esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 5 Simulation Analysis
### Reference Multicast Schemes
On the contrary, BIER does not require the construction of the MDT; it only needs to setup the unique identifiers for each router once at the beginning (or if a change/failure affects these identifiers) and then fully populates the BIRT. Therefore there is no need to interchange messages between the routers during the construction of the MDT; thus, we assume the communication cost only involves the interchange of the BFR-id. Finally, for sake of simplicity, we do not consider the IGMP protocol required by ST, PIM-SSM, and BIER to subscribe hosts to their local router to join a multicast group. Therefore, any additional communication or transmission cost due to IGMP is not accounted in the following results. Please note that this is a simplification that goes in favour of ST, PIM-SSM and BIER as GCMR does not require IGMP.
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 5 Implementation and Experimental Results
### Use Case 3: RUL Prediction on the Lifetime Dataset  
This use case deals with RUL prediction by processing the Toyota dataset, which describes the whole lifetime of the battery until its capacity reaches 80% of its nominal capacity. It should be noted that in order to perform RUL prediction, it is necessary to use a dataset that contains the whole lifetime of the battery to train the ML models, and it is to be used as the reference value of the prediction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pipeline** & \begin{tabular}{c} **Reference Value** \\ **Estimation** \\ \end{tabular}  & \begin{tabular}{c} **Definition of** \\ **Training and Testing** \\ **Set** \\ \end{tabular}  & \begin{tabular}{c} **Features Selection** \\ \end{tabular}  & \begin{tabular}{c} **ML Model** \\ **Selection** \\ \end{tabular}  & \multirow{2}{*}{**RMSE (\%)**} \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{ \begin{tabular}{c} residual to \\ nominal capacity \\ method \\ \end{tabular}  } & set of datasets & \(\succ\) & V, T & \multirow{4}{*}{ \begin{tabular}{c} \\ statistics \\ testing: 2 \\ \end{tabular}  } & \multirow{4}{*}{LSTM} & \multirow{4}{*}{0.6} \\  & & training: 5 & & & & \\ \cline{1-1}  & & testing: 2 & & & & \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{ \begin{tabular}{c} current to \\ maximum capacity method \\ \end{tabular}  } & set of consecutive & raw data & \multirow{4}{*}{LSTM} & \multirow{4}{*}{0.25} \\  & current to & cycles & & & & \\ \cline{1-1}  & & cycles & & & & \\ \cline{1-1}  & & training: 5 & & & & \\ \cline{1-1}  & & testing: 1 & & & & \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{\ \begin{tabular}{c} current to \\ maximum capacity method \\ \end{tabular}  } & set of consecutive & raw data & \multirow{4}{*}{ \begin{tabular}{c} \\ values \\ testing: 1 \\ \end{tabular}  } & \multirow{4}{*}{\begin{  
Figure 11: The actual and predicted values of SoC for pipeline 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Cycle Number** & **RMSE (\%)** \\ \hline
75 & 3.24 \\ \hline
175 & 2.70 \\ \hline
275 & 3.09 \\ \hline
375 & 3.56 \\ \hline
475 & 2.82 \\ \hline
575 & 3.63 \\ \hline
675 & 3.71 \\ \hline
775 & 4.65 \\ \hline
875 & 6.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The evaluation of pipeline 11 for RUL prediction in several cycles. Figure 10: The evaluation of different pipelines for SoC prediction in use case 2 by the RMSE metric. For this implementation, cycle-based features (i.e., features that are based on the cycle number) and features extracted by interpolation have been selected as features, which have been extracted from the first 100 cycles of each dataset. Table 9 presents the features that have been applied to this use case. In this use case, the most suitable metric is accuracy, which indicates the deviation of the predicted RUL from the reference value of the RUL, and it is measured in percentage (%), while the RUL is expressed in cycles.
## 2 Materials and Methods
### Methanogenic Pathway of Predominant Species Determined by Genomic Binning  
CONCOCT software was used for contig binning, which is based on sequence composition and coverage [37]. Subsequently, mapping of the high-quality reads was carried out using Bowtie2 [34] to determine the coverage of these contigs before conducting the CONCOCT step. CheckM [38] was used to assess the completeness and contamination of the recovered metagenome-assembled genomes (MAGs). MAGs with completeness \(>90\%\) and contamination \(<10\%\) were used for detailed downstream analyses. The abundance of each MAG of the samples was estimated by mapping the high-quality reads of individual datasets to the contigs from the MAGs.  
