esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis
#### 2.4.2 Accuracy Assessment  
We conducted a preliminary assessment of the accuracy and the result was evaluated using an error matrix approach to generate accuracy parameters; i.e., confusion matrices, user's and producer's accuracies, Kappa Coefficient, and overall accuracies for the entire target region. Typically, an accuracy assessment using an error matrix (or confusion matrix) is conducted by selecting a sample of pixels, blocks of pixels, or polygons from within the mapped area and obtaining the reference classification for each sampled location, where the reference classification is defined as the best available assessment of ground condition [54]. In this study, the assessment unit is assumed to be a pixel. The matrix provides a quantitative method of characterizing classification accuracy where it compares ground truth data with the results of classification. Kappa coefficient assesses the viability of the classification compared to a random classification, subject to the observed marginal in the true classes [55].  
Figure 4: Rule images of Mixture Tuned Matched Filtering (MTMF) scores: (**a**) pre-fire MTMF, (**b**) post fire MTMF.
## 2 Literature Review
Additionally, the authors measured the prediction latency, which was found to be under 10 milliseconds, making the system ideal for deployment in IoV environments, even on devices with limited computational resources. In [33], the author emphasizes the importance of data privacy and ownership control when sharing information among devices, thereby maximizing the potential benefits of data-driven applications. Data sharing is a key element in various learning paradigms, such as federated learning and the ensemble learning approach proposed in this paper. The author suggests a robust mechanism that stores data on an external server and incorporates a blockchain framework to maintain a transaction record of the data while sharing the data among devices. This proposed framework not only provides excellent data privacy but also guarantees remarkable ownership control. Furthermore, it accommodates a diverse range of data types, making it highly suitable for various applications.
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 4 Experiment and Evaluation
### Dataset Construction
#### 4.1.1 Open-Source Dataset  
When it comes to using sensors for user behavior recognition, the choice of dataset is crucial. The PAMAP2 [49] dataset is evidently a very comprehensive resource, providing this research with information on various physical activities, as well as data from multiple subjects. These datasets include eight males and one female, which helps to consider the potential impact of gender differences on user behavior recognition.  
In the PAMAP 2 dataset, each subject wore three IMUs and a heart rate monitor, with one IMU placed on the wrist of the dominant hand, producing data that were very similar to that generated by common wrist-worn smartwatches. This makes the PAMAP2 dataset particularly suitable for simulating scenarios of user behavior recognition using smartwatches.  
By selecting data only from the wrist sensors, this study can better simulate real-world application scenarios, which is very helpful for the development and testing of user behavior recognition algorithms in wearable devices such as smartwatches. This method of selecting datasets helps ensure that the algorithm performs with higher accuracy and reliability in real-world usage scenarios.  
The PAMAP 2 dataset encompasses various daily human activities such as sitting, lying down, walking, running, cycling, and climbing stairs, which are crucial for the analysis of human daily behaviors. From the training data provided by PAMAP2, one can understand the proportion of various behaviors exhibited by the subjects during the testing period, thereby allowing for the assessment of the subjects' physical condition and self-care abilities. When conducting research using the PAMAP2 dataset, different machine learning and deep learning methods can be explored to identify and classify various physical behaviors. This has huge application potential in areas such as health monitoring, sports analysis, and quality of life improvement.  
Therefore, the PAMAP2 dataset not only provides a rich source of data but also supports this research in better simulating real-world usage scenarios, which will provide a stronger foundation for the research and application development presented in this paper.
