esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nanotechnology and Nanomedicine
### Porosimetry  
Determination of the surface area to volume ratio of a nanoparticles is essential for their characterization considering the titanic influence of the surface area on the properties and performance of the nanoparticles both in vivo and in vitro [40]. Nitrogen adsorption and desorption isotherm is use for the determination of nanomaterial porosimetry in order to estimate the pore volume, specific surface area and pore size distribution [128]. [PERSON] et al. [17] and [PERSON] et al. [18] reported  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Author’s Name**} & \multirow{2}{*}{**Drug Used**} & \multicolumn{2}{c}{**Zeta Potential (mV)**} & \multirow{2}{*}{**Zeta Potential (mV)**} & \multirow{2}{*}{**Size of**} & \multirow{2}{*}{**Shape of**} \\  & & & before Drug & after Drug Loading & & Nanoparticles (nm) \\ \hline [PERSON] et al. [28] & Ciprofloxacin & \(-15.3\pm 2.0\) & \(-13.0\pm 1.9\) & 11.93–22.12 & Spherical \\ [PERSON] et al. [15] & Doxorubicin & \(-46.17\pm 3.82\) & \(-40.57\pm 3.80\) & 20–60 & Spherical \\ [PERSON] et al. [16] & Teripariparamide (PTH 1-34) & Nil & \(-27.6\pm 8.9\) & 30 \(\pm\) 50 & Spherical \\ [PERSON] et al. [105] & Xanes (Docetaxel) & \(-15.54\pm 0.9\) & \(-21.7\pm 0.1\) & 42.22 & Spherical \\ [PERSON] et al. [32] & Doxorubicin & \(-12.1\) & \(-34.7\) & 24.90 & Spherical \\ [PERSON] et al. [6] & Nil & \(-9.4\pm 0.8\) & \(-2.89\pm 0.1\) & \(<\)100 & Spherical \\ [PERSON] et al. [82] & Nil & Nil & \(-16.4\pm 3.81\) & \(35\pm 16\) & Spherical \\ [PERSON] et al. [20] & Doxorubicin & \(-19.1\pm 3.9\) & \(-17.8\pm 4.6\) & 35.50 & Spherical \\ [PERSON] et al. [124] & Cytarabine & \(-11\) & \(-13.2\) & 20–50 & Spherical \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variations in the negative potential charge exhibited by cockle shell-derived calcium carbonate nanoparticles (CSCaCO\({}_{3}\)NPs) before and after loading with different drugs.  
a specific surface area for CSCaCO\({}_{3}\)NP to be 6.181 m\({}^{2}\)/g and 6.9529 m\({}^{2}\)/g respectively and further categorised the CSCaCO\({}_{3}\)NP curve of the Brunauer-Emmet-Teller (BET) as a Type III with hysteresis loop indicating multiple layers based on the classification of adsorption-desorption isotherm.
# A Fading Suppression Method for \(\Phi\)-OTDR Systems Based on Multi-Domain Multiplexing
## 3 Schematic of the Proposed Multi-Domain Multiplexing System
It can effectively eliminate the noise interference caused by clock asynchrony, reduce the phase noise of the system, and improve the signal-to-noise ratio of the intermediate-frequency signal. For the data capture by the DAQ, the maximum frequency of the intermediate-frequency signal output at the detector end is 150 MHz. According to the Nyquist sampling theorem, the sampling rate of the data acquisition card should reach at least twice the maximum frequency, that is, \(f_{s}\) = 300 MSA/s, to reconstruct this intermediate-frequency signal. In addition to meeting the Nyquist sampling theorem, to ensure a good reconstruction of the intermediate-frequency signal, the sampling rate should be an integral multiple of the carrier frequency of the intermediate-frequency signal. For the intermediate-frequency signals with frequencies of 80 MHz, 100 MHz, 120 MHz, and 150 MHz, the sampling rate of the data acquisition card should preferably be selected as the common multiple  
Figure 2: The generation process of the multifrequency probe pulse light. of these intermediate-frequency signal frequencies. Therefore, the sampling rate can be selected as 600 MSa/s, which can meet the requirement of integral multiple sampling for the intermediate-frequency signals of 100 MHz, 120 MHz, and 150 MHz. For the intermediate-frequency signal of 80 MHz, although the sampling rate does not satisfy the integral multiple relationship, since the sampling rate is several times the signal frequency, it can still ensure that sufficient sampling points are obtained for each signal period, thus ensuring the integrity of the signal. Figure 3a shows the IF signal waveforms of the P and S states, and the signal energy gradually decays as the fiber distance increases. Figure 3b shows the local waveform of the IF signal, and the P state and the S state are roughly complementary. Figure 3c shows the short-time Fourier transform (STFT) spectrum of the IF signal. The yellow area indicates that the IF signal contains energy peaks at four frequencies of 150 MHz, 120 MHz, 100 MHz, and 80 MHz, and these frequencies are highly overlapped in time. Figure 3d shows the power spectrum of the IF signal. The four frequency points of 150 MHz, 120 MHz, 100 MHz, and 80 MHz have the same peak intensity. The main peaks all have a high SNR and a narrow spectral width, and the side lobes are suppressed well. Therefore, the IF signal shows good spectral characteristics.