Taxonomic analysis of the MAGs was performed by different methods, and the results were compared to obtain a consensus assignment. First, all the protein sequences translated in silico from these MAGs were input to PhyloPhlAn [39] to facilitate the phylogenetics of the MAGs, which were based on 400 universal proteins. Second, the ORFs predicted from each MAG were checked by sequence similarity to the NCBI-nr database using BlastX with a maximum allowed e-value of \(1\times 10^{-10}\). The BlastX results were parsed by MEGAN [40] to access the species and genus phylum-level taxonomic annotation of each cluster of bins. The minimum bit score used for the analysis was 60, and a minimum support of 5% for each taxonomic category was used for the LCA algorithm. A taxon of a MAG was assigned when at least 85% of the identified ORFs resulted in a concordant taxonomy. We also constructed a phylogenetic tree of the recovered MAGs and their closest species' genome using the FastTree algorithm with default settings by PhyloPhlAn.  
Genes were predicted across each MAG using FragGeneScan [41], and functional prediction of the identified genes was conducted by similarity searching against the KEGG and CAZy databases using an e-value of \(10^{-5}\) and an aligned length larger than 200 nt. The proteins of interest in carbohydrate and methanogenic metabolism were retrieved from the gene information in comparison with the KEGG database, and the abundance of methanogenesis-associated genes in each MAG was calculated using the mapping information of each gene.
#### 3.1.3 Residual Predictive Deviation (RPD) of Major Elements of Mixed Coals  
The residual predictive deviation (RPD) parameter was introduced to examine the accuracy of the PLSR model and can be defined by the following equation:  
\[\text{RPD}=\frac{\text{SD}}{\text{RMSECV}}, \tag{4}\]  
where SD is the standard deviation of each element's concentration obtained from conventional industrial analysis, and RMSECV is the RMSE of cross-validation for the PLSR. When the RPD is lower than 2.0, the model is considered as insufficient. When the RPD lie between 2.0 and 2.5, the model is approximately precise and is recommended for use in the estimation of composition. When the RPD is in the range from 2.5 to 3.0, the model is very precise. When the RPD is higher than 3.0, the model is considered as an excellent model [18; 19; 20].  
In Table 2, the PLSR model of each element after the application of the SG derivative method shows that the RPD values of carbon and hydrogen are 3.10 and 2.63. Thus, both of the PLSR models are considered to be sufficiently precise. The RPD of oxygen is 1.70, which indicates that the PLSR model for oxygen is insufficient. However, the RPD of sulfur is 3.53, which proves that the PLSR model for sulfur is excellent, although the sulfur concentration is extremely low compared to the concentrations of other elements.
## 2 Materials and Methods
### Functions for Correcting the Contact Position Due to the Effect of Overlapping Span  
Although the assembly of a rigid catenary canton is undertaken using a series of spans in which the beams are assumed to be straight, in the case of overlapping spans, it may be advisable to previously deform the beam by giving it a sloped shape, and thus obtain the smoothest possible transition between cantons. This makes it difficult to simulate because the finite element model used is based on the hypothesis of straight beams. To overcome this disadvantage, it is assumed that the rigidity of the beam is not altered by the previous deformation. Thus, the integration of the differential Equation (18) is carried out as if the catenary is configured as a perfectly straight beam, both in the normal spans and in the section span. Then, to take into account the previous deformation of the beam and its influence on the contact force, the vertical position of the contact element calculated from Equation (10) is supplemented by the correction functions \(v_{1}\) and \(v_{2}\). These functions take into account the variation of the position of these elements to then calculate the contact forces according to Equations (21) and (22). These functions depend on the geometry of the deformation and their effect is only taken into account when the pantograph is circulating through the section slopes. In the simulation proposed in the work, a linear slope is also assumed due to its simplicity; however, this methodology can also be adapted to any beam configuration with slight modifications.  
