esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
These enzymes are secreted by the BRF in response to the dyes and may degrade or completely mineralize them into less or non-toxic forms [26; 30; 31]. The microbial biodegradation of effluents degrade most of the dyes while producing the other less toxic amines in small quantity [32]. The previous studies indicate that the secondary amines produced during the current study have a very low toxicity [33]. Figure 7: HPLC chromatogram of standard secondary amines (**A**)—N-methylaniline (\(t_{\text{R}}\) = 3.570 min). (**B**)—3-Methyldiphenylamine (\(t_{\text{R}}\) = 3.870 min) and (**C**)—Diphenylamine (\(t_{\text{R}}\) = 3.690 min). Figure 2:0 HPLC chromatogram of MTIE (**A**)—untreated sample (**B**)—treated sample, peak 1 for N-methylaniline (\(t_{\rm R}\) = 3.570 min) and peak 2 (\(t_{\rm R}\) = 4.001 min) and 3 (\(t_{\rm R}\) = 4.839 min) for unknown compound. Figure 2:1 HPLC chromatogram of FSIE (**A**)—untreated sample (**B**)—treated sample, peak 1 for N-methylaniline (\(t_{\rm R}\) = 3.570 min) and peak 2 (\(t_{\rm R}\) = 4.679 min) for unknown compounds. **Biological oxygen demand (BOD) and Chemical oxygen demand (COD):** The textile industrial effluents have higher values of BOD and COD than recommended by the WHO, i.e., below 80 mg/L for BOD and 250 mg/L for COD, due to the high concentration of the chemicals and contaminants in them. The initial values of BOD were 358 mg/L, 347 mg/L and 412 mg/L for MT, ST and FST industrial effluents, respectively, while, the initial values of COD were 1032 g/L, 1176 mg/L and 1237 mg/L for MT, ST and FST Industrial effluents, respectively. The reduction in BOD and COD, respectively, after the optimization of physical parameters were 48.32% and 56.20% for the MT effluent, 44.09% and 56.46% of the ST effluent and 47.81% and 56.10% of the FST effluent. After optimization of nutritional parameters, the biodegradation process was enhanced, and there was further reduction in the BOD and COD values of each effluent. The reduction of BOD and COD, respectively, were 86.31% and 82.94% for the MT effluent, 83.57% and 81.03% of the ST effluent and 82.76% and 79.70% of the FST effluent. The process of the biodegradation reduced the values within recommended values, which confirmed the significance of this process. High BOD and COD make aquatic life difficult, while a reduction in these values make water suitable for aquatic life [15].
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 3 Space Threats and Planetary Defense History  
### Serious Threats from Outer Space  
#### 3.1.1 Cretaceous-Paleogene Extinction  
Threats such as asteroids and comets from somewhere outside Earth can result in enormously catastrophic consequences. The Cretaceous-Paleogene (K-Pg) extinction event is also known as the Cretaceous-Tertiary (K-T) extinction; it was a sudden mass extinction of around 75% of the plant and animal species on Earth, which happened approximately 66 million years ago [57, 58]. With the exception of some exothermic species, no tetrapods weighing more than 25 kg survived. A large number of species were erased from Earth in the K-Pg extinction, and the best-known are the non-avian dinosaurs. Moreover, it destroyed numerous other terrestrial organisms such as mammals, birds [59], lizards and snakes [60], insects, plants, teleost fishes [61], pterosaurs, pleisosaurus, and mosaurs [58].  
The root cause of the K-Pg extinction event was originally proposed to have been the impact of a massive comet or asteroid 10 to 15 km wide about 66 million years ago by a team of scientists led by Nobel Prize-winning physicist [PERSON] and his son [PERSON]. The impact hypothesis is also known as the [PERSON] hypothesis and was supported by the discovery of the 180 km sized Chicxulub crater in the Gulf of Mexico's Yucatan Peninsula which was summarized in the 1991 publication of [62]. Moreover, authors in [63] have presented more conclusive evidence that the K-Pg boundary clay represented the debris (unusually high levels of the metal iridium) from an asteroid impact. Such a massive impact would have instantly led to devastating shock waves, a large heat pulse, and global tsunamis. Moreover, the release of enormous quantities of dust, debris, and gases would have resulted in a prolonged cooling of Earth's surface, low light levels, and ocean acidification that would have decimated primary producers including microscopic marine algae as well as those species reliant upon them [63].  