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 3 Prediction Model for Dementia Diagnosis
### Preprocessing

Table 3: Statistical Information for the Collected MMSE-KC (Phase1). values in our data may come from patients who did not properly understand the test. The results for this kind of patient considered cognitive decline based on family interviews and other parts of test results. Thus, if data type is categorical, we changed missing values to abnormal and, if numeric, to 0. Data normalization changes values in different ranges to values in the same range, preventing an attribute with a larger range of values from having a larger weight than a smaller range of attributes. For numeric data, there are four approaches to normalizing data: firstly, to convert to a range between 0 and 1; secondly to use a value between \(-1\) and 1; thirdly to find the average and standard deviation of the attribute; and, fourthly, to normalize the data using the log value. In the case of categorical data, algorithms using neural networks and statistical methods among machine learning algorithms cannot process categorical data, so they are converted to binary data or one-hot encoding [17]. For example, in regard to one-hot encoding, if there are three categorical data such as red, blue, and green in the color attribute, they are converted to a format such as (100), (010), (001). In this study, we use the maximum-minimum normalization method for numerical data and one-hot encoding for categorical data among various normalization methods. Equation (1) shows the maximum-minimum normalization formula. \[\text{Normalization Value}\left(d\right)=\frac{original\ Value-oldMin}{oldMax- oldMin} \tag{1}\]
## 7 Simulation Results
### Comparison with Other Methods
In the same case, a frequency overshoot is experienced, and the frequency finally reached a steady state after 22 s whereas in our case the frequency reached a steady state after 8 s. However, to make the comparison closer to our objective, we solve the proposed optimization problem in our paper using PSO, and compare the obtained result from PSO with the suggested ICA-based optimal multi-stage UFLS scheme. With these adjustments, there was a significant improvement with proposed PSO algorithm compared to the suggested PSO-based UFLS in [40], the minimum frequency reached was almost 48.8 Hz, and the steady-state frequency was reached after about 18 s. This prove that the proposed UFLS optimization problem in this paper is better than [40]. However, our proposed ICA-based optimal multi-stage UFLS scheme still gave better results as we had earlier shedding and the overshoot was smaller due to the suggested optimization problem and the performance of the adopted algorithm. The SFR model is widely used in research to do with LFC as well as UFLS due to its simplicity and reduced computational burden. Studies also show that once the parameters are deduced the SFR models gives technically sound results. However, the process of obtaining these parameters is extensive and rigorous. From the additional simulations provided in our paper it can be seen from Figures 15-17 that the changes in some parameters such as the fraction of the total power generated by the HP turbine (F) and the time constant (T) due to uncertainties and inaccurate validation, have little effect on the overall frequency behavior. In our simulations, even a 50% change in F and T introduced slight changes to the frequency. This is due to the fact that the used gains were smaller compared to the overall introduced disturbance. Although the parameters T and F of the system can be accurately estimated and used in SFR model, the proposed technique has good performance in case of the uncertain estimation of these parameters. The proposed method is also tested under unusual variation in the total inertia of the system (under 10%), where its performance has been preserved. In addition to SFR model validation of the adopted system done in [37], the aforementioned results verify that SFR model can be used in UFLS with high accuracy. Figure 14: Frequency behavior of the proposed UFLS technique compared to others optimization-based techniques: PSO and [40]. Figure 16: Frequency behavior of the proposed UFLS technique under uncertainties in determining the constant time of the governor-turbine system, T, in SFR model. Figure 17: Frequency behavior of the proposed UFLS technique under uncertainties in estimating the total inertia, H, in SFR model. Figure 15: Frequency behavior of the proposed UFLS technique under uncertainties in determining the parameter, F, in SFR model.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 4 Applications Related to the Water Cycle
### Hydrology and Water Management  
Serrat-Capdevila et al. [282] have identified several issues for the applicability of satellite precipitation data that are worthy of special attention: (1) error reduction at the source; (2) the availability of rapid observation updates and data assimilation; (3) consideration of additional rainfall system information; (4) local algorithm calibration; and (5) the evaluation of uncertainty or error in the estimates. Five years since the publication of this appraisal, we can safely say that most of these issues have been addressed, although not completely.  
