esa-sceva/satcom-chunk-collection huggingface dataset:
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
###### Abstract  
In a three-coil wireless power transfer system with relay coils, the transmission efficiency and output power of the system decreases with changes in the adjacent coupling coefficients. Controlling the power of three-coil wireless power transfer systems is also a significant challenge. To solve these issues, a three-coil wireless power transfer system based on parity-time symmetry is proposed in this paper. First, a three-coil parity-time wireless power transfer system was modeled based on a circuit model. Then, the transmission and gain characteristics of the three-coil parity-time wireless power transfer system were analyzed. It was found that when the system is in a parity-time-exact region, it can maintain a constant transmission efficiency and output power, and its output power is independent of the coupling coefficient. In addition, based on the output characteristics of the three-coil parity-time wireless power transfer system, a power control method and a working range detection method were proposed to attain a constant power output. Finally, a three-coil parity-time wireless power transfer system was experimentally tested.  
wireless power transfer (WPT); parity-time (PT) symmetry; relay coil; power control Article  
A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 2 Background
### Life Cycle of Smart Contracts  
The authors in [22] outline four different stages of the smart contract: creation, deployment, execution, and completion.  
* _Creation_. In its creation stage, an EVM can be targeted by multiple high-level languages, such as Solidity [4], Serpent [23] or Bamboo [24]. The most commonly used one is Solidity. It is a JavaScript-like language and Turing-complete. EVM cannot run the Solidity codes directly, so they are compiled to opcodes(low-level instructions used by EVM) and encoded to bytecodes for storage reasons.
* _Deployment_. The Ethereum protocol outlines two kinds of transactions: one that invokes message calls, and the other that results in contract deployment [21]. The developer initiates a transaction that contains bytecodes (stored in a field called _init_ in the transaction structure), and this action returns another fragment of the code that will be stored in the EVM running environment and will be executed later.
* _Execution_. While in the execution stage, a smart contract is a running program, like a process or thread in a stand-alone computer. It will receive transactions (the first type) and the data that will be passed to the contract as parameters. Then, the EVMexecutes the instructions one by one until finished or the gas limitation is reached. This process happens at the time a new block is mined.
* _Completion_. After the execution, states are updated and stored in blockchains together with transactions. This completes the life cycle of the smart contract.
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 3 The Proposed Method
### Optimization of SVR Parameters
#### 3.2.5 Mutation  
In genetic algorithms, Gaussian mutation is a frequently used mutation operation that creates new individuals by randomly perturbing the chromosomes of the individuals in a way that follows a Gaussian distribution. This mutation operation can preserve population diversity to a certain degree and assist the algorithm in breaking out of the local optimum. As shown in Figure 3, for each individual \(S_{i}\), a random number \(r_{j}\sim N(\mu,\sigma^{2})\) is created, where \(N(\mu,\sigma^{2})\) is a Gaussian distribution with variance \(\sigma^{2}\) and mean \(\mu\). \(\sigma\) is the standard deviation of the Gaussian mutation, which controls the strength of the mutation. A larger \(\sigma\) leads to a larger magnitude of the mutation, which increases the diversity of the population. A smaller \(\sigma\) leads to a more localized mutation, which helps to perform a fine-grained search in the neighborhood of the current solution search. Next, we choose whether the random number \(r_{j}\) is added to the gene according to the mutation probability \(P_{m}\) to obtain the mutated individual \(S_{i}\prime\). After performing Gaussian mutation, the mutated parameter values are checked to determine whether they exceed the predefined bounds. If any parameter falls outside the allowable range, it is adjusted to the nearest boundary value. This ensures that all parameter values remain within the preset limits, maintaining the validity and feasibility of the solution space during the evolutionary process.  
Figure 3: Variant operation.  
Figure 2: Crossover operation.
