esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis Model
#### 2.2.2 How the Data Analysis Model Works  
In Figure 6, the operation of the data analysis model is described. In the process, it has two parts. In the first, the analysis of educational data of the face-to-face modality is established. These data are available from different sources and dates or pre-pandemic period and must be established. The knowledge generated from this analysis aims to identify the deficiencies or sufficiency of the students at the time of generating learning. Once Hadoop finishes processing and validates the results, if they are as expected, it will continue with the process. Otherwise, it generates a loop and returns to the data sources and it integrates a new source or new data from a system into the process. This process does it, until the results are optimal to be able to get out of the loop. The results that have been validated are stored and the process continues until the next phase.  
The framework follows a process similar to the previous stage; however, the data and sources that are considered are post-pandemic. In this analysis, the data contains information about the change that has occurred in student performance. This subject is done under two edges: in the first, the grades are considered and in the second, edge learning is involved. This variation in the analysis is carried out by the indices that the university has detected in quick reviews where a deficiency in learning is exposed without there being a negative change in grades.  
The process enters a stage of validation of the results, if any variable is needed, and sources and related data are integrated to adjust the process and results. With the optimized results, they are processed to combine them and establish the existing differences, the results are presented, and the analysis process is finalized. By clearly establishing the differences based on data analysis, the areas in charge have a key input to define what the parameters are to consider in the execution of a hybrid education model.  
Figure 6: Block diagram of the operation of data analysis in a university education model.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 2 Height Systems  
In geodesy, the determination of points on or near the Earth's surface typically involves three coordinates: latitude, longitude, and height. The last coordinate, height, represents the metric distance of a point from a reference surface measured along the perpendicular line to that surface [1]. Although this concept may appear straightforward, the height of a point can be described in various subtly different ways, each resulting in a distinct height value for the same location. Therefore, it is essential to exercise precision and clarity when defining and utilizing the term \"height\". Heights, in the broadest sense, can be categorized into three main types: those defined without assumptions about the distribution of masses within the Earth's interior, those that are physically defined, and geometrical heights [2]. Geometrical heights are the easiest to define among the listed ones because they are measured along straight lines. This group includes ellipsoidal heights which are measured along the normal to the reference ellipsoid. These heights can be directly determined from satellite observations but have limited practical application because they do not account for the Earth's gravity field [3]. Heights defined without assumptions about the distribution of masses include geopotential numbers and dynamic heights, while physically defined heights include normal, orthometric, and normal-orthometric heights.
## 3 Description of the New Control Approach
### Adaptive Control Using a Gain Scheduling Approach  
The dynamics of large wind turbines present a nonlinear behaviour. On the other hand, the controllers are linear, and therefore it is expected that the control system performance will deteriorate as the required magnitude of power reduction increases. The same takes place with the CPC. Hence, it is necessary to adapt the controller parameter according to the changes in the operating point. The simplest adaptive control approach is the gain scheduling concept.  
In the case of the CPC, it is possible to find analytical equations to recalculate the controller parameters as a function of a scheduling parameter by using a pole placement approach [20]. However, this procedure is not practicable in the case of the power tracking controller because it is internally nested with the CPC, making the derivation very cumbersome. Instead, a numerical procedure will be used here by tabulating the controller gains and activating them according to the power reference value. Between values interpolation is used.
## 3 Routing in Satellite Networks
### Satellite Networks  
The satellite acts as a relay located in space, receiving signals sent from the Earth and sending back the signal it received. Satellite networks, on the other hand, communicate via satellite. Satellite networks consist of nodes that provide data transfer between two points on Earth and transmit data at high speeds, such as GigaHertz. The classification of satellite orbits is based on several parameters. One of these parameters is the orbit that the satellites follow. According to the orbit they follow, satellites use circular and elliptical orbits. In those that have a circular orbit, the midpoint of the globe is the same as the center point of the orbit they follow. In elliptical versions, the center point of the Earth is the same as one of the focal points of the ellipse. Another classification parameter is the classification according to the plane of the orbit that the satellites follow relative to the Earth. In this classification, it can be located in the equatorial plane, in the plane of the poles, or have a certain angle. The third classification metric sees satellites divided into three classes with respect to their interval and the globe: low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary Earth orbit (GEO). LEO satellites have altitudes between 500-2000 kilometers (km) from the Earth. Their coverage area is approximately 8000 km and is narrow compared to other satellites. They have the lowest latency compared to other satellite networks. MEO satellites are located at an interval of 5000-15,000 km from the globe. They are widely used in GPS applications. GEO satellites are situated about 36,000 km from the globe, and these satellites move at the same speed as the Earth. For this reason, the point at which they are located on the Earth is fixed. Three GEO satellites can cover the entire globe. They are the satellites with the greatest delay due to the altitude at which they are located [90, 91, 92, 93, 94, 95].  
