esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical and Sensory Evaluation of Silicone and
## 2 Results and Discussion
### Chemical Analyses Results
#### 2.1.3 Color and Lactic Acid Analyses  
The results for the color analyses are presented in Table 1. Significant although small differences between treatments were found for some of the color parameters (ANOVA; \(F\) = 15.5 \(p\) < 0.05), however overall, treatment with polymers had minimal effect. Lactic acid concentrations in PLA treated and control wines (g/L \(\pm\) SD) were also determined as follows: control spiked, 0.52 \(\pm\) 0.2; 50 cm\({}^{2}\)/L, 0.53 \(\pm\) 0.2; 200 cm\({}^{2}\)/L, 0.54 \(\pm\) 0.1; 600 cm\({}^{2}\)/L, 0.43 \(\pm\) 0.2. The decrease in lactic acid concentration in the PLA 600 treatment may be due to absorption of lactic acid by the polymer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Treatment** & **SO2 Resistant** & **Total Red** & **Wine Color** & **Wine Hue** & **Red Pigment** & **Total Phenolics** \\  & **Pigments (A.U.)** & **Pigments (A.U.)** & **Density (A.U.)** & **(A.U.)** & **Coloration (A.U.)** & **(A.U.)** \\ \hline Control spiked & 2.89 \({}^{\mathrm{a.b}}\) & 12.37 & 6.16 \({}^{\mathrm{a.b}}\) & 0.83 & 0.27 & 49.53 \({}^{\mathrm{a.b}}\) \\ Polyalactic acid 50 & 2.92 \({}^{\mathrm{a.b}}\) & 11.17 & 6.07 \({}^{\mathrm{a.b}}\) & 0.83 & 0.30 & 50.73 \({}^{\mathrm{b}}\) \\ Polyalactic acid 200 & 2.89 \({}^{\mathrm{a.b}}\) & 11.35 & 6.14 \({}^{\mathrm{a.b}}\) & 0.85 & 0.30 & 50.85 \({}^{\mathrm{b}}\) \\ Polyalactic acid 600 & 2.81 \({}^{\mathrm{a}}\) & 10.97 & 6.02 \({}^{\mathrm{a}}\) & 0.86 & 0.30 & 48.63 \({}^{\mathrm{a}}\) \\ Silicone 50 & 2.87 \({}^{\mathrm{a.b}}\) & 13.03 & 6.11 \({}^{\mathrm{a.b}}\) & 0.85 & 0.27 & 50.17 \({}^{\mathrm{a.b}}\) \\ Silicone 200 & 3.08 \({}^{\mathrm{a.b}}\) & 10.23 & 6.39 \({}^{\mathrm{a.b}}\) & 0.87 & 0.34 & 50.77 \({}^{\mathrm{b}}\) \\ Silicone 600 & 2.95 \({}^{\mathrm{b}}\) & 11.00 & 6.28 \({}^{\mathrm{a.c}}\) & 0.86 & 0.31 & 50.30 \({}^{\mathrm{a.b}}\) \\ \hline \hline \end{tabular}  
* Means with different letters are significantly different (ANOVA followed by Tukey’s HSD\({}_{0.05}\)).  
\end{table}
Table 1: Color parameters for untreated (‘control spiked’) wine and wine treated with poly-lactic acid or silicone of varying surface areas (50 cm\({}^{2}\)/L, 200 cm\({}^{2}\)/L and 600 cm\({}^{2}\)/L) determined through spectrophotometric analyses. Data represent mean values * \(\pm\) standard deviations.  
Figure 3: Concentrations of (**a**) ethyl isovalerate, ethyl 2-methylbutyrate; (**b**) ethyl butyrate, hexanol, ethyl hexanoate, isoamyl acetate, ethyl octanoate; (**c**) phenylethanol and (**d**) hexanoic acid and octanoic acid in untreated (‘control spiked’) wine and wine treated with polylactic acid of varying surface areas (50 cm\({}^{2}\)/L, 200 cm\({}^{2}\)/L and 600 cm\({}^{2}\)/L). Data represent mean values \(\pm\) standard deviations. Wines with different letters have significantly different concentrations for each compound (Tukey’s HSD\({}_{0.05}\)).
