esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Vehicle Lateral and Rollover Stability  
The vehicle's center of gravity has a particularly strong influence on its self-steering behavior. A basic distinction can be made between over-steering (unstable) and understeering (stable) driving behavior [24]. An oversteering vehicle is characterized by the fact that above a critical velocity the yaw amplification factor becomes infinitely large, so that small steering movements cause large and uncontrollable vehicle reactions. Based on a single track model as described in [25], the critical velocity can be calculated according to Equation (10) from the understeer gradient \(K\) given in Equation (9). Equation (11) provides the characteristic velocity for understeering vehicles. \(c_{a,r}\) and \(c_{a,r}\) are the cornering stiffnesses of the rear and front axles.  
\[K=\frac{m}{l}\ \left(\frac{l_{r}c_{a,r}-l_{f}c_{a,f}}{c_{a,r}\ c_{a,f}}\right) \tag{9}\]  
\[v_{crit}=\sqrt{-\frac{l}{K}} \tag{10}\]  
\[v_{char}=\sqrt{\frac{l}{K}} \tag{11}\]  
According to [PERSON] [26], stable driving behavior for a 2F1R-vehicle requires the COG to be in the front third of the vehicle (under the assumption of identical tire properties on the front and rear axles and neglection of longitudinal forces). For a four-wheeled (4W) vehicle, the COG must be in the front half. In addition to driving stability and steering sensitivity, the COG position also influences a vehicle's rollover stability in particular. The static stability factor (SSF) can be used as a measure of rollover stability [27]. The SSF describes the maximum possible lateral acceleration of a vehicle, i.e., the lateral acceleration at which the wheel on the inside of the curve lifts off. Research shows that SSF is highly suitable as a measure of rollover stability and correlates with actual rollover accident occurrence for passenger cars [28]. According to [PERSON] [26], the SSF for a three-wheeled vehicle in the tadpole configuration can be calculated according to Equation (12) with the maximum lateral acceleration \(a_{y,max}\), the gravitational constant \(g\), the track width on the front axle \(t_{f}\), COG height \(h_{z}\) and COG position \(l_{r}\), and wheelbase \(l\). Influences of the wheel suspension are neglected for the SSF.  
\[SSF_{2F1R}=\frac{a_{y,max}}{g}=\frac{t_{f}}{2h_{z}}\cdot\frac{l_{r}}{l} \tag{12}\]  
The static stability factor of a four-wheeled vehicle with different track widths on front (\(t_{f}\)) and rear axle (\(t_{r}\)) can be calculated according to Equation (13). In the case where \(t_{r}=0\)Equation (12) is again obtained for the 2F1R vehicle. A derivation of Equation (13) can be found in Appendix A.  
\[SSF_{4W}=\frac{a_{y,max}}{g}=\frac{1}{2h_{z}}\frac{t_{f}l_{r}+t_{r}l_{f}}{l} \tag{13}\]
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 4 Efficiency of Creating PAM versus Its Dimensions
### Result Analysis  
The results presented in this part of the paper relate to studies carried out for various sizes of the analyzed area (square with a side length of \(2R_{0}\)). The angular resolution \(\Delta\alpha\) for all tests are equal to \(1^{\circ}\). Figure 7 shows the effect of increasing the radius \(R_{0}\) on the calculated points ratio (i.e., \(CPR\)) values (blue color) and the calculation time (i.e., \(CT\)) needed to obtain the results (orange color).  
As mentioned in the previous section, the \(CT\) metric depends on the processing power and is used for comparison purposes. The greater the value of the radius \(R_{0}\), the longer the time needed to perform the calculations. For example, in the case of a tenfold increase in \(R_{0}\) (from 2 to 20 km), the \(CT\) increases about six times. In the case of analyzing small areas, the vast majority of points on the PAM are calculated based on PEM (some points even multiple times, as shown in the following figures-see Figure 8). The larger the area of the analysis, the \(CPR\) value significantly decreases, and more points must be determined using the interpolation method presented in Figure 2. In the case of the radius \(R_{0}\) equal to 1 km,about 90% of the points on the PAM are determined based on calculations. Increasing \(R_{0}\) to 10 km results in a decrease in \(CPR\) to the level of only about 21%. In this case, 79% of points must be interpolated.  