The community is producing results that are very promising for monitoring, planning, and designing infrastructures in poorly-gauged areas. Water security is one of the most critical issues in this context, and satellite remote sensing techniques are of particular importance for emerging regions with inadequate in situ gauge observations [283]. Research attention is to be focused on precipitation frequency, especially of extreme precipitation, to appropriately manage weather-related risks and in the design of hydraulic structures [284]. Spatially-distributed hydrologic modeling for water management is feasible for the large basins under the scenario of inadequate in situ data availability; satellite precipitation datasets provide the necessary skill for water balance studies on interannual and inter-seasonal scales [285; 286]. Community models are being developed to simulate water resource availability considering human activities [287]. The utility of satellite observations through data assimilation for water resource monitoring can vary as a function of dominant hydrological processes. Opportunities for improvement are mainly related to the development of more accurate and higher spatial and temporal resolution precipitation products, and to the use of a wider range of remote sensing products in a priori model parameter estimation, model evaluation, and data assimilation [288]. The use of satellite precipitation products is prompting such approaches in poorly-gauged or ungauged regions through the use of intensity-duration-frequency (IDF) curves [289; 290], which have mostly been used in small watersheds, and now need to be applied more widely. Hydrological applications also call for the availability of long-time records that are consistent across satellite platforms, and are specifically designed for hydrology. A recent example of such a dataset is the MW-based hydrological bundle climate data record (CDR), designed by NOAA [291] ([[https://www.ncdc.noaa.gov/cdr/atmospheric/hydrological-properties](https://www.ncdc.noaa.gov/cdr/atmospheric/hydrological-properties)]([https://www.ncdc.noaa.gov/cdr/atmospheric/hydrological-properties](https://www.ncdc.noaa.gov/cdr/atmospheric/hydrological-properties)), last accessed 24 September 2019).
## 4 Discussion  
The conducted study proved that the use of a low-cost multi-GNSS receiver as a position source for the autopilot popular in unmanned systems makes it possible to obtain a very high precision of steering for a hydrographic USV. Such a solution allows a hydrographic vessel to be maintained along the sounding profile with a transverse error (XTE) not exceeding 2 m. In traditional (manned) hydrography, sailing along sounding profiles is typically performed using systems based on a professional autopilot cooperating with a gyrocompass, satellite compass, or, less frequently, a magnetic compass. The typical accuracy of the course maintenance by gyrocompass devices is 1-1.5\({}^{\circ}\)[38], with that of satellite compasses being similar. However, under unfavourable conditions, it can even temporarily reach a value of 10\({}^{\circ}\)[39]. Moreover, expensive geodetic GNSS receivers are most frequently used in professional hydrography as a positioning system [9].  
The proposed solution in this paper is an alternative to the professional measuring systems used in hydrography. The main factor affecting the accuracy of steering a hydrographic USV along a sounding profile using an autopilot is the operational characteristics of the multi-GNSS receiver. Thanks to the continuous development and extension of the GNSS satellite system constellation (GPS, GLONASS, BDS, Galileo), multi-GNSS receivers continue to improve their operational characteristics, including positioning accuracy. Many studies have attempted to verify the positioning accuracy, availability, continuity, and reliability with multi-GNSS receivers from urban [40; 41; 42] and maritime [43] to pedestrian positioning applications [44] using different methods [45].  
