esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluate Options Sub-tool
Getting Final Output from the Simulation  
Figure 10: Evaluate Options flowchart. There are three economic indicators applied in this paper to check the profitability of the various projects. They are net present value (NPV), rate of return on investment (ROR), and payback period (PBP). NPV is used as the primary economic indicator in this research, while the others (PBP and ROR) are used as secondary indicators to check the project's profitability. NPV is preferred over other possibilities because it recognises the time value of money, it avoids the problems associated with accounting adjustments in business projects by using cashflows, and it only indicates the absolute excess of present value of cash inflows over cash outflows. The NPV estimation technique analyses all the future cashflows using predetermined discount rate and incorporating the effects of inflation into the discounted cashflow calculations to adjust the cashflow forecast using a specific price increase to arrive at the present value, which is then compared with the initial outlay to give either a positive, negative, or zero result. The project with positive NPV is selected. The formula for NPV is given as Equation (3):  
\[\text{NPV}=\sum_{t-1}^{T}\frac{C_{t}}{\left(1+r\right)^{t}}-C_{0} \tag{3}\]  
where  
\(C_{t}\) = net adjusted cash inflow during the period \(t\);  
\(C_{0}\) = total initial investment costs;  
\(r\) = discount rate;  
\(t\) = number of periods. The economic assumptions used in this paper for the application of this tool are 25 years and 10%, 5%, and 20% for the following economic variables: plant life, discount rate, inflation rate, and income tax rate. Table 6 represents the economic formulas applied in this paper (see Appendix A, Table A3 for other economic formulas).
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 3 Principle and Analysis
### Design Guideline  
Based on the above discussion, a design guideline for extending the proposed antenna for other frequency bands is summarized as follows:  
1. _Determining the initial values of the antenna structure._ The antenna is supposed to operate under the resonances of TM\({}_{1/2,2}\) and TM\({}_{3/2,0}\) modes. For a conventional PIFA (\(L/W\approx 2\)), the initial size can be estimated by referring to [27].
2. _Loading Slots 1 for wide bandwidth._ According to the current distributions in Table 2, the position to etch Slots 1 can be chosen as \(D_{1}\approx W/3\). As shown in Figure 5a,b,  
Figure 5: (**a**) Two resonant frequencies; (**b**) \(|\,\)S\({}_{11}\,|\)s under different lengths of _Cp_ of Antenna III.  
Figure 6: Simulated \(|\,\)S\({}_{11}\,|\)s under (**a**) different lengths of _Ls_, and (**b**) different lengths of _Ws_ of Antenna III.  
tuning the length (_cp_) of Slots 1 to make TM\({}_{3/2,0}\) and TM\({}_{1/2,2}\) modes merge together can obtain a wide operating band.
3. _Loading Slots 2 to improve impedance matching._ Adjusting the length (_Ws_) and position (_Ls_) of Slots 2 can lead to better impedance matching, as shown in Figure 6a,b.
4. _Optimizing the final structure._ The parameters of slots (_cp_, _Ws_, _Ls_, _D_\({}_{1}\)_, and _D_\({}_{3}\)) and feed structure (_D_\({}_{2}\)) can be further adjusted to obtain an optimized performance.
## 3 Inverter Modeling and Control
### Control System Design
#### 3.5.1 Speed Control  
The demand torque of IM comes from the speed controller. The PI controller is designed to track the reference speed for the IM. The proportional gain \(K_{p}\) and integral gain \(K_{i}\) are tuned to achieve the desired overshoot, rise time, settling time, and crossover frequency (\(\omega_{cp}\)). The speed controller is designed using a plant model and sensor delay with load torque \(T_{I}\), as shown in Figure 6, where \(T_{e}^{*}\), \(T_{I}\), \(B\), and \(J\) are electrical torque, load torque, coefficient of friction, and the moment of inertia, respectively [4,5,29].  
Figure 6: Feedback speed control loop.