## 1 Introduction
Moreover, neat PODE\({}_{3}\) was capable of emitting low NO\({}_{\text{x}}\) and soot emissions with a lower EGR amount than those of PODE/diesel blends, mitigating the negative combustion implication of EGR at high levels. Experiments conducted on the influence of EGR rates on the combustion and emission characteristics of n-butanol/diesel/PODE\({}_{3.4}\) blends showed that at an EGR rate below 30%, as the EGR rate grows, the effects on the emissions of soot, CO, and HC are not significant, while the emissions of NO\({}_{\text{x}}\) are sharply reduced; when the EGR rate is above 30%, as it grows, the emissions of soot, CO, and HC drastically rise. Therefore, the further exploration of the optimal EGR rate of the engine using PODE/diesel blends is of great significance to improve the engine performance. At present, although there are many experimental investigations on the application of PODE in diesel engines, numerical investigations on the spray characteristics of PODE, the combustion process, and pollution generation mechanisms of engines are still relatively rare. In addition, the influence of the blended fuel properties on the spray model was ignored in the previous numerical studies on PODE. Therefore, in order to develop a numerical model \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Component** & **Melting** & **Boiling** & **Cetane** & **Density** & **Viscosity** & **Low** & **Oxygen** \\
**Point** (\({}^{\circ}\)**C) & **Point** (\({}^{\circ}\)**C) & **Number** & **(g/mL@25 \({}^{\circ}\)**C)** & **(MPa s@25 \({}^{\circ}\)**C)** & **Value** & **Content** \\  & & & & & & **(MJ/kg)** & **(wt\%)** \\ \hline PODE1 & \(-\)105 & 42 & 30 & 0.86 & 0.58 & 22.4 & 42.1 \\ PODE2 & \(-\)69.7 & 105 & 63 & 0.98 & 0.64 & 20.6 & 45.3 \\ PODE3 & \(-\)42.5 & 156 & 67 & 1.03 & 1.05 & 19.4 & 47.1 \\ PODE4 & \(-\)9.8 & 202 & 76 & 107 & 1.75 & 18.7 & 48.2 \\ PODE5 & 18.3 & 242 & 90 & 1.11 & 2.24 & 18.1 & 48.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of components in PODE with n ranging from 1 to 5.  
applicable to PODE engines, this paper adopts a more accurate cavitation and spray model developed by authors [23]. This paper investigates the influence of the blend ratio of PODE, the EGR rate, and the injection strategy on the combustion and emissions characteristics. These results can provide an important reference value for developing and improving the PODE engine, and the numerical model can also be used in the further numerical simulation process of engine development.
## 3 Results and Discussion
### Sensory Quality
The quality of the tested beverages was also influenced by the degree of saturation with carbon dioxide. The intensity of carbonation was observed to a greater extent in unpasteurized samples (BG) (\(p\) < 0.05). This observation is consistent with the results of microbiological analyses and the presence of live yeast cells. It was also observed that the degree of carbonation positively correlates with the intensity of sour and acetic flavors. On the other hand, no significant (\(p\) > 0.05) influence on the sensory quality of distinguishing features such as beer and other odors and flavors as well as the sour smell, was observed. Flavor additives used in the production of craft beers include distinct ingredients such as honey, fruits, cassava, pumpkin, herbs, and ginger, among other agents, to cause positive sensory changes on the final product [46, 47]. In the presented research, a simple composition (wort, fruit juice), properly selected technological conditions, and, most importantly, a well-designed starter culture ensured a high sensory quality, without the need to add other flavor additives. When comparing the sensory quality of the two types of studied beer beverages (light and dark), it was found that all kinds of light beer samples, especially trial BPGL with the addition of _G. oxydans_ H32 starter culture, were characterized by a significantly (\(p\) < 0.05) better overall sensory quality. Additionally, all kinds of pasteurized and inoculated light beer samples were characterized by the most balanced sensory attributes profile, ultimately shaping the high overall quality (\(p\) < 0.05). Only a few types of research can be found regarding the possibility of using LAB as a starter culture of pro-health and probiotictic sour beer production [1, 12, 13, 33]. To the best of our knowledge, no comparable studies have been conducted using AAB as a starter culture with beneficial properties. On the other hand, AAB are often perceived as contaminants in brewing, negatively affecting the final sensory quality of the beer [39, 48]. Based on the conducted research, it was shown that a well-selected strain of AAB bacteria is a proper solution for controlling the light sour beer re-fermentation process. It has also been proven that AAB are not only associated with creating beer spoilage but can shape the appropriate sensory characteristics. Furthermore, the suitable AAB strain and method of introducing  
Figure 3: Principal component analysis (PCA) in the QDP sensory evaluation of beer beverages. Explanatory notes: projection of variables (sensory discriminants) and cases (tested samples) onto the plane of the principal components (PC1 and PC2). o.: odor; int. : intensity; \(f\): flavor; (**a**)—light beer; (**b**)—dark beer. it into the beer may positively affect the sensory quality of sour beer and also provide health-promoting properties.