In the case of a linear slope, the variable \(x\) defines the horizontal displacement of the pantograph from the beginning of the route, \(x_{1}\) is the position where the ascending slope begins in the canton of outgoing catenary 1, and \(x_{2}\) is the starting position of the descending slope in the canton of incoming catenary 2, with \(x_{1}>x_{2}\), as shown in Figure 7. Let \(L_{1}\) be the length of catenary slope 1, \(L_{2}\) be the length of catenary slope 2, and \(h_{1}\) and \(h_{2}\) be the respective heights at the end of each slope. This is compiled for \(v_{1}\) as:  
\[\begin{array}{c}u_{1}=x-x_{1}\\ x<x_{1}\cup x>x_{1}+L_{1},\ \ v_{1}=0\\ x\geq x_{1}\cap x\leq x_{1}+L_{1},\ \ v_{1}=\frac{h_{1}}{L_{1}}u_{1}\end{array} \tag{25}\]  
and for \(v_{2}\) as:  
\[\begin{array}{c}u_{2}=x-x_{2}\\ x<x_{2}\cup x>x_{2}+L_{2},\ \ v_{2}=0\\ x\geq x_{2}\cap x\leq x_{2}+L_{2},\ \ v_{2}=h_{2}-\frac{h_{2}}{L_{2}}u_{2} \end{array} \tag{26}\]
## 3 Model and Problem
### Sidelobe Suppression Model  
As previously mentioned, the portion of the signal that is not the mainlobe belongs to the sidelobes. Therefore, it is difficult for us to evaluate the suppression of the sidelobes in a beam pattern. Generally speaking, we use the maximum sidelobe level (SLL) as a performance index to evaluate the suppression of the total sidelobes. This is because once the maximum SLL is suppressed, all the sidelobes will be suppressed. Mathematically, the calculation of the maximum SLL is as follows:  
\[f_{sll}(\mathbf{I})=\frac{\mathbf{B}|AF(\theta_{SLL})|}{|AF(\theta_{ML})|}, \tag{4}\]  
where \(\mathbf{I}=\{I_{1},I_{2},...,I_{N_{T}}\}\) is a vector denote the excitation current weights of the total antennas.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
###### Abstract  
_Camabis sativa_ L. is a dioiceous plant belonging to the _Camnbaceae_ family. The main phytochemicals that are found in this plant are represented by cannabinoids, flavones, and terpenes. Some biological activities of cannabinoids are known to be enhanced by the presence of terpenes and flavonoids in the extracts, due to a synergistic action. In the light of all the above, the present study was aimed at the multi-component analysis of the bioactive compounds present in fibre-type _C. sativa_ (hemp) infloreserences of different varieties by means of innovative HPLC and GC methods. In particular, the profiling of non-psychoactive cannabinoids was carried out by means of HPLC-UV/DAD, ESI-MS, and MS\({}^{2}\). The content of preplated flavones in hemp extracts, including cannflavins A and B, was also evaluated by HPLC. The study on _Camabis_ volatile compounds was performed by developing a new method based on headspace solid-phase microextraction (HS-SPME) coupled with GC-MS and GC-FID. Cannabidiolic acid (CBD) and cannabidiol (CBD) were found to be the most abundant cannabinoids in the hemp samples analysed, while \(\beta\)-myrcene and \(\beta\)-caryophyllene were the major terpenes. As regards flavonoids, cannflavin A was observed to be the main compound in almost all the samples. The methods developed in this work are suitable for the comprehensive chemical analysis of both hemp plant material and related pharmaceutical or nutraceuticalI products in order to ensure their quality, efficacy, and safety.  
_Camabis sativa_ L.; hemp; cannabinoids; flavonoids; terpenes; HPLC; GC; MS.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 4 Discussion
### Comparisons of the Seasonal Dynamics of Biophysical Profiles on Post-Fire Areas  
Although the post-fire EVI, LST and LSWI changes were consistent with the results from previous studies [29; 47; 50; 51], few prior studies incorporated seasonal changes and fewer still compared and analyzed index differentials and relationships. In this study, four remote sensing indices (after normalization) were used to perform a comparative analysis of the annual changes from fire disturbance and seasonality. The regional climate is known to determine seasonal cycles. An extended period of snow cover and a short growing season are obvious features of boreal forests; in particular, this study measured significant unique seasonal changes in the EVI, LST and LSWI. The maximum intra-annual temperature was the highest in July because of insolation effects (Figure 2). However, the maximum LST was observed in June, and the EVI peaked in July, which decreased July's LST. The effects of fires on vegetation activities and energy flow were difficult to observe during winter because the EVI and GPP values reached a minimum (close to zero) for the entire seasonal cycle, even in the control area. The LST and LSWI's yearly maximum and minimum values are mainly affected by winter-snow conditions, which do not meet the requirements for photosynthesis. The LST began to increase in March, but the GPP, EVI and LSWI did not increase until April. Temperature is the limiting factor for vegetation growth. Figure 8 shows the immediate post-fire changes in the EVI, LST and LSWI, but the EVI changes were clearly more persistent than those of the LSWI and LST. The integrated analysis of the LST and LSWI suggested that fire created a more arid environment, which reduced the effect of climate, as demonstrated with the higher LST and lower LSWI. In addition, the effect of fires in winter is small, but fires can still have an effect. We found that the post-fire EVI values were lower than those of the control area in winter, indicating that fires affected the evergreen forest. The GPP value was 0, suggesting that the MODIS could only roughly record the energy flow.
