esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Supercritical Fluid Technology
#### 2.1.2 MIP-Supported Materials Preparation in scCO\({}_{2}\)  
MIP-supported materials have been shown to be a very appealing area, were the properties of the core materials can be improved or changed, which can have a strong impact on the materials' performance, such as in the production of a MIP layer on supports as silica, titanium oxide, quantum dots or polymers, among others.  
Supercritical CO\({}_{2}\)-assisted synthesis of an ultrasensitive amphibious quantum dot-molecularly imprinted sensor to BPA was reported by [PERSON] et al. [51] where CdTe quantum dots were previously synthesized and functionalized in a conventional way with mixed thiols, to confer both stability and reactivity. Then, the MIP containing the quantum dot was produced in scCO\({}_{2}\), using MAA as functional monomer, EGDMA as crosslinker and BPA as template, by FRP. Quantum Dots (QDs) as spherical discrete particles were obtained with particle size diameter of 5-10 nm with a low degree of aggregation. On the other hand, CdTe@MIPs presented larger sizes, due to their surface polymer coating. QDs could be successfully incorporated within molecularly imprinted matrices using supercritical carbon dioxide, and sensing properties of CdTe@MIP towards BPA was evaluated by fluorescence spectroscopy by using only 2 mg of material in the presence of different concentrationsof BPA aqueous solutions, up to 500 nM. CdTe@MIP demonstrated a well-defined highly sensitive quenching of the fluorescence, showing it to be a promising candidate for the development of BPA sensors (4 nM detection limit).  
[PERSON]'s group has developed large MIP-layered core-shell beads using supercritical carbon dioxide technology, for gravity-driven purification systems to remove impurities from pharmaceutical crude mixtures (Figure 1) [52]. scCO\({}_{2}\) was used to pre-functionalized core-shell particles and perform the MIP-layer synthesis over large silica beads (75-200 \(\upmu\)m), where MAA was used as monomer, EGDMA as crosslinker, and ACET as a model pharmaceutical impurity. Particles were loaded on a SPE column, and their performance evaluated in gravitational mode using a 250 ppm ACET solution in ACN. Imprinted particles could remove 550 \(\upmu\)g\(\text{{}_{ACET}}\cdot\text{{g}_{support}}^{-1},24\%\) more than the control particles.
## 4 Case Study
### Simulation Results and Analysis
Therefore, the reduced carbon transaction cost in Scenario 4 is very limited. The sequestered CO2 is stored in large quantities for a long time, thus increasing the storage cost, and the total system cost is higher than that in Scenario 1. After considering P2G power stations, Scenario 5 and Scenario 6 solve the problem of long-term storage of a large amount of CO2. Compared with Scenario 4, the carbon trading costs are reduced by 13.98% and 16.72%, respectively, and the transmission and storage costs are reduced by 24.78% and 19.84%, respectively. In Scenarios 2 and 3, the operating cost of P2G is affected by the additional carbon purchase cost, which reduces the ability to consume wind power. In Scenarios 5 and 6, the methane production process of P2G is provided with carbon source by carbon capture device, without considering the additional carbon purchase cost, wind power and CO2 can be consumed while generating income. Compared with Scenario 2 and Scenario 3, the abandonment cost is reduced by 24.02% and 43.52%, respectively. Figure 7 shows the thermal output of CHP units in Scenarios 5 and 6. As can be seen from Figure 8, when wind abandonment is severe (from 3 to 6), the thermal output of CHP unit in Scenario 6 is reduced, so that its forced electrical output continues to decrease, leaving more room for wind power consumption. Figure 7: Wind power curtailment of different scenarios. In conclusion, compared with the introduction of carbon capture, P2G and heat recovery alone, the optimized scheduling model of power-gas-heat integrated energy system proposed in this paper, which combines P2G heat recovery and carbon capture, has more significant effects on wind power consumption and CO\({}_{2}\) emission reduction.
