esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 2 Literature Review
Regarding renewable energy's impact on electricity carbon quotas, [PERSON] et al. [47] found that the green label price under the Renewable Energy Certificate (REC) policy has a non-monotonic impact on investment in renewable energy, influenced significantly by the quota set under the Renewable Energy Standard (RES) policy. [PERSON] et al. [48] developed a carbon transaction cost model for wind and thermal energy and adopted a carbon quota distribution strategy that used the entropy weight method to adjust the proportions of benchmark emission components in regional power systems. The majority of current research on carbon allocation in the power industry focuses primarily on thermal power generation, with little attention paid to the impact of renewable energy on emissions. This is based on both domestic and international research on the influence of CO\({}_{2}\) emissions from electricity, the determination of total carbon quota allocations, and allocation methods. Furthermore, the determination of total carbon quotas often relies on a single factor, overlooking the potential risk of \"carbon transfer\" within the power industry, leading to allocation schemes that lack fairness. To address these gaps, this study builds a carbon emissions impact indicator system for the power sector and improves the current prediction model by using the PSO algorithm, which gets around the drawbacks of conventional neural networks in global optimization. In an attempt to identify a more accurate carbon emissions forecasting model under the effect of several factors, the paper combines the GM(1,1) gray prediction model with BPNN. The forecasting precision of the synthesized model is compared with that of the standalone GM(1,1) model. With the total carbon emission control unchanged by 2030, the shared emission reduction responsibility coefficient is introduced to address the challenge of dividing emission reduction responsibilities within the power system. This allows for a quantitative analysis of the emission reduction responsibilities of each regional power grid, ensuring a fair allocation of carbon quotas and facilitating the proposal of reasonable and effective emission reduction measures based on the quota standard.
## 4 The Proposed Multi-Population Global-Best Modified Brain Storm Optimization with Migration
### Reduction of Search Space  
Conventionally and usually, from the current operating conditions of various facilities, search space is calculated using the maximum decrease/increase of energy output/input values of facilities, upper/lower bounds of energy output/input values of facilities. The authors have proposed a reduction method of search space considering not only characteristics of various facilities but also considering electric and natural gas hourly cost and hourly load characteristics, and smooth transition to the next day facility operation in weekdays. As an example, the reduction of search space of the heat storage tank is explained in this section. The upper and lower bounds of the stored heat energies are calculated from the initial condition (\(HST_{initial}\)) [11; 12]. The bounds are calculated by the equations shown below:  
\[HST_{0}=HST_{initial} \tag{19}\]  
\[ForMax_{i}=HST_{i-1}-HinMax_{i}+Hout_{i}+Loss \tag{20}\]  
\[ForMin_{i}=HST_{i-1}-HinMin_{i}+Hout_{i}+Loss \tag{21}\]  
where \(HST_{i}\) is the stored heat energies at hour \(i\), \(ForMax_{i}\) is the upper bound of stored heat energies in the heat storage tank at hour \(i\), \(ForMin_{i}\) is the lower bound of stored heat energies in the heat storage tank at hour \(i\), \(HinMax_{i}\) is the upper bound of heat input value at hour \(i\), \(HinMin_{i}\) is the lower bound of heat input value at hour \(i\), \(Hout_{i}\) is heat output at hour \(i\), and \(Loss\) is hourly heat loss.  
Solid lines in Figure 9 show \(ForMax_{i}\) and \(ForMin_{i}\) and dash-dotted lines in Figure 9 show \(BackMax_{i}\) and \(BackMin_{i}\). Using the same calculation concept of (20) and (21), \(BackMax_{i}\) and \(BackMin_{i}\) can be calculated using the opposite direction from \(T\) hour considering smooth transition to the next day facility operation in weekdays. Finally, considering the upper and lower bounds explained above, the following (22) and (23) shows the reduced search space at each hour.  
\[TMax_{i}=Min\ \{ForMax_{i},BackMax_{i},HSTMax\} \tag{22}\]  
\[TMinx_{i}=Max[ForMin_{i},BackMin_{i},HSTMin] \tag{23}\]  
where \(TMax_{i}\) is the upper bound of the stored heat energies in the heat storage tank in the reduced search space at hour \(i\), \(TMinx_{i}\) is the lower bound of the stored heat energies in the heat storage tank in the reduced search space at hour \(i\), \(HSTMax\) is an upper bound of heat energies in the heat storage tank, and \(HSTMin\) a lower bound of heat energies in the heat storage tank.  
