esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The objective of this study was to compare BEVs with ICEVs using LCAs.  
The study showed that the relevance of BEVs is increasing as the number of vehicles in circulation has increased. In addition, the operation of BEVs is becoming easier as the autonomy of these vehicles has increased significantly.  
BEVs showed significant environmental benefits compared to ICE vehicles, even in scenarios where the electrical grid is carbon intensive, such as in Poland. In several scenarios, BEVs consistently outperformed diesel and petrol vehicles in terms of GHG emissions.  
In the best-case scenario, in which BEVs are recharged using clean electricity and equipped with batteries produced using renewable energy, the environmental impact was reduced by up to six times compared to conventional ICE vehicles.  
It is important to highlight that BEVs can offset the initial \"carbon debt\" incurred during battery production in just over a year of operation on average, and in the best-case scenario, after half a year, it will have already paid off that \"carbon debt\". Over their entire life cycle, BEVs are expected to save between 20 and 50 tCO2e for the worst-case and best-case scenarios, respectively, compared to their ICEV counterparts, highlighting their critical role in mitigating climate change. It was also observed that pollutant emissions from these vehicles were lower for smaller vehicle sizes/weights.  
In short, from both environmental and financial perspectives, BEVs offer superior performance and long-term benefits, making them a critical element in the transition to sustainable transportation.  
These analyses and results are critical to guiding policymakers and original equipment manufacturers (OEMs) in designing policies and strategic decisions based on long-term goals for countries, states, and cities.  
Figure 11: CO2e equivalent emissions over the life cycle of a vehicle, per vehicle segment, driven in Portugal, for BEVs with batteries produced in China and Sweden.  
To build on this work, it seems appropriate, as a next step, to compare BEVs and ICEVs using other criteria, such as total cost of ownership.  
Conceptualization, V.V. and L.R.; methodology, V.V.; software, V.V.; validation, J.M., A.B. and A.C.; formal analysis, A.C.; investigation, V.V.; writing--original draft preparation, V.V.; writing--review and editing; supervision, G.F.P., A.B. and L.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results  
### Elemental Composition and pH of the Cellulose Pyrochar  
The pyrolysis of cellulose at a temperature of 350 \({}^{\circ}\)C for 1 h resulted in a weight loss of 64% (Table 1) corresponding to a C-loss of 55% which is in line with observations that thermal degradation of cellulose starts around 300 \({}^{\circ}\)C [15]. However, pyrolysis seems to be less destructive since oxic charring led to a weight loss of 85% and 89% already after 4 min and 8 min heating at the same temperature [29,30]. The C content, H/C\({}_{\rm atm}\) and O/C\({}_{\rm atm}\) of pyrochars are often used as an indication of their condensation degree. The present pyrochar reveals a C concentration of 70% and the ratios decrease from 1.7 in the pure cellulose to 0.7 and from 0.9 to 0.3, respectively, indicating dehydration during pyrolysis. Whereas the O/C\({}_{\rm atm}\) of the pyrochar is below the threshold adjusted by the European Biochar Certificate to be classified as biochar [31] the H/C\({}_{\rm atm}\) is just at the threshold.  
Commonly, an increase of the pH value with increasing charring temperature of wood is reported in the literature [13]. However, this behavior is not confirmed for the pyrolyzed cellulose, analyzed here. In contrary, the pH decreases from 7.2 for the fresh cellulose to 5.0 for its pyrochar (Table 1). A possible explanation of the contradicting result may be the fact that the used cellulose contained negligible amounts of ash and other impurities which could accumulate due to the combustion of organic matter. Therefore, the pH change is explainable solely by heat-induced chemical alterations, which may have been the removal of OH-groups of the cellulose by dehydrogenation and the formation of functional groups which increase the acidity of the pyrolysate. Bearing this in mind, one can suspect that high alkalinity in biochars derived from plant residues is related to the content of inorganic cations (Ca, Mg, etc.) in the feedstock rather than being a general property of such materials.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 4 Analysis
### LEO-Enhanced PPP and PPP-AR
#### 4.2.1 PPP and PPP-AR Enhanced with Different LEO Constellations
Figure 5: Comparison of average convergence times of PPP enhanced using different LEO constellations. directions was 0.40, 0.34, and 1.20 cm, respectively, and that of ambiguity-fixed solution PPP was 0.28, 0.27, and 1.00 cm, respectively. With the help of the LEO constellation, the positioning accuracy of the float and fixed solutions was improved to varying degrees. The L+M constellation in the E and U directions was superior to the M and L constellations. However, the overall improvement was very limited. Even in the U-direction with the poorest positioning accuracy, the improvement was less than half a centimeter.
