esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Simulation Test for Strengthening the Weighting Coefficients of the First Scheduling Interval
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Values**} & \multicolumn{5}{c}{**Time (min)**} \\ \cline{2-6}  & **435** & **440** & **445** & **450** & **455** \\ \hline \(p_{t}^{\text{\tiny{plan}}}\) /MW & 12.18 & 12.19 & 12.20 & 12.21 & 12.26 \\ \(p_{t}^{\text{\tiny{uu}}}\) /MW & 20.15 & 20.16 & 16.26 & 18.45 & 17.39 \\ \(p_{t}^{\text{\tiny{pa}}}\) /MW & 6.39 & 6.39 & 6.39 & 4.51 & 4.51 \\ \(p_{t}^{\text{\tiny{fa}}}\) /MW & 1.581 & 1.507 & 0 & 1.729 & 0.619 \\ \(p_{t}^{\text{\tiny{fp}}}\) /MWh & 0 & 0 & 2.34 & 0 & 0 \\ \(p_{t}^{\text{\tiny{rad}}}\) /MW & 12.18 & 12.19 & 9.86 & 12.21 & 12.26 \\ \(E_{t}^{\text{\tiny{pa}}}\) /MW\(\cdot\)h & 98.58 & 99.03 & 99.48 & 99.80 & 100.1 \\ \(E_{t}^{\text{\tiny{fe}}}\) /MW\(\cdot\)h & 4.881 & 5.0 & 4.80 & 4.98 & 4.89 \\ \(p_{t}^{\text{\tiny{uu}}}\) /MW & 0 & 0.917 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Case analysis of the increased weights. Figure 18: Simulation results for the second objective after strengthening the weight. Table 5 shows the MPC decision result for the enhanced weight. Note that for this test, minute 435 is the current time, and minutes 440-455 are future times. Comparing Table 5 with Table 4, we find that only the amounts of \(p_{i}^{\text{\tiny{th}}}\), \(p_{i}^{\text{\tiny{th}}}\) and \(E_{i}^{\text{\tiny{th}}}\) change. From Table 5, the wind curtailment does not occur at the current moment (minute 435) but shifts to a future time (minute 440). According to the MPC decision process, the current time decision value is used, while the rest of the decision values are discarded. Moreover, when the next scheduling times arrive, the MPC makes decisions, and the result for minute 440 does not exhibit wind curtailment. Therefore, at minute 435, all the wind energy is utilized, without any waste. The results suggested that the method of enhancing weight could assist the MPC in reducing the number of times of occurrence and the amount of wind curtailment. Figure 19 shows the comparison result of \(p_{i}^{\text{\tiny{nd}}}\) using for the enhanced and original weights respectively. In Figure 19, two power signals are shown: the enhanced weight of \(p_{i}^{\text{\tiny{nd}}}\) (solid, black) and the original weight of \(p_{i}^{\text{\tiny{nd}}}\) (solid, gray). Figure 19 shows that when wind \(<\) plan, both \(p_{i}^{\text{\tiny{nd}}}\) values are basically equal to wind, while both \(p_{i}^{\text{\tiny{nd}}}\) values are very close to plan when wind \(>\) plan. Therefore, the third objective is achieved. However, the comparison result shows that the enhanced weight \(p_{i}^{\text{\tiny{nd}}}\) is slightly less than the original weight \(p_{i}^{\text{\tiny{nd}}}\). One possible explanation for this result is that \(p_{i}^{\text{\tiny{nd}}}\) is sacrificed to reduce the wind curtailment \(p_{i}^{\text{\tiny{nd}}}\).