The reduced search space is shown with a shaded area in Figure 9 considering not only characteristics of various facilities but also smooth transition to the next day facility operation in weekdays and heat hourly load characteristics. Consequently, the reduction of search space can realize effective search to the target problem.
## 4 Results and Discussion
### Influence of flank Installation Height on Thermal Performance  
A typical meteorological day (11 January) in January, the coldest month of Guangzhou, is chosen from TMY weather data of Guangzhou for the evaluation of the influence of the tank installation height on thermal performance. The variations in daily thermal efficiency with the changes of tank installation height were computed for various panel tilt angles (14\({}^{\circ}\)-38\({}^{\circ}\)), and the results are showed in Figure 7a. In view of the limited space above the window for tank installation, the tank installation height ranges from 0.4 to 1.0 m.  
Figure 6: The monthly thermal efficiencies of [PERSON] et al.’s work [22] and this work.  
As seen from Figure 7b, the vertical distance between the panel center and tank bottom increases with the increase of the tilt angle. Due to the increase of thermosyphon pressure, the daily thermal efficiency increases with the increase of the tilt angle when the tank installation height is constant. When the tilt angle is constant, the daily thermal efficiency increases considerably when the tank installation height increases from 0.4 to 0.6 m. This is because the increasing thermosyphon pressure resulting from the increase of tank installation height becomes a dominating factor which affects the thermal efficiency. When the tank installation height increases from 0.6 to 1.0 m, the thermal efficiency increases very slightly for the contribution of increasing thermosyphon pressure is, to a certain extent, offset by the increase of flow resistance and pipe heat losses. Considering that the position of the PV/T panel mounted above windows is rather high, it is suggested that the tank installation height is 0.6\(\sim\)0.8 m from the viewpoint of convenient maintenance.
## 2 Simplified NREL 5 MW Wind Turbine
### WTGS Model  
A variable-speed wind turbine generally consists of an aeroturbine, a gearbox and a generator. The aerodynamic power captured by the rotor is given by the nonlinear expression as:  
\[P_{r}=\frac{1}{2}\rho\pi R^{2}C_{p}(\lambda,\beta)V^{3}, \tag{1}\]  
where \(\omega_{r}\) is the rotor speed, \(R\) is the rotor radius, and \(\rho\) is the air density. The power coefficient \(C_{p}\) is a nonlinear function defined based on the blade pitch angle \(\beta\), and the tip-speed ratio \(\lambda\), where the tip speed ratio is:  
\[\lambda=\frac{\omega_{r}R}{V}. \tag{2}\]  
\(C_{p}\) is used to establish the aerodynamic model for the capturing ability of wind energy. For different wind turbines, the polynomial expressions of \(C_{p}\) are usually with a similar form but different parameters. The adopted \(C_{p}\) for the simulated wind turbine can be referred to [18]. Similar to the aerodynamic power, the aerodynamic torque is defined as follows:  
\[T_{r}=\frac{1}{2}\rho\pi R^{3}C_{q}(\lambda,\beta)V^{2}, \tag{3}\]  
where\[C_{q}(\lambda,\beta)=\frac{C_{p}(\lambda,\beta)}{\lambda}. \tag{4}\]  
The dynamic response of the wind turbine rotor driven at a speed \(\omega_{r}\) by the aerodynamic torque \(T_{r}\) is shown to be  
\[J_{r}\dot{\omega}_{r}=T_{r}-T_{ls}-K_{r}\omega_{r}. \tag{5}\]  
The low-speed shaft torque \(T_{ls}\) acts as braking torque on the rotor. It results from the torsion and friction effects due to the difference between \(\omega_{r}\) and low speed shaft speed \(\omega_{ls}\):  
\[T_{ls}=K_{ls}(\theta_{r}-\theta_{ls})+B_{ls}(\omega_{r}-\omega_{ls}), \tag{6}\]  
where \(\theta_{r}\) and \(\theta_{ls}\) are the angular positions of the rotor and shaft, respectively. The generator is driven by the high-speed shaft torque \(T_{hs}\) and braked by the generator electromagnetic torque \(T_{em}\):  
\[J_{g}\dot{\omega}_{g}=T_{hs}-T_{em}-K_{g}\omega_{g}. \tag{7}\]  
Assuming an ideal gearbox with transmission ratio \(n_{g}\), the following equation can be established  
