esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Feature Points Matching Strategy
#### 2.3.2 Random Sampling Consistency Method Purification  
In the rough matching point set after the previous two-way matching, there are still some wrong matching point pairs. This includes two types. The first type of mismatched points come from unmatched regional scenes, which are mismatched due to approximate descriptors; the second type of mismatched point pairs come from matched regional scenes, however, due to noise and local areas of the image deformation leads to a deviation in positioning. If such matching point pairs are not eliminated, the calculated transformation model will deviate from the actual situation, resulting in a decrease in registration accuracy. For the above two considerations, it is necessary to adopt a certain method to purify the set of rough matching points. Among the many methods, the random sampling consensus algorithm proposed by [PERSON] et al. [16] is the most commonly used. This method is a robust parameter estimation method. Its main idea is to fit the model parameters in an iterative manner in the search set, set a threshold to filter the interior points, continuously expand the set of support points, and find all the data pairs that meet the model parameters to obtain the optimal solution.
## 1 Introduction
### Contribution  
Our proposed framework solves the following problems: (1) Communication volume and security risks between the headquarters and the manufacturing plant; (2) Security problems caused by frequent cross-class communication in the classical smart manufacturing process; (3) Prevents unknown and untrusted nodes from joining the smart manufacturing system. Overall, the main contributions of this paper are as follows:  
* Blockchain provides authentication and identification capabilities. Only devices registered in the Blockchain can participate in SM. This can prevent illegal users from accessing the network and ensure that the data used in SM are legal and valid.
* DT implements a true copy of the physical environment. Data can be scheduled here, and after data are scheduled or when it is necessary to work with other manufacturing plants, decision requests can be sent to headquarters outside of the DT. The request consists only of the result of processing the data. Even if an attacker eavesdrops during transmission, it is difficult to ascribe meaning to the intercepted data.
* Manufacturing in DT no longer needs to receive data from all directions; scheduling decisions from headquarters can ensure collaboration with other manufacturers even in a massive data environment. In this paper, we create clusters from which the DT schedules data flows at the discretion of headquarters. This creates a scalable SM environment capable of supporting the needs of the rapidly growing IIoT industry.
* Furthermore, DRL integration into DT and assisting it with decisions on when data should be transmitted is novel. To the best of our knowledge, our work is the first to consider such intelligent decisions based on the PDQN model. We deployed and tested classic DQN and PDQN, and our results showed PDQN model performs admirably in terms of convergence time, stability, and dependability.
## 2 Results
### Structural Elucidation Using Spectroscopic and Spectrometric Methods  
Here, we report one of the most extensive structural characterizations of SL. 1H- and 13C-Nuclear Magnetic Resonance (NMR) Spectroscopy and Mass Spectrometry (MS) were performed on the purified compound. Additional 1H-1H COSY and 1H-1H TOCSY, 1H-13C HSQC and 1H-13C HMBC (Supplementary Materials, Figures S2-S5) combined with MS results, confirmed that only one acyl group (laurate adduct) was bound to a primary hydroxyl group of sorbitol. From the 1D- and 2D-NMR experiments, the chemical shifts and their assignments are fully detailed in Table 3.  