## 2 Materials and Methods
### Determination of Antioxidant Capacity  
Determination of antioxidant capacity of aqueous herbal extracts was performed by a simple assay using the stable 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical. The extracts were analyzed according to previously reported methods [22; 23]. In brief, the samples (30 \(\upmu\)L) were mixed with 3 mL of methanolic DPPH solution (4% w/v) into well plates. After incubation at room temperature for 60 min, the absorption of the reaction mixture was measured at 517 nm using a spectrophotometer (V-1200). Each sample was analyzed in triplicate. The results were calculated and expressed as the percentage of reduction (inhibition) of the DPPH (I %), which is described by following expression: I = 100 \(\times\) (A\({}_{\text{control}}\) - A\({}_{\text{sample}}\))/A\({}_{\text{control}}\), where A\({}_{\text{control}}\) is the initial absorbance and A\({}_{\text{sample}}\) is the value of the absorbance after the reaction. Finally, Trolox was used as a standard compound and the inhibition of DPPH by aqueous extracts was finally expressed as \(\upmu\)g Trolox equivalents (TE) per mL of sample. A Trolox calibration curve was prepared with concentration range of 2 to 60 mM Trolox.
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 2 Sources of PHAs
### Microbial Synthesis of PHAsopolymers  
When two or more different types of monomers are linked in the same polymer chain, copolymers are formed [84]. All the mcl PHAs are copolymers of C6-C12. P3 HB is the most commonly used PHA; due to its exceptional properties, such as high thermal stability and excellent mechanical properties, P3 HB based copolymers have attracted industrial interest [6]. Various copolymers such as P3 HBV, P3 HB4 HB, and P3 HBHrk have been produced on an industrial scale for several applications, but the non-3-hydroxybutyrate (3 HB) monomer of these copolymers is not economical and adds additional cost to the overall synthesis. For example, to produce copolymers of 3-hydroxyalerate (3 HV), 4-hyrovybutyrate (4 HB), and 3-hydroxyhexanoate (3 HHx) based on 3 HB, propionate, 1,4-butandiol, and lauric acid are required. Moreover, these non-3 HB monomers are toxic and challenging to control in the cell growth processes. As a consequence, researchers started using genetically engineered metabolic pathways and low-cost non-fatty acid substrates, such as glycerol, gluconate, and glucose, for the production of PHA copolymers [48; 85]. [PERSON] et al. [86], reported the synthesis of PHBV from 3 HV-unrelated resources glucose and glycerol without the genetic manipulation of _Haloferax mediterranei_ strain. [PERSON] et al., [87] successfully synthesized P3 HBV by adopting a metabolically engineered pathway. The authors used a strain of _Salmonella enterica_ serovar Typhimurium to metabolize propionyl coenzyme A (propionyl-CoA). The _S. enterica_ accumulated significant amounts of P3 HBV when grown aerobically using glycerol as a glucose source. [PERSON] et al., [85]
## 3 Results and Discussion
### Effect of Coir Fiber Addition on the Bulk Density of Geopolymer Specim  
Figure 10 shows metakaolin-based geopolymer composite bulk density at varying coir fiber contents. It was seen that the bulk density of the geopolymer composites decreased as the weight percent of coir fiber increased. There was a strong correlation between the amount of coir fibers (wt %) present in the geopolymer composite and the bulk density of the CFRMG composite. The geopolymer control sample (MGP Control) had the highest bulk density of 2113 kg/m\({}^{3}\), whereas the geopolymer composite reinforced with 2.0 wt % of coir  
Figure 8: Flexural strength of specimens at different coir fiber contents tested after 28 days.  
