esa-sceva/satcom-chunk-collection huggingface dataset:
# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 4 Experimental Results and Analysis
### Parameter Impact and Selection
#### 4.1.2 Impact and Selection of Loss Function  
The loss function affects the learning ability of a network. In this study, four loss functions are used by CNNs for softmax classifiers, of which three are SVM loss, and the other is categorical cross-entropy loss. Among the data loss functions of SVM are hinge loss, squared hinge loss, and categorical hinge loss. Multiple SVM models are formed using one-versus-rest to solve multiple classification problems using hinge loss and squared hinge loss by CNNs. Figure 3 shows the impact of the four loss functions on network training and the results of AlexNet trained on Cifar10.  
The experiment shows that SVM is unsuitable for cross-entropy loss, so the accuracy of training results is low, and the loss is high. However, the accuracy of classification using SVM or CASVM is greater than that of softmax, regardless of whether the attention mechanism is embedded, and whether softmax uses cross-entropy loss or hinge loss. Figure 3, shows that the hinge loss has a lower error than cross-entropy loss, and there is some difference in performance between SVM and softmax.  
The shallow network CNN1 and the deep network ResNet18 are chosen to compare the performance of the two classifiers without using the embedded attention mechanism. Figure 4 shows the accuracy and loss values obtained by the CNN1 and ResNet18 network models, using Animal10, Cifar100, Cifar10, and Fashion-MNIST.  
The images of all datasets were compressed to 32 \(\times\) 32 when training CNN1, and the network model using softmax used cross-entropy loss. From Figure 4a, it is clear that the network model combining CNN1 with SVM had less accuracy improvement on small datasets. Figure 4b, shows that ResNet18 combined with SVM showed some improvement on each dataset, with more obvious improvement on image datasets with 100 classifications like Cifar100 and on the Animal10 large dataset. It can be concluded that the loss function of the SVM classifier shows some improvement in network classification performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Params (M)** & **Flops (G)** & **Model Size (MB)** \\ \hline CASVM (CNN1) & 4.23 & 0.01 & 16.12 \\ CNN1 & 4.23 & 0.01 & 16.12 \\ CASVM (CNN2) & 7.54 & 0.21 & 28.77 \\ CNN2 & 7.54 & 0.21 & 28.75 \\ CASVM (AlexNet) & 38.38 & 0.32 & 146.41 \\ AlexNet & 38.37 & 0.31 & 146.38 \\ CASVM (ResNet18) & 11.19 & 54.60 & 42.70 \\ ResNet18 & 11.18 & 54.50 & 42.66 \\ CASVM (ResNet152) & 57.99 & 365.61 & 221.23 \\ ResNet152 & 57.93 & 365.45 & 220.99 \\ CASVM (VGG19) & 139.67 & 39.30 & 532.81 \\ VGG19 & 139.67 & 39.30 & 532.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of model parameters and calculation volume.
## 3 Results and Discussion
### Changes in Ion Concentration of Leachate  
At the end of the permeability test conducted on a soil mixture specimen with 11% bentonite content, the ionic concentration of the leachate that penetrated the soil column specimen was measured. Figure 7 shows the variation of ion concentration of the leachate with zeolite content and ZnCl\({}_{2}\) concentration. The initial Zn\({}^{2+}\) concentration of the 0.01 mol/L ZnCl\({}_{2}\) solution was 654 mg/L at 0% ion in zeolite content, while the Zn\({}^{2+}\) concentration of the leachate after the test was 42.30 mg/L. The Zn\({}^{2+}\) concentration of the leachate was further reduced to 3.18,1.14 or 0.57 mg/L after adding 12.50, 25, or 50% zeolite, respectively. The Zn\({}^{2+}\) concentrations decreased by 92.48, 97.29, and 98.65%, respectively, compared to the case without zeolite, which indicated that the increase of zeolite addition significantly increased the Zn\({}^{2+}\) adsorption capacity of the mixed soil, and the 50% zeolite doping had the best adsorption effect.
