esa-sceva/satcom-chunk-collection huggingface dataset:
# A Chaotic Secure Communication System Design Based on Iterative Learning Control Theory
## 5 Conclusions  
This paper has described an application of the ILC theory to secure communication system design by using a chaotic signal obtained from the chaotic-logistic map. It was also evidenced that the main important benefit of the proposed ILC given in [8] is robustness, even on variation in the resetting initial conditions and exogenous noise. From our secure communication system, an important design parameter is the \(T\)-time duration of each cycle. By reducing it, we may be able to employ our system to more \"faster-time\" message signals. It is worth mentioning that the ILC method belongs to the _data-driven_ systems, due to data measurement used in its learning algorithm [17].  
Finally, we believe that it is important to highlight that the main aim of this paper is to illustrate a kind of academic example of an application of the ILC theory to secure communication system design based on chaotic signals. However, and because it is beyond the author's expertise, an additional analysis test is missing; for instance, the bit error rate curve over AWGN (Additive White Gaussian Noise) channel, including the possibility to expand our design by using the latest works on chaotic waveform to convey data such as DCSK (Differential Chaos Shift Keying), NR-DCSK (Noise Reduction Differential Chaos Shift Keying), and so on. All of these are left for a future work.  
Research supported in part by the Spanish Ministry of Economy and Competitiveness through the research grant project DPI2015-64170-R/FEDER.  
The author declares no conflict of interest.  
The following abbreviation is used in this manuscript:  
ILC Iterative Leaning Control  
ZOH Zero-Order-Holder
## 4 Performance Evaluation
### Replacing One Mobile Anchor Node by Fixed Anchor Nodes  
We will study the case where we replace one mobile anchor node by fixed anchor nodes in a MATLAB simulation environment with the specifications depicted in Section 4.2. The number of fixed nodes equals the number of pausing points of the mobile anchor node.  
In Figure 28, we compare between energy dissipated by one mobile anchor node including the energy dissipated through sending messages and the total energy dissipated by all fixed anchor nodes replaced by one mobile anchor node. As shown, the mobile anchor node consumes more energy by fixed anchor nodes due the power dissipated through moving. Moreover, the cost of using fixed anchor nodes rather than one mobile node is very high.
## 4 Results and Discussion
### Micro-Tests after the Addition of B. pasteurii to CG and FA
#### 4.1.4 SEM/EDS and FTIR Precipitation Test  
All of the biochemical reaction products under _B. pasteurii_ mineralization were also investigated by means of SEM/EDS and FTIR, in order to analyze their morphology and composition.  
In Figure 6a,b, the structure of the bioprecipitate can be clearly observed using SEM at different magnifications, indicating that plate-like layers were stacked to form ellipsoids. A similar morphology was also observed in other studies [52; 53; 54]. A clear rod-shaped bacterial structure (a typical shape of _B. pasteurii_) of diameter 0.5-1.5 \(\upmu\)m, which is consistent with other researchers [55], along with calcite crystals, can also be observed in Figure 6a. Thus, during the mineralization process, bacteria functioned as nucleation sites [56]. The presence of _Bacillus_ species surrounded by mineralization products was described in detail by [PERSON] et al. [57]. Finally, EDS quantification revealed that the elemental composition of the bioprecipitate shown in Figure 6c was mostly calcium, carbon and oxygen (see Figure 6d).  
Figure 5: **(a)** A pre-test: Mineralization occurring from a macroscopic perspective. After the introduction of calcium ions, a white bioprecipitate was formed immediately. **(b–d)** Phenomena after the simultaneous addition of CaCl\({}_{2}\) solution to three sets of test tubes at the 12 h, 36 h and 48 h incubation time points (from left to right), respectively. The test tubes comprise CG group_2, FA group_1 and Reference_0. Different symbols correspond to different test tubes.  