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 6 Conclusions  
Robustness and sensitivity metric-based tuning of the Kalman filter gain for single-channel speech enhancement has been investigated in this paper. At first, the noise variance was computed from the estimated noise for each noisy speech frame using a speech presence probability method. A whitening filter was also constructed to pre-whiten each noisy speech frame prior to computing LPC parameters. Then, the robustness and the sensitivity metrics were incorporated differently depending on the speech activity of the noisy speech to dynamically _offset_ the bias in Kalman gain. The noise variance and the AR model parameters were adopted as a speech activity detector. It is shown that the  
Figure 14: The mean preference score (%) comparison between the proposed and benchmark SEAs for the utterance sp27 corrupted with 5 dB colored \(f16\) noise.  
Figure 13: The mean preference score (%) comparison between the proposed and benchmark SEAs for the utterance sp05 corrupted with 5 dB non-stationary _babble_ noise.  
proposed tuning algorithm yields a significant reduced-biased Kalman gain, which enables the KF to minimize the residual noise and distortion in the enhanced speech. Extensive objective and subjective scores on the NOIZEUS corpus demonstrate that the proposed method outperforms the benchmark methods in real-life noise conditions for a wide range of SNR levels.  
The contribution of [PERSON] includes: preliminary experiments, experiment design, conducted the experiments, code writing, design of models, analysis of results, literature review, and writing of manuscript. [PERSON] provided supervision and aided the editing the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The subjective AB listening tests were conducted with the approval of Griffith University Human Research Ethics: database protocol number 2018/671.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Solution Times with Different Turbulence Modelling Approaches  
This study performed CFD solutions on an Intel(r) Xeon(r) CPU Process @3.50 GHz, 16 GB RAM, NVIDIA Quadro K4200 workstation operating on 12 CPUs. Mesh structures and their qualities may increase computational cost, and a higher-performance computer is required. However, LBM based on a particle-based approach uses a uniform Cartesian grid  
Figure 4: Adaptive Wake Refinement for Darrieus Vertical Axis Wind Turbine (5,000,0000 lattice cells). The spatial resolution varied from 0.00125 m to 0.05 m in this case. The wind direction using a white arrow, and we’ll also represent the plane’s orientation using a red arrow for the x-axis and a blue arrow for the z-axis.  
that can be refined as it is required depending on geometry and flow field. To see the effect of the number of lattices on the solution time, the CPU times of solutions with different turbulence modelling approaches were given in Table 1.  
The Courant Number plays a crucial role in governing the choice of time step scheme. By increasing the time step size, computational speed can be accelerated, making it desirable to set the Courant Number as high as feasible. On the other hand, smaller numbers mean a more stable solution but will be slower since it is doing more steps. The relation between the courant number and stable solution can be taken into consideration of the stability parameter. The stability parameter must be less than 1, so the configuration of the simulation time was adjusted to the given value of the Courant number of 0.8.  
When the solution time displayed in Table 1 is compared, the RANS solution with the Spalart-Allmaras model had a lower computational time than the others for 2,500,000 and 5,000,000 lattices. Large-Eddy Simulation (LES) method, WALE and Smagorinsky turbulence models have greater values owing to the influence of time-dependent solution, as shown in Table 1. In other words, The RANS approach relies on ensemble-averaged equations, which limits its ability to predict local flow fluctuations. In contrast, the LES approach utilizes spatially filtered equations, allowing for capturing large-scale flow structures based on the chosen filter size.