# Numerical Investigation of Air Entrapment Dynamics for High-Speed Thermal Spraying
## 5 Conclusions  
In this paper, the air entrapment of a molten nickel droplet impacting on a stainless-steel has been numerically investigated. Unlike the previous models, which mainly concerned the entrapped air around the center, in this paper, it is found that the entrapped air bubbles can be divided into impacting air bubbles (IM bubbles) and spreading air bubbles (SP) according to the stage at which they are entrapped. The pores found at the splat's edge are attributed to the entrapped SP bubbles, which are related to the competition between the solidification rate \(r_{\rm sd}\) and spreading rate \(r_{\rm sp}\). Moreover, the final bubble volume involved is related to the impacting velocity. For a low impacting speed (\(v=50\) m/s), even though a low entrapped-bubble ratio \(P_{\rm b}\) could be obtained, the formed coating splat is thick and surrounded by cut-off fingers, which will negatively affect the subsequent molten droplet deposition. A medium impacting speed (\(v=75\) m/s-\(125\) m/s) would result in a high entrapped-bubble ratio \(P_{\rm b}\), and large SP bubbles could be found on the edge of the splat. For a high impacting speed (\(v=200\) m/s), a relatively thin splat with small SP bubbles on edge is obtained. However, too high an impacting speed would cause serious splashing. To achieve an even coating with low porosity, a proper impacting speed should be carefully chosen according to the initial condition and physical characteristics of the material, which could affect the solidification rate or the spreading rate of the molten droplet, such as the temperature, the viscosity, thermal conductivity, etc.  
Conceptualization, H.G. and D.Y.; validation, K.W., J.C., R.Z. and M.L.; writing-original draft preparation, H.G., J.C. and R.Z.; writing-review and editing, D.Y. and M.L.; supervision, H.G.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 2020 Research Program of Sanya Yazhou Bay Science and Technology City, grant number SKYC-2020-01-001. Finance Science and Technology Project ofHainan Province, grant number ZDKJ202019. Key Special Project for Introduced Talents Team of South-ern Marine Science and Engineering Guangdong Laboratory (Guangzhou), grant number ML2019 ZD0506. Key R&D Programmes of Guangdong, grant number 212021032611700002.  
Not applicable.  
The authors acknowledge Xinyan Ma for technical assistance.  
The authors declare no conflict of interest.
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 3 Proposed FOCV MPPT Algorithm
### Scan Phase  
#### 3.1.1 Current-To-Voltage (I2V) Converter and Voltage-To-PWM (V2 PWM) Converter  
Figure 9 shows the block diagrams of the proposed I2V converter and the proposed V2 PWM converter, and the timing diagrams of the I2V and the V2 PWM. The I2V converter that uses the current mirror technique sweeps and scans the voltage and the current of a PV cell and makes the I-V curve. Once the enable signal (EN) is high and the switch between the PV cell and the I2V converter turns on, the I2V sweeps and scans the current  
Figure 8: The flow chart of the proposed FOCV MPPT using an I-V curve tracer.  
Figure 7: The block diagram of the proposed FOCV MPPT using an I-V curve tracer.  
of the PV cell (\(I_{PV}\)) from the short-circuit current (\(I_{SC}\)) to 0 A, and the voltage of the PV cell (\(V_{PV}\)) from 0 V to \(V_{OC}\). At the same time, \(I_{PV}\), which is mirrored by two current mirrors, is converted into voltage form (\(I_{PV\_V}\)) by being multiplied with a resistor, \(R_{1}\). The relation between \(I_{PV}\) and \(I_{PV\_V}\) can be expressed as  
\[I_{PV\_V}(n)=R_{1}I_{PV}(n), \tag{2}\]  
where \(n\) is the number of iterations. For better linearity, two OP AMPs are implemented in the I2V by making each \(V_{D}\) of MOSFET that is connected to the input of OP AMP the same.  
The V2 PWM converter is composed of two circuits, a ramp generator and a comparator. The ramp generator generates a ramp signal (\(V_{RAMP}\)) whose peak voltage (\(\max(V_{RAMP})\)) is higher than the maximum value of \(I_{PV\_V}\), \(\max(I_{PV\_V})\). Comparing \(I_{PV\_V}\) with \(V_{RAMP}\), the comparator generates the PWM duty signal, \(I_{PV\_PWM}\). Using the constancy of the slope of \(V_{RAMP}\), the slope can be expressed as  
\[\frac{\max(V_{RAMP})}{T}=\frac{I_{PV\_V}(n)}{T_{PWM}(n)} \tag{3}\]  
where \(T\) is the period of the ramp signal and \(T_{PWM}(n)\) is the width of the duty signal of each iteration. Therefore, \(T_{PWM}(n)\) can be expressed as Equation (4).  
\[T_{PWM}(n)=\frac{T\times I_{PV\_V}(n)}{\max(V_{RAMP})} \tag{4}\]  
As shown in Equation (4) and Figure (c)c, the higher the current is, the wider the width of the duty signal is.
