esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Nanoparticles Characterization
Data represent mean \(\pm\) SD (\(n=3\)). discontinuous phase (aqueous phase). In the second emulsification, some plasmid could migrate to the continuous water phase, decreasing the amount of plasmid that could be encapsulated. If the initial amount of plasmid used in the preparation is increased, then more plasmid could be retained in the discontinuous water phase, resulting in more plasmid physically entrapped into the nanoparticles, until some saturation point is reached. Comparing to other research reports, [PERSON] et al. obtained an encapsulation efficiency of 53% in their experiments, but as previously noted, these experiments lead to larger particle size [28]. When encapsulating drugs such as cancer treatment medication, higher efficiencies can be achieved, as [PERSON] et al. reported an efficiency of 87.3% [12]. [PERSON] et al. encapsulated an anticancer drug conjugated with PEG-FA with a drug loading efficiency of 97.2% [60]. [PERSON] et al. performed plasmid encapsulation in chitosan and obtained a particle loading capacity of 51.2% with an encapsulation efficiency of over 75% [33]. [PERSON] et al. reported encapsulating plasmid using PEG-PEI polyplexes with PLGA with encapsulation efficiencies of 26.4%-27.8% [11]. [PERSON] et al. obtained a drug load in the range of 0.71%-0.77% which led to 88%-92% encapsulation efficiency when encapsulating a combination of cancer medication and plasmid DNA in porous PLGA nanoparticles and as of plasmid only, load was 0.036%-0.041% with plasmid encapsulation efficiency of 36%-40% [61]. It is noticeable that, higher efficiencies can be achieved during encapsulating medication in comparison with plasmid DNA, as negative charge of plasmid increases its encapsulation efficiency in PEI 25K [11; 61]. However, PEI is non-degradable and molecular weight affects cytotoxicity and gene transfer activity; as high molecular weight PEI (PEI 25K) showed high transfection efficiency with significant cytotoxicity [62]. Although encapsulation efficiency obtained using PEI is larger, in this project, the polymers used in formulation of nanoparticles are degradable and/or do not present cytotoxicity issues. The morphology of pDNA-NPs is smooth and spherical as confirmed by SEM and AFM images as shown in Figure 4, with particles size in the range of 200-250 nm. Spherical morphology is congruent by other observations reported in literature for a similar system [13; 28; 61]. Figure 4: **(A)** Scanning electron microscopy (SEM) images of pDNA-NP with 0.8% theoretical pVAX1-NH36 loadings (TDL). Atomic force microscopy (AFM) images of pDNA-NP with **(B)** 0.2% TDL; and **(C)** 0.5% TDL.
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation
### Solution of the Charger Deployment
#### 5.3.1 Varying the Number of PoIs  
The scalability of the GH and the PSO solutions is verified by varying the number of PoIs to increase the problem scale. Figure 7 shows that as the number of PoIs increases, the number of deployed chargers also increases, and the growth trend gradually slows down. Both the GH and the PSO solutions based on the GHDSAE solution can always deploy fewer chargers than the GH and the PSO solutions based on the GHDS solution. In Figure 7a, the GHDSAE-based GH solution saves 8.09-27.19% (with an average of 19.27%) chargers than that of the GHDS-based GH solution. In Figure 7, the GHDSAE-based GH solution saves 0.14-18.58% (with an average of 9.92%) solution time than that of the GHDS-based GH solution. In Figure 7c, the GHDSAE-based PSO solution saves 1.97-21.72% (with an average of 14.44%) chargers than that of the GHDS-based PSO solution. In Figure 7d, the GHDSAE-based PSO solution overspends 2.23-25.95% (with an average of 16.86%) solution time than that of the GHDS-based PSO solution. As a result, the aggregation effect of sensors can effectively reduce the deployment of chargers at the cost of slightly incremental computing time. Likewise, both the PSO solution based on the GHDSAE and the GHDS solutions can always deploy fewer chargers than that of the GH solution based on the GHDSAE and the GHDS solutions. In Figure 7a,c, the GHDS-based PSO solution saves 17.71-40.34% (with an average of 23.98%) chargers than the GHDS-based GH solution. Furthermore, the GHDSAE-based PSO solution saves 11.80-36.36% (with an average of 19.00%) chargers than the GHDSAE-based GH solution. In Figure 7b,d, the GHDS-based PSO solution consumes 237.36-354.39% (with an average of 310.35%) more solving time than that of the GHDS-based GH solution. The GHDSAE-based PSO solution consumes 230.29-314.03% (with an average of 277.63%) more solving time than that of the GHDSAE-based GH solution. This is mainly due to the fact that the PSO solution is a random population intelligence-based optimization algorithm that requires several iterations to search for the optimal solution, and thus achieves delicate search and high-precision deployment. However, the PSO solution can always converge to an optimal solution with the cost of acceptable solving time. In summary, both the GH and PSO solutions are scalable as the problem scale grows, and the PSO solution always outperforms the GH solution by deploying fewer chargers with the cost of acceptable calculation time expenses, and the aggregation effect of sensors can effectively reduce the deployment of chargers at the cost of slightly incremental computing time.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 5 Experimental Results
### Impact of Non-IID Data on FedAvg Performance
#### 5.1.3 Results on CIFAR-10 Dataset  
Finally, we evaluate the performance of the FedAvg algorithm on the CIFAR-10 dataset under IID and non-IID data distributions.  