In the context of integrating satellite communication with 6G networks, it is crucial to compare LEO, MEO, and GEO satellites, particularly in terms of latency and instantaneous positioning [92]. LEO satellites, typically orbiting at altitudes of 500-2000 km, offer the lowest latency (around 20-40 ms) due to their proximity to Earth, making them suitable for real-time applications. However, their fast movement relative to the Earth's surface requires a large constellation and frequent handovers to ensure continuous coverage. MEO satellites, located at altitudes of 2000-20,000 km, provide moderate latency (around 100-150 ms) and a balance between coverage area and mobility. GEO satellites, positioned at approximately 35,786 km, offer wide coverage and fixed positioning relative to the Earth, simplifying ground station tracking. However, they suffer from high latency (around 500-600 ms), which can be problematic for latency-sensitive applications. Each orbit class presents trade-offs between coverage, latency, and infrastructure complexity, and understanding these differences is key to optimizing satellite-augmented 6G networks.
## 3 Results and Discussion
### Analysis of Volatile Compounds
TDN has been pointed out as an ageing marker in sparkling wine, along with diethyl succinate (ester) and vitispirane (terpene) [37]. It was identified in both SB+L and B+L but not in the respective controls. Those compounds were found by [PERSON] et al. (2009) [17] in sparkling wine lees surface, as well as in our Cava lees analysis (Table S1). Lastly, the results obtained were subjected to a PCA to determine the differences between the breads produced with and without sourdough and Cava lees. Figure 3 shows the result of a previous correlation analysis and Figure 4 presents de PCA biplot obtained. The PC1 and PC2 explain 79.02% of the total variability. The first principal component (PC1) explains a 52.37% of the samples variances while the second one (PC2) explains a 26.64%. Most of the volatile compounds were found in the positive side of PC1, especially esters, acids, and linear aldehydes, whereas branched aldehydes, alcohols and ketones were situated on the negative axis of PC1. On the other hand, the positive axis of PC2 contained a greater number of volatiles, including alcohols, linear aldehydes, ketones, and esters; while branched aldehydes and terpen were situated in the negative side of PC2. Figure 3: Heatmap of the correlation matrix of the volatile compounds (\(p<0.05\)). Numbers correspond to the volatile compounds identified in Table 4. Positive correlations are shown in yellow; negative correlations in blue; absence of correlation in white. It can be observed that both controls (SBC and BC) were placed in the same quadrant. In fact, both controls and SB+L were positioned opposite bread with Cava lees (B+L). SBC and BC were characterized by alcohols and ketones, while B+L was described by esters and terpen. Moreover, a greater quantity of different volatiles was identified in B+L samples.
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 4 Fusion of Multiple Observations
### Method Performance  
In this section, we present graphical representations of our method's performance, focusing on the trajectory and positional data from Groups 2 and 3 as outlined in the experimental setup table, where the positioning information obtained through our method is compared against the true positions provided by the simulation software.  
Figure 9: Method comparison with/without geometric constraints. (**a**) Trajectory; (**b**) X; (**c**) Y; (**d**) Yaw.  
Figure 11 (Group 3): The motion trajectory of the vehicle is portrayed under more complex conditions, where movement is compounded by the presence of obstacles. The increased presence of outliers, particularly at corners where occlusions affect observation quality, is evident. Despite these challenges, our method demonstrates its robustness by producing a trajectory that is significantly smoother and more aligned with the true path compared to the raw data.  
Based on the experimental setup outlined in Table 1 and the data presented in Table 4, we conclude the following:  
Motion State: The motion state of the vehicle significantly affects positioning accuracy, with moving Groups 2 and 5 showing higher positioning errors compared to the stationary Groups 1 and 4. Our method, however, reduces these errors, bringing the positioning accuracy of moving states close to that of stationary states, indicating its effectiveness in handling motion challenges.  