# Regeneration Performance of Activated Carbon for Desulfurization
## Results and Discussion
### SO2 Adsorption  
#### 3.2.1 Effect of AC Dosage on SO2 Adsorption  
As seen in Figure 4, at amounts of 2 g, 4 g, 6 g, 8 g, and 10 g, the average SO2 adsorption rates of AC in ten minutes were 42.3%, 55.8%, 62.3%, 68.5%, and 79.7%, respectively. The desulfurization efficiency is improved by the increase of the AC amount. This indicates that the SO2 saturation adsorption capacity of the samples is highly influenced by the amount of AC. On increase of dosage, more active  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cycles** & **Micropore Area(m2/g)** & **BET Surface(m2/g)** &
\begin{tabular}{c} **Total Pore** \\ **Volume(cm3/g)** \\ \end{tabular} & **Average Pore Size(nm)** \\ \hline
0 & 356.449 & 463.601 & 0.246 & 2.594 \\
2 & 387.726 & 498.530 & 0.273 & 3.074 \\
4 & 395.423 & 518.272 & 0.291 & 3.299 \\
6 & 435.656 & 563.622 & 0.321 & 3.229 \\
8 & 463.761 & 589.350 & 0.319 & 5.027 \\
10 & 443.978 & 664.601 & 0.373 & 2.793 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Surface area and pore volume analysis.  
Figure 3: The N2 sorption isotherms (**a**) and pore size distributions (**b**) of the samples (where AC. stands for fresh AC, Reg-ACh stands for the nth regenerated AC).  
surface areas and sites are provided from the AC, which develops the removal capacity of SO\({}_{2}\)[26]. The best option combines both the cost and desulfurization efficiency, so we fixed the AC dosage for the rest of the experiment at an amount of 4 g.
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 3 Materials and Methods  
Differential evolution algorithm is an optimization algorithm that uses real number coding to perform heuristic random search in a continuous space. Because this method can better solve some of the difficulties encountered by traditional genetic algorithms in solving complex problems, it has received widespread attention and application. In video coding, differential evolution algorithms can be used to find optimal coding parameters to achieve higher video coding quality and compression rate. Quantization is a key module in video coding, and the quantization parameters in the process are crucial. However, under the current encoding standard, under the default encoding configuration, the quantization parameter of each CTU is a fixed value and does not change adaptively according to the video content or scene complexity. This flaw leaves room for improvement in the encoding compression rate. This section applies the differential evolution algorithm to the quantization parameter decision-making process in video coding, proposes a quantization parameter decision-making scheme based on the differential evolution algorithm, and allocates quantization parameters for each CTU, which makes the coding efficiency higher while reducing mismatch rate. The process of quantization parameter decision scheme improved by using differential evolution algorithm is shown in Figure 1.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### Data Security and Encryption Methods  
In blockchain-based systems, data security is a fundamental criterion that ensures data integrity, confidentiality, and protection [110; 114]. These technologies rely on various cryptographic techniques to maintain secure and tamper-proof data management [115; 116; 109]. Security and privacy are particularly critical in sensitive data environments, such as the healthcare sector, where strict data protection measures are required [24; 101; 103].
## 2 Related Works
In [22], an MLWDF based joint scheduling has been proposed with multiple traffic classes, where the priority of traffic class and waiting time in the queue of each packet are considered. Although the authors assume that the average SINR for each user is the same, they also ignore the Channel Quality Indicator (CQI) feedback.
# Respiration Monitoring for Premature Neonates in NICU
## 1 Introduction  
Vital signs, such as heart rate, blood pressure, respiratory rate, and body temperature, are physical parameters that can be measured and used to assess physiological state and functioning. Monitoring of vital parameters is a crucial topic in neonatal daily care. Premature infants have an immature respiratory control that predisposes them to apnea/periodic breathing, haemoglobin oxygen desaturation, and bradycardia [1; 2]. Apnea is defined as the status of cessation of respiratory airflow, whereas periodic breathing is characterized by groups of respiratory movements interrupted by small intervals of apnea [3]. Continuous monitoring of respiration for premature infants is critical to detect abnormalities in breathing and help develop early treatments to prevent significant hypoxia and central depression from apnea. A long-term continuous monitoring approach of respiration may also be used to assess sleep stage, which varies with different clinical conditions [4; 5].  
