esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods for Identification and Quantification of Coumarins
### Analysis
#### 5.2.4 Spectrometric Detectors  
Along with the chromatographic methods for the separation, spectrophotometry/spectrofluorimetry has also been used for coumarin analysis in spirit beverages (Table 4). The use of a spectrophotometer relies on the absorption capacity of coumarins in the ultraviolet range related to the transitions of \(\pi\) electrons from bonding to antibonding molecular orbitals. However, it requires a reference sample for a more accurate determination [162].  
Technological advancements have led to the development of new equipment, which has improved the precision and accuracy of existing techniques used to detect and quantify coumarins even in very low concentration. These techniques include HPLC with diode array detection (DAD) and fluorescence detector (FLD) coupled to mass spectrometry (MS), GC or LC coupled to ESI-MS, MS-time of flight (TOF), MS-ion trap and MS-MS tandem quadrupole (Q)-TOF [170; 171; 172; 173; 174; 175; 176; 177; 178].
### Research Scope  
In this study, the hardware and software necessary to develop a management system for proper wearing of safety helmets based on three-axis accelerometer sensing were established. This work resolves the shortcomings of existing studies in which sensors were employed and the focus was on whether or not the safety helmet was being worn. This research was focused on developing a method of using worker motion data captured by three-axis accelerometer sensing to determine whether safety helmets are being worn improperly at construction sites. The specific steps performed in this study are described as follows:  
First, statistical data on Korean construction site safety accidents were analyzed to understand the importance of not wearing safety helmets, and the necessity of developing a system for monitoring the proper wearing of safety helmets was identified. Existing studies on monitoring the proper wearing of safety helmets have been limited to determining when safety helmets are not being worn. As such, it was observed that improper wearing, such as when the chinstrap and harness of the safety helmet are not fastened correctly, have not been monitored adequately in previous studies.  
Second, a sensing safety helmet measurement system was developed in which the safety helmet has a three-axis accelerometer sensor that can identify user activities to monitor whether the safety helmet is properly worn, not worn, or improperly worn.  
Third, the raw data from the three-axis accelerometer sensor of the safety helmet were converted into a signal vector magnitude (SVM). The recognition rate for each activity was found to be high, but it was difficult to distinguish between proper and improper wearing.  
Fourth, to resolve this problem, the SVM was converted into a frequency by performing a fast Fourier transformation (FFT), and specific features were found at certain frequencies. These frequency features were used to classify the data by work activity and to analyze them.  
Fifth, the Waikato Environment for Knowledge Analysis (WEKA) data mining program was used to verify that it is possible to differentiate among proper wearing, not wearing, and improper wearing of the safety helmet during work for each of the work activities.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Variables in 3D Printing  
#### 1.3.1 Print Orientation  
The alignment of an object within the space of the build volume may influence the accuracy and precision of the final 3D-printed product. It is recommended that large, flat surfaces are orientated at an angle between 10\({}^{\circ}\) to 20\({}^{\circ}\) to reduce the surface area of each layer and the amount of force applied to the print as the build platform elevates after building each layer [19].  
In addition, any objects with two parts of the object that eventually merge together, like an OSS which is horseshoe-shaped, should be orientated in such a way that a shared base is formed first before the layers branch into individual paths. Theoretically, this helps to prevent any cracks or splits forming at the intersection.  
All objects should ideally be oriented to self-support themselves with no or minimal amount of overhang where possible. The risk of dimensional changes during post-processing can be reduced by eliminating, or minimising, the number of support structures required.
## 3 Methodology
### Transformer and Channel Attention Module (CAM)  
Given that remote sensing images cover wide areas, change targets are discrete and have complex associations with the surrounding environment. Due to the inherent limitations of the local nature of convolutional operations, methods based solely on CNNs still  
Figure 4: (**a**) The details of the DPEM. Information Flow (**a**): After the input of features at two moments, the change-related channels are first enhanced via global pooling and 1D convolution. Then, change-related regions are highlighted through max/average pooling and \(7\times 7\) convolution. Finally, spatio-temporal differences are amplified by pixel-wise subtraction. (**b**) The Channel Attention Module. Information Flow (**b**): Input features capture change-related channel statistics via dual pooling (average/max); they learn channel attention weights through a shared MLP, and then generate channel masks via sigmoid operation; finally, refined features are output through element-wise multiplication.  