It should be assumed that multi-GNSS receivers used in mobile devices will soon significantly increase their positioning accuracy thanks to the use of dual-frequency receivers [46] and other measurement techniques [40]. On 21 September 2017, Broadcom announced the world's first mass-market, dual-frequency GNSS receiver device, the BCM47755. It is a very strong innovation  
Figure 11: The difference between the probabilities of the occurrence of variable XTE for the empirical and theoretical generalised gamma distribution.  
destined to bring a revolution in the field of survey and geolocalisation. With these kinds of sensors, an accuracy of a few centimetres could be obtainable even with mobile devices [45]. The widespread introduction of multi-frequency GNSS receivers with an accuracy of a few centimetres is undoubtedly a major factor contributing to the improvement of unmanned vessel automatic control precision.
## 4 Materials and Methodology
### Performance Metrics for Classification  
The performance of various models on the testing dataset was assessed upon completion of the training phase. The evaluation focused on key performance metrics, including sensitivity (Sen.), specificity (Spe.), precision (Pre.), F1-score, and accuracy (Acc.). The following equations were used to calculate these metrics [56]:  
\[\text{Sensitivity}(\%)=\frac{(TP)}{(TP+FN)}\times 100 \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & & **CNN** & \\ \hline
**Layer** & **Kernel Size** & **Filter Number** & **Parameters** \\ \hline Conv1 & 3 \(\times\) 3 & 32 & 320 \\ Max-Pooling1 & 4 \(\times\) 4 & - & 0 \\ Conv2 & 3 \(\times\) 3 & 64 & 18,496 \\ Max-Pooling2 & 4 \(\times\) 4 & - & 0 \\ FC1 & 128 & - & 991,360 \\ FC2 & 256 & - & 33,024 \\ Softmax & 4 & - & 1028 \\ \hline \end{tabular}
\end{table}
Table 1: Details of the proposed CNN architecture.  
Figure 13: Loss function diagram showing the model training of the CNN, where the training loss is 0.02, and the validation loss is 0.1 [31].  
\[\text{Specificity}(\%)=\frac{(TN)}{(TN+FP)}\times 100 \tag{6}\]  
\[\text{Precision}=\frac{TP}{TP+FP}\times 100 \tag{7}\]  
\[F1=2\times\frac{\text{Precision}\times\text{Sensitivity}}{\text{Precision}+ \text{Sensitivity}}\times 100 \tag{8}\]  
\[\text{Accuracy}(\%)=\frac{(TP+TN)}{(TP+FP+TN+FN)}\times 100 \tag{9}\]  
The terms true positive (TP), true negative (TN), false positive (FP), and false negative (FN) were used.
## 2 Wastewater Treatment by Microalgae
### Agricultural Wastewater
#### 2.2.2 Pretreatment
\begin{table}
\begin{tabular}{c c c} \hline
**Wastewater** & **Pretreatment Method** & **Microalgae** & **References** \\ \hline Swine & Settled for 24 h, then centrifuged at 8000 rpm for 10 min & _Chorella_ sp. HL & [79] \\ Swine & Centrifuged at 7000 rpm (8656\(\times\)_g_) for 5 min, filtered with filter of & _Scenedesums_ sp. & [30] \\ Swine & pore diameter 8 \(\upmu\)m & _Chorella_ & _thermophila_ \\ Cattle & Filtered using Whatman filter paper 42 & (MF179624) & [71] \\ Poultry litter extract & Acid pretreatment using 5 M of HCl or H\({}_{2}\)SO\({}_{4}\) & _Acutodesums obliquus_ & [42] \\ Swine & Natural precipitation method for 1 day; and added with 5\% & _Chorella vulgaris_ & MBFJNU-1 & [57] \\ Aquaculture & Filtered using a filter pump (pore size: 22 \(\upmu\)m), then filtered with & _Tetraselmis_ sp. & [78] \\ Fishery & Sterilized in an autoclave at 121 \({}^{\circ}\)C and 150 kPa for 30 min. & _Thalassisisisis pseudonama_ and & [81] \\ Poultry slaughterhouse & Acid precipitation (H\({}_{2}\)SO\({}_{4}\)) at pH 4 & _Chorella vulgaris_ & [80] \\ Swine & Sedimentation for 2 days, and anaerobically digested & _Chorella pyrenoidosis_ & [59] \\ Swine & Filtered with a sieve of mesh number 140 (100 \(\upmu\)m) & _Chorella_ & [58] \\ Anaerobic