esa-sceva/satcom-chunk-collection huggingface dataset:
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 2 Types of Wastewater Treatment Plants Where _Thiothrix_ Morphotype Can Occur
### Activated Sludge  
Activated sludge is a community of microorganisms contributing to the biological treatment of wastewater. Although filamentous bacteria are usually present in activated sludge in relatively low numbers, they can cause negative phenomena. Under certain conditions, they proliferate to such an extent that they significantly affect the performance of the wastewater treatment plant, causing sludge bulking and foaming [35]. Wastewater treatment plants in the food [34; 35; 36] and pulp and paper industries [28; 29] face similar problems.  
Most filamentous bacteria in activated sludge belong to the _Thiothrix_ morphotype [52]. An analysis of the bacterial populations of 29 industrial wastewater treatment plants using 165 rRNA amplicon sequencing showed that activated sludge bulking was associated with the _Thiothrix_ morphotype in 72% of cases [24].  
Pure cultures belonging to the genus _Thiothrix_ were repeatedly isolated from activated sludge samples [53; 54]. Some isolated strains were subsequently described as representatives of new species and genera: _Thiothrix eikeloomii_ (later reclassified as a member of the genus _Thiolinea_[10]), _Thiothrix uzzi, Thiothrix fructosivorans_[8], _Thiothrix disciformis_[9] = _Thiolinea disciformis_[10] and _Thiothrix flexilis_[9] = _Thiofilm flexile_[10]. Based on the genome obtained from the metagenome (MAG) of activated sludge, a new species, _Thiothrix winogradskyi_, was described [55].  
Besides pure cultures, new species of _Thiothrix_ with _Candidatus_ status were described in activated sludge. In this way, '_Candidatus_ Thiothrix moskovensis' and '_Ca_. Thiothrix singaporensis' were described based on metagenome-assembled genomes (MAG) obtained from laboratory-scale bioreactors with the enhanced biological removal of phosphorus [56].
## 5 Results and Discussion
### Unstable Network Experimental Result
While the proposed FedLO framework shows promising results, it is essential to acknowledge certain limitations that should be addressed in future research:  
* Scalability Challenges: The scalability of FedLO may face challenges in extremely-large-scale federated environments. Exploring strategies to optimize the algorithm's performance as the number of edge devices increases is crucial for broader applicability. * Sensitivity to Hyperparameters: The performance of FedLO may be sensitive to the choice of hyperparameters. * Dependency on Network Conditions: FedLO's performance relies on stable communication networks between the central server and edge devices. Adverse network conditions or high latency may impact the algorithm's effectiveness. Investigating techniques to mitigate the impact of unreliable networks is essential. * Model Heterogeneity: The current version of FedLO assumes homogeneity among edge devices, considering them equal participants. Addressing the challenges posed by heterogeneous models on edge devices, such as varying architectures or capabilities, is an important avenue for future research. * Security Concerns: FL, in general, raises security and privacy concerns, and FedLO is not exempt. Future work should include robust security mechanisms to safeguard sensitive data during the FL process. * Real-World Implementation Challenges: The deployment of FL frameworks, including FedLO, in real-world scenarios may encounter practical challenges such as device dropout, varying data distributions, or diverse data types. Addressing these challenges is crucial for successfully adopting FL in practical applications. In addition to the mentioned restrictions, it is crucial to consider the extent of the FedLO framework in contrast to conventional methods. Traditional approaches frequently depend on centralized data processing, which may only sometimes be practical or desirable in certain circumstances due to concerns regarding privacy, data locality, or regulatory restrictions. FedLO, which utilizes FL, presents a decentralized approach that permits model training to occur locally on edge devices, thereby mitigating privacy risks linked to centralizing data. Additionally, FedLO facilitates collaborative model training across distributed edge devices while safeguarding data privacy, making it well-suited for applications where data cannot be conveniently centralized.