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 3 Simulated Data Analysis
### Five-DOF System

Table 4: Monte Carlo simulation results obtained from the simulated EMA of the five-DOF system: mean natural frequencies and damping rations and their respective relative bias error computed from the LSCF estimates and from the estimates obtained with the MLE-PMM technique after 10 iterations. Figure 6: Monte Carlo simulation results for the five-DOF system example with 10% noise: sample standard deviation of the natural frequencies (**a**) and damping ratios (**b**) estimated for the 3 rd mode both with the MLE-PMM and MLE-MM [14] and their corresponding standard deviation estimates obtained after 10 iterations. These results also demonstrate the MLE-PMM capability of providing very accurate confidence regions for the optimized modal properties. The MLE-PMM capability of predicting the sample standard deviations for all the identified modes is illustrated in Figure 9 and Table 5. In the former, the sample standard deviations computed from the 5000 sets of estimates obtained with the Monte Carlo simulations are compared to the means of the standard deviation estimates provided by the MLE-PMM. In the latter, this comparison is established, in terms of relative difference, both for the MLE-PMM and the MLE-MM estimates. Analysing these results, one verifies that the MLE-PMM provided accurate predictions for the sample standard deviation of all the identified modes, which are as well in good agreement with the MLE-MM estimates. Figure 8: Covariances on the estimates for the natural frequencies and damping ratios obtained with the MLE-PMM for all modes of the 5-DOF system: estimates obtained after 10 iterations of the MLE-PMM over the Monte Carlo realizations (blue dots); 95% elliptical confidence regions obtained from the sample covariance, \(\text{Cov}(\hat{f}_{n},\hat{\xi}_{n})\) (continuous black line); and 95% elliptical confidence regions obtained from the mean over the covariance estimates provided by the MLE-PMM over the Monte Carlo simulations (dashed red line). Figure 7: Monte Carlo simulation results for the five-DOF system example with 10% noise: sample standard deviation of the natural frequencies (**a**) and damping ratios (**b**) estimated for the 5 th mode both with the MLE-PMM and MLE-MM [14] and their corresponding standard deviation estimates obtained after 10 iterations. Although it is verified from Figures 6 and 7 and from Table 5 that the MLE-MM provides slightly lower uncertainty bounds for optimized estimates, the MLE-PMM algorithm is significantly less time consuming. This is verified in Figure 10 where the performance of the ML-PMM algorithm is compared, in terms of time consumed over the performed Monte Carlo simulations, to its ML-MM counterpart. The significant difference in processing time between the two algorithms is explained by the fact that the dimensions of the system matrices obtained with the MLE-PMM is significantly lower than those of the MLE-MM. Figure 10: Time consumed by MLE-PMM (red line) and MLE-MM [14] (blue line) algorithms over the Monte Carlo runs.
## 3 Results
### Routine Detection of MRSA Strains  
According to the results obtained, out of 48 _S. aureus_ isolates, 22 (45.83%) strains presented \(\leq\)21 mm and \(\leq\)10 mm cefoxitin and oxacillin inhibitory zones, respectively. They were categorized as MRSA according to the Clinical Laboratory Standard Institute's (CLSI) protocols. Meanwhile, the rest of the isolates (54.17%) were identified as MSSA.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**S. aureus**} & \multirow{2}{*}{**Cogulase Test**} & \multirow{2}{*}{**Sleigh ID 32 System**} & \multicolumn{4}{c}{**BBLTMStaphyloslideTM**} & \multicolumn{2}{c}{**Vitek 2 Compact**} \\ \cline{3-10}  & & No. of CI & **Stains** & **No. of CI** & **Stains** & **No. of CI** & **Stains** & **No. of CI** & **Stains** & **Stains** \\ \hline
48 & 45 & 93.75\% & 42 & 87.5\% & 48 & 100\% & 46 & 95.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between different phenotypic techniques used for recognition of _S. aureus_ recovered from clinical samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Gene**} & \multirow{2}{*}{**Primer Sequence (5’-3’)**} & \multirow{2}{*}{**Base Pair**} & \multicolumn{4}{c}{**Conditions of Real-Time PCR**} \\ \cline{3-8}  & & & **Initial** & **Denaturation** & **Annealing** & **Extension** & **Final Extension** \\ \hline _mecA_ & GTAGAATG
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
## 5 Conclusions  
We discussed the deviation revision of the measured meteorological data caused by the impact of the local wind environment and the accurate value of reference wind pressure in Shenzhen during Super Typhoon Mangkhut. Relevant studies on the two sites of NDLI and PAFC were carried out by WTT and CFD numerical simulation methods. The conclusions are summarized as follows:  
1. As for NDLI, which was affected by terrain interference, the overall trend of the fitted \(C_{VI}\) with wind direction by means of WTT and numerical simulation is the same. However, differences in \(C_{VI}\) were found under the same wind direction, and the \(C_{VI}\) value in the WTT was higher than that in the CFD numerical simulation. The existence of NDLI accelerated the measured wind speed by 12% in the sensitive wind direction.
