esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Trajectory Generation
### Bezier Curve  
We use a Bernstein polynomial basis to represent each trip between two target spots. The position of HUAV for each trip in each dimension out of \([x,y,z]\) can be written as:  
\[f_{j}^{x,y,z}(t)=\sum_{i=0}^{u}c_{i}\binom{u}{i}*t^{i}*(1-t)^{u-i} \tag{12}\]  
Since variable \(t\) of Bernstein polynomials is defined on a fixed interval \([0,1]\), as in [37], we use scale \(s\) to scale the parameter time \(t\) to an arbitrarily allocated time for each segment. Then the whole trajectory that contains \(n\) piecewise trajectory with the degree of \(u\) can be generalized to Equation (13). In the following part of this section, we will briefly use \(F\) to represent the whole trajectory, and \(f_{j}\) to describe the \(j_{th}\) piecewise trajectory as well.  
\[F^{x,y,z}(t)=\left\{\begin{array}{l}s_{1}f_{0}^{x,y,z}\big{(}\frac{t-T_{0}}{ s_{1}}\big{)},t\in[T_{0},T_{1}]\\ s_{2}f_{1}^{x,y,z}\big{(}\frac{t-T_{1}}{s_{2}}\big{)},t\in[T_{1},T_{2}]\\ \vdots\\ s_{n}f_{x,y,z}^{n}\big{(}\frac{t-T_{n-1}}{s_{n}}\big{)},t\in[T_{n-1},T_{n}] \end{array}\right. \tag{13}\]  
We set the time \(T_{j}\) to demonstrate the time in which the HAUV is supposed to achieve the path segment, by assuming the HAUV travels with trapezoidal velocity, which will be introduced in the following section.
## 3 Methodology
### Flow-Aware Dynamic Graph Learning  
As the relationships between features may change with varying input data, the Flow-aware dynamic graph learning method is proposed to establish a connection between the generated graph and real-time data. As shown in Figure 2, at first, an initial adjacency matrix is generated using Kaiming initialization to represent the relationships among features. Then, two linear transformation layers are designed to separately transform the real-time data input X and the adjacency matrix \(\text{E}_{\text{f}}\). The transformed vectors are subsequently concatenated and subjected to an additional linear transformation, resulting in a new embedding, \(\text{E}_{\text{d}}\). The transformation process can be expressed by the following formula:  
\[\text{E}_{\text{d}}=\text{W}_{1}[\text{W}_{2}\text{X}+\text{b}_{2};\text{W}_{ 3}\text{X}+\text{b}_{3}]+\text{b}_{1} \tag{3}\]  
where \(\text{W}_{1}\), \(\text{W}_{2}\), and \(\text{W}_{3}\) are learnable weight matrices.  
Figure 1: The structure of the proposed DHGAR model.  
Figure 2: Structure of the flow-aware dynamic graph learning.  
To generate dynamic graphs based on the current input data, a Data-Adaptive Graph Generation (DAGG) method is employed. This method was first introduced in the Diffusion Convolutional GRU (DCGRN) model and is capable of generating and refining graph structures according to the data patterns. The general DAGG method primarily consists of dot product operations, a ReLU layer, and a softmax function. It takes an adjacency matrix as the input and outputs the generated graph. In this study, since the input adjacency matrix \(\text{E}_{\text{d}}\) is learnable and incorporates a real-time data stream, the generated graph possesses the ability to dynamically reflect changes in the data. As shown in Figure 2, \(\text{E}_{\text{d}}\) is first subjected to a dot product operation and then processed by a ReLU layer and a softmax function. Ultimately, the dynamic graph is obtained. This process can be expressed as follows:  
\[\text{G}_{\text{dynamic}}=\text{softmax}\Big{(}\text{ReLU}\Big{(}\text{E}_{ \text{d}}\text{E}_{\text{d}}^{\text{T}}\Big{)}\Big{)} \tag{4}\]  
It is worth noting that the dynamic graph adjusts in real time according to different input data, not only during training, but also during testing, generating varying graph structures. Therefore, this method can effectively capture changes in the impact of different environmental features on wind power.
