esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 2 Background  
The modern power network consists of various complex machines designed to play significant roles in generating, transmitting, and distributing electricity to numerous households connected to the grid. Dynamic state estimation (DSE) assessments show the complex systems that need to be operational to guarantee the optimal conversion, transformation, and delivery of electrical energy [18; 35; 36]. Figure 2 presents an infinite-bus power grid model consisting of a synchronous generator powered by a mechanical source in a three-phase circuit arrangement. The transformers and power measurement units serve to deliver optimal power to the asynchronous machine and other loads. During the operation of the power system network, the static state estimation cannot appropriately capture the dynamics of the operational environment [35; 36]. This presents a huge problem because the power system circuitry consistently fluctuates to adjust to the evolving realities of the energy systems. Despite this, SSE methods are still used to design an EMS, but the approach proves insufficient for solving evolving problems and providing sufficient information needed to monitor power system architecture [36; 37]. In asynchronous generators and machines, for example, EMS systems with the SSE configuration have been difficult to monitor and control since observability is often lagging and unresponsive in situations of optimal operation [30; 37; 38]. Dynamic state estimation (DSE) is capable of accurately capturing and assessing the dynamic system states and plays a crucial role in further monitoring and controlling by providing the maximum observability of the power system network [8; 26; 37; 39]. For protection to be appropriately achieved, the dynamic states must be accurately captured to provide a unique state of awareness of the power system network [39]. The SCADA system was adopted to fulfill this responsibility in the differing state of the power system network. SCADA systems can assess two independent data points from the power system network and report the data to the control infrastructure [8; 40]. One problem with SCADA systems, as stated earlier, is their limited number of parameters that can be assessed and observed at a time together within a period of 8-10 s before another cycle of data can be collected [8; 37; 41]. In a complex modern power system network,these limitations could cause significant damage as individual components are consistently exposed to fault occurrences on the line and other complex energy components whose damage could result in a partial or total system collapse of the grid. Additionally, present research into the role of varying DSE parameters and how they could be implemented in an EMS has increased the demand for a more robust communication and monitoring infrastructure to be developed [38].
## 2 Theoretical Background
### Fractal Dimension
#### 2.3.3 Petrosian's Fractal Dimension (\(Pfd\))  
To calculate the \(PFD\) value, the process consists of three steps [35]:  
1. Take a signal, \(X\), with \(N\) samples, and binarize it according to the criteria in Equation (26). \[z_{i}=\ \left\{\begin{array}{ll}1,&x_{i}>mean(X)\\ -1,&x_{i}\leq mean(X)\end{array}\right.,\ i=1,\ 2,\ldots,\ N\] (26)2. Find the total number of sign changes in the binarized signal, \(Z\), using Equation (27). \[N_{\Delta}=\sum_{i=1}^{N-2}\left|\frac{z_{i+1}-z_{i}}{2}\right|\] (27)
3. Calculate the \(PFD\) as described in Equation (28). \[PFD=\frac{\log_{10}N}{\log_{10}N+\log_{10}\left(\frac{N}{N+04N_{\Delta}}\right)}\] (28)
## 3 Establishment of Multi-Objective Optimization Algorithm
### Multi-Objective Beluga Optimization Algorithm (NSGA-III-LBWO)
To enhance the efficiency of the optimization process, the number of offspring individuals sampled from the non-dominated Pareto set \(C_{t}^{\text{non-dominated}}\) will increase (Equation (12)), so as to search near the current Pareto set, which facilitates jumping out of the local optimum. \[C_{t}^{\text{non-dominated}}=\alpha\cdot t\cdot S \tag{12}\]  
where \(t\) is the cycle number, \(\alpha\) is the constant scaling factor, and \(S\) is the population size. Before generating initial positions based on the Pareto set in the LBWO algorithm, it is necessary to evaluate the individuals in the Pareto set according to Equation (13) and select the first \(C_{t}^{\text{non-dominated}}\) non-dominated individuals to generate initial positions. \[val_{p}=\sum_{i=1}^{\zeta}w_{i}\cdot f_{i}(p) \tag{13}\]  
where \(val_{p}\) is the value of individual \(p\) and \(w_{i}\) is the weight of the \(i\)th objective function. The centrifugal fan performance optimization problem in this paper can be expressed as follows:  
\[find\ X^{*}=\{\beta_{i}{}^{*},\beta_{0}{}^{*},R_{\sigma_{S}}{}^{*},\theta{}^{*}\} \tag{14}\]  
The objective functions can be described as follows:  
\[\left\{ \begin{array}{l}\max(P_{total}(X^{*}))\\ \max(\eta(X^{*}))\\ \min(L_{p}(X^{*}))\end{array} \right. \tag{15}\]  
where \(X^{*}\) is an ideal design variable combination that should maximize the total pressure and efficiency of the fan and, at the same time, minimize the noise. The multi-objective optimization steps of the prototype fan are based on NSGA-III-LBWO, as shown in Figure 6.  
