esa-sceva/satcom-chunk-collection huggingface dataset:
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 1 Introduction
### Outline and Scope of This Work  
The approach in this work is to develop a single dispatching scheme that coordinates a pair of prosumers to optimize their overall renewable energy penetration. This is unique in that it utilizes future predictions of supply and load for both prosumers and creates a single objective function to optimize. The control scheme determines the flows within each prosumer, as well as the energy trading between them, to optimize an objective, effectively turning the two prosumers into a single operating unit. This is conceived here as a centralized approach that could be implemented by a third party who receives data from both prosumers, as well as weather forecasts, and transmits real-time energy allocation instructions back to the prosumers. Future work will focus on a decentralized version of this concept, operating in a manner like the prosumer network in New York mentioned earlier [11]. The scheme would also require cooperation from the local utility company, in terms of reasonable tariffs for utilizing their infrastructure and for purchasing excess energy from the prosumers. The two prosumers can be pre-existing entities (prosumers) that are ideally selected to be complementary, such that one is likely to have an energy deficit when the other has a surplus. The proposed control scheme enables better overall penetration for the two prosumers and reduces the exchanges between them and external providers. The behavior of the algorithm can be modified through the objective function, by including terms that reflect battery storage lifetime, for example.
# Promoting Urban Farming for Creating Sustainable Cities in Nepal
## 3 Materials and Methods  
### Materials  
We used (a) demographic data and other socioeconomic data from the Central Bureau of Statistics (CBS), Nepal; and (b) land-use and land-cover data at 10 m \(\times\) 10 m pixel levels that were taken from the global land-cover Sentinel-2 surface reflectance for UTM Zones 44 and 45 N from the Esri website [22]. Using a deep-learning model, we categorized satellite photos by using more than 5 billion manually annotated Sentinel-2 pixels collected from more than 20,000 locations spread across all the world's major biomes. Only a fraction of Nepal's land-use and -cover information for 2017 until 2021 was retrieved, using a shapefile to extract Nepal's information. We identified seven types of land use within the urban administrative territories of Nepal. These include (1) a forest with dense (over 40%) tree canopy cover, (2) shrubland with 10-15% tree crown coverage spread throughout, (3) agricultural land with various crops, (4) snow/ice, (5) glacier-covered places, (6) urban areas, and (7) water (Figure 2). Our primary objective was to investigate how urban dynamics in Nepal's three ecological areas are changing the way land is used and covered. More importantly, our major goals were to see the status of urban agriculture from 2017 to 2021 because Nepal's rural areas have been massively annexed to population centers, and these areas were promoted to an urban definition despite their rural characteristics. We desired to also include the year 2022, but we could not find the appropriate data that would be useful for this research.  
Figure 4: Benefits of urban farming. Graphics showing some benefits of locally produced food in urban areas. Conceptualized and drawn by the authors, using some generic images from the web.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 5 Conclusions  
In this paper, we proposed a reinforcement learning-based approach to address the rendezvous problem in cognitive radio networks, specifically focusing on scenarios where the available channels differ among secondary users. Through extensive simulations, we demonstrated that the proposed method offers notable improvements in time-to-rendezvous (TTR) when compared to traditional algorithms, such as the JS algorithm.  
In asymmetric scenarios, where some channels are either occupied by primary users or are unsuitable for communication, the reinforcement learning-based algorithm significantly outperformed the JS algorithm, achieving much lower ETTR similarly to the symmetric scenarios. This performance boost can be attributed to the algorithm's ability to adaptively learn and optimize channel selection strategies, even in environments where channel availability is unpredictable.  
In conclusion, the proposed algorithm demonstrates improved performance over conventional methods, though it also highlights areas for potential enhancement. Specifically, expanding the agent's ability to select a more diverse range of actions during channel search could help avoid repetitive patterns that may delay successful rendezvous. Additionally, reward convergence shows room for improvement, suggesting that further refinement is needed. Future research will therefore focus on optimizing the rendezvous process by designing specialized actions suited for rendezvous scenarios and incorporating techniques to ensure stability within the reinforcement learning framework.  
