esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
* The joint PAM constellation and reflectance optimization problem is formulated by maximizing the minimum distance between any two adjacent received constellation points to achieve better BER performance. * The optimization problem is solved in two steps. In step one, we solve a convex optimization problem for the optimal received constellation. In step two, a feasible problem is decomposed into two alternative optimizing sub-problems to find the transmit constellation and IRSs' reflectances corresponding to the optimal received constellation. * The BER performance is shown and analyzed in terms of the BER curves and the constellation distributions. The extra dimming constraint relaxation benefit of the tunable IRS are illustrated compared with the mirror-based IRS. The rest of this paper is organized as follows. In Section 2, the tunable IRS-aided SISO VLC system model is introduced, and the joint constellation and reflectance optimization problem is formulated. In Section 3, we propose a two-step algorithm to solve the optimization problem. Numerical results are provided in Section 4, and Section 5 concludes the paper.
## Materials and Methods  
### Participants and Meteorological Variables  
The study was carried out between April and August 2002. Thirty-seven participants were included their personal exposure monitors to PM\({}_{2.5}\) were used, and they represented the first measurement of PM\({}_{2.5}\) air pollution in Mexico City. Exposure was measured using a \(p\)DR nephelometer calibrated according to technical information [29].  
Personal exposure periods were selected if the address was available and had a duration of at least 1 and half hours, then by means of participants' written diaries. PM\({}_{2.5}\) exposure periods were classified as \"daytime\" from 11:00 to 12:30, and included 12 measurements, whereas those between 21:30 and 23:00 were classified as \"nighttime\" and included 25 measurements. Moreover, measurements were also classified as within indoor or outdoor microenvironments in 30 min periods, thus, home or office addresses were geo-localized as described below. No measurements were taken during weekends. All participants lived in Mexico City in 13 of the 16 boroughs (see Figure 1). A detailed specification of the equipment and the sampling method utilized in this research have been published elsewhere [30].  
Average annual measurements of meteorological variables, such as relative humidity (RH, %), temperature (TEMP, \({}^{\circ}\)C), wind speed (WSP, m/s), and wind direction (WDR, the azimuth direction from which it originates) were taken from the meteorological network stations [31] located in the areas closest to the participants' addresses (distribution by geographical location of annual average relative humidity and temperature is shown in Supplementary Materials Figure S1). Since \(p\)DR measurements can be biased with an RH above 70%, the information was double-checked on the days PM\({}_{2.5}\) was measured and RH was lower than 70% according to RAMA [32; 33; 34]. Several measurements were close to each other; thus, the median was calculated to improve the locations and reduce the sample redundancy.  
Weather conditions and PM\({}_{2.5}\) personal measurements were compared between daytime and nighttime and simultaneously among geographical locations. A non-parametric method was used (Friedman test) since data distribution was different from the normal standard (Shapiro-Wilks \(p<0.05\)). Data are presented as median (interquartile range (IQR)). Statistical significance was considered with a \(p\)-value \(>0.05\).
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 2 Data
### Data Preprocessing  
In PM\({}_{2.5}\) concentration prediction studies, rigorous data preprocessing is required to improve the quality and consistency of the input data for the model and to fully utilize the combined information from remote sensing satellite imagery and meteorological observations. For satellite imagery, radiometric calibration, geometric correction, and cloud masking are essential steps to minimize observational errors and ensure the physical authenticity of the images. Radiometric calibration converts raw digital numbers into physical radiance or surface reflectance, eliminating the effects of sensor gain, offset, and atmospheric conditions, and ensuring the comparability of images across different times and locations. Geometric correction aligns the images with a standard geographic coordinate system using ground control points (GCPs) and mathematical transformation models to ensure accurate matching with ground-based observations. Because cloud cover interferes with surface information acquisition, the Fmask algorithm is used in combination with short-wave infrared (SWIR) and thermal infrared (TIR) bands to identify cloud-contaminated pixels. Gaussian interpolation is then applied to fill cloud-occluded regions, mitigating the effects of data loss on subsequent analyses.  
Meteorological data preprocessing includes data cleaning, spatial interpolation, and temporal alignment to ensure data integrity and accuracy. Meteorological observations often contain missing values and outliers; thus, the Z-score method is applied to remove outliers, and linear interpolation is used to fill missing values, reducing observational errors. Given the differences in temporal resolution between satellite imagery and meteorological data, temporal alignment is necessary. In this study, high-temporal-resolution meteorological data were downsampled using mean aggregation, and linear interpolation was performed based on the observation times of the satellite imagery. This ensures temporal consistency between the two datasets and improves the coherence and quality of model inputs.  