2. As for PAFC, which was affected by the building and instruments, the WTT results revealed that the wind speed in directions of 45\({}^{\circ}\) and nearby were sheltered by the giant pillars at the corner and reduced by 20%. The wind speeds of the other wind directions were mainly affected by the instruments in the hollow-layer, and the acceleration effect was presented when the instruments were absent. In addition, the shielding effect was presented when the instruments were present. In the dominant wind direction, the wind speed influence coefficients were 1.163 and 0.593 when undisturbed and disturbed by instruments.
3. The \(C_{VI}\) results were used to correct the recorded wind speed at the NLDI and PAFC sites during Super Typhoon Manghut; they were converted into the 10 min mean wind pressure value at 10 m height under the standard landform. Hence, the reference wind pressure values calculated were 0.526 and 0.505 kPa. The analogous conversion values achieved the purpose of mutual verification. Meanwhile, the effectiveness and reliability of the methodologies are presented.  
Conceptualization, J.D. and Z.X.; methodology, J.D.; formal analysis, X.C.; writing--original draft preparation, J.D.; writing--review and editing, Z.X. and L.Z.; supervision, J.D., Z.X. and L.Z. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper is fully supported by the National Natural Science Foundation of China (Grant Nos. 51908226 and 52078221). The financial support is gratefully acknowledged.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Other Considerations
#### 5.3.4 Ergonomic Design of Ground Control Systems  
An area that has only received limited attention is the design of the ground control systems in such a way that takes into account the human body and expectations around how things should be controlled. Anecdotally, the authors have come across systems that cause discomfort or are confusing for a human operator. There are no international standards enforced upon the design of ground control systems, however, and that is true for many aspects of unmanned aviation. Some have suggested that standards could be borrowed from closely related areas, such as general-purpose computer workstations [99]. However, others have suggested that a combination of human-computer interface standards and conventional cockpit design is required as ground control systems for unmanned aircraft resemble both systems, though they also have distinct differences [100]. Either way, as BVLOS operations become more prevalent, it is critical that the principles of ergonomics are also applied to the design of ground control systems in order to minimise the potential for design-induced errors.
## 3 Results and Discussion
### Optimization of CuAAC Reactions with PBG and Synthesis of G0 Copolymers
The SEC peaks obtained for all three arborescent copolymers after 24 h of reaction are relatively symmetrical, without signs of degradation, demonstrating that CuSO\({}_{4}\)/NaAsc was useful under these conditions. The range of grafting yields listed in Table 2 (54-93%) suggests that the nature of the side chains influences the grafting yield. Given the limited amount of data available, it is unclear whether this variation is related to characteristics, such as the chemical composition or the bulkiness of the side chains. While the G0 PBG substrate was useful to optimize the CuAAC reaction for the synthesis of arborescent copolymers, coupling of the higher generation substrates of arborescent PBG (G1-G3) with CuSO\({}_{4}\)/NaAsc may provide further insight into these reactions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Copolymer**1  & \multicolumn{5}{c}{**PBG Substrate**} & \multicolumn{5}{c}{**G\({}_{y}\)5 **} & \multicolumn{5}{c}{**G\({}_{\textbf{n}}\)(g/mol)**6  } & \multicolumn{5}{c}{**\(M_{\textbf{n}}\)(g/mol)**7  } & \multicolumn{5}{c}{**\(M_{\textbf{n}}\)(g/mol)**8  } & \multicolumn{5}{c}{**\(M_{\textbf{n}}\)(g/mol)**9  } & \multicolumn{5}{c}{**\(f_{\textbf{n}}\)10  } \\ \hline G0 PBG-\(g\)-P(HEA-TMS) & 51,000 & 18 & 57 & 324,000 & 1.17 & 25 & 84 \\ G0 PBG-\(g\)-PGly & 51,000 & 18 & 93 & 331,000 & 1.02 & 40 & 85 \\ G0 PBG-\(g\)-PEO & 51,000 & 18 & 54 & 172,000 & 1.09 & 24 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of randomly alkyne-functionalized G0 PBG substrates CuAAC-grafted with various \(\alpha\)-azido side chains using CuSO\({}_{4}\)/NaAsc.