struggle to capture long-range dependency features and achieve long-distance context modeling, even after multi-scale aggregation and differential perception enhancement. Therefore, as shown in Figure 1, we pass the features \(G_{i}^{(j)}(\forall i\in\{1,2\},\forall j\in\{1,2,3\})\) and \(G_{3}^{(j)}\) after multi-scale aggregation and differential perception enhancement through the Transformer module and CAM, respectively, to generate the feature maps \(G_{i}^{(j)}(\forall i,j\in\{1,2,3\})\). The Transformer uses encoder and decoder modules. Various Transformer modules [24, 48] are plug-and-play in the proposed change detection network. We use the Transformer to model global context information, while the CAM models channel context information by highlighting channels related to changes. The following will introduce the CAM in detail. As shown in Figure 4b, the channel attention mask of the CAM is obtained through a dual-pooling strategy (average and max-pooling), shared MLP, and sigmoid normalization, as shown in Equation (7):  
\[M_{i}^{j(k)}=\sigma(MLP(Avg(G_{i}^{j(k)}))+MLP(Max(G_{i}^{j(k)}))) \tag{7}\]  
where \(G_{i}^{j(k)}\) represents the feature maps at different stages in Figure 1 and \(M_{i}^{j(k)}\) represents the channel attention mask corresponding to the input feature \(G_{i}^{j(k)}\) in Figure 4b, where \(i,j\in\{1,2,3\},k\in\{2,3\}\). \(MLP(\cdot)\) represents a multi-layer perceptron, which consists of two linear layers and a ReLU activation function.  
Define \(T(G_{i}^{j(k)})\) as the output of \(G_{i}^{j(k)}\) when passed into the Transformer module. Finally, the feature map \(G_{i}^{j(4)}\) is obtained through the CAM, as shown in Equation (8):  
\[G_{i}^{j(4)}=T(G_{i}^{j(k)})\otimes M_{i}^{j(k)} \tag{8}\]
## 2 The Forecasting Model  
### Ensemble Empirical Mode Decomposition  
#### 2.1.1 Empirical Mode Decomposition (EMD)  
Empirical mode decomposition (EMD) is a new adaptive signal time frequency processing method, proposed by [PERSON] in 1998 [38], that is especially suitable for the analysis and processing of nonlinear nonstationary signals. The method is considered to be a significant breakthrough in the linear and steady-state spectrum analysis based on the Fourier transform. The complex signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition, and the intrinsic mode functions show the intrinsic vibrational form of the signal. The IMF components must meet the following two conditions:  
1. The maximum difference between the number of extremes and the number of zero points is one;
2. The upper envelope and the lower envelope should be symmetrical. For the signal \(X(t)\), the calculation flow of EMD is as follows:  
1. Determine all maxima and minima of signal \(X(t)\);
2. According to the maximum and minimum of signal, construct the upper envelope \(f_{a}(t)\) and lower envelope \(f_{b}(t)\) of \(X(t)\) using three spline interpolation method;
3. Find the local average of the signal, \(f_{m}(t)=\left(f_{a}(t)+f_{b}(t)\right)/2\);
4. Calculate the difference between \(X(t)\) and \(f_{m}(t)\), \(E(t)=X(t)-f_{m}(t)\);
5. Determine whether \(E(t)\) satisfies the conditions of IMF, and if the conditions are satisfied, the first IMF component can be obtained; otherwise, repeat the above steps until the signal meets the IMF conditions.
6. Define \(r(t)=X(t)-imf_{1}(t)\), and determine whether \(r(t)\) needs to be decomposed. If so, repeat the above steps by replacing \(X(t)\) with \(r(t)\), otherwise end the decomposition.  
After all these steps, the process of EMD for the signal \(X(t)\) can be ended and the signal \(X(t)\) is decomposed into the sum of a number of IMF components \(imf_{i}(t)\) and the remaining components \(r(t)\), that is \(X(t)=\sum\limits_{i=1}^{n}imf_{i}(t)+r(t)\).