Because of inconsistencies among various types of data, multi-source heterogeneous data were integrated into a unified input feature matrix. This integration leverages the complementary advantages of different datasets, improving the predictive accuracy of the model. Given the differences in spatial resolution, a 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) (\(\sim\)1 km \(\times\) 1 km) grid was established to facilitate data integration. All datasets were resampled to 0.01\({}^{\circ}\) resolution using bilinear interpolation. Compared with the nearest-neighbor method, bilinear interpolation produces smoother interpolated values [33]. Daily average PM\({}_{2.5}\) concentrations were assigned to the corresponding grid cells. If a grid contained two or more monitoring stations, their average value was calculated to represent the PM\({}_{2.5}\) concentration within that cell.
## 3 Results and Discussion
### Effect of pH  
BPA is weakly acidic in nature, being ionizable to form organic anions at sufficiently high pH levels. Its behavior in the environment is complex. pH is an important parameter affecting the adsorption process. The zeta-potential of Mag-PVP as a function of pH was shown in Figure 7a. The effect of pH on the adsorption of BPA was shown in Figure 7b. XAD-4, with a cross-linked bridge and benzene ring structure, was selected as the control [15]. Compared to XAD-4, Mag-PVP has N elements in its structure. The adsorption of BPA by Mag-PVP increased slightly from 3.0 to 5.0, and obtained the largest adsorption amount at pH 5.0, which was then reduced with an increase of pH. However, the pH had no significant effect on XAD-4 adsorption capacity until it was higher than 8.0.  
The point of zero zeta-potential of Mag-PVP was at pH 5.0. Mag-PVP was positively-charged when the pH was less than 5.0. The largest adsorption amount of Mag-PVP at pH 5.0 was probably attributed to hydrogen bonding. The nitrogen of PVP can form hydrogen bonding with the phenol hydroxyl of BPA at pH 5.0. At pH 5.0-9.0, BPA existed in a neutral form, and the nitrogen-containing surface functional groups were deprotonated. The hydrogen bonding interaction decreased, which resulted in the reduced adsorption capacity. BPA is a weak acid with a pKa value of 9.6-10.2 [8]. Its particles release a proton and form bisphenolate anions in alkaline solution. The reduction of BPA adsorption when pH > pKa was obvious because of growing repulsion forces and a reduction of \(\pi\)-\(\pi\) interactions between bisphenolate anions and the surface of the sorbent. The phenomenon was consistent with XAD-4 when the pH was above 8.0. There was no change in the BPA adsorption capacity of XAD-4 at pH < 8.0, indicating that the adsorption mainly depended on the interaction of hydrophobic or \(\pi\)-\(\pi\) interactions, excluding the effect of hydrogen bonding. Above all, three main mechanisms can explain the adsorption of Mag-PVP. Hydrogen bonding, electrostatic interaction and \(\pi\)-\(\pi\) interactions are mutually responsible for the adsorption of BPA onto Mag-PVP. The possible schematic illustration of BPA adsorption onto Mag-PVP was presented in Figure 8. These results also indicate that Mag-PVP could be efficiently applied to weakly acidic and alkaline water for the removal of BPA.  
Figure 6: Effect of coexisting Cl\({}^{-}\) (**a**), Ca\({}^{2+}\) (**b**), and humic acid (**c**) on BPA adsorption onto Mag-PVP (BPA concentration = 10 mg/L; adsorbent dosage = 0.2 g/L; shaking time = 4 h). Error bars represent standard deviation values from three replicates.
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 4 Grow Lights Monitoring System
### Light Control
#### 4.3.1 LED Dimming  
LED illuminance is controlled by changing the forward current flowing through it. The relationship between the current of an LED and its brightness is approximately linear, contrary to its relation with the voltage. Therefore, an LED driver is used to change the forward current based on either an amplitude modulation (AM) method or a pulse width modulation (PWM) method [67]. The AM method directly changes the amplitude of the forward current, which is easier to control, but it does not ensure efficient dimming, and it changes the chromaticity of the emitted light, which makes it unsuitable for providing the plant light requirements discussed earlier. PWM, on the other hand, changes the duration of the forward current flow through the LED; it does not affect the LED chromaticity since the current amplitude is maintained constant and is therefore suitable for providing the plant light requirements in indoor farming environments. PWM is performed by generating a square-form signal that rapidly switches between the on and off states at a specific frequency. The power delivered to the LED is controlled by changing the on-time of the signal, also called the duty-cycle, which is given by the ratio of on-time in one period of the square signal. The majority of microcontrollers have built-in PWM outputs that can be used to control the brightness of the LEDs only by changing the duty-cycle.
