esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
## 1 Introduction
The fractional-order PID (FOPID) controller affords more flexibility in PID controller design and more robustness than the integer-order one [14; 15; 16; 17; 18; 19; 20; 21; 22]. The tuning rules of the FOPI/PID controller have been reported in some of the literature for single-input single-output (SISO) systems and multi-input multi-output (MIMO) processes. However, there are only a few studies that use fractional-order controllers for PCCS in the literature. In Ref. [9], [PERSON] and [PERSON] proposed fractional order controllers for the parallel cascade scheme including the Smith predictor for the large time delay. The design concept was based on IMC structure and an extra controller was also suggested to stabilize unstable, integrating primary processes. However, the authors used the Adam-Bashforth-Moulton (ABM) algorithm, which is quite different from most of the other works in the fractional-order control field that are used to simulate fractional-order systems. In accordance with the literature, the tuning methods of PI\({}^{\lambda}\)D\({}^{\mu}\) can be generally classified as analytic and heuristic ones [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. In fact, most of the analytic methods are often tuned by considering the objective functions, which are normally considered as phase and gain margin specifications as well as constraints using the maximum sensitivity  
Figure 1: Block diagram of parallel cascade control structure. function (\(M_{s}\)) for robustness [14; 15; 16; 17; 18; 19; 20; 21; 22]. On the other hand, heuristic methods including genetic algorithms (GA) and particle swarm optimization (PSO) are often adopted to find out suitable parameters for the fractional-order PID controller [23; 24; 25; 26]. In this work, our aim is to design an analytic method of a generalized FOPI controller for the primary loop of a PCCS with a time delay to enhance the performances of designed systems. It is mainly based on the concepts of fractional calculus and the IMC approach by using the frequency domain [18]. The tuning rule of the FOPI controller for primary processes can be derived directly without introducing any nonlinear objective function, and the proposed method can be applied for many typical process models including stable, unstable, and integrating primary processes.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 4 Fundamental Measurement Limits
### Particle Motion
#### 4.2.2 Brownian Motion  
Due to the Brownian motion of the molecules, the particle velocity fluctuates and a random error results for the flow velocity measurement [154]. The two-sided noise power spectral density \(S_{\text{p}}(f)\) of the velocity is [155; 156]:  
\[S_{\text{p}_{\text{p}}}(f)=\frac{2D}{1+\frac{f^{2}}{f_{\text{g}}^{2}}},\qquad \text{with }D=\frac{k_{\text{B}}T}{3\pi\eta d_{\text{p}}}=\frac{k_{\text{B}}T\tau_{ \text{p}}}{m_{\text{p}}}, \tag{14}\]  
with \(D\) as diffusion constant, which is connected with the Boltzmann constant \(k_{\text{B}}\), the temperature \(T\), as well as the particle diameter \(d_{\text{p}}\) or the particle mass \(m_{\text{p}}=\rho_{\text{p}}\frac{1}{6}\pi d_{\text{p}}^{3}=3\pi\eta d_{\text{ p}}\tau_{\text{p}}\). According to [PERSON]'s theorem, the maximal resulting standard deviation of the measured flow velocity reads:  
\[\max\left(\sigma_{\text{p}}\right)=\sqrt{\int\limits_{-\infty}^{+\infty}S_{ \text{p}_{\text{p}}}(f)\text{d}f}=\sqrt{\frac{D}{\tau_{\text{p}}}}=\sqrt{ \frac{k_{\text{B}}T}{m_{\text{p}}}}. \tag{15}\]  
Figure 8: Deviation of (**a**) the amplitude and (**b**) the phase of a cosinusoidal excitation due to the particle-following behavior of the particles listed in Table 1.  
For the particles listed in Table 1 in air flows at room temperature (\(\eta=15\times 10^{-6}\) Pas, \(\rho=1.2\) kg/m\({}^{3}\)), the uncertainty amounts to 3 mm/s and 6 mm/s, respectively. The order of magnitude of the uncertainty indicates that the Brownian particle motion is in particular of interest for the measurement of slow fluid motions or the usage of light particles. Actually, the resulting measurement uncertainty due to Brownian motion is even smaller, because the flow velocity measurement is averaged over a time period \(\Delta t\) and usually also over the velocity of \(N_{\text{p}}\) particles. For \(\Delta t\gg 2\tau_{\text{p}}\) and approximating the spectral noise density as band limited white noise, the measurement uncertainty limit then is:  
\[\sigma_{v}\approx\sqrt{\frac{2D}{N_{\text{p}}\Delta t}}. \tag{16}\]  
Note that the number of particles results from the particle concentration and the spatial resolution of the measurement system. Hence, the Brownian motion of the particles leads to a fundamental uncertainty relation between flow velocity, space and time.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 5 Justification of EOSSA
### Methods of Clustering the Signal Roots  
In step 4 of Algorithm 2, one can perform an elementary clustering, where the index clusters \(G_{j}\) consist of either a real root number or two complex-conjugate root numbers. For the algorithm to work in the case of close roots, a more sophisticated clustering is necessary.  