## 4 Simulation and Experimental Results Analysis  
The simulation and experimental test platforms were created to evaluate the performance of the proposed control strategies. The simulation testbed is constructed in the RT-BOX3/PLECS environment. RT-Box3 is used as a real-time simulator for the power unit. The F38379D LaunchPad manufactured by TI is used as a control board to implement the proposed compensation strategy. LaunchPad and RT-Box3 are connected through the RT-Box LaunchPad. The HIL block diagram of the co-simulation between RT-Box3 and F28379D LaunchPad is shown in Figure 1. The whole HIL system is divided into two parts: the control unit and the power supply circuit. These two parts are loaded into the F28379D LaunchPad and RT-Box3 through PLECS (Version 4.7) software, respectively. Meanwhile, in order to more clearly characterize the compensation performance of the proposed strategy, a microgrid without a compensation device is set up as a control group. Figure 1 shows the block diagram of the natural gas generator system based on super capacitor compensation device. Its main components are as follows: (i) two natural gas generators (300 GF31-T), its main parameters are displayed in Table 1; (ii) super capacitor cabinet, which comprises 20 modules connected in series, boasting a total capacity of 8 F and a rated voltage of 960 V. Specifically, each individual super capacitor modules features a voltage level of 48 V and a capacitance value of 165 F; (iii) power inverter; (iv) voltage and current sensors used for measuring and calculating system power; (v) the NAMIMO control algorithm and super capacitor battery management strategy is implemented with two separated TMS320F28379D. In addition, the resistive loads in Figure 1 were used to equate the line impedance as well as the household power in the microgrid, which was set to 40 kW. Figure 2 shows that the physical platform was built according to the control strategy proposed in this paper.
## 4 Simulation and Comparative Analysis
In order to validate the robustness of the PID-IDA-PBC algorithm, constant-size perturbations of different sizes are added to the drive motors of joint 1 and joint 2 at 4 s. The result is shown in Figure 6. The PID-IDA-PBC algorithm has excellent disturbance-resistance performance. According to the above, it can be concluded that a single control algorithm finds it difficult to balance both dynamic performance and steady state performance. Therefore, this paper proposes a hybrid control strategy that attempts to combine the advantages of the two control algorithms. The simulation experiment of the hybrid control strategy was divided into three parts. First, we compared the control effects of hybrid control and single control algorithms. Then, a disturbance was added at 4 s to verify the immunity of the hybrid control. Finally, a simulation experiment was designed to analyze the effect of switching parameters on the effects of hybrid control. The hybrid controller parameter selection is \(c_{1}=0.08,c_{2}=0.008,l_{1}=0.03,l_{2}=0.003,h_{1}=0.7,h_{2}=0.2\). According to the simulation results in Figure 7, it can be concluded that the hybrid control strategy combines the advantages of the two control algorithms well, and has good dynamic performance and high tracking accuracy. Figure 8 shows the simulation curves of \(u_{d},u_{q}\) under Hybrid control method. The smoothing function curve in Figure 9 reflects the proportion of PID-IDA-PBC control during the switching process. Figure 10 is the trajectory curves when there is a disturbance imposed at the time of \(t=4\) s. The result shows that even if the system generates small high-frequency chattering due to disturbance, it can eventually eliminate chattering. The simulation results in Figure 11 show that the control effect of the hybrid control strategy is related to the switching parameter \(c\). The curve with smooth switching function under different parameters is shown in Figure 12. According to the results in Figures 11 and 12, it can be concluded that the effect of the hybrid control is affected by the parameter \(c\) and the switching interval \([l,h]\). The larger the \(c\) value, the earlier the position error change ratio is affected by the PID-IDA-PBC algorithm. According to the value of \(c\), the appropriate switching interval is selected to achieve smooth switching, and the abrupt change of the switching is avoided. Figure 6: Curves with effect of interference of each joint. Figure 8: Curve with control voltage of each joint motor. Figure 7: Curves with different control algorithm of each joint. Figure 10: Curve with hybrid control tracking error under the influence of disturbance. Figure 9: Curve with smoothing function.