In particular, there is broad consensus that the Chicxulub impactor was an asteroid rather than a comet with a broadly accepted diameter of around 10 km. The impact released estimated energy of between \(1.3\times 10^{24}\) and \(5.8\times 10^{25}\) joules (1.3-58 yottajoules), or 21-921 billion Hiroshima A-bombs [64]. In 2013, authors with expertise in modeling nuclear winter published their research about the impact winter [65], which indicated that the entire terrestrial biosphere might have burned based on the amount of soot in the global debris layer. Supposedly, a global soot-cloud blocked the sun and created a long-term winter effect. More recently, in 2020, authors published the climate-modeling of the extinction event supporting the asteroid impact instead of volcanism [66]. However, the extinction also provided other opportunities to enable many groups to radiate. For example, mammals diversified in the Paleogene [67] and evolved new forms such as horses, whales, bats, and primates which made the emergency of the Homo sapiens possible.
## 4 Discussion  
### Model of Acoustic Waves Propagation in Pipes  
For comparison of the propagation velocities of structure-borne acoustic waves inside the pipe material determined by distributed acoustic sensing to theoretical predictions, a numerical simulation of the propagation of acoustic waves was carried out for the chosen pipe geometry. The calculated dispersion curves for the different acoustic modes expected in a cylindrical waveguide are presented in Figure 11. As the frequency spectrum of the excitation acquired by fiber optic sensing is dominated by frequencies below 7 kHz (data not shown), group speeds are analyzed in the low-frequency region. The group speed of the longitudinal mode L (0,1), which is the fundamental mode in a cylindrical waveguide, amounts to 5047 m/s in the low frequency limit (bar speed). All the experimentally determined acoustic velocities of the main mode are in good agreement with this value.  
Interestingly, the (frequency independent) group speed of the torsional mode T (0,1) is 3130 m/s indicating this as the second mode observed in some of the experiments with velocities in a range of 3000 m/s to 3300 m/s.  
Recently, a study [34] was presented targeting the localization of CO\({}_{2}\) leakage from transportation pipes. Propagation velocities of acoustic waves traveling in the pipe material were determined in a laboratory scale experiment on a steel pipe of comparable dimensions as the ones used here, i.e., 6 m length, 50 mm outer diameter, and 46 mm inner diameter, employing two acoustic emission sensors and Nielsen-Hsu pencil lead break tests as excitation source. Two main modes with velocities of 5070 m/s und 3268 m/s were derived in agreement with the findings of the present study.
###### Abstract  
Chlorination is the preferred method to control biofouling in a power plant cooling system due to its comparative effectiveness and low cost. If a power plant is located in a coastal area, chlorine can be electrochemically generated in-situ using seawater, which is called in-situ electro-chlorination; this approach has several advantages including fewer harmful chlorination byproducts and no need for chlorine storage. Nonetheless, this electrochemical process is still in its infancy in practice. In this study, a parallel first-order kinetics was applied to simulate chlorine decay in a pilot-scale cooling system. Since the decay occurs along the water-intake pipe, the kinetics was incorporated into computational fluid dynamics (CFD) codes, which were subsequently applied to simulate chlorine behavior in the pipe. The experiment and the simulation data indicated that chlorine concentrations along the pipe wall were incremental, even under the condition where a strong turbulent flow was formed. The fact that chlorine remained much more concentrated along the pipe surface than in the middle allowed for the reduction of the overall chlorine demand of the system based on the electro-chlorination. The cooling system, with an in-situ electro-chlorination, consumed only 1/3 of the chlorine dose demanded by the direct injection method. Therefore, it was concluded that in-situ electro-chlorination could serve as a cost-effective and environmentally friendly approach for biofouling control at power plants on coastal areas.  