Figure 10: Experimental results of Group 2. (**a**) Trajectory; (**b**) \(X_{i}\) (**c**) \(Y_{i}\) (**d**) Yaw.  
Tag Size: Comparisons across different tag sizes demonstrate that our method maintains low positioning errors regardless of tag size, whether larger (Groups 1 and 2) or smaller (Groups 4 and 5). This finding suggests that our method is compatible and consistent with various tag dimensions.  
Obstacle Obstruction: The presence of obstacles in Groups 3 and 6, as opposed to the absence of obstructions in Groups 2 and 5, results in decreased positioning accuracy. However, our method's identification and removal of outliers effectively lessen the impact of obstructions on positioning outcomes.
## 4 Electromagnetic Performance Analysis
### Back-EMF Analysis  
The back-EMF is the induced voltage calculated by the time derivative of flux linkage. Figure 8 shows the open-circuit back-EMF waveforms and their harmonic spectra of the proposed HE-DSDPM structures with various \(I_{dc}\). As indicated in Figure 8a, the sinusoidal waveforms of the back-EMF are found in both proposed and conventional structures because of containing a symmetrical magnetic flux path. When exciting by \(I_{dc}\) of 0 A, the highest back-EMF of the multi-tooth HE-DSDPM is about 18.76 V and also 24.54% more than the conventional one because its multi-tooth modification helpfully improves the magnetic flux path and flux regulation.  
The variance back-EMF percentage with \(I_{dc}\) variations is indicated in Figure 8b. It is found that the back-EMF percentage of the multi-tooth HE-DSDPM is decreased by 34.24% when excited by \(I_{dc}\) of \(-5\) A and improved by 33.32% when excited by \(I_{dc}\) of \(+5\) A. Especially, the highest back-EMF of the multi-tooth HE-DSDPM has 21.38% smaller and 29.98% higher than that of the HE-DSDPM with \(I_{dc}\) of \(-5\) and \(+5\) A, respectively. This is because of the effects of flux linkage percentage and flux regulation quality, as described previously.  
The harmonic spectra back-EMF of the proposed structures is presented at different \(I_{dc}\). The THD percentage represents the harmonic spectra system of the back-EMF waveform. It can be seen that the THD percentage is reduced by more than 3% when using the structural multi-tooth modification in the HE-DSDPM. This is explained that the back-EMF of multi-tooth HE-DSDPM receives high smoothness waveform. Furthermore, the THD percentage of both machines is changed with varying \(I_{dc}\), which significantly impacts the behaviour of ripple torque. From these results, the back-EMF and THD percentages will be applied for the explanation of the torque characteristics.  
Figure 7: Open-circuit flux linkage of the proposed HE-DSDPM with various \(I_{dc}\).
###### Abstract  
In order to minimize the influence of decorrelation noise on multi-temporal interferometric synthetic aperture radar (MT-InSAR) applications, a series of phase reconstruction methods have been proposed in recent years. Unfortunately, current phase reconstruction methods generally exhibit a low computational efficiency due to their high non-linearity, in particular in the case that the dimension of a SAR stack is high. In this paper, a new approach is proposed to efficiently resolve phase reconstruction problems. This approach is inspired by the theory of probabilistic principle component analysis. A complex valued probability generative model is constructed to portray a phase reconstruction process. Moreover, in order to resolve such a model, a targeted algorithm based on the idea of expectation maximization is designed and implemented. For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. The experimental results demonstrate that the proposed method can accelerate the phase reconstruction process drastically, in particular when a high-dimensional SAR stack is required to be processed.  
MT-InSAR; phase reconstruction; computational efficiency; probability generative model; probabilistic principal component analysis +
Footnote †: journal:  
0021
# Impact of Fat Content and Lactose Presence on Refractive Index in Different Types of Cow Milk
###### Abstract  
This study unveils an advanced methodology for characterizing various types of cow's milk based on their optical properties, aiming to establish a straightforward yet comprehensive method. This study uses fundamental principles such as Snell's Law and Fresnel coefficients to determine and demonstrate critical angles for total internal reflection and reflectance at p polarization. Notably, milk composition, particularly fat content, significantly and remarkably influences its refractive index, with higher fat content leading to elevated values. Additionally, the extinction coefficient, derived through the Beer-Lambert law, provides valuable and essential information regarding light absorption and scattering within the milk samples. The significance of this research relies upon its ability to comprehensively analyze various optical properties of milk, including critical angles, reflectance, and extinction coefficients. By doing so, it offers an exhaustive and detailed understanding of how milk responds to light across different wavelengths and angles of incidence. Moreover, the technique effectively distinguishes milk types based on their fat content and particle characteristics. This novel characterization technique holds promise for various applications within the dairy industry, such as milk quality control, classification, and adulteration detection, which is crucial for maintaining consumer trust and safety.  