In Figure 7, strong absorption bands were observed at 875 cm-1 and approximately 1414 cm-1, which is consistent with other researchers [58; 59]. They belong to the C-O bond characteristic of CaCO3, mainly present in CG group_1 and FA group_2, and confirm the MICP process. These characteristic peaks are not present in only FA (Figure 7b). Although very small vibrational bands were also present in only CG (Figure 7a), this suggests that the latter contains very low levels of carbonate derived from natural chemical processes. The absorption located at around 795 cm-1 is assigned to Si-O stretching vibrations, which suggests the presence of quartz in CG (Figure 7a) and FA (Figure 7b). It must be remarked that the results of FTIR spectroscopy were consistent with those of both the SEM/EDS analyses and the MICP process.  
Figure 6: Scanning electron microscopy images of the bioprecipitate (**a**) at 7500 times magnification and (**b**) at 20,000 times magnification. (**c**) SEM image and (**d**) EDS analysis of the bioprecipitate. EDS data are shown from one of several determinations (abscissa unit: keV).  
Figure 7: FTIR spectra of (**a**) the bioprecipitate in the CG group compared with only CG and of (**b**) the bioprecipitate in the FA group compared with only FA. A small amount of CG or FA substrate is mixed in the bioprecipitate.
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 7 Conclusions  
The primary objective of this research is to develop an advanced path tracking controller for intelligent vehicles that can effectively navigate roads with varying curvatures while maintaining high accuracy and stability. The aim is to address the limitations of traditional model predictive control (MPC) methods, which struggle with adaptive control under changing road conditions, particularly in terms of curvature.  
The current research on path tracking control for autonomous vehicles predominantly relies on traditional MPC methods with fixed parameters. These methods exhibit significant limitations when confronted with roads of varying curvatures. The inflexibility in parameter adjustment leads to suboptimal tracking performance, especially in complex curved road scenarios, where the vehicle's ability to accurately follow the planned path is compromised. This lack of adaptability undermines the overall safety and efficiency of autonomous driving systems.  
To overcome the aforementioned limitations, this paper proposes an innovative path tracking controller based on the model predictive control (MPC) theory, which is optimized using a genetic algorithm (GA) and incorporates adaptive constraint conditions. The three-degree-of-freedom vehicle dynamics model is first subjected to linearization and discretization processes, forming the foundation for the controller design. The key innovation lies in the integration of adaptive constraints based on the yaw rate and lateral velocity, which are dynamically adjusted according to the road curvature. These constraints serve as stability boundaries, ensuring that the vehicle's motion remains within safe and controllable limits. The GA is employed to optimize the time domain parameters of the MPC controller for each specific curvature scenario, thereby enhancing its adaptability and tracking accuracy.  
The effectiveness of the proposed controller is validated through extensive simulations conducted using CarSim/Simulink software v.2021 and the HCU hardware-in-the-loop (HIL) platform. The experimental findings reveal that, in comparison to the traditional MPC controller, the conditional adaptive MPC controller introduced in this study demonstrates the ability to dynamically adjust time domain control parameters in response to varying road curvatures. Under variable curvature conditions, the adaptive MPC controller significantly enhances path tracking accuracy, with lateral errors reduced to less than 0.15 m. The proposed controller reduces lateral tracking errors by over 50% compared to the traditional MPC, with a corresponding dramatic improvement in implied SSE (thoughnot explicitly calculated, as the error reduction is visually and quantitatively evident in Figures 10 and 11). Notably, the HIL results indicate a remarkable 22% improvement in tracking precision under complex curved road conditions, underscoring the superior performance of the proposed approach.