## 3 Results
### Descriptive Analysis and Differences between Lipid Mass Estimation Formulas  
Descriptive analysis of BMI, sum of 4, 6, and 8 skinkolds and lipid percentages estimated by the different formulas for males and females are shown in Table 2. Differences between lipid percentage formulas were significant for both men (mean diff = -9.88-9.03; \(p\) < 0.001-0.016;ES = 0.05-0.70; 95%CI = \(-\)11.57-10.33) and women (mean diff = \(-\)12.01-13.46; \(p<\) 0.001-0.010; ES = 0.00-5.37; 95%CI = \(-\)12.79-14.27). Therefore, a pairwise comparison of the formulas was performed, the results of which are shown in Tables 3 and 4 for males and females, respectively. Significant differences were found in the pairwise comparison for most of the formulas for both males and females. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Men (\(\boldsymbol{n}\) = 37)**} & \multicolumn{2}{c}{**Women (\(\boldsymbol{n}\) = 54)**} \\  & **Mean \(\pm\) SD** & **Max.–Min.** & **Mean \(\pm\) SD** & **Max.–Min.** \\ \hline BMI & 23.03 \(\pm\) 1.39 & 24.48–19.35 & 22.07 \(\pm\) 1.54 & 24.50–18.50 \\ \(\sum^{4}\) skinfolds (mm) & 35.50 \(\pm\) 11.71 & 63.50–19.75 & 46.60 \(\pm\) 14.22 & 80.10–22.25 \\ \(\sum^{6}\) skinfolds (mm) & 60.14 \(\pm\) 20.64 & 120.25–36.25 & 85.26 \(\pm\) 21.61 & 133.75–42.50 \\ \(\sum^{8}\) skinfolds (mm) & 78.30 \(\pm\) 27.35 & 153.25–44.75 & 106.83 \(\pm\) 28.28 & 170.25–51.25 \\ \% [PERSON] & 8.90 \(\pm\) 2.17 & 15.21–6.38 & 16.77 \(\pm\) 3.34 & 24.28–10.15 \\ \% [PERSON] & 12.40 \(\pm\) 2.65 & 20.22–9.35 & - & - \\ \% [PERSON] & 12.95 \(\pm\) 3.76 & 21.63–7.21 & 23.38 \(\pm\) 5.07 & 35.72–13.03 \\ \% [PERSON] & 17.72 \(\pm\) 3.17 & 27.24–13.92 & 15.33 \(\pm\) 2.94 & 21.88–8.81 \\ \% [PERSON] & 12.11 \(\pm\) 2.15 & 18.02–9.49 & 18.90 \(\pm\) 3.56 & 27.98–13.11 \\ \% [PERSON] & 17.91 \(\pm\) 2.84 & 24.84–14.17 & - & - \\ \% [PERSON] & 10.71 \(\pm\) 3.04 & 19.17–6.81 & 18.37 \(\pm\) 2.93 & 25.63–13.02 \\ \% [PERSON] & 16.04 \(\pm\) 4.89 & 33.39–10.47 & 22.73 \(\pm\) 5.41 & 39.71–10.57 \\ \% [PERSON] & 14.50 \(\pm\) 4.59 & 24.84–7.25 & 26.31 \(\pm\) 4.11 & 35.51–17.56 \\ \% [PERSON] & 18.78 \(\pm\) 4.30 & 29.56–12.00 & 28.79 \(\pm\) 3.30 & 33.88–21.88 \\ \% [PERSON] & 11.45 \(\pm\) 2.33 & 18.10–8.84 & - & - \\ \% [PERSON] & 9.73 \(\pm\) 3.67 & 21.01–5.48 & 21.46 \(\pm\) 3.54 & 29.06–14.94 \\ \% [PERSON] & - & - & 20.03 \(\pm\) 5.55 & 33.20–10.67 \\ \% [PERSON] & 14.03 \(\pm\) 3.10 & 23.48–10.06 & 23.92 \(\pm\) 2.03 & 28.43–20.51 \\ \% [PERSON] & 8.90 \(\pm\) 2.17 & 15.22–6.39 & 16.77 \(\pm\) 3.34 & 24.28–10.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive analysis of anthropometric variables and lipid percentages in men and women.
