.1 m. Then different formation shapes are reconfigured by coordinating the virtual joint angles \(\Theta_{d}\) and the virtual linkage extract expansion rates \(\lambda_{d}\). It is worth mentioning that instead of refreshing the relative positions of all the agents, the virtual linkage approach can be reconfigured into different shapes by only changing \(\Theta_{d}\) and \(\lambda_{d}\).  
In the following simulations, the required trajectory and formation shapes are specified by:  
\[\xi_{contr}=(r_{d},q_{d},\Theta_{d},\lambda_{d}) \tag{12}\]
## 4 Case Study of a Real-World Power Grid
### Scale-Free Characteristics and Vulnerability of the Indian Power Grid  
Utilizing real data of the Indian power grid with its 572 nodes and 871 edges, its robustness and vulnerability are simulated and analysed as follows. Normally, it is assumed that the node with the largest load is the most important one, while the nodes with light loads are always ignored. However, nodes with light loads cannot be excluded from consideration and are taken into account in this case study. We notice that:  
* Figure 5a,b shows that the probability of very large cascading failures (\(a_{m}>0.98\), marked in red) grows with as \(\alpha\) increases under HLA, but decreases under HLA.
* It is obvious that HLA can lead to a definite cascading failure in the power grids of interest. In the case of the Indian cascading failure on 30 and 31 July 2012, it was caused by the failure of the Bina-Gwalior-Agra line, which took the heaviest load in the power grid. This fact shows great agreement with the simulation conducted in this study. Hence, in reality, special care needs to be taken for the nodes with the highest loads, e.g., more back-up capacities should be provided or more protective measures should be taken to ensure the stability of the power system.
* LLA is normally ignored, but can also cause a definite cascading failure, i.e., the light loads in power systems are vulnerable under LLA. Hence, it is necessary to pay more attention to light loads.  
Figure 4: Probabilities of cascading failures in the Indian power grid under natural failure conditions.  
Figure 5a,b shows the dependence of the avalanche index \(a_{m}\) of Equation (8) for the Indian power grid on the parameter \(\alpha\) and the tolerance parameters \(\beta\) and \(\gamma\) under HLA and LLA, respectively.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 1 Introduction
The main goal of RLQR is to reach high retrieval effectiveness as well as lower communications cost by reducing the number of contacted peers. The main contributions of this study are as follows:  
1. We introduce a fully distributed query routing approach for pure unstructured P2P systems, in which each peer learns only from its local gathered data. 2. We introduce a new formulation for the K-Neighbors-Selection (K-NS) problem different from that of the existing approaches. Indeed, we consider that the natural model for this issue are formalizations from reinforcement learning, in which an agent learns by taking actions that produce rewards. The goal of the agent is to find a selection policy of actions that maximizes the cumulative reward. In the query routing setting, the forward peer could run a learning algorithm (e.g., agent) that selects \(k\) neighbors (e.g., actions) for each search query. Each selected neighbor yields a binomial reward which expresses its ability to produce pertinent documents to the query. Indeed, the agent must learn a K-NS policy to maximize the cumulative rewards leading to higher user satisfaction. 3. We address the cold-start issue during training, which is considered as the main hindrance of query routing approaches based on supervised machine learning or data-mining methods. Indeed, a cold start happens when a new peer joins the network. Existing query-oriented methods assume that each peer has submitted certain number of queries and received replies from relevant peers. Hence, the gathered data from previous queries are stored in the sender log file then used as training data. However, this assumption is not fulfilled in the case of a newly joined peer. Therefore, existing methods [7; 9; 15; 16] required to randomly flood a certain number of queries to gather training data, which undoubtedly leads to achieving low performances during the training phase. To tackle this problem, we introduce here reinforcement learning into the neighbors selection algorithm. Indeed, our approach RLQR balances between exploration (pulling different neighbors to learn new information) and exploitation (i.e., pulling the neighbor with the highest estimated reward based on previous queries). Doing so, RLQR improves the routing performance continuously and therefore goes quickly through the cold-start phase [7; 17; 18; 19]. The rest of this paper is structured as follows. In Section 2, we review the related work. In Section 3, we first formalize the query routing issue as the reinforcement learning problem then we thoroughly describe our approach. The simulation settings and the evaluation of the proposed approach are presented in Section 4. Section 5 concludes this study and sketches of future directions.