Keywords:computational fluid dynamics; power plant; cooling system; electro-chlorination; in-situ chlorination +
Footnote †: journal: Applied Physics Letters B
In contrast, the forward Euler method is simple to implement, yet only first-order accurate; when the curves exhibit steep slopes or strong nonlinearities, significant integration errors may occur unless a very small step size is used. Consequently, improving accuracy with the forward Euler method typically requires increasing the number of iterations, which reduces computational efficiency in practical applications. Among these approaches, the uniform-step forward Euler method is the most commonly used. However, its limited accuracy often necessitates finer discretization, further increasing computational costs. For instance, [PERSON] et al. [17] developed the ErosOpt software for parameter calibration, which can conveniently call the user material subroutine (UMAT)in ABAQUS to compute theoretical response curves for various constitutive models. Nevertheless, because ErosOpt employs the uniform-step forward Euler method and requires assembling the element matrix and solving the system of equations based on boundary conditions at each incremental step, the process consumes significant computational resources, resulting in relatively slow parameter inversion. Soil stress-strain curves typically exhibit nonlinear characteristics, with significant variations in slope at different stages. Existing methods for solving theoretical constitutive model curves primarily rely on FEM or explicit iteration methods with uniform step sizes, which do not fully consider the influence of stress-strain curve slope variations on the required iteration step size. To improve the computational efficiency of the CSUH model, this study proposes a dynamic iteration method with adaptive step size adjustment. This method adjusts iteration step sizes according to variations in the stress-strain curve slope, significantly reducing the number of iterations while maintaining computational accuracy. This paper first introduces the flexibility matrix of general elastoplastic constitutive models and presents the stress-strain incremental iteration equations under different stress paths. Subsequently, the fundamental theoretical framework of the CSUH model is outlined, followed by a detailed explanation of the proposed dynamic iteration method. The effectiveness of the dynamic step size iteration method is verified through comparisons with traditional uniform step size iteration methods. Finally, this method is employed for parameter inversion of the CSUH model using Fujinomori clay, calcareous sand, and Changhe dam rockfill material. The prediction results demonstrate that the CSUH model accurately describes the mechanical behavior of different soil types under various stress paths. The findings of this study provide valuable insights for improving the computational efficiency of the CSUH model and other elastoplastic constitutive models.
## 2 Finite Element Modeling and Validation  
### Experimental Introduction  
In order to investigate the shear behavior of RC beams strengthened with an FRP grid-PCM composite, this paper uses the test beams in Reference [13] for finite element simulation. The loading method, cross-sectional dimensions, reinforcement details, and strengthening positions of the test beams are shown in Figure 1. The strengthening method is to embed the BFRP grid in the PCM and externally bond it to the two side surfaces of the shear span zone of the beam. The cross-sectional dimension of the test beam is b \(\times\) h = 150 mm \(\times\) 300 mm, and the effective height \(h_{0}\) is 270 mm, the shear reinforcement ratio is 0.19%, and the thickness of the PCM layer is 20 mm. There are three different shear span ratios of 1.6, 2.0, and 2.4, and the distances from the support to the loading point are 432, 540, and 648 mm, respectively. The naming rule is as follows: C represents the unstrengthened beam, S represents the strengthened beam, and the following number is the shear span ratio. For the strengthened beam, among the subsequent three fields, P refers to PCM, G refers to the grid, and 0 indicates the arrangement angle where the grid is vertically placed. In this section, a total of four beams are simulated, including one unstrengthened beam with a shear span ratio of 2.0 and three strengthened beams with different shear span ratios. The unstrengthened beam is used as a reference to compare and evaluate the performance improvement of the strengthened beams, and the accuracy of its simulation is the key to judging the strengthening effect; the three strengthened beams provide a basis for the subsequent analysis of influencing factors, and the shear span ratio is one of the important parameters in shear research. All the strengthened beams are strengthened with a BFRP grid with a thickness of 5 mm, a width of 10 mm, and a spacing of 50 mm \(\times\) 50 mm. The mechanical properties of various materials are shown in Table 1.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method