## 2 The Theoretical Basis of Tightly-Coupled INS/RTK Integrations
### Definition of State Vectors and Motion Model
The simplified measurement models of the accelerometer and gyroscope are:  
\[\vec{f}_{ib}^{b} =(\mathbf{I}_{3}+\mathbf{M}_{a})\vec{f}_{ib}^{b}+\mathbf{b}_{a}+\mathbf{\varepsilon}_ {a} \tag{8}\] \[\vec{\Omega}_{ib}^{b} =(\mathbf{I}_{3}+\mathbf{M}_{g})\mathbf{\omega}_{ib}^{b}+\mathbf{b}_{g}+\mathbf{ \varepsilon}_{g} \tag{9}\]  
where \(\vec{f}_{ib}^{b}\) and \(\vec{\mathbf{\omega}}_{ib}^{b}\) are the output vectors of inertial sensors; \(\mathbf{\varepsilon}_{a}\) and \(\mathbf{\varepsilon}_{g}\) represent the random noise; and \(\mathbf{b}_{a}\) and \(\mathbf{b}_{g}\) respectively represent the accelerometer and gyroscope biases that are contained in the state vector. The biases are modeled as constant vectors and corrected by the GNSS solutions. \(\mathbf{I}_{3}\) is the identity matrix, and \(\mathbf{M}_{a}\) and \(\mathbf{M}_{g}\) denote the matrices of the scale factor and cross-coupling errors for accelerometer and gyroscope, respectively.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 3 Method
### The Accuracy Assessment of Extraction Results  
To evaluate the accuracy of the model, we introduced the following two indexes that were widely used in the performance evaluation of machine learning methods: precision and recall [48]. Precision describes how many positive samples were distinguished exactly in the predicted positive samples. Recall describes how many positive samples were distinguished exactly in the total positive samples. For the better explain of precision and recall, a confusion matrix of the classification results is shown in Table 2.  
Where _TP_ represents the true positive samples, _FP_ represents the false positive samples, _FN_ represents the false negative samples and _TN_ represents the true negative samples. Generally, the two indexes are both meaningful in the description of model equality; however, they are also contradictory: higher recall only means a larger sample data set, thus leading to lower precision. Therefore, an \(F\)1-score, which is the harmonic mean value of precision and recall, is often used to describe the quality of the following binary classification models:  
\[P=\frac{TP}{TP+FP} \tag{19}\]  
\[R=\frac{TP}{TP+FN} \tag{20}\]  
\[\frac{1}{F1}=\frac{1}{2}\times\left(\frac{1}{P}+\frac{1}{R}\right) \tag{21}\]  
\[F1=\frac{2\times P\times R}{P+R} \tag{22}\]  
where \(P\) represents the precision and \(R\) represents the recall. The higher \(F\)1-score indicated the higher classification quality of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**True Situation** & **Predicted Positive** & **Predicted Negative** \\ \hline Positive & _TP_ & _FN_ \\ Negative & _FP_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of classification results.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 3 Hardware Design of RFID Reader/Router
### Hardware Design of Wireless RFID Reader/Router
#### 3.2.3 Circuit Design of ZigBee Coordinator  
The role of ZigBee coordinator is sending ZigBee terminal nodes' information to the host computer through the serial port, and at the same time, sending control instructions from the host computer to the terminal nodes through wireless network. Because TTL level is adopted in CC2530 and EIA level in host computer communication, level translation is needed for effective data communications. MAX232 chip is used to achieve the translation between the two electrical levels. The circuit of ZigBee coordinator is shown in Figure 4b. It is consistent with the terminal node circuits and is set as the coordinator in the Z-stack protocol stack only.
