esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 Extreme Gradient Boosting (XGBoost) Algorithm  
Extreme Gradient Boosting (XGBoost) is a machine learning algorithm that combines the outputs of multiple weak learners, typically decision trees, to make a strong prediction [33]. XGBoost is known for its speed and efficiency, as it incorporates techniques like parallelization, tree pruning, and approximate learning to improve both training time and accuracy [34].  
The core objective of XGBoost is to minimize the regularized loss function. At each step, XGBoost adds a decision tree to the ensemble, and the model's objective function \(L(\theta)\) is defined as below [35]:  
\[L(\theta)=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum_{k=1}^{K}\Omega(f_{k}) \tag{1}\]  
where \(l(y_{i},\hat{y}_{i})\) is the loss function, measuring the difference between the true value \(y_{i}\) and the predicted value \(\hat{y}_{i}\) for each training instance. \(\Omega(f_{k})\) is the regularization term for the complexity of the \(k\)-th tree (or weak learner), ensuring that the model does not overfit.  
In this study, the 'Poisson log-likelihood' function was selected as the loss function, as the dependent variables (accident frequency and deaths) represent the count data. The Poisson log-likelihood loss function is expressed as follows:  
\[l(y_{i},\hat{y}_{i})=\hat{y}_{i}-y_{i}\text{log}(\hat{y}_{i})+\text{log}(y_{i}!) \tag{2}\]  
This function is well suited for non-negative integer values and helps effectively model the over-dispersed nature of accident frequency data. While alternative loss functions such as MAE and RMSE were considered, the Poisson log-likelihood function demonstrated superior performance in capturing the distribution characteristics of the data in this study.
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 2 Nanofluids
### Effect of Nanoparticles on Reservoir and Fluid Properties
#### 2.2.2 Effect on Rheology and Viscoelasticity
The formation of this network is attributed to the chemical bonds created between carbonyl groups in the polymer backbone and silanol functionalities in the nanoparticles, considered to act as a sort of physical crosslinker among the macromolecules. Below a certain critical aggregation concentration (CAC), the intramolecular hydrophobic association is dominant in HAPAM solutions, forming small aggregates. In this condition, the polymer chains have a small chance of interacting with silica nanoparticles. As the polymer concentration increases to the CAC, inter-molecular hydrophobic associations become more predominant in the solution, thus creating a network of polymer chains. At even higher concentrations (>CAC), the strong associations caused by the number of hydrophobic groups have a negative effect on the interaction between polymer and silica nanoparticles (Figure 5). Figure 4: Rheological properties and EOR performance of the nano-copolymer and hydrolyzed polyacrylamide (HPAM). Effect of shear rate on viscosity for polymer solutions (0.2 wt%) prepared with distilled water (**a**); viscosity versus temperature for HPAM and AM/AA/NSFM solutions (0.5 wt%) (\(\dot{\gamma}=7.34\) s\({}^{-1}\)) (**b**); and core flooding experimental results of AM/AA/NSFM and HPAM (0.2 wt%) at 65 \({}^{\circ}\)C (**c**). Figure reproduced from Ref. [63]. AM/AA/NSFM: acrylamide/acrylic acid/nano-SiO\({}_{2}\) functional monomer.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 4 GNSS Meta-Signal and HO-BOC Signals Delay/Phase Estimation Results
Therefore, as discussed above for the time-delay, large secondary peaks directly degraded the convergence to the CRB, which would have an impact on carrier phase-based positioning techniques (see Section 6). It is important to notice also the performance gain around 15 dB (a factor of 30) between delay and phase estimates. We could conclude that in terms of robustness (i.e., being able to operate at lower SNRs), the best choices were Galileo E5 and the combinations with the full-bandwidth E5 and B2, i.e., E5 + E6 and B2 + B3. The time-delay/phase MLEs and the corresponding CRBs results for the different HO-BOC signals are summarized in Figure 6. The results were also compared to the MLEs and CRB obtained with Galileo E5. Taking into account the ACF shape in Figure 4, again, the impact of the secondary peaks was clear, which induced false locks and therefore a convergence degradation, which translated to a MLE threshold behavior that was shifted to the right. This effect was similar to the one discussed for the GNSS meta-signals, shown in Figure 5. For the HO-BOC signals considered, we can point out the following:Figure 5: The comparison of the different time-delay CRBs (m) is shown in Subplot (**a**). Time-delay and phase RMSE (m) and the corresponding CRBs (m) for the different GNSS meta-signals are shown in Subplots (**b**) Galileo E5, (**c**) GPS L2C + L5Q, (**d**) Galileo E5B + E6B, (**e**) BeiDou B2 AQ + B3Q, (**f**) Galileo E5A + E6B, (**g**) BeiDou B2 + B3Q and (**h**) Galileo E5 + E6B. * As already anticipated, the achievable performance for the time-delay estimation, with the E1-PRS and the B1A signals, was almost the same compared to Galileo E5. This was determined by the shape of the ACF main peak and the signal bandwidth considered. * Because of the much larger secondary peaks of the E1-PRS and the B1A signals, which were roughly located at the same position, the convergence region to the CRB was degraded w.r.t. the Galileo E5 signal (i.e., [2; 3] dB). * Regarding the L1-M and E6-PRS signals, because the ACF main peaks were wider, then the time-delay CRB was slightly degraded compared to E5 (i.e., roughly 2 dB). * Because the secondary peaks of the L1-M and E6-PRS signals were further apart from the main one and their value was not extremely large, w.r.t. Galileo E5, the convergence region was not affected. * Again, as for the different GNSS meta-signals, the convergence of the phase MLE was driven by the convergence of the time-delay MLE; therefore, only the L1-M and E6-PRS signals provided a performance similar to the Galileo E5 signal. We could conclude that in terms of robustness and with only a slight performance degradation w.r.t. the Galileo E5 signal, the best choices were the L1-M and E6-PRS HO-BOC signals (i.e., BOCsin(10,5) and BOCcos(10,5)). In the results provided, notice that these two signals used a sampling frequency \(F_{s}=30\) MHz, which was half of the bandwidth exploited for Galileo E5, i.e., using \(F_{s}=60\) MHz.
###### Abstract  
In this work, a series of thiosemicarbazones with phenol fragments were used as starting compounds for the synthesis of new effective antioxidants containing both a phenol substituent and a heterocyclic fragment: thiazole and thiazoltionone. To determine the most stable conformation of thiosemicarbazone, a potential energy scan was used, along with NOESY NMR spectroscopy data. A number of thiazole derivatives were obtained due the interaction of thiosemicarbazones with several bromokotones: bromoacetophenone, bromodinedone, and bromoacetylcolumnar. The product yields varied from 71 to 94%. Thiazoltionone derivatives were obtained through the reaction between thiosemicarbazones and chloroacetic acid or maleic anhydride with good yields of 82-95%. The antioxidant activities of all the products were determined in vitro: the radical cation scavenging activity was estimated using 2,7-azino-bis(3-ethylbenzothazoline-6-sulfonate), while the ferric reducing capacity was determined using the ferricyanide/Prussian blue method. It was found that the antioxidant activity of most synthesized substances in both tests exceeds the activity of 4-methyl-2,6-di-_tert_-butylphenol, while derivatives with a fragment of 2,6-di-_tert_-butylphenol have the highest activity.  
synthesis; phenols; heterocycles; antioxidants; quantum-chemical calculations; conformers +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
The new generation Chinese hyperspectral imager AHSI on board the GF-5 satellite has the potential to retrieve accurate water clarity values, as it has continuous narrow spectral bands from 400-2500 \(\upmu\)m. Also, AHSI imagery is suitable for monitoring the lakes and reservoirs that are not very large, due to its relatively high spatial resolution. In this study, we tested the AHSI data for its ability to monitor inland water clarity, as well as a validation of the accuracy in terms of the semi-analytical methods used for the routine \(Z_{sd}\) image production of AHSI imagery. Four inland lakes and reservoirs in China were selected as the study regions. Concurrent in situ measurements were conducted when AHSI had acquired images. An accuracy analysis of the retrieved \(Z_{sd}\) from the AHSI images shows that only the semi-analytical method incorporating the \(QAA_{V5}\) algorithm can yield relatively high accuracies. For 84 concurrent sampling sites in a \(Z_{sd}\) range of 0.3-4.5 m, the MAE and MRE were 0.35 m and 25.3%, respectively. Experimental results also indicate that the AHSI has sufficient bands for the implementation of the semi-analytical method, which can achieve desirable \(Z_{sd}\) estimations with roughly estimated \(R_{rs}\) AHSI images. Although further validation is still required for highly turbid and clear waters, our results indicate that the AHSI is effective to retrieve accurate water clarity data at different levels of transparency.  
