esa-sceva/satcom-chunk-collection huggingface dataset:
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 5 Simulations
### Control Performance Comparison  
The control performance of the proposed method is compared with the ordinary MPC-based method using constant horizon parameters (\(t_{\text{c}}\) = 30 s, \(t_{\text{p}}\) = 60 s). The fault scenario, system parameter and other controller parameter settings are the same as case B. The control result is shown in Figure 16. All the voltages meet the constraint requirements after 4 optimizations are implemented. The performance comparison is listed in Table 5. An index of average voltage deviation of all the load buses is introduced as:  
\[\Delta V_{\text{offset}}=\frac{1}{19}\sum_{i=1}^{19}\left(\frac{1}{\Delta t} \int_{t_{1}}^{t_{1}+\Delta t}\big{|}V_{\tau}-\hat{V}_{i}(t)\big{|}dt\right) \tag{23}\]  
Figure 16: Voltage evolution with control in case B using constant horizon parameters.  
Figure 15: Voltage evolution using AH-MPC with both disturbance and model mismatch occurred during the control process.  
The integration time \(\Delta t=300\) s. Smaller value of \(\Delta V_{\text{offset}}\) indicates faster voltage recovery speed and better control performance.  
The comparison results of Figures 12 and 16 and Table 5 show that both methods are able to stabilize the system voltage within a desire range, and the AH-MPC-based method outperforms MPC-based scheme with constant horizon in computation effort and voltage recovery speed.  
The proposed scheme applies decreasing prediction parameters and control horizon. When \(n=1\), both methods applies the same horizon parameters and cost almost the same computation time. With the increase of \(n\), the time consuming per optimization of the AH-MPC-based scheme is remarkably reduced. This is mainly because the numerical simulation needed in the model prediction is implemented within a shorter prediction horizon, and the control sequence consists of less  
The measurement collection and optimization starts at each sampling point, and the control decisions are applied to the practical system a sampling interval (\(t_{\rm s}=10\) s) later, which offers sufficient time for the telecommunication and computation. In a real-life application, it is confidently expected that the computation time can be further reduced:  
* The simulations were carried out on a personal computer with the Matlab/Simulink-based tool. Both the hardware and software configuration can be significantly improved in a real-life application [27].
* The optimization problem can be solved concentrating on areas close to the disturbance with a simple representation used for other areas [21].
## 4 Enhancement of Brillouin Stokes Signal (I): Induction of Stimulated Scattering
### Motivation and Principle  
The Brillouin scattering in PFGI-POFs observed in the previous experimental setup [27,28] was not stimulated but spontaneous, because the Brillouin threshold of PFGI-POFs was estimated to be as high as 24 W, as already described in Section 3. Consequently, the power of the reflected Stokes light was so low that we had to face the following four problems: (1) a PFGI-POF longer than several meters was required, (2) the polarization state had to be optimized, (3) averaging of the spectral data had to be conducted several tens of times, and (4) signal-to-noise (S/N) ratio of the BGS was quite low, even when (1), (2), and (3) were cleared. In order to implement practical Brillouin sensors and other systems using PFGI-POFs, these problems need to be resolved.  
One solution is to employ so-called pump-probe technique. As described in Section 2, when the pump power is higher than the Brillouin threshold, a transition from SpBS to SBS occurs, leading to drastic enhancement of the Stokes light. On the other hand, when probe light at the same frequency as the Stokes light is also injected into the other end of the FUT, SBS is induced even when the power of the pump light is much lower than the Brillouin threshold, because the probe light itself acts as a seed of stimulated scattering [38]. This technique, called pump-probe technique, has been used to develop Brillouin systems with high S/N ratio [17].