## 1 Introduction  
Until about ten years ago, talking about X-Ray Fluorescence (XRF) imaging meant talking about Synchrotron Radiation of advanced generation for tomography and holography [1]. Nowadays, XRF imaging in Cultural Heritage (CH) field immediately recalls XRF mapping (or Macro-XRF, MA-XRF), which is the recording of XRF spectra together with their spatial coordinates, so to allow the reconstruction of distribution maps for chemical elements in the sample. This can be obtained, in principle, by either scanning the sample or using a pinhole and a 2D detector, the so-called full-field XRF (FF-XRF) [2; 3; 4].  
MA-XRF mobile instruments scan samples, as painted artworks, with an X-ray beam whose dimensions can vary from one to several hundred micrometers in diameter. The detected characteristic X-ray fluorescence emission, stimulated by ingoing radiation, allows creating the images of the elemental distribution. Technical details are beyond the purpose of this paper, aiming to discuss the consequences in CH field and the possibilities linked to a good post-processing of data. Indeed, the physical background of MA-XRF does not differ from that of XRF, but the possibility of mapping elemental distribution on samples gives this technique the dignity of a new analytical method and makes it one of the most useful tools to support restoration and conservation interventions. One of the characteristics which makes MA-XRF a routine technique not only on painting or CH materials but also in many other fields (such as geology, biology, forensic [5; 6; 7; 8; 9; 10]), is the multispectral capabilities and the possibility of visualizing the distribution of chemical elements at and below the surface of paintings, according to the self-absorption limits for X-rays. Indeed, maps are a more intuitive tool that can be easily interpreted by outsiders, such as restorers, art-historians, curators, and conservators.  
The first experiments in 2007 were performed with synchrotron radiation [11]. Ten years until then, MA-XRF regularly applied in museums, archeological sites, conservation studios, and the first workshop on MA-XRF, Macro X-ray Fluorescence Scanning in Conservation, Art, and Archaeology was held in Trieste [12].  
Nowadays, portable MA-XRF (p-MA-XRF) has moved over, with the possibility of taking into account also the shape of the scanned surface [13; 14] and combining its results with all the other well-established imaging techniques [15; 16; 17; 18; 19; 20; 21]. Recent instrumentation organizes data in 3D spectral image cubes (one of the axes is the spectral one, the other two axes describe the position) [22]. Moreover, and this is the focus of our paper, data analysis has been more and more developed and optimized [23; 24; 25] until the interpretation using neural networks [26].
# Parametric Investigation of Die-Sinking EDM of Ti6 Al4V Using the Hybrid Taguchi-Rams-RATMI Method
## 2 Experimental Methodology  
### Work Material  
Ti6 Al4V, is a popular titanium alloy that is well known for its exceptional corrosion resistance, low weight, and high strength (Table 1). For high-performance applications, its remarkable mechanical qualities, resistance to corrosion, and biocompatibility make it the ideal choice. It is still relevant and significant in a variety of sectors due to continuous improvements in processing and application techniques. It is utilized in exhaust systems, offshore constructions, maritime gear, surgical implants, orthopedic devices, high-performance engine components, aviation parts, chemical processing equipment, and heat exchangers. Ti6 Al4V may be produced via a number of industrial processes, including powder metallurgy, casting, and forging. However, because of its great strength and limited heat conductivity, machining this alloy can be difficult. To accomplish accurate and effective machining, methods such as EDM are frequently used. A workpiece measuring 120 \(\times\) 100 \(\times\) 7 mm was utilized in its base form in the machining process. Four workpieces of the same size were used to accommodate each run (Figure 1).  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Composition**} & \multicolumn{1}{c}{**Properties**} \\ \hline Titanium (Ti): Balance & Ultimate tensile strength: \(\sim\)950 MPa \\ Aluminum (Al): 6\% & Yield strength: \(\sim\)880 MPa \\ Vanadium (V): 4\% & Elongation: \(\sim\)14\% \\ Iron (Fe): \(\leq\)0.25\% & Modulus of elasticity: \(\sim\)110 GPa \\ Oxygen (O): \(\leq\)0.20\% & Density: \(\sim\)4.43 g/cm\({}^{3}\) \\ Carbon (C): \(\leq\)0.08\% & Melting point: \(\sim\)1660 \({}^{\circ}\)C \\ Nitrogen (N): \(\leq\)0.05\% & Thermal conductivity: \(\sim\)6.7 W/m K \\ Hydrogen (H): \(\leq\)0.015\% & Coefficient of thermal expansion: \(\sim\)8.6 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition and properties of Ti6 Al4V [2].