## 6 Detection Methods: Optical Frequency Comb Spectroscopy
### Cavity-Enhanced Direct Frequency Comb Spectroscopy  
CE-DFCS combines broad spectral bandwidth, high spectral resolution, precise frequency calibration, and ultrahigh detection sensitivity all in one experimental platform based on an optical frequency comb interacting with a high-finesse optical cavity [166; 182]. [PERSON] et al. demonstrate a minimum detectable absorption of 8 \(\times\) 10\({}^{-10}\) cm\({}^{-1}\), a spectral resolution of 800 MHz, and 200 nm of spectral coverage [183]. Moreover, combined with VIPA technology, the minimum detectable concentration is 1.7 \(\times\) 10\({}^{11}\) cm\({}^{-3}\)[184]. Whereas, a great deal of CE-DFCS applications are in the visible and NIR spectral region, MIR CE-DFCS is attracting a lot of attention [182; 185; 186]. It is noteworthy that the mode spacing between the cavity and comb should be well matched, or the operation will always too be complicated [186; 187].
## 3 Results and Discussion
### Green Metric Considerations  
The green metrics (AE: Atom Efficiency, RME: Reaction Mass Efficiency, MRP: Mass Recovery Percentage, 1/SF: 1 over the Stoichiometric Factor) were calculated according to a previous published article [64].  
The graphs of Figure 9 illustrate the assessment of green chemistry metrics for the oxidation of \(\beta\)-myrcene (a) and \(\beta\)-caryophyllene (b) under different reaction conditions. A wider distribution in the radar plots signifies a greener process, while a more squeezed shape indicates lower performance in terms of the considered green parameters.  
Figure 9: Radial distribution with green matrix of (**BmIm\(\mathbf{\mu}\)\(\mathbf{PMo_{11}VO_{40}}\)** with \(\beta\)-myrcene (**a**) and \(\beta\)-caryophyllene (**b**) for different entries in different conditions, where blue (H\({}_{2}\)O\({}_{2\text{aq}}\)), orange (H\({}_{2}\)O\({}_{2\text{aq}}\) + solvent; toluene in (**a**) and ethyl acetate in (**b**)), gray (TBHP\({}_{\text{ad}}\)), yellow (TBHP\({}_{\text{dec}}\)), purple (grafted form (**MRImd@PMo\({}_{\text{11}VO_{40}}\)**) with TBHP\({}_{\text{ad}}\)) and green (grafted form (**MRImd@PMo\({}_{\text{11}VO_{40}}\)**) with TBHP\({}_{\text{dec}}\)) are presented.  
In the case of \(\beta\)-myrcene, the gray curve (M9, TBHP\({}_{\text{aq}}\)) stands out with the best atom economy and the highest yield. However, despite its efficiency, the catalyst was not recovered, making the process less sustainable. In contrast, the grafted catalyst with TBHP\({}_{\text{dec}}\) (green) showed improved catalyst recovery but at the cost of a slightly lower yield, demonstrating a trade-off between recyclability and reaction efficiency. The same behavior can be observed between M5 (H\({}_{2}\)O\({}_{2\text{aq}}\)) and M6 (H\({}_{2}\)O\({}_{2\text{aq}}\) + toluene) where better yields cost lower MRPs and have more waste. The side reactions in \(\beta\)-myrcene oxidation affected the metrics, further constraining the green profile.  
On the other hand, for \(\beta\)-caryophyllene oxidation, all processes displayed better green metrics, with blue (H\({}_{2}\)O\({}_{2\text{aq}}\)) being the least efficient, yielding the lowest values in both the yield and sustainability parameters. Notably, the grafted catalyst with TBHP\({}_{\text{aq}}\) (C17, purple) exhibited similar yields to the TBHP\({}_{\text{dec}}\) (C18, green) system but with significantly improved mass recovery (MRP), emphasizing its advantage in catalyst recyclability. These results highlight that for greener oxidation, the grafted catalyst, particularly with aqueous TBHP\({}_{\text{aq}}\), offers a balance between efficiency and sustainability, making it a more environmentally friendly alternative, especially in such transformations with limited side reactions. This demonstrates that such products can be obtained with green procedures involving catalyst recovery and reuse.
## 4 Hardware Control  
The actuator is mainly controlled by pneumatic actuation. It is required to inflate and deflate the air chambers of the actuator in a systematic way to render a specific kind of feedback. By controlling the amount of air inside the air chambers, it is possible to control the pressure and vibration feedback of the actuator.  