The existing methods for monitoring respiration include nasal thermocouples, spirometers, transthoracic inductance, respiratory effort belt transducer, piezoelectric transducer, optical sensor (pulse oximetry), strain gauge, impedance plethysmography, and electrocardiogram (ECG). Currently, the respiration of premature infants in a neonatal intensive care unit (NICU) is monitored by bedside monitors. ECG is considered as the standard reference measurement for respiration, since ECG can provide stable and robust monitoring for a NICU. However, the pressure of the contact sensor may also change the local skin perfusion, which yields not a true measurement as compared to the non-contact sensor. The electrodes may exert pressure to the skin, yielding to tissue compression and vascular insufficiency. As a consequence, applying the ECG electrodes on infant skin for a long timeincreases the risk of trauma and infections. Removing the adhesives from the immature skin, as part of regular care, can damage the immature skin of preterm infants, as well as cause stress and pain [6; 7]. This technique is impractical for home care, since the sensors have to be placed by skilled caregivers, and wearing the sensors also causes inconvenience for everyday life [8; 9].  
A non-contact monitoring system is a good alternative to improve infant comfort and safety and it also has the potential for monitoring at home. Few works to date have investigated video-based contactless methods for monitoring respiration. In this study, we developed and evaluated a contactless respiration measurement method based on video monitoring. The contribution of our method is: (1) we propose an approach for non-contact respiration monitoring; and (2) we validated the method on clinical data acquired in NICU.
## 4 Discussion  
### Assessment of MODIS NDVI Trends and Climate Variables  
The pixel-based results of the annual and seasonal trends of vegetation dynamics in the Horn of Africa (Figure 3) revealed that there were areas of vegetation change in the last two decades. There were some changes in MODIS NDVI trend towards an overall positive trend, however, 49.3% of the pixels in the region experienced a decreasing NDVI trend mainly because of the increasing climate variability, recurrent drought and vegetation degradation. [PERSON] et al. [24] identified that the NDVI trend was increasing in 47% of the pixels and decreasing in 49% of the pixels across East Africa during 1998-2015. Our result showed a slight improvement in the average annual NDVI trend in comparison to [PERSON] et al. [47] which concluded the average NDVI in the Horn of Africa slowly decreased from 1998 to 2013, this could be due to the difference in the study duration as we included some recent years (until 2019) in the analysis, and the break-in 1998 was another factor to bring slight variation in the trend across the region with fluctuations in the intensity of precipitation [22].  
The weakest NDVI trends in the Horn of Africa were observed in the grassland cover and the tropic-cool/arid zones in the last two decades. The grassland in the arid lands was most affected because of the increasing aridity and recurrent drought with climate variability in the region, and the grassland and shrubland covers are more sensitive to the impacts of climate change [1; 25; 33]. The highest vegetation increase was observed in from September to November season, the time lag effect of the short rainy season from June to August in Ethiopia, Eritrea and Somalia, and the effect of ENSO might be large on the NDVI variations [24].  
The main climatic drivers for the vegetation changes in the Horn of Africa showed that the LST was negatively and significantly correlated with NDVI, while precipitation was positively and significantly correlated in the past 20 years (Figure 6). The results match with the findings of [PERSON] et al. [25] to the reverse proportional and significant correlation between NDVI and temperature (LST in our case) which could be attributed to global warming, average net warming and non-radiative processes [28], and the correlation showed the highest significance in the tropical warm-subhumid zone. On the other hand, they reported there was insignificant proportional relationship between NDVI and Precipitation, however, our result reveals that the response of NDVI to rainfall was significant in the region as the intensity of precipitation and fluctuation has been increasing [1]. The response of vegetation to precipitation was poor in the tropical cool-humid zone, this can be associated basically to the influence of'saturation' of the NDVI which reacted slowly through time even with the increase in the intensity and fluctuation of rainfall in humid areas [48; 49].