# High-Density 3D Printable Chipless RFID Tag with Structure of Passive Slot Rings
###### Abstract  
A three-dimensional (3D) printable chipless radio frequency identification (RFID) tag, with high density and sensitivity, is proposed and fulfilled on insulator substrates. By printing a rectangular slot ring and designing specific geometry on the substrate, the printed structure shows high sensitivity in a resonant manner, with the benefits of high density and low cost. Considering the multiple rectangular rings with different sizes in a concentric distribution, a bit coding sequence can be observed in frequency spectra because of the corresponding different resonant frequencies aroused by the printed slots. In this way, the 3D printable chipless RFID tag can be fulfilled by adopting the structure of the rectangular slot ring on the insulated substrates. The main characteristics of the designed rectangular slot rings are verified on both flexible and solid substrates. A 12-bit chipless tag based on the slot ring structures is designed and implemented. The simulation and experiment results show good agreement on its characteristics. The frequency response reveals the fact that the 2 th, 3 th and 4 th harmonic do not exist, which is a unique merit for improving the encoding capacity and the sensitivity of the corresponding reader. The electric field direction of the electromagnetic wave of the reader excitation tag is demonstrated to be wide, up to 90\({}^{\circ}\) on the tag horizontal plane, 30\({}^{\circ}\) on the vertical direction.  
Keywords:chipless tag; radio frequency identification (RFID); radar cross-section (RCS); rectangular slot ring +
Footnote †: journal: Sensors
## 5 Testing Results  
Upon receipt of \(\Delta d\) messages, the actuator regenerates each \(b_{k1},i+b_{k2},i\), which can be calculated by an equation.  
To validate the effectiveness of the proposed system, we demonstrated a framework with C++ and tested it on NS3. The tests were conducted on a tablet PC with a 3.0 GHz Intel inside Core i3 CPU and 4 GB of Smash. The test machine used a 64-bit adaptation of Windows 8. The network includes the parameters listed in Table 2. EDB-CPS was compared with the state-of-the-art methods: A-CPS [26], BGNIC [27], and StreamLAB [33]. Based on the testing process, the following results were calculated:  
* throughput;
* hop-to-hop delay.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Used Parameters** & **Detailed Parameters** \\ \hline Transmission range & 30 m \\ Sensing range of the node & 25 m \\ Initial energy of the node & 5 Joules \\ Bandwidth of the node & 45 Kb/s \\ Simulation time & 36 min \\ Number of sensors & 360 \\ Network size & 600 \(\times\) 600 m\({}^{2}\) \\ Number of hops in the network & 18 Maximum \\ Number of clusters & 06 \\ Buffering capacity & 50 Packets buffering capacity at each node \\ Mobility model & Lattice mobility model [34]. \\ Mobility (Speed of the nodes) & 0 m/s to 15 m/s \\ Data packet size & 128 bytes \\ Initial pause time & 30 Seconds \\ \(R_{x}\) energy & 14 Mw \\ \(T_{x}\) energy & 18 Mw \\ Power intensity & \(-18\) dBm to 12 dBm \\ Sink location in each region & (0, 230) \\ Contending paradigm & IA-CPS [26], BGNIC [27], and StreamLAB [33] \\ Mobility (speed of the nodes) & 0 m/s to 20 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in the simulation setup.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(A_{net}\) & Network area \\ \(A_{rec}\) & Recruiting actuator \\ \(D_{a}vg\) & Mean distance from actor to base station \\ \(d_{N}\) & Distance of the recruited sensor from the recruiting cluster head sensor \\ \(\partial_{p1,p}\) & Distance of the recruited sensor from its domain to the recruiting sensor’s domain \\ \(FS_{e}\) & Amplifier energy of free space \\ \(M_{p}\) & Monitoring point \\ \(MP_{e}\) & Multipath energy \\ \(N_{r(d)}\) & Recruited sensor sensing data \\ \(N_{r}\) & Recruited sensor \\ \(p_{r}^{\prime}\) & New vector that indicates the probability of a grid station \\ \(P_{r}\) & Recruited sensor sensing data \\ \(R\) & Probability of the recruited sensor \\ \(S_{n}\) & Number of sensor nodes \\ \(t\) & Recruitment time \\ \(C_{ndj}\) & Adjacent cluster domain or Nonadjacent cluster domain \\ \(N_{rec}\) & Number of recruited sensors \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables used in the recruitment process.