Step 1: Determine population size \(N\), number of iterations \(t_{max}\), individual attributes, and objective functions. Initialize the population \(P_{0}\), calculate the objective functions values of each individual, and perform non-dominated sorting on the individuals. Step 2: Based on the current Pareto set and Equation (10), generate the offspring population \(C_{t}\) in LBWO. Step 3: Merge the original population \(P_{t}\) and the offspring population \(C_{t}\) to obtain population \(R_{t}\). Step 4: According to the non-dominated strategy and reference point mechanism, \(N\) individuals are selected from population \(R_{t}\) to join the new population \(P_{t+1}\). Step 5: Update the number of iterations \(t\) to determine if the end condition is met. If it is, end and output the optimization result; otherwise, return to step 2.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 2 System Model  
### Network Model  
As shown in Figure 1, we consider a multi-tier heterogeneous WPCN in which the BSs of each tier are spatially distributed to provide seamless access service over the whole \(\mathbb{R}^{2}\) plane. Let \(m_{i}^{(k)}\) denote the location of BS \(i\) in tier \(k\in\{1,...,K\}\). The location of BSs in tier \(k\) is denoted by \(\Phi_{k}=\left\{m_{i}^{(k)};i=1,2,3,...\right\}\), where the transmit power of BSs in tier \(k\) is \(P_{t,k}\) and the bias factor is \(B_{k}\), where \(B_{k}\geq 1\) for any \(k\in\{1,...,K\}\). The bias factor is the factor which is used to adjust the association probability among different tiers in multi-tiers HetNets. The transmit power of BSs can multiply the bias factor to obtain \"more\" transmit power to let more users associate with the BSs. However, the actual transmit power of BSs cannot increase by multiplying the bias factor. The achievable rate of information transmission and the amount of wireless power transfer will not be impacted by the bias factor. We assume \(\Phi_{k}\) following an independent homogeneous Poisson point process (PPP) with density \(\lambda_{k},k\in\{1,...,K\}\). The superposition of \(K\) tiers can be denoted as \(\Phi=\bigcup_{k}\Phi_{k}\) and forms a weighted Poisson Voronoi tessellation due to the inhomogeneous transmit powers of the BSs in different tiers.  
In addition, the nodes obey a homogeneous PPP \(\Phi_{u}\) with density \(\lambda_{u}\), which is independent of \(\Phi_{k}\). We assume that \(\lambda_{u}\) is large enough so that each BS serves at least one associated UE per channel, i.e., \(\lambda_{u}\gg\lambda_{k}\) for any \(k\in\{1,...,K\}\). That is, the downlink channels are fully occupied such as in saturated conditions. Nodes in the downlink will receive the interference signal from other BSs serving their own nodes on the same channels. Each node is assumed to be equipped with single-antenna and a rechargeable battery with a large storage. In the downlink multi-tier heterogeneous WPCNs,  
\begin{table}
\begin{tabular}{c c} \hline \(\Phi_{k}\), \(\lambda_{k}\) & PPP of \(k\)-th tier BS and the corresponding density \\ \(\Phi_{k}\), \(\lambda_{u}\) & PPP of UEs and the corresponding density \\ \(P_{t,k}\), \(P_{s,k}\) & Dynamical transmit power and static power of \(k\)-th tier BS \\ \(B_{k}\) & Bias factor of \(k\)-th tier BS \\ \(\alpha\) & Path loss exponent \\ \(h_{x,y}\) & Small scale fading between nodes located \(x\) and \(y\) \\ \(W\), \(N\) & Total bandwidth and number of UEs sharing the spectrum \\ \hline \end{tabular}
\end{table}
Table 1: Notations used in the paper.  
Figure 1: System model of a multi-tier heterogeneous wireless powered communication network (WPCN).  
each node can receive the desired information from its associated BS and harvest energy from both the serving BS and the interfering BSs. It can be seen from the Figure 1 that some of UEs in WPCN receive the information and others harvest energy from the BSs over the whole plane.
## 6 Conclusions and Future Work  
### Conclusions  
To address the current challenges of requirement management chaos and representation difficulties, this paper innovatively proposes a prototype system construction method for requirement representation based on prompt engineering. This system leverages the powerful information extraction capabilities of large language models and the structural representation advantages of knowledge graphs, implementing the intelligent management of requirement documents using a three-layer architecture of \"requirement analysis--information extraction--graph construction\". Based on the SysML standard, the requirements are classified, and specialized information extraction templates and prompt templates are designed for different types of requirements. GPT-4 is used for structured data extraction, combined with automated processing and manual corrections, resulting in a knowledge graph that clearly presents the semantic relationships and hierarchical structure of the requirements, providing intuitive and efficient support for requirement analysis and management.  
