esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antimicrobial Activity of _G. hederacea Extracts_  
#### 2.5.1 Indicator Microorganisms  
In the experiment, four Gram-positive bacteria--_Microoccus lute_ ATCC 10240, _Staphylococcus aureus_ ATCC 33862, _Bacillus subtilis_ ATCC 11774 and _Enterococcus faecalis_ ATCC 19433--as well as three Gram-negative bacteria--_Escherichia coli_ ATCC 8739, _Pseudomonas aeruginosa_ ATCC 9027 and _Salmonella enterica_ ser. Enteritidis ATCC 13076--were used. All bacterial strains were purchased from the American Type Culture Collection (ATCC) and cultivated on liquid media: trypticasein soy broth (TSB) for _M. luteus_, nutrient broth (NB) for _S. aureus_, _B. subtilis_, _E. coli_, _P. aeruginosa_ and brain heart infusion (BHI) for _E. faecalis_ and _S. enteritidis_ under optimal temperature conditions (30 \({}^{\circ}\)C for _M. luteus_ and 37 \({}^{\circ}\)C for the remaining bacteria). The fungistatic activity of the tested extracts was determined against one strain of yeast and five filamentous fungi. The yeast strain (_C. albicans_ ATCC 10231) was purchased from the American Type Culture Collection and cultured on Sabouraud Dextrose Broth (SAB) at 37 \({}^{\circ}\)C under aerobic conditions. Two species of the genus _Fusarium_ (_F. graminearum_ KZF 1 and _F. culmorum_ KZF 5), _Alternaria alternata_ KZF 13 and _Sclerotinia sclerotorium_ KZF 23 were obtained from the collection of the Research Centre for Registration of Agrochemicals, whereas _Botrytis cinerea_ BPR 187 was from the Bank of Plant Pathogens and Research on their Biodiversity, Institute of Plant Protection, National Research Institute in Poznan, Poland. The tested filamentous fungi were cultivated in Petri dishes (55 mm diameter) on a Potato Dextrose Agar (PDA) at 25 \({}^{\circ}\)C for 5-10 days.
## 3 Our Proposed Method
### LTRSH Model Building
#### 3.1.2 Local Texture Pattern Detection  
Local texture information is detected on the color quantization in HSV space by coding the local texture pattern to obtain one corresponding CS-LTP map \(T\left(T(x,y)=x,x\in\left\{0,1,...,X-1\right\}\right)\).  
The CS-LTP map can be obtained through the following steps:  
First, for each point \(p_{i}(x_{i},y_{i})\) ( \(x_{i}\) and \(y_{i}\) represent the coordinates of the point \(p_{i}\)) on image\(X\), starting from the right of point \(p_{i}(x_{i},y_{i})\), we can locate \(N\) ( \(N>0,N\in N^{+}\)) points \(\{p_{i}^{k}(x_{i}^{k},y_{i}^{k})\}\) (\(k=1,2,...,N\)) orderly by tracing a circle with radius \(R(R>0)\) in the clockwise direction, satisfying the constraint that is the distance between \(p_{i}\) and the \(kth\) point \(p_{i}^{k}\) is equal to \(x_{i}^{k}-x_{i}=R\cos\theta\), \(y_{i}^{k}-y_{i}=-R\sin\theta\). Then, a pair of points \(\{p_{i}^{k},p_{i}^{k+N/2}\}\) can be obtained, and the quantized value of \(\{p_{i}^{k},p_{i}^{k+N/2}\}\) is denoted as \(n_{i}^{k}\) and \(n_{i}^{k+N/2}\) for \(k=1,2,...,N/2\).  
Next, the CS-LTP coding \(T(x_{i},y_{i})\) of point \(p_{i}(x_{i},y_{i})\) is defined as (\(T(x_{i},y_{i})=x,x\in\left\{0,1,...,X-1\right\}\)) (Equation (2)):  
\[T_{R,N}(x_{i},y_{i})=\sum_{k=0}^{\frac{N}{2}-1}s(n_{i}^{k}-n_{i}^{k+\frac{N}{2 }})^{3}\ \,\ \ s(j)=\left\{\begin{array}{ll}2,&j\geq t\\ 1,&-t<j<t\\ 0,&j\leq-t\end{array}\right. \tag{2}\]  
where \(t\) is the user-specified threshold. Here, we use the tolerance interval \(t=0.8\). Thus, we know that when \(R=1\) and \(N=8\), CS-LTP is defined as having a length of 4 bits with the indicator \(s(j)\), which is replaced with a three-valued function. In this way, \(X\) of the CS-LTP map is \(\sum_{i=0}^{3}2\times 3^{i}=81\). The reason for using CS-LTP is that CS-LTP is more resistant to noise, but is no longer strictly invariant to gray-level transformations [27].  
