esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of ACM Controller
### \(H_{2}\) and \(H_{\infty}\) Control  
In order to facilitate the use of \(\text{H}_{2}\) and \(\text{H}_{\infty}\) control algorithms for various control problems, a general control block diagram is used, as shown in Figure 5[27].  
Figure 5: \(\text{H}_{2}\) and \(\text{H}_{\infty}\) control block diagram.  
The control block diagram can be described as:  
\[\left[\begin{array}{c}\mathbf{z}\\ \mathbf{y}\end{array}\right]=\mathbf{G}(s)\left[\begin{array}{c}\mathbf{w}\\ \mathbf{u}\end{array}\right]=\left[\begin{array}{cc}\mathbf{G}_{11}(s)&\mathbf{G}_{12}(s) \\ \mathbf{G}_{21}(s)&\mathbf{G}_{22}(s)\end{array}\right]\left[\begin{array}{c}\mathbf{w}\\ \mathbf{u}\end{array}\right] \tag{30}\] \[\mathbf{u}=\mathbf{K}(s)\mathbf{y}\]  
where \(\mathbf{G}\)(s) is the transfer matrix of the controlled object, \(\mathbf{K}\)(s) is the controller.  
The state-space form of the controlled object \(G\) can be presented as:  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}=\mathbf{Ax}+\mathbf{B}_{1}\mathbf{w}+\mathbf{B}_{2}\mathbf{u }\\ \mathbf{z}=\mathbf{C}_{1}\mathbf{x}+\mathbf{D}_{11}\mathbf{w}+\mathbf{D}_{12}\mathbf{u}\\ \mathbf{y}=\mathbf{C}_{2}\mathbf{x}+\mathbf{D}_{21}\mathbf{w}+\mathbf{D}_{22}\mathbf{u}\end{array}\right. \tag{31}\]  
where \(\mathbf{x}\) is the state vector of the plant, \(\mathbf{w}\) is the input variable which represents all exogenous signals, \(\mathbf{z}\) is the output performance variable, \(\mathbf{u}\) is the control variable, \(\mathbf{y}\) is the measured output signal, the variable \(\mathbf{u}\) and \(\mathbf{y}\) are the output and input of the controller to be designed, respectively. The goal of control design is to minimize \(\mathbf{z}\).  
Using the following closed-loop transfer matrix to represent the input \(\mathbf{w}\) and output \(\mathbf{z}\) relationship.  
\[\mathbf{T}_{zw}(s)=\mathrm{F}_{1}(\mathbf{G},\,\mathbf{K})=\mathbf{G}_{11}(s)\mathbf{+G}_{12}(s) \mathbf{K}(s)[\mathbf{I}\mathbf{-G}_{22}(s)\mathbf{K}(s)]^{-1}\mathbf{G}_{21}(s) \tag{32}\]  
The controller \(\mathbf{K}(s)\) of the controlled system is designed to meet the performance index [28]: \(\min\limits_{\mathbf{K}}\lVert T_{zw}\rVert_{2}\) (H\({}_{2}\) control) or \(\min\limits_{\mathbf{K}}\lVert T_{zw}\rVert_{\infty}\) (H\({}_{\infty}\) control).  
For the H\({}_{\infty}\) (H\({}_{2}\)) controller of the ACM, selecting the state variable: \(\mathbf{x}=\left[y_{1}\,\,\dot{y}_{1}\,\,y_{3}\,\,\dot{y}_{3}\right]^{\mathrm{T}}\). To see the performance of the ACM and the controller, selecting the output performance variable \(\mathbf{z}=\left[\!\!\!\!\!\!\!\!\!\!\!  
The conventional PID control principle is simple, easy to use and robust. PID controller is a correction device that can change the original frequency domain characteristics of the system. Therefore, according to the system characteristics of ACM, set the frequency domain performance indicators desired by the system: amplitude crossover frequency and phase margin, and then the three parameters of the PID controller can be determined.
