esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Result and Comparative Study
### The Performance of the Proposed Method  
In this part, the performance of the proposed method is discussed for the diagnosis of each manufacturing defect. The testing set for each type of manufacturing defect has 300 samples. The testing results of the proposed method are presented by confusion matrix and t-SNE.  
The confusion matrix of the proposed method is recorded ten times, and the average value is shown in Figure 8. Figure 8 clearly shows that the diagnostic accuracy of the #2, #3 and #4 are 100%; the diagnostic accuracy of the #0, #1 and #5 are 90%, 92.3% and 88.1%, respectively. It can be seen that most of the misclassified samples of #0 and #1 are diagnosed as #5, and the misclassified sample of #5 is diagnosed as #0 and #1. It can be proved that the proposed method can extract different features for classification in #2, #3 and #4. The extracted features of the proposed method are very similar in #0, #1 and #5. It can be proved that the data description of typical manufacturing defects is correct in the previous part.  
The \"t-SNE\" can express the feature extraction ability of the model and analyze the clustering of features more intuitively. The visualization tool \"t-SNE\" implements high-dimensional feature mapping to visualize the learned characteristics, as shown in Figure 9.  
Figure 8: Confusion matrix of the proposed method.  
Figure 7: The HHT time-frequency image. (#0) Normal hermetic refrigeration compressor (#1) Discharge pipe contacting stator; (#2) Crankshaft contacting discharge pipe; (#3) Shedding of support spring; (#4) Incorrect position of silencer; (#5) Foreign body in suction valve.  
In Figure 9, it can be seen that the proposed model effectively learned feature information, which helps diagnose and classify a manufacturing defect, and high-level features show prominent clustering characteristics. It is concluded that the proposed method has better diagnostic performance and learned characteristics capabilities.
# Application of Artificial Intelligence in Wind Power Systems
## 4 Artificial Intelligence Algorithms
### Algorithms for Wind Turbine Design  
Evolutionary algorithms (genetic algorithms and particle swarm optimization) are often used to iteratively improve wind turbine components such as blade aerodynamics, tower dimensions and foundations [22; 23; 31; 32; 33; 34]. Representative examples are the use of genetic algorithms to refine the aerodynamic profile and particle swarm optimization for structural designs that balance mass and deflection.  
Neural networks (fully connected networks and convolutional networks) are used to predict aerodynamic performance metrics (lift, drag, turbulence) and to optimize geometry by rapidly evaluating design candidates [25; 28; 32; 33; 35]. Convolutional neural networks can map the shape of airfoils to aerodynamic coefficients, while feedforward neural networks help to predict the results of foundation designs without expensive finite element analyses.  
Support vector machines, relevance vector machines and genetic programming have also been explored for the control of blade design processes, especially for the determination of optimal tip speed ratios (TSR) [31].
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Free Localization
#### 4.2.2 Magnetic Sensor-Based Localization  
In magnetic sensor-based localization, the building plot is drawn without capturing GPS signal and by ignoring magnetic fields since advanced construction materials possess magnetic inscription features. An underground pipeline monitoring scheme developed in [65] perceives and traces leakages in concurrent time from distinct sensor nodes lying inside and around the pipeline. The manifestation that the peculiarities use the Earth's magnetic field for accurate wayfinding inspired the authors of [66]. It developed a localization technique for large buildings having long corridors connecting distinct regions. Local signals originating through the earth's magnetic field are used for location estimates. [PERSON] et al. [67] used resident characteristics of the variable magnetic field to extant a physical amplitude in three-dimensional fruition environment realization. The magnetic field is a promising technology for geo-localization and optimal accuracy.
# High-Harmonic and Terahertz Spectroscopy (HATS): Methods and Applications
## 4 Prospective  
### HATS for Polar Molecules  
The detection of HATS from gas has focused on atoms or non-polar molecules with relatively simple structure and high degree of symmetry. However, in nature, many molecules exhibit polarity, which makes a well-defined orientation a prerequisite to study their angle-dependent property. Due to the inherent dipole moment, polar molecules are indeed the next potential targets for HATS detection to investigate electron dynamics under strong laser fields.  