The hardware used in this system is shown in Figure 3. To control the flow of air inside the air chambers, we used four electrically controlled air solenoid valves (SC0526 GC; Skoocom Technology Co. Ltd.: Shenzhen, Guangdong, China) for the two air chambers. Two of the valves were used as positive ones, and the other two were used as negative ones. By using positive solenoid valves, air fills the chamber from the air source, while the negative solenoid valves are used to exhaust air to the environment. A diode (1N4007) was used with each solenoid valve to control and eliminate the transient voltage when the magnetic coil of the solenoid valves suddenly loses power. A 16 g threaded CO\({}_{2}\) cartridge from TRAVELO was used as an air source. The Arduino Uno micro-controller was used for maintaining the serial communication of the computer and actuator. The digital input from the computer goes to the MOSFET transistor circuit through the Arduino using serial communication. By controlling the air solenoid valves, specific haptic feedback is rendered. The chambers of the actuator inflate when air is pumped inside, while they deflate when air is exhausted. The pressure regulator used for our actuator ranges from 0 psi to 30 psi. However, we used air pressure in the range of 5 psi to 10 psi.  
Figure 3: Hardware component for pneumatically controlling the actuator. (**a**) Electronics including the micro-controller, (**b**) the air cartridge with solenoid valves and silicone tube, and (**c**) the silicone-made pneumatic fingertip actuator.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 2 System Model  
Considering the user's needs, the spectrum bandwidth to be sensed by the SUs is greater than 2 GHz in this work. If the wideband spectrum sensing is directly performed, a complex FB is required, which is unacceptable in practical systems, especially mobile devices, where power consumption and its size are critical. If only wideband spectrum prediction is performed, the perceived accuracy may not reach the needs of the cognitive user. Therefore, the research method adopted in this work is a combination of rough estimation and fine sensing. In the rough estimation stage, the wideband spectrum is firstly divided into multiple sub-bands, which then are clustered based on the correlation between sub-bands. Subsequently, one detection sub-band is selected for direct detection of the clustered sub-bands, while the state of other sub-bands in the same group is obtained by way of prediction or estimation. Finally, the sub-bands selected by the rough estimation with different transmission services are accurately sensed based on RFB, as shown in Figure 1.  
The conventional narrowband energy detection (ED) is able to determine whether the channel is occupied by comparing the detection statistic of the received signal with the pre-set threshold. For wideband spectrum, the RFB divides the received wideband signals into \(M\) sub-bands, applying the ED for each independent sub-band for spectrum sensing. Judging by the number of detected idle or occupied sub-bands, the number of RFB sub-bands is adjusted accordingly. The system block diagram is shown in Figure 2. The detection statistic of each sub-band is compared with the same threshold, and the decision center is responsible for determining the adjustment of the FB sub-band number. The SU would no longer sense the channels during a data transmission period--that is, the channel state is assumed to be unchanged. If the data transmission fails, the channels need to be re-sensed. Otherwise, the channels states are obtained again prior to the next data transmission period.
## 4 Case Study
### System Failure Prognostics
#### 4.2.2 RUL Prediction with Disturbance  
There were many influencing factors in the actual system, and the health condition changed dynamically, so it was difficult to accurately evaluate the RUL of the system in one prediction. For example, there was redundant equipment in the system, and equipment switching would lead to a substantial recovery of function. Obviously, if the previous prediction method is used, the error could be very large. Therefore, it was necessary to achieve continuous tracking of the health condition of the system and the dynamic prediction. Dataset B was used to test the prediction of the health condition when some equipment of the system switched.  
It can be seen from Figure 11 that if the equipment was not switched, according to the previous degradation law, the health condition of the system would reach the failure threshold at T2 h. In the vicinity of region B, when the pump was switched, system function  
Figure 9: Decomposition of SHI.  
was restored and the degradation curve changed. At that time, using RUL prediction would not be able to accurately predict the remaining life. Therefore, to realize the adaptive prediction of health conditions, updating the training sample is necessary. With the new feature data added to the training set, the final life T1 was close to the real situation. The dynamic prediction method could track the change of system health condition and realize the dynamic prediction of failure time.