# Discovery of an Intact Quaternary Paleosol, Georgia Bight, USA
## 2 Objectives and Methods
### X-Ray Diffraction (XRD)  
The University of Georgia's (UGA) XRD system is a Bruker D8-Advance model diffractometer. Computers in the XRD lab are equipped with a wide range of crystallographic software for pattern simulations (NEWMOD and CrystalDiffract), cell refinements (Rietveld refinement), crystal structure presentation (CrystalMaker), and the ICDD PDF-4+ database. The Bruker Eva program presents raw data and graphs known as diffractograms with patterns and peak positions identified  
Sediments were examined using X-ray powder diffraction (XRD) with the University of Georgia Bruker D8-Advance diffractometer. Samples were initially ground using a corundum mortar and pestle and then further ground in ethanol using a McCrone micromizing mill for 10 min to reduce particle size to \(<\)10. Powders were backfilled against a glass plate and pressed up to 400 psi to create a flat, self-supporting mount with minimal sample transparency. The D8 optics included a 250 cm goniometer radius, 0.6 mm divergence slit, and Bragg-Bentono geometry. A knife-edge blade was set 2 mm above the sample surface, and data was collected using a cobalt radiation source (K\({}_{\alpha 1}\) = 1.7890 A and K\({}_{\alpha 2}\) = 1.7928 A) that was operated at 35 kV and 40 mA with a position-sensitive Lynx-Eye detector. To verify alignment and calibration within 0.01\({}^{\circ}\) 2-theta tolerance of the certificate value for the brightest reflection peak position, an external NIST Reference standard SRM1976b corundum (\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)) [30] was used. Using a locked-coupled continuous scan mode with a step size of 0.01\({}^{\circ}\) 20 and a count rate of 0.2 s per step, the scan range was enhanced from 2 to 70\({}^{\circ}\) 20. The Bruker Eva program presents raw data and graphs known as diffractograms with patterns and peak positions identified. The raw data was K\({}_{\alpha 2}\)-stripped. Peak locations were compared to information from the powder diffraction file (PDF-4+) database of the International Centre for Diffraction Data (ICDD). Eva software V6 was used with the database to identify the best-fit phases for mineral identification.  
Energy-Dispersive Spectroscopy (EDS) is a common analytical approach to evaluate mineralogy in combination with XRD. Identification of individual elements alone is not enough to determine mineralogy. This can be carried out for the elements that appear in major abundances by recalculating the structural formula using an assumed stoichiometric model. We opted to forego EDS analysis of the argillic fraction as our XRD results wereconfirmatory of a clay-dominated sediment. XRD's more direct mineralogical identification capability precludes additional reliance on stoichiometry.
# A Polarization Independent Quasi-TEM Metamaterial Absorber for X and Ku Band Sensing Applications
## 3 Absorption and Pressure Sensor Results and Discussion
### Absorption Based on Simulation
where,\(f\) represents the frequency and \(h^{\prime}\) is the thickness of the substrate. At the resonance frequency of both 13.78 GHz and 15.3 GHz, it clearly presents negative values of \(-0.887\) and \(-0.460\), respectively. A comparative study based on simulation of the proposed MM absorber, is given in Table 3 where the FR-4 material shows significantly good performance regarding absorption capacity compared to Rogers RT-5880 substrate. Moreover, a dual-band resonance with wide bandwidth (13.67-13.88 GHz, 15.19-15.4 GHz approx.) gives a flexibility option in FR-4 compared to RT-5880 where it shows a narrowband absorption. Additionally, the proposed absorber shows LHM properties which can be apply in sensing applications and the cost of this material is low compared to Rogers RT-5880 or other available materials.
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology
### Comprehensive Evaluation Method of Stuck Pipe Based on Fuzzy Mathematics
#### 3.3.1 Establishment of Fuzzy Set for Stuck Pipe  
The American scientist [PERSON] [30] extended the value of the ordinary set characteristic function from {0, 1} to {0, 1}, thus creating the fuzzy set theory, and has the following definition:  
Let \(X\) be a space of points (objects), with a generic element of \(X\) denoted by \(x\). Thus, \(X=\{x\}\), if there is a real-valued function \(\mu_{\rm A}\), such that,  
\[\mu_{\rm A}:X\rightarrow[0,1] \tag{19}\]  
Then A is a fuzzy set on \(X\). In order to distinguish it from ordinary sets, the characteristic function \(\mu_{\rm A}(x)\) is called a membership function. This function describes that the element \(x\) in the \(X\) belongs to the set A to the degree \(\mu_{\rm A}(x)\).  
Different values of membership function \(\mu_{\rm A}(x)\) correspond to different membership degrees of \(x\) to set A.  
When \(\mu_{\rm A}(x)=0\), \(x\) does not belong to A at all; When \(\mu_{\rm A}(x)=1\), \(x\) belongs to A completely; When \(0<\mu_{\rm A}(x)<1\), \(x\) belongs to A to the extent of \(\mu_{\rm A}(x)\).  