# Trepenoids in Marine Heterobranch Molluscs
## 4 Diterpenoids
### Tetracyclic Diterpenoids  
Only doridacean slugs and one sea hare contain tetracyclic diterpenoids. In British Columbia, _Cadlina luteomarginata_ presents five diterpenoids, cadlinaldehyde (71), spongian, secospongian, 20-acetoxy-12-marginatone, and lutenolide (72) (Figure 9) in their external extracts, some of them resulting from degradation of other terpenoids. This species is reported to be able to biosynthesize some of its terpenoids [147; 148]. _Chromodoris hamiltoni_ from Mozambique presents two spongiane diterpene lactones, 78,118-diacetoxy-16-oxospongian-17-al and 78,118-diacetoxy-16-oxospongi-12-enal, along with the macrolide latrunculin B [208; 239]. The Japanese slug _Chromodoris obsoleta_ possesses seven cytotoxic sponge diterpenoids, the dorisenones A-D (73) (Figure 9), and some related compounds [240]. The slug _Chromodoris petechialis_ from Hawai'i presents three sponge diterpenes, probably from its diet sponge _Chelonaplysila_ sp. [152], one of them being cytotoxic [240]. _Chromodoris mandapanensis_ from India contains spongiadiol (74) (Figure 9), which is also present in _Glossodoris cincta_ (cited as _G. atromarginata_) from Egypt, and was previously isolated from Australian sponges, within a mixture of related spongiane compounds [170]. _C. mandapanensis_ was found on an unidentified sponge also containing spongiadiol, and thus supporting its dietary origin. Spongiadiol displayed antiviral activity against herpes simplex virus [241]. _Chromodoris elisabethina_ from Australia contains the oxygenated diterpene puupehenone (75) (Figure 9), known as cytotoxic and with antitubercular activity but with unknown ecological activity [90]. _Glossodoris cincta_ from Egypt presents five spongian diterpenes in its digestive gland and dorsal mantle [170; 242; 243], which are also found in specimens from Sri Lanka [46; 244]. Some of these metabolites have been reported to be cytotoxic and antiviral in the laboratory [241; 245]. _C. kunei_ from Okinawa possesses a cytotoxic spongian diterpene, probably also from its diet sponge _Dysidea_ cf. _arenaria_[246]. _C. reticulata_ from Australia provided six oxygenated diterpenes, two of which contain cyclic imine functionality, together with seventeen known diterpenes more, among which aplyroseol-2 was the major compound in the mantle along with some dialdehydes [175].  
Regarding the only example from sea hare, in the Eastern Pacific, _Aplysia vaccaria_ contains in its digestive gland several ichthyotoxic non-halogenated diterpenoids, crenulides (76) (Figure 9), derived from its diet, the brown algae _Dictyota crenulata_[247; 248].
## 2 Materials and Methods
### Dataset  
The dataset used in this study was compiled from four different groups of experiments previously carried out by the research group [15; 25; 26; 27; 28], with the aim of producing MELs.  
In all experiments, the concentrations of biomass, carbon, and nitrogen were assessed at the beginning of the experiment (day 0) and on day 2, day 4, and day 7 and, for longer experiments, on days 10, 14, and 18 as well. Furthermore, the MEL and lipid concentrations were measured on days 4 and 7 and, for longer experiments, on days 10, 14, and 18 as well. Possible carbon sources, both hydrophilic (glucose, glycerol, cheese whey, or no carbon sources) and hydrophobic (soybean oil, fish oil, sunflower oil, rapeseed oil, and waste frying oil), constituted the categorical variables. Sodium nitrate was consistently used as the nitrogen source. The final dataset is composed of 47 samples, with each shake flask run considered a sample, and includes 42 features. Although the experiments aimed at the production of MELs, they followed different strategies, varying in the carbon source used, feeding strategy, initial biomass, headspace volume, carbon-to-nitrogen ratio, and duration.  
Figure 1: Simplified workflow for this study which depicts the research strategy used. The research strategy can be divided into five parts: construction of the dataset, data preprocessing, feature engineering techniques, model training coupled with hyperparameter tuning, and finally, model testing.  
The shortest experiment was concluded after 7 days of fermentation, while the longest experiment was extended to 18 days. Figure S1 (Supplementary Material, Figure S1) shows the evolution of biomass throughout the first 7 days for all of the experiments. Figure S2 (Supplementary Material, Figure S2) and Figure S3 (Supplementary Material, Figure S3) depict the consumption of carbon and nitrogen, respectively, throughout the first 7 days for all experiments.  
* Experiment 1 included 18 samples, a duration of 18 days, and a volume of 50 mL. D-glucose or glycerol (or simply no hydrophilic carbon source) was used in combination with a hydrophobic carbon source, such as soybean oil, rapeseed oil, or waste frying oil.
* Experiment 2 included 10 samples, a duration of 10 days, and a volume of 50 mL. Waste frying oil was used as the hydrophobic carbon source, while D-glucose and cheese whey (CW) were used as the hydrophilic carbon sources.
* Experiment 3 comprised 8 samples, a duration of 7 days, and the consistent use of D-glucose and waste frying oil as the hydrophilic and hydrophobic carbon sources, respectively. Four different volumes were tested (200 mL, 100 mL, 50 mL, and 25 mL), with each set of conditions run in duplicate.
* Experiment 4 included 11 samples, with each running for 10 days with a volume of 50 mL. D-glucose was used as the hydrophilic carbon source, while the hydrophobic carbon sources were varied between residual fish oil and sunflower oil.