Figure 5 shows the evaluation accuracy over the training rounds for the CIFAR-10 dataset with IID and non-IID distributions, trained with a fixed learning rate of 0.02 using the SGD optimizer.  
Figure 4: Fashion MNIST IID vs. Fashion MNIST non-IID with fixed learning rate.  
Figure 5: CIFAR-10 IID vs. CIFAR-10 non-IID with fixed learning rate.  
Table 3 summarizes the maximum accuracy achieved after 200 training rounds and the number of rounds needed to reach 37% evaluation accuracy. The data indicate that the CIFAR-10 dataset, trained with non-IID distribution, suffered from significant performance degradation compared to the IID distribution, both in terms of maximum accuracy and the number of communication rounds required to reach the target accuracy.
## 2 Materials and Methods
### Analysis of Network Simulations
#### 2.2.3 Complexity Indices  
- Lempel-Ziv Complexity  
The spatiotemporal differentiation of the spontaneous instantaneous firing rate was calculated by the Lempel-Ziv complexity (LZc) [44]. The optimal time window to compute LZc was examined by selecting temporal segments of variable lengths (from 1 s to 800 s). LZc decreased when increasing the temporal segments and reached a plateau around 200 s, and starting from 400 s, the variability, quantified by the coefficient of variation (CV), was less than 0.0004. For each condition, we calculated LZc over 10 segments of spontaneous activity (400 s each) and reported its mean \(\pm\) SD.  
- Perturbational Complexity Index  
The Perturbational Complexity Index (PCI) quantifies the spatiotemporal complexity of brain activity evoked by an external perturbation [17]. To compute PCI, we followed the procedure outlined in [17], with the notable difference that we applied it to the IFR time series of the brain nodes.  
In short, the significant evoked activities of each node, with respect to baseline, were determined by applying a non-parametric permutation test to the IFR. The baseline activity (pre-stimulus, time window = [\(-\)1000, 0 ms]) was used to determine, for each node, thresholds of significance (significance level set to 0.01, 500 bootstraps) and accounting for multiple comparison testing using the maximum test statistic. In this way, we derived a spatiotemporal matrix of significant activity [SS(x,t)], where SS(x,t) = 1 for significant activity in node x at time t, and SS(x,t) = 0 otherwise. The SS matrix was then sorted according to the amount of significant activity during the post-stimulus interval of each node (e.g., most/less active at the bottom/top rows). Then, the Lempel-Ziv complexity (CL) was calculated on the binary matrix [SS(x,t)] of the significant IFR (first 500 ms after the pulse).  
Finally, PCIIZ is defined as the normalized CL of the evoked spatiotemporal patterns SS(x,t):  
\[\text{PCI}=\text{ CL}\cdot\frac{\log_{2}(\text{L})}{(\text{L-Hsrc}(\text{L}))} \tag{13}\]  
where L is the size of the matrix [SS(x,t)] (L = number of nodes \(\times\) number of time samples), and Hsrc is the source entropy given by:  
\[\text{Hsrc}(\text{L})=-p_{1}\cdot\log_{2}(p_{1})-(1-p_{1})\cdot\log_{2}(1-p_{ 1}) \tag{14}\]  
where \(p_{1}\) and \((1-p_{1})\) are the fraction of \"1\" (significant activity) and \"0\" (non-significant activity) in the binary matrix.  
Spatial temporal maps were represented based on SS and the <\(\Delta\)IFR(t)> (Equation (10)). In particular, for each time point t, the \(\Delta\)IFR(t) of the nodes with significant activity (i.e., SS(., t) = 1) were reported.  