The procedure for local texture pattern coding is illustrated in Figure 5. For example, the third variation texture in Figure 5 is encoded as \(\left[0020\right]_{3}\), and then, it is converted to the decimal number 18. In this case, the point \(p_{i}(x_{i},y_{i})\) is encoded as 18 as the gray value of the CS-LTP map \(T\).  
Figure 5: An example of CS-LTP encoding (\(t=0.8\)).
## 3 Results and Discussion
### Spatial Consistency Quality Assessment  
The spatial consistency results are highlighted in Table 1 below.  
Results are reflective of the correlation between the Laplacian filtered bands of the pan sharpened image and the Laplacian filtered panchromatic band. The domain value range from \(-1\) to \(+1\) and ideal values should be close to 1. The ideal value is 1. The results show the best spatial consistency results  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**BAND \#** & **BAY** & **BRO** & **CNS** & **EHLERS** & **GRS** & **IMVM** & **MIHS** & **PANSHARP** & **PCA** & **RCS** & **WAVELET** \\ \hline
1 & 0.854 & 0.761 & 0.853 & 0.801 & 0.853 & 0.785 & 0.826 & 0.845 & 0.846 & 0.844 & 0.542 \\
2 & 0.854 & 0.834 & 0.852 & 0.801 & 0.853 & 0.784 & 0.825 & 0.845 & 0.845 & 0.845 & 0.542 \\
3 & 0.854 & 0.847 & 0.851 & 0.801 & 0.853 & 0.783 & 0.824 & 0.845 & 0.837 & 0.845 & 0.543 \\ \hline AVERAGE & **0.854** & 0.814 & 0.852 & 0.801 & 0.853 & 0.784 & 0.825 & 0.845 & 0.843 & 0.844 & 0.542 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial consistency: correlation coefficient (CC) Laplacian filtering. Abbreviations: Bayesian fusion (BAY); Brovey transform(BRO); Color Normalized Spectral sharpening (CNS); Ehlers fusion technique (EHLERS); Gram–Schmidt (GRS); Local Mean and Variance Matching (IMVM), Modified Intensity Hue Saturation (MIHS), Pansharp algorithm (PANSHARP), Principal component analysis (PCA); Ratio Component Substitution (RCS); WAVELET, Wavelet Resolution merge fusion (WAVELET).  
were produced by the Baysian pansharpening method with Gram-Schmidt in second place and CNS in third place. The wavelet pansharpening technique produced the worst spatial consistency results.
## 3 Results
### Effects of Laser Irradiation  
Laser irradiation modified the surface of the Co-Cr alloy substrate while heating the reaction medium (CP solution). The laser heating effect was verified by a control experiment, in which the LAB process was performed on the Co-Cr alloy substrate in the absence of a temperature-controlled water bath. As shown in Figure 4, the temperature of the CP solution increased with the laser irradiation time for the Co-Cr alloy substrate. Without the substrate, the temperature remained almost constant during irradiation up to 30 min. The laser-induced surface modification of the substrate was confirmed by a control experiment in which laser irradiation was applied to the substrate in ultrapure water. The substrate surface was roughened at the micronscale within 5 min of irradiation in ultrapure water (Figure 5a), similarly to that in the CP solution (Figure 2a). The contact angle of a water droplet on the substrate significantly decreased after irradiation (Figure 5b). These results clarified that the Co-Cr alloy substrate undergoes surface modification, i.e., micro-roughening and wetting by laser irradiation in ultrapure water.  
Figure 4: Variation of the temperature of the supersaturated calcium phosphate (CaP) solution (CP solution) with the laser irradiation time for the Co–Cr alloy substrate (without using the temperature-controlled water bath).  