The first concern is the effect of the inherent dipole moment on the emissions. The inherent dipole moment of the polar molecule is predicted to introduce even harmonics under the interaction with the fundamental laser solely, which is different from the typical dual-color scheme of adding a weak second harmonic [98]. For the dual-color method, the symmetric motion of electrons is broken during  
Figure 10: The normalized yield of even harmonics from H28 to H32 as a function of the aligned angle \(\Theta\) and the dual-color relative phases with the THz phase reference for nitrogen. Experimental data are normalized under each alignment of different harmonics and THz wave, respectively. The red arrows help to find the phase variations of H32 from N\({}_{2}\) between the parallel and perpendicular alignments.  
the consecutive two half cycles of the laser field when a suitable dual-color relative phase is applied, producing significant intensities of the even-order high harmonics and THz emissions. Therefore, even harmonics are closely associated with the emission of THz wave when they are generated by the common asymmetric electron dynamics, which underlies the HATS detection. The ionization rates of polar molecules under different orientations are mainly determined by the spatial distribution of the electronic wave function, which introduces additional asymmetry for the electrons released in the directions parallel and anti-parallel to the dipole. Therefore, even only with a single color fundamental laser, the oriented polar molecules can still generate the HATS [99; 100]. Based on the discussions in Section 3.1, the orientation dependent THz yields can also be applied to detect the angle-dependent ionization of the polar molecules, or to reconstruct the angle-dependent differential PICS.  
The second concern is the asymmetric Coulomb potential of polar molecules. The rescattering dynamics of polar molecules has been shown theoretically to be different substantially from that of homonuclear molecules, as reported in Refs. [101; 102]. With the help of HATS technique, the asymmetry of nuclei attraction and the electron dynamics on THz wave and high harmonics may be identified, allowing more insights into the electron localization of polar molecules. The understanding of polar molecules can also help push HATS into more complex matter, such as the polymer, cluster and even the crystal.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 3 Experimental Setup
### Monopole Antenna for 4.2-4.4 GHz  
As mentioned in Section 1, commercial antennas do not support 4.2-4.4 GHz bands since those are allocated for WAIC systems by ITU. For example, VERT 2450 antenna, commonly used with SDR device, only supports dual-band (2.4-2.5 GHz and 4.9-5.9 GHz) for Wi-Fi. Thus, we self-produced monopole antennas for our experiment (3 in Figure 2), which operate in WAIC bands.  
Figure 3 shows an overall structure of designed monopole antenna and its prototype. We prepared two prototype antenna modules and measured a single transmit-receive link by separately connecting them to two USRPs. Before the feasibility test, we first measured the scattering parameter (\(S\)-parameter) and the radiation beam pattern to verify the performance of the designed monopole antenna.  
Figure 2: Experimental setup.  
\(S\)-parameters are used to describe the electrical behavior of linear electrical networks [11]. In general, antenna matching is described as a simple two-port network and \(S_{11}\) parameter indicates the input power reflection coefficient. Thus, \(S_{11}\) value should be low to radiate input signals to the air.  
The radiation beam pattern indicates the directional (angular) dependence of the strength of the radio waves from the antenna [12]. In general, the relative amplitude normalized to the total radiated power is plotted, separately in the electric field plane (\(E\)-plane) and the magnetic field plane (\(H\)-plane).  
Figure 4 shows the measured performance of the designed monopole antenna. Figure 3(a) shows measured \(S_{11}\) values for varying operating frequency. For each measurement, low \(S_{11}\) values are observed at WAIC bands (\(-17.42\) and \(-17.19\) dB at \(4.3\) GHz), which indicates that input power fed to transmit antenna is well radiated into the air, approximately \(98\%\) of input signal. Figure 3(b) shows the radiation beam patterns at \(4.3\) GHz. \(xy\)-plane and \(yz\)-plane at the designed antenna indicates \(E\)-plane and \(H\)-plane, respectively. Measurement results agreed well with simulation ones. Additionally, the maximum antenna gain aws \(3.89\) dBi and half-power bandwidth (HPBW) at \(E\)-plane was about \(60^{\circ}\). Note that, based on measurements, we verified that the designed monopole antenna was appropriate to transmit and receive signals at WAIC bands.