# An Improved Hybrid Control Scheme of a Switched Reluctance Motor for Torque Ripple Reduction
## 5 Experimental Result
The output torque is positively correlated with the load torque. The proposed method is superior in terms anti-disturbance ability and torque ripple reduction. As for MPTC, the torque performance is controlled by the angles. The proposed method influences the torque ripple, dynamic response, and anti-disturbance ability. Figure 12 shows the phase current waveforms of the three control methods under 0.4 Nm and 0.6 Nm when the reference speed is set as 1000 r/min. The current waveforms and amplitudes of different schemes are similar. The peak current under 0.4 Nm is approximately 6 A, which is smaller than that under 0.6 Nm (8 A). Moreover, the current ripple of method 3 is lower than that of the other methods, which is positively correlated with torque ripple. Figure 11: Speed and torque under load torque change. (**a**) Method 1, (**b**) method 2, and (**c**) method 3. The intuitive comparison results of the three methods under different working conditions are listed in Table 1. The critical performance information including startup time, response time, speed overshoot, and torque ripple range demonstrates the effectiveness of the proposed method. The above results reveal that the proposed method can provide much better control performance, including speed tracking, dynamic response, torque ripple reduction, anti-disturbance ability, and robustness, compared to the two other schemes.
## 3 Results and Discussion  
### Films Characterization  
#### 3.1.1 SEM Analysis  
SEM micrographs of surface and cross-section of neat PCL and active films (PCL3 and PCL6) are shown in Figure 1. An homogeneous and smooth surface was observed for all films without the presenceof pores or cavities. However, some isolated spots (micrographs **c** and **e**) were shown with the addition of ASE to PCL, although no significant structural modifications were observed (micrographs **d** and **f**). This behaviour might be due to the hydrophilicity of polyphenic compounds present in ASE. Similar microstructural findings were reported for chitosan-based films containing chive root extract [45], agar [46] and carrageenan [47] films incorporated with grapefruit seed extract, and gelatin-based films additivated with curcumin ethanol extract [48]. In conclusion, it was considered that ASE compounds added at the studied concentrations by melting at 80 \({}^{\circ}\)C followed by a compression-moulding at 120 \({}^{\circ}\)C, as previously described, were dispersed in the polymer matrix uniformly without any significant particles aggregation resulting in homogeneous films with uniform thickness and good appearance.
## 5 Results and Discussions
### Model Applicability  
Since the SubwayBJ dataset only contains the flow data of Beijing Metro Line 13, to prove the good applicability of our ST-RANet model, we additionally processed the data of Beijing Metro Line 15 for the experiments. A \(4\times 5\) feature map was created for a total of 20 stations, and other settings are the same as the SubwayBJ dataset. The prediction results of the inbound traffic at a station of Line 15 are shown in Figure 12. The predictive indicators obtained by the model are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE(\%)** \\ \hline ST-ResNet & 56.60 & 27.37 & 1.24 \\ ST-RANetExt & 54.99 & 27.45 & 1.02 \\ ST-ResNetExt & 57.41 & 27.75 & 1.16 \\
**ST-RANet** & **54.24** & **26.84** & **0.78** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiments on SubwayBJ.  
Figure 12: Prediction results of ST-RANet on Line 15.  
Line 15 has an increase in network dimensions compared to the data in line 13. However, based on the results, it can be seen that on the dataset of Line 15, our ST-RANet model has a similar or even better prediction performance compared to Line 13, with a mean absolute error of less than 1% of the real data. With an accuracy of over 99%, it proves that our model has good generalization ability and applicability. When the model size increases, we can also adjust epochs, batch_size, and other hyperparameters in Table 2 to control the convergence speed and overfitting degree of model training, making the model have good scalability. In the future, the model can be applied to the entire Beijing metro network to predict the passenger flow of the whole network simultaneously.