Figure 8 depicts the effect of duplicate calculation of points in generating the final PAM. In particular, the greatest redundancy of calculation is for the area next to the TX. Therefore, the increase in the number of calculations (i.e., \(CN\)) does not translate proportionally to the increase in \(CPR\).  
Another metric, independent of the processing power of the used computer, is the total number of calculations (i.e., \(CN\)). The absolute value of this measure for three distances \(R_{0}\) is presented in Figure 9a. The normalized values of the calculations for these three cases are shown in Figure 9b.  
Figure 8: Redundancy \(r\) for points on sparse PAM matrix illustrated for (**a**) whole map area and (**b**) area with highest number of calculations.  
Figure 7: Calculated points ratio \(CPR\) and computation time \(CT\) for different radius \(R_{0}\).  
Figure 10 shows the normalized number of points on the map for which the attenuation calculations are repetitive. In this case, the parameters \(NCP_{2}\) and \(NCP_{3}\) mean that 37% and 14% of points are calculated two or three times, respectively. On the other hand, \(NCP_{>5}\) means the sum of points on the map is calculated 6 or more times. For PAM with a larger radius, i.e., \(R_{0}=5\) km, the number of points without redundancy (\(NCP_{1}\)) is 92% and and the number of points without redundancy (\(NCP_{1}\)) is 97%.
## 2 KGs Extraction
### Final Summary  
In the **Pre-Construction Preparation** phase, the focus is on laying a robust foundation for KG construction through two key steps. First, **Named Entity Recognition** employs advanced techniques such as BERT-based models, Bi-LSTM, CRF, and graph-based methods to accurately identify key entities and map them to predefined ontologies [5; 6; 7; 8; 9; 10]. Next, **Relation Extraction** leverages methods like dependency parsing, semantic feature extraction, multi-head graph convolution networks, and attention mechanisms to extract subject-relation-object triples from unstructured text [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]  
Building on these initial tasks, the **Advanced Extraction** stage further refines the process. In this stage, **Graph-based Methods**--using graph neural networks (GNNs) and attention mechanisms--organize entities and relations into structured graphs [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. Additionally, **Multimodal and Domain-Specific Extraction** integrates diverse data sources (including text, images, and sensor inputs) to enhance precision in specialized fields such as healthcare, cybersecurity, and industrial applications [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50].  
Finally, during the **Construction** phase, the process is extended and automated by incorporating **Intelligent Reasoning Methods**--such as multi-hop reasoning, causal inference, and quantum embeddings--to support complex inference tasks [61; 62; 63; 64; 65]. This is complemented by **Rule-based Approaches** that utilize predefined rules, knowledge-guided attention, and logic-based models to further refine extraction accuracy in specialized domains [66; 67; 68; 69; 70; 71; 72]. Moreover, advanced techniques for **Entity and Relationship Alignment** ensure consistency and seamless integration across heterogeneous knowledge graphs [73; 74; 75; 76; 77; 78].
## 3 Tire-Road Contact Force Equations through Numerical Analysis
### Tire Contact Force Equations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tire Type** & \begin{tabular}{c} **Inflation** \\ **Pressure (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Vertical** \\ **Deflection (mm)** \\ \end{tabular} & \begin{tabular}{c} **Simulation** \\ **Force (N)** \\ \end{tabular} & \begin{tabular}{c} **Rectangular** \\ **Equation (N)** \\ \end{tabular} & \
\begin{tabular}{c} **Huang** \\ **Equation (N)** \\ \end{tabular} &
\begin{tabular}{c} **Modified** \\ **Equation (N)** \\ \end{tabular} \\ \hline
11.00R20 & 0.8 & 10 & 14,758.4 & 37,623.7 & 17,676.5 & 15,936.2 \\
11.00R20 & 0.8 & 15 & 23,037.7 & 56,194.2 & 26,389.2 & 23,791.1 \\
11.00R20 & 0.8 & 20 & 31,357.9 & 74,603.8 & 35,018.4 & 31,570.7 \\
11.00R20 & 0.8 & 25 & 38,156.4 & 92,852.4 & 43,563.9 & 39,274.9 \\
0.7 & 20 & 26,268.3 & 65,278.3 & 30,641.1 & 27,624.4 \\
0.9 & 20 & 34,197.8 & 83,929.3 & 39,395.7 & 35,517.1 \\
1 & 20 & 36,414.2 & 93,254.7 & 43,773.0 & 39,463.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Contact forces of truck tire. According to the analysis in this section, the traditional footprint assumptions are not reasonable; thus, the tire force equation has a large error in the calculation of tire force. Actually, the footprint approximates different shapes at different deformation stages of tires. FE models of different tires were established to analyze the deformation process of the tire. The simulation results show that the tire footprint approximates different shapes at different deformation stages. For passenger cars, the tire footprint is not always an oval shape as assumed. It is actually a rhombus when the contact width is smaller than the tread width, and a hexagon when the contact width reaches the tread width. As for the tires of heavy trucks, the footprint is an oval when the contact width is smaller than the tread width, and a rectangle with two half circles on each side when the contact width reaches the tread width. The equation based on the modified footprint achieves better results, and the errors are all within 10%.