Conceptualization, Y.L.; Formal analysis, Y.L.; Funding acquisition, Y.L. and J.L.; Resources, F.Z. and S.W.; Supervision, C.X. and J.L.; Writing-original draft, Y.L.; Writing-review & editing, J.L., F.Z. and S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 41901304 and 41971318.  
The authors declare no conflict of interest.
# CNT-PUFs: Highly Robust and Heat-Tolerant Carbon-Nanotube-Based Physical Unclonable Functions +
## 5 Evaluation of the Fabricated CNT-PUFs
### Evaluation of CNT-PUFs under Normal Conditions
#### 5.2.3 Unpredictability  
In order to test whether the generated raw responses of our CNT-PUF were suitable for use as cryptographic keys, we checked their unpredictability. Unlike uniformity or uniqueness, no single formula can be used for checking this metric. Usually, a whole series of different statistical tests is performed to assess the unpredictability of PUF responses. For this purpose, we used the well-established NIST SP 800-22 test suite [24], applied on individual binary PUF responses. Table 1 indicates how many of the 30 PUF responses passed a specific test. The average \(p\)-value is provided for each test applied on all responses. According to the criteria defined in the NIST SP 800-22 test suite [24], a sequence is deemed random if its corresponding \(p\)-value \(\geq\) 0.01. However, from these criteria, it can be seen that almost all PUF responses passed the most-important tests, indicating that they had a high degree of unpredictability and were, therefore, suitable for use as primitives in cryptographic applications. Nevertheless, it was not possible to obtain statistically sound values from the Random Excursions and Random Excursions Variant tests for our CNT-PUFs due to the high recommended input lengths above 144 bits, as shown in Table 1.  
Furthermore, each PUF response can be represented as a two-dimensional array (matrix), facilitating the application of dedicated spatial correlation analysis techniques. Results obtained using established metrics such as [PERSON]'s \(I\), [PERSON]'s \(C\), Join Count \(J_{BW}\), and even the Getis-Ord \(G\) were published in a paper by [PERSON] et al. [42]. The findings demonstrated a consistent positive spatial auto-correlation in four out of four PUF responses obtained from our proposed PUF structure. This auto-correlation primarily arises from the clustering of logical 1s.  
Figure 9: Figure **(a)** shows the comparisons of each of the 30 CNT-PUFs with each other using the inter-device Hamming distance. This figure was adapted and modified from our previous work [19], which this work extends. Figure **(b)** visualises the distribution of these inter-device Hamming distances as a histogram. Each bar represents the relative amount of comparisons within that area. The average inter-device Hamming distance is 0.48, which is close to the optimum.
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Adsorption Studies  
#### 3.8.1 Effect of Adsorbent Dose  
The impact of an adsorbent dose of pine sawdust and cellulose fibres (CF) on removing chromium ions from an aqueous solution is shown in Figure 11. The results reveal an increased removal efficiency for both adsorbents, as they were loaded with chromium solution. The increase in the removal percentage as the amount of adsorbent dose increases can be attributed to the larger surface area of the cellulose fibres (CF)'s adsorbent [47; 48]. The outcome indicates that cellulose fibres expanded the microporous structure of the biomass, thereby creating additional sites for adsorption and increasing the uptake of chromium ions. Consequently, the solubilisation of lignin pretreatment leads to the exposure of adsorption sites in the biomass [48]. The lower removal percentage of untreated pine sawdust can be attributed to limited surface area, inadequate functional groups, reduced affinity for chromium ions, and particle size/structure limitations [49].  
Figure 10: TGA and DTA thermograms of (**a**) untreated pine sawdust (UPSD) and (**b**) cellulose fibres (CF).