Figure 5: **(a)** SEM images and **(b)** contact angles of water droplets of the surfaces of the Co–Cr alloy substrates after laser irradiation in ultrapure water for 0 (untreated), 5, 10 and 30 min.
## 2 The Basic Theory  
### Rotor System  
The simplified model of a SFD-rotor-bearing system supported by deep groove ball bearing is shown in Figure 1. Rotor equivalent lumped mass points are \(M_{\text{s1}}\), \(M_{\text{d1}}\), \(M_{\text{s2}}\), \(M_{\text{s3}}\), \(M_{\text{s4}}\), \(M_{\text{s5}}\), \(M_{\text{d2}}\), \(M_{\text{s6}}\) respectively, \(M_{\text{o1}}\) and \(M_{\text{d2}}\) are lumped mass points of rolling bearing outer ring. \(K\) is stiffness of the flexible shaft; \(K_{\text{a}}\) is the supporting stiffness of squirrel cage, \(C_{\text{z}}\) and \(C_{\text{p}}\) are damping coefficients of rotor in bearing and disk. \(\delta\) is the gap between rotor and stator; \(e_{1}\) and \(e_{2}\) are the eccentricities of the disk; \(\alpha_{1}\) and \(\alpha_{2}\) are the eccentric angles of the disk; \(k_{\text{s}}\) is the stiffness coefficient.  
According to the Lagrange equation, the differential equation of motion of rotor system can be obtained as follow:  
\[[M]\ddot{q}]+[C][\dot{q}]+[K]q]=\{Q\}, \tag{1}\]  
In the formula, \([M]\), \([C]\), \([K]\) are the mass matrix, damping matrix and stiffness matrix, respectively. Their details have been further described in Appendix A.  
The system non-dimensional transformation is introduced: \(\tau=\omega t\), \([\overline{q}]=\frac{[q]}{\mu}\), \(\dot{[\overline{q}]}=\frac{[\dot{q}]}{\mu\omega}\), \(\dot{[\overline{q}]}=\frac{[\dot{q}]}{\mu\omega^{2}}\), where \(\omega\) is rotational angular velocity of the shaft; \(\mu\) is the clearance of rolling bearing. Formula (1) transforms into non-dimensional equation:  
\[\dot{[\overline{q}]}=-\frac{[M]^{-1}[C]}{\omega}\dot{[\overline{q}]}-\frac{[ M]^{-1}[K]}{\omega^{2}}\dot{[\overline{q}]}+\frac{[M]^{-1}\{\overline{Q} \}}{\mu\omega^{2}}. \tag{2}\]  
Figure 1: The simplified model of SFD-rotor-bearing system.
# Comparative Effectiveness of a Commercial Mouthwash and an Herbal Infusion in Oral Health Care
## 1 Introduction  
The formation of the dental biofilm is initially influenced by factors such as the surface free energy, which affects the wettability and the surface tension, the surface roughness, and the adhesion of the salivary proteins [1, 2]. It is known that the initial attachment of bacterial biofilm to the dental surface is a result of hydrophobic and electrostatic interactions, respectively, in bacterial attachment to the dental surface [3]. Dental plaque-induced gingival inflammation is modified by various systemic and oral factors.  
The prevention of the formation of dental biofilm, respectively, the occurrence of dental cavities and gingival inflammation, is done by several means. The most common are the use of toothpaste with anti-cavity and anti-inflammatory effects and through additionalmeans such as dental floss or mouthwash. Mouthwash is a mean often used to prevent the occurrence of dental biofilm and decrease the inflammation it causes [1].  
The search for effective, efficient, safe, and economical alternatives has led to a rise in the use of natural phytochemicals derived from plants in treating human diseases. A large body of evidence exists to substantiate the use of herbs for preventing and treating human diseases [4].  
Among the medicinal herbs that exhibit antimicrobial activity, _Glechoma hederacea_ L. ([PERSON]), _Matricaria chamomilla_ L. ([PERSON]), and _Polygonum aviculae_ L. ([PERSON]) have a large number of phenolic groups with bactericidal action besides the main ingredients [5; 6; 7].  