### Frequency Synchronization Algorithm Based on AGC Gain Compensation  
#### 3.3.1. AGC Impact Analysis for Low-Overhead Frame Structure  
In conventional burst OFDM systems, the AGC gain adjustment is completed within the AGC guard interval. Otherwise, the input signal for frequency synchronization may suffer amplitude distortion, potentially leading to significant performance loss. When the frame structure design illustrated in Figure 4 is used, the AGC gain adjustment is carried out within the preamble. The relationship between the received signal before and after AGC adjustment can be expressed as  
\[\gamma(n)=g(n)r(n), \tag{18}\]  
where \(g(n)\) denotes the AGC gain. In practical systems, the AGC adjustment step is controlled by the power of received signal samples, and we convert the gain \(g(n)\) into dB by  
\[G(n)_{dB}=20\log_{10}g(n). \tag{19}\]  
The AGC gain is changed from an initial value \(G(i)_{dB}\) to a steady-state value, and the gain \(G(n)\) is updated with step \(\lambda_{k}\) for time interval \(\tau\). The AGC gain \(G(n)_{dB}\) at each moment \(n\) can be expressed as  
\[G(n)=G(i)u(n)+\sum_{k=0}^{K-1}\lambda_{k}u(n-k\tau), \tag{20}\]  
where \(G(i)_{dB}\) denotes the initial gain of the AGC and \(u(n)\) is the unit step function. The overall convergence time of the AGC is determined by the gain-adjusted time interval \(\tau\) and gain adjustment step \(\lambda_{k}\).  
Figure 6 shows an example of the received preamble with AGC adjustment, where I and Q denote the in-phase and quadrature components of ZC sequence, respectively. The preamble consists of two ZC sequences, each with a length of 128, and a CP with a length of 64. The time domain waveform of the received signal after the AGC adjustment is illustrated in Figure 7. When the preamble is not present, the received signal only contains noise at a low power level. The AGC gain is initially set to the maximum value and gradually decreases so that the received signal power reaches the reference power. Therefore, an overload occurs within the CP at the very beginning of the preamble.
# A Flexible and Simple Lossless DWT Filter Bank Using a MAXFLAT FIR Half-Band Filter
## 2 Design of Two-Band Lossless Single(s-) Filter Banks
Consequently, the filter form of Equation (8) for the lossless s-filter bank can allow unusual flexibility in choosing a best filter for analysis and synthesis, i.e., the overall passband and stopband distortion (even if not flat) of the filter has no effect on producing the result of Equation (9). Further, these filters can lead to computationally efficient implementations due to the fact that all odd-order coefficients are zeros. The computational superiority of the proposed lossless s-filter bank can be confirmed even more by realizing a linear-phase half-band FIR filter, where \(Q_{0}(z)\) has symmetric integer (even-order) coefficients of \(h_{2n}=h_{4K-2-2n}\;(n=0,1,\cdots,K-1)\). Such linear-phase half-band FIR filter design will be discussed in next section. Comments for additional designsUsing Equation (6), we can consider the other set of \(N\) and \(m\) (i.e., odd \(m\) if \(N\) is even) for the design of lossless s-filter banks. In this case, the PR condition is identical to the form of Equation (8) above, and only the system delay is different. Hence, similarly to the case of even \(m\) and odd \(N\), the choice of \(m\) in addition to the determination of \(H_{0}(z)\) has to be also seriously considered for the various applications of s-filter banks. This is because \(m\) for a given \(N\) has a significant effect on the residual band characteristics. Further Figure 2a,b have identical input/output behavior for analysis and synthesis, while providing different image compression and denoising effects if \(H_{0}(z)\) is not a half-band filter. For example, if \(H_{0}(z)\) is a lowpass filter with narrow passband, upper subband of Figure 2a has larger high-frequency bandwidth than that of Figure 2b, which results in relatively low image compression ratio and poor denoising efficiency. In other words, to achieve higher image compression ratio and denoising efficiency, the s-filter bank of Figure 2a is better to choose a large passband filter, while Figure 2b is better to  
Figure 2: Two-band PR s-filter bank structures with (**a**) lower-band subtraction loop (upper-band prediction) and (**b**) upper-band subtraction loop (lower-band prediction). use a narrow passband filter. For this reason, so that the lower and upper subbands have a symmetric bandwidth, the design of FIR half-band lowpass filter with linear phase is also significantly important. More importantly, the value of even \(m\) (\(>0\)), defined as input sample delay of \((m-N)\) in Equation (4) for a given \(N=2K-1\), is decided from the relation of \(m-2K+1=1\) (i.e., \(m=2K\)) to minimize the redundancy between lower and upper subbands.