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 3 System Model  
In this section, the system model used to evaluate positioning performance will be described in detail. First, the indoor environment considered in this study is shown in Figure 1. The size of the indoor environment is 5 m \(\times\) 5 m \(\times\) 3 m, and it is assumed to be an empty space.  
In this environment, it is assumed that a total of four LED access points (APs) are placed at a height of 3 m, and the receiver moves parallel to the ground at a height of 0.7 m. The transceiver characteristics are summarized in Table 1.  
Next, we analyze the channel characteristics of the line of sight (LOS) and non-line of sight (NLOS) paths in an indoor environment. The path of visible light in the indoor VLC environment is illustrated in Figure 2. The path of visible light can be divided into two components. The green line represents the LOS path while the red and yellow lines represent the NLOS path. The NLOS path can be further subdivided into an incident wave from the LED AP to the wall (red line) and a reflected wave from the wall to the receiver (yellow line). The channel characteristics associated with each path are as follows:  
\[h_{u,\ dir.}^{i}=P_{t}\frac{A(m+1)}{2\pi d^{2}}cos^{m}(\theta)T_{s}(\psi)C( \psi)\cos(\psi) \tag{1}\]  
\[h_{u,\ non-dir.}^{i}=P_{t}\frac{A(m+1)}{2\pi d_{1}^{2}d_{2}^{2}}\rho cos^{m}( \theta)dA_{wall}\cos(\alpha)\cos(\beta)T_{s}(\psi_{r})C(\psi_{r})\cos(\psi_{r}) \tag{2}\]  
where (1) and (2) are the RSS according to the LOS and NLOS paths, respectively. First, \(h_{u,\ dir.}^{i}\) and \(h_{u,\ non-dir.}^{i}\) denote the RSS of each path and \(P_{t}\) denotes the LED AP transmit power. \(A\) is the active area of the receiver and \(m\) is the Lambertian order. \(d\) is the distance from the LED AP to the receiver. \(d_{1}\) is the distance from the LED AP to the reflective surface, and \(d_{2}\) is the distance from the reflective surface to the receiver. \(\rho\) is the reflection coefficient of the wall and \(dA_{wall}\) is the surface element of the wall. Also, \(T_{s}(\psi)\) and \(C(\psi)\) denote the optical filter gain and optical concentration gain, respectively, and \(\psi_{c}\) means the FOV of the receiver.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Transmitters} & Transmit power & 10 W \\  & Half power semi-angle & 60\({}^{\circ}\) \\  & Elevation & \(-90^{\circ}\) \\  & Field of view (FOV) & 60\({}^{\circ}\) \\ Receiver & Active area & 1 cm\({}^{2}\) \\  & Gain of optical filter & 1 \\  & Gain of optical concentrator & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transceiver characteristics.  
Figure 1: Indoor VLC system model.  
Based on the above Equations (1) and (2), the overall RSS distribution of the indoor environment applied in this paper is shown in Figure 3.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.5 Naive Bayes  
NB originates from classical mathematical theory. It is a classification method based on Bayesian theory and an independent hypothesis of feature conditions. It makes classification predictions by calculating the conditional probability of each feature being classified separately [30]. The basic idea of the NB algorithm is: for the given item to be classified, calculate the probability of each category under the condition of the occurrence of this item and determine to which category the item to be classified belongs. This algorithm requires few parameters to be estimated and has fast operation speed and stable performance. However, this algorithm ignores the correlation between variables and the difference in the influence of attribute variables on decision variables, which results in the classification structure being greatly affected by the input data.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning
### Representation of a Railway Network
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline
**TR** & set of train runs (trains) in the timetable & \(T^{\text{DL}}\) & maximal dwell time of a train at a station \\ \(TR\) & number of trains in the timetable & \(TT^{\text{G}}\) & vector describing the original timetable of trains \\ \(\mathbf{R}_{p}^{\text{T}}\) & binary matrix representing the routes of trains & \(TT^{\text{G}}\) & element of \(TT^{\text{G}}\) describing the timetable of train \(p\) (route with corresponding arrival/departure times) \\ \(R_{p,i,j,k}^{\text{T}}\) & element of \(R_{p}^{\text{T}}\) referring to track \(k\) connecting stations \(i\) and \(j\) and being a part of the route of train \(p\). & \(TR^{\text{OB}}\) & number of obligatory trains \\ \(t_{i,p}^{\text{ARR}}\) & planned arrival time of train \(p\) at station \(i\) & \(T^{\text{E}}\) & limit of the total travel time extension of a train \\ \(t_{i,p}^{\text{DEP}}\) & planned departure time of train \(p\) from station & \(T^{\text{E}}\) & regarding the original timetable \(TT^{\text{G}}\) \\ \(T_{i,p}^{\text{ARR}}\) & maximal lateness of arrival of train \(p\) to station & \(D^{\text{L}}\) & travel distance extension of the train regarding the original timetable \(TT^{\text{G}}\) used to limit travel time \\ \(T_{i,p}^{\text{DEP}}\) & maximal lateness of departure of train \(p\) from station \(i\) & \(T^{\text{DE}}\) & limit of the travel time extension relating to the distance \(D^{\text{L}}\) \\ \(T_{i,p}^{\text{D}}\) & minimal dwell time of train \(p\) at station \(i\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notation regarding train run model. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline
**C** & set of all closures & \(T_{p}^{\text{C}}\) & duration of closure \(n\) \\ C & number of all closures & \(t_{n}^{\text{CES}}\) & earliest start time of closure \(n\) \\ C\({}^{\text{OB}}\) & number of obligatory closures & \(t_{n}^{\text{CLS}}\) & latest start time of closure \(n\) \\ \(\mathbf{S}^{\text{G}}\) & matrix of closure parameters & \(\mathbf{O}\) & binary matrix of precedence constraints \\ \(S_{n}^{\text{G}}\) & element of \(\mathbf{S}^{\text{G}}\) representing a tuple describing & \(o_{n,m}\) & element of \(\mathbf{O}\) representing a precedence constraint between closures \(n\) and \(m\) \\ \(\mathbf{R}_{n}^{\text{C}}\) & matrix of tracks blocked by closure \(n\) & \(T^{\text{H}}\) & time horizon of the investigated problem \\ \(R_{n,i,j,k}^{\text{C}}\) & element of \(\mathbf{R}_{n}^{\text{C}}\) representing the existence of a & & \\ blockade of track \(k\) between stations \(i\) and \(j\) to & & & \\  & be performed by closure \(n\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Notation regarding track closure model.
###### Abstract  
Rapid development in the printing and dying industry produces large amounts of wastewater, and its discharge in the environment causes pollution. Keeping in view the carcinogenic and mutagenic properties of various dyes, it is important to treat dyed wastewater. Maple leaf biochars were produced at different pyrolysis temperatures, i.e., 350 \({}^{\circ}\)C, 550 \({}^{\circ}\)C, and 750 \({}^{\circ}\)C, characterized for physicochemical properties and used for the removal of cationic (methylene blue (MB)) and anionic dye (congo red (CR)). Response surface methodology (RSM) using three variables, i.e., pH (4, 7, and 10), pyrolysis temperature (350 \({}^{\circ}\)C, 550 \({}^{\circ}\)C, and 750 \({}^{\circ}\)C), and adsorption temperature (20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C), was designed to find the optimum condition for dyes removal. X-ray diffraction (XRD) analysis showed an increase in CaCO\({}_{3}\) crystallinity and a decrease in MgCO\({}_{3}\) crystallinity with the increase of pyrolysis temperature. RSM design results showed that maple biochar showed maximum adsorption capacity for cationic dye at higher pH (9-10) and for anionic dye at pH 4-6, respectively. Under the selected condition of pH 7 and an adsorption temperature of 30 \({}^{\circ}\)C, biochar MB550 was able to remove MB and CR by 68% and 74%, respectively, from dye mixtures. Fourier transform infrared (FTIR) and X-ray photoelectron spectroscopy (XPS) analyses showed that MB550 was able to remove both dyes simultaneously from the aqueous mixtures.  
biochar; methylene blue; congo red; response surface methodology; maple leaf +
Footnote †: journal: Applied Sciences