## 4 Fractional Nonlinear Damage Creep Model  
### Definition of Fractional Calculus  
The concept of fractional calculus involves differential and integral operations that extend beyond traditional integer-order calculus. In practical applications, some typical fractional calculus are commonly used such as the Riemann-Liouville (R-L) type, the Caputo type, and the Grunwald-Letnikov type. Due to its simpler definition, the Riemann-Liouville (R-L) fractional calculus is widely used in geotechnical rheology analysis.  
Let \(f(t)\) be a piecewise continuous function on the interval \((a,+\infty)\) and integrable on any finite subinterval. For \(n\in C\), where \(C\) is the set of complex numbers and the real part of the complex number \(n\) is greater than 0, i.e., \(\text{Re}(n)>0\), the \(n\) order Riemann-Liouville fractional integral of the function \(f(t)\) is defined as follows:  
\[l^{n}f(t)=\frac{1}{\Gamma(n)}\int_{a}^{t}(t-x)^{n-1}f(x)dx \tag{9}\]  
where \(\Gamma(n)\) is the Gamma function, \(\Gamma(n)=\int_{0}^{\infty}e^{-t}t^{n-1}dt\).  
Figure 11: The functional relationship curve between \(\beta\) and \(N\).  
The Riemann-Liouville (R-L) fractional derivative is the inverse operation of the fractional integral. The \(n\) order R-L fractional derivative is defined as follows:  
\[D^{n}f(t)=\frac{d^{\lceil n\rceil}}{dt^{\lceil n\rceil}}\Big{(}I^{\lceil n \rceil-n}f(t)\Big{)} \tag{10}\]  
where \(\lceil n\rceil\) denotes the smallest integer greater than \(n\).  
Equation (10) can also be expressed as follows:  
\[D^{n}f(t)=\frac{1}{\Gamma(\lceil n\rceil-n)}\frac{d}{dt^{\lceil n\rceil}}\int_ {a}^{t}(t-x)^{\lceil n\rceil-n-1}f(x)dx \tag{11}\]  
When \(n\in(0,1)\), Equation (10) can be expressed as follows:  
\[D^{n}f(t)=I^{1-n}Df(t) \tag{12}\]
# Recovery/Reuse of Heterogeneous Supported Spent Catalysts
## 4 Methods for Recovering Metals from Spent Catalysts  
### Traditional Methods of Recovery: Hydrometallurgy and Pironetallurgy  
Since spent catalysts are considered valuable sources [83] of metals and hazardous residues [84] at the same time, their recovery offers both economic and environmental benefits. Hence, after reaching their useful life, spent catalysts are further treated before final disposal for the recovery of metal content and/or separation of various components. _Hydrometallurgy_ and _pyrometallurgy_ are two traditional technologies widely applied for these purposes (Figure 6).  
Hydrometallurgy, known as the leaching method, consists of metals dissolution by acid or base solutions and their selective separation in a pure form by solvent extraction. In this last operation, the leach liquors are brought into contact with an organic phase in order to transfer, selectively, the metallic ions from solutions to organic phase [85]. Precipitation, distillation, and ion exchange are other separation procedures used as an alternative to solvent extraction [86; 87]. [PERSON] et al. used a combination of acid leaching followed by solvent extraction to recovery selectively Mo and V from spent petroleum catalyst [86]. [PERSON] et al. used an ion exchange method for the recovery of tungsten (W) from the leach liquor after leaching of the SCR catalyst [88]. Hydrometallurgical processing is effective and easy to operate but produces a large amount of acidic waste liquid [89].  
Pyrometallurgical processing requires chemical treatments at high temperatures such as incineration, pyrolysis, and melting. However, treatments involve production of toxic gases such as dioxins and furans, which energy consuming and expensive [89; 90].  