Figure 9: Mode of failure of the tested prism.  
fiber (MGP\(-\)2% PCF) had the lowest (2045 kg/m\({}^{3}\)). The bulk densities of the geopolymer composites were observed to decrease with increasing coil fiber content. Comparing these results with other investigators on composites, an agreement in the trend of decreasing values can be observed. From the result of [PERSON] et al. [65], for instance, coconut fiber as a reinforcing element in cement composite decreased the bulk density with increasing fiber content. In addition, the study by [PERSON] et al. [23] on the use of cotton fibers as reinforcing elements in fly ash-based geopolymer composite showed decreased geopolymer composite density with increasing cotton fiber content.  
Similarly, the study on wood-fiber-reinforced geopolymer composites, as reported by [PERSON] et al. [29] and [PERSON] et al. [66], showed that density decreased in value with increasing fiber content. The reduced bulk densities in these studies could be attributed to the specific gravity of reinforcing fibers being lower than that of the geopolymer matrix. Moreover, the density of geopolymer-based composites tends to decrease with an increase in fiber content because fibers clump together during the mixing process, entrapping water-filled spaces that, in turn, become porosities or air voids [10]. Furthermore, as lighter materials result in easier handling in the construction sector, the decreased bulk density of the composites as coir fiber content increases will promote the ease of handling the geopolymer composites in service.
## 2 Materials and Methods
### Dynamic Wind Analysis  
The wind speed flow in the installation area is the most important property to validate. The installation of an anemometer at a determined distance from SHAWT implies that between them, there may be buildings or trees that are an obstacle in the wind path. An obstacle could cause a decrease in wind speed or turbulence. Figure 2 shows the effect of a building on the wind flow and shows the turbulence formed around it [6].  
The finite-volume numerical method is used for wind flow analysis with ANSYS FLUENT's Computational Fluid Dynamics (CFD) software [25]. A dimensional analysis of the objects near the SHAWT is carried out. The air volume evaluated in the CFD model is from the SHAWT to the anemometer location. The total volume is divided into cells. The mesh size is reduced by sections from the outer limits of the volume to the wall of the object to be analyzed.  
The analysis is carried out under a condition of wind flow around the buildings in the stable state, for this RANS equation (Reynolds-averaged Navier-Stokes) is solved in combination with the K-epsilon realizable turbulence model. The pressure-velocity coupling method used is the semi-implicit method for pressure linked equations-consistent algorithmic. The spatial discretization is second-order to pressure and second-order up  
Figure 2: Effect of an obstacle on the wind flow (own elaboration).  
wind to momentum. This model is selected as a turbulence model in this study because it performs well in predicting the flow around objects.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Datasets
#### 2.2.3 Vegetation Indices  
A wide range of remote sensing data are available to monitor global vegetation conditions [60]. The use of vis is more popular because they can better reflect vegetation health conditions [44]. In this study, we use NDVI and EVI as the input for machine learning models. Equations (1) and (2) depict the mathematical formulae to calculate these two indices, respectively. NDVI characterizes the normalized difference between red and near-infrared (NIR) bands. It is very useful since vegetation strongly reflects NIR light and absorbs red light. The NDVI value for each pixel is between \(-1\) and \(1\). Larger positive values represent increasing green vegetation, while negative values usually indicate nonvegetated surfaces [61; 62]. EVI is another modified form of NDVI which can better model areas with large vegetation biomass and minimize the effects of soil and other atmospheric factors. C1 and C2 in Equation (2) represent the coefficients of the aerosol resistance term (C1 and C2 are 6 and 7.5, respectively, for the MODIS EVI product). In this study, we use a MODIS product [54] (NDVI and EVI), which has a spatial resolution of 250 m and a temporal resolution of 16 days [54]. We extracted NDVI and EVI from MOD13Q1.006 and used them because the MODIS data were consistent for our research period.  
\[NDVI=NIR-Red/NIR+Red \tag{1}\]  
\[EVI=2.5\times\frac{NIR-Red}{(NIR+C1\times Red-C2\times Blue+1)} \tag{2}\]
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review  
This section details EEG-based preference recognition, specifically, the neural correlation of the preference, predictive features of the preference, and preference classification algorithms.  