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
## 5 Numerical Simulation Analysis and Experimental Verification
### Internal Flow Analysis  
The streamline distribution of the impeller before and after optimization is shown in Figure 15. When the incoming flow makes contact with the leading edge of the blade, the flow velocity increases rapidly due to the rotation of the impeller, and then decreases with the development of the blade passage. The high-speed range of the optimized impeller is obviously larger than that of the original impeller. The whole streamline is relatively flat, and there is no obvious bad flow area.  
To further study the characteristics of internal flow, four planes are selected in the moving blade and guide vane basin along the direction of the medium flow to divide the basin equally. The sections selected on the impeller are A\({}_{1}\) and A\({}_{2}\), and the sections selected on the guide vane are B\({}_{1}\) and B\({}_{2}\), as shown in Figure 16.  
The velocity distribution of each section is shown in Figure 17. Overall, the velocity changes, decreasing gradually along the airflow direction. After optimization, the airflow velocity in the impeller basin becomes significantly larger. The intuitive manifestation is that the fan flow rate is significantly improved, which is consistent with the previous optimization calculation results. At the same time, the velocity of the airflow in the guide vane basin is much lower than that in the blade basin. This is because the guide vane is a stationary component and cannot provide power. It only plays a role in changing the  
Figure 16: Cross-section of blade in different flow directions.  
Figure 15: Flow chart of impeller region. (**a**) Original. (**b**) Optimized.  
flow direction and diffusion. Therefore, a local low-speed zone appears near the guide-vane wall.  
Considering the cross-section shown in Figure 16, the pressure changes in the fan flow channel before and after optimization is shown in Figure 18.  
Overall, globally, the static pressure of the impeller pressure surface is higher than that of suction surface; compared to the local guide vane channel of the guide vane, the pressure distribution of the local impeller channel of the impeller shows stronger periodicity. It is not difficult to find that the overall static pressure of the optimized guide vane is significantly higher than that before optimization. It is caused by the conversion of more kinetic energy into pressure energy. Therefore, the energy conversion ability of the optimized fan is improved.
# Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans
## 7 Field Navigation
### Experimental Results  
The performance of the navigation system of Andabata has been tested in an urban park with several lampposts, stone benchs and trees (see Figure 8). The park contains soft slopes and different terrains such as grass and sand.  
The experiment shown in Figure 16 consists of an outdoor trip from a start point (white cross) to reach two GPS goals (blue dots), which are fulfilled when the robot is closer than \(10\,\mathrm{m}\) (red circles). The path followed by Andabata is shown with black dots representing the GPS positions obtainedfrom the onboard smartphone. The longitudinal speed of the robot was set to \(v=0.34\,\mathrm{m}\,\mathrm{s}^{-1}\) and the trip took \(540\,\mathrm{s}\), approximately. Figure 16 has been generated with the help of _Google Earth_[36].  
Changes in the yaw angle during this experiment (obtained with the magnetic compass) can be observed in Figure 17. Remarkable changes occur when the robot tries to aim to the first and second goal points at the beginning and the middle of the trajectory, respectively. Once the goal direction is obtained, some corrections are made in order to cross through traversable areas.  
Figure 18 shows a local FEM and its FRM generated during autonomous navigation. Small black arrows have been superimposed to represent the successive positions of the mobile robot in local coordinates (provided by the Local_positioning node). It can observed how obstacle avoidance is performed while moving towards the goal point.  
Figure 16: GPS positions of Andabata during navigation (top view).  
Figure 17: Yaw variations during the experiment.
# Evaluation and Improvement of Internet Printing Protocol Based on HCPN Model Detection Method
## 3 Establishment of HCPN Model for IPP Authentication Process
### Consistency Verification
#### 3.3.1 Expected Results  
When analyzing the spatial state of the protocol model, researchers need to investigate its state-space nodes, main markings (main-state nodes), dead markings (dead-state nodes), and transitions, and then compare them with the expected results to judge whether the model was constructed correctly.  