## 3 Experiments and Results  
To evaluate the performance of our proposed method, we conducted a series of experiments comparing it against four established federated learning algorithms: FedAvg [1], FedProx [4], PerfedAvg [14], pFedMe [13] and FedGKD [28]. These experiments were performed on two datasets--MNIST and a synthetic dataset--both configured with non-IID data distributions to simulate realistic federated learning scenarios with heterogeneous client data. For instance, our general settings are adaptable to scenarios like smart mobile devices, where user-specific app data varies; edge nodes, such as IoT devices with diversesensor inputs; or data silos, like hospitals with distinct patient distributions, all exhibiting non-IID characteristics. In the following subsections, we describe the experimental settings, including the datasets, their non-IID partitioning, and the model architectures used, followed by a detailed discussion of the hyperparameter configurations for all methods.
## 1 Introduction
### Damping the Resonant Modes  
Appearing resonant modes in the system is the first challenge introduced by employing LCL-filters. In the existing studies, two general approaches have been proposed for damping the resonance modes: (i) passive damping (PD) and (ii) active damping (AD). In the PD method, an extra series or parallel resistor is added to the filter elements. This damping method is simple; however, it increases power loss. Alternatively, AD methods dampen resonant modes by employing proper control algorithms, which are efficient and cost effective [2]. Various AD methods have been presented in literature, which are generally categorized into two groups of (i) filter-based (single loop) and (ii) feedback-based (multi loop) methods [5].  
In filter-based methods, only grid-side or inverter-side current is measured, and the resonant modes are dampened using proper control schemes. This method is called the single loop cascade control method [6; 7; 8]. The filter-based methods are usually implemented by a notch filter with the characteristic frequency equal to the resonant frequency. This method does not demand an extra sensor, which makes it cost effective. However, its performance, stability margin, and robustness are not acceptable, and the dependency of the scheme on the resonant mode frequency and the variation of the filter parameters intensifies the mentioned deficiencies [5; 9]. Meanwhile, the bandwidth of the control feedback should be low enough and should be reduced to about \(1/10\) of the resonance frequency [10]. This is a large deficiency, especially in systems with low-resonance frequency. In feedback-based methods, in addition to using the state variable of grid-side current, other state variables are used in the control loop, which is known as the multi loop method. In this method, the output capacitor current [11; 12; 13], the output capacitor voltage [14], the inverter-side current [15; 16], and combination of multiple states [4; 17; 18] are utilized as inner loop feedback signals. Among the different feedback signals, capacitor current and inverter-side current provide superior stability characteristics for resonance damping with simple implementation.  
Further to the filter-based and feedback-based methods, predictive control [19; 20; 21; 22], robust control [23; 24; 25], adaptive control [26; 27], and predictive adaptive robust control [28] have also been presented in the literature. However, the complexity of the schemes has limited the practical implementation of these control schemes.
## 2 System Structure and Working Principle
### Modeling of Energy Storage System
#### 2.2.2 Supercapacitor  
Compared with conventional capacitors and batteries, the advantages of SCs are mainly reflected in their fast response, high power density, and long cycle life [27]. Because of its high price, a large-capacity SC increases system acquisition costs and reduces system economics. The open circuit voltage \(U_{\text{SC}}\) of the SC has a linear relationship with its state of charge \(SOC_{\text{SC}}\).  
\[SOC_{\text{SC}}=\frac{U_{\text{SC}}}{U_{\text{SC}\_\text{max}}} \tag{3}\]  
Normally, when \(SOC_{\text{SC}}\) is less than 50%, it will not continue to be used unless it is charged. This is because the power contained in the SC is as follows:  
\[Q_{\text{SC}}=0.5U_{\text{SC}\_\text{max}}C_{\text{SC}}(1-SOC_{\text{SC}}) \tag{4}\]  
here, \(Q_{\text{SC}}\) is the energy released when the SC discharges to \(SOC_{\text{SC}}\), and \(U_{\text{SC}\_\text{max}(\text{min})}\) is its maximum (minimum) voltage. When \(SOC_{\text{SC}}\) is 50%, the SC would release 75% of its energy. When the \(SOC_{\text{SC}}\) is low, the efficiency of the SC is low. The capacity unit of the SC can be converted to the same capacity unit (Wh) as the battery as follows:  
\[C_{\text{SC}}=\frac{1}{2}C\left(U_{\text{SC}\_\text{max}}^{2}-U_{\text{SC}\_ \text{min}}^{2}\right)/3600 \tag{5}\]
## 5 Performance Evaluation
### Travel Time Comparison  
This subsection evaluates the travel time for both DQN and FLDQN. We utilize the same map and random traffic conditions discussed in the previous section. The evaluation involves 5 agents and 1200 randomly generated vehicles. The travel time for each agent is measured from their starting point to their designated destination. As previously mentioned, we set 5000 episodes for DQN and 100 episodes for FLDQN. In FLDQN, we introduce rounds, with 50 rounds specified, resulting in a total of 100 (episodes) \(\times\) 50 (rounds) = 5000 episodes for FLDQN. To observe the changes in travel time across 5000 episodes, we compare the travel time of a randomly selected agent as illustrated in Figure 5.  