Conceptualization, D.J. and J.-W.J.; methodology, D.J.; software, D.J.; validation, M.J., J.-W.J. and G.K.; formal analysis, D.J.; investigation, J.-W.J.; resources, M.J.; data curation, D.J.; writing--original draft preparation, D.J. and M.J.; writing--review and editing, [PERSON]-[PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]-[PERSON] and G.K.; funding acquisition, [PERSON]-W.J. and G.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-003) for [PERSON], [PERSON], and [PERSON]. Also, this research was financially supported by Hansung University for [PERSON].  
Not applicable.  
The data will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Numerical Investigation on Handling Stability of a Heavy Tractor Semi-Trailer under Crosswind
## 1 Introduction
[14] constructed a gust wind model and applied to a parameterized bus. The results showed that the crosswind velocity was the main factor on directional deviation, and the yaw moment and lateral force coefficient were also the most significant considerations among the aerodynamic coefficients. The previous research showed that the research related to crosswind aerodynamics and crosswind stability were relatively separated from each other, which did not consider the interaction between the two parts [15]. It is undeniable that the crosswind will influence the aerodynamic characteristics and may induce lateral displacement. Moreover, by considering the change of aerodynamic characteristics under crosswind, the handling stability analysis is more convincing. Therefore, in this work, a coupling approach was adopted to investigate the crosswind aerodynamic and crosswind stability of a heavy  
Figure 1: Wind-induced rollover of a tractor trailer. duty tractor trailer, systematically. By this means, the aerodynamic characteristics under different crosswinds [16] were computed by computational fluid dynamics (CFD), and a mathematical model was constructed to describe the relationship between aerodynamic forces and crosswind. Then a full vehicle dynamic model of a tractor semi-trailer was constructed, and the effect of the crosswind on stability was investigated by considering the mathematical model of the aerodynamic force as an input. Based on this coupling method, the heavy tractor trailer with and without a deflector was used to evaluate the aerodynamic characteristics and dynamic response of the truck in different crosswinds. The results have demonstrated that the deflector, which can reduce the lateral force and yawing moment, is also beneficial to improve the handling stability greatly under crosswind, and has a certain significance for engineering applications.
## 3 Development and Validation of CFD/CSD Coupled Solvers
### Structural Dynamics Equation  
The fluid-structure coupling vibration satisfies the following equation:  
\[M\ddot{u}+C\dot{u}+Ku=f(t), \tag{12}\]  
In the above equation, \(M\),\(C\),\(K\) are the mass matrix, damping matrix and stiffness matrix of the system, respectively. _i,u,u_, respectively, correspond to the acceleration, velocity and displacement vector of structure movement; \(f(t)\) is the time-dependent external excitation on the system. For aeroelastic systems, \(f(t)\) is the aerodynamic force acting on the structure.  
In this paper, the modal coordinate transformation method is used to solve the structural dynamics equation. Introduce coordinate transformation:  
\[u=\Phi q, \tag{13}\]  
In the above equation, \(u\) is the coordinate in physical space, and \(q\) is the modal coordinate, so:  
\[\dot{u}=\Phi\dot{q}\quad\ddot{u}=\Phi\ddot{q}, \tag{14}\]  
Equation (13) and Equation (14) are substituted into Equation (12):  
\[M\ddot{\Phi}q+C\Phi\dot{q}+K\Phi q=f(t), \tag{15}\]  
Multiply the left and right sides of the equation by \(\Phi^{T}\) to obtain:  
\[\Phi^{T}M\ddot{\Phi}\dot{q}+\Phi^{T}C\Phi\dot{q}+\Phi^{T}K\Phi q=\Phi^{T}f(t), \tag{16}\]Substituting Equation (14) into Equation (16) yields the following equation.  