## 4 Discussion
Due to the genetic similarities between flaviviruses, it would be expected that antibodies for other flaviviruses, such as YFV, may also have the ability to enhance or neutralize ZIKV infection. In this study, the EBs from co-cultures had lower viral loads as compared to the placental cell lines. This supports reports of ZIKV titers in fetal and placental tissues in vivo [65; 28; 66]. Since macrophages or other monocytes were not used in this study, the enhancement we observed was likely due to antibody-mediated enhancement (AME). AME occurs when antibodies bind to virus particles, forming complexes. These complexes interact with cell surface receptors and promote entry into host cells, leading to increased levels of viral replication via the suppression of innate immune processes and inflammatory cascades [86; 24]. While this process is associated with Fc receptor-bearing monocytes, it is also possible for these virus-antibody complexes to infect other cell types and suppress innate immunity [24]. The data presented here highlight the impact of various cells on the neutralization or enhancement of ZIKV with YFV antisera. The cell-specific responses of ZIKV and other flaviviruses is well documented and should be recognized when developing cross-reactivity assays [87; 48]. The data also highlight the impact of placental cells and maternal antibodies on the establishment of congenital ZIKV infections. A limitation of these studies was the unavailability of anti-monkey IgM secondary antibody. For this research, we utilized a secondary antibody that detects IgA, IgM, and IgG. This secondary antibody detected IgG antibody but not IgM. Whether this was due to a lack of sensitivity of the antibody or the lack of IgM in the antisera is unknown. A second limitation of this study was the omission of Hofbauer cells from the model. Hofbauer cells have been found to play a role in ZIKV transmission through the placental barrier [88; 89; 90; 83]. They are placental macrophages that also have Fc receptors, play a role in early angiogenesis within trophoblast cells, and have been found to transfer viral particles into the fetal endothelial cells and blood supply [81; 91; 92; 93]. Since they have been found to further facilitate the vertical transmission of ZIKV, not including them in the transwell co-culture assay limited the findings within this study. DENV antibodies have already been found to enhance ZIKV infection within Hofbauer cells, and as such, not including them could have impacted the results [83]. We did not include Hofbauer cells in this assay since they are primary cells that have strict culture requirements that were not compatible with the other cells in this assay.
## 7 EMC Analysis and Human Exposure in EV  
As mentioned previously, the coupling of the transmitter, located on the ground, and the receiver, placed under the bottom of the vehicle, is done through a large gap. Such a large gap in the mid of the two ICT coils in static or dynamic cases infers a high level of stray field close to the coils. This condition poses a challenging exposure to magnetic fields for vehicle travelers or people (or things) expected to be close to the vehicle in the course of charging maneuvers. Therefore, to conform to the health safety, it is required to assess the degree of exposure with regard to the international safety guidelines labelled by the International Commission for the Protection against Non-Ionizing Radiation (ICNIRP) [50]; the highest limits are 27 \(\mu\)T for the magnetic induction B and 4.05 V/m for electric field E.  