To determine which primary carbon of the _meso_- and the pseudo-asymmetric polyol is bound to laurate, we measured a 1H-13C-HSQC with high resolution in the direct dimension and no decoupling. Extraction of 1D slices along the respective carbon frequency allowed estimation of vicinal 1H-1 HJ couplings. Figure A1 shows the signals that belong to C\({}^{4}\)H and C\({}^{3}\)H of the sorbitol. C\({}^{4}\)H is a triplet with coupling constants of 4.8 Hz, indicating that both vicinal protons to this group are in _cis_ position. C\({}^{3}\)H, on the other hand, displayed a doublet of doublets with two different coupling constants of 5.3 Hz and 7.4 Hz; thus, here,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Molecular Group** & **13C Shift (ppm)** & **1H Shift (ppm)** & **Multiplicity** & **Coupling (Hz)** \\ \hline Sorbitol & & & & \\ -C\({}^{1}\)H\({}_{2}\)-O- * & 66.66 & 4.35, 4.18 & dd, dd & 3.0, 11.5, 6.3 \\ -C\({}^{2}\)H- & 70.69 & 3.91 & m & - \\ -C\({}^{3}\)H- & 73.24 & 3.68 & dd & 5.28, 7.37 \\ -C\({}^{4}\)H- & 70.27 & 3.93 & t & 4.84 \\ -C\({}^{5}\)H- & 74.32 & 3.83 & m & - \\ OH-C\({}^{6}\)H\({}_{2}\)- & 64.25 & 3.71 & m & - \\ \hline Laurate & & & & \\ O=C\({}^{1}\)-OH * & 174.46 & - & - & - \\ -C\({}^{2}\)H\({}_{2}\)- * & 34.64 & 2.33 & t & 7.6 \\ -C\({}^{3}\)H\({}_{2}\)- & 25.46 & 1.61 & m & - \\ -C\({}^{4}\)H\({}_{2}\)- & 29.72 & 1.32 & m & - \\ -C\({}^{5}\)H\({}_{2}\)- & 30.12 & 1.28 & m & - \\ -C\({}^{6}\)H\({}_{2}\)- & 30.12 & 1.28 & m & - \\ -C\({}^{7}\)H\({}_{2}\)- & 30.12 & 1.28 & m & - \\ -C\({}^{6}\)H\({}_{2}\)- & 30.12 & 1.28 & m & - \\ -C\({}^{6}\)H\({}_{2}\)- & 30.12 & 1.28 & m & - \\ -C\({}^{6}\)H\({}_{2}\)- & 30.12 & 1.28 & m & - \\ -C\({}^{11}\)H\({}_{2}\)- & 23.22 & 1.29 & m & - \\ -C\({}^{12}\)H\({}_{3}\) & 14.35 & 0.88 & t & 7.00 \\ \hline \hline \end{tabular}  
* Acylation site; d = doublet; t = triplet; m = multiplet.  
\end{table}
Table 3: 1H- and 13C-NMR chemical shifts of sorbitol-6-O-laurate (SL) with their molecular assignments.  
the vicinal protons are different, best explained by one in _cis_ and the other in _trans_ position. This proves that the laurate adduct is connected via the ester function to the C\({}^{1}\)H end of the polyol.  
MS was performed with ElectroSpray Ionization (ESI) (Supplementary Materials, Figure S6). The spectrometric analysis of the synthesized SL is shown below in Table 4. In the latter, 5 clear adducts of SL have been observed and correlated to possible adducts.
# Quad-Band Circular Polarized Antenna for GNSS, 5G and WIFI-6E Applications
###### Abstract  
A quad-band circularly-polarized antenna, for applications of a global navigation satellite system (GNSS), 5G, and WIFI-6E, is designed, fabricated, and measured. The proposed antenna is formed by an L-shaped radiator, a rectangular frame ground with an L-shaped stub, and a rectangular strip at the opposite corner. The microstrip antenna can generate four frequency bands, covering WIFI-6E (5925-7125 MHz), 5G n77 (3300-4200 MHz), n78 (3300-3800 MHz), and the GNSS bands. This antenna generates right hand circular polarization (RHCP) waves in the low frequency band (0.95-2.11 GHz), covering GPS, BDS, GLOASS, and GALILEO applications. Moreover, an L-shaped aperture and three rectangular slits are cut on the ground to broaden the axial-ratio bandwidth at the upper band. A prototype is fabricated and measured to verify the performance of this design. It is shown that the agreement between the simulation and measurement is satisfactorily good. The measured \(-10\) dB bandwidths for each band are 75.8% (0.95-2.11 GHz), 55.8% (3.05-5.39 GHz), 39.9% (5.84-8.19 GHz) and 10.3% (9.14-10.68 GHz), respectively. While the measured 3 dB axial-ratio bandwidths are 59.4% (1.16-2.14 GHz), 35.8% (3.23-4.64 GHz), 8.4% (5.70-6.20 GHz), and 2.6% (7.51-7.71 GHz), the measured gains are 4.56, 2.28, 4.26, and 4.30 dBi at 1.5 GHz, 3.8 GHz, 6 GHz, and 7.6 GHz, respectively.  