## 3 Computational Results
### Experimental Settings  
The FCM-based GA is used to classify the first 110 job records into \(G\) clusters. The obtained cluster centers are fed into the BPN. After learning and training, 10 new testing jobs are given to the trained BPN predictor to predict their cycle times, where the prediction performance is assessed in terms of the mean absolute error (MAE), mean absolute percentage error (MAPE), and root-mean-squared error (RMSE), which are calculated as  
\[\text{MAE}=\frac{\sum_{n=1}^{\mathcal{N}}\left|Y_{n}-\hat{Y}_{n}\right|}{ \mathcal{N}}, \tag{15}\]  
\[\text{MAPE}=\frac{\sum_{n=1}^{\mathcal{N}}\left|Y_{n}-\hat{Y}_{n}\right|}{ \mathcal{N}\cdot Y_{i}}, \tag{16}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{n=1}^{\mathcal{N}}\left(Y_{n}-\hat{Y}_{n}\right) ^{2}}{\mathcal{N}}}, \tag{17}\]  
where \(\left|Y_{n}-\hat{Y}_{n}\right|\) is the absolute difference between the predicted and observed values.  
Many parameters affect the accuracy of the job cycle time prediction. Herein, we test the performance and compare the results by using different cluster numbers \(G\), different neuron numbers \(\hat{k}\), in the hidden layer, and different threshold values \(\delta\). In addition, all experiments are conducted using populations with the same number of individuals. The remaining parameters used for the FCM-based GA and BPN are summarized in Table 1.  
We implement the proposed hybrid approach in C++ using Visual Studio 2013. The experiments are run on a computer with an Intel Core i7-4790 CPU @3.6 GHz and 16 GB of memory under Windows 10 Professional edition. Because the BPN performance is sensitive to the initial conditions of random weights [39], the experiment is repeated at least five times with different initial conditions. The best and average predicted values are recorded for subsequent analysis.  
Figure 7: Entire dataset of job records.
## 4 Evolutionary Algorithms for Task Scheduling Problem
### Modified Particle Swarm Optimization
#### 4.1.5 Particle Updating  
The velocities of particles are updated regularly based on its best experience \(pBest\) and the leader \(gBest\). The below equation indicates how \(k\)th particle updates its velocity.  
\[V_{k}^{(t+1)}(i,j)=w.V_{k}^{t}(i,j)+c_{1}r_{1}(pBest_{k}^{t}(i,j)-X_{k}^{t}(i,j ))+c_{2}r_{2}(gBest_{k}^{t}(i,j)-X_{k}^{t}(i,j)). \tag{16}\]  
In Equation (16), each element in velocity matrix is updated. \((i,j)\) indicates the element in \(i\)th rows and \(j\)th column of the matrix. \(c_{1}\) snf \(c_{2}\) are positive acceleration constants, which control the influence of \(pBest\) and \(gBest\) on the search process, \(r_{1}\) and \(r_{2}\) follow uniform distributions in the range of \([0,1]\), and \(w\) is inertia weight, which usually decreases from a large value (e.g., 0.9) in the beginning to a small value (e.g., 0.1) in the last iteration to control the exploration and exploitation abilities of the swarm.  
The new position of \(k\)th particle is defined as follows.  
\[X_{k}^{(t+1)}(i,j)=\begin{cases}1,&if\left(V_{k}^{(t+1)}(i,j)=max_{i}\left\{ V_{k}^{(t+1)}(i,j)\right\}\right),i\in\left\{1,2,\cdots,m\right\};\\ 0,&\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
value in its column is assigned a value of 1, while others in this column are labeled 0. If a column of velocity matrix has many elements with max value, then one will be selected randomly and 1 assigned to its corresponding element of position matrix.