Two characteristics relate to the interactions of the IPT structure and the human body. The first is to evaluate the electromagnetic fields (**B**, **E** and **J**) induced in the human body at the IPT frequency. The second is to inspect the effect of the IPT system input current on the radiation heights and to make accessible the effective data to determine the degree of the design freedom of the IPT systems. It should be noted that owing to electromagnetic compatibility and energy efficiency, the IPT system for EV commonly functions in a comparatively small frequency scale (from a few kilohertz to about 100 kHz).  
For the prediction of radiated fields, we need adequate modeling tools [81; 82; 83; 84; 85]. The assessment of exposure of alive matters to magnetic fields requests in general suitable modeling approaches founded on 3D computations used for solving the electromagnetic problem Equations (1)-(4) containing the IPT system, the vehicle and the humans (in the vehicle or situated nearby). In such computations, the considered model of human body is very significant. The most decisive aspects ruling the acceptance of such a model are the consistency with physical biological qualities of faithful circumstance and the agreement to the used computational approach.
# A Review of Smart Grid Anomaly Detection Approaches
## 2 Materials and Methods
### Definition of Research Questions  
The aim of the review is to identify research studies pertaining to anomaly detection in smart electrical grids as well as analyze the trends and methodologies employed. As a result, the answers to the following questions are sought:  
* What is currently known about anomaly detection?
* What are the most common AI methods?
* What is the trend in this field?  
The Mendeley library management tool and electronic resources, whose search tools allow the localization of documents relevant to the review, were used: ACM Digital Library, Compendex, ScienceDirect--Elsevier, IEEE Xplore, ISI Web of Science, SpringerLink, and Wiley Inter Science Journal Finder.  
In the exploration and search for manuscripts the following keywords were combined: smart grid, cyber physical systems, anomaly detection, and artificial intelligence.
## 2 Review of Literature and Standards
### MES from the Manufacturing Viewpoint  
A MES can be defined by its functionalities according to editor from MPDV Mikrolab (DE) in [2]. Many different MES views and definitions are present in parallel; however, there are some very prominent international or nationally de-facto well-accepted industrial foundations and standardization associations, whose role is outstanding in the MES scene. The biggest three are the Manufacturing Enterprise Solutions Association International (MESA, USA), the National Institute of Standards and Technology (NIST, USA), and the Zentralverband Elektrotechnik- und Elektronikindustrie eV (ZVEI), which are the most well-known in industry.  
MESA's MESA-11 model stood the test of time. It was published in 1997, yet, over the years, it obtained many extensions (integrated MES, collaborative MES); however, the eleven _core manufacturing functionalities_ stayed the same. They defined MES as an information delivering component from order launch to finished goods, which enables the optimization of production activities and based on the data acquisition functionality it (i.) guides, (ii.) initiates, (iii.) responds to, and (iv.) reports on plant activities as they occur in nearly real time [46]. The numbered activities highlight the crucial functionality expected from a MES.  
According to the NIST, MES is a system that uses network computing to automate production control and process automation by downloading routings and schedules, furthermore, uploading production reports. Simply put, the MES bridges the information gap between the business and the shop-floor (control) layer [47]. Here, again, this bridge role reappears and the demand to operate automatically. This is a less specific functionality requirement list; however, NIST released the SIMA (Systems Integration of Manufacturing Applications) Reference Architecture, which contains the accurate manufacturing activities necessary to realize a MES [48].  
The ZVEI's VDI (Verein Deutscher Ingenieure) Guideline 5600 categorizes MES in the area of discrete manufacturing as a \"process-oriented manufacturing management system\" and as the \"comprehensive driving force for the organization and execution of the production process\" [49]. SEMATECH's (Semiconductor Manufacturing Technology Consortium) CIM (Computer Integrated Manufacturing) Framework defines Manufacturing Information and Execution Systems (MIES), which focus primarily on operations in a factory: resource integration and workpiece management and movement [50].  
The ideal MES according to [2], from a functional point of view, consists of guarantees of communication with corporate management applications and with the manufacturing environment, in addition to available function groups that can be activated and used depending on the requirements. These three uncovered function groups are (i.) production, (ii.) quality, and (iii.) personnel, from which the production is relevant in this case and its seven specified sub functionalities. The previously presented MES concepts and their functionalities are listed in Table 1.