esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sorption Properties  
#### 3.2.1 Effect of pH  
Figure 2 compares the effects of the pH\({}_{\rm eq}\) on Ce(III) sorption using MC, MC-UR, and MC-UR/S (the triplicated experiments showed a good reproducibility). These results show that the sorption capacity increases with the pH, almost linearly for MC and more marked for MC-UR and MC-UR/S.  
For the functionalized solvents, the sorption slightly increases between 1.2 and 1.5 (with sorption capacities below 0.2 and 0.5 mmol Ce g\({}^{-1}\)). A steep increase is observed up to pH\({}_{\rm eq}\) 4-4.3 (q\({}_{\rm eq}\) tends toward 0.8 and 1.3 mmol Ce g\({}^{-1}\), for MC-UR and MC-UR/S, respectively). Above pH\({}_{\rm eq}\) 4.5, the sorption capacities stabilize. In order to avoid any risk of  
Figure 2: Influence of pH on Ce(III) sorption using MC, MC-UR, and MC-UR/S sorbents (sorbent dose, SD: 400 mg L\({}^{-1}\); C\({}_{0}\): 0.366 mmol Ce L\({}^{-1}\); time: 48 h; v: 210 rpm; T: 22 \(\pm\) 1 \({}^{\circ}\)C).  
precipitation (especially at high concentrations, such as those used for sorption isotherms), a pH above 6 was not tested, and further experiments were performed at pH\({}_{0}\): 5.  
Figure S11 shows the distribution of Ce(III) species as a function of the pH (under experimental conditions selected for the study of the pH effect). At pH > 2, cerium is present only as free Ce\({}^{3+}\) species. In acidic solutions, the protonation of reactive groups (as protonated amine groups, sulfonic acid moieties) involves the repulsion of Ce\({}^{3+}\) cations (and CeCl\({}^{2+}\) at pH < 2) and limited sorption capacities. As the pH increases, this repulsion decreases, and the sorption steeply increases, with this effect being especially notable below pH\({}_{\rm eq}\) 4-4.5. The sulfonated derivative shows a higher sorption capacity due to the increased density of the reactive groups (amino and sulfonate groups).  
Figure S12 shows that for MC and MC-UR, the sorption of Ce(III) is systematically followed by a weak increase in the pH (by less than 0.3 units). On the other hand, for MC-UR/S, consistent with its lower pH\({}_{\rm ZC}\) value, the Ce(III) sorption slightly increases the pH below pH 3, while above the equilibrium, the pH tends to decrease.  
In ion exchange processes, the slope of the log\({}_{10}\) plot of the distribution ratio D (D: \(\rm q_{eq}/C_{eq}\), L g\({}^{-1}\)) vs. pH gives the ionic charge of the exchanged metal ion [69]. Figure S13 shows that the linear plot for the functionalized sorbent is close to 0.5. This value is not consistent with the theoretical interpretation described here, meaning that the binding mechanism is not purely ion exchange.
# Assessment of the Water Quality of WWTPs' Effluents through the Use of Wastewater Quality Index
## 2 Materials and Methods
### Sampling and Sample Preparation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**WWTP**} & \multicolumn{6}{c}{**Maximum Allowable Concentrations**} \\ \cline{2-10}  & **COD** & **TSS** & **TN** & **TP** & **pH** & **EC** & **Cd** & **Cr** & **Ni** & **Pb** \\  & **mg/L O\({}_{2}\)** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** & **mg/L** \\ \hline BLG & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ DMG & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ DPN & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ GAB & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ KNL & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ K2N & 125 & 35 & 15 & 2 & 6.5–8.5 & 750 & 0.45 & 32 & 34 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximum allowable concentrations for the WWTPs’ effluents according to Directive 91/271/EEC (COD, TSS, TN and TP) [8], Directive 2013/39/EU (Cd, Ni and Pb) [9] and Ordinance N-4 (pH, EC and Cr) [10]. For the determination of the physical-chemical parameters, the water samples were collected in glass bottles and directly stored at 4 \({}^{\circ}\)C. The 50 mL samples for ICP-MS analysis were filtered with a 25 mm PES sterile syringe filter (0.45 \(\upmu\)m), inserted into glass bottles, acidified with 1.5 mL conc. HNO\({}_{3}\) (67-69% Fisher Chemicals, Pittsburgh, PA, USA, TraceMetal Grade), stored at 4 \({}^{\circ}\)C and transported to the laboratory premises [41]. Upon receipt, the samples were analyzed in triplicate.