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 1 Introduction  
Small-angle scattering (SAS) is widely used to investigate inhomogeneities in many different types of materials. It is a versatile technique providing quantitative results in a non-destructive way. Tens of thousands of studies have been published over the last few years with results obtained by SAS in many different scientific fields, signalling significant interest from the scientific community. During the first period of SAS applications, experimental data were not calibrated, and users mainly limited their analysis to the calculation of the gyration radius and molecular weight. Polydispersity of the inhomogeneities that create forward scattering was rarely investigated due to the complications of the required process. In the decades to follow, the analysis of SAS data became more sophisticated and more complete results are now obtained from SAS data, including many corrections of experimental smearings that may affect the experimental data. Nevertheless, SAS is widely used, and the inhomogeneities are rarely monodisperse, especially in the case of precipitates in alloys, in gas bubbles in irradiated materials, and in many other materials, where polydispersion affects the mechanical properties of the materials. An exhaustive introduction to the application of SAS to materials science can be found in [1]. Therefore, efficient polydispersion analyses have been implemented to obtain information on the size distribution function (SDF) of the inhomogeneities. In all the SAS manuscripts published in the last few years, around 10% use the lognormal SDF for its simple mathematical handling and because it serves to mimic the polydispersion of inhomogeneities in many different materials. It has been successfully employed in a very large number of diverse applications. Among them are the investigations on composite particles of ferrofluids [2, 3], on managing steel [4], on the structure of ferritic alloys [5], on the \(a\)-Fe-Nb-C system [6], on Ni-Fe superalloys [7], on dispersed silver nanoparticles, surrounded by a stabilizing polymeric shell of poly (acrylic acid) [8], on irradiated Fe-Cu-Mn alloys [9], on Mg-Zn-Al(-Ca) alloys [10], on microlovided steels [11], on the mesoscopic metallic system [12], on gold nanoparticles [13], on the Al-Zn-Mg-Cu alloy [14], on non-porous nanoparticles [15], oncolloidal ThO\({}_{2}\) sols [16], on aerosol nanoparticles [17; 18], and on Ni/SiO\({}_{2}\) catalysts [19]. As shown above, the characterization of the SDF of nanoparticles is important in many areas, and a potential field of application could be, for instance, the investigation of metallic nanoparticles for catalysis applications [20; 21]  
The applications of the lognormal SDF have been also extensively discussed in a large number of methodological manuscripts, among which is [22], and used in software developed to support the scientific community in the analysis of SAS data, including SASFIT [23], FLAC [24], and IRENA [25]; a comprehensive list of the software packages was compiled by [26].
# Elastodontic Therapy with Oral Bioactivator Devices: A Review
## 4 Elastodontic Appliances
### Advantages and Limits  
Bioactivators are an evolution of elastodontic therapy; they simultaneously harmonise, maxilla, and mandible, they work tridimensionally, and they normalise the inclination and rotation of occlusal planes to obtain a correct function and a good balance of the masticatory system [5].  
Many bioactivators' advantages derive from their structure and elastomeric material.  
The material of the appliances is made of a polymer/elastomer mixture, which has these characteristics: very elastic, comfortable, thermoactivable, and adaptable to different arch shapes.  
It is not predisposed to caries, it does not cause lesions to the periodontium, and it does not cause root resorption (still scarce literature on this subject).  
Possible interference caused by the flanges can be modified with instruments suitable for heat. The device can also be expanded by immersing it in the water at about 70 \({}^{\circ}\)C  
Figure 6: Measures of bioactivators for mixed and permanent dentition.  
Figure 7: Measures of bioactivators for deciduous dentition.  
for 30 s and, to fix it in the new shape, immersing it in cold water. The adjustments and shortenings are made with a rubber brush, and shine is restored with a flame tool [1].  
This bioactivator material, together with the myofunctional exercises, restores the balance between the oral and lingual musculature. Normal muscular function is restored by proper relation and balance between skeletal and dentoalveolar components [1; 12; 22].  
A study at the University of Aquila analysed two Class II bioactivators of two different marks on a group of adolescent patients. Both techniques studied improved the degree of OJ and OB between T0 and T1, even 1 year after the start of therapy [23].  
Various bioactivator models and types suit several kinds of arches, vertical planes, and faces. No construction bite is needed, so it allows us to avoid the impression manoeuvre, which is not well-tolerated. They are cheap for the patient, and the therapy time is short.  