# Notch Fatigue Life Research Based on Critical Distance Theory
## 4 Finite Element Analysis
### Fatigue Life Prediction Model
The logarithmic values of the point method characteristic stress, line method characteristic stress and fatigue life are calculated, respectively, and their correspondence is established as shown in Figure 11. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Incion Radius/mm**} & \multicolumn{2}{c}{**Characteristic Stress/MPa**} & \multirow{2}{*}{**Fatigue Life/Number**} \\  & **Point Method** & **Line Method** & **of Cycles** \\ \hline
10 & 244.40 & 215.75 & 1,142,878 \\
20 & 221.41 & 203.13 & 1,436,151 \\
30 & 206.37 & 193.62 & 1,722,225 \\ \hline \end{tabular}
\end{table} 
Table 4: Characteristic stresses and fatigue life of three notched specimens. Figure 10: Relationship between the peak point of the first principal stress and the distance from its location to the peak point. Point method prediction model:  
\[\text{lg}\sigma_{\text{ave}}+0.4135\text{lg}N=4.8922\]  
\[N=10^{(11.8312-2.4183\text{lg}\sigma_{\text{ave}})}\]  
Line method prediction model:  
\[\text{lg}\sigma_{\text{ave}}+0.2640\text{lg}\ N=3.9283\]  
\[N=10^{(14.8799-3.7879\text{lg}\sigma_{\text{ave}})}\]
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
## 4 Measurement Results of Vertical Earth Pressure Acting on Box Culvert  
### Theoretical Vertical Earth Pressure on Box Culvert  
The theoretical vertical earth pressure acting on the top of the box culvert in the centrifuge model test process can be calculated using Equation (1). Here, \(\sigma_{v,thno.}\) is theoretical vertical earth pressure, \(\gamma\) is the unit weight of sand when the degree of compaction is 95% (1.66 t/m\({}^{3}\)), \(h\) is the cover depth in the centrifuge model test (4, 8, or 12 cm), and \(g\) denotes gravitational acceleration.  
\[\sigma_{v,thno}\!\!=\!\gamma\!\cdot\!h\!\cdot\!g \tag{1}\]
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 4 Performance Evaluation
### Environmental Setting  
The experiments are conducted on a floor of a campus office building, as shown in Figure 3. A total of 9 iBeacon tags are used in the experiment; see Figure 4a. The deployment scenario of iBeacons is shown in Figure 4c. They are mounted on the ceiling of the corridors at the height of 2.5 m above the ground with the same technical configuration. The horizontal interval between the iBeacon tags is approximately 4 m. A Huawei smartphone with Android 9.0 is used to collect the IMU data, as shown in Figure 4b. The phone is handled by a volunteer pointing to the heading direction with a screen up during the experiments. Since the major purpose of our work is to locate the smartphone user continuously in real-time instead of occasionally executing measurements statically, continuous trajectories are tested, which consist of two parts of the corridors, with distances of 20.8 m and 11.2 m, respectively; see the red lines in Figure 3. We compare our proposed method with the other two different methods: pure PDR and pure BLE-based methods. Ten groups of experiments on the same trajectory are independently conducted by applying the three methods. Thirty-two measurement points on the trajectory are selected to demonstrate the positioning performance.  
Figure 3: The map of the experimental area, with colored regions indicating different rooms (some have room numbers as shown on the map, while others like toilets and distribution rooms do not have numbers).