## 2 Dataset  
### Particle Settling Experiments  
The details of the particle settling experiments are provided in three papers published by our team [16,29,41]. The experimental setup for particle settling included a 1.5 m height tube, a high-speed camera, and a computer for data acquisition. A total of 114 particles, varying in density, shape, and size, were used. Both Newtonian and non-Newtonian fluids were employed, as shown in Table 2. Please refer to the Supplementary Materials File S1 for a comprehensive dataset comprising a total of 931 experimental data points.  
The dataset comprising 931 experimental data points was meticulously collected under controlled laboratory conditions. Each experiment was repeated multiple times to ensure consistency and to minimize experimental errors. The range of experimental conditions was chosen to represent a wide spectrum of scenarios encountered in particle dynamics, particularly in petroleum engineering contexts. It should be noted that uncertainties in data acquisition are inevitable in any experimental process. These uncertainties may arise from various sources, such as measurement errors (e.g., in the measurement of particle size or fluid properties), environmental conditions, and intrinsic variations in the materials used. To address these issues, experiments are conducted under consistent environmental conditions. Moreover, multiple repetitions are performed for each experimental condition to help average out random errors.  
Figure 1 demonstrates that various non-spherical particles differ in settling orientation, with some particles even changing orientation during settling. At low Reynolds numbers, the initial settling orientation remains consistent, while at high Reynolds numbers, the settling orientation changes continuously throughout the settling process, making it more complex.
# PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software
## 4 Experiments and Results  
### Evaluation Metrics  
Let true positive (TP) denote the number of vulnerable samples detected correctly, false positive (FP) denote the number of normal samples detected incorrectly, false negative (FN) denote the number of vulnerable samples undetected, and true negative (TN) denotes the number of clean samples classified correctly. Running time and memory were considered for testing resource consumption.  
We used five metrics to measure vulnerability detection results. The \(FP\) rate (\(FPR\)) metric measures the ratio of falsely classified normal samples to all normal samples.  
\[FalsePositiveRate(FPR)=FP/(FP+TN) \tag{11}\]  
False negative rate (\(FNR\)) measures the ratio of vulnerable samples classified falsely to all vulnerable samples.  
\[FalseNegativeRate(FNR)=FN/(FN+TP) \tag{12}\]  
Precision measures the correctness of the detected vulnerabilities.  
\[Precision(P)=TP/(TP+FP) \tag{13}\]  
Recall represents the ability of a classifier to discover vulnerabilities from all vulnerable samples.  
\[Recall(R)=TP/(TP+FN) \tag{14}\]  
The \(F1\) measure considers both precision and recall.  
\[F1-Measure(F1)=2*P*R/(P+R) \tag{15}\]  
The low \(FPR\) and \(FNR\), and high \(P\), \(R\), and \(F1\) metrics indicated the excellent performance in the experimental results. Low resource consumption is also vital.
## 3 Results  
### Study Selection  
As a result of the comprehensive evaluation, 364 studies were found in electronic databases including Pubmed (128), WOS (150), and Scopus (80), and via the review of references (6). In terms of duplication, 12 studies were excluded. A total of 352 studies were screened and title and abstract sections were read. After abstract screening, 327 studies were excluded because they did not meet the inclusion criteria. When the full text of the remaining 25 studies were analyzed, 8 studies were rejected. Three had no control group, three had no measurement of stability, and two had miniscrews of different size and shape.  
Figure 2: The PRISMA 2020 flow diagram.  
Finally, 17 articles were included in the systematic review. The publication dates of the included articles range from 2009 to 2022. Study selection is shown in Figure 2.
## 4 Results
### Comparing Models
#### 4.4.1 Comparing Model Performance across Datasets
Considering the overall trends by models and methods by analyzing n-grams and n-grams and LIWC, the logistic regression model also achieved the maximum compared to the other models with a total of 20/36 above-average predictions for both the model and the method. By comparing the performance of the models on the results obtained from the LIWC analysis, the logistic regression model statistically achieved the best results with the above-average predictions given for each of the three datasets, DS1, DS2, and DS3, while the other models predicted somewhat less favorable with 2/3 above-average predictions. Statistically, the datasets on which the LIWC analysis achieved the most above-average results using all models are DS2 and DS3 with 3/4 above-average predictions.
## 1 Introduction  
Microalgae are photosynthetic microorganisms capable of producing compounds such as proteins, carbohydrates, lipids, fatty acids, biopeptides, and pigments that can be used to develop foods, drugs, cosmetics, biofuels, and animal feed [1]. The cellular growth of microalgae varies according to a combination of parameters, such as nutrient composition, temperature, and light [2].  