## 4 Data and Estimation Results
### DCC Model  
To examine carbon-energy correlation dynamics, we incorporate the DCC setting into the bivariate GARCH model and present the estimated results in Table 4. The table shows that the two estimated DCC parameters, \(\pi\) and \(\lambda\), are positive and significant (\(p\)-value \(<\) 0.01). This result supports the correlation-clustering phenomenon--a high/low correlation follows with another high/low correlation.
## 4 Potential of Bioactive Compounds Obtained from By-Products of the Wine Industry
### Feed and Food Industry
The method is simple, and the primary expense is related to the electricity utilized for drying the moist material. Therefore, these infusions can be used as health-promoting compounds that function as nutraceuticall components [178]. In contrast, the creation and advancement of new products through innovative processes have led to the emergence of a distinct category of items known as functional foods. In this context, grape pomace extract can be used in the preparation of chicken products. Furthermore, the bioactive compounds present in grape extracts positively contribute to their well-known antioxidant and protective properties. Therefore, [PERSON] et al. [179] evaluated the effect of adding grape pomace extract and mechanically deboned chicken meat on the nutritional and sensory properties of chicken nuggets. The results show that adding mechanically deboned chicken meat to the ingot formulation mainly increased the fat content. In addition, the conduction of a suitability analysis confirmed these results and identified positive attributes, such as 'light color'. Based on the findings, it can be inferred that adding grape pomace extract up to 120 mg.kg\({}^{-1}\) and mechanically deboned chicken up to 15 g.100 g\({}^{-1}\) did not have a negative impact on the visual appeal of chicken nuggets. In another study, [PERSON] et al. [180] studied whether the polyphenol chemicals found in grape seeds can be employed in the development of novel animal-derived goods as a source of natural antioxidants. This study examined how different concentrations (0.1%, 0.2%, and 0.5%) of a grape seed extract containing 40% ethanol (GSE) affected the quality of dry-cured pork neck. After conducting the tests, it was found that the use of extracts with higher concentrations resulted in better lipid stability compared to the lot containing only 0.1%. According to the study, the GSE extract effectively inhibited lipid hydrolysis and limited oxidative processes in the meats during curing when added in a concentration of at least 0.2% [180]. [PERSON] et al. [181] studied the potential use of white grape pomace, the most abundant waste from the white wine industry, in modifying the volatilome and phenolic content of beer. The pomace was added to beer after primary fermentation in varying concentrations (10% and 20% \(w/w\)) and subjected to different pre-treatments (pasteurized and unpasteurized). The results show that in all the beers tested, there was an increase in the concentration of phenolic compounds of 321.584 to 501,459 mg GAE.dm\({}^{-3}\), with the insertion of white grape pomace. There was also, with the insertion of white grape pomace, a reduction in the composition of volatiles in beers (the concentration of the acetaldehyde content was 17.425-31.425 mg.dm\({}^{-3}\), much lower than the control sample, with 134.050 mg.dm\({}^{-3}\)), and an increase in relation to the antioxidant activity of the beers against the DPPH\(\bullet\), FRAP, and ABTS+\(\bullet\) assays [181].
# Name-Based Security for Information-Centric Networking Architectures
## 5 Implementation and Evaluation
### DID Performance Evaluation  
#### 5.1.1 DID Registry Management Overhead  
We implemented a proof of concept DID registry using Hyperledger Fabric (henceforth, we will simply refer to it as Fabric). Fabric is a private, permissioned blockchain technology, i.e., a blockchain system where membership is controlled. Fabric involves no monetary cost, low computational complexity, and insignificant delays (as opposed, for example, to public permissionless blockchains, such as the Ethereum [20]).  