Oral use mouthwashes have been created to combat dental plaque, to increase the strength of dental structures, and for the prophylactic or curative treatment of periodontal disease or oral mucosa inflammation. Depending on the active substance concentration, mouthwashes can be used by vigorous rinsing or by oral irrigation. Mouthwashes can have cosmetic or therapeutic purposes. They can also be classified according to how they are prepared in commercial and extemperaneous preparations. Their compositions include common ingredients (water, alcohol, flavorings, and sweeteners) and active ingredients (oxidizing substances-hydrogen peroxide, sodium perborate and urea peroxide, asrtingents-zinc chloride, zinc acetate, tannic acid, acetic acid, and citric acid) [8; 9; 10].  
Recently, there has been an increase in the use of natural plant-derived phytochemicals in the treatment of human diseases, in search of efficient, safe, and inexpensive treatment alternatives [4]. Numerous studies demonstrate the possibility of using medicinal plant species in therapy for their rich content in phenolic compounds [11; 12; 13; 14]. Among the medicinal herbs, _Polygonum aviculare_ L. and _Glechoma hederacea_ L. have a large number of phenolic compounds with antimicrobial, antioxidant, and anti-inflammatory action in their main ingredients [11]. The purpose of this study was to comparatively evaluate the effectiveness of a commercial mouthwash and of an herbal infusion on dental plaque formation and gingival inflammation.
## 4 FE-Based Method
### Application of the Method to a Case Study
#### 4.4.2 FE Models  
The two FE models of the floor cell considered as a case study were made using SAP2000 software [39]. The 3D model was realized according to step 1 of the method described in Section 4.3. In particular, for both ceiling bricks and RC elements of the floor, solid FE with dimensions 6 \(\times\) 6 \(\times\) 4 cm\({}^{3}\) were used, while for the perimeter beams, solid FE with dimensions 6 \(\times\) 5 \(\times\) 4 cm\({}^{3}\) or 5 \(\times\) 6 \(\times\) 4 cm\({}^{3}\) were used, respectively, for beams parallel to the X or Y direction. In the intersection regions, solid elements 5 \(\times\) 5 \(\times\) 4 cm\({}^{3}\) in size were used. The beams were modeled with the extrados coplanar to that of the slab. Out-of-plane displacements of the floor cell were restrained at the slab top surface.  
The 2D model was realized according to step 2 of the method described in Section 4.3. In particular, the thickness of membrane elements is set equal to that of the floor RC slab, i.e., 4 cm. For the elastic properties of the homogenized material constituting 2D elements, those reported in Section 4.4.1 for concrete C25/30 are adopted.  
The mesh size was chosen according to a sensitivity analysis in which the mesh size was reduced until the floor displacement of the 2D model under in-plane compression loads had a maximum scatter of 1% with respect to a fitter mesh. A discretization with 16 \(\times\) 16 square finite elements was adopted.
## 3 Methodology
### The Traffic Spatiotemporal Parameters Considering Density and Headaway  
In Figure 2, the length of the road section is denoted by \(L\), the number of vehicles is \(N\), and the corresponding traffic density is \(k\). The intervehicle distance is \(d_{i}\), the headway between the first vehicle and the preceding vehicle is \(d_{out}\), and the headway between the last vehicle and its following vehicle is \(d_{in}\). The relationship between density, vehicle number, headway, road length, \(d_{out}\), and \(d_{in}\) can reveal the following.  
\[k=\frac{N}{L} \tag{6}\]  
\[L=\sum\limits_{N}d_{i}+\Delta=\sum\limits_{N}d_{i}+\beta_{1}d_{out}+\beta_{2}d _{in} \tag{7}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are weight parameters of the distance at the downstream exit position and upstream entrance position of the road section, respectively, with a value range of [0, 1].  
Formulas (6) and (7) are transformed to obtain Formula (8), the reciprocal of Formula (8) is taken to obtain Formula (9), and the reciprocal is transformed to obtain Formula (10).  