Today, _bioleading_ seems instead to be a more promising approach to value such resources due to its environmental friendliness, and low cost and low energy requirements [91] (Figure 6). This sustainable method consists of the use of microorganisms such as bacteria or fungi which can secrete organic or inorganic acids able to solubilize and extract selectively metals from solid compounds [92]. The use of microorganisms makes this process require a longer time in comparison to the conventional chemical leaching [91].
## 5 Results and Discussion
### Instantaneous Goodput  
#### 5.2.1 Healthy Network  
Figure 15 shows that goodput increases with a notable trend after every 20-30 s. The system takes the first 9 s to generate the packets, and after that, the system finds the rerouting and shows a rapid increase due to rerouting. Hence, the value moves up to 2500 kbps at 9 s. Later, the system finds the optimal path due to rerouting every 20 to 30 s and shows the increase in goodput up to 20,000 kbps at 200 s.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### Flow Diagram  
The process flow of SMTrust is illustrated in Figure 3. After the RPL initialization steps and topology creation along with the deployment of attacker nodes in the network, a node is required to select a parent for itself in order to forward the packets. A list of potential parents is established, and trust is computed and stored in trust table for all the neighbouring nodes.  
In the parent selection procedure, the potential parent is checked for trustworthiness. If the potential parent is trustworthy according to the set threshold value, then it is selected as the preferred parent. Otherwise, another node from the potential parent list is checked for the selection of preferred parent. Once the preferred parent node is selected, it is checked for malicious node. If the node is detected as an attacker, it is put in the suspicious list and another parent needs to be selected. If the node is detected as a normal and trustworthy node, it is forwarded for routing in the network.
## 3 Active Guidance Controller Design for IRWs Based on MARL
### Cooperative Markov Games  
In the context of MARL, the single-agent MDP approach is expanded to cooperative Markov games, where each agent's actions influence their outcomes and those of others, creating a dynamic environment of interdependent decision making. This process can be represented by the \(\{N,\,S,\,\{A_{i}\},\,\{O_{i}\},\,T,\,\{R_{i}\},\,\gamma\}\) tuples, where \(N\) is the number of agents, \(S\) is the state space of the environment, \(A_{i}\) is the action space of the \(i\)-th agent, \(O_{i}\) is the observation space of agent \(i\), \(T\): \(S\times A_{1}\times\ldots\times A_{N}\times S\rightarrow[0,1]\) is the transition function, defining the probability of the environment changing to the next state after all agents act, \(R_{i}\): \(S\times A_{1}\times\ldots\times A_{N}\to R\) denotes the reward function of agent \(i\), providing instant rewards for each agent, and \(\gamma\) is the discount factor used to calculate the present value of future rewards.  
Figure 2: Closed-loop control via SIMPACK cosimulation.  
Given the localized observation and independent decision-making characteristics of IRW collaborative controllers, we use the Dec-POMDP model for modeling, as shown in Figure 3. Each agent receives a local observation \(o_{i,t}\in O_{i}\) from the environment at time step \(t\) and selects action \(a_{i,t}\in A_{i}\) based on the behavior strategy \(\mu_{i}\left(\cdot\,1\,o_{i,t}\right)\). After the joint action \(a_{t}=(a_{1,t},\,\ldots,\,a_{N,t})\in A\) is executed, the rewards \(r_{i,t}\in R_{i}\) are received by the agents. The environment then transitions to the next state \(S_{t+1}\) determined by the transition function \(T\).  