Limitations: it does not rotate canines, molars, and premolars; it does not correct eighth disodontiasis and mesialised teeth; and it cannot be used in patients with mobile dental elements.  
A common problem is excessive salivation, which gradually reduces after a few days [4]. As for all removable devices, the patient's compliance is necessary. Instructing the patient to keep the device in the mouth, especially the oral respirators, is essential to not spitting it out at night.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Modes Control
#### 3.2.3 Constant Power Charging  
The constant power charging mode adjusts the current and voltage to uphold a consistent power level throughout the charging procedure. This mode optimizes the charging speed and ensures an efficient utilization of available resources by dynamically adapting the charging conditions based on the battery's status and temperature [128]. When the battery's SoC is low, it enables quicker charging rates and automatically lowers the charging power when the battery gets close to capacity. Dynamic power control also enables load management and helps balance the power demand and supply within the charging infrastructure.
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
## 3 Simulation Results and Performance Analysis
### The BER performance in the AWGN channel  
In this section, the conventional DCSK using logistic map [21], NR-DCSK [23], RM-DCSK [27], HE-DCSK [28], PS-DCSK [30], CDSK [31], and the proposed method with the same transmission rate are compared in the AWGN channel with different bit energy to noise power spectral density (\(E_{b}/N_{0}\)) ratios. Figure 5 shows BER for the different methods versus \(E_{b}/N_{0}\) with the data transmission rate \(R_{b}=1000\) bits/s, where the spreading gain \(L=150\), \(n_{samp}=10\), \(N_{s}=15\), and the frequency of the sinusoidal signal is \(f=50\) kHz. The RM-DCSK [27], HE-DCSK [28], PS-DCSK [30], and CDSK [31] uses the logistic map as the chaotic sequences, and the spreading gain is also \(L=150\) in order to achieve the same bit transmission rate. The HE-DCSK and RM-DCSK have lower BER and PS-DCSK has similar BER as compared to the conventional DCSK. CDSK shows the worst BER performance because of the interactions between the reference signal and the information bearing signal, which introduces an inter-symbol interference in the demodulation process at the receiver. NR-DCSK (duplicated times \(p=5\)) has better BER compared to the proposed method. However, the data transmission rate of the NR-DCSK is half of that of the proposed method using \(p\) times bandwidth with respect to the proposed method. In particular, similar system structure as the PS-DCSK is used in the proposed method, except for the special first order hybrid system and the corresponding matched filter in our proposed method. Consequently, our proposed method outperforms all other methods (except for the NR-DCSK). The reason is that the hybrid system and the corresponding matched filter are used to obtain maximal SNR at the receiver. Moreover, the correlator of the proposed method uses the optimal SNR points rather than the entire filter output to perform correlation to decode the bit information. The result shows that our proposed method has slightly better BER as compared to the decoding method using the entire filter output [12; 36]. This is because the multiple points with maximal SNR convolution operation further reduces the effect of noise.  
Figure 4: Block diagram of the proposed scheme.
## 3 Potential Security Implications of IoT in 6G Environment
### 6G Usage Scenario-Related Vulnerabilities
#### 3.2.3 Massive Communication  
Massive communication in 6G, characterized by the interconnection of a vast number of IoT devices across diverse sectors such as smart cities, healthcare, and agriculture, presents considerable security challenges. The extensive scale of connected devices not only expands the potential attack surface but also complicates the management and security of the network [5]. Many of these IoT devices, particularly those designed for low-power or extended battery life, often lack the computational capabilities necessary to implement robust security measures, rendering them vulnerable to unauthorized data access and device manipulation [45]. Furthermore, varying network conditions--ranging from fluctuating data rates to differing mobility requirements--add another layer of complexity to the enforcement of security protocols. As these devices transition between environments characterized by low bandwidth and high-speed connectivity, inconsistencies in network performance may create critical vulnerabilities that attackers can exploit [47].