From a high-level perspective (For more details about the Fabric architecture, interested users are referred to [21]), Fabric allows special nodes, referred to as _endorsing peers_, to store and execute arbitrary programs, called _chaincode_. Chaincodes are replicated to many endorsing peers and the state associated with them is stored in a ledger distributed to a set of _committing peers_ (In reality a peer can be at the same time both endorsing and committing).A chaincode is executed simultaneously and in parallel by many endorsing peers, and the final output of the execution is determined based on an _endorsement policy_ defined by the chaincode owner. An endorsement policy can be, for example, that \(n\) out of \(m\) endorsing peers should execute the chaincode and produce the same output, otherwise the execution is not accepted. If the output of the execution has to be stored in the ledger, then it is forwarded to a special node referred to as the _orderer_. The orderer instructs committing peers to re-validate that the endorsement policy has been respected and to update the state of the chaincode in the ledger. Any \"client application\" can invoke a chaincode by interacting with a (single) _gateway_ using the Fabric API. The gateway is responsible for: instructing all endorsing peers to execute the chaincode, collecting the final outcome, verifying the endorsement policy, and returning the outcome to the application.  
In order to evaluate the overhead of our solution, we created a network composed of six peers, and we stored in all peers a chaincode that implements our registry. Our endorsement policy defines that at least three out six peers should produce the same output in order for that output to be accepted. The basic operations of our chaincodes are the following: check if a DID exists, retrieve the DIDdocument that corresponds to a particular DID, and create a new DID document. Table 1 reports the amount of time required to perform these operations.  
As it can be seen except from the DID document creation operations, which requires \(\sim 2.6\) s, all other operations are executed in less than 50 ms. Our registry has been implemented using HashMaps therefore DID lookup complexity is O(1) no matter the number of entries. Furthermore, the size of DID document is a few bytes (and less than 1 KB); hence, a DID registry should be able to handle a very large number of documents (each corresponding to a name root).
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Performance Evaluation  
In this section, the performance of the CFA is evaluated in three-fold. First, the CFA is compared against its counterparts, the GSO and the FA. Secondly, the CFA is compared to other kinds of metaheuristics. Thirdly, the performance of CFA is further justified on the CEC 2005 dataset. For the first two experiments, all compared algorithms were executed 30 times for each test function. For each run, the program can perform 160,000 FEs. However, for the third experiment, the evaluation criteria in the original paper are respected where the competing method is executed in 25 independent runs and in each run the method is evaluated at different numbers of consumed FEs. To compare the performance between two competing algorithms, we employ the performance index defined by [PERSON] et al. [6] as follows. Given two competing algorithms, p and q, the performance merit of p against q on a test function is defined by the formula,  
\[\text{Merit(p, q)}=(\text{f}_{\text{p}}-\hat{\text{f}}^{\ast}+\varepsilon)/( \text{f}_{\text{q}}-\hat{\text{f}}^{\ast}+\varepsilon) \tag{11}\]  
Figure 4: Worst-case analysis with the Griewank (30) function. (**a**) The worst fitness obtained by the CFA program as the number of repetitive runs increases. (**b**) The number of program runs with which each performance level is reached.  
Figure 3: Worst-case analysis with the Rastrigin (30) function. (**a**) The worst fitness obtained by the CFA program as the number of repetitive runs increases. (**b**) The number of program runs with which each performance level is reached.  
where \(\varepsilon\) is a small constant equal to \(5\times 10^{-7}\), \(\mathrm{f_{p}}\) and \(\mathrm{f_{q}}\) are the mean best function values obtained by the competing algorithms \(\mathrm{p}\) and \(\mathrm{q}\), and \(\mathrm{f^{*}}\) is the global minimum of the test function. As all the test functions involve minimization, we realize \(\mathrm{p}\) outperforms \(\mathrm{q}\) if Merit(\(\mathrm{p}\), \(\mathrm{q}\)) \(<\) 1.0, \(\mathrm{p}\) is inferior to \(\mathrm{q}\) if Merit(\(\mathrm{p}\), \(\mathrm{q}\)) \(>\) 1.0, and \(\mathrm{p}\) and \(\mathrm{q}\) perform equally well if Merit(\(\mathrm{p}\), \(\mathrm{q}\)) \(=\) 1.0.