# Biomethane Potential of Sludges from a Brackish Water Fish Hatchery
###### Abstract
In fact, it should be considered that biogas could contain harmful concentrations of NH\({}_{3}\) and H\({}_{2}\)S [17] and it must be consequently purified before combustion, to avoid corrosion and other technical issues: biogas treatments such as biological desulphurization by thiobacillus [18; 19], scrubbing or filtration in activated carbon reactors are common practices in the sector, but also determine capital and management costs. Other contaminants that have to be monitored in the biogas include volatile organic compounds (VOCs), carbon monoxide (CO), nitrogen oxides (NO\({}_{\text{x}}\)), formaldehyde, HCl, SO\({}_{2}\) [17]. In recent years, few studies have been addressed to the determination of the methane potential in AD from marine or brackish aquaculture effluents, mainly operating with lab-scale self-built apparatus; nonetheless, the reported results were particularly promising. [PERSON] treated marine aquaculture sludges with a continuously stirred tank reactor (CSTR), but the sludge was diluted 1:1 with tap water (5.3 g Na\({}^{+}\)/L) to overcome inhibition and maintain a stable process, obtaining a methane production of 0.26-0.28 L CH\({}_{4}\)/g of volatile solids (VS) [16]. Operating with an up-flow anaerobic sludge blanket (UASB) reactor and with a sludge salinity of 15-17 g/L, [PERSON] et al. found a maximum specific methane production of 0.45 L CH\({}_{4}\)/L of volume reactor/day [8]. [PERSON] and colleagues [20] employed 0.5 L batch reactors, reaching methane yields of 0.27-0.33 NmL CH\({}_{4}\)/g VS in the treatment of sludge from brackish fish farm; however, they used specific additives like glycine betaine and trehalose to increase the energy yield. Biochemical methane potential (BMP) assays allow to establish through a standardized laboratory tool the methane production of liquid and solid substrates in the AD process, giving useful information about substrate's biodegradability and methane generation kinetics. The BMP procedure involves adding small amounts of an active anaerobic inoculum and a selected substrate into serum bottles, creating anaerobic conditions and measuring gas production over time [21]. The biochemical methane potential (BMP) of effluents from a typical fish of Mediterranean production, that is gift-head sea bream (_Sparus aurata_), was never determined before, according to our knowledge. Consequently, the aim of the present study was to find out if the AD could be technically applicable to treat sludges from brackish gift-head farming. Moreover, in order to have first indicationson the feasibility of the AD process and on the eventual potential and requirements for a scale-up, it is fundamental to understand the optimal inoculum to substrate (I/S) ratio and the total methane potential of this substrate.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
###### Abstract  
Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matern to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.  
hierarchical; Bayesian; spatiotemporal
## 2 Materials and Methods
### GBDT Model
After one of the subsamples was randomly used as a testing set with the rest as a training set, the GBDT model was constructed, and the performance was evaluated. This process was repeated 10 times. By grid research, all the potential setting was traversed with 10-fold cross-validation, and the parameters with best performance were given. In this study, the mean square error (MSE) was set as a loss function, and the learning rate was set as 0.1. The number of the CART decision trees was set as 100, and the max depth of the decision tree was set as 3. The inversion accuracy of the remote sensing inversion model was assessed using the mean absolute error (_MAE_), mean absolute percentage error (_MAPE_), and root mean square error (_RMSE_), which are defined as follows:  
Figure 3:0  
Figure 3:1  
Figure 3:2  
where \(n\) is the number of data pairs, the subscript \(i\) denotes individual data points, and \(y\) and \(f\) represent the measured and estimated values, respectively. The correlation coefficient (R\({}^{2}\)) was also measured to show how well the variation of one model explained the variation in the concentration of Chl-a. Generally, the largest R\({}^{2}\) with the smallest RMSE gives the best prediction model. In this study, the models with correlation coefficients (R\({}^{2}\)) exceeding 0.7 would be selected to verify the inversion accuracy. Theil-Sen and Mann-Kendall trend analysis were used to analyze the variation of Chl-a concentrations in the coastal sea surface of the Beibu Gulf of Guangxi. Theil-Sen and Mann-Kendall trend analysis includes Theil-Sen slope estimation and the Mann-Kendall significance test, which does not require the dataset to meet the normal distribution on the time series, nor does it require a dataset correlation between time series, which is insensitive to outliers in time series and has a strong ability to avoid measurement errors in datasets or discrete data. The remote sensing images with sensing dates ranging from 2013 to 2020 were converted into the trained GBDT model, and the output results were imported into ArcGIS 10.5 for raster processing. After elimination of the outlier values and Inverse Distance Weighted (IDW) interpolation, the spatial and temporal distributions of chlorophyll were visualized for analysis.