A mandatory requirement for clustering is that trend components should not be in the same cluster as non-trend components. Describe three possible ways of clustering in step 4.  
1. The aim is to obtain small distances between points \((\mathsf{Re}(\mu_{i}),|\mathsf{Im}(\mu_{i})|)\) within one cluster, while distances between points from different clusters should be large. Then _distance clustering_ is carried out as follows. Denote \(D=|\{\mu_{i}:\mathsf{Im}(\mu_{i})\geq 0\}|\). The \(k\)-means method is sequentially applied for \(k=1,\ldots,D\) to \(\{(\mathsf{Re}(\mu_{i}),|\mathsf{Im}(\mu_{i})|),\ i=1,\ldots,t\}\). At each step, the ratio of the within-cluster sum of squared distances between points to the total sum of squared distances is calculated. If this value is less than the parameter \(\delta\), the procedure stops, and we take the result of clustering with the current \(k\) as the desired clustering. Thus, the parameter of Algorithm 2 is \(\delta\), and the number of groups is included in the result of the algorithm. The result of the distance clustering is similar to elementary clustering; however, close roots will be in the same cluster.
2. Perform the _hierarchical clustering_ for pairs \((\mathsf{Re}(\mu_{i}),|\mathsf{Im}(\mu_{i})|)\) using Euclidean distance and complete linkage, with a given number of clusters \(k\). For trend extraction, \(k=2\). This version of clustering is implemented in the current version of Rssa [13]. For this type of clustering, trend components can be mixed with non-trend ones.
3. For the special case of clustering into two clusters, when all trend components must fall into one group, a special way of clustering (_frequency clustering_) is proposed. Since the concept of trend is based on its low-frequency behaviour, i.e., on the fact that its periodogram is mainly concentrated in a given frequency range \([0,\omega_{0}]\), it is suggested that the roots \(\mu=\rho e^{2\pi\omega i}\) with \(|\omega|\leq\omega_{0}\) are included in the trend cluster.  
In the hierarchical (with \(k=2\)) and frequency clustering methods, clustering is used instead of grouping at Trend Identification step of Algorithm 1. Since automatic grouping appears to be more stable, we will further use distance clustering.
# Evaluation of Blue(r)m Mouthwash vs. Chlorhexidine
###### Abstract  
Chlorhexidine is widely regarded as a gold standard antiplaque agent, and it is used commonly in periodontal patients. However, due to its side effects, a suitable alternative agent has been searched for several years. The present study compared the efficacy of a novel, oxygen-releasing, natural mouthwash Blue(r)m (Bluem Europe, Zwolle, The Netherlands) with chlorhexidine in patients with gingivitis. A total of 50 patients were selected and randomly divided into two groups who received a baseline scaling by a trained periodontitis. The test group received Blue(r)m mouthwash as an adjunct for 2 weeks, whereas the control group received chlorhexidine mouthwash for 2 weeks. The primary outcomes of the Full-Mouth Gingival Index, Full-Mouth Plaque Index, and a percentage of the bleeding sites were recorded at the baseline. The same parameters were recorded after 2 weeks and 1 month from the baseline. Both the study and control groups exhibited equal efficacy in terms of reduction in the gingival index and bleeding percentage, both at 2 weeks and 1 month post scaling. However, the Blue(r)m group displayed statistically significant reduction in plaque index at 1 month as compared with chlorhexidine. As per the objectives of the study, it can be concluded that Blue(r)m reported a better antiplaque efficacy compared with chlorhexidine.  