Luminosity is one of the most important factors that, in addition to modifying the composition of the biomass, influence the economic efficiency of the process. This factor can vary in intensity and frequency of microorganisms' exposure. Several studies have demonstrated the interference of photoperiod alterations on the content of proteins, carbohydrates, and fatty acids in microalgal cultures [3, 4]. In addition, changes in light intensity and photoperiod are the main factors that determine the growth rate of microalgae cultures, as they mainly interfere with the mechanism of obtaining energy from cells [5].  
Alterations to photoperiods can have significant effects, especially when combined with other factors that influence cell growth and photosynthesis, such as the use of organic compounds such as glycerol to supplement the culture medium [3, 6]. Glycerol is a byproduct of the transesterification reaction of oils during biodiesel production. Purifiedglycerol has several applications; however, in its raw form, its commercial value is much lower [7]. In addition to the high cost of the purification process, especially for small industries, the market demand for this product is lower than the amount generated by the biodiesel industry [8].  
Glycerol has positively influenced the cell growth of microalgal cultures [9; 10; 11] and biomass productivity. For the profitability of high-value add products through microalgae, it is essential to obtain a balance between the compounds of interest and biomass productivity, as some applied conditions can reduce its yield. _Spirulina_ cultured in glycerol has already shown remarkable growth and protein production, reaching 384.15 mg L\({}^{-1}\) in only 36 h of culture [9]. However, results regarding the association of glycerol with _Spirulina_ cultures with different photoperiods have not yet been reported.  
_Spirulina_ strain is an important commercial microalga certified with GRAS (generally recognized as safe by the Food and Drug Administration, USA) since 2002 and is on the European Novel Food list (EU, 2017/2470). The produced biomass can be applied to proteins and unsaturated fatty acids [9; 10; 11]. These compounds can be used to improve the nutritional and functional value of foods [12], as a supplement [13], as animal feed [14], and as biofuel [15; 16; 17]. In this context, this study aimed to evaluate the influence of organic carbon source addition in the form of glycerol on a _Spirulina_ sp. LEB 18 culture subjected to different photoperiods and its effect on biomass composition and cell growth.
## Materials and Methods
### 5\({}^{\prime}\). Rapid-Amplification-of-cDNA-Ends (5\({}^{\prime}\) RACE) PCR  
The transcriptional start sites of _SLI_4383/_pptA_ and _SLI_4382_ were established by 5\({}^{\prime}\) RACE PCR as described previously [51]. Briefly, 5 \(\upmu\)g of total RNA isolated from a 48 h-old culture of _S. lividans_ TK24 was individually used for reverse phase transcriptions with 2 picomoles of specific primers of _pptA_-GSP1 (Table S3), using Superscript III first-strand synthesis supermix (Invitrogen, Carlsbad, CA, USA). After purification with a NucleoSpin Extract IIs column (Macherey-Nagel, Duren, Germany), the synthesized first-strand cDNA was added with a homopolymeric dA tail at the 3\({}^{\prime}\) end using terminal deoxynucleotideyl transferase (Thermo Scientific, Waltham, MA, USA) according to the manufacturer's instructions. The poly(dA)-tailed cDNA was again purified, and was then used as a template for the subsequent PCR amplification using a poly(dT) primer (70 homopolymeric T) and a second inner-gene-specific primer, _pptA_-GSP2 (Table S3). An additional round of PCR was carried out using the original PCR product with a 1000-fold dilution as a template, with the poly(dT) primer and a nested _pptA_-GSP3 primer (Table S3), to obtain a single specific band. The final PCR products were then sent for sequencing the nested _pptA_-GSP3 primer (Table S3). The nucleotide immediately preceding the stretch of T residues was recognized as the transcriptional start site.