## 4 Modeling the Flexural Rigidity Considering Natural Bonding Effect  
Section 2 proved that there were three stages in the load-slip curves when the SIP form was used, for example, S-4 (Figure 13a). Therefore, an idealized curve can be used to simplify the actual curve (Figure 13b). Accordingly, the actual load-deflection curve can be idealized as a three-stage curve (Figure 14). First, the FRP-concrete hybrid beam specimens behaved as a full composite action structure when the load was lower than a certain level, denoted by \(P_{bond}\). After the load exceeded \(P_{bond}\), the load remained constant and the deflection increased to a certain level. After the plateau, the load increased nearly proportionally with the deflection. Three assumptions were adopted in the idealized relationship: (1) Before the natural bonding failure, there was no slip, so the beam could be modeled as full composite action; and (2) After the debonding stage, the shear force-slip relationship was modeled as a line with a fixed slope, which meant that a constant slip modulus was used. Moreover, the line started from zero if extended toward a horizontal axis, which means without the effect of slip. It should be noted that line BC in Figure 13a shows a softening phenomenon (decreasing slope) with the increase of load, which could be attributed to the damage of the FRP, the plasticity of the steel bolts, and concrete cracking. Therefore, the straight line BC in Figure 13b is probably not suitable for high load levels. Similarly, the interfacial softening and concrete plasticity in the FRP-concrete hybrid beam could induce the decreasing slope in the BC stage in Figure 14. Therefore, the application of the idealized curve in Figure 14 is suggested for use in the design of the serviceability limit state.  
Figure 12: Load versus strain relationship of points along the mid-span section.  
In this paper, only four push-out test specimens and two beams were tested, so the determination of \(\tau_{bond}\) should be validated using more test data for future research. Moreover, the influence of concrete strength, the depth of ribs in the SIP forms, and the sizes of bolts will be researched in future work.
# Sleep Apnea Classification Using the Mean Euler-Poincare Characteristic and AI Techniques
These works use techniques that involve RR variability [26; 32; 33], frequency domain features using wavelet transform [29; 34], and machine learning techniques as neural networks [17; 29; 34; 35; 36], and so on. Artificial intelligence models currently play a huge role in medicine, especially artificial neural networks, which have been used for the classification and detection of diseases such as diabetes [37], heart diseases [38], chronic kidney disease [39], and in oncological diagnostics [40]. Due to the efficiency and utility of these tools, we use neural networks in this work. Our study makes significant contributions to the field of ECG analysis by introducing an innovative algorithm that integrates geometric models as features and inputs for AI models, specifically designed to classify and detect sleep apnea and heart diseases. This algorithm leverages geometric features that closely match the behavior of the ECG, enhancing the accuracy of classifications. We ensure relevance and continuity in ECG research by addressing the challenges posed by limited and imbalanced datasets and aligning our work with historical benchmarks established in the 2000 challenge [41] using the Apnea-ECG database [42]. Overall, this comprehensive approach not only deepens our understanding of ECG signal behavior but also lays the groundwork for future studies involving larger and more balanced datasets, ultimately improving diagnostic capabilities and patient outcomes. Existing OSA detection methods often rely on handcrafted features and struggle to achieve high accuracy with single-lead ECG signals. To address these limitations, this article introduces a novel algorithm that uses random algebraic polynomials (RAP) for signal smoothing and creates a non-Gaussian physiological random field (NGPRF) to capture intricate, non-Gaussian ECG features. This innovative framework models critical ECG peaks (P-waves, QRS-complex, T-waves) and extracts their geometrical characteristics as Euler-Poincare characteristics (EPCs). By capturing unique geometrical forms in the ECG signal, the EPCs provide robust inputs to our classification system. Unlike conventional approaches, we integrate these advanced geometric descriptors with machine learning classifiers, including ensemble K-nearest neighbors (EKNN) and feedforward multi-layer neural networks (FNNs), to significantly improve OSA detection accuracy. This novel combination of RAP-based signal processing and advanced classification methods bridges existing gaps, making single-lead ECG-based OSA detection more accurate and potentially adaptable for real-time and diverse clinical applications. This article is organized as follows: In Section 2, we describe the mathematics involved. In Section 3, we describe the data used. In Section 4, we discuss the methodology applied to the cases. Then, in Section 5, we present the results and discussion. Finally, a conclusion and future works are provided in Section 6.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 1 Introduction