## 2 Mathematical Model of the Wheel Loader
### Tribological Parameters of Revolute Joints  
The members of the loader manipulator's kinematic chain and drive mechanisms are linked through fifth-class revolute joints, forming kinematic pairs. The general structure of the revolute joint elements (Figure 2) consists of a clevis pin 1 and a sliding sleeve 2 which in the kinematic pair of members \(L_{i}\)-\(L_{i+1}\) form a sliding contact joint with an appropriate clearance fit. The clevis pin is firmly attached to one member of the kinematic pair, and the sliding sleeve to the other, while the sliding sleeve can have relative movement in relation to the pin and vice versa.  
Functional parameters of joints refer to the relative velocities and the loads acting on the joints during a manipulation task. Structural parameters of joints refer to the geometric dimensions of the joint elements, while tribological parameters relate to the moments of frictional resistance and the forces required to overcome this resistance within joints.  
During manipulation tasks, tribological phenomena--friction and wear between joint elements--occur due to relative motion and loading of kinematic chain members and  
Figure 2: Joint Forces and Moments in the Loader Manipulator.  
mechanisms. Friction in the joints increases resistance, resulting in power loss and reduced energy efficiency of the manipulator's drive mechanisms.  
Wear of the joint elements increases the clearances in the loose fit of the sliding joint. As a result, when the direction of relative movement between the members of the kinematic pair changes, dynamic shocks occur, leading to increased stress and reduced service life of the components in the kinematic chain of the manipulator mechanism.  
It is characteristic that during certain operations of loader manipulation tasks, the members of the kinematic chain and mechanisms, including joint elements, have significant relative motion leading to increased wear and dynamic loading [33; 34]. Previous analyses have shown that during the material loading and unloading operations, the total range of relative motion of the bucket in both directions is approximately \(\theta_{40}\cong 100^{\circ}\), while the total range of relative motion of the arm during the transport of material from the loading to the unloading position and its return to a new loading position is approximately \(\theta_{40}\cong 90^{\circ}\). The range of relative motion of the two-arm lever and coupling rod is smaller compared to that of the arm and bucket.  
These significant ranges of relative motion, particularly during the material loading phase, are accompanied by the highest forces acting on the manipulator's joints. During the loading operation, the manipulator must overcome the resistance of the material, which results in large reaction forces transmitted through the kinematic chain. The joints located near the end-effector, such as those of the bucket and the arm, are especially exposed to these loads.
# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 4 Discussion
### Inference Models and Results
The ANN's architecture and training process enable it to model the nonlinear interactions between F, n, P, and I effectively within the given dataset. Its high accuracy confirms it \"understands\" these relationships. However, the absence of variables like real cutting parameters means it does not capture the full spectrum of influences and interactions in actual turning, limiting its practical scope. Expanding the experiment to a factorial DOE--incorporating additional factors--would generate a richer dataset, enabling the ANN to \"understand\" the process holistically and yield more robust, generalizable results. The current forward and inverse ANN models successfully predict machining parameters with high accuracy, demonstrating their suitability for adaptive recalibration. However, to fully realize the objective of a self-adaptive system, further enhancements are needed to enable the automatic detection of process drift and real-time model adjustments. By integrating continuous monitoring, incremental learning, and dynamic model selection, the system can evolve towards a fully adaptive recalibration framework, ensuring robust and optimized performance even as machining conditions change over time.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
## 3 Simulation Results
### State of Charge Estimation for LITHIUM-Ion Batteries
For the system with less dynamic changes, the proposed constrained method can obtain more performance improvement than the unconstrained method (please note that the HWFET driving cycle in Figure 4a has more stationary current than the USABC driving cycle in Figure 4b, and the maximum SOC estimation error of the two methods decreases from \(2.46\%\) to \(1.60\%\) for the HWFET cycle, while it decreases from \(3.61\%\) to \(3.55\%\) for the USABC cycle, which is much less). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(K_{\theta}\) & \(R\) & \(K_{\mathbf{I}}\) & \(K_{2}\) & \(K_{3}\) & \(K_{\mathbf{4}}\) \\ \hline
3.3738 & \(-0.0050\) & \(0.0000\) & \(-0.1197\) & \(0.0935\) & \(-0.0198\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameters for the tested battery. Figure 4: Simulated currents of the battery under different driving cycles: (**a**) the Highway Fuel Economy Driving Schedule (HWFET) driving cycle; (**b**) the United States Advanced Battery Consortium (USABC) driving cycle.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 6 Conclusions  
In this paper, we systematically investigated eight de-speckling algorithms, the WTF, GLF, WF, LF, BF, PMD, WJD, and TV, for BUS image processing. Among the filtering algorithms in different categories, the VMR and LRD are chosen to evaluate key performance, i.e., the degree of smoothness in the homogeneous region and the clarity of detailed features in the edge region. We then demonstrate the filtering effects of these filters with respect to tumor region segmentation accuracy. Finally, referring to the experimental results, a general conclusion is drawn as the following.  