## 4 Discussion
### Comparison of Big-Leaf Lue-Gpp Models
Furthermore, the MVPM added \(W_{\rm s\_VPD}\) to characterize the constraints of atmospheric water requirement on leaf photosynthesis. However, the \(R^{2}\) value was lower between \(GPP_{\rm max}\times W_{\rm s\_MVPM}\) and \(GPP_{\rm EC}\) than between \(GPP_{\rm max}\times W_{\rm s\_VPM}\) and \(GPP_{\rm EC}\), indicating that adding a linear function constraint of \(VPD\) yields inaccurate estimates. Moreover, the \(W_{\rm s}\) algorithm used in the MODIS might not fully quantify the effect of water stress on productivity, since the consistency between \(GPP_{\rm max}\times W_{\rm s\_MODIS}\) and \(GPP_{\rm EC}\) is not ideal. The decoupled relationship between \(VPD\) and \(GPP\) was also reported in corn and soybean field sites [52]. Several studies have indicated that VPD may not comprehensively capture the limitations of moisture on plant photosynthesis, as it solely accounts for atmospheric water availability while disregarding soil water supply [53]. The joint constraint combination of \(T_{\rm s}\) and \(W_{\rm s}\) better simulated the environmental constraints imposed on \(GPP\) than the maximum constraint combinations. This is because \(T_{\rm s}\) and \(W_{\rm s}\) were both set to \(<\)1, and the maximum limit of \(T_{\rm s}\) or \(W_{\rm s}\) may underestimate the effect of certain environmental constraints on estimating \(GPP\). The combination of environmental factors in the form of a product (joint constraint combination) greatly improved the simulation accuracy of the EF-LUE model [11], which was consistent with our results. However, a specific LUE-GPP model is consistently superior over other models across various ecosystems. For instance, the VPM showed superior performance in deciduous broadleaf forest sites with deep root systems, as \(W_{\rm s}\) effectively characterizes the effect of leaf age and canopy water availability on photosynthesis. However, the EF-LUE model had the best performance in non-forest sites [11]. In addition, compared to \(T_{\rm s\_EF}\), \(T_{\rm s\_CASA}\) might not be optimal in quantifying temperature constraints, but it can improve model performance when combined with any form of \(W_{\rm s}\) in big-leaf LUE-GPP models.
## 2 Operation and Modelling of the Bearingless Switched Reluctance Motor  
### Operating Principle  
The BSRM has salient poles both in the rotor and stator, and the excitation is limited to stator windings only. The excitation currents are unidirectional and discontinuous. The stator phases are sequentially excited to obtain continuous rotation [12; 13; 14]. Hence, the design of the controllers for the BSRM is more complicated due to its nonlinear magnetic field distribution. The basic 12/14 BSRM salient pole structure is illustrated in Figure 1. Two regulated individual DC voltages are given to the suspension and torque coils to realise the decoupled performance between the net suspension winding's force and main winding's torque. The suspension winding coils Is1 and Is3 produce the radial force in the Y-directional, and coils Is2 and Is4 produce the radial force in the X-directional. Positive suspension forces are produced from Is2 and Is1; similarly, Is4 and Is3 poles produce negative suspension forces. The resultant rotational torque will be produced from the stator main phase coils called phase-A and phase-B. The operating parameters and ratings of the 12/14 BSRM are given in Table 1.  