As shown in Figures 7 and 9, when the communication parties verified each other's identities, they first decrypted the other party's identity information and the corresponding hash value, and then used the agreed-upon algorithm to calculate another set of hash values for the identity information. Comparing the consistency of the two sets of hash values was key to determining whether the authentication process was successful. When the verification was successful, the token \"OK\" was returned, and the protocol entity continued to execute the protocol (green); otherwise, \"err\" was returned, and the protocol entity triggered the reset transition RESET to re-execute or terminate the protocol (red); therefore, it could be predicted that transitions RESET0 and RESET1 were two dead transitions in the model.  
In addition, the original authentication model had to reach the terminal and end operations after the authentication was complete, so the following could be determined: The model coverage state was full; there was no live transition in the terminal state, only one main marking and one dead marking. The main marking and dead marking, though their names might be unknown, had to be consistent and numbered equal to the terminal marking's number. Table 2 presents the expected results for the state space of the original protocol model.
## 4 Physical and Mechanical Parameters
### UCS and Tensile Strength  
Figure 2d,e shows the effect of high temperature on the UCS and tensile strength, respectively. Both parameters decrease with increasing temperature, consistent with thermally induced degradation in rock strength. UCS indicates low correlation with temperature, while the tensile strength shows a high correlation with temperature. This can be explained by the fact that high temperature tends to transform granite samples from a more brittle material to a more ductile material.
## 3 Methods  
This article obtained the cumulative surface deformation data of Zhengzhou City using VV polarized Sentinel-1A images over a time span from 14 July 2019 to 12 June 2022, using the PS InSAR method. Then, we interpolated into grid form and applied SSA method to extract residuals on each grid time series. Afterwards, the boxplot method was used to calculate all residuals on each grid and filter out outliers.
# Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform
## 4 Results and Discussion
### Hydrodynamic Performance in Regular Waves  
This study employs the software OrcaFlex 11.0a to conduct a coupled analysis of the platform's dynamic responses. OrcaFlex is a software for dynamic analysis of marine systems, which can simulate various types of structures. A fully nonlinear numerical model is constructed in OrcaFlex.  
Figures 17-19 illustrate the surge, heave, and pitch RAOs of the moored platform with and without nets in regular waves with a heading angle of 180\({}^{\circ}\). The wave heights considered are 1 m and 3.5 m, with the periods ranging from 5 s to 30 s. It is evident that the surge, heave, and pitch RAOs of the platform are quite small within the wave  
Figure 16: The mean drift force of yaw at the 90\({}^{\circ}\) heading angle.  
Figure 15: The mean drift force of sway at the 90\({}^{\circ}\) heading angle.  
period of 5-15 s, where the majority of energy is concentrated, regardless of the presence of nets. The corresponding surge RAOs are less than 1 m, the heave RAOs are significantly smaller than 1 m, and the pitch RAOs are less than 1\({}^{\circ}\). These results demonstrate the excellent hydrodynamic performance of the new platform. For wave periods larger than 15 s, under the conditions of H = 1 m, the impact of nets on the surge, heave, and pitch RAOs is minimal for most periods. However, the presence of nets reduces the RAOs near the natural period of heave and pitch, which may be attributed to the increased damping effect induced by the nets. Similar effects of nets on the peak RAOs of heave and pitch can be found in Figure 9 of [48]. For wave periods larger than 15 s, under the conditions of H = 3.5 m, the effects of nets on the RAOs are complex. The existence of nets reduces the values of surge and heave RAOs for most periods, but the opposite results are observed for the roll RAOs. Additionally, incident waves with larger wave height correspond to smaller surge, heave, and pitch RAOs of the platform for wave periods larger than 15 s.  
Figure 17: Surge RAOs for the moored platform with/without nets.  
Figure 18: Heave RAOs for the moored platform with/without nets.