The evaluation results presented in Figure 5 indicate distinct behaviors between DQN and FLDQN. DQN exhibits continuous fluctuations, with some episodes taking over 200 s between 4000 to 5000. In contrast, FLDQN experiences more pronounced fluctuations during its initial 1000 episodes, followed by stabilization efforts from episodes 1000 to 2000. Subsequently, the agent learns and converges to a travel time of less than 100 s from episode 2000 up to 5000.  
This initial inconsistency and variability can be attributed to the \"Train-starter\" hyperparameter (as specified in Table 1), which dictates that agents should explore the environment before initiating their model training. This exploration phase is designed to enhance the cumulative reward in the long term. Since the agents are taking random actions, they are not receiving the expected rewards due to the lack of optimal actions during the initial episodes. Additional communication rounds are imperative for the models to converge through shared updates. However, after sufficient global aggregation steps, beyond 2000 episodes, FLDQN accrues the benefits of model cooperation with other agents, evidenced by the drastic performance improvement. The stabilization and rapid descent to low travel times demonstrate the ability of FLDQN to effectively learn collaborative behaviors. In contrast to the performance improvements seen in FLDQN, the continued instability of the standard DQN algorithm over episodes underscores the advantages of using FL approaches compared to standalone RL agents.  
In summary, FLDQN enables agents to exchange their learned experiences by aggregating updates across decentralized models, while DQN utilizes individual RL agents. This results in FLDQN achieving quicker progress towards optimal travel time after an initial divergence, facilitating accelerated learning compared to DQN.  
Figure 5: Comparison of travel time for a randomly selected agent: FLDQN achieves a significant reduction in travel time to 46.47 s, surpassing the DQN (98.52 s) under identical map and random traffic conditions.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 4 Evaluation Matrices  
For a comparative analysis of the different models, the following evaluation metrics were used: RMSE and R\({}^{2}\)[63].  
Root Mean Square Error:  
Information regarding the standard deviation of the forecast error is given by the root mean square error (RMSE) value. The forecasted value spread with respect to the original value is measured by RMSE. The lower the RMSE value, the better the forecast accuracy of any model.  
\[RMSE=\ \sqrt{\sum_{i=1}^{n}\frac{(\hat{y_{i}}-y_{i})^{2}}{n}} \tag{9}\]  
\(\hat{y_{i}}\) is the forecasted value.  
\(y_{i}\) is the actual or observed value.  
\(n\) is the number of observations.  
Coefficient of Determination (R\({}^{2}\)):  
\"R-squared\" is a measure of the goodness of fit of a model. The coefficient of determination is calculated using the following equation.  
\[R^{2}=1-\frac{\sum(\hat{y_{i}}-y_{i})^{2}}{\sum(\overline{y_{i}}-y_{i})^{2}} \tag{10}\]  
\(\overline{y_{i}}\) is the mean of all values of \(y\).