Keywords:gingivitis; chlorhexidine; topical oxygen therapy; mouthwash Article
## 3 Results
On the other hand, NEX8 showed 0.9 \(\pm\) 0.03 compared to the input image and 0.96 \(\pm\) 0.01 (\(p\) = 0.14) compared to the reconstructed image, which was not significantly different (Figure 4B). The MRI imaging times used in this study were 3 min 56 s for NEX2, 7 min 53 s for NEX4, 15 min 47 s for NEX8, and 23 min 40 s for NEX12. The reconstruction time by RDN was 2.11 s per case; therefore, the imaging time was successfully reduced by 83% for NEX2, 67% for NEX4, and 33% for NEX8. The above SNR results are summarized in Table 1, and the PSNR and SSIM results are in Table 2. The values in the table are the mean and SD of five test data animals for each NEX. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNR** & **Cortex** & **White Matter** & **Hippocampus** & **Thalamus** \\ \hline NEX2 & 10.38 \(\pm\) 0.82 & 9.06 \(\pm\) 2.81 & 11.37 \(\pm\) 2.47 & 8.98 \(\pm\) 0.64 \\ NEX4 & 14.95 \(\pm\) 1.63 & 10.7 \(\pm\) 3.24 & 13.12 \(\pm\) 2.51 & 11.79 \(\pm\) 0.96 \\ NEX8 & 19.69 \(\pm\) 4.68 & 12.29 \(\pm\) 3.81 & 19.82 \(\pm\) 5.91 & 15.29 \(\pm\) 1.25 \\ NEX12 & 19.59 \(\pm\) 7.86 & 15.54 \(\pm\) 5.59 & 22.74 \(\pm\) 7.11 & 16.98 \(\pm\) 1.49 \\ NEX2 to12 & 32.08 \(\pm\) 6.09 & 24.86 \(\pm\) 11.79 & 41.49 \(\pm\) 19.17 & 18.01 \(\pm\) 2.36 \\ NEX4 to12 & 45.03 \(\pm\) 14.94 & 23.85 \(\pm\) 13.92 & 59.08 \(\pm\) 51.19 & 22.54 \(\pm\) 7.68 \\ NEX8 to12 & 46.7 \(\pm\) 120.00 & 16.78 \(\pm\) 7.60 & 53.71 \(\pm\) 25.86 & 24.55 \(\pm\) 4.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table summarizes SNR and SD for the cortex, white matter, hippocampus, and thalamus. Figure 4: Graphs of PSNR (**A**) and SSIM (**B**). The right side of each NEX was the value obtained by comparing the reconstructed image to the teacher image, and the left side was the value obtained by comparing the input image to the teacher image. The PSNR was significantly increased at NEX2, and SSIM showed a significant increase at NEX2 and 4 (*: \(p\)! 0.05, **: \(p\)! 0.01, ***: \(p\)! 0.001). PSNR, peak signal-to-noise ratio; SSIM, structural similarity index; NEX, number of excitations; n.s, not significant.
## 4 Discussion  
The proposed DNN model with 3 hidden layers and 64 hidden neurons trained with a batch size of 15 and epoch size of 250 is used to predict the zero-crossing point class in real time as an optimal model. The proposed DNN model is validated by comparing it with some existing models, such as decision tree [26] and support vector machine (SVM) [27], as shown in Figure 15. From Figure 15, it has been observed that the training accuracy of the proposed model is a bit low if only either noisy or THD distorted signals are considered, but if the signal is highly distorted due to both noise and harmonics then the proposed model learning is good with better training accuracy. However, the proposed model is more generalized in comparison with decision tree and support vector machine, with better testing accuracy of 97.97%, 98.87%, 100% and 99.5% on Dataset-1, Dataset-2, Dataset-3 and Dataset-4, respectively, and that is required for real-time deployment.  
Figure 14: Training and testing performance of DNN model with respect to various number of epochs. (**a**) Noise level: 10 to 50 (Dataset-1). (**b**) THD level: 10 to 50 (Dataset-2). (**c**) THD level: 50, noise level: 10 to 40 (Dataset-3). (**d**) Noise level: 5 to 20 (Dataset-4).