In summary, speckle suppression filtering is very important when processing ultrasound images of breast tumors. In this study, it is shown that the eight typical filters all have their merits. The images filtered by the BF present a good visual effect and it is more likely to obtain good segmentation results. Additionally, it is relatively fast, and its modified model is supposed to be acceptable for real-time processing. The LF can obtain good visual result, but fluctuates significantly. The PMD is not only fast but also good at edge preservation. However, it is not suitable for being applied to the tumor region segmentation. Ultrasound images with breast tumors processed by the WJD and TV can achieve splendid segmentation accuracies. However, the searching of similar pixels in the WJD and TV will bring a heavy burden to its computation. Taking speed into account, the WJD and TV would not be good choices for de-speckling BUS images.  
This work is supported by National Natural Science Funds of China (No. 61271314, 61372007, 61401286, and 61571193), the International Cooperation Project of Science and Technology of Guangdong Province (No. 2014A050503020) and Guangzhou Key Lab of Body Data Science (No. 201605030011). [PERSON] envisioned and coordinated the invited paper preparation; [PERSON] contributed to manuscript preparation, most of the experiments, and partial writing; [PERSON] contributed to manuscript writing and paper revision including some of the experiments and experimental analysis. [PERSON] and [PERSON] have made equal contribution to this paper. The authors declare no conflict of interest. The authors declare no conflict of interest.
## 3 Improved SSA Based on Halton Sequence and LX  
### Population Initialization Based on the Halton Sequence  
The Halton sequence [30; 31; 32] is a common multidimensional low-discrepancy sequence with an optimal order difference of \(N^{-1}\left(\log(N)\right)^{d}\), where \(d\) is the dimension. As shown in Figure 1, the solutions generated with the Halton sequence are more uniformly distributed in space than those generated using random initialization.  
The definition of the Halton sequence is based on the inverse radical function, and its defining function is expressed as follows:  
\[\phi_{p}(n)=\frac{b_{0}}{p}+\frac{b_{1}}{p^{2}}+\cdots+\frac{b_{m}}{p^{m+1}} \tag{6}\]  
where \(p\) is a prime number, and \(b_{k}\) is the \(k\)-th digit of n in the base-\(b\) expansion (\(0\leq k\leq m\)).  
\[n=b_{0}+b_{1}p+\cdots+b_{m}p^{m} \tag{7}\]  
The Halton sequence in d dimensions can be represented with the following formula:  
\[HaltonSet_{n}=\left(\phi_{p_{1}}(n),\phi_{p_{2}}(n),\cdots,\phi_{p_{d}}(n)\right) \tag{8}\]  
The initial positions for the population can be generated using the following formula:  
\[x_{i,j}=lb_{j}+HaltonSet_{n}\times\left(ub_{j}-lb_{j}\right) \tag{9}\]  
where \(lb_{j}\) and \(ub_{j}\) denote the lower and upper limits of the positions of the sparrow population in the \(j\)-th dimension (\(1\leq i\leq n,1\leq j\leq d\)).