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 1 Introduction
* Two fitness functions are proposed to determine the optimal positions of the circular areas in our PSO method. The \(G_{best}\) fitness function is to achieve the maximum coverage across the network. The fitness function used to determine \(P_{best}\) is the absolute value of the difference between the actual number of nodes covered by the circular area and the number of ideal coverage nodes, which makes each circular area contain the number of nodes that match its size. * A connecting line aided route construction method is proposed to determine a multi-hop route. The method considers the distance from the candidate CH node to the connecting line between the source CH node and the BS, the transmission distance from the source CH node to the candidate CH node, and the residual energy of candidate CH node. Hence, the energy consumption of the transmission is reduced. The rest of the paper is organized as follows. The related work is described in Section 2. The network and energy models are proposed in Section 3. The overview of PSO is introduced in Section 4. Section 5 introduces the detailed PUDCRP protocol. Experiment results are discussed in Section 6 by comparing with other protocols. The conclusion is presented in Section 7.
# Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
## 3 AI-Based Approaches in Building Energy Management
### Commonly Used Machine Learning Methods
#### 3.2.5 Artificial Neural Networks (ANNs)  
Artificial neural networks are widely used in the modeling of non-linear processes. It is useful in a wide range of applications, such as natural language processing, decision making, classification, dimension reduction, regression problems, clustering, decision making, anomaly detection, and computer vision. ANNs have been broadly utilized to forecast electricity consumption and the cooling and heating loads in buildings.  
A \"deep neural network\" is a type of deep learning model that is an artificial neural network with multiple layers. The concept is centered on the modeling of the hierarchical characterization behind the patterns used to predict data. This is achieved by creating modules comprised of multiple layers of information for processing.  
ANNs are used in combination with building information models (BIMs) to evaluate thermal comfort. Suggestions for optimizing savings on interior energy consumptions are made by forecasting the indoor climate, with a focus on temperature and humidity [71, 72]. Real-time monitoring is also possible using the ANN method. One of the advantages of this method is that it can detect non-linearity between the input and output datasets.  
Deep Learning (DL) is an artificial neural network method that aims to incorporate multilayer information processing modules. Examples include a convolutional neural network (CNN), radial basis function (RBF) networks, restricted Boltzmann machines (RBMs), and recurrent neural networks (RNNs). The DL method can be used in estimating the power consumption of individual appliances in the distribution system [73, 74].
## 3 Background
### Resource Management Algorithms
* By choosing the server with the greatest resource capacity to match incoming tasks or the largest memory block that is accessible, the worst-fit [19] scheduling algorithm adopts the opposite strategy from the best-fit method. This technique seeks to maximize resource consumption while minimizing resource fragmentation, despite its contradictory name. The utilization of the worst-fit method maximizes system efficiency by assigning tasks to the greatest available resources, hence mitigating the possibility of minor gaps between resources. This method ensures smoother resource management in computer systems and helps prevent excessive fragmentation, even though it may result in a not ideal resource optimization result. It is used alongside the best-fit algorithm to serve as a baseline for our setup [19]. * The power-saving algorithm [20] prioritizes reducing energy consumption by dynamically allocating resources according to workload trends and device conditions to the least power-consuming node. Because of their outdoor or remote locations, edge servers sometimes have trouble obtaining dependable power supplies, which makes energy management more difficult and calls for effective power-saving techniques. By dynamically assigning tasks based on energy-efficient servers' availability, it optimizes energy usage without compromising performance [20]. * The location-based scheduling algorithm [21] uses geographic data to improve user experience and optimize resource consumption in edge cloud environments. This algorithm handles workloads that are dynamic in nature by dynamically allocating tasks to edge servers according to users' geographical proximity to the servers. The method makes sure that jobs are directed to the closest edge server by using location-based data, which reduces latency and improves end-user response times. Moreover, the algorithm places the needs of the user first by attempting to provide services as quickly as possible, which enhances system responsiveness and user experience in general. Practically speaking, the location-based algorithm works by continually tracking the geographic coordinates of edge servers and users, altering workload assignments in real-time to account for shifts in server availability and user distribution. Through the use of this proactive strategy, the algorithm efficiently maximizes resource utilization and reduces reaction latency, especially in applications where latency is critical, including real-time communication. The location-based algorithm is an example of a strategic approach to resource management that is in line with the changing needs of edge computing applications because of its user-centric design and usage of location-based data.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 10 AI Support for 5G Operations and Services
### Network and Cloud Resource Management  
5G networks are likely to co-exist with 2G/3G/4G for the foreseeable future, which will bring a significant challenge in operating a 5G network, especially about the traditional operating model [115]. Namely, the coexistence of 2G, 3G, 4G and 5G networks brings difficulties in network synergy and interoperability, difficulties in fault demarcation and location under the hierarchical decoupling architecture, and also challenges of unified resource scheduling and operations due to the dynamic change of cloud and virtual networks. Therefore, the challenges of network operations that come with the 5G era will be significant.  