## 3 Hierarchical Dispersion Entropy
### Comparison with Other Methods  
In order to verify that the proposed hierarchical dispersion entropy method is better than the current information entropy method, this paper compares the hierarchical dispersion entropy with multi-scale sample entropy, hierarchical sample entropy, multi-scale fuzzy entropy, hierarchical fuzzy entropy, and multi-scale dispersion entropy. A total of 40 sets of white noise and 1_/f_ noise were used as information entropy to calculate the samples, and the coefficient of variation CV of the same decomposition node of each information entropy was compared. The information entropy parameter selection is as follows: the decomposition layer number \(k=3\), the signal length \(N=1024\), the class number \(c=3\), the embedding dimension \(m=2\), the scale factor \(\tau~{}=~{}8\). The comparison results are shown in Figure 8 and Table 4. The MSE and HSE are compared as examples. As the decomposition scale increases, the stability of HSE is significantly higher than that of MSE, and the CV value of HSE of decomposition node 4 is also smaller than MSE, indicating that the hierarchical entropy performance is significantly better than multi-scale entropy; the hierarchical entropy can consider the high and low frequency components, and extract the time mode information more comprehensively and accurately; taking MDE, MSE and MFE as examples to compare, with the increase of the decomposition scale, MDE is more stable than MSE and MFE. The CV value of the decomposition node 4 is also the smallest MDE and the error bar is the smallest, indicating that the performance of the dispersion entropy is significantly better than the sample entropy and the fuzzy entropy. The anti-noise of the dispersion entropy is better, and the bandwidth variation is more sensitive, and it is able to map status information more accurately. It shows that the CV value of HDE is the smallest and the calculation is the most stable. The HDE method is better than the existing public information entropy method, which not only improves the stability of entropy calculation but also reduces the bit error rate of entropy calculation.  
Figure 7: (**a**) Mean and SD results of HDE for 40 different white noise time series. (**b**) The mean and SD results of HDE for 40 different classes of 1_/f_ noise time series.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class \(c\)** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline White noise & 0.0053 & 0.0082 & 0.0105 & 0.0115 & 0.0115 & 0.0168 & 0.0128 & 0.0123 \\
1_/f_ noise & 0.0014 & 0.0022 & 0.0024 & 0.0024 & 0.0021 & 0.0029 & 0.0029 & 0.0043 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CV values of node 4 of hierarchical decomposition of different classes.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### Algorithm
#### 3.4.3 Trajectory Determination  
The trajectory is determined by solving a TSP on graph \(\mathbf{G}\). The time required for filming each virtual node is calculated in accordance with the formula presented in (9). To ensure reliable data transmission, the drone allocates sufficient time to film each sensor node. This duration is calculated using (11), which determines the required filming time based on sensor data size and transmission rate. Meeting this condition guarantees successful reception of all data from each sensor node. Consequently, the receiver drone can efficiently collect data from all sensor nodes in the farm.
## 2 Related Work
### Privacy-Preserving Techniques
In addition, the majority of recent FL works fail to address essential challenges that are inherent to FL settings, including user drop-off and poor data quality, which are characteristic of NG-IoT settings. In study [21], a new hybrid privacy-preserving federated approach that incorporated synchronous and asynchronous communication was suggested. The model captured some key issues in FL-based NG-IoT and used Two-Trapdoor Homomorphic Encryption (TTHE) to ensure that all the components in the model were kept private. To reduce the effects of irregular users, a server protocol was proposed and the hybrid LEGATO algorithm, which is an asynchronous approach, was proposed for handling the user dropout scenario. In order to deal with the problem of poor-quality data from some users, a data sharing approach was also used. A novel privacy-preserving edge FL framework based on LDP (PPeFL) was proposed by study [25]. In particular, they designed three LDP mechanisms to address privacy issues within the FL process. To this end, the Filtering and Screening with Exponential Mechanism (FS-EM) was proposed to reduce the effective dimension of the problem being solved by filtering out parameters that are found to be optimal for global aggregation according to their contribution to the neural network. In addition to mitigating the spike in privacy budget related to local perturbation mechanisms, this approach drastically decreased communication costs. Additionally, the study presented a Data Perturbation Mechanism with Stronger Privacy (DPM-SP), which provided an extra layer of scrambling to participants' original data, ensuring enhanced security. To address the variance introduced by perturbation, a Data Perturbation Mechanism with Enhanced Utility (DPM-EU) was proposed, aiming to improve utility. Although these techniques were effective, most of the available works did not optimize them together or dynamically adapt noise to model sensitivity, thereby causing either high computational cost or lowered accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 3 Mechanical Performance of Fe-SMA