## 4 Enhanced LMS Methods with IMC
### Novel Adaptive Filtering Algorithm Based on the IMC 2: Recursive Method
#### 4.2.2 Numerical Validation
Similar trends to the AM signal case are observed for up to 0.45 s. In the transient region, for up to around 0.05 s, the IM-LMS 2 method shows a much better tracking performance but a slower convergence than the FX-LMS, for around 0.4 s (0.05 \(\leq t\leq\) 0.45) in the steady-state region. However, after that point, the peak-to-peak value of the estimation error with the IM-LMS 2 is about three times better than that of FX-LMS. Figure 11 compares the predicted spectral contents of the uncontrolled FM signal and controlled FM signal for both of the methods. Although a broadband reduction cannot be achieved with either FX-LMS or IM-LMS 2, they show significant reductions at primary peaks (\(\omega_{of}\), \(\omega_{of}-n\omega_{of}\), \(\omega_{cf}+n\omega_{of}\)). The amplitude reduction levels of IM-LMS 2 at primary peaks are higher than those of FX-LMS, except for at \(\omega_{of}\), while FX-LMS shows a severe spillover at a higher frequency. In addition, the IM-LMS 2 method shows considerable attenuation at around 50 Hz, which is the natural frequency of the  
Figure 10: Control of a frequency-modulated (FM) signal. (**a**) Time-domain tracking with LMS and internal model LMS 2 methods; (**b**) estimation error. target plant, whereas FX-LMS demonstrates similar amplitude levels around this frequency. Thus, the tracking performance of IM-LMS 2 is improved when increasing the complexity and frequency components that were to be controlled in the output of the disturbance plant, which should be tracked.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Rover** & \begin{tabular}{c} **Country** \\ **(Agency)** \\ \end{tabular} & **Status** & **Year** & **Notes** \\ \hline \multirow{2}{*}{Sojourner} & USA (NASA) & Inactive & 1997 & \begin{tabular}{l} Part of Mars Pathfinder mission; \\ operated for 85 days. \\ \end{tabular} \\ \hline \multirow{2}{*}{Spirit} & USA (NASA) & Inactive & 2004 & \begin{tabular}{l} Last contact in 2010; explored \\ Gusev Crater. \\ \end{tabular} \\ \hline \multirow{2}{*}{Opportunity} & USA (NASA) & Inactive & 2004 & \begin{tabular}{l} Lost in a 2018 dust storm after \\ nearly 15 years. \\ \end{tabular} \\ \hline \multirow{2}{*}{Curiosity} & USA (NASA) & Active & 2012 & \begin{tabular}{l} Nuclear-powered; ongoing science \\ in Gale Crater. \\ \end{tabular} \\ \hline \multirow{2}{*}{Perseverance} & USA (NASA) & Active & 2021 & \begin{tabular}{l} Operating in Jezero Crater; \\ collecting rock samples. \\ \end{tabular} \\ \hline \multirow{2}{*}{Zhurong} & China (CNSA) & Inactive & 2021 & \begin{tabular}{l} Entered hibernation in 2022; \\ presumed inactive. \\ \end{tabular} \\ \hline \multirow{2}{*}{\begin{tabular}{l} Rosalind \\ Franklin \\ \end{tabular} } & Europe (ESA) & Planned & 2030 & \begin{tabular}{l} Delayed ExoMars rover; will \\ search for signs of past life. \\ \end{tabular} \\ \hline \multirow{2}{*}{\begin{tabular}{l} Tianwen-3 \\ Helicopters \\ \end{tabular} } & \multirow{2}{*}{China (CNSA)} & Planned & 2030 & Mars sample return mission. \\ \cline{2-2} \cline{4-4}  & & & & \\ \hline \multirow{2}{*}{IDEX (IDEFIX)} & \begin{tabular}{l} France/Germany \\ (CNES/DLR) \\ \end{tabular} & Planned & 2027 & \
\begin{tabular}{l} Without Mars’ moon Phobos; \\ part of MMX mission. \\ \end{tabular} \\ \hline \multirow{2}{*}{Mangalyaan-2} & India (ISRO) & Planned & 2026 &
\begin{tabular}{l} Orbiter, lander, rover, and \\ helicopter; direct Mars entry with \\ sky crane landing. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of active, inactive, and planned Mars rover missions. to complete their tasks as effectively and efficiently as possible. As a key contribution to the state of the art, we provide an overview of the research challenges involved with deploying such vehicle swarms on Mars. Inspired by terrestrial-based advancements, we suggest technological solutions for sustainable and efficient robotic exploration of the Red Planet. The remainder of this paper is visually outlined in Figure 1 and structured as follows. Section 2 describes the expected application scenario in which rovers, drones, and satellites all work together for effective and efficient Mars exploration. Following the key components of NASA's Mars infrastructure [10], Sections 3-5 discuss novel technologies and paradigms for energy management, communication, and collaborative task scheduling, respectively. The way forward is discussed in Sections 6 and 7 concludes the paper.
## 6 Simulation Results
### Robustness against Common Signal-Processing Effects
#### 6.3.1 Robustness in Noisy Environments  
Bio-signals can be affected by noise during transmission between medical services in the IoMTs ecosystem. Therefore, it is necessary to test the robustness of the proposed scheme in noisy environments. For this purpose, we used an ECG test signal named \"Record_112\". This signal was then affected by a \"Gaussian\" noise as below [59]:  
\[S_{N}=S_{O}+kG \tag{26}\]  
where \(S_{O}\) denotes the original bio-signal, \(S_{N}\) is the signal contaminated by \"Gaussian\" noise noted G, and k represents the noise strength.  