In this study, we consider a fully cooperative environment in which all agents work together to achieve a common goal rather than only promoting individual rewards. The policy parameters of the \(i\)-th agent are denoted as \(\theta_{i}\), with the policy set of all agents as \(\mu=\{\mu_{1},\,\ldots,\,\mu_{N}\}\) and the policy parameter set as \(\theta=\{\theta_{1},\,\ldots,\,\theta_{N}\}\). The target function for optimal control is shown in Equation (8). Each agent updates its behavior strategy to maximize its cumulative discounted reward, and the optimization target for each agent is expressed by Equation (9).  
\[J(\theta)=\mathrm{E}_{\tau\sim\mu}\left[\sum_{t=0}^{\infty}\gamma^{t}\sum_{i= 1}^{N}R_{i}(s_{t},a_{t})\right] \tag{8}\]  
\[J_{i}(\theta_{i})=\mathrm{E}_{s\sim T,\mu_{i}\sim\mu_{i}\left(\cdot\{o_{i}\}_{ a_{-i}}\sim\mu_{-i}\left(\cdot\{o_{-i}\}\right)\right.}\left[\sum_{t=0}^{ \infty}\gamma^{t}R_{i}(s_{t},a_{i,t},a_{-i,t})\right] \tag{9}\]  
where \(\tau=(o_{1,0},\,a_{1,0},\,\ldots,\,\,o_{N,0},\,a_{N,0},\,o_{1,1},\,a_{1,1},\,\ldots)\) is the sequence of observations and actions, \(\mu_{i}\) is the policy of agent \(i\), \(\mu_{-i}\) is the policy set of all agents except agent \(i\), \(o_{-i}\) is the collection of local observations of all other agents, and \(a_{-i}\) is the action set of all other agents.
## 4 Conclusions  
In this work, testing for islanding detection was conducted when a PV system and EESS were operated in parallel at the same grid connection point. In the test of islanding detection by one inverter, the inverter detected the islanding and stopped stably. However, when two inverters were supplying power to the grid utility, the islanding detection failed and the inverters operated continuously. Generally, the EESS inverter in charging mode operates as the R (Resistance) load. When the EESS inverter was charging with energy supplied from the PV inverter, islanding was detected. However, this caused an overcurrent error of the PV inverter IGBT. When the EESS inverter was charging with some of the energy supplied by the PV inverter, the inverters failed to detect islanding. In some cases, the EESS inverter does not apply the algorithm for islanding detection while operating in charging mode, so the inverter could fail to detect islanding. From the experimental results, islanding detection could fail due to the different methods and measuring periods used by each manufacturer of inverters for detecting islanding states. The conclusions can be summarized as follows.  
1. When two inverters by different manufacturers were operated in parallel at the same grid connection point, the inverters failed to detect islanding and supplied power continuously to the load or grid utility. Because the method and detection period for islanding detection of each manufacturer are different, detection can fail.
2. When the EESS inverter was charging with all the energy supplied by the PV inverter, islanding was detected. However, the PV inverter stopped due to an overcurrent error of the IGBT.  
Figure 10: Test result of case 6 (PV 20 kW, EESS +5 kW).  
Figure 9: Test result of case 5 (PV 20 kW, EESS +10 kW).  
When the EESS inverter was charging with only some of the energy supplied from the PV inverter, islanding was not detected.
4. Because the EESS inverter can operate in parallel with other inverters of distributed power sources, an effective algorithm for islanding detection has to be applied in charging mode.  
It is necessary to make a new test standard for islanding detection because in some cases two inverters in parallel might not detect an islanding.  
[PERSON] wrote the main part of the paper, in particular, the results of the experiment were analyzed. [PERSON] and [PERSON] provided technical ideas and assistance in establishing the experiment. [PERSON] and [PERSON] established the research direction of the paper and suggested a method for conducting the experiment. [PERSON] and [PERSON] analyzed the data using the paper and provided important comments. [PERSON] reviewed and revised the overall content of the paper.  
This work was supported by a grant from the Standardization Certification of Korea Energy Agency (KEA) funded by the Ministry of Trade, Industry and Energy, Republic of Korea (Nos. 71000145, 71000106).  
The authors declare no conflict of interest.
## 4 Experimental Verification
### Analysis of Test Results
In Figure 14, the diagnostic accuracy of STMF data is the most stable under different speed conditions, and the overall accuracy of OSD is low. The fault diagnosis accuracy and volatility of AWF and NNF will vary with speed conditions. In short, the STMF method has good adaptability and accuracy.