refractive index; cow's milk; effective medium theory; optical reflectance; lactose Article
## 3 Results
### Model Training and Test Results  
#### 3.1.1 Model Training  
To validate the superiority of the ResNet_Attention model over other sequence processing models, comparative experiments were conducted using the same training dataset. The initial learning rate for all models was set to 0.001, and the model weights were randomly initialized. A batch size of 32 was used, and the models were trained for 20 epochs. The results are shown in Figure 10.
###### Abstract  
To effectively process textual data, many approaches have been proposed to create text representations. The transformation of a text into a form of numbers that can be computed using computers is crucial for further applications in downstream tasks such as document classification, document summarization, and so forth. In our work, we study the quality of text representations using statistical methods and compare them to approaches based on neural networks. We describe in detail nine different algorithms used for text representation and then we evaluate five diverse datasets: BBCSport, BBC, Ohsumed, 20 Newsgroups, and Reuters. The selected statistical models include Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TFIDF) weighting, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). For the second group of deep neural networks, Partition-Smooth Inverse Frequency (P-SIF), Doc2 Vec-Distributed Bag of Words Paragraph Vector (Doc2 Vec-DBoW), Doc2 Vec-Memory Model of Paragraph Vectors (Doc2 Vec-DM), Hierarchical Attention Network (HAN) and Longformer were selected. The text representation methods were benchmarked in the document classification task and BoW and TFIDF models were used were used as a baseline. Based on the identified weaknesses of the HAN method, an improvement in the form of a Hierarchical Weighted Attention Network (HWAN) was proposed. The incorporation of statistical features into HAN latent representations improves or provides comparable results on four out of five datasets. The article presents how the length of the processed text affects the results of HAN and variants of HWAN models.  
n _Applied_  
22 June 2021  
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## 1 Introduction
IFC focuses on representing and managing building-oriented information because the previous targets of BIM have been buildings; however, there has been a consistent demand for the application of IFC to facilities other than buildings. Accordingly, many researchers have proposed methods for applying IFC to alignment curves and infrastructure facilities such as bridges and tunnels [22; 23; 24; 25]. bSI also intended to extend IFC to various facilities; it officially announced the latest version of the schema (IFC4 \(\times\) 2), in which alignment elements had been added to the existing schema [26]. However, IFC4 \(\times\) 2 still presents an insufficient number of elements for representing railway track information, and its expansion is difficult because methodologies for converting it into a model based on the existing IFC schema have not been established yet. In contrast, BIM software supports the creation of a railway structure but cannot connect the object to a new IFC entity. [PERSON] et al. [27] proposed a methodology to convert a model based on the existing IFC into a model based on the extended IFC by inputting the physical and spatial information of model elements into a user-defined property set. This method can be useful in areas where IFC standards have not been established yet; however, it is time-consuming and requires highly skilled operators because users need to insert information for each component. [PERSON] et al. [28] performed a schema extension for the creation of railway track information models and represented railway structures using the developed software; however, this method requires the user to invest considerable time and effort in representing each component because individual algorithms need to be created. Conversely, the method of generating an information model for a horizontal structure using alignment information and converting it into a suitable extended IFC element enables the generation of a railway track model regardless of the skills of the modeler. This study proposes a methodology to create IFC-based information models for 3D alignment-based railway tracks. The information models of railway tracks were created using the linkage between software. This methodology can also employ other functions of existing software because AMT and BAT software are linked via their alignment information; thus, the individual attributes of each object can be managed. The information models are converted into IFC-based information models that can secure interoperability. The railway components are semantically assigned identical IFC entities, which are extended by referring to a classification system. The proposed methodologies are applied to actual railway projects under construction, and their effectiveness is verified.