\[diag[M_{r}]\ddot{q}+\Phi^{T}C\Phi\dot{q}+diag[K_{r}]q=\Phi^{T}f(t), \tag{17}\]  
In this paper, the damping of the structure is assumed to be 0, and then the above equation can be simplified as follows:  
\[diag[M_{r}]\ddot{q}+diag[K_{r}]q=\Phi^{T}f(t) \tag{18}\]  
For Equation (18), introduce the state variable:  
\[E=\begin{bmatrix}q\\ \dot{q}\end{bmatrix} \tag{19}\]  
Combined, Equation (18) can be converted to:  
\[\dot{E}=\begin{bmatrix}0&I\\ diag[M_{r}]\dot{q}diag[K_{r}]q&0\end{bmatrix}E+\begin{bmatrix}0\\ diag^{-1}[M_{r}]\Phi^{T}f(t)\end{bmatrix} \tag{20}\]  
The classical four-step Runge-Kutta iterative method is adopted to solve the equations, and the iteration format is as follows:  
\[E^{(0)} =E^{(n)} \tag{21}\] \[E^{(1)} =E^{(0)}+\tfrac{\Delta t}{2}\dot{E}^{(0)}\]
# Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices
## 5 Conclusions  
The proposed method in this paper, Edge-YOLO, is a lightweight IR target detection approach that aims to ensure good performance in road scenes and is suitable for edge-embedded devices. The algorithm utilizes an optimized bounding box loss function, the improved EX-IoU, to enhance the regression accuracy of the bounding box. Moreover, to improve the up-sampling effect, the algorithm adopts the improved CAU-Lite up-sampling operator, which perceives the contextual content. Lastly, the lightweight ShuffleBlock replaces the backbone feature extraction part of the network, and the strip depthwise convolutional attention module is used to enhance the extraction capability of strip-shaped targets and other salient features present in the IR feature map for the ShuffleBlock, thus further enhancing the detection accuracy of the model. The experimental results on the FLIR dataset demonstrate that Edge-YOLO is essentially equivalent to YOLOv5m in terms  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **FPS(CPU)** & **FPS(NPU)** \\ \hline YOLO-FIRI & 73.0 & 0.8 & 21.6 \\ YOLOv5m & 79.1 & 0.5 & 14.5 \\ YOLOv7 & **81.2** & 0.3 & 8.8 \\
**Edge-YOLO** & 78.6 & **1.1** & **31.9** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Testing results on the RK3588 platform.  
Figure 9: The RK3588 embedded development board.  
of accuracy, while reducing the number of network parameters, computation, and model size by 72.2%, 70.3%, and 71.6%, respectively. Additionally, the detection speed is increased by 44.4%, making the algorithm more suitable for embedded device applications.  
Conceptualization, J.Y.; methodology, J.L.; software, J.L.; validation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and J.Y.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Impulsive Noise Reduction
#### 3.3.2 Coefficient Scaling  
Subsequently, a scaling is made to the detail coefficients \(d_{ji}\) of each device \(i\). In this process, 0 is assigned to the coefficient with the smallest value, 1 is assigned to the coefficient with the largest value, and a value between 0 and 1 is assigned to the remaining coefficients \(d_{ji}\) as a function of its original value, resulting in the \(dd_{ji}\) signal, as it is shown in Figure 7.
# Graph Dilated Network with Rejection Mechanism
## 6 Experiments
### Results for Node Classification Task  
To fully evaluate the performance of GraphDRej (including RM and MGDCK) and baselines, we not only report the classification accuracy (shown in Table 2) of these models with the same two layers but also report the best results (presented in Table 3) achieved by these models with different numbers of layers. These results provide the positive evidence to question **Q1**: GraphDRej significantly outperforms previous models both on the two-layer results and on the best results over all data sets. For the tasks with the two-layer models, GraphDRej achieves an improvement ranging from 0.68% to 6.18% over all data sets. Also, compared with the best results, GraphDRej achieves  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & **\#Nodes** & **\#Edges** & **\#Class** & **\#Features** \\ \hline Cora & 2708 & 5429 & 7 & 1433 \\ Citeseer & 3327 & 4732 & 6 & 3703 \\ Pubmed & 19,717 & 44,338 & 3 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data set information.  