## 2 What Is Terrestrial Storage of Biomass (TSB)?
### Summary of Decomposition Chemistry  
The overall chemistry of the aerobic and anaerobic decomposition of starch begins with depolymerization by hydrolysis, which emphasizes the need for water to start the process, as follows:  
\[\text{n (C}_{6}\text{H}_{10}\text{O}_{5})+\text{n}\text{H}_{2}\text{O}\to\text{n }\text{C}_{6}\text{H}_{12}\text{O}_{6} \tag{8}\]  
Aerobic oxidation produces CO2 and water, as follows:  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}+\text{O}_{2}\to 6\text{ CO}_{2}+6\text{ H}_{2}\text{O} \tag{9}\]  
The water produced in this reaction can lead to further hydrolysis via the previous reaction.  
There may be several pathways for anaerobic decomposition. As an example, a path for anaerobic decomposition of glucose can proceed as follows:  
Step 1: Anaerobic Fermentation  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}\to 2\text{ C}_{2}\text{H}_{5}\text{OH}+2\text{ CO}_{2} \tag{10}\]  
This is the reaction that converts glucose monomers to bioethanol in the corn bioethanol process.  
Step 2: Hydrogen and Acetic Acid Formation  
\[2\text{ C}_{2}\text{H}_{5}\text{OH}+\text{H}_{2}\text{O}\to 2\text{ CH}_{3}\text{COOH}+4\text{ H}_{2} \tag{11}\]  
Step 3a: Methane Formation  
\[\text{CO}_{2}+4\text{ H}_{2}\to\text{CH}_{4}+2\text{ H}_{2}\text{O}\text{ (CO}_{2}\text{ Reduction)} \tag{12}\]  
Step 3b: Acetic Acid Decarboxylation  
\[\text{CH}_{3}\text{COOH}\to\text{CH}_{4}+\text{ CO}_{2}\text{ (Acetic Acid Decarboxylation)} \tag{13}\]The overall anaerobic decomposition reaction is as follows:  
\[C_{6}H_{12}O_{6}\to 3\;CH_{4}+3\;CO_{2} \tag{14}\]  
Note that there is also a path for aerobic decomposition that goes through C3 (propionic and citric acids) instead of C2 intermediates, which is called the Krebs cycle [47].
## Materials and Methods
### Datasets and Analysis
#### 2.2.2 Forest Cover Data  
To sample the SIF values from non-disturbed forested areas since the year 2000, we used a LANDSAT global forest change dataset for the period 2000-2018 ([52]; \"canopy cover\" and \"forest loss\" data sets, respectively, in the following). The canopy cover dataset defined trees as vegetation taller than 5 m in height at a 30 \(\times\) 30 m spatial resolution by the year 2000 and specified the percentage of canopy cover per pixel. The forest loss dataset indicated the first year of stand disturbance since 2000, and the characterization did not include selective removals within the forest stands that do not lead to a non-forest state. We focused on areas that were covered 95% or more by tree canopies according to the canopy cover data set and used the forest loss dataset to identify stand-replacing disturbances. Each pixel is encoded either as a 0 (no forest loss) or a value in the range of 1-18, representing the first respective loss detection times in 2001-2018. The dataset was recoded as 0 (a forest with 95% canopy cover by the year 2000 and without disturbances since then), and 1 (disturbed area) for each year between 2014 and 2018. Focal statistics on 0.98-km\({}^{2}\) moving windows were used to determine which windows had at least 95% undisturbed forest cover. Finally, we sampled SIF retrievals only from these areas.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## Material and Method  
### Bird Data  
Bird data were obtained from the French Breeding Bird Survey (FBBS), a standardized monitoring program in which volunteer ornithologists identify and record breeding birds year after year [30]. In the FBBS program, each observer provides a point location, and a randomly 2 km \(\times\) 2 km survey square shaped plot is selected around a radius of 10 km from this location. Each plot contain 10 point counts with 100 m radii which are separated from each other by 300 m at a minimum and scattered in the landscape in order to best represent the diversity of habitats (Figure 1). Each point count is performed twice a year during the breeding season (from April to mid-June) to record early singing species as well as last migrants. In each point count, heard or seen species are recorded during 5 min between 1 h and 4 h after sunrise. In our study, we focused the analyses on the year 2010 for which the sampling effort was high and well distributed at the national scale (a total of 1091 plots). For each point count in the surveyed plot squares, the maximum count over the two visits was retained for each species. We excluded raptors that have home range sizes (several tens of hectares) much higher than point count areas. We also excluded urban and wetland species because their habitats were poorly sampled compared to agricultural and woodland habitats. Moreover, the DHI components based on fPAR is is not available in the heart of the cities.  