digested swine & Sterilized in an autoclave at 121 \({}^{\circ}\)C and 150 kPa for 30 min. & _Chorella zofingiensis_ & [60] \\ Fishery & Sterilized in an autoclave at 121 \({}^{\circ}\)C and 150 kPa for 30 min. & _Chorella_ & [59] \\ Piggery & Centrifuged at 10,000\(\times\)_g_ for 10 min at 4 \({}^{\circ}\)C and pre-outcalaved at & 121 \({}^{\circ}\)C for 20 min \\ Milkyery & Filtered through a filter cloth then filtered using 1.5 \(\upmu\)m glass & _Haematococcus plucvialis_ & [75] \\  & microfiber filters, autoclaved at 121 \({}^{\circ}\)C and 15 psi for 20 min. & _Chorella Sorokiniana_ & [64] \\ Aquaculture & Centrifuged at 3000\(\times\)_g_ for 5 min. & _Chorella pyrenoidosis_ & [62] \\ Piggery & Sedimented and sterilized by passing through a 0.45 \(\upmu\)m filter & _Acutodesums obliquus_ & [66] \\ Piggery & Technulated and filtered through 0.2 \(\upmu\)m cellulose acetate & _Acutodesums obliquus_ & [66] \\  & membranes followed by stripping with air using an acrylic & _Chorella_ sp. GD & [65] \\  & column (ID 5 cm \(\times\) H 100 cm) & _Chorella_ sp. GD & [69] \\ Aquaculture & Filtered using 0.45 mm Whatman GF/C filter papers followed by & _Chorella_ sp. GD & [65] \\  & autoclaving & _Chorella_ sp. GD & [69] \\ Piggery & Autoclaved for 30 min at 121 \({}^{\circ}\)C then centrifuged & _Chorella_ sp. GD & [69] \\  & (3000\(\times\)_g_ for 10 min) & _Chorella_ sp. GD & [69] \\ \hline \end{tabular}
\end{table} 
Table 6: Pretreatment methods of agricultural wastewater before microalgae cultivation.
# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
## Results
### Correlation Between GPP, Climatic Variables and EVI  
The relationship between GPP and Rn was positive during the flooded period (rho = 0.2, \(p<0.05\)) and negative but not significant during non-flooded months (rho = \(-0.19\), \(p=0.12\)) (Figure 4a). GPP was positively associated with ET during both flooded (rho = 0.25, \(p<0.05\)) and non-flooded months (rho = 0.38, \(p<0.05\)) (Figure 4b).  
Our analyses show that soil moisture and VPD are correlated with GPP in two contrasting ways. During the flooded period, soil moisture is negatively associated with GPP (rho = \(-0.22\)), while VPD is positively associated (rho = 0.15). However, during the non-flooded months, soil moisture is positively associated with GPP (rho = 0.34) and VPD negatively (rho = \(-0.33\)) (Figure 4c,d).  
Figure 3: Shuttle Radar Topographic Mission (SRTM) classified as areas below and above the tower level, which is located at 181 meters of altitude (red dot). Grey squares represent the Moderate Resolution Imaging Spectroradiometer (MODIS) pixels within the tower footprint (green circle, [PERSON] et al. [42]) selected to extract mean EVI and be analyzed.  
As GPP is in phase with soil moisture when the forest is not flooded, it decreases at the end of the dry season (September) and starts to increase in the onset of the rainy season (October). Nevertheless, a negative trend was observed between GPP and rainfall during non-flooded months (rho = \(-\)0.26, \(p<0.05\)) (Figure 4e). Air temperature was negatively associated with GPP during non-flooded months (rho = \(-\)0.24, \(p<0.05\)) and was not significant during the flooded months (rho = 0.081, \(p=0.27\)) (Figure 4f).  
Monthly CWD had the strongest positive association with GPP during non-flooded months (rho = 0.81, \(p<0.05\)), to the extent that GPP increases when CWD gets less negative. No trend was observed during flooded months (rho = 0.01, \(p=0.96\)) (Figure 4g).  
As a proxy of photosynthetic capacity, EVI was positively correlated with GPP during non-flooded months (r = 0.50, \(p<0.05\)) and despite not being significant, a positive trend was also observed during flooded months (r = 0.53, \(p=0.09\)) (Figure 4h and Figure S6).