## 1 Introduction
[14] presents a system design and reliability evaluation of isolated hybrid energy systems for rural health clinics. A review that explores study feasibility, optimal sizing and a technical analysis of hybrid energy systems for the electrification of a healthcare center in the Global South is presented in [1]. Ref. [2] presents a techno-economic evaluation of isolated hybrid renewable energy systems for healthcare clinics in remote locations in Nigeria. In [15], the system design and technical analysis of both grid-connected and off-grid systems for the rural electrification of healthcare clinics in the Philippines is presented. Ref. [3] presents an analysis of a hybrid renewable energy system for an isolated microgrid powering rural healthcare clinics in South Africa. The aforementioned literatures provide relevant information and also serve as the bedrock for this paper in terms of a techno-economic analysis of the energy design and sizing of the proposed system. However, a critical review of some of the literature as shown in Table 1 reveals that certain studies do not consider wind energy as a source of energy supply, while diesel generators as an alternative source of power supply in off-grid systems, due to the intermittent nature of renewable energy sources, were not considered in other studies. An evaluation of the emissions contribution of diesel generators was also not considered in certain studies, which is an indication that environmental consideration has not been seriously examined in most rural electrification. Therefore, this paper comprehensively analyse the techno-economic and environmental performance of hybrid energy systems, which consist of wind turbines, solar PV, diesel generators and a battery storage system for powering a rural healthcare facility center in Nigeria. Furthermore, this research aimed at minimizing the electricity costs and emissions associated with diesel generators and to also carry out a sensitivity analysis for the optimal configuration of hybrid energy systems in rural communities for healthcare facilities. The optimization model was solved using algebraic modelling and different case studies were investigated to determine the suitable optimal configuration for the hybrid systems. The remaining section of this article is organized as follows: Section 2 describes the hybrid energy system configuration; Section 3 presents the proposed simulation methodology; Section 4 discussed the simulation results and the paper is concluded in Section 5.
## 3 Results and Discussion
### Mechanical Properties
#### 3.2.2 Tensile Strength  
The tensile properties, including the yield strength (YS) and ultimate tensile strength (UTS), of all the experimental cases are presented in Figure 9. The average tensile properties (YS and UTS) of Case 1 (344 MPa and 539 MPa) and Case 2 (337 MPa and 541 MPa) exhibited comparable values, however, both measures were improved in Case 3 (377 MPa and 564 MPa) and Case 4 (374 MPa and 585 MPa). The average elongations in Cases 1, 2, 3, and 4 were 44%, 42%, 38%, and 37%, respectively.  
When comparing Cases 1 and 2, it was noted that applying a pressure of 10,000 N to Inconel625 had a minimal impact on the tensile strength. However, when comparing Cases 2, 3, and 4, a substantial increase in the longitudinal tensile strength of the bimetallic structure was observed, particularly when pressure was applied to the SS308L side, which possessed a weaker mechanical strength.  
With an increasing rolling pressure, the tensile strength rose and the elongation decreased. This enhancement in tensile strength and associated reduction in elongation were directly proportional to the rolling pressure applied. As the rolling pressure ascended, both the strain and dislocation density increased, leading to an improvement in the yield strength (YS) and ultimate tensile stress (UTS), concurrently causing a decrease in the elongation.  
The SEM images of the fracture surfaces, presented in Figure 10, reveal characteristic dimple-like features, through higher-magnification fractography of the center regions in all experimental cases. The equiaxed shape of the dimples, as seen in Figure 10, suggests the dominant influence of uniaxial tensile stress in this specific area, and this demonstrates that the GMAWAM 308L featured a good toughness [22]. This hints at a ductile fracture mechanism facilitated by the coalescence of microvoids. The initiation of these microvoids at the secondary phase and their eventual amalgamation led to the final fracture of the test specimen [23].  