### Paper Organization  
The remainder of this paper is organized as follows: a comprehensive literature review will be done from three perspectives in Part 2: single mathematical theory-based forecasting models, hybrid theory-based forecasting models, and the models specific to air traffic forecasting. Then, the methodology based on EMD-SARIMA that has been employed in this paper will be presented in Part 3, which is also threefold: at first, the original time series data is decomposed into components called intrinsic mode functions (IMFs); then, each component is modeled with the SARIMA forecasting model; finally, all of the results are integrated together so as to form a combined forecasting result. After that, the air cargo and passenger flow data which is used in this paper to do forecasting from the official website of the CAAC is described in detail in Part 4, as well as how the components have been decomposed from the original time series data in the experimental setup. Later, in Part 5, the case studies made based on the China civil aviation data between the years 2006 and 2014 are discussed, including how the hybrid EMD-SARIMA theory is applied in air passenger flow forecasting, and how its performance compares with other forecasting models. Last but not least, the summary, conclusions, and comments upon future work are made in Part 6.
## 3 Additive Manufacturing of Pla-Metal Biodegradable Composites
In order to manufacture 3D-printed PLA/silver composites, investigators constructed and used a small extruder with a short heating duration of 3 cm to reduce thermal deterioration during TS-SOC filament production [80]. Additionally, they generate dense conductive percolation networks by uniformly forming silver nanoparticles (SNPs) with a diameter of 70-90 nm through surface dechelating and chemical reduction. A few studies have been carried out on PLA-cobalt biocomposites. Recommended printing parameters according to the materials data sheets are employed, which include a printing temperature of 210 \({}^{\circ}\)C for both materials, a heated printing bed at 40 \({}^{\circ}\)C, a printing speed of 20 mm/s for the initial layers and 40 mm/s for the intermediate and upper layers of the constructs, and a layer height of 200 \(\upmu\)m. An infill of 100% is applied to work with bulk materials and promote adhesion within and across layers [81]. PLA/nickel filaments were used for the manufacturing of 3D printed composites by various researchers. Experiments showed that using a 75 \(\upmu\)m thick wire with a 1 mm nozzle, a layering height of 0.6 mm, a speed of 7 mm/s, and a nozzle temperature of 175 \({}^{\circ}\)C usually led to consistent prints [82]. Table 1 enlists various 3D printing process parameters for the manufacturing of PLA-metal biocomposites. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S.No.** & **Biocomposite** & **AM Technique** & **Process Parameters** & **Reference** \\ \hline \multirow{2}{*}{1} & PLA with 5–20\% Mg & \multirow{2}{*}{FDM} & Nozzle diameter—0.4 mm, Layer thickness—0.2 mm & [46] \\  & biocomposites & & Nozzle temperature—245 \({}^{\circ}\)C and & \\  & & & bed temperature—60 \({}^{\circ}\)C, Infill & \\
2 & PAL coated AM60 alloy composites & & speed—60 mm/s, Nozzle diameter—0.4 mm, Layer thickness—50 μm & [83] \\  & & & & thickness—50 μm & \\ \hline \multirow{3}{*}{3} & PLA with 15\% and & \multirow{3}{*}{FDM} & Layer height—0.25—0.35 mm, & \\  & 80\% of brass & & Printing speed—20–40 mm/s, Infill & [84] \\ \cline{1-1}  & & & percentage—50\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three-dimensional printing process parameters for manufacturing PLA/Metal biocomposites.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis
### Experimental Results  
#### 4.2.1 Implementation Details  
All of the experiments were implemented under the Darknet deep learning framework [16]. When considering the GPU capacity and training efficiency of Jetson TX1, we used a PC with a 11G memory NVIDIA GeForce GTX 1080 Ti (16 GB RAM, Intel (R) Xeon(R) CPU, e3-1226 v3, @3.30 GHz) to train the model. The operating system was 64-bit Ubuntu16.04, the CUDA8.0 and its matching cuDNN were installed in the system. Subsequently we ported the well-trained model into Jetson TX1.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Application and Scenarios