Based on the above fuzzy mathematics theory, the following fuzzy set of stuck pipe, \(S\), can be established. The fuzzy set \(S\) indicates that \"stuck pipe may occur\", Suppose that the membership degree of each element in the \(P=\{P_{1},P_{2},\cdots,P_{n}\}\) to the fuzzy set \(S\) is \(\mu_{\rm s}(P_{1}),\mu_{\rm s}(P_{2}),\cdots,\mu_{\rm s}(P_{n})\), then the stuck pipe fuzzy set \(S\) can be expressed in the following form:  
\[S=(\mu_{\rm s}(P_{1}),\mu_{\rm s}(P_{2}),\cdots,\mu_{\rm s}(P_{n})) \tag{20}\]  
\(P_{1},P_{2},\cdots,P_{n}\) are the various evaluation indexes of stuck pipe. Two evaluation indexes were used, friction coefficient and probability of stuck pipe. \(\mu_{\rm S}(x)\) is the membership function of stuck pipe, and the final fuzzy set \(S\) can be simplified into the following form,  
\[S=(\mu_{\rm s}(P_{1}),\mu_{\rm s}(P_{2})) \tag{21}\]
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 3 System Model
### Definition and Properties of Voronoi Cells  
**Definition 1**.: _Given a set of \(n\) points \(S=\{p_{1},p_{2},\cdots,p_{n}\}\) in the field, the Voronoi diagram can be constructed by drawing the perpendicular bisector of line segment of each pair. Those bisector line segments form the boundaries of Voronoi cells are called Voronoi edges, and it can be defined as:_  
\[V(p_{i})=\underset{j\
eq i}{\cap}\left\{p|d(p,p_{i})<d(p,p_{j})\right\},\ i=1,2, \cdots,n \tag{1}\]  
_where \(d(p,p_{i})\) denotes the Euclidean distance between points \(p\) and \(p_{i}\), and the area \(V(p_{i})\) is said to be the Voronoi cell of \(p_{i}\). Obviously, the Euclidian distance from the point in the region of \(V(p_{i})\) will be shorter than the points outside._  
Voronoi graphs have many interesting and surprising mathematical properties [37; 38], such as influence region, lateral adjacency, liner behavior, largest empty circle, local dynamic characteristics, etc. These characteristics all provide a powerful tool to solve problems in spatial analysis, geography science, computer science and mathematics [39; 40]. Among them, there are three main properties:  
1. Each Voronoi node is the intersection of the three Voronoi edges. If any node in the graph make a circle, which goes through the Voronoi edges corresponding to all the vertices (three or more), cannot incorporate any other vertex. The circle with the largest radius is called the maximum empty circle.
2. For Voronoi polygons, Euler's Regulation demonstrates that no more than six adjacent space targets can be influenced while a vertex is being deleted or added. This feature is consistent with the practical characteristics of node deployment and network topology in wireless sensor networks.
3. For the points \(a,b\in V\), the edge \(ab\) is a Delaunay edge if there is a circle through \(a\) and \(b\) so that all other points of \(V\) lie outside the circle. The collection of Delaunay edges defines a plane geometric graph \(D(V)\) known as the Delaunay triangulation of \(V\). In the non-degenerate case, which excludes four or more points on a common circle, \(D(V)\) is indeed a triangulation. Even in degenerate cases, the faces of \(D(V)\) are convex polygons, and these can be further subdivided into triangles using additional edges.
## 1 Introduction
### New Paradigm of WSN with CR: Cognitive Radio Wireless Sensor Networks (CR-WSN)  
CR-wireless sensor networks (CR-WSNs) are a specialized _ad hoc_ network of distributed wireless sensors that are equipped with cognitive radio capabilities. CR-WSN is different in many aspects with a conventional WSN and conventional distributed cognitive radio networks (CRNs). The following section details the differences in the aspects among _ad hoc_ CRNs, WSNs, and CR-WSNs. CR-WSNs normally involve a large number of spatially distributed energy-constrained, self-configuring, self-aware WS nodes with cognitive capabilities. They require cognition capacity for a high degree of cooperation and adaptation to perform the desired coordinated tasks. They have not only to transfer data packets, but also to protect incumbent license users. More explicitly, this is a system that employs most of the capabilities required for a CR system, as defined by International Telecommunication Union (ITU) [12] and also for WSNs.  
According to [PERSON] _et al._[7], a CR-WSN is defined as a distributed network of wireless cognitive radio wireless sensor (CRWS) nodes, which sense an event signal and collaboratively communicate their readings dynamically over the available spectrum bands in a multi-hop manner, ultimately to satisfy the application-specific requirements.  