Figure 10: SEM image of fracture surface (Case 4).  
Figure 9: Results of tensile strength.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 5 Mixed Coordination Algorithm Proposal
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline \multirow{2}{*}{**Scenario ID**} & **Number** & **Nodes** & **CPU** & **BW** & **Number** & **Total** & **Density** & **Absolute** & **Relative** \\  & **VNRs** & **per VNR** & **Cycles** & **Units** & **of Links** & **Demand** & **Index** & **Intensity Index** & **Intensity Index** \\ \hline \multirow{2}{*}{s-id1} & 50 & 3 & 15 & 15 & 166 & 4740 & 0.06 & 284.4 & 0.0227 \\ \cline{2-10}  & 50 & 3 & 15 & 30 & 166 & 7230 & 0.06 & 433.8 & 0.0347 \\ \hline \multirow{2}{*}{s-id3} & 50 & 3 & 15 & 50 & 166 & 10,550 & 0.06 & 633 & 0.0506 \\ \cline{2-10}  & 50 & 3 & 15 & 70 & 166 & 13,870 & 0.06 & 832.2 & 0.0665 \\ \hline \multirow{2}{*}{s-id5} & 50 & 3 & 30 & 15 & 166 & 6990 & 0.06 & 419.4 & 0.0335 \\ \cline{2-10}  & 50 & 3 & 30 & 30 & 166 & 9480 & 0.06 & 568.8 & 0.0455 \\ \hline \multirow{2}{*}{s-id7} & 50 & 3 & 30 & 50 & 166 & 12,800 & 0.06 & 768 & 0.0614 \\ \cline{2-10}  & 50 & 3 & 30 & 70 & 166 & 16,120 & 0.06 & 967.2 & 0.0773 \\ \hline \multirow{2}{*}{s-id9} & 50 & 3 & 50 & 15 & 166 & 9990 & 0.06 & 599.4 & 0.0479 \\ \cline{2-10}  & 50 & 3 & 50 & 30 & 166 & 12,480 & 0.06 & 748.8 & 0.0598 \\ \hline \multirow{2}{*}{s-id11} & 50 & 3 & 50 & 50 & 166 & 15,800 & 0.06 & 948 & 0.0758 \\ \cline{2-10}  & 50 & 3 & 50 & 70 & 166 & 19,120 & 0.06 & 1147.2 & 0.0917 \\ \hline \multirow{2}{*}{s-id13} & 50 & 3 & 70 & 15 & 166 & 12,990 & 0.06 & 779.4 & 0.0623 \\ \cline{2-10}  & 50 & 3 & 70 & 30 & 166 & 15,480 & 0.06 & 928.8 & 0.0742 \\ \hline \multirow{2}{*}{s-id15} & 50 & 3 & 70 & 50 & 166 & 18,800 & 0.06 & 1128 & 0.0901 \\ \cline{2-10}  & 50 & 3 & 70 & 70 & 166 & 22,120 & 0.06 & 1327.2 & 0.1061 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RII of R-ViNE-PS in the many-small parameter configuration.
# Research on Flexible Traceability System of _Agaricus bisporus_ Supply Chain
## 4 Design of the System
### Optimization of the Traceability Process  
The flexible traceability model described in this paper adopts a distributed, modular design approach. By decoupling and encapsulating different segments of the supply chain into independent modules, the entire traceability system becomes more flexible and scalable. This optimized process is better suited to adapt to the continuously changing dynamics of the supply chain, allowing seamless integration based on actual circumstances, achieving a high degree of flexible traceability. Additionally, each functional module encompasses data processing logic and blockchain components, enabling independent transaction processing and data storage. Modules communicate and invoke one another through data flows, thereby realizing coordination across the supply chain segments and facilitating data sharing. The concept of a flexible and reliable traceability process is illustrated in Figure 4.