gains from 0.15% to 4.77% over all data sets. Furthermore, compared with the GNNs (i.e., JK, PN, and MADReg), which consider the over-smoothing problem, GraphDRej also achieves the best performances. Although GG-NN adopts a gate mechanism which is similar to our proposed rejection mechanism, GraphDRej can also outperform GG-NN (further analysis can be found in Section 6.6). These analyses show the effectiveness of GraphDRej on learning a more meaningful node representation.
## 6 Control System Simulation Analysis Based on Simulink
### Dynamics Simulation Analysis of Each Stamping Stage  
As shown in Figure 20, the operation process of the servo high-precision press can be divided into the following steps: from T0 to T1, the crankshaft angular velocity gradually decreases; from T1 to T2, the slider linear speed is constant to conduct stamping; from T2 to T3, pressure is held; from T3 to T4, the slider rises; from T4 to T5, stay still and wait [29].  
According to Figure 21, the main drive structure analysis model was established in Ansys Workbench to perform rigid body dynamics analysis. Analysis parameters include a transmission ratio i = 71/14, the density of each moving part is 7850 kg/m\({}^{3}\), and the dynamic friction coefficient is 0.02. In addition, considering the role of the balancer, its own gravity is ignored [30].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Rated torque & 6000 Nm & Back electromotive force constant & 1368 V/1000 rpm \\ Rated current & 275 A & Stator phase resistance & 0.01094 \(\Omega\) \\ rated power & 157.1 kW & Direct axis inductance & 1.389 mH \\ Rated speed & 250 r/min & Quadrature axis inductance & 1.389 mH \\ Inverter output voltage & 380 V & Motor moment inertia & 7.78 kg\(\cdot\)m\({}^{2}\) \\ Peak torque & 15,000 Nm & Passive mechanism inertia & 19.372 kg\(\cdot\)m\({}^{2}\) \\ Current at maximum torque & 530 A & Connecting rod coefficient & 0.7143 \\ Rated torque constant & 21.8 Nm/A & Rear transmission ratio & 71/14 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation model test parameters.  
Figure 19: Simulink-based simulation model of servo high-precision press closed-loop control system.  
During the time period T0 to T1, the angular velocity of the crankshaft undergoes a decreasing process. The crankshaft angular velocity is 62.78 rad/s, and the crankshaft torque load conforms to the diagram shown in Figure 22, consistent with the design expectations.  
From T1 to T2, the uniform slider linear speed stamping stage is simulated. Figure 23 shows the step response of the position loop. Under a stamping force of 2000 kN, the slider moves at a linear speed of 0.01 m/s, and the crankshaft torque load conforms to the design expectations, as shown in Figure 24.  
During the pressure-holding stage from T2 to T3, in order to better adapt to engineering practice, the position loop is modified from the crankshaft angle to the slider position. Constant load mode is used during this stage, and current feedforward control is introduced, while the load disturbance that may be caused by workpiece elastic deformation is considered [31]. Figure 25 shows the displacement response under no load, while Figure 26 shows the pressure response curve under no load.  
Figure 21: Main drive structure model.  
Figure 22: Crankshaft torque load curve.  
Figure 23: Step response of the position loop.  
Figure 24: Crankshaft torque load diagram.  
Figure 25: Displacement response curve under no load.