## 4 Result and Discussion
### Two-Stage Linear Shut-Off Valve Water Hammer Change Rules
#### 4.2.2 Change Rule of Water Hammer Pressure at the End Valve  
Based on the previous sections, it is evident that the differences in the maximum water hammer pressures among the four pipeline units were minimal. Consequently, this analysis focused on the variations in the water hammer pressure at the end valve of unit X2. Figure 8 illustrates the changes in the water hammer pressure at the end valve of the X2 pipeline unit under two-stage linear valve closure conditions. The extreme values of the water hammer pressure for closure conditions T2-1, T2-2, T2-3, T3-1, T3-2, and T3-3 ranged from 2180 to 5482 kPa, 2323 to 4680 kPa, 2676 to 4294 kPa, 2881 to 4845 kPa, 3009 to 4256 kPa, and 3138 to 3979 kPa, respectively. It can be observed that, at the same time, the water hammer pressure fluctuations at the end valve for the 80% fast closure were greater than those for the 60% fast closure. As time progressed, the amplitude of the water hammer pressure waves gradually stabilized. In this study, under the closure conditions T2-1 and T3-1, the maximum extreme value of the water hammer pressure ultimately approached 4500 kPa, exceeding 1.3 times the normal operating head, thereby failing to meet the safety requirements for the operation of the power station.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Valve Closure Condition** & \begin{tabular}{c} **Max Pressure** \\ **to D1 (KPa)** \\ \end{tabular} & \begin{tabular}{c} **Max Pressure to** \\ **D2 (KPa)** \\ \end{tabular} & \begin{tabular}{c} **Max Pressure** \\ **to X1 (KPa)** \\ \end{tabular} & \begin{tabular}{c} **Max Pressure to** \\ **X2 (KPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Max Pipeline** \\ **Pressure (KPa)** \\ \end{tabular} &
\begin{tabular}{c} **Negative Pressure** \\ **Occurrence** \\ \end{tabular} \\ \hline T2-2 & 4724 & 4718 & 4692 & 4680 & 5000 & No \\ T2-3 & 4280 & 4277 & 4303 & 4294 & 5000 & No \\ T3-2 & 4263 & 4260 & 4264 & 4256 & 5000 & No \\ T3-3 & 3985 & 3983 & 3985 & 3979 & 5000 & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic pressure table of each pipeline under two-stage shut-off mode (unit: Kpa).  
Figure 8: Variation of end water hammer pressure under two-stage shut-off valve at different times of X2.  
Therefore, the valve closure rate should not be excessively high to avoid generating a water pressure that surpasses the maximum allowable pressure of the unit in practical engineering operations.
## 4 Analysis of Results and Discussion  
### Comparison of Model Prediction Performance before and after GAN Augmented Training Set  
To validate the effectiveness of the GAN data augmentation technique in handling small-sample STRC shear wall data, this study employed six models, including traditional ML models (BPNN, KNN), boosting ensemble learning models (Catboost, LightGBM), bagging ensemble learning model (RF), and the proposed Blending-CRKL model, to predict the failure mode of STRC shear walls. By comparing these six models, we gain a comprehensive understanding of the changes in prediction performance metrics for different types of models before and after applying GAN data augmentation. These models were trained using both the initial training set and the GAN-augmented training set; the changes in prediction performance metrics are presented in Figure 8, and detailed results are provided in Table 2.  
Figure 8: Performance comparison of STRC shear walls failure modes prediction models before and after GAN augmented training set: (**a**) accuracy; (**b**) precision; (**c**) recall; (**d**) F1-score.  
From Figure 8, it can be observed that the accuracy, precision, recall, and F1 scores of all six models have significantly improved after using GAN for data augmentation. The accuracy of the six models has increased from 0.75-0.88 to 0.88-0.97. Particularly, the proposed Blending-CRKL model achieved an accuracy of 0.97 after improvement. It is also evident that both in the GAN-augmented dataset and the original dataset, the Blending-CRKL model outperforms other models in terms of accuracy, precision, recall, and F1-score. According to Table 2, compared to the initial training set, the average accuracy, precision, recall, and F1 scores of the six models have improved by 13%, 81%, 48%, and 67%, respectively. The proposed Blending-CRKL model in this paper has also shown improvement in all four metrics, with increases of 10%, 94%, 48%, and 50%, respectively.  
In summary, for the failure modes prediction of STRC shear walls, GANs address the issue of insufficient training samples by generating new samples. The use of GANs to augment the training set significantly improves the predictive performance of the model, indicating that GANs are highly effective in handling small sample databases.