In all conditions (control and manipulations), each stimulus was repeated 500 times. Then, 10 resamples of 300 trials (without repetitions) were formed, and the PCI was computed on each subgroup. We assessed the stability of PCI versus the number of trials (ranging from 10 to 500 trials) and observed that the PCI values reached a plateau around 200 trials, and starting from 300 trials, the variability (CV) was less than 0.01.
# Development of Effective Infrared Reflective Coatings
## 3 Results and Discussions
### Total Solar Reflectance (TSR) and Infrared Solar Reflectance (IR-SR) Parameters  
Measuring the parameters of TSR and IR-SR allowed for a numerical evaluation of these important factors and helped to obtain a more clear comparison of the samples under investigation.  
Spectral solar irradiation \(i_{solar}\) plays important role in the calculation of solar reflectance; \(\rho_{solar}\) (solar absorbance \(\alpha_{solar}\)) is calculated from the measured spectral reflectance \(\rho(\lambda)\):  
\[\rho_{solar}=\frac{\int_{\lambda_{1}}^{\lambda_{2}}\rho(\lambda)\times i_{ solar}\,d\lambda}{\int_{\lambda_{1}}^{\lambda_{2}}i_{solar}\,d\lambda} \tag{1}\]  
Figure 3: Reflection behavior of different coatings applied on black primer measured in the wavelength range between 335 nm and 2500 nm (FE: coatings developed on black primer; La: Lacquer).  
Numerical values for the total solar reflectance (TSR) and the infrared solar reflectance (IR-SR) depicted in the same diagram (Figure 4) allow for a better comparison of the heat-reflective usefulness of the coatings. In the case of the coatings developed with the RAL7016 Anthracite grey color, there are significant differences between the infrared solar reflectance and the total solar reflectance.  
According to Figure 4, the worst results were obtained with a coating using traditional pigments. Coatings that contained covering infrared reflective pigments produced better results. The best examples are those that were made of infrared transparent pigments, represented by the L95/CR28, S84/A80, S84/CR28, and S84/CR28/Lacquer compositions. The columns in Figure 4 prove that the white primer is beneficial when a coating with high total solar reflection is required. In all cases, the infrared solar reflection values are higher than the total solar reflection ones. This means that the coatings under investigation in the wavelength range between 700 and 2500 nm show better reflection than in the full spectrum (330-2500 nm), which contains the absorption of UV and visible light.  
Table 6 demonstrates more clearly the differences between the TSR and the ISR values by numerical value.  
As all coatings used Anthracite grey (RAL7016), they were unable show differences in the visible wavelength, only in the infrared range. A high reflection value (close to 100) means significant light reflection, which means that the surface does not really absorb infrared light, and that is why it is much less warm. We could reach this aim using coatings S84/CR28 (TSR: 48.2%; IR-SR: 76.6%) and of S84/CR28/Lacquer on white primer (TSR: 47.7%; IR-SR: 75.9%); on the other hand, these coatings on black primer are less effective. The worst results were observed using traditional coatings (TSR: 7.3%, IR-SR: 6.3%).  
Figure 4: Summary of the total solar reflectance and the infrared solar reflectance values measured on the coatings developed according to the ASTM Standard G173-03 (FE: coatings developed on black primer; FR coatings developed on white primer; La: Lacquer).
## 2 Background
The proposed HMABCFA approach combines a modified artificial bee colony (ABC) and firefly algorithm (FA) to find the optimal CH. The FA updating position feature was merged with the ABC algorithm to avoid low convergence speed during the CH selection process. HMABCFA was implemented using Python 3.6 with supporting libraries. The performance of the proposed HMABCFA technique was evaluated in terms of energy stability, network lifetime, and latency. As a result, the proposed HMABCFA improved the lifespan by 23.21%, reduced latency by 22.88%, and improved energy stability by 19.84% compared with MBABCOA, KHOGACP, and GSAC. However, a longer time was required for convergence owing to the hybrid approach. Various benchmark studies on CH selection in WSN have been conducted by adapting different optimization techniques. However, the limitations of the related works have been observed, which include a longer network time, increased energy consumption, and poor network stability. The limitations of various CH selection optimization algorithms are listed in Table 1.  