## 3 Experiments and Discussion
### Measurement Verification  
Displacement measurement experiments were conducted to evaluate the performance of the grid pattern-based structured-light vision method. A plane board was placed on the electrical sliding table and carried to several positions in front of the system. The electrical sliding table was equipped with a grating ruler (1 \(\upmu\)m accuracy). The moving distances measured by the grating ruler were regarded as the ground truth. Three-dimensional coordinates of the grid pattern points on the board were obtained by the proposed method and fitted to planes at each location. The plane at the first position was regarded as the base plane. The distances from the 3D points on the other planes to the base plane were calculated sequentially. The measurement results and the corresponding ground truth values are listed in Table 5. The maximum relative error was 0.840%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & \([A_{\text{{\it over}}},B_{\text{{\it over}}},C_{\text{{\it over}}},D_{\text{{\it over}}}]\) & \([A_{\text{{\it Hor}}},B_{\text{{\it Hor}}},C_{\text{{\it Hor}}},D_{\text{{\it Hor }}}]\) \\ \hline
1 & \([0.807,0.009,0.59,-224.019]\) & \([-0.041,0.996,0.807,6.870]\) \\
2 & \([0.814,0.009,0.579,-224.667]\) & \([-0.035,0.997,0.071,6.137]\) \\
3 & \([0.821,0.008,0.569,-225.443]\) & \([0.029,-0.998,-0.061,-5.225]\) \\
4 & \([0.829,0.008,0.558,-225.711]\) & \([-0.023,0.999,0.05,4704]\) \\
5 & \([0.836,0.007,0.548,-226.566]\) & \([-0.016,0.999,0.039,4.195]\) \\
6 & \([0.843,0.007,0.537,-226.901]\) & \([-0.011,0.999,0.028,3.694]\) \\
7 & \([0.849,0.007,0.527,-227.53]\) & \([-0.003,0.999,0.017,3.244]\) \\
8 & \([0.856,0.006,0.516,-227.949]\) & \([0.004,1.000,0.006,2.82]\) \\
9 & \([0.863,0.005,0.505,-228.439]\) & \([-0.01,-0.999,0.005,-2.447]\) \\
10 & \([0.869,0.006,0.493,-228.702]\) & \([-0.017,-0.999,-0.015,1.838]\) \\
11 & \([0.875,0.004,0.483,-229.223]\) & \([0.023,-0.999,-0.028,1.98]\) \\
12 & \([0.881,0.004,0.471,-229.522]\) & \([0.029,0.998,-0.037,1.143]\) \\
13 & \([0.897,0.004,0.46,-229.653]\) & \([-0.036,-0.998,0.049,-1.109]\) \\
14 & \([0.893,0.004,0.449,-230.287]\) & \([0.043,0.997,-0.059,0.085]\) \\
15 & \([0.898,0.003,0.438,-230.369]\) & \([0.048,0.996,-0.069,-0.395]\) \\
16 & \([0.903,0.003,0.428,-231.273]\) & \([-0.054,-0.995,0.078,1.719]\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of the light planes.  
Figure 14: The multiple light planes in the camera coordinate system. (**a**) Vertical light planes. (**b**) Horizontal light planes. (**c**) Space enclosed by the light planes.  
The nominal radius of the gypsum sphere was 85 mm. The 3D reconstruction data from our system and from the D455 camera were fitted to the sphere equation to calculate the measured radius. The measured radius of our approach and of that using D455 are listed in Table 6.
## 4 Discussion
### General Comparison of the Number of \"Pre-\" and \"Post-\" Earthquake Anomalies
We recognize that such investigations are biased by the fact that higher magnitude earthquakes tend to show anomalies ahead of time, but such earthquakes are fewer in number (according to the Gutenberg-Richter magnitude distribution [42]), so most of the weight on the anomalies is posed by lower magnitude events. Even if 8 years of data seems like quite a long time, only a longer satellite dataset could help to better investigate this aspect. For this reason, we would like to underline the importance of maintaining the orbiting of the _Swarm_ constellation (and other missions equipped with magnetometers, such as CSES) for as long as possible. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter, Period Band** & **Anomalies before the Earthquake** & **Anomalies after the Earthquake** & **Difference of the Anomalies** & **Estimated Uncertainty** & **Is the Result Significant?** \\ \hline Y, no-band & 16,630 & 16,436 & 194 (1.2\%) & 129 & No \({}^{1}\) \\  & Norm: 16,584 & Norm: 16,481 & Norm: 103 (0.6\%) & 129 & No \({}^{1}\) \\ \hline Y, 2–10 s & 4959 & 4563 & 396 (8.7\%) & 70 & Yes \\  & Norm: 4953 & Norm: 4568 & Norm: 385 (8.4\%) & 70 & Yes \\ \hline Y, 10–25 s & 5078 & 4917 & 161 (3.3\%) & 71 & Yes \\  & Norm: 5066 & Norm: 4928 & Norm: 137 (2.8\%) & 70 & Yes \\ \hline Y, 25–50 s & 6297 & 5858 & 439 (7.5\%) & 79 & Yes \\  & Norm: 6273 & Norm: 5880 & Norm: 392 (6.7\%) & 79 & Yes \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline
**_Ne_, no-band & 15,504 & 15,618 & \(-114\) (\(-0.7\%\)) & 125 & Yes, but \\  & Norm: 15,469 & Norm: 15,652 & \(-182\) (\(-1.2\%\)) & 125 & post-seismic \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the number of anomalies in the closer bin to the epicenter before and after the earthquake for the symmetrical investigations (from \(-\)500 days to +500 days) of 8 years of _Swarm_ data correlated with M5.5+ earthquakes by the WSC algorithm. A normalization (”Norm”) is provided to take into account possible different geomagnetic activities occurring before and after each investigated event.