###### Abstract  
Besides alveolar bone quality, the drilling protocol applied in conjunction with the design of an implant are the major determinants of primary implant stability. Surgical trauma and bone compression resulting from implant insertion may constitute one cause for marginal bone resorption. Inserting two current bone-level implant designs (Astraa; Straumann; \(n=5\)) in bovine ribs, primary stability, strain development on the buccal bone plate and histologic signs of bone damage were recorded. Besides comparing the implant designs (Welch \(t\)-tests), all measurement parameters were checked for potential correlations (Pearson product moment correlation coefficients) with the level of significance set at \(\alpha=0.05\). Considerable numbers of crack formation and plastic deformation of bone were observed after implant insertion. Straumann implants showed slightly greater values for insertion torque (\(p=0.772\)), strain development (\(p=0.893\)) and implant stability (\(p=0.642\)). Significantly greater bone to implant contact (cortical \(p=0.014\); trabecular \(p=0.016\)) was observed in Straumann implants, while Astra implants caused a significantly greater number of microcracks in cortical bone (\(p=0.020\)). In Straumann implants, insertion torque correlated with bone to implant contact in the cortical area (\(p=0.029\)) and the number of macrocracks in trabecular bone correlated with bone to implant contact (\(p=0.029\)). In Astra implants, insertion torque and bone to implant contact in the trabecular area correlated (\(p=0.007\)) as well as the number of macrocracks in trabecular bone and implant stability (\(p=0.016\)). Additionally, in the area of cortical bone, the number of macrocracks correlated with bone to implant contact (\(p=0.019\)). Implant placement results in bone damage of varying magnitude, which is governed by the drill protocol, implant macrodesign and bone quality.  
Keywords:implant design; primary implant stability; mechanical trauma; strain development; crack formation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Fabrication of a Functional Plastic Scintillator  
A styrene-based plastic scintillator was fabricated by a polymerization method, and its performance was compared and analyzed with a PVT-based commercial scintillator (EJ-200). Plastics were manufactured by the process shown in Figure 2. The materials used in the manufacture of plastics were styrene, PPO (2,5-diphenyloxazole), POPOP (1,4 di[2-(5 phenyloxazolyl)]benzene), CdTe, and plastics with a diameter of 50 mm and a thickness of 30 mm. Styrene was used as the primary solvent, PPO was used as the primary fluorophore, and POPOP was used as the secondary fluorophore. Although the types of nanomaterials are various, CdTe was used. The contents (wt%) of organic dye and CdTe were selected due to previous studies [31]. The amounts of materials added to the styrene were PPO (0.4 wt%), POPOP (0.01 wt%), and nanomaterials (0.2 wt%). The plastic scintillator used styrene as a basic matrix material, and a nanomaterial having an emission wavelength range of 500-600 nm was selected. CdTe nanomaterial was mixed with the matrix material, and stirred at 60 \({}^{\circ}\)C. After the stirring process for about 2-3 h was completed, the weight suitable for the thickness of the plastic was measured. The sample was placed in a vial for polymerization and fine bubbles generated during stirring were removed. Fine bubbles can cause cracking due to internal stress during the polymerization process, and since it reduces optical properties. Bubble removal process of about 1-2 h is required. The de-bubbled sample is polymerized in a vacuum oven at a temperature of up to 120 \({}^{\circ}\)C. At this time, it is important to increase the temperature slowly, and if the temperature is increased rapidly, bubbles in the sample may be regenerated. The polymerization process takes about 70 h. Plastics that have gone through the polymerization process are finished after cutting and polishing the surface to increase the transmittance.  
Figure 2: Manufacturing process of a functional plastic scintillator.
## 2 UWA Signal Preprocessing  
The received UWA signal can be expressed as:  
\[r(t)=s(t)*h(t)+n(t) \tag{1}\]  
where \(s(t)\) represents the transmitted signal, \(h(t)\) represents the UWA channel, and \(n(t)\) represents channel noise. The SPWVD [22] employs double smoothing processing with time domain and frequency domain windows. Compared with short-time Fourier transform (STFT) and wavelet transform (WT), SPWVD can provide higher time-frequency resolution and more effective suppression of cross-term interference, thereby offering a clearer representation of the signal's TFR. The mathematical expression of SPWVD is given by:  
\[\text{SPWVD}_{x}(\text{t},\text{f})=\int\limits_{-\infty}^{+\infty}\int\limits_{- \infty}^{+\infty}g(v)h(\tau)e^{-j2\pi/\tau}r(t-v+\frac{\tau}{2})r^{*}(t-v+ \frac{\tau}{2})dvd\tau \tag{2}\]  
where \(g(v)\) and \(h(\tau)\) denote window functions, and * represents complex conjugation. The TFR obtained via SPWVD initially has a size of 4800 \(\times\) 4800, Among them, the time domain window type is the Hamming window, the window length is 480, and the overlap rate is 240; the frequency domain window type is the Hamming window, the window length is 1200, and the overlap length is 600. To remove irrelevant information, the diagram is first cropped to 1000 \(\times\) 4800 and then processed using bicubic interpolation to resize it to 100 \(\times\) 480. The final TFR of the UWA signal is shown in Figure 1.  