## 3 Background and Preliminaries
### General Classification of Routing Protocols in WSNs  
Is mentioned, according to the network structure, routing protocols in WSNs can be divided into three categories [21]: data-centric (flat), hierarchical, and geographic (location-based). They are described as follows:  
* _Data-Centric protocols_: Multi-hop data-centric routing protocols are basically the first class to be introduced in WSNs. Considering a large number of nodes in sensor networks, flat algorithms employ _query-based_ mechanisms in which the sink node only requests the desired data in order to prevent continuous data transmissions and thus save power. In this group, Sensor Protocols for Information via Negotiation (_SPIN_) [28], Directed Diffusion [29], Energy-Aware Routing (_EAR_) [30], Rumor Routing [31] and Minimum Cost Forwarding Algorithm (_MCFA_) [32] are some of the most famous flat algorithm paradigms.  
* _Hierarchical protocols_: Different from the flat category, in hierarchical protocols that utilize a clustering scheme, nodes are assigned different roles or functionality. In fact, energy conservation can be achieved in these protocols by some aggregation and reduction of data in so-called cluster heads (CHs). In this class, Two Tier Data Dissemination (_TTDD_) [33], Low-Energy Adaptive Clustering Hierarchy (_LEACH_) [34], Threshold-Sensitive Energy-Efficient Sensor Network Protocol (_TEEN_) [35], Adaptive Periodic Threshold-Sensitive Energy-Efficient Sensor Network Protocol (_APTEEN_) [36] and Power-Efficient Gathering in Sensor Information Systems (_PEGASIS_) [37] are some inspiring protocols.
* _Location-Based protocols_: The possibility to apply position information in routing schemes will be used in location-based algorithms to route data towards the desired regions in the sensor field. This can save energy by limiting the flooding through the network [22]. _GPSR_[38], _GAF_[12], and _GEAR_[13] fall in this class.
# PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything
## 1 Introduction
Before progressing further, it is crucial to identify, supervise and eliminate the factors responsible for creating security concerns in wireless networks in order to build a strong and transparent relationship among the involved entities and develop an impenetrable ecosystem. Delicate factors that are mainly responsible for susceptibility include data integrity, device heterogeneity, data management, ownership and access control, synchronization, location spoofing, hardware/software tampering, and power or energy constraints [12; 13; 14; 15; 16]. With the fast expansion of smart devices employed in numerous technical domains, both heterogeneity and energy consumption have increased tremendously [17]; energy efficiency is a critical factor in sensor-based networks [18]. Understanding how energy is being consumed by sensor nodes allows for modelling and deployment of effective solutions for evaluation, the intent being to align with real-world sensor network applications. To identify non-cooperative nodes [19], an approach that can integrate an efficient processing algorithm for energy calculation and communication-routing protocols is required [20], with the feedback taking the form of incentives from the neighbour nodes to overcome energy shortages [21]. Figure 1: Internet of Everything. There are primarily two types of data encryption techniques: symmetric and asymmetric (or public key) cryptography. AES, DES, RSA, ECC, and other famous algorithms assist in protecting against data security breaches. All of these cryptographic approaches, however, are insufficient to guard against quantum assaults [22]. The quantum algorithm of [PERSON] and Grover is noteworthy for breaking symmetric and asymmetric key cryptosystems that are based on integer factorization and discrete logarithmic computational problems. [PERSON]'s period-finding technique mainly relies on quantum superposition, or the ability to exist in several states at the same time [22]. Traditional key exchange, encryption, and signature systems are ineffective in dealing with massive amounts of IoE data and can be easily breached. The worst-case hardness [23] of solving geometrical problems over high-vector lattices, such as the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP) [24; 25], both of which rely on the evaluation of a basis function on the lattice [23], is the cornerstone of lattice cryptography. Lattice vectors create a lattice grid by generating a large number of mathematical basis vectors. Due to the great complexity of the lattice grid, quantum computers may or may not be able to solve these problems [26]. Additionally, reducing a problem from worst-case to average-case offers an advantage in integrating learning with errors (LWE) [25] and lattice-based cryptography for applications to real-world security challenges in a trouble-free manner, eventually leading to the creation of a fail-safe cryptographic system especially suited for the breakneck IoE ecosystem.