Keywords:quad-band; circularly polarized; global navigation satellite system; 5G +
Footnote †: journal: Electronics
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation  
All the formulations presented in Section 3 were implemented in our academic software _code_Carmel_ ([[https://code-carmel.univ-lille.fr/](https://code-carmel.univ-lille.fr/)]([https://code-carmel.univ-lille.fr/](https://code-carmel.univ-lille.fr/)), accessed on 20 November 2021), which provided the numerical simulation results. In the following, two different examples were carried out. For the first example, we validated the simulation results obtained with he Darwin model by considering an industrial transformer. The experimental measurement data, in a common mode test, were provided until 100 MHz. However, based on the measurement results, it should be mentioned here that the [PERSON] model is not valid when the frequency is superior to 10 MHz, since the radiation effects become non-neglected. As the frequency increases, to handle the skin effect in the periphery of the windings, more elements are needed in the mesh, which introduces a scientific challenge at the numerical aspect. For our computation, the total DoFs reached 40 million elements. The second one, which represents an inductance example, is studied to investigate the influence of the frequency on the electromagnetic field distributions and compare the impedance curves computed by each model, namely, EQS, MQS, and Darwin models.
## 2 Propagation on Stems with Rectangular Section  
In solid media, there are two basic ways of propagating energy: one associated with the compression and the medium rarefaction and other related to the shear movements between the perpendicular planes to the propagated direction [5]. From these two types of propagation two kinds of waves emerge in infinite solid media: the P (dilatational) and S (equivoluminial) waves. The P and S waves have a velocity propagation \(\epsilon_{\mathbf{p}}=\sqrt{(\lambda^{\prime}+2\mu)/\rho}\) and \(\epsilon_{\mathbf{s}}=\sqrt{\mu/\rho}\), respectively, where \(\lambda^{\prime}\) and \(\mu\) are the Lame constants [Pa] and \(\rho\) is the density of the medium [\(\mathbf{kg/m}^{3}\)]. When the P and S waves find the solid interface, part of the incident wave refracted out and the other part of it is reflected into the body.  
The relationship between the amplitudes of refracted and reflected waves depends on the elastic properties and the density of the interface media, as well as the refraction and reflection angles of the reflected waves which are governed by the Snell's law [7; 8; 9]. If the propagation medium (body) has one or two finite dimensions, it is called a waveguide. The P and S waves that propagate in waveguides interact with the guide boundaries and generate patterns of energy propagation known as modes [6; 8]. The propagation modes, also called modal waves, depend not only on the material properties but also on its geometry.  
Every structure considered as waveguide has its propagation characteristics, and the rectangular section has been widely investigated, among other works, it is possible to cite [8; 10; 11; 12; 13]. Following the theoretical basis model of wave propagation in rectangular rods is presented.
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 3 Proposed Approach
### Supervised Learning Classifiers  
At the final stage, we select a collection of classifiers in a supervised manner to quantitatively evaluate the representative capability of the integrated features, as follows.  
* Gaussian discriminant analysis (GDA) [52]: based on the generative learning algorithm property, we learn the model \(P(y)\) distributed according to Bernoulli and \(P(x|y=k)\), where k is one of the 14 classes distributed according to the multivariate normal distribution; then, \(P(y|x)\) can be expressed as Sigmoid function.
* K-nearest neighbor (KNN) [53]: we initialize K = 30 for the number of neighbors to capture and locate similar data points. We gradually increase the value of K so that our KNN predictions become more stable and accurate based on majority voting and averaging.