\[n_{g}=\frac{T_{ls}}{T_{hs}}=\frac{\omega_{g}}{\omega_{ls}}=\frac{\theta_{g}}{ \theta_{ls}}. \tag{8}\]  
It should be noticed that the wind turbine introduced is modeled with a flexible driveshaft, but the simulated wind turbine in FAST can be developed with more DOFs (degree of freedoms) like the flapwise and edgewise blade mode and fore-aft and side-to-side tower bending-mode, but the simplified WTGS in \(SimWindFarm\) is designed with a few DOFs like generator control and flexible drivetrain. Such simplification could simplify the simulation process and reduce the running time while preserving the basic characteristics of wind turbines.  
In the baseline NREL 5 MW wind turbine model, the generator model is usually user defined, but a simple first order generator model is used in the \(SimWindFarm\) benchmark. The simple generator model can be expressed as:  
\[\dot{T}_{em}=\frac{1}{t_{gen}}(\frac{P_{set}}{\omega_{g}}-T_{em}) \tag{9}\]  
in which \(P_{set}\) is the active power set-point for the WTGS and \(t_{gen}\) is the generator time constant.
# A Sustainable Polygeneration System for a Residential Building
###### Abstract  
In line with the decarbonization of the domestic sector to meet the 2050 climate neutrality targets, this paper describes the energy, economic, and environmental analysis of a set of different novel configurations of polygeneration installations to provide electricity, air conditioning, domestic hot water, and desalinated water for a building of 80 dwellings. All arrangements were designed to cover 100% of the five demands required in the building with renewable energy only, from photovoltaic (PV) and photovoltaic-thermal (PVT) panels and biomass backup boilers (BB). Electricity can be sold to or purchased from the grid without electrical storage with batteries. Additional electricity generation with thermoelectric generators (TEG) coupled to the PVTs, and the BB was explicitly analyzed. The choice of electrically or thermally activated technologies (heat pump, HP/single-effect absorption chiller, SEAC for cooling and multi-effect distillation, MED/reverse osmosis, RO for desalination) created four configurations from the basic structure based on solar and biomass sources. Thus, the paper has studied four designs in detail and applied them to three case studies corresponding to different locations in Spain. They were modeled with TRNSYS and included specific models for desalination technologies. Both structures provide important energy and CO\({}_{2}\) savings concerning the conventional supply of the building demands. The novel life-cycle analysis approach further increases the lifetime CO\({}_{2}\) savings for all configurations as well. The electric option (the combination of HP and RO for cooling and desalting) was, by far, the most attractive solution in terms of liability and lower investment required in the three case studies.  
polygeneration; desalination; buildings; renewable sources self-consumption +
Footnote †: journal: Applied Sciences
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 2 Related Works
### Security Situational Awareness  
Over the literature, security situational awareness has been an active research topic for recent years. There are already many promising research results in smart grids. [PERSON] et al. (2020) suggested that SSA is key to the safe operation of smart grids. Inadequate SSA could jeopardize the stability of the grid system and cause significant undesirable effects. The literature ([PERSON] et al., 2020) pointed out that the scale and complexity of the future smart grids will continue to increase, and a statistical metric system based on statistical quantities is proposed to meet the challenge of high dimensional complexity of the situational elements. [PERSON] et al. (2020) indicated that cybersecurity threats extend from computer networks to smart grids and proposes an SSA mechanism based on big data analysis to improve awareness efficiency. An SSA approach that utilized consensus decision information from multiple power terminals to enhance system integrity protection has been proposed in ([PERSON] et al., 2020). This approach has been employed to address the impact of a single point of failure caused by a network attack on power system integrity.  