To address these concerns, this paper proposes a novel approach for selecting energy-efficient cluster heads in a WSN using the COA. The proposed EACH-COA method aims to enhance the overall lifespan of the network.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 6 Conclusions  
A comprehensive description of different circuit techniques based on QFG transistors and their application to energy-efficient amplifiers has been presented. Moreover, a new, low-power CMOS amplifier, using adaptive local common-mode feedback with current starving, WTA tail current biasing and QFG transistors for adaptive cascode biasing, has been proposed. Measurement results of the amplifier fabricated in a 130 nm process show very large SR and GBW, maintaining very low static power consumption. The amplifier can be applied in ultra-low-power switched capacitor systems and in general, when both large capacitive loads and low quiescent power are required.  
Conceptualization, A.L.-M. and J.R.-A.; methodology, A.L.-M.; software, M.P.G.; validation, M.P.G.; formal analysis, A.L.-M.; investigation, A.L.-M., J.M.A.-M. and J.R.-A.; resources, A.L.-M.; data curation, M.P.G. and J.B.-L.; writing--original draft preparation, A.L.-M. and M.P.G.; writing--review and editing, J.M.A.-M., J.B.-L., R.G.C. and J.R.-A.; visualization, M.P.G. and J.B.-L.; supervision, A.L.-M.; project administration, A.L.-M.; funding acquisition, A.L.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by AEI/FEDER, grant number PID2019-107258 RB-C32.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 1 Introduction
The fundamental component of this methodology is deterioration patterns that reflect a building component's typical degradation patterns, both standard wear conditions and under the influence of failure mechanisms, with the aim of enabling statistical analysis of the data to determine the component's predicted service life. The methodology was developed by comparing common lightweight partitions in residential construction (gypsum board partitions and autoclaved aerated concrete partitions) to conventional hollow concrete block partitions. The results and findings illustrate the methodology's advantages (and disadvantages), highlighting its potential as an effective tool for maintenance policy management. The research contributes a statistical estimation of the service life of conventional and lightweight partitions in residential buildings, providing a basis for planning the life cycle of these components, and providing a foundation for ascertaining the life cycle costs of manufacturing processes in residential building construction. The findings also offer estimates for the life cycle planning of interior partitions. This analysis enables an examination of the economic aspect of lightweight partitions in buildings that takes into account the service life of the building, and enables the for quantification of costs that result from typical component failures.
###### Abstract  
The treatment of metal-contaminated sediment generated in environmental dredging projects often requires both reduction and remediation, and the electric field has good application prospects in the integration of reduction and remediation. In this study, based on the electro-osmosis, vacuum, and vacuum electro-osmosis methods, a detachable test system was made. Experiments of the three methods were carried out independently on the reduction and remediation of dredged sediment from Tai Lake under pollution-free and Cu-contaminated conditions. The results show that copper contamination weakens the effect of reduction, and the production of copper precipitates makes the soil more prone to cracking and blocking drainage channels, which has the greatest impact on the electro-osmosis method. In terms of copper concentration, vacuum electro-osmosis achieves the transport and discharge of contaminants, and has the best remediation effect. The removal rates at the anode and cathode are 45.1% and 50.0%, respectively. A correlation model based on electrical conductivity, moisture content, and contaminant concentration was established to facilitate the determination of contaminant concentrations in actual projects. Electro-migration plays a dominant role in the remediation process, and the reduction affects the electric field distribution and, thus, the migration efficiency.  
dredged sediment; heavy metal contamination; reduction and remediation; electro-osmosis; vacuum preloading +
Footnote †: journal: Applied sciences
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Optical Activity in Solids: a-Quartz
#### 4.2.2 Method II  
Figure 5 shows \(C_{\rm B}\) from a differential decomposition of the data in Figure 4. The circular dichroism \(C_{\rm D}\) is very close to zero, and all linear effects \(L_{\rm B},C_{\rm B},L_{\rm B}^{\prime}\) and \(C_{\rm B}^{\prime}\) are small (\(<\)10\({}^{-2}\) for \(\lambda>300\) nm) and are not shown. Observe that the solid curve for short wavelengths shows \(C_{\rm B}\) offset-adjusted by \(2\pi\) to provide first-order values, whereas the dashed curve shows the unfolded data from the decomposition. At \(\lambda=589\) nm, we find \(C_{\rm B}=0.8143\) rad, which corresponds to a rotation of \(23.33^{\circ}\), and, with a specific rotation of \(\alpha\)-quartz equal to \(21.73^{\circ}\)[18], we find a thickness of 1.074 mm. As a second test, we use \(C_{\rm B}=2.204\) rad at \(\lambda=373\) nm and with a specific rotation of \(58.89^{\circ}\)[18], we find a thickness of 1.073 mm.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sample** & **A (-)** & **B (\(\mu\)m\({}^{2}\))** & **C (\(\mu\)m\({}^{4}\))** & **D (\(\mu\)m\({}^{6}\))** \\ \hline D-glucose & \(7.24\times 10^{-8}(8)\) & \(5.41\times 10^{-8}(4)\) & \(-2.14\times 10^{-9}(6)\) & \(9.8\times 10^{-11}(2)\) \\ \(\alpha\)-quartz & \(7.27\times 10^{-6}(2)\) & \(1.771\times 10^{-5}(2)\) & \(9.62\times 10^{-7}(3)\) & \(-7.10\times 10^{-9}(6)\) \\ \hline \end{tabular}
\end{table}
Table 2: Cauchy parameters for \(\kappa\)-fits for D-glucose and \(\alpha\)-quartz. Values in parenthesis are the confidence limits in the last decimal.  