## 3 Results
### Correlation between Endocrine-Metabolic Setting and Blood Pressure and Body Composition
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Correlations** & **TSH** & \multicolumn{4}{c}{**FT3**} & \multicolumn{4}{c}{**FT4**} & \multicolumn{4}{c}{**FT3/FT4 Ratio**} \\ \hline  & \(r\) & \(p\) & \(r\) & \(p\) & \(r\) & \(p\) & \(R\) & \(p\) \\ \hline B-Systolic BP (mmHg) & \(-0.07\) & 0.77 & 0.11 & 0.65 & 0.09 & 0.72 & 0.13 & 0.62 \\ \hline B-Diastolic BP (mmHg) & \(-0.11\) & 0.67 & \(-0.04\) & 0.88 & 0.21 & 0.40 & \(-0.01\) & 0.96 \\ \hline HR (ppm) & \(-0.21\) & 0.41 & \(-0.38\) & 0.12 & 0.37 & 0.14 & \(\mathbf{-0.50}\) & **0.03** \\ \hline C-Systolic BP (mmHg) & \(-0.15\) & 0.56 & 0.16 & 0.53 & 0.15 & 0.57 & 0.08 & 0.76 \\ \hline C-Diastolic BP (mmHg) & \(-0.01\) & 0.68 & \(-0.04\) & 0.90 & 0.20 & 0.43 & 0.00 & 0.97 \\ \hline AI@75 & 0.22 & 0.39 & \(-0.27\) & 0.28 & \(-0.07\) & 0.80 & \(-0.15\) & 0.54 \\ \hline PWA & 0.00 & 0.99 & 0.17 & 0.51 & \(-0.25\) & 0.32 & 0.31 & 0.21 \\ \hline Weight (Kg) & \(-0.16\) & 0.52 & \(-0.41\) & 0.08 & 0.30 & 0.22 & \(\mathbf{-0.58}\) & **0.01** \\ \hline BMI (Kg/m\({}^{2}\)) & \(-0.40\) & 0.09 & \(-0.43\) & 0.07 & 0.33 & 0.17 & \(\mathbf{-0.59}\) & **0.009** \\ \hline WHR & 0.02 & 0.91 & \(-0.05\) & 0.83 & 0.16 & 0.51 & \(-0.10\) & 0.68 \\ \hline Total FM (\%) & \(-0.04\) & 0.87 & \(-0.45\) & 0.06 & 0.23 & 0.37 & \(\mathbf{-0.63}\) & **0.006** \\ \hline Lean/Fat Mass & \(-0.03\) & 0.90 & 0.39 & 0.11 & \(-0.23\) & 0.36 & **0.61** & **0.009** \\ \hline VAT (g) & \(-0.10\) & 0.70 & \(-0.17\) & 0.53 & 0.31 & 0.25 & \(-0.32\) & 0.23 \\ \hline IMAT (Kg) & \(-0.08\) & 0.74 & \(-0.49\) & 0.05 & 0.22 & 0.41 & \(\mathbf{-0.68}\) & **0.003** \\ \hline ASMI & \(\mathbf{-0.62}\) & **0.01** & \(-0.00\) & 0.99 & \(-0.05\) & 0.86 & 0.17 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Thyroid laboratory profile correlations. [PERSON]’s coefficient correlation coefficient was used. Bold numbers represent significant correlations with \(p<0.05\). TSH: Thyroid-Stimulating Hormone; FT3: Free Triiodothyronine; FT4: Free Thyrroxine. \(r=\) [PERSON]’s correlation coefficient; \(p=p\) value. FT4 levels demonstrate weak to negligible correlations with the variables measured in the study, suggesting a limited influence on blood pressure regulation and body composition. Table 5.  