Most of the works mentioned above have improved the smart grid systems' SSA from an individual perspective. However, they have ignored the awareness environment's continuity and the SSA deployment scheme with dynamically changing environmental information.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## Appendix A.2.4 Computational Time and Model Accuracy
### Performance Metrics  
We use four different metrics to quantify the performance of our models. In the formulas below, \(s(x,t)\) represents the predicted traffic state (i.e., traffic density \(\rho\) or traffic state \(u\)) at location \(x\) and time \(t\), and \(\delta\) represents the ground-truth.  
1. **Relative Error (RE)** measures the relative difference between the predicted and ground-truth traffic states. For the deterministic TSE, RE is calculated by: \[\text{RE}_{s}(x,t)=\frac{\sqrt{\sum_{x}\sum_{t}(s(x,t)-\hat{s}(x,t))^{2}}}{\sqrt{ \sum_{x}\sum_{t}\hat{s}(x,t)^{2}}}.\]
2. **Squared Error (SE)** measures the squared difference between the predicted and ground-truth traffic states at location \(x\) and time \(t\). For the deterministic TSE, SE is calculated by: \[\text{SE}_{s}(x,t)=(s(x,t)-\hat{s}(x,t))^{2}.\]
3. **Mean Squared Error (MSE)** calculates the average SE over all locations and times, which is depicted as follows: \[MSE_{s}(x,t)=\sum_{x}\sum_{t}SE_{s}(x,t)/N\] where \(N\) is the total number of data.
4. **Kullback-Leibler divergence (KL)** measures the difference between two distributions \(P(x)\) and \(Q(x)\), which is depicted as follows: \[\text{KL}(P\|Q)=\sum_{x}P(x)\log\biggl{(}\frac{P(x)}{Q(x)}\biggr{)}.\]  
Note that for UQ-TSE, the traffic state \(s(x,t)\) follows a distribution, and the aforementioned errors measure the difference between the predicted and the ground-truth means instead. For example, RE and SE for the UQ-TSE are defined as:  
\[\text{RE}_{s}(x,t)=\frac{\sqrt{\sum_{x}\sum_{t}(\mathbb{E}[s(x,t)]-\mathbb{E} [\hat{s}(x,t)])^{2}}}{\sqrt{\sum_{x}\sum_{t}\mathbb{E}[\hat{s}(x,t)]^{2}}}.\]  
\[\text{SE}_{s}(x,t)=(\mathbb{E}[s(x,t)]-\mathbb{E}[\hat{s}(x,t)])^{2}.\]
## 1 Introduction  
Smart farming refers to the management of farms using information and communication technologies to increase the quantity and quality of plants and crops. By placing smart agriculture sensors in greenhouses or in the field, various sensing data such as lighting, temperature, soil nutrient levels, leaf color, and humidity can be collected. Given the vast amount of sensing data, crop growth can be evaluated using data analysis tools to enable farmers to make data-driven decisions. In other words, farmers can determine optimal amounts of water, fertilizers, and pesticides to minimize resources and raise better and healthier crops.  
Particularly, crop disease diagnosis in a timely manner is important to prevent diseases from spreading at an immature state and prevent economic damages to farmers. A large team of experts and farmers can identify crop diseases based on the symptoms on the leaves; however, this manual observation is time consuming and costly. In addition, it is inefficient to continuously monitor all the crops on a large field area. Therefore, the automatic detection of crop diseases is necessary.  
With the rapid advance in computer vision enabled by deep learning, image-based crop disease detection has garnered particular attention. Among deep learning models, the deep convolutional neural network (DCNN) [1] has demonstrated powerful performance for image classification and detection. Therefore, image-based approaches have been actively studied using digital cameras built on autonomous agricultural vehicles for crop disease identification and detection. This study only deals with the identification and detection of apple leaf diseases.