As shown in Figure 1, the TFR demonstrates strong capability in discriminating frequency-modulated signals (such as 2 FSK, 4 FSK, etc.) but performs poorly in distinguishing phase-modulated signals (such as 2 PSK, 4 PSK, etc.). Therefore, another feature needs to be extracted to further aid the model in improving its ability to extract phase information from phase-modulated signals. References [6; 23] demonstrate how HOS is applied to modulation recognition and provide related verification. Experimental results show that this method can effectively extract the phase information of phase-modulated signals. Therefore, this paper performs feature extraction of HOS for the signal following the above method. Figure 1: TFR of UWA signal. (a) 2 FSK; (b) 4 FSK; (c) 2 PSK; (d) 4 PSK; (e) OFDM; (f) DSSS; (g) 4 DSSS; (h) 9 DSSS. For a zero-mean complex stationary random process \(y(n)\), its second-order statistics are defined in two different ways based on the position of complex conjugation as:  
\[C_{20}=\mathrm{E}\Big{[}y^{2}(n)\Big{]}\text{ and }C_{21}=\mathrm{E}\Big{[}|y(n)|^{2} \Big{]} \tag{3}\]  
Similarly, the fourth-order statistic can be defined as:  
\[C_{40}=\mathrm{cum}(y(n),y(n),y(n),y(n)) \tag{4}\] \[C_{41}=\mathrm{cum}(y(n),y(n),y(n),y*(n))\] \[C_{42}=\mathrm{cum}(y(n),y(n),y*(n),y*(n))\]  
where * denotes complex conjugation.
# High-Precision Elastoplastic Four-Node Quadrilateral Shell Element
###### Abstract  
In order to enhance the accuracy of calculations for four-node quadrilateral shell elements, modifications have to be made to the computation of the membrane strain rate and transverse shear strain rate. For membrane strain rate calculations, the interpolation of the quadratic displacement of the nodes along the edges of the quadrilateral shell element is implemented, along with the introduction of a degree of freedom for rotation around the normal. Additionally, the elimination of the zero-energy mode of additional stiffness is achieved through a penalty function. When computing the transverse shear strain rate, the covariant component is expressed in the tensor of the natural coordinate system, followed by the elimination of shear self-locking in the element coordinate system. The strain-updating calculation and stress-updating calculation for the quadrilateral shell element, utilizing the model and J2 flow theory, respectively, are suitable for small deformations, geometric nonlinearity, and elastic-plastic problems. The improved quadrilateral shell element successfully undergoes in-plane and bending fragment inspections, demonstrating good reliability and calculation accuracy for the dynamic analysis of planar shells, curved shells, and twisted shells.  
quadrilateral shell element; membrane strain rate; transverse shear strain rate; penalty function; zero-energy mode; shear locking Article
## 4 Simulations and Performance Evaluations
### Heterogeneous Client Compensation
Imagine a circumstance where a pair of patrons are tasked with handling the preparation assortment of CIFAR10, with one individual managing the initial 25,000 samples and the other in command of the residual 25,000 samples. These two data groups do not overlap, thus utilizing the entire training set. Expanding this approach to five clients would require dividing the data set into five categories, each with 10,000 samples, with no overlaps or sample duplications. The second approach, however, was based on the random selection of samples from the dataset, allowing for a single data piece to be chosen several times within the same client. Moreover, data could be common to different clients, even if the sum of the samples from both clients  
Figure 9: Overfitting for different numbers of local epochs. exceeds the total number of samples from the training set. Thus, this method may yield a generally substandard model performance that aligns more with actual situations. It is essential to consider that performance may vary from simulation to simulation, even with the same conditions, due to the random variables of these tests. As a result, the tests involving random samples were repeated multiple times, and the results presented represent their average. The impact of random selection, with identical data amounts and epoch numbers, is evident in Figure 10, which compares two centralized learnings. The aforementioned phenomenon is similarly observed in a federated scenario, as illustrated in Figure 11 by comparing two basic FLs involving two clients, each containing 25,000 images. The likelihood of encompassing a greater portion of the dataset and subsequently enhancing the ultimate accuracy increases as the number of randomly selected training samples increases. As the quantity of randomly selected samples utilized for training increases, the probability of encompassing a more significant portion of the dataset also increases. This subsequently results in an enhancement of the final accuracy. This correlation is visually depicted in Figure 12, showcasing three FL evaluations conducted on two devices. The number of samples on each client has been progressively augmented from one simulation to the next, positively impacting the accuracy metric at the conclusion of each federated round. Figure 11: Federated Learning of 2 clients with and without random data selection. Figure 10: Centralized Learning with and without random data selection.