## 5 Conclusions  
Based on a geological investigation of the cavity depth beneath the entrance of the Yuanjue Grottos, a numerical model was established to calculate and analyze the influence of the cavity depth on the vertical displacement of the wallrocks at the entrance of the Yuanjue Grottos and the vertical displacement of the baseplate. The conclusions can be summarized as follows.  
1. With increases in the cavity depth, the front side of the entrance of the Yuanjue Grottos presents an overall forward trend, which may result in the formation of fissures in the front side of the Yuanjue Grottos.
2. The weathering cavities mainly affect the stress deformation of the front side of the entrance and the front side of the baseplate of the Yuanjue Grottos.
3. The settlement trend of the fissured baseplate presents an obvious accelerated change when the cavity depth exceeds 4 m, and the settlement trend of the fissured baseplate shows obvious differences when the cavity depth exceeds 5 m. The depth of the weathering cavities beneath the entrance of the Yuanjue Grottos should be controlled within 5 m.  
Conceptualization, B.L. and J.L.; methodology, B.L.; software, Y.L.; validation, J.C. and F.Z.; formal analysis, Y.L.; investigation, B.L. and J.C.; resources, J.C.; data curation, Y.L.; writing--original draft preparation, B.L. and J.L.; writing--review and editing, J.L., J.C. and Y.L.; visualization, J.C. and Y.L.; supervision, J.L.; project administration, J.L.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Key Research and Development Program of China (2022 YFC2905400), the Key Projects for Technological Innovation and Application Development in Chongqing (CSTB2022 TIAD-KPX0095), and the National Natural Science Foundation of China (42202055).  
Not applicable  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Thanks are given to [PERSON] from the Northeastern University (China) for his constructive suggestions and comments.  
The authors declare that there are no conflicts of interest.
## 2 Materials and Methods
### Biofilm Milieu  
(a) Hydroxyapatite (HA) Discs, utilized as a surface on which biofilms formed, were prepared from hydroxyapatite (HA) powder with particle sizes of 9.6 um in diameter (MT3300, LowWet; Tomita Pharmaceutical, Kumamoto City, Japan) and of an impurity below the detection threshold. No binder was used to press the powder. Sintering was conducted at a temperature of 900 \({}^{\circ}\)C. The discs were compressed using the universal testing system for static tensile, compression, and bending tests (Instron model 3384, Instron, Norwood, MA, USA). The diameter of disks was 12, while their height was 3 mm. To check the quality of HA discs, a LEXT OLS4000 microscope (Olympus, Center Valley, PA, USA) and microcomputed tomograph (microCT) Metrotom 1500 microtomograph (Carl Zeiss, Oberkochen, Germany) were used.  
(b) The agar discs used as another biofilm formation surface were cut out using cork-borers 10 mm in diameter from 2% Muller-Hinton (Argenta, Poznan, Poland) agar.  
(c) Artificial Saliva (AS) was prepared with the use of mucin (2.5 g/L), sodium chloride (0.25 g/L), potassium chloride (0.2 g/L), calcium chloride (0.2 g/L), yeast extract (2.0 g/L), protease peptone (5.0 g/L), 40% urea (1.25 mL/L) and sucrose (3.0 g/L). The Artificial Saliva's pH was adjusted to a value of 6.
## 6 Conclusions  
In this paper we describe a full framework that supports automated verification and documentation of the assembly process via the proposed BOA. This ensures traceability throughout the supply chain, for a given assembly process. The related documentation and repeated confirmations, authorizations, and certifications, require substantial effort and have numerous chances for human errors. Therefore, digitalization and automation of these processes has a great value. To facilitate this digitalization, we propose the extended bill of assembly (BOA) as the backbone of the traceable documentation of each assembled product. For automating the entire control and documentation process, we suggest a framework that uses smart-contract technology enforcing the extended BOA. This framework offers traceable and protected documentation of the assembly process, providing security, safety, and full traceability, for the full assembled product. In other words, the proposed framework provides security measures needed to prevent counterfeit parts, hostile hacking, and an unauthorized approach to the assembly documentation throughout the entire supply chain. Moreover, using the proposed framework with a smart contract for each assembly activity automates the control, documentation, authorization, and certification activities throughout the entire assembly process.  
Practitioners can directly benefit from this paper by implementing the BOA structure in their respective organizations. Maintaining a BOA (even manually) enables full built-in traceability for components and sub-assemblies. Automating the validation and some of the documentation of the BOA by implementing smart contracts is another benefit for practitioners.  