### Stress Relaxation and Creep  
Previous studies have found that stress relaxation and creep are notable characteristics of Fe-SMA. [PERSON] et al. [88] obtained a recovery stress of 316 MPa of Fe-SMA, but the recovery stress decreased 19 MPa (accounting for 6% of the total recovery stress) after maintaining the displacement for 10 h. [PERSON] et al. [89] discovered that stress relaxation is slightly dependent on recovery stress, but further investigation is necessary to thoroughly understand their relationship. In the fatigue loading process, [PERSON] et al. [83] found that the stress relaxation mostly took place in the early stage, and the recovery stress decreased by roughly 10-20% under different strain amplitudes after 2 \(\times\) 10\({}^{6}\) loading cycles. Therefore, the stress loss caused by stress relaxation should be considered when using Fe-SMA to reinforce structures, and stress compensation shall be performed by secondary activation under special conditions.  
Except for normal room temperature, the relaxation and creep of Fe-SMA are also affected by different ambient temperatures. [PERSON] et al. [90] investigated the stress relaxation and creep of Fe-SMA in the temperature range of \(-\)45 \({}^{\circ}\)C-50 \({}^{\circ}\)C, and found that both the two increased with the decrease in temperatures. Keeping the temperature at 45 \({}^{\circ}\)C, the alloy experienced a 0.6% creep with the constant stress of 600 MPa in 30 min, or incurred a stress relaxation of 10% when the strain remained constant. [PERSON] et al. [69] carried out  
Figure 6: Loading curves of Fe-SMA [83]: (**a**) under pseudo-static load, (**b**) under fatigue load after activation.  
a series of transient total deformation tests under high temperatures. The initial creep and failure temperatures decreased with the increase in load level, while all the initial creep temperatures were above 500 \({}^{\circ}\)C. This research is very significant for the potential fire hazards faced by Fe-SMA in engineering reinforcement.
## 3 Results and Discussion
### Analysis of ANN  
Results of the implementations of Figures 7 and 8 are displayed in Table 1 in the Appendix A. The data shows good results marked in blue with averages of the absolute values for errors being as low as 2.5% for training and 3.0% for test data (~0.3 mm), some average fittings with accuracy between 10% and 30% (~up to 1.3 mm) as well as some outliers.  
Below, the results from the enhanced and standard ANNs are visualized and compared. For better comparability of the following diagrams, exactly the same experiments as datasets, as well as the same ANN-modeling concepts, were used for the enhanced and standard ANNs. For a better analysis of the behavior, an enhanced ANN with very good error values for unedited data and the standard ANN with the same unedited dataset for the conventional inputs of welding parameters as well as the amount of beads are shown in Figure 9. The average absolute error for enhanced is 0.32 mm and 1.325 mm for standard. The approach to predict distortion based on the additional input of resulting geometry does result in a lower average absolute error of 0.84 mm, but this is still significantly worse than the enhanced approach. Additionally, a distortion prediction based on the resulting geometry of the welded structure is not feasible for a real use case, so it is not examined further. In these diagrams, the \(x\)-axis shows the measured distortion, and the \(y\)-axis the predicted distortion.  
Figure 8: Plots for the different settings of implemented ANNs for the means of the absolute values of the absolute prediction errors highlighted by setting.  
All test and training data were plotted. The target line indicates the values for a perfectly accurate prediction. In both diagrams, the prediction dots were scattered very closely along this line, which proves an accurate prediction model for the created ANNs. In comparison to each other, the enhanced ANN scatters less, which is also supported by the minimum and maximum errors of (4 mm/\(-\)7 mm) for the enhanced ANN as well as (6 mm/\(-\)9 mm) for the standard ANN. Groups of large errors cannot be clearly assigned to an area of real distortion. Hence, the ANN should work well for all examined distortion values. The prediction also does not result in data points scattered more on one side of the target line, which means that there is no imbalance, such as a shifted prediction towards high distortions.