To verify the feasibility of this method, the paper uses Chapter 18-Chapter 22 of the national standard 'Code for Design of Metro' (GB 50157-2013) as a case study to construct a dataset, dividing it into 80% training data and 20% test data. The experiment compares the performance of GPT-4 with GPT-3.5-turbo, BERT, and RoBERTa under the same evaluation criteria. The results show that GPT-4 achieves an extraction accuracy of 84.76%, significantly higher than GPT-3.5-turbo (79.05%), BERT (59.05%), and RoBERTa (59.05%). This demonstrates that the proposed method, which combines an information extraction template with GPT-4, exhibits excellent performance in SR information extraction tasks, far outperforming current mainstream deep learning models. Furthermore, GPT-4 can perform SR information extraction tasks without the need for extensive data labeling, achieving high-accuracy and high-efficiency extraction with only a few examples. This highlights the vast application potential of large language models combined with prompt engineering in requirement management and representation.  
Furthermore, based on the extracted structured data, this paper successfully constructs a requirement representation knowledge graph for the metro domain, further validating the effectiveness of the large language model combined with prompt engineering and knowledge graph construction systems. This provides a new technological pathway for intelligent requirement management.
## 3 Methods and Materials
### Application Logic Unit Layer  
The _Application Logic Unit Layer_ controls the whole system by interfacing the user through the _Human-Machine Interface Layer_ and by using the data provided by the _Hardware layer_. Indeed, it exploits the image data collected by the _Camera Node_, contained in the _Hardware layer_, to feed an Artificial Intelligence (AI) algorithm based on _You Only Look Once (YOLO) v2_ convolutional neural network. The latter realizes the conceptual _Object Recognizer_, as better explained in Section 3.4.1. In fact, the _YOLOv2_ network recognizes multiple different objects in the image and estimates their positions over different frames.  
Figure 4: The topics (rectangles) and the nodes (ellipses) of the Robotic Operating System (ROS).  
The acquired images and the relevant bounding boxes are shown to the user through the _Human-Machine Interface Layer_, which also collects the user commands. When one of the recognized objects is selected, the _Human-Machine Interface Layer_ sends the correspondent bounding box corner coordinates to the _Target Stabilizer_. It is realized through the _Kernelized Correlation Filter (KCF)_ algorithm, described in Section 3.4.2. The KCF algorithm is used to improve the object position estimation over consecutive frames. The low quality camera produces a lot of noise which is bad interpreted by the network or the _KCF_. To keep the cost of the entire system low, a combination of the two algorithms is used. In fact, when the user selects a target, the two algorithms start communicating each other to improve the location of the target during the movement of the manipulator.  
The improved object coordinates are exploited by the _Robot Movement Control System_, which implements the conceptual _Robot Logic_ unit. The _Robot Movement Control_ algorithm uses the information obtained by the _KCF_ algorithm as target point for the arm end-effector. To do that, it implements a _Finite State Machine (FSM)_, described in Section 3.4.3, which drives the manipulator to the target position by performing an established sequence of movements. To guarantee the arrival to the right position at every step, the _Robot Movement Control_ block exploits a close feedback loop algorithm called _Image Based Visual Servoing (IBVS)_, which permits to drive a manipulator exploiting a 2D algorithm. It is better described in Section 3.4.2.  
The following sections report detailed descriptions of the _YOLO Deep Neural Network_ (Section 3.4.1), the _Kernelized Correlation Filter_ (Section 3.4.2) and the _Robot Movement Control System_ (Section 3.4.3).
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 3 The Proposed Algorithm
### Calculating the Stretched Pitch Contour Slopes  
To find the significant changes in F0s, the slopes between points in the pitch contour are needed. Figure 3 illustrates the process of calculating the slopes: in the left-hand panel, (a), the estimated pitch contour is plotted; the graph in the middle panel, (b), shows a stretched pitch contour of the contour in panel (a) as discussed in Section 3.2, while that the right-hand panel, (c), depicts the slopes between the F0s of the stretched pitch contour. It is computed by differentiating the contour. The vertical red lines in Figure 3 show the possible offset points, and the vertical green lines are the possible onset points.