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 3 Fast SAR Image Simulation Based on Spatial Segmentation
### CUDA Thread Organization Based on Spatial Segmentation
Each 2D envelope window is mainly created based on the 2D sampling time axis. The CUDA thread organization architecture based on the spatial segmentation we designed effectively coordinates the relationship among the SAR phase reference system characteristics, the echo matrix cells, and the hardware resources of the CUDA platform. The echo matrix cell method with regard to the target 2D echo matrix cell \((x,y)\) is used as the subfield of the target backscatter field, which is equivalent to assigning each thread to each echo matrix cell, and each thread handles one latticed block \((P_{z},z<Z)\). This provides the basic conditions for the CUDA platform to efficiently call the GPU multi-threads to execute the SAR echo simulation task in parallel.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
###### Abstract  
In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO\({}_{2}\), NO\({}_{2}\) and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture.  
air quality; edge computing; high accuracy; IoT; Kalman Filter; prediction; Raspberry Pi
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
## 2 Topological Modeling
### Node Activation Mechanism  
In WSN, most member nodes should be dormant to save energy consumption. However, the nodes which are located at each group boundary or perform specific tasks should be in an operational state, i.e., The network does not need all nodes in work mode. If a reasonable sleep/wake mechanism is used, we can greatly reduce the network energy costs. Most sensor networks adopt IEEE 802.15.4 as the Media Access Control (MAC) layer protocol. It already contains a periodic sleep/wake mechanism. However, in the band topology, if the wake cycle of each node in the network is exactly the same, it will easily cause a packet internal node to generate large congestion when sending and receiving data information. As a result, the energy load of each node will generally increase. In order to adapt the network model based on energy-efficient strategy, the algorithm should use a sleep/wake-up scheduling mechanism based on a dynamic reconfiguration strategy as follows:  
1. When the target node enters the monitoring area, the sensor node starts searching and receiving the broadcast flood message \"Request-MSG\" issued by the target. According to the chronological order, sink node records the minimum hop number information \"Hop-count\" and its own identification information \"Node-ID\". Subsequently, all information about anchor nodes in the neighbor area of the target node is stored and the initial positioning tree is constructed.
2. Based on the network topology of the target node and neighbor anchor nodes in the sensing area, the two sets of nodes which need to wake up or keep the sleeping (low power state) are estimated and sorted. Once they are determined, the message \"Wakeup-MSG\" is immediately sent to the target, wake up and activate anchor nodes with \(h\) hop from the target node, launch them into working state and holding. After all above actions are completed, the message \"Prune-MSG\" is sent to the target, and the anchor nodes are cleared once again when the locating tree is created.
3. According to the business type, we constantly and dynamically reconstruct the locating tree. As the target node moves and the sensing area continually changes, the anchor nodes that need to participate in the location continue to wake up or to remain dormant.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### Wireless Power Transfer  
This method extends the operational lifetime of power-constrained devices [175; 176; 177; 178]. Solar, thermoelectric, and wind effects are used in energy harvesting; however, these are not practical, because they are dependent on the time of day, the location, and the setting. This challenge has been handled by harvesting RF signals by allowing battery-constrained systems to maintain their energy.  
The initial focus of wireless power transfer (WPT) has been on high-power and long-distance transmission; however, the continued development has been constrained by the possible reduction in transmission efficiency and associated health risks brought on by high-power applications. Alternative wireless information and power transmission techniques have proven to be crucial not only for theoretical study but also for the operational cost reduction and the sustainable expansion of wireless communications due to the massive energy consumption growth with the ever-increasing number of linked devices. The distance between the base station and the device in a communication network is important for both information and power transfer [179]. Therefore, more advancements in far-field WPT techniques are required.  
Implementing NOMA with wireless power transfer for IoT applications using SWIPT protocol must be solved in order to enhance network EE, as discussed in Section 3.2.2.