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 4 Filter Simulation  
The proposed filter layout using the folded-arms square open-loop resonator is developed following the assembly in Figure 7. The bandpass filter layout electromagnetic (EM) simulation is conducted on the Keysight Technologies USA, full-wave ADS EM simulator, with the filter layout and simulation responses presented in Figure 9. Looking at the simulation responses, the filter transmits at the expected centre frequency of 2.2 GHz, as expected. The EM simulation return loss is better that 22.6 dB, with a minimum insertion loss of 0.8 dB. The EM simulation uses a copper conductor with a cladding thickness of 35 micron (\(\upmu\)m) and conductivity of \(5.8\times 10^{7}\) S/m for the microstrip metals (top and bottom layers). The EM simulation did not consider the thickness variation of the filter substrate material and metal surface layer roughness.  
Figure 8: Microstrip layouts and EM simulation responses: (**a**) Coupling coefficient determination; (**b**) External quality factor determination.
## 2 Cluster Head Selection
### Multi-Factor Evaluation Schemes
#### 2.3.1.1 Fuzzy Logic Controller
According to the two parts of cluster formation and cluster migration introduced in ACE, FSCA proposes to use two FL modules: Initial Fuzzy Module (IFM) and Migration Fuzzy Module (MFM), which are responsible for initiating new clusters and decreasing the overlap between clusters, respectively. To determine the CHs and to generate cluster sizes equal to or greater than network density, two inputs of node lifetime since the protocol starts and the number of Loyal Followers of the node are applied to IFM in initial stage. Selection of time as one of the inputs is for decreasing the restriction on cluster size to cover the un-clustered nodes in completion of initial stage. By the end of the initial stage,nodes are in one of three states: CH, cluster member or un-clustered. In the migration phase, CHs broadcast a POLL message to their cluster members. By receiving the message, each cluster member counts its Loyal Followers, which are the nodes that are un-clustered or clustered with only one potential CH. By applying the energy level and number of Loyal Followers to MFM, each node calculates its chance to be the next CH and sends it back to the CH, which selects the node having the highest chance for the next round. According to the simulation comparison presented by the paper, the FSCA protocol distributes clusters uniformly throughout the network by migrating close clusters apart, with an advantage over ACE that it also prolongs the network lifetime.
# Quiescent Optical Solitons with Quadratic-Cubic and Generalized Quadratic-Cubic Nonlinearities
## 1 Introduction  
The dynamics of soliton propagation through optical fibers and other forms of optical waveguides is a scientifically rich phenomena. Occasionally, it so happens that such an engineering marvel encounters several road blocks. A few of them are soliton radiation and consequently shedding of soliton energy, collision-induced timing jitter, four-wave mixing and many others. Another lesser known and lesser studied detrimental phenomena is the formation of quiescent optical solitons when the chromatic dispersion (CD) is rendered to be nonlinear [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. This can be triggered from several possible unwanted situations that arise during the course of soliton transmittal across intercontinental distances. These could include rough handling of fibers, twisting and bending of fibers, water pressure from undersea cables, and many others. This would lead to solitons getting stalled during its transmittal. Such a phenomenon is discussed in this paper from a mathematical perspective.  
There are two types of nonlinear refractive index structures that are taken into account in this paper. They are quadratic-cubic (QC) and its generalized version that is referred to as generalized QC nonlinearity. The enhanced Kudryashov's scheme makes the retrieval of the stationary solitons possible. It is dark and singular stationary solitons that are yielded for the model with the two forms of self-phase modulation (SPM) considered in this paper.  