## 3 Results
### Dynamic Light Scattering (DLS) and Zeta Potential (Z-Potential)  
The colloidal stability and the hydrodynamic diameter of the a-TiO\({}_{2}\) based NPs (a-TiO\({}_{2}\), a-TiO\({}_{2}\)-NH\({}_{2}\) and a-TiO\({}_{2}\)-NH\({}_{2}\)/DOPC-DOTAP) were evaluated through Z-potential and DLS (Figure 3).  
The Z-potential evidences the surface chemistry on colloidal stability (Figure 3a). Amorphous-TiO\({}_{2}\) NPs present the most negative Z-potential value (\(\varsigma=-40.0\) mV, outside \(|\varsigma|\geq\pm 30\) mV risky range) allowing us to conclude about their colloidal stability.  
Amine-groups protonation is responsible for the increase in zeta potential of a-TiO\({}_{2}\)-NH\({}_{2}\) NPs up to \(-24.2\) mV. Although this value is an indirect proof of amine-functionalization, it also evidences the flocculation and/or coagulation risks associated with these amine-functionalized nanosystems.  
Finally, the biomimetic a-TiO\({}_{2}\)-NH\({}_{2}\)/DOPC-DOTAP NPs present the highest positive Z-potential (69.1 mV, outside the aggregation risk range). This fact proves the effective coupling between the positively charged lipids and a-TiO\({}_{2}\)-NH\({}_{2}\) NPs and it is also an evidence of their colloidal stability.  
The hydrodynamic diameter evaluation through DLS showed that no micro-scale aggregates were present, which suggests a relatively good dispersion of all the samples (Figure 3b). However, the a-TiO\({}_{2}\)-NH\({}_{2}\) NPs suspension (the one within the risky zeta potential range) shows the highest hydrodynamic diameter (centered at \(\approx\)156.4 nm) and the widest peak size distribution (showing the possibility of aggregation), in accordance to the lowest Z-potential values, as previously discussed. On the other hand, the lipidic shell formation on a-TiO\({}_{2}\)-NH\({}_{2}\) NPs contributes to an improvement of sample colloidal dispersion (lowest size distribution diameter, \(\approx\)43.7 nm, with sharp peak) in accordance with the recorded Z-potential values.  
Figure 2: Bandgap energy of a-TiO\({}_{2}\) based NPs: (**a**) UV-Vis spectra; (**b**) [PERSON]’s plot of a-TiO\({}_{2}\) indirect band gap; (**c**) [PERSON]’s plot TiO\({}_{2}\)-NH\({}_{2}\) indirect bandgap.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Decision  
After channel equalization, a constellation decision is required to recover the original data. The most common decision scheme is based on the Euclidean distance between the received symbols and the standard constellation points, because the decision scheme is supposed to have the best performance in the additive Gaussian white noise (AWGN)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Equalizers** & **GK-DNN** & **FSDNN** & **TFDNet** & **MPANN** & **DBMLP** & **PCVNN** & **LSTM** \\ \hline
**Main types of NN** & MLP & MLP & MLP & MLP & MLP & MLP & RNN \\
**Number of hidden** & 2 & 1 & 1 & 1 & 1 & 1 \\
**layers** & & & & & & & \\
**Activation function** & ReLU & ReLU & ReLU & ReLU & Tanh & ReLU & Tanh, \\
**Optimizer** & Adagrad & Adam & Adam & Adam & Adam & Adam & Adam \\
**Complexity** & Moderate & Low & High & Low & High & Low & High \\
**Convergence speed** & Fast & Moderate & Moderate & Moderate & Slow & Slow & Slow \\
**Pre-equ.** & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
**Post-equ.** & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
**Deployment location** & Waveform & Waveform & Waveform & Waveform & Symbol & Symbol \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarization of machine learning algorithms for channel equalization.  
channel. However, as mentioned in Section 2, the VLC channel is not a simple AWGN channel, but has nonuniform noise distribution and nonlinear effect. As a result, the distortion of the constellation diagram may not exhibit a uniform Gaussian distribution around the standard constellation points. On the contrary, the constellation clusters may exhibit deviation or exhibit distortion highly relevant to the signal power. Apparently, the conventional decision is not the optimal decision scheme, and novel algorithms that take the statistical characteristics into account are required. Machine-learning-based decision schemes have been widely investigated in visible light communication systems and great improvement has been reported. These schemes can be divided into two categories: classification and clustering.