The advanced automated operations are gradually forming a gap with the traditional operations based on expert experience. Automated and intelligent network operations will be the just need in the 5G era. AI technology has intrinsic advantages in solving high-volume data analysis, cross-domain feature mining, and dynamic strategy generation, and will create new modes and capabilities for 5G network operations [121]. The mobile network automation will rely on automatic closed-loop under the operator provided intent or policy [115].  
In [122] the authors proposed AI as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of ETSI to embed an AI engine in the network, they described a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI.
## 2 Related Work  
### Last-Mile Delivery and Urban Logistics  
Last-mile delivery (LMD) is one of the most studied challenges in urban logistics due to its high operational cost, significant contribution to urban congestion, and environmental impact [6; 9]. It represents the final segment of the supply chain, linking urban distribution centers (UDCs) to customers. In densely populated areas, the complexity of traffic conditions, regulatory constraints, and customer expectations for rapid, reliable delivery make LMD both critical and costly.  
To reduce these impacts, cities and logistics providers have experimented with alternative modes and strategies, such as relay points, electric vehicles, autonomous ground robots, and micro-hubs. Among these, unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their ability to bypass road congestion and directly reach delivery points, particularly in difficult or high-density areas [5; 8]. However, UAV deployment at scale is still limited by regulatory, safety, and energy constraints.
## 3 Results
### Performance of the Random Forest Model in SIF Prediction  
To better understand the performance of explanatory variables for \(\mathit{SIF}_{normalized}\), we calculated the importance of the variables in the model using RF; the results are shown in Figure 5.  
Figure 5 shows that \(\cos\) (SZA\({}_{0}\)) and the air temperature proved to be the most important input parameters for \(\mathit{SIF}_{normalized}\); the green and NIR reflectance and NDVI were the next most important. Healthy plants have a high reflectivity in the NIR band. The results for the performance of RF with six explanatory variables are shown as Figure 6.  
The results show that the RF model performed well at simulating TanSat SIF (Figure 6), with R\({}^{2}\) values of 0.75, 0.73 and 0.81, RMSE values of 0.32, 0.30 and 0.32 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\), and RD values of -0.32, 0.02 and -0.14 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\), for the 2017, 2018 and 2019 datasets, respectively.  
A comparison between models that did and did not include \(\cos\) (SZA\({}_{0}\)) was also made. Taking the 2018 dataset, for example, the RF model gave a better accuracy if \(\cos\) (SZA\({}_{0}\)) was included, giving an R\({}^{2}\) of 0.72, an RMSE of 0.30 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) and an bias of 0.22 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\); this was against an R\({}^{2}\) of 0.65, an RMSE of 0.34 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) and an bias of 0.26 mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) without \(\cos\) (SZA\({}_{0}\)). In comparison, R\({}^{2}\) was improved by 0.07, RMSE was reduced by 0.04, and bias was reduced by 0.04.  
Figure 4: The relationship between \(\mathit{SIF}_{normalized}\) (apparent \(\mathit{SIF}_{yield}\) within the dense vegetation area in TROPOMI ignoring the canopy escape probability) and \(\cos\) (SZA\({}_{0}\)) according to TROPOMI for a dense forest region from February 2018 to February 2019. Cos (SZA\({}_{0}\)) is the cosine of the solar zenith angle at noon, which was used to represent the solar radiation intensity.  
Figure 5: Feature importance in the Random Forest (RF) model. T is the air temperature. Red, NIR, Blue and Green are the BRDF-corrected reflectances at the corresponding bands, \(\cos\) (SZA\({}_{0}\)) is the cosine of the solar zenith angle at noon, and NDVI is the normalized difference vegetation index.