Figure 4: The transmission Mueller matrix of an \(\alpha\)-cut quartz sample.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations
### Thermal Management  
Large, complicated UAS platforms, such as the one in this study, require significant pre-flight preparation before takeoff. During ground test setup, hot weather and the sun heated the aircraft and its electronics, even without the engine running. This substantial thermal load dictated removal of switching circuitry. The only components that failed were IGBTs, despite use of large heatsinks and no electrical load. Future hybrid aircraft that utilize IGBTs for electrical mode switching and battery management will need an active cooling solution, such as a fan and/or radiator for electronics, during ground hot-day operation.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 3 Results
### Intercomparisons with Jason-3 SSH Products  
During the study period from 11 July to 29 December 2023, we identified 127,862 crossover points for SSH product accuracy assessment. Figure 11 presents their global distribution, limited to \(\pm\)50\({}^{\circ}\) latitude to exclude potential sea ice effects (the iGNSS-R altimeter's full observational range extends to \(\pm\)81\({}^{\circ}\) latitude). The distribution demonstrates comprehensive coverage across all major ocean basins within this latitudinal zone.  
Figure 12 presents the evaluation of iGNSS-R SSH against Jason-3 SSH at crossover points. Figure 12a shows the scatter plot comparison between the two datasets. To better illustrate the retrieval errors of iGNSS-R SSH, Figure 12b displays the statistical distribution of errors across 127,862 crossover points. The results indicate that the iGNSS-R SSH has a root mean square error (RMSE) of 17.14 cm and a bias of \(-\)0.01 cm, with the overall error distribution closely approximating a normal distribution (Gaussian).  
Figure 11: Crossover points between Jason-3 and iGNSS-R altimeter.  
Figure 10: Typical daily sea surface height product generated by iGNSS-R altimeter (1 October 2023 UTC).  
Figure 13 presents the evaluation results of iGNSS-R altimeter SSH error corrections using Jason-3-derived parameters at crossover points, including dry/wet tropospheric delays, ocean tides, pole tides, solid Earth tides, and DAC. In the subfigures, the blue dashed lines represent the 1:1 lines, while the red solid lines show the results of the least-squares linear fit. With a 0.2 h time window for ocean tides, pole tides, solid Earth tides, and DAC matching, 668 crossover points were available for comparison. With a 0.5 h time window for dry/wet tropospheric delay correction matching, 650 crossover points were available for comparison.  
Figure 12: Comparative analysis of sea surface height measurements between Jason-3 radar altimeter and iGNSS-R altimeter. (**a**) Scatter plot of coincident measurements. (**b**) Distribution histogram of measurement differences.  
Figure 13: _Cont._  
Figure 13a,b show RMSEs of 1.303 cm for wet tropospheric delay correction and 0.755 cm for dry tropospheric delay correction at a 0\({}^{\circ}\) incidence angle. When scaled by the cosine of the incidence angle to account for the maximum 35\({}^{\circ}\) viewing geometry of iGNSS-R, these errors increase to 1.59 cm (wet) and 0.92 cm (dry), respectively.  
Figure 13c-f reveal RMSEs for other correction terms: 3.23 cm (ocean tide), 0.49 cm (pole tide), 0.43 cm (solid Earth tide), and 0.39 cm (DAC).  
Table 2 summarizes the overall accuracy assessment of iGNSS-R SSH products, using Jason-3 as reference.