# Recent Progress and Challenges Regarding Magnetic-Based Nanoparticles for Targeted Drug Delivery
## 3.3. **Magnetite-Based Nanomaterials for Specific Cancer Treatment**  
Cancer stands as a significant global health challenge. Consequently, it is unsurprising that the quest for efficient carriers for highly toxic anticancer drugs tailored for targeted therapy garners substantial interest within the scientific community. However, the application of magnetite nanoparticles in cancer therapy is strongly affected by numerous factors that must be met--control of size, shape, and composition; toxicity; biocompatibility; and high delivery efficiency to the targeted tumor environment. The primary challenge currently impeding the translation of systems studied in research laboratories to clinical applications is presumably the limited efficiency in delivering nanocarriers to the targeted tumor area. The external magnetic field should control the movement of the nanocarrier but is limited by low tissue penetration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Components of** & **Fabrication/** & **Loaded Drug** & **Responsiveness * & **Reference** \\  
* Each drug delivery system demonstrates magnetic responsiveness.  
\end{table}
Table 2. Diverse synthetic solutions for potential optimization of targeted drug delivery using magnetite nanoparticle-based systems in cancer therapy.  
Despite the range of different cancers, particular attention is focused on materials for potential use in breast cancer therapy. This particular cancer type contributes to the highest number of new cases, although the diagnoses of prostate and lung cancer also remain notably high [42; 43]. Therefore, assessing the research progress dedicated to utilizing a specific carrier to treat a particular cancer type is worthwhile. Further analysis of recent research aims to determine whether the methodology for developing a potentially optimal carrier for targeted delivery aligns with advanced investigations into such a drug formulation for cancer therapy. It is assumed that magnetite NPs, deposited on the surface of the targeted cell membrane, are uptaken by cells (through endocytosis), and then the active substance is released intracellular under the influence of a specific stimulus (e.g., pH). However, at the moment, knowledge about these intracellular interactions is limited.
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design  
In this section, we first clarify our motivation to utilize the ICN functionalities to enhance CDN and present the general description of the architecture design and its key components. Next, we explain the working mechanism of the architecture by presenting the workflow of a content request and delivery process. Finally, we introduce the details of our solution to several issues in converting from the HTTP paradigm to the ICN paradigm.
## 9 Stokes Space Polarization and Field Recovery Principle  
System AIR enhancement demands the employment of polarization diversity in the fiber channel. As previously mentioned, during transmission, the polarization states of optical signals change randomly, and they have to be acquired at the receiver for signal retrieval. In this context, various polarization recovery schemes for estimating the rotation matrix have been presented. In this section, we give a comprehensive review of the polarization recovery schemes. Moreover, a broad overview of the Stokes space field recovery techniques is presented.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
## 3 Results and Discussion
### Thermal Analyses  
The thermal analyses of the prepared materials were investigated by Differential Thermal Analysis (DTA) and thermogravimetric analysis (TGA). Figure 5 showed DTA curves of Zn-Al LDHs, the prepared graphene and their Zn-Al-GO composites. For the pure LDHs, two endothermic peaks were observed at 188 \({}^{\circ}\)C and 293 \({}^{\circ}\)C. These peaks are due to removal of the interlayered water and carbonate anions; respectively. The dehydroxylation reactions of the layers of LDHs overlapped with the removal of carbonates anions, while, for graphene, three exothermic peaks were observed at 300, 520 and 602 \({}^{\circ}\)C. The first exothermic peak corresponded to the removal of unstable oxygen-containing groups of graphene. The second and third exothermic peaks were attributed to the decomposition and combustion of graphene.  
In the case of the Zn-Al-GO composites, they showed the characteristic peaks of both LDHs and graphene. DTA curve of Zn-Al-GO1 showed two endothermic peaks for the removal of the interlayered water and carbonate anions in addition to dehydroxylation reactions. In addition, the decomposition and oxidation reactions of the graphene layers were observed as broad exothermic peaks at 691 \({}^{\circ}\)C and 757 \({}^{\circ}\)C. In addition, the composites Zn-Al-GO2 and Zn-Al-GO3 showed similar behavior for Zn-Al-G1. These results indicated that the decomposition and oxidation reactions of graphene layers shifted to higher temperature after combining with the layers of LDHs. This finding confirms the presence of a link between the layers of LDH and the sheets of graphene oxides.  
The TGA curves of Zn-Al LDHs and their composites with graphene oxide are displayed in Figure 6. They showed four mass losses in the range of 25-700 \({}^{\circ}\)C. Lower than 200 \({}^{\circ}\)C, all samples showed weight losses due to the removal of the surface and interlayer water agreeing with DTA results. Above 200 \({}^{\circ}\)C, the main mass loss was observed corresponding to the removal of carbonate anions and dehydroxylation reactions of the layers of LDH. For all composites, the final mass loss was slightly lower than that of the pure Zn-Al LDHs indicating that the combined graphene sheets with the layers of LDHs caused relatively high stability for their composites at high temperature.  
Figure 5: Differential Thermal Analysis (DTA) curves of: (**a**) Zn-Al LDH, (**b**) Zn-Al-GO1, (**c**) Zn-Al-GO2, (**d**) Zn-Al-GO3 and (**e**) the prepared graphene.