* Naive Bayes [54]: based on the so-called Bayesian theorem, which calculates the posterior probability \(P(c|x)\) from \((x)\), \(P(c)\), and \(P(x|c)\), the Naive Bayes assumes that the effect on a given class \((x)\) (x is independent of the predictor, called class conditional independence.
* Support vector machine (SVM) [55]: we apply SVM algorithms to find an optimal hyperplane acting as a decision boundary in \(N\)-dimensional space that can distinctly classify our feature points. We maximize the margin of the classifier when support vectors affect the position and orientation of the hyperplane. The tuning parameters, including the kernel, regularization, and gamma, are carefully chosen.
* Adaptive boosting (AdaBoost) [56]: we sequentially add a set of weak classifiers and trains using the weighted training data. First, we initialize the weight for each data point, fit weak classifiers to the dataset, and compute the weight of all weak classifiers. After 100 iterations, we obtain the final prediction with the updated weight for each classifier by the formula \(F(x)=sign\Big{(}\sum_{n=1}^{N}W_{n}f_{n}(x)\Big{)}\), where \(f_{n}\) is the \(n^{th}\) weak classifier and \(W_{n}\) is the corresponding weight.
* Random Forest [57]: this comprises multiple random decision trees. A random sample from our original dataset forms into each tree. A subset of \(K\) features presenting each tree node \(d\) is randomly selected to generate the best split. Then, we split the node into daughter nodes and repeating these steps \(n\) times to create \(n\) trees.
* Extreme learning machine (ELM) [58]: the ELM includes an input layer, a hidden layer, and an output layer. We set the specific number of hidden neurons, randomize the weight and the bias between input and hidden layers in the execution process, calculate the weight between hidden and output layers by the Moore-Penrose pseudoinverse with a sigmoid activation function, and fit the results with the least-squares method.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 3 ISLT Timescale Algorithm
### Inter-Satellite Time Comparison
\begin{array}{l}\Delta\rho_{\rm AB}=\left|\overline{R}_{\rm A}(t_{\rm r 1})-\overline{R}_{\rm B}(t_{\rm s1})\right|-\left|\overline{R}_{\rm A}(t_{0})- \overline{R}_{\rm B}(t_{0})\right|+c\left[\left|\delta\overline{t}_{\rm A}(t_{ \rm r1})-\delta\overline{t}_{\rm B}(t_{\rm s1})\right|-\left[\delta\overline{t} _{\rm A}(t_{0})-\delta\overline{t}_{\rm B}(t_{0})\right]\right]\\ \Delta\rho_{\rm BA}=\left|\overline{R}_{\rm B}(t_{\rm r2})-\overline{R}_{\rm A}(t_ {\rm s2})\right|-\left|\overline{R}_{\rm A}(t_{0})-\overline{R}_{\rm B}(t_{0}) \right|+c\left[\left|\delta\overline{t}_{\rm B}(t_{\rm r2})-\delta\overline{t}_{ \rm A}(t_{\rm s2})\right|-\left[\delta\overline{t}_{\rm B}(t_{0})-\delta \overline{t}_{\rm A}(t_{0})\right]\right]\end{array} \right. \tag{14}\]The superscript \"\(\sim\)\" stands for predicted values. By correcting the original pseudoranges with items above, we can get the reduction pseudoranges:  
\[\left\{ \begin{array}{l}\widetilde{\rho}_{\text{AB}}=\left|R_{\text{A}}(t_{0})-R _{\text{B}}(t_{0})\right|+c[\delta t_{\text{A}}(t_{0})-\delta t_{\text{B}}(t_{0 })]+\widetilde{\varepsilon}_{\text{AB}}\\ \widetilde{\rho}_{\text{BA}}=\left|R_{\text{B}}(t_{0})-R_{\text{A}}(t_{0}) \right|+c[\delta t_{\text{B}}(t_{0})-\delta t_{\text{A}}(t_{0})]+\widetilde{ \varepsilon}_{\text{BA}}\end{array} \right. \tag{15}\]  
The difference of the two reduction pseudoranges is free of the orbits and could be used to determine the inter-satellite clock deviation at \(t_{0}\):  
\[\delta t_{\text{AB}}(t_{0})=\delta t_{A}(t_{0})-\delta t_{B}(t_{0})\ =\frac{ \widetilde{\rho}_{\text{AB}}-\widetilde{\rho}_{\text{BA}}}{2c}+\varepsilon \tag{16}\]  
where \(\varepsilon\) is the equivalent error of inter-satellite time comparison.