In CR-WSNs, a wireless sensor node selects the most appropriate channel once an idle channel is identified and vacates the channel when the arrival of a licensed user on the channel is detected. The cognitive radio technique is probably one of the most promising techniques for improving the  
Figure 2: CR-WSNs model.  
efficiency of the WSNs. CR-WSNs increase spectrum utilization, and fulfills the end-to-end goal, increase network efficiency and extend the lifetime of WSNs. Figure 2 presents a CR-WSNs model.
## 4 Survey Results and Analysis
### Preliminary Data Analysis
#### 4.1.1 Reliability Analysis ([PERSON]'s Alpha Test)  
The first analysis performed immediately after receiving and validating the answers to the final questionnaire was to check the internal consistency of the section of the onlinetraining questionnaire (items I.01-I.14) and also the section regarding the satisfaction associated with this type of training (items I.09-I.14).  
In this case, it was verified that the questions of the section were formulated in such a way that they refer only to a single reality studied in accordance with the general objective of the research, namely: the satisfaction of the online training of employees in OSH. A [PERSON]'s alpha value of 0.75 or more is accepted as a rule to denote an agreeable level of reliability [48]. The results of the Cronbach's alpha value for all fourteen items (I.01-I.14) are 0.82, and for the six items (I.09-I.14) regarding the satisfaction of the online training of employees, the value is 0.95; the level of reliability is very good, specific to situations where important decisions must be made, and as a result, the data were reliable and valid [35; 36; 37; 42].
## 2 Materials and Methods
### Instrumentation Procedures (SPME-GC/MS)  
To extract the VOCs from each specimen, solid phase microextraction (SPME) was used. The SPME fiber utilized in this study was a 50/30 \(\upmu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fiber (Supelco, Sigma Aldrich, St. Louis, MO, USA). Each SPME fiber was conditioned for 30 min at 250 \({}^{\circ}\)C at least three times to guarantee each fiber was clean and ready to be used prior to specimen sampling. A blank fiber instrument run was then performed to ensure no contaminants or lingering volatiles remained on the fiber.  
An Agilent Technologies GC 7890A model with an Agilent Technologies 5975C inert XL MSD with triple-axis detector (Agilent Technologies, Santa Clara, CA, USA) was used for the separation and analysis of the compounds extracted by the employed SPME fiber (33). An Rtx(r)-5 capillary 30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m column (Restek Corporation, Bellefonte, PA, USA) was used. Before analyzing the samples, an instrument blank was run on the GC-MS instrument to check its performance with blank vials for calibration purposes. This also validated that the instrument was running correctly prior to sample analyses. Ultra-high purity helium was used as a carrier gas at a flow rate of 1.0 mL/min. Samples were injected in the splitless mode. The oven temperature started at 35 \({}^{\circ}\)C for 5 min, increased to 240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min then held again for 4 min. The total run time for analysis was 29.5 min. Mass spectra were repeatedly scanned from 45 to 550 amu. Compounds were identified using the NIST 17 (2017, Version 2.3) mass spectral reference library and verified with external standard chemical calibration where possible (anisole and dodecane). The criteria for the compounds identified were those with detected peaks greater than or equal to a mass spectral match quality of 90% or above.
## 2 Methods
### Considering Distance Scale Factor  
In the TLSC algorithm, according to the least square adjustment principle, the Gaussian function is fitted mainly by establishing the mapping relation between distance and covariance. However, negative values often appear in covariance calculation results, which violates the positive definiteness of the Gaussian function. If the negative value is removed and the positive value is retained, a large amount of data will be lost, resulting in poor fitting and estimation. Therefore, Gaussian function considering distance scale factor is constructed (DLSC). The prior Gaussian function model can be established as follows:  
\[F(d)=C(0)exp(-k^{2}d^{2}) \tag{9}\]  
where \(C(0)\) is the self-covariance of the observation signal, which can be solved based on Equation (7). According to the regularity of the stationary random function (Gaussian function) that \(C(d)\leq C(0)\), if the distance \(d\) goes to infinity, the covariance \(C(d)\) will be close to zero. Therefore, when the maximum distance between the two observation points is defined (\(d_{max}\)), the covariance \(C(d_{max})\) is much less than \(C(0)\), namely:  
\[C(d_{max})\ll C(0) \tag{10}\]  
Defining \(C(d_{max})=RC(0)\), \(R<<1\), where \(R\) is the distance scale factor of signal covariance. Taking into account the Equation (9), there is:  
\[F(d_{max})=RC(0) \tag{11}\]  
\[RC(0)=C(0)\exp\Bigl{(}-k^{2}d_{max}{}^{2}\Bigr{)} \tag{12}\]  
\[R=\exp(-k^{2}d_{max}{}^{2}) \tag{13}\]  
\[-k^{2}=\ln R/d_{max}{}^{2} \tag{14}\]  
The corresponding value of \(k\) can be determined by taking different values of \(R\). Then the Gaussian function can be established to determine the covariance matrix of signals between observation points. From the root mean square error (RMS) of the checking residuals, the \(k\) value with the smallest RMS value will be selected appropriately adjusting the \(R\) value.
# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Data Analysis and Experimental Platform
The total number of cases constitutes 42.86 percent of the study sample. The company with the lowest overall score is LB, with \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**District** & **EW-TOPSIS** & **Rankings** & **D-TOPSIS** & **Rankings** & **EW-D-TOPSIS** & **Rankings** \\ \hline NN & 0.5856 & 9 & 0.5852 & 4 & 0.5854 & 8 \\ LZ & 0.5854 & 12 & 0.5835 & 9 & 0.5846 & 10 \\ GL & 0.5857 & 4 & 0.5844 & 6 & 0.5855 & 5 \\ WZ & 0.5857 & 6 & 0.5843 & 7 & 0.5855 & 6 \\ BH & 0.5857 & 5 & 0.5856 & 2 & 0.5857 & 3 \\ FCG & 0.5855 & 10 & 0.5790 & 11 & 0.5843 & 11 \\ QZ & 0.5858 & 2 & 0.5856 & 3 & 0.5857 & 2 \\ GG & 0.5858 & 3 & 0.5848 & 5 & 0.5856 & 4 \\ YL & 0.5857 & 7 & 0.5837 & 8 & 0.5854 & 7 \\ BS & 0.5854 & 11 & 0.5769 & 12 & 0.5840 & 12 \\ HZ & 0.5851 & 14 & 0.5750 & 13 & 0.5828 & 13 \\ HEC & 0.5858 & 1 & 0.5858 & 1 & 0.5858 & 1 \\ LB & 0.5851 & 13 & 0.5696 & 14 & 0.5825 & 14 \\ CZ & 0.5856 & 8 & 0.5795 & 10 & 0.5849 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: DEMATEL-Entropy Weight-TOPSIS model ablation study scores. a score of 0.5825. This is a significant discrepancy from the management ability of the first-ranked enterprise, indicating that there is considerable potential for improvement in line loss management ability. Research on the Whole-Process Evaluation System of Line Loss Management in a City of Guangxi District Based on DEMATEL-Entropy Weight-TOPSIS  
The collection of indicator data is conducted in accordance with the established evaluation indicator system. The determination of the qualified value is based on a combination of national standards, industry standards or norms, and expert experience. Table 5 presents a comprehensive overview of the scores for each urban area in Guangxi, based on the combined evaluation conducted between 2014 and 2016. It also illustrates the scores for each secondary indicator.
## 2 Related Work  
### Zero-Shot Learning  
Zero-shot learning (ZSL) [11; 12] is a machine learning method that allows a model to classify objects in specific categories not encountered during training. This is especially useful when gathering training data for every possible category is unfeasible.  
The key concepts of zero-shot learning (ZSL) include semantic space, projection functions, and generalized zero-shot learning (GZSL). ZSL relies on a semantic space, encompassing both seen and unseen categories, which can be constructed using attributes, word embeddings, or other high-level category descriptions. Projection functions map visual features into this semantic space. While traditional models typically project visual features into the semantic space, newer approaches suggest reverse projection or joint embeddingspaces. Generalized zero-shot learning extends ZSL by including both seen and unseen categories during the testing phase, making it more realistic and challenging as the model must simultaneously distinguish between them.  
ZSL has applications in various fields, such as image classification, natural language processing, and remote sensing. For instance, in remote sensing, ZSL can classify new types of land cover without labeled data; in NLP, ZSL can enhance the ability of language models to handle new tasks without task-specific training data.  
Current research [13; 14; 15] focuses on improving the robustness of ZSL models, developing more comprehensive benchmarks, applying ZSL to a broader range of complex real-world problems, and combining ZSL with self-supervised learning to explore its potential in dynamic environments where categories evolve.