Preference can be defined as a human attitude toward a collection of entities that can be mirrored in an explicit decision-making procedure. This aspect can also be an evaluative judgment in the sense of liking or disliking an object [19]. The possibility of measuring the conscious and unconscious brain activity in assessing advertisements, through processing the consumer's processing of the advertisement message, cognitive workload, and emotional state, cannot be disregarded. The idea of a 'buy button' in the brain may be overexpressed; however, the research efforts to utilize the neural measures in monitoring the consumer thought processes are not trivial [20]. Understanding the neural process behind the preference, feelings, and decision-making can enhance the prediction of the user preferences and choices, and neuromarketing provides a precise objective determination of the implicit preferences of the consumers [21].  
Several studies [10; 22; 23; 24] have shown that the EEG can be used to determine the consumer preferences. To better utilize the EEG in consumer neuroscience research, the psychological processes underlying the consumer preferences must be understood.  
In the following subsection, we describe the neural correlations of the EEG-based preference. Next, we classify the relevant studies into: (1) predictive features; and (2) classification algorithms of the preference recognition. Finally, we explain how the preferences can be detected using BCI.
## 2 Integrated Navigation System Model
### Combined Navigation Model
#### 2.2.3. Observation Equation
\begin{cases}\mathbf{H}_{\rho}(t)=\begin{bmatrix}\mathbf{0}_{m\times 6}&eD_{a}& \mathbf{0}_{m\times 6}&-eD_{a}M_{pp}C_{b}^{n}&eD_{a}M_{pp}v_{INS}^{n}&D_{tu}& \mathbf{0}_{m\times 1}\\ \mathbf{H}_{\rho}(t)=\begin{bmatrix}\mathbf{0}_{m\times 3}&D_{v}&D_{p}& \mathbf{0}_{m\times 6}&-D_{v}C_{b}^{n}(\omega_{eb}^{n}\times)-D_{p}M_{pp}C_{b}^{n}&D_{v} a^{n}+D_{p}M_{pp}v_{INS}^{n}&\mathbf{0}_{m\times 1}&D_{tru}\end{bmatrix}\end{cases}  \tag{15}\]  
In vector \(\mathbf{Z}_{G}\), \(\rho_{\text{Gi}}-\rho_{\text{ii}}(i=1,2,\cdots,m)\) denotes the difference between the pseudo-range of the i-th satellite measured by the GNSS and the pseudo-range given by the inertial navigation calculation, and \(\dot{\rho}_{\text{Gi}}-\dot{\rho}_{\text{ii}}(i=1,2,\cdots,m)\) denotes the difference between the pseudo-range rate of the second satellite measured by the GNSS and the pseudo-range rate given by the inertial navigation calculation; detailed definitions of the submatrices of matrix \(\mathbf{H}_{G}\) can be found in reference [23]; and \(\mathbf{V}_{G}\) is the noise vector of the pseudo-range and the pseudo-range rate observation. The systematic observation equations in this paper can be obtained by combining Equations (9) and (13), and the systematic observation matrix can be obtained by combining Equations (10) and (14):  
\[\mathbf{Z}(t)= \begin{bmatrix}\mathbf{Z}_{\text{I}}(t)\\ \mathbf{Z}_{\text{G}}(t)\end{bmatrix} = \begin{bmatrix}\mathbf{H}_{\text{I}}(t )\\ \mathbf{H}_{\text{G}}(t)\end{bmatrix} \mathbf{X}(t)+ \begin{bmatrix}\mathbf{V}_{ \text{I}}(t)\\ \mathbf{V}_{\text{G}}(t)\end{bmatrix}  \tag{16}\]  
i.e., \(\mathbf{Z}(t)=\mathbf{H}(t)\mathbf{X}(t)+\mathbf{V}(t)\).