## 2 Materials and Methods
### Fully Connected Deep Neural Networks
#### 2.2.1 FCDNN Structure  
The FCDNN used in this study had a six-layer network structure, consisting of an input layer, four hidden layers, and an output layer, each composed of a plurality of neurons that could be calculated in parallel. The connection between the hidden layers and between the first hidden layer and the input layer was connected by an activation function. The details of the structure of the proposed FCDNN are as following:  
1. Fully-connected layer (or dense layer)  
The fully connected layers were able to learn non-linear combinations of input features considerably efficiently. Neurons in a fully connected layer have full connections to all activations in the previous layer. Their activations can hence be computed with a matrix multiplication followed by a bias offset [29]:  
\[H(x)=Wx+b, \tag{2}\]  
where \(W\in R^{(K,n)}\) is the weight matrix and \(b\in R^{K}\) is the bias offset.
2. ELU activation layer Exponential Linear Unit (ELU) is a function that tends to converge cost faster and generate more accurate results [30]:  
\[\text{ELU(x)}\ =\ \left\{\begin{array}{c}\alpha(e^{x}-1)\ if\ x\leq 0\\ x\ if\ x>0\end{array}\right\} \tag{3}\]  
ELU uses the activation function in order to achieve mean zero, as the learning can be made faster. For the ELU activation function, an \(\alpha\) value is picked; a common value is between 0.1 and 0.3. Hence it is a good option against activation functions like ReLU (Rectified Linear Unit) since it decreases the bias shift by pushing the mean activation towards zero. Unlike ReLU, ELU can produce negative outputs.
3. Dropout layer Dropout is a sort of regularisation that randomly drops some proportion of the nodes that feed into a fully connected layer. Dropping a node means that its contribution to the corresponding activation function is set to zero, and therefore it prevents the network from memorizing the training data (overfitting). With dropout, training loss will no longer tend rapidly toward zero, even for extensive deep networks.  
Figure 6: Proposed fully-connected deep neural networks (FCDNN) with multiple hidden layers and a dropout layer.  
* Linear activation layer
* A linear activation function takes the form [28]: \[A=cx,\] (4) where \(c\) is a constant number and activation is proportional to the input. This way, it provides a range of activations, so it is not binary activation.
## 3 Materials and Methods  
### Data Augmentation Method for Plant Leaf Disease Recognition  
The plant data augmentation method proposed in this study was studied by borrowing the structure of CycleGAN, as shown in Figure 1, and the main suggestion is to improve the expression ability of the model using the Pre-Activation Residual Attention Block (PaRAB) and Binary Mask. These improvements allow the proposed model to generate visually plausible plant leaf data. The proposed method solves the bias problem of early diagnosis models of plants by contributing to increasing the diversity of samples by making diseased samples with insufficient numbers from normal plant leaves.  