## Materials and Methods
### Chemical Characterization
#### 2.3.5 Profile of Volatile Compounds with HS-SPME-GC/MS  
A SPME procedure was adapted from a published report [17]. An aliquot (1 mL) of a saturated NaCl solution was placed into a 20 mL glass vial, then 8 mL of wine sample was added. After that, 50 \(\upmu\)L of 2-methyl-3-pentanol internal standard (I.S.) solution (dilution 1/50 of I.S. in ethanol, corresponding to a final spiked concentration of 103 mg\(\cdot\)L\({}^{-1}\)) was added and the vial was closed with a perforable screw cap, being kept in a heating bath at 40 \({}^{\circ}\)C for 5 min with continuous stirring at 300 rpm. Afterwards, stirring was stopped and a SPME fiber (DVB/CAR/PDMS, 50/30 \(\upmu\)m, 1 cm) was exposed to the headspace of the 20 mL glass vial for 30 min under continuous heating (40 \({}^{\circ}\)C) without stirring, in a configuration similar to the one reported in a published recommended practice [18].  
The GC/MS analysis was carried out with manual injection on an Agilent 7890A gas chromatograph coupled to an Agilent 5975 quadrupole mass detector (Agilent Technologies Italia SpA, Cernusco sul Naviglio, Milano, Italy). The thermal desorption took place at 240 \({}^{\circ}\)C for 6 min. The separation was performed on a MEGA-WAX Spirit column (0.30 \(\upmu\)m/0.18 mm/40 m) in split mode (1:10). Helium carrier gas flow rate was 0.7 mg\(\cdot\)L\({}^{-1}\) (constant flow). The temperature program was: 40 \({}^{\circ}\)C for 0.2 min, 40-180 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\), then 180-230 \({}^{\circ}\)C with 10 \({}^{\circ}\)C min\({}^{-1}\) rate, and finally 3 min at 230 \({}^{\circ}\)C. The mass spectrometer operated in EI mode at 70 eV. The mass range was m/z 34-360 at 1 spectrum-s\({}^{-1}\); the temperature of the ion source and quadrupole were 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively.  
The samples were analyzed in a random order to avoid biases. The data integration was performed automatically using the provided tool (Chemstation, Agilent). The total ion current (TIC) peaks were expressed as areas vs. retention time and were manually aligned using the MS spectra to monitor the correctness of the alignment. The peaks were assigned using an integrated approach: (a) by comparison with reference mass spectra (NIST 2011 database); (b) possible isomers/analogues were assigned by calculating the linear retention indexes (LRI) on the base of the elution series of linear alkanes standard (analytical standards C\({}_{7}\)-C\({}_{40}\) in dichloromethane; Sigma-Aldrich, Milano, Italy), plus additional injection of pure hexane and pentane. Consequently, non-isothermal linear retention indexes (LRI) were calculated for the identified compounds from the reference alkane standards retention times [19]. The NIST library was searched for matching the acquired and reference MS spectra and for comparing measured and theoretical values of LRI for the most likely assignments and the most similar stationary phases.
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 3 CAN Network Elements
### Spectral Converters
#### 3.3.1 Functionality and Operating Principle
Frequency detuning range is defined as the difference between the centre frequencies of the input optical signal and the desired output optical component, as shown in the output signal spectrum in Figure 11. It is easy to appreciate that the four RF driving signals and three DC bias voltages can be controlled to dynamically optimise the operating conditions of the spectral converter to simultaneously achieve three critical and complimentary performance criteria, which are (i) maximisation of the conversion efficiency of the desired optical component, (ii) an enhanced frequency difference between two adjacent converted optical components for a specific driving RF signal frequency and (iii) minimisation of the number of unwanted parasitic optical components. The optimum spectral converter operating conditions for a specific detuning range have been identified analytically in [11], where performance was also explored statistically using  
Figure 10: Example of spectral converter application in a CAN. (Insets shows spectra at indicated ports). a wide range of randomly generated operating conditions in order to fully confirm the validity of the identified optimum operating conditions. To control a spectral converter's configuration in the dynamically reconfigurable CAN, the spectral converter would include an embedded DSP controller, which periodically communicates with a centralised SDN controller, via an extended OpenFlow protocol, in order to dynamically set and adjust the converter's configuration. The centralised SDN controller would also exchanges control information with other DSP controllers embedded in other spectral converters, transceivers and soft-ROADMs to coordinate and manage the overall connectivity within the CAN. Therefore, the highly flexible spectral converters are fully compatible with SDN-controlled CAN network scenarios.