Future research may be pursued in several directions: (1) performing case studies to validate and improve the suggested framework. (2) Extending the proposed approach to facilitate the usage of the BOA in constructing a digital twin (Section 5 briefly discusses this option). (3) Using the BOA accumulated data of each product, in a shopfloor, to better plan and schedule the assembly process.  
Conceptualization, Y.C. and S.R.; methodology, Y.C. and S.R.; validation, S.R.; formal analysis, Y.C.; investigation, S.R.; writing--original draft preparation, Y.C. and S.R.; writing--review and editing, Y.C. and S.R.; visualization, S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** The authors of this paper would like to thank the diligent anonymous reviewers for their time and efforts. We also would like to thank the MDPI editorial team for their availability, dedication and assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 3 Nonlinear Chaotic Grey Wolf Optimization (NCGWO) Algorithm
### Logistic-Tent Double Mapping  
Logistic mapping is a single-peaked mapping, which perfectly inherits the advantages of randomness and the initial value of sensitivity inherent in chaos [24]. The logistic mapping is expressed as follows:  
\[X_{n+1}=UX_{n}(1-X_{n}) \tag{11}\]  
where \(X_{n}\) is the iteration result \(X_{n}\in(0,1)\), and \(U\) is a parameter \(U\in(0,4)\).  
The tent chaotic system is a segmented linear one-dimensional mapping with chaotic properties, uniform probability density and power spectral density, and a relatively good autocorrelation [25]. It is mathematically derived as follows:  
\[X_{n+1}=\left\{\begin{array}{l}ax_{n},0\leq x_{n}<0.5\\ a(1-x_{n}),0.5\leq x_{n}\leq 1\end{array}\right. \tag{12}\]  
where \(x\) is the system variable and \(x\in[0,1]\), and \(a\) control parameter is between (0 and 1) and it is \(a\
eq 0.5\) when the system is chaotic.  
Compared to logistic mapping, tent mapping has a uniform probability density, a uniform power spectral density, ideal correlation properties, and a faster iteration speed [26].  
This study uses the logistic-tent double mapping [27], which combines the complex chaotic dynamics properties of the logistic mapping with a faster iteration speed, greater autocorrelation, and the applicability to many sequences of the tent chaotic system. Its mathematical formulation is as follows:  
\[X_{n+1}=\left\{\begin{array}{l}\left[rx_{n}(1-x_{n})+\frac{(4-r)}{2}x_{n} \right]\text{mod}1,\text{if}x_{n}<0.5\\ \left[rx_{n}(1-x_{n})+\frac{(4-r)(1-x_{n})}{2}\right]\text{mod}1,\text{if}x_{n} \geq 0.5\end{array}\right. \tag{13}\]  
where \(X\) is the system variable, \(r\) is the control parameter \(x\in[0,1]\), and \(r\in[0,4]\). In this study, the logistic-tent double mapping is used for initialization, so that the initial solutions are distributed as uniformly as possible in the solution space, as shown in Figure 3.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 4 Results  
It has already been mentioned that the system is capable of receiving RTCM corrections by the UART2 of the GNSS 1 receiver, which would allow the reported position to have errors of approximately 2 cm under ideal conditions. These corrections are not used in the system validation, as it is intended to demonstrate the quality of attitude estimations in a system that is fully autonomous and independent of external corrections.  
Figure 5: Attitude and positioning system prototype.