The entire training process of the proposed method and each loss function can be confirmed in Figure 1. The red line represents the elements needed to calculate each loss function, and the black line represents the elements used as inputs for each model. Basically, two generators \(G:X\to Y\) and \(F:Y\to X\) are utilized, where \(X\) and \(Y\) are the domains to be transformed, \(X\) means a healthy leaf class, and \(Y\) means a disease-affected leaf class  
Figure 1: Data Augmentation Method for Plant Leaf Disease Recognition.  
to be transformed. Consequently, the two generators are converted to domains that are opposite to each other. In addition, the discriminators competing with the generators \(D_{Y}\) and \(D_{X}\) are required for training, where \(D_{Y}\) distinguishes the fake leaf image \(G(x)\) from the real leaf image \(y\in Y\), and \(D_{X}\) distinguishes the fake leaf image \(F(y)\) from the real leaf image \(x\in X\). \(G\), \(D_{Y}\), and \(F\), \(D_{X}\), which correspond to each other as a pair, proceed with adversarial learning and perform transformation between both domains.  
In addition, we utilize the binary mask \(M_{x}\) and \(M_{y}\) required to regulate the generator from converting to unnecessary areas. The binary mask explicitly denotes leaf areas, \(M_{x}\) points to healthy leaf areas, and \(M_{y}\) points to disease-infected leaf areas. The leaf mask is obtained using a pre-trained Mask R-CNN [29] model, and for time efficiency, all mask data used for learning were extracted before training. the binary mask data is used by the generator to learn the background loss function and is also passed along as input to the discriminator as additional information about the leaf region. Since this method is used only as an input of a discriminator that is not actually used during the test process, it is reasonable because there is no need to have mask data when making a new sample by the generator during the test process. Additionally, giving additional information to discriminators is already well known to further enhance the model's expressiveness in previous studies such as CGAN and ACGAN. This allows the generator to create new samples that preserve the shape of the input leaves well and assists the attention module within PaRAB to quickly focus on the leaf region, excluding backgrounds where transformation cannot be performed.
## 4 Functionality and Application of Functional Ingredients from Jackfruit
### Applications  
#### 4.2.1 Pharmaceutical Industry  
Some of the most relevant applications for the functional ingredients present in jackfruit are related to the pharmaceutical industry. The antioxidant properties of phenolic and carotenoid compounds, in addition to the anti-inflammatory properties of flavonoids and the antibacterial effects of extracts obtained from different parts of jackfruit, are of great interest owing to their potential incorporation into [4], for example, therapeutic agents for treating infectious diseases and their potential health benefits by positively affecting heart's function, conditions of the skin, and prevention of ulcers and cancer development [3].  
The jackfruit seed crude extracts contain jacalin, a type of lectin that presents a high affinity to the Thomsen-Friedenreich disaccharide antigen (Gal$1-3 GalNAc), specifically its O-glycoside [3]. This protein also recognizes and reversibly binds to galactose [31]. Jacalin is suitable for studying O-linked glycoproteins and evaluating the immune system of patients infected with certain viruses, especially the human immunodeficiency virus 1 (HIV-1) [3]. Based on jacalin's ability to target overexpressed disaccharides in tumor cells, [PERSON] et al. [46] reported the development of surface-modified gold nanoparticles containing inositol hexophosphate (IP6) and jacalin to maximize the apoptotic effect of IP6 against lines colon cancer cells (HCT-15). Their results showed significant apoptotic effects, concluding that the findings may raise the hope for a new drug delivery strategy to attack colon cancer [46].  
In the case of jackfruit pectin, a multifunctional polysaccharide, its applications include usage as a binder for tablet formulations and as a versatile delivery agent for encapsulating drugs [47]. [PERSON] et al. [48] generated pectin (P)/hydroxyapatite (P/HA) bionanocomposites hybrids. These authors tested their cytocompatibility, alkaline phosphatase (ALP), anti-inflammatory activity, cell adhesion, and effects on fibroblast stem cells to establish their biocompatibility and feasibility as a bone graft [48]. On the other hand, [PERSON] & [PERSON] [49] developed calcium pectinate-jackfruit seed starch mucoadhesive beads containing metformin HCl to evaluate the encapsulating effects of the drug and its cumulative release after some time. This study found positive effects on the yields, mucoadhesivity, and a significant hypoglycemic effect in diabetic rats after oral administration [49].