\[k=\frac{N}{\sum\limits_{N}d_{i}+\beta_{1}d_{out}+\beta_{2}d_{in}} \tag{8}\]  
\[\frac{1}{k}=\frac{\sum\limits_{N}d_{i}+\beta_{1}d_{out}+\beta_{2}d_{in}}{N}= \frac{\sum\limits_{N}d_{i}}{N}+\frac{\beta_{1}d_{out}}{N}+\frac{\beta_{2}d_{ in}}{N} \tag{9}\]  
\[\frac{\sum\limits_{N}d_{i}}{N}=\frac{1}{k}-\frac{\beta_{1}d_{out}}{N}-\frac{ \beta_{2}d_{in}}{N} \tag{10}\]  
The average headway and density of vehicles within a road segment are not entirely inversely proportional to each other, which is similar to the conclusion obtained in the literature [44]. According to Formula (10) and the definition of headway, Formula (11) is obtained, which is the product of density and headway.  
\[k\cdot\overline{h}_{s}=\frac{N}{\sum\limits_{N}d_{i}+\beta_{1}d_{out}+\beta_{ 2}d_{in}}\cdot\frac{1}{N}{\sum\limits_{N}d_{i}}=\frac{1}{1+\beta_{1}\frac{d_{ out}}{N}+\beta_{2}\frac{d_{in}}{N}} \tag{11}\]  
Figure 2: Relationship between density and headway.  
Combining Formula (5) and Formula (11), the calculation formula for the traffic spatiotemporal state is obtained, as shown in Formula (12).  
\[\begin{array}{ll}TP&=a\cdot\frac{T_{1}+\frac{1}{h}\sum\limits_{i=2}^{n}(\tau_{ \text{d}i})-\frac{1}{h}\sum\limits_{i=2}^{n}(\tau_{\text{a}i})-T_{\text{f}f}}{T _{\text{f}f}}+(1-a)\cdot\frac{\overline{t}_{\text{exp}}\cdot k_{\text{exp}}\cdot k _{\text{exp}}\cdot l_{\text{exp}}\cdot l_{\text{exp}}\cdot l_{\text{exp}}\cdot l _{\text{exp}}}{L_{\text{eff}}+L_{\text{eff}}}\\ &=a\cdot\frac{T_{1}+\frac{1}{h}\sum\limits_{i=2}^{n}(\tau_{\text{d}i})-\frac{1} {h}\sum\limits_{i=2}^{n}(\tau_{\text{a}i})-T_{\text{eff}f}}{T_{\text{f}f}}\\ &\quad\frac{1}{1+\beta_{1-\text{exp}}\frac{\text{det}-\text{exp}}{\text{det}- \text{exp}}\cdot k_{\text{exp}}\cdot k_{\text{exp}}\cdot l_{\text{exp}}\cdot l _{\text{exp}}\cdot l_{\text{exp}}\cdot l_{\text{exp}}\cdot l_{\text{exp}} \cdot l_{\text{exp}}}{L_{\text{eff}}+L_{\text{eff}}}\end{array} \tag{12}\]
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 3 The Mean Square Convergence Analysis
### The Energy Conversion Relation  
Consider a learning system that can estimate the nonlinear mapping relation of \(f(\cdot)\)  
\[d(n)=f(n)+v(n) \tag{15}\]  
where \(f(n)=\mathbf{\Omega}(n)^{T}\varphi(\mathbf{x}(n))\) is the mapping function to estimate, \(\varphi(\mathbf{x})\) is the transformed feature vector of the input, \(\mathbf{\Omega}\) is the weight vector to estimate an optimal vector \(\mathbf{\Omega}^{T}\), and \(v(n)\) is uncorrelated interference noise. A different mapping method \(\varphi(\cdot)\) can represent a class of KLMS algorithms. The weight update of the estimator \(f(n)\) can be expressed as  
\[\mathbf{\Omega}(n+1)=\mathbf{\Omega}(n)+\mu e(n)\varphi(\mathbf{x}(n)) \tag{16}\]  
Subtracting \(\mathbf{\Omega}^{T}\) from both sides of Equation (16) yields  
\[\mathbf{e}_{w}(n+1)=\mathbf{e}_{w}(n)+\mu e(n)\varphi(\mathbf{x}(n)) \tag{17}\]  
where \(\mathbf{e}_{w}(n)\) is the weight error vector in iteration n. Define the priori error \(e_{a}(n)\) and the posteriori error \(e_{p}(n)\) as  
\[\begin{split} e_{a}(n)&=e_{w}(n)\varphi(\mathbf{x}(n) )\\ e_{p}(n)&=e_{w}(n+1)\varphi(\mathbf{x}(n))\end{split} \tag{18}\]  
The relation between the priori error and the posteriori error is  
\[e_{p}(n)=e_{a}(n)-\mu e(n)||\varphi(\mathbf{x}(n))||^{2} \tag{19}\]  
where \(||\varphi(\mathbf{x}(n))||^{2}=\varphi(\mathbf{x}(n))^{T}\varphi(\mathbf{x}(n))\). Substituting Equation (19) into Equation (18), we have  
\[e_{w}(n+1)=e_{w}(n)-\frac{(e_{a}(n)-e_{p}(n))\varphi(\mathbf{x}(n))}{||\varphi(\bm {x}(n))||^{2}} \tag{20}\]To obtain the energy conversion relation, square both sides of Equation (20)  