Plots were filtered according to the quality of the satellite images available on their locations (see Section 2.2) and 759 plots were finally considered in the analyses. Because birds are expected to respond differently to landscape habitats according to their ecological requirements, four metrics of species richness (SR) per plot square were computed from the species observed at point counts. The first two metrics of SR were related to woodland species and farmland species (specialists of woodland and farmland habitats). The others were related to habitat generalists, and total species richness computed as the sum of all species (see Appendix A for species names). These bird groups (guilds) were based on species information available at the national level ([[http://vigienature.mnhn.fr](http://vigienature.mnhn.fr)]([http://vigienature.mnhn.fr](http://vigienature.mnhn.fr))).
## 4 Experimental Setup
### Dataset and Preprocessing  
The collection contains 259,200 data points from 24 September to 26 September 2021. Temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10 are among the eight variables listed in Table 1. In this experiment, the algorithm contains linear interpolation, which is employed to fill the data if there are any missing values. Linear interpolation produces the best results for all percentages of missing values if compared to other methods, such as the mean method [31]. The authors found no missing data from raw data, which indicates that this is the advantage of the oneM2M system to gather the data [32]. We utilize Equation (9) to normalize the data before inputting it into the proposed method [33]:  
\[\overline{x}_{normalized}=\frac{x-x_{min}}{x_{max-}x_{min}} \tag{10}\]  
where \(x_{min}\) denotes minimum data, \(x_{max}\) is minimum data, and \(x\) is the original data. It is critical to create supervised time series data. The input matrices and output matrices are shown below in their many configurations.  
For 1 h ahead prediction:  
\[\text{Input matrix}=\left[\begin{array}{ccccc}x_{n(t=0)}&x_{n(t=1)}&x_{n(t=2)}& \ldots&x_{n(t=119)}\\ x_{n(t=1)}&x_{n(t=2)}&x_{n(t=3)}&\ldots&x_{n(t=120)}\\ \ldots&\ldots&\ldots&\ldots\\ x_{n(t=T-119)}&x_{n(t=T-118)}&x_{n(t=T-117)}&\ldots&x_{n(t=T)}\end{array}\right]\]  
\[\text{Output matrix}=\left[\begin{array}{ccccc}x_{n(t=120)}&x_{n(t=121)}& x_{n(t=122)}&\ldots&x_{n(t=179)}\\ x_{n(t=121)}&x_{n(t=122)}&x_{n(t=123)}&\ldots&x_{n(t=180)}\\ \ldots&\ldots&\ldots&\ldots\\ x_{n(t=T+1)}&x_{n(t=T+2)}&x_{n(t=T+3)}&\ldots&x_{n(t+60)}\end{array}\right]\]  
For 2 h ahead prediction:  
\[\text{Input matrix}=\left[\begin{array}{ccccc}x_{n(t=0)}&x_{n(t=1)}&x_{n(t =2)}&\ldots&x_{n(t=239)}\\ x_{n(t=1)}&x_{n(t=2)}&x_{n(t=3)}&\ldots&x_{n(t=240)}\\ \ldots&\ldots&\ldots&\ldots\\ x_{n(t=T-239)}&x_{n(t=T-238)}&x_{n(t=T-237)}&\ldots&x_{n(t=T)}\end{array}\right]\]  
\[\text{Output matrix}=\left[\begin{array}{ccccc}x_{n(t=240)}&x_{n(t=241)}& x_{n(t=242)}&\ldots&x_{n(t=359)}\\ x_{n(t=241)}&x_{n(t=242)}&x_{n(t=243)}&\ldots&x_{n(t=360)}\\ \ldots&\ldots&\ldots&\ldots\\ x_{n(t=T+1)}&x_{n(t=T+2)}&x_{n(t=T+3)}&\ldots&x_{n(t=T+120)}\end{array}\right]\]  
For 3 h ahead prediction:  
\[\text{Input matrix}=\left[\begin{array}{ccccc}x_{n(t=0)}&x_{n(t=1)}&x_{n(t =2)}&\ldots&x_{n(t=479)}\\ x_{n(t=1)}&x_{n(t=2)}&x_{n(t=3)}&\ldots&x_{n(t=480)}\\ \ldots&\ldots&\ldots&\ldots\\ x_{n(t=T-479)}&x_{n(t=T-478)}&x_{n(t=T-477)}&\ldots&x_{n(t=T)}\end{array}\right]\]  
\[\text{Output matrix}=for testing data is because this is empirically the best partition into the training and the testing sets [34].
# Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller
## 3 Modelling
### Results of Case 2: Positive and Negative Road Disturbances  
In the graph of vertical displacement PN shown in Figure 15, the peak of vertical displacement has gone to \(0.26\) m but now, the peak has reduced to \(0.08\) m while on the other side the negative peak without controller is \(-0.13\) m has reduced to nearly \(-0.02\) m by using the controller.  
In the graph of pitch displacement shown in Figure 16, the peak of pitch displacement has gone to \(0.125\) m but now, the peak has reduced to \(0\) m while on the other side the negative peak without controller is \(-0.14\) m has reduced to nearly \(-0.07\) m by using control law and the controller.  
In the graph of vertical acceleration shown in Figure 17, the peak of vertical acceleration has gone to \(16\) but now, the peak has reduced to \(5\), similarly there are several other peaks so its better to see RMS value in this case which is given in Table 3.  
In the graph of pitch acceleration shown in Figure 18, the peak of pitch acceleration has gone to \(8.5\) but now, the peak has reduced to \(3\).  
In the graph of front suspension travel shown in Figure 19, the peak of front suspension travel has gone to \(0.0125\) but now, the peak has reduced to \(0.03\) while on the negative side it has reached to \(-0.15\) but now it has reduced to almost \(-0.03\).  
In the graph of rear suspension travel shown in Figure 20, the peak of rear suspension travel has gone to \(0.15\) but now, the peak has reduced to \(0.03\) and same reduction level on the other side.  
Effect of road disturbances is reduced up to great extent in all the states. The results obtained in RMS values for Case 1 are compared with the previous techniques are given in Table 2. The values obtained from figures of Case 2 are given in Table 3 in which the values of states are compared without controller and with controller are given and the improvement is given in percentage. This technique has demonstrated the results which proved it to be the most economical solution for stabilizing and implementing this nonlinear system in the real world. This technique is cost effective as compared to other techniques i.e., road adaptive algorithm, PID and many others. Comparative analysis with the work [8,10] shows that states are much more stable in comparison to several previous techniques and the RMS values of the given states are much more reduced than as obtained in the above stated work.The simulation results exhibited that the half-car model considering road input conditions presented in [17] was found to have a maximum accuracy of \(65\%\) while the design proposed in this paper proves to have a maximum accuracy of \(85\%\) of which is significantly higher.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Moving Mesh Technique (MMT)  
The Moving Mesh Technique (MMT) is an advanced computational method used in finite element modeling (FEM) to address the challenges associated with crack growth simulation [182; 183]. This technique allows the mesh to adapt dynamically as cracks propagate, improving accuracy and efficiency in modeling complex crack behavior. The mesh is updated in response to the evolving crack front, ensuring that the element sizes and shapes remain appropriate for accurately capturing stress gradients and crack paths. New elements are created as the crack grows, and existing elements are reconfigured to accommodate the new geometry, preventing distortion and maintaining solution quality. The mesh can be refined near the crack tip, where stress concentrations are highest, ensuring that the critical region is accurately modeled without requiring extensive computational resources for the entire domain. A numerical model based on FEM analysis is introduced by [PERSON] et al. [184]. In this model, the MMT is integrated to mimic fatigue crack growth in material components exposed to cyclic loading. In this approach, the computational mesh adapts dynamically throughout the simulation in accordance with fracture mechanics criteria. Unlike traditional FE methods that necessitate remeshing at each crack length increment, this model selectively updates the mesh only when significant distortion of mesh nodes occurs due to their movement. Consequently, this strategy notably alleviates computational overhead.  
Table 1 presents a comparison of advanced techniques in finite element modeling for fatigue crack propagation, including case studies, advantages, and limitations.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Case Study** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{Extended Finite Element Method (XFEM)} & [PERSON] [23] reported the following case studies: & & \\  & Charpy specimen with initial crack at the notch tip. & XFEM is a versatile tool for simulation of FCG, providing an excellent option for precise and reliable fatigue life of a cracked component. & A very fine and high-quality mesh is essential for accurately capturing the steep gradients of the stress field near the crack tips and crack fronts.