#### 2.4.2 Processing Module Scenarios
The transceiver is configured to be in continuous reception mode throughout these tests. In order to test this mode, there are four alternative commands that a reader can send to our tag: read one datum from EEPROM, read all data from EEPROM, read data saved at a specific date, and store a datum in the EEPROM. These commands explain the cases that are the most similar to the cases of our application: the tag receives a command, processes it and, if required, transmits an answer. The value in the supercapacitor for the measures is 4.2 V.  
1. \"Read one\" datum from EEPROM: a reader system requires the last datum saved in the tag. The transceiver receives a package from a reader with a \"read one\" command and sends it to the microcontroller, which performs a read operation in the EEPROM. Thereafter, the datum is transmitted back to the reader. 2. \"Read all\" data command from EEPROM: here there are three transactions, because the transceiver is not able to send all the available data in one packet. The process is the same as for transmitting all the data in transmission scenario for the first transaction of this command. This first transaction sends all possible bytes. The second transaction sends the bytes that could not be sent in the previous transmission. The last transaction is to confirm that the transmission is ended, and this means that this is a package with the lowest size, 2 bytes. 3. \"Read date\": here, the tag stores information from different days. This command retrieves the information in the tag associated to a specific date. The transceiver changes from reception mode to sleep mode. EEPROM searches for the specific date, after which the date found reaches the microcontroller again, and the latter sends the date to the transceiver. 4. \"Write\": here a reader wants to write a datum in the EEPROM of the tag. The process is as follows: the transceiver detects a command, changes from reception to sleep, the command is processed in the microcontroller, it reaches the EEPROM, and after an examination of where to save the datum, it is written. Then, the microcontroller checks that everything was correct and changes the transceiver to reception mode again. This command does not have an acknowledgement transmission packet; if we want to know if it has been saved correctly, we have to send a \"read date\" command or \"read one\" datum command.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 2 Related Work
### Functional Mock-Up Interface  
One standardized numeric representation of simulation models is given by the FMI standard for Model Exchange [6; 12; 13]. At the time of writing, two versions, 1.0 and 2.0, of the FMI have been released. The second FMI version introduces several optional capabilities such as persisting the state of a model. Each numerical model is encapsulated into a single file, which is referred to as Functional Mock-up Unit (FMU), and can be instantiated and accessed by other tools, for instance to create a comprehensive simulation with models from various sources. The FMU itself contains a static XML-based model description and a set of C functions that implement the dynamic behavior of the model, represented by a set of Ordinary Differential Equations (ODEs) and discontinuities including discrete changes, called events [12; 13]. Equations are defined in terms of C functions and require an external numerical solver to compute the results of a simulation. An FMU may either contain a compiled platform-specific binary executable of the C functions or the source code itself.  
In order to create a generic virtual component that connects simulation with automation infrastructure, one needs to couple the continuous or hybrid FMI-based component model and the surrounding discretized system. Although the FMI does not focus on purely discrete systems, several authors have successfully described the integration of FMUs, and hybrid models in general, into discrete event-based simulations [7; 8; 14]. [PERSON] et al. presented a structured approach for coupling heterogeneous models, such as continuous-time and discrete event-based models, into a joint simulation that forms a tree of locally homogeneous submodels [14].  
A predictive approach for integrating FMUs into discrete event-based simulations was proposed in [7; 8] and implemented as an external library called FMI++. The approach allows bidirectionally synchronizing the operation according to the timing of scheduled events.