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 4 Surface Modification  
Targeted drug delivery is a kind of smart drug delivery system which delivers the drug to targeted parts of the body (e.g., organs/tissues/cells). The concentration of the drug in the desired targeted part of the body is increased and this improves the efficacy of treatment by reducing the side effects. By delivering the drug to the desired part of the body, the desired plasma concentration is maintained, and healthy cells/tissues are avoided in the case of cancer. When designing a targeted drug delivery system, the following factors should be considered: drug properties, side effects of drugs, route of drug administration, site of target and the disease [96]. In 1986, [PERSON] and [PERSON] [97] found out that the solid tumours have defective architecture in their blood vessels. Large molecules with molecular mass over 40 kDa leak out from the tumour vessels and they are therefore accumulated in the tumour tissues, which does not happen in healthy tissues [98]. This is called the enhanced permeability and retention (EPR) effect. The EPR effect in combination with the rapid clearance of some anticancer agents and drugs in general from the bloodstream created the necessity of finding an additional way to maintain the successful targeted delivery of a therapeutic drug. Surface modification of the drug delivery systems was then identified that can help in overcoming these issues [99].  
Surface modification may be achieved by using various targeting ligands such as proteins, antibodies, peptides, carbohydrates or other small molecules in order to improve the selectivity process. Binding these ligands onto the surface of a drug delivery system can bind them to specific receptors in order for them to be accumulated locally, and as a consequence the therapeutic outcome is enhanced and a lower dose might also then be required [98; 99]. Other advantages of surface modification include decreased side effects with improved patient compliance, controlled biodistribution as well as increased localisation [100].
# Blockchain-Assisted Verifiable and Multi-User Fuzzy Search Encryption Scheme
## 5 Proposed Scheme
### Trapdoor Generation
#### 5.2.4 The Subsequent Work  
In this section, we focus on verification, decryption of files, and updating the index tree.  
Verification  
\(V\text{erify}(C_{i},MAC_{C_{i}},K_{2})\): Suppose \(k\) encrypted identifiers \(FID=\{FID_{1},FID_{2}\dots,FID_{k}\}\) are returned in a search result, \(DU\) first determines whether \(FID\) from \(CS\) is the same as \(FID\) from \(SC\). Then, the \(MAC_{C_{i}}\) of the file is calculated locally by \(k_{2}\) to determine whether the \(CS\) has tampered with search information. \(SC\) sends \(\$search\) to \(DO\), sends \(\$search\_price*\$gas\_price\) to the staff member who executed the transaction, and refunds the transaction deposit from \(DU\).  
Decryption  
\(Dec(SK_{u},C_{K_{i}},C_{w_{q}})\): After getting the key ciphertext \(C_{K_{i}}\), the user checks whether the \(SK_{u}\) matches with the ciphertext access policy, if it matches, \(DU\) can get \(A=e(g,g)^{rs}\) and recover the \(K_{i}\), otherwise, \(DU\) has no access to the file. The decryption of \(K_{i}\) is shown in the Decryption Correctness section.  
\(Update(Uptype,I_{T_{c}})\): The updating of the index tree is the process of replacing the old subtree with the new encrypted one. \(DO\) retains the unencrypted index tree locally and denotes \(T_{c}\) as the set of nodes that may be changed during the update process. As an example, in Figure 4, if the file \(F_{1}\) is deleted in an update operation, then \(T_{c}=\{L_{11},L_{21},Root,F_{1}\}\).  
When \(Uptype=delete\), delete the leaf node \(F_{i}\) and recompute the other node index vectors in \(T_{c}\), encrypt node index vectors in \(T_{c}\) by \(KS\). If the operation destroys the balance of the tree, replace it with a pseudo node, then a new indexed subtree \(I_{T_{c}}\) is generated. \(DO\) sends \(I_{T_{c}}\) and \(Uptype=delete\) to \(SC\), which replaces the original indexed subtree with the help of \(I_{T_{c}}\).When \(Uptype=add\), the leaf node \(F_{i}\) needs to store the key ciphertext \(C_{K_{i}}\), the file ciphertext message authentication code \(MAC_{F_{i}}\), encrypted identifier \(FID_{F_{i}}\) and the other operations are the same as the deletion operation. The index update process will not destroy the balance of the tree because the new nodes will be added to the pseudo nodes.