The test signal was contaminated by Gaussian noise using different values of k. Then, the proposed method was used to construct the zero-watermark image and then to check the signal copyrights according to the diagrams presented in Figures 4 and 9, respectively. In Figure 16, the results of the current test are displayed, and Figure 17 shows an example of 512-bit sequences of the encrypted feature vector (\(EI_{1}\)) and the extracted feature vector (\(EI_{1}^{*}\)) from the original signal and those contaminated by \"Gaussian\" noise. These binary sequences were used in the BER calculation for an ECG signal contaminated by \"Gaussian\" noise with \(k=0.06\). From Figure 16, we notice that the BER value increased with the increase in k-strength. However, the BER values remained low and tended to be zero, indicating that our scheme is resistant in noisy environments. This result can be explained by the fact that orthogonal moment is known by the property of noise resistance, especially at lower orders.
## 6 Summary and Conclusions  
TANSO, onboard the GOSAT satellite launched on January 23, 2009, is the first and only orbiting greenhouse gas observing satellite. Although SCIAMACHY, onboard ENVISAT, has also been able to measure greenhouse gas concentrations since 2002, it is not specifically designed for studying GHG, and the data require calibration. Approximately 300 stations are far apart from accurately global GHG concentration measure. The present study demonstrates that MODIS derived indices have great potential for CO\({}_{2}\) concentration measurement on regional as well as global scales.  
The assessment of CO\({}_{2}\) concentration from remote sensing data has been conceived and attempted in the present work. First, we examined the relationships between the MODIS derived indices and CO\({}_{2}\) concentration on each continent and found that CO\({}_{2}\) concentration is dominated by different factors in different continents. LST, NDVI and GPP are the main factors that affect the CO\({}_{2}\) concentration and their relative contributions are different in different areas. Second, we developed a TVP model for CO\({}_{2}\) concentration measurement based on the relationships between the MODIS derived indices and CO\({}_{2}\) concentration and got satisfactory results. The accuracy of TVP model is different on different continents: the highest correlation coefficient between the modeled and observed value is in Eurasia and South America (R\({}^{2}\) = 0.75, RMSE = 1.16 and 1.17, respectively); the lowest value is found in Australia (RMSE = 0.73). Compared with the TANSO XCO\({}_{2}\), we found that the accuracy across the study area is between -2.56-3.14 ppm. From the results we also found that the TVP model underestimates high CO\({}_{2}\) concentration and overestimates low values.  
Increasing the accuracy of CO\({}_{2}\) concentration that derived from remote sensing data remains beyond current capabilities. The TVP model that proposed in this study also contains some uncertainties (described in Section 5.3). With the releasing of CO\({}_{2}\) concentration data by GOSAT project, validated data at different time scales should be used in future studies.  
This study was supported by Scientific Research (B), 23405005 (PI: [PERSON], Hokkaido University, Japan) and National Natural Science Foundation of China (41201159, Study on the effect mechanism of commercial center pattern on traffic carbon emissions in Shenyang city. PI: [PERSON], Chinese Academy of Sciences, China). We also thank the GOSAT Project of Japan, NASA and ESA for providing data that were used in this study.
# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 3 Method  
### Notation Explanation  
In the observation and reconstruction algorithm based on DCSGM, some symbols are defined and their meanings explained, as shown in Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meanings** \\ \hline \(x\) & the original signal \\ \hline \(\Phi\) & the observation model \\ \hline \(y\) & the observed vector \\ \hline \(\hat{x}\) & the generated signal \\ \hline \(\mathbf{G}_{\theta}\) & the generative model \\ \hline \(z\) & the input of generative model from normalized the observed vector \\ \hline \(\hat{z}\) & the input that is optimized \\ \hline \(\hat{y}\) & the observed vector that is optimized \\ \hline \(L_{2}\) & the Euclidean norm \\ \hline \(\mathcal{L}_{G}\) & the loss of generative model \\ \hline \(\mathcal{L}_{\Phi}\) & the loss of observation model \\ \hline \(\alpha\) & the inner loop optimization rate \\ \hline \(\beta\) & the outer loop optimization rate \\ \hline \(\theta\) & the parameter of model \\ \hline T & the inner loop iteration \\ \hline N & the outer loop iteration \\ \hline \(Loss_{\mathit{linear\_loop}}\) & the loss of inner loop \\ \hline \(Loss_{\mathit{sqrt\_loop}}\) & the loss of outer loop \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and their meanings.