## 1 Introduction
### Background Study of This Research
Therefore, real-time detection of DoS JSA and HDSA utilizing IEEE 802.11p, which is based upon VFC, requires investigation to evaluate end-to-end delay and jitter in VANET, due to DoS JSA and HDSA attack, (congestion and collision) for trust evaluation. The authors in previous studies [14; 15; 16] have conducted investigations separately using Cuckoo/CSA (ABC) and Firefly Genetic Algorithm (GA), respectively. The investigations were performed to evaluate delay sensitivity for real-time detection for only DoS JSA attacks in VANET, which also utilized DSRC technology. However, based upon the investigation conducted with the Cuckoo/CSA (ABC) scheme, it revealed that it was not centered on VFC. In addition, most of the schemes' investigations dwell on only the unicast method for data transmission. However, this method did not achieve trustworthiness in the network. The authors have conducted an investigation on Firefly (GA), and utilized the concept of VANET as key enabler of future ITS, utilizing real-time detection of DoS attacks. The authors also trained the misbehavior of the nodes on the paths of vehicles delayed in VANET. They also utilized the DSRC technology and multicast data transmission. However, the author's investigation was limited. This is based upon the fact that the investigation does not include all forms of attacks, including HDSA attacks, such as DoS JSA, PD, and RCRCO, in the network. In addition, the absence of the VFC method was also a major limitation observed in the schemes. Therefore, it can be concluded that there is a trustworthiness limitation in VANET. This still presents the greatest challenge. To address these challenges in VANET, in this paper we consider all forms of attacks, including all forms of DoS attack detection in VANET, which includes but is not limited to DoS JSA, HDSA (congestion and collusion), PD, and RCRCO/DoS attacks in our proposed scheme VIA models. We also consider the hybrid deployment of OAs with VFCs and integrate full authentication/KDE trust mechanism deployment in the VANET. These will be used to evaluate the end-to-end delay and jitter in real-time IEEE 802.11p hybrid multicast and unicast data transmission in VANET. Therefore, in this paper we propose real-time detection of DoS attacks in IEEE 802.11p using VFC in a Secure Intelligent Vehicular Network.
## 3 Special Model: Exponentiated Exponential-Pareto Distribution (EEP)
### Survival Function and Tail Properties  
Since (11) is a continuous differentiable pdf, the cumulative distribution function (CDF) can be derived. By the definition of CDF, we have the following:  
\[F_{T}(t;\theta,\eta)=\begin{cases}c\Big{(}1-e^{-\frac{(\alpha+1)t^{\eta}}{ \theta}}\Big{)}&t<\theta^{\frac{1}{\eta}}\\ c\Big{(}2-e^{-(\alpha+1)}-\frac{\theta\pi}{t^{\eta\eta}}\Big{)}&t\geq\theta^ {\frac{1}{\eta}}.\end{cases} \tag{13}\]  
Notice that the development of EP distribution guarantees that \(c(2-e^{-(\alpha+1)})=1\). Therefore,  
\[F_{T}(t;\theta,\eta)=\begin{cases}c\  
that is,  
\[S_{T}(t;\theta,\eta)=\begin{cases}1-c\bigg{(}1-e^{-\frac{(\alpha+1)t}{\theta}} \bigg{)}&t<\theta^{\frac{1}{\eta}}\\ c\left(\frac{\theta^{\alpha}}{t^{\beta\alpha}}\right)&t\geq\theta^{\frac{1}{ \eta}}.\end{cases} \tag{15}\]  
Now, given that we derived the formula of CDF and the survival function of \(T\), we can further discuss the tail properties of the EEP distribution.  
Proposition 2: If \(T\) follows an EEP distribution with a CDF \(F\), then the distribution of \(T\) has a heavy tail.  
Proof of Proposition 2: We only prove that \(\lim_{t\rightarrow\infty}e^{\lambda t}S_{T}(t)=\lim_{t\rightarrow\infty} \left(\frac{c\theta^{\alpha}e^{\lambda t}}{t^{\beta\alpha}}\right)=\infty\) for all \(\lambda>0\).  
Notice \(\eta\alpha>0\) based on our assumption. Denote \(\lceil\eta\alpha\rceil\) as the smallest integer greater than \(\eta\alpha\).  
Then, since \(\lim_{t\rightarrow\infty}c\theta^{\alpha}e^{\lambda t}=\infty\) and \(\lim_{t\rightarrow\infty}t^{\eta\alpha}=\infty\), apply L'Hospital rule \(\lceil\eta\alpha\rceil\) times. We have \(\lim_{t\rightarrow\infty}e^{\lambda t}S_{T}(t)=\lim_{t\rightarrow\infty} \left(\frac{c\lambda(\eta\alpha)\sigma e^{\lambda t}}{\prod_{i=1}^{|\eta|}( \eta\alpha-i)t^{\eta\alpha-\lceil\eta\alpha\rceil}}\right)=\infty\).  
Therefore, the distribution of \(T\) has a heavy tail.  
Furthermore, we could also specify the conditions that an EEP distribution has a fat tail. Suppose that the survival function of a random variable \(X\) satisfies the following:  
\[S(x)\sim x^{-\beta}\text{ as }x\rightarrow\infty,\alpha>0\]  
Notice that a distribution is said to have a fat tail when the tail index \(\beta<2\). Notice that, for an EEP random variable \(T\), \(S_{T}(t)\sim t^{-\eta\alpha}\). Therefore, only when \(\eta\alpha<2\), the EEP distribution is considered to have a fat tail.