## 2 Related Works
### Organization Pattern of Network Structure
#### 2.3.2 Virtual Structure Based Pattern  
Many algorithms adopt virtual structure to collect data. TTDD [26], proposed by [PERSON] et al., adopts square virtual grid structure to collect data. When a source node appears, it sponsors the establishment of the virtual grid structure. The source node is appointed as a crossing point of the virtual structure. It relays establishment information to all other crossing points, called dissemination points. The nodes which are closest to the virtual crossing points are responsible for the crossing points. As each source node establishes its own virtual structure, that means the virtual structure will be reestablished for many times. In this way, excessive energy is consumed. In [24], VGDRA can avoid the energy consumption of reestablishment. The establishment of virtual structure has nothing to do with source node in this algorithm. VGDRA partitions target area into uniform square cell. The number of square cell is based on the total number of sensor nodes. VGDRA adopts the heuristics used in LEACH [36], TEEN [37], and APTEEN [15] which consider 5% of the total number of sensor nodes. The node which is closer to center of virtual cell and has more residual energy will be chosen as a head node in each grid cell. The sensory data is relayed among head nodes to the mobile sink. Although the reestablishment is avoided, communication range and side length of target area are not taken into consideration.  
In the algorithms with virtual structure, nodes are organized by corresponding virtual cell and head node. Sensory data is easy to be routed to sink by virtual structure when sink is moving. However, the attendant challenge is that the maintenance of some virtual structure is complex.
## 6 Results
### DC Power  
The computed results of the second set of data related to DC_CurrentString1 using (i) the method of Ordinary Least Squares, (ii) the Ridge Method, (iii) the LASSO method, and (iv) the Elastic Net Method are given in Table 2.  
The results of modeling the DC power of the Multiple Linear Regression Model show that the RMSE ranges from 191.401 to 629.97, and the best-performing model with the parameters/factors (X2, X5) has the lowest RMSE (191.401) and highest R-squared (0.9335). In addition, the worst model has highest value of RMSE (2.018) and the parameters/factors X3 and X7. The coefficients for the predictor variables vary widely across different combinations, indicating diverse influences of the variables on the DC power.  
Regarding the LAR models, the results of RMSE show a slight improvement in RMSE compared to the MLR model, with values ranging from 190.768 to 629.77. The R-squared values are slightly higher or comparable to those of the Linear Regression models, indicating a better or similar fit, and the best-performing model with the parameters/factors (X2, X5) has the lowest RMSE (191.401) and highest R-squared (0.9335). In addition, the worst model has highest value of RMSE (2.018) and with parameters/factors X3 and X7. Additionally, some coefficients are shrunk to zero, indicating variable selection by the Lasso method, which helps in simplifying the model.  
The RR models have RMSE values ranging from 185.92 to 628.53, showing slight variations compared to the other methods. The R-squared values are generally high, indicating a good fit, with the highest being 0.9373 for the (X2, X5) combination; i.e., it is the best-performing model. Also, the worst model has the highest value of RMSE (2.018) and with the parameters/factors X3 and X7. The coefficients are generally more stable compared to the LR due to the regularization effect of RR, which helps manage multicollinearity.  
Finally, the ENR models also show RMSE values ranging from 190.44 to 629.77, similar to Lasso. The R-squared values are high and higher than those of Lasso and Ridge, with the highest being 0.9342 for the (X2, X5) combination; i.e., it is the best-performing model. Also, the worst model has the highest value of RMSE (0.2809) and the parameters/factors X3 and X7. The coefficients of the ENR models reflect a balance between Lasso and Ridge regularization, with some coefficients shrunk to zero, combining the strengths of both methods.  
All regression models show high R-squared values, indicating they can explain a large portion of the variability in the DC power due to including the parameters/factors. The RMSE values suggest that the (X2, X5) combination is particularly effective across different regression methods. Lasso and Elastic Net perform similarly in terms of RMSE and R-squared, and both slightly outperform LR. RR shows consistent performance with slightly higher RMSE in some cases. Lasso and Elastic Net perform variable selection, setting some coefficients to zero, which can simplify the model and potentially improve interpretability.