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure
#### 2.2.3 Utilizing Lyapunov Functions to Ensure System Stability  
The first step to solving the optimization problem is to form the following closed-loop system [16]:  
\[\dot{\hat{x}}(t)=f(\hat{x}(t).\ h(\hat{x}(t))) \tag{27}\]In Equation (27), the variables \(\hat{x}(\mathbf{t})\) represent the system state variables obtained from the nonlinear DFIG model and the control input \(\mathbf{u}=\mathbf{h}(\hat{\mathbf{x}}(\mathbf{t}))\) is determined through the Lyapunov function-based feedback control law. The aim of employing the Lyapunov function in the control scheme is to secure the stability of the DFIG system. Incorporating the Lyapunov function in the control aims to ensure the stability of the DFIG system. For this purpose, the chosen Lyapunov function is quadratic, and its structure is as follows:  
\[V(x)=X^{T}\;P\;X \tag{28}\]  
The Lyapunov function in this framework is represented by the vector \(X\). Additionally, within the structure of the Lyapunov function, the matrix \(P\) is a positive-definite matrix determined by solving the Riccati equation defined as follows:  
\[A^{T}P\;A-P-A^{T}P\;B\;\Big{(}B^{T}P\;B+R\Big{)}^{-1}B^{T}P\;A+Q=0 \tag{29}\]  
where \(A\) and \(B\) are the systematic matrices of the linear model of the DFIG system, and \(Q\) and \(R\) are the weighted matrices in the cost function structure. Following the establishment of the Lyapunov function, the next step in shaping a closed-loop system involves computing the control signal \(\mathbf{u}=\mathbf{h}(\hat{\mathbf{x}}(\mathbf{t}))\), defined as follows:  
\[h(x)=\left\{\begin{array}{cc}-\frac{L_{f}V+\sqrt{\left(L_{f}V\right)^{2}+ \left(L_{g}V\right)^{4}}}{L_{g}V}&if\;L_{g}V\
eq 0\\ 0&if\;L_{g}V=0\end{array}\right. \tag{30}\]  
where \(f\) and \(g\) are the nonlinear functions of the DFIG system, and the \(L\) operator is defined as follows:  
\[L_{f}V=\frac{\partial V(x)}{\partial x}f,L_{g}V=\frac{\partial V(x)}{\partial x }g \tag{31}\]  
As a result, the closed-loop system was formed using the nonlinear model data of the DFIG system.
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 1 Introduction
### Aim and Organization of the Paper  
In this paper, we address the issue of noise and outliers in GMMs for anomaly detection. This is pursued by introducing a novel entropy-based procedure for initializing a uniform noise component in the Gaussian mixture model.  
This paper is organized as follows. Section 2 provides an overview of the mixture-based approach that includes an additional noise component to improve the robustness of GMMs in the presence of contaminants. Next, a novel solution based on estimating the entropy of a GMM is presented and discussed. This solution can be used to initialize the noise component in the EM algorithm and mitigate the impact of noise and outliers on the performance of GMMs in anomaly detection tasks. Section 3 presents some examples of data analysis using both simulated and real datasets to illustrate the effectiveness of the proposed approach. The final section of this paper concludes by highlighting the key contributions of our work.