We recognized the limitations posed by the small sample size and emphasize the need for caution in interpreting these findings. We confirmed our results using both the Mann-Whitney U test and [PERSON]'s correlation.
## 5 Validation
### Experiments  
The results from experiment 1 were validated automatically with help from the MiniZinc Python library, while the results from experiments 2 and 3 had to be validated manually, but only once for both sets of experiments:  
1. Evaluation of Generated Constraint Model's Syntax Correctness  
In this experiment, we quantified the percentage of generated constraint models that exhibited syntactical correctness, defined as the absence of errors or warnings during parsing and validation.
2. Evaluation of Generated Constraint Model's Solution Correctness  
We further assessed the percentage of generated constraint models that, while syntactically correct (i.e., free of errors or warnings), failed to align with the ground truth solution derived from the original problem definition. These models, though valid in syntax, produced solutions that deviated from the expected behavior. Such models may represent plausible solutions to related or analogous problems but do not accurately address the specific constraints and requirements of the target problem.
3. Evaluation of Generated Constraint Model's Validity  
Finally, we measured the percentage of constraint models generated by the ACMG engine that not only passed the MiniZinc validation checks without errors or warnings but also produced solutions consistent with the ground truth. These models were deemed fully valid, as they exhibited both syntactical correctness and functional alignment with the expected behavior defined by the input prompt. A valid model must precisely reflect the user's intent and adhere to the problem's specified constraints.
## 5 Conclusions
42090012) and Open Fund of State Key Laboratory of Remote Sensing Science and Beijing Engineering Research Center for Global Land Remote Sensing Products (Grant No. OF202312). **Data Availability Statement:** Datasets can be downloaded at following links. Human density (HD) and gross domestic product (GDP) can be obtained from links [[https://www.worldpop.org/](https://www.worldpop.org/)]([https://www.worldpop.org/](https://www.worldpop.org/)) and [[https://figshare.com/articles/dataset/17004523/1](https://figshare.com/articles/dataset/17004523/1)]([https://figshare.com/articles/dataset/17004523/1](https://figshare.com/articles/dataset/17004523/1)) (accessed on 16 November 2022), respectively. Leaf area index (LAI) and gross primary productivity (GPP) is available at [[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), accessed on 21 November 2022. NDVI data can be downloaded from the link of MOD13A1 v061--[[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 8 February 2023). The land cover (LC) data is available at [[https://zenodo.org/records/8176941](https://zenodo.org/records/8176941)]([https://zenodo.org/records/8176941](https://zenodo.org/records/8176941)), accessed on 3 October 2022. The temperature (Tmp) and precipitation (Pre) datasets are available at [[https://poles.tpdc.ac.cn/zh-hans/](https://poles.tpdc.ac.cn/zh-hans/)]([https://poles.tpdc.ac.cn/zh-hans/](https://poles.tpdc.ac.cn/zh-hans/)), accessed on 9 November 2022. The PM2.5 data can be downloaded from [[https://zenodo.org/record/6398971](https://zenodo.org/record/6398971)]([https://zenodo.org/record/6398971](https://zenodo.org/record/6398971)), accessed on 21 November 2022. Finally, the soil nutrient availability (SNA) comes from [[https://www.fao.org/](https://www.fao.org/)]([https://www.fao.org/](https://www.fao.org/)) soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/ (accessed on 16 November 2022), and the urban built-up dataset for dividing core and expansion regions is available at [[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 15 October 2022. **Acknowledgments:** We appreciate the support of the Ministry of Ecology and Environment. We appreciate the \"National Earth System Science Data Center, National Science & Technology Infrastructure of China\" for providing GPP and LAI data. We appreciate the \"National Tibetan Plateau/Third Pole Environment Data Center\" for providing urban built-up data. We appreciate \"A Big Earth Data Platform for Three Poles\" for providing temperature and precipitation data. We also appreciate the \"Food and Agricultural Organization of the United Nations\" for providing data of soil nutrient availability. We also thank the anonymous reviewers and editor for their valuable comments on the manuscript. **Conflicts of Interest:** The authors declare no conflicts of interest.