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 3 GCUM and the Energy Consumption Model
### GCUM Model  
1. The structure of the GCUM includes many tunnels that cross and create a complex network environment.
2. In the clustering process, anchor nodes are chosen based on specific priorities, and cluster head nodes are selected. However, when special circumstances, such as accidents, occur, if the original anchor nodes are damaged, mobile nodes with high residual energy demands may become the new cluster head nodes.
3. The three types of nodes are anchor, multifunction, and original nodes. Each node has a different initial energy level, and their functions are influenced by the heterogeneous factors of the GCUM.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 2 System Model and Signal Processing Methods
### Signal Acquisition and Tracking Methods
In this paper, we use the carrier phase correction amount to correct the carrier phase measurements of the tracking loop and obtain accurate carrier phase measurements.
## 2 Conceptual Model and Research Hypotheses
### Social Influence and Hedonic Motivation to Use EESS  
A main direction of the analysis of the present research is the relationship between social influence and the hedonic motivation to use EESS. The inclusion of this correlation in the conceptual model is argued by the fact that EESS devices are generally programmed through software applications, and the degree of acceptance of these types of applications is conditioned for ease of use and hedonic motivation. Within the UTAUT2 model [16], hedonic motivation is defined as \"the fun or pleasure derived from using a technology\". [PERSON] and [PERSON] [22] define hedonic motivation from the perspective of perceived enjoyment, specifically that the use of a certain technology is enjoyable. According to other authors [46], hedonic motivation is the strongest predictor of users' behavioral intentions. In a study in the education sector, [PERSON] and [PERSON] [47] show that pleasant experience in operation positively influences the intention to use. In the field of EESS technologies, hedonic motivation is a relevant influencing factor, as it allows one to increase the degree of interaction with technology [5]. The role of social influence in stimulating hedonic motivation is also addressed in several pieces of EESS research. [PERSON], [PERSON], and [PERSON] [4] point out that, from both a marketing and a public policy perspective, among the factors that decisively influence the intention to adopt green technologies, organizations should focus on influencing hedonic motivation. On the other hand, the same authors show that hedonic motivation is the least targeted determinant of established policy instruments. Other studies [48] show the correlation between social influence, the confidence in a certain technology, and the hedonic motivation. [PERSON] et al. [18] show the role of hedonic motivation as one of the main factors in accepting EESS technologies such as smart meters, while showing the significant positive effect of hedonic motivation on the behavioral intent to use. Therefore, we make the following hypotheses:  
**Hypothesis 5 (H5).**_Social influence (SI), specifically (a), the adoption of EESS at the level of the network of friends, colleagues, or family (SI_FF), and (b), the promotion of the use of EESS in the public space (SI_PS), will have positive effects on the hedonic motivation to use them (HM)._  
**Hypothesis 6 (H6).**_HM will mediate the relationship between (a) SI_FF and (b) SI_PS, and the behavioral intention to use EESS (BIOU)._
## 2 Materials and Methods
### MB Adsorption  
MB adsorption tests were performed in a thermo-stated shaker by using Erlenmeyer flasks (100 mL). Typically, 10 mg MPC and 50 mL MB solution with different initial concentrations (45-55 mg L\({}^{-1}\)) at natural pH were transferred into the Erlenmeyer flask, followed by shaking with a speed of 200 rpm at 25 \({}^{\circ}\)C. To calculate the adsorption capacity, the sample was withdrawn from the Erlenmeyer flask at predetermined time interval. Then, the sample was analyzed with a Unico UV-4802S spectrophotometer to determine the retained MB concentration. The adsorption capacity was evaluated according to the reported method [11].  
The adsorption tests were performed at different pH ranging from 3 to 11 to study the corresponding changes in adsorption capacity of MB on MPC. The initial pH of the MB solution was adapted with the  
Figure 1: Schematic illustration of the synthesis and application of the matrix porous carbon (MPC) for MB adsorption.  
use of HCl or NaOH aqueous solution. Additionally, the adsorption of MB with MPC was conducted at different temperatures of 25, 35, and 45 \({}^{\circ}\)C to determine the adsorption isotherms.  
To study the regeneration and reusability of MPC for MB adsorption, the MPC was isolated from the solution with the help of magnet after the first adsorption test was finished (Scheme 1). The recovered MPC was rinsed by ethanol several times and then dried under vacuum at 80 \({}^{\circ}\)C overnight. After fresh MB solution with a concentration of 55 mg L\({}^{-1}\) (pH = 11) was introduced, the next reusability test of MPC was started.