\[\begin{split}& e_{w}(n+1)^{T}e_{w}(n+1)=\left(e_{w}(n)-\frac{(e_{a }(n)-e_{p}(n))\varphi(\mathbf{x}(n))}{||\varphi(\mathbf{x}(n))||^{2}}\right)^{T}\\ &\left(e_{w}(n)-\frac{(e_{a}(n)-e_{p}(n))\varphi(\mathbf{x}(n))}{|| \varphi(\mathbf{x}(n))||^{2}}\right)\\ &=e_{w}(n)^{T}e_{w}(n)-2e_{a}(n)\frac{(e_{a}(n)-e_{p}(n))\varphi( \mathbf{x}(n))}{||\varphi(\mathbf{x}(n))||^{2}}\\ &+\frac{(e_{a}(n)-e_{p}(n))^{2}}{||\varphi(\mathbf{x}(n))||^{2}}\end{split} \tag{21}\]  
After some function derivation, the energy conversion relation can be obtained:  
\[||e_{w}(n+1)||^{2}=||e_{w}(n)||^{2}+\frac{|e_{a}(n)|^{2}-|e_{p}(n)|^{2}}{|| \varphi(\mathbf{x}(n))||^{2}} \tag{22}\]  
which is similar to the linear adaptive filter and different to that of the QKLMS based on implicit mapping method ([14], Equation (18)).
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 3 System Overview  
This section describes how ML and vehicle network data can be used together to implement a full prediction system. It also explains how the predictions can be used in safety applications. The safety application can be implemented in the vehicle and in the infrastructure system by transferring the prediction results to the infrastructure. The proposed system block diagram is summarized in Figure 1. The proposed system includes the following components:  
* Vehicle network: The in-vehicle data to be fed to the ML system are collected through a vehicle network.
* Data logging system: The data logging system collects data from the vehicle network.
* Machine learning system: The machine learning system receives the data from the logging system and then classifies them. A training dataset is required to train the ML algorithms. The training dataset should be labeled correctly to the required classes.
* Vehicle to Infrastructure communication (V2I): This network is used to transfer the result of the ML predictions to the infrastructure system.
* Vehicle application system: This system uses the ML results to provide in-vehicle safety functions for the driver. For example, if the road traffic is classified as high, then a warning is issued to drive carefully.
* Infrastructure application system: This system uses the ML classification result to provide functions in the infrastructure level. for example, a road maintenance request is issued if the road surface is detected as being full of holes.  
Three algorithms were implemented for in-vehicle data classifications; decision trees, random forest and Support Vector Machine (SVM). A labeled dataset collected from the CAN network was used to train and test these algorithms. Results of the classification were analyzed with respect to algorithm accuracy, precision, recall and F-score.
## 4 Application of Hyperchaotic Sequence in Secure Communication  
Due to the unpredictability and ergodicity of chaotic systems, chaotic systems are widely used to transmit data safely through various networks. When a chaotic system is used for data transmission, the distribution of its output has great influence on the performance of transmission error resistance. In this section, we take the three chaotic sequences as the chaotic sequence generators of the chaotic keying communication model and evaluate the sequences by the bit error rate of signal in the process of transmission with noise. In this paper, a chaotic keying communication model is used as the reference modulation-differential chaotic shift keying model (RM-DCSK). The RM-DCSK scheme consists of a transmitter and a receiver. The transmitter first uses a chaotic sequence to encode the information bits to generate the transmission signal, and then sends the transmission signal to the receiver. The receiver decodes the received signal to recover bits of information.