Hence, the BSRM drive needs a total of six hysteresis controllers to regulate both the suspension force and torque. Out of six, two are used as individual phase current controllers, and the remaining four suspension force current controllers are used to control the suspension currents. The suspension force pole arc is carefully chosen not to be less than one-rotor pole pitch to maintain the continuous levitation force. Therefore, the pole arcs of both the suspension pole and rotor pole are equal in dimensions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Rated power(motor) & 1 kW \\ Maximum motor Current/phase & 4 amp \\ Voltage/phase & 250 volts \\ Net torque & 1 Nm \\ Speed & 9000 rpm \\ Toque winding per phase resistance & 0.86 ohms \\ Suspension winding per phase resistance & 0.32 ohms \\ Suspension voltage & 250 volts \\ Maximum suspension current & 4 amp \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating details of the 12/14 bearingless switched reluctance motor (BSRM).  
Figure 1: Structure and winding pattern of the stator.
## 3 Results
### Discussion  
Firstly, the MM/PBSA result for the selected compounds BTB14738, RH00608, SPB02705, and CD01000 is given in detail in Table 3. It has been shown that Van der Waals interaction contributed more towards the favorable binding of compounds while the contribution of electrostatic interaction was estimated to be lower. Hence, Van der Waals forces are an important form of interaction between the ligand and the protein.  
It is noteworthy that enoyl-acyl carrier protein reductases have been reported as attractive targets for the development of novel antibiotics. In one such study, the crystal structure of enoyl-ACP reductase (FabK) from Streptococcus pneumoniae in complex with phenylimidazole derivative inhibitor has been reported [53] (Figure 3). This implies that the enoyl reductase domain of FAS-I can be a promising target because it is crucial in the regulation of the pathway. The inhibitor has been reported to bind competitively with respect to NADH. The thiazole ring and a part of ureido moeity is involved in the \(\pi\)-\(\pi\) stacking interaction with the isoalloxazine ring of the FMN molecule. Similarly, the active site of Mtb FAS-I consists of the tightly bound FMN molecule and the catalytic residue His584; hence, ligands containing aromatic rings possess potential for strong \(\pi\)-\(\pi\) interactions with the FMN molecule. The presence of residues Thr436, His584, and Arg868 facilitates the formation of a hydrogen bond. Furthermore, the active site is large and open in nature, offering wide scope for optimization of compounds through additional groups. The proposed hit compounds can be pioneer inhibitors of Mtb FAS-I.
## 3 Results  
### Factors Affecting the Performance of Fully Grouted Rock Bolts  
Rock-bolting is one of the two techniques (reinforcement and support) for excavation stabilization and is the output of empirical systems during the preliminary support design of tunnels. Comprising four components (rock-mass, bolt, internal, and external fixtures), rock bolts can be classified into the following three types, based on the load transfer between the rock mass and bolt [12]:  
1. Continuously mechanically coupled (CMC);
2. Continuously frictionally coupled (CFC);
3. Discretely mechanically or frictionally coupled (DMFC).  
Fully grouted (cement or resin) rock bolts belong to CMC systems, inflated steel-tube rock bolts are CFC systems, and expansion shell bolts belong to the DMFC category. The first type is comparatively simple in installation, versatile, effective in reinforcement, and also economical. This type is widely used in underground excavation projects for stability [19; 20; 21], and, owing to this reason, empirical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Parameter**} \\ \hline
**Rock Mass** & **Rock-Bolt** & **Rock Bolt** & **Curing Time** & **Grout Type** & **Ground Water** \\  & **Type** & **Length (L)** & **Curing Time** & **Grout Type** & **Condition** \\ \hline Moderate (50) & Fully grouted & L = 3 m (11) & 12 h (6) & & Dry (109) \\ Soft (40) & (125) & L = 4 m (81) & 24 h (6) & & Wet (33) \\ \hline Weathered (52) &
\begin{tabular}{c} inflated steel \\ tube (17) \\ \end{tabular} & L = 5 m (50) & 36 h (6) & Resin (22) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of field tests (in parentheses) against each parameter.  
systems also focus on fully grouted rock bolts. The CMC bolts are strong and stiff bolts compared to the last two types, which are flexible and absorb large ground deformation. However, after the development of D-bolts (also fully grouted), the D-bolt is preferred over the expansion shell, inflated steel-tube rock bolt, and split-set type, owing to its high strength and large deformation capacity [22]. A few types of bolts used for stability are shown in Figure 2[23].