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 3 The SS Method  
### The Theoretical Basis of the SS Method  
Section 2 proves that BRBs generates amplitude modulation and phase modulation on the fundamental frequency component of current signals. The three-phase current signals can be expressed as Equation (10), and \(I(t)\) is the amplitude signal. When the motor is healthy, \(I(t)\) is a constant, and when the motor is a faulty motor with BRB, \(I(t)\) contains the signature frequency components of BRB.  
Assuming \(SS(t)\) is the sum-of-squares of three-phase current signals, \(SS(t)\) can be expressed as  
\[\begin{array}{rcl}SS(t)&=&\sum\dot{I}^{2}_{Abrb}(t)+\dot{t}^{2}_{Bbrb}(t)+ \dot{t}^{2}_{Cbrb}(t)\\ &=&I^{2}(t)\cdot\left\{\sin^{2}[2\pi f_{e}t+\beta\sin(2\pi f_{brb}t)]\right.\\ &&+\sin^{2}[2\pi f_{e}t+\beta\sin(2\pi f_{brb}t)+\frac{2\pi}{3}]\\ &&+\sin^{2}\left[2\pi f_{e}t+\beta\sin(2\pi f_{brb}t)-\frac{2\pi}{3}\right] \right\}\\ &=&\frac{3}{2}I^{2}(t)\end{array} \tag{11}\]  
According to the theoretical derivation in Section 2, \(I(t)\) of the faulty motor can be expressed as  
\[I(t)=D+\lambda\cos(2\pi f_{brb}t) \tag{12}\]  
where \(D=-\frac{\sqrt{2}\phi}{Z}\cdot 2\pi f_{e}\) and \(\lambda=\frac{3p\phi l_{brb}}{2\pi f_{brb}l}\).  
Consequently, \(SS(t)\) of the faulty motor can be expressed by integrating (12) into (11):  
\[\begin{array}{rl}SS(t)&=\frac{3}{2}I^{2}(t)\\ &=3\cdot\lambda D\cdot\cos(2\pi f_{brb}t)+\frac{3\lambda^{2}}{2}\cos^{2}(2\pi f _{brb}t)+\frac{3D^{2}}{2}\\ &=3\cdot\lambda D\cdot\cos(2\pi f_{brb}t)+\frac{3\lambda^{2}}{4}\cos(2\pi\cdot 2 f_{brb}t)+\frac{3\lambda^{2}}{4}+\frac{3D^{2}}{2}\end{array} \tag{13}\]  
Therefore, when the motor is a faulty motor with BRB, \(SS(t)\) is a variable and contains the signature frequency components. In contrast, \(SS(t)\) is a constant when the motor is healthy. The health condition of the motor can be diagnosed by finding the signature frequency components from the spectrum of the SS of three-phase current signals.
## 3 Markov Chain Monte Carlo Algorithms
### Transitional Markov Chain Monte Carlo (TMCMC)  
#### 3.2.1 Concept of TMCMC  
To enhance the sampling efficiency of MH, [PERSON] and [PERSON] [16] proposed the generation of samples from a sequence of distributions that approach the target posterior rather than directly sampling from the nontrivial target distribution. A kernel sampling density is sequentially updated based on previous sampling results and then used as a new global proposal. However, a significant disadvantage of this method is that when the dimension is high, substantial samples are required, leading to intensive computational demands. The transitional Markov Chain Monte Carlo (TMCMC) developed by [PERSON] and [PERSON] [19] uses a similar idea, employing a series of intermediate or transitional distributions to generate samples that rapidly converge to the target posterior. Unlike [16], TMCMC does not require kernel density estimation. Instead, the intermediate distributions are autonomously defined based on multiple strategies, such as reweighting, resampling, and adaptive random walk at each level.