The temporal evolution is initially considered to be linear and thereafter its generalized version is taken up. The results are derived and exhibited in the rest of the paper. These are penned in detail after a succinct introduction to the model and a quick re-visitation of the integration scheme. To step back in time, the concept of nonlinear CD was introduced by [PERSON] during 2006 that led to the formation of quiescent optical solitons [10]. Thereafter, this concept was further explored for a wide variety of models, which otherwise gave way to mobile solitons, such as the complex Ginzburg-Landau equation, Sasa-Satsuma equation, Lakshmanan-Porsezian-Daniel equation, and many more [11; 12; 13; 14; 15]. The current paper revisits the familiar nonlinear Schrodinger's equation (NLSE) with quadratic-cubic and generalized quadratic-cubic nonlinearities. In this context, for both nonlinear structures, linear temporal evolution as well as generalized temporal evolutions are considered. It must be transparent that the derivation of these quiescent soliton solutions was successful by the aid of direct computer software usage that gave way to implicit forms of quiescent solitons [11; 12; 13; 14; 15]. The analytical derivation of such solitons, however, was carried out earlier by the usage of [PERSON]'s elliptic function expansion and extended the trial function approach [1,2]. The current paper implements enhanced [PERSON]'s mechanism to recover quiescent solitons to the NLSE.
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 5 Conclusions  
This paper considers a low complexity SCL decoding for polar codes that utilizes multiple CRC codes. In contrast with the other schemes using multiple CRC codes, we define the operational complexity of the SCL decoding in consideration of the hardware implementation, and optimize the CRC positions in terms of the complexity in combination with the ID-SCL decoding. Consequently, the proposed decoding obtains not only the complexity reduction from CRC-based early stopping, but also additional reduction from the reduced number of decoding paths accompanying negligible performance loss. It is also observed that, according to \(J\), there exists a tradeoff in complexity reduction in the low and high SNR regions.  
This work was supported by the research fund of Signal Intelligence Research Center supervised by Defense Acquisition Program Administration and Agency for Defense Development of Korea. This work was also supported in part by Samsung Electronics (Suwon, Korea).  
[PERSON] and [PERSON] analyzed decoding complexity, developed the algorithms and wrote the paper; [PERSON] analyzed implementation complexity of the decoder; [PERSON] designed the experiments and analyzed data. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Predicting Missing Soil Heavy-Metal Values Based on SA-FOA-BP  
Incomplete or incorrect soil sample data in collection or laboratory sample analysis is problematic. Simply deleting these data can reduce the experimental accuracy. Because there is a correlation between heavy metals in soil samples, interpolating missing data through correlation algorithms can ensure the accuracy of soil heavy-metal data prediction. In this study, the simulated annealing algorithm was improved by the fruit fly optimization algorithm to optimize the parameters of the BP neural network, improve the prediction accuracy of missing soil heavy-metal values, and overcome the poor convergence and easy local optimization of the BP neural network.
## 1 Introduction
Initially, it was developed to improve the quality of goods produced (manufacturing process development), but later its application was extended to many other fields of engineering, such as biotechnology, etc. Professional statisticians recognized [PERSON]'s contribution, especially to the development of designs for studying variation [30; 31]. A complete factor design identifies all possible combinations for a given set of factors affecting a process, known as control and noise parameters. The Taguchi method involves identifying the appropriate control agents to achieve optimal process results. Orthogonal arrays (OAs) are used to perform a series of experiments, and are used as a procedure to study all the parameters with a smaller number of experiments. Since most industrial experiments involve a significant number of factors, a complete factorial design may involve a large number of experiments. The results of these experiments are used to analyze the data and predict the quality of the data produced. The whole method is of the philosophy \"the larger the better\" when evaluating the results. The investigative results are modified into signal-to-noise ratio values (S/N) using available statistical software [26; 28; 32]. In this study, the S/N ratio is equivalent to the final degradation efficiency. In this work, presented chemically modified N-TiO\({}_{2}\) nanoparticles were synthesized by the sol-gel process. Full characterization, utilizing several techniques such as X-ray Powder Diffraction (XRD), micro-Raman, X-ray Photoelectron Spectroscopy (XPS), Field Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscopy (TEM) and Ultraviolet-visible (UV-Vis) Spectroscopy, was executed to confirm that the particles are on the nano-scale, as well as exhibiting the desirable physicochemical properties and morphology. A Taguchi design experiment of four factors and three levels was composed to evaluate and optimize the N-doped TiO\({}_{2}\) powder photocatalysis. All the photocatalytic results from the different Taguchi experiments were used to assess the factors mostly affecting the photocatalytic process, in order to finally propose a protocol for photocatalytic testing.