esa-sceva/satcom-chunk-collection huggingface dataset:
# Bar-Wave Calibration of Acoustic Emission Sensors
## 2 Experimental Procedures  
An aluminum bar of 25.4 mm \(\times\) 6.4 mm \(\times\) 3660 mm was shaped as shown in Figure 1. The material was UNS A96061 (6061-T6511 temper from Kaiser Aluminum, Spokane, WA, USA) with the mass density of 2.70 Mg/m\({}^{3}\), the longitudinal wave velocity = 6.42 mm/us, and the transverse wave velocity = 3.04 mm/us. On one end face, a transmitter (FC500 from Acoustic Emission Technology Corp., Sacramento, CA, USA) was glued using epoxy, such that guided waves produced by the transmitter were directed along the length direction of the bar. The broad face of the bar was polished and its normal displacement was determined using a laser interferometer (Thales Laser, LH-140, Orsay, France) at distance L of 300 mm from the bar end. More details on the set-up were given in reference [8]. The following changes were made in the set-up from the previous tests: (1) The pulse generator was modified, providing step-pulses of 241, 350 or 405 V with FC500 connected; (2) A different FC500 from the previous tests was used with the front alumina facing replaced by an aluminum disc of 1.6-mm thick. This disc was glued to the piezoelectric element using silver-loaded conductive epoxy. This modification reduced radial oscillations of the transmitter significantly, as will be shown later.  
The response of an AE sensor was obtained by placing its center at L = 300 mm. Couplant was Vaseline and the sensor was held under 1 N force for at least 15 min before measurements were recorded. The sensor output was terminated with 10 k\(\Omega\) at the input of Pico Scope (Pico Tech 3405A, Pico Technology, St. Neots, UK) and digitized at 2 ns time interval. When a sensor includes internal buffer electronics, its output was connected to a power supply and the output signal was routed to the ac-coupled scope input by a tee. No termination was used in this case as a load resistor was a part of the power supply circuit. Data analysis procedures were given previously [4; 5]. Notice that signals levels in this study usually exceeded 10 mV and no cable movement occurred during measurements. No special preparation was thus required to avoid triboelectric effects, if any.  
Table 1 lists AE sensors tested in this study. The transmitter data (FC500) is also shown. The nominal values of resonance frequency and sensing element diameter are given along with the model number and manufacturer. The selection of these sensors is dictated by the availability for the lack of financial resources, but most of them are commonly utilized in research and in practical applications.
## 5 Summary of Findings
### Infrastructure Gaps and Teacher Training  
The cost and complexity of rolling out high-density networks, edge computing servers, and immersive devices pose genuine barriers. While urban districts or well-funded institutions can launch 5G pilot programs, under-resourced schools remain vulnerable to connectivity shortfalls. Professional development is also paramount: teachers need strong support to interpret AI recommendations, design interactive lessons, and safeguard student data.  
The next section proposes concrete recommendations and highlights the remaining research gaps, paving the way toward a cohesive and future-ready vision of smart education.
## 3 Proposed Work
### Network Structure  
Protocols usually use a randomly organized allocation of nodes in the deployed area, thus the inefficient use of nodes energy can be observed. In EZ-SEP algorithm, the deployed area is separated into three fields as zone 1, zone 2 and zone 3 to optimize the energy consumption at the nodes. To distribute the network load and due to nodes assigned initial energy value, nodes are categorized into two groups as normal and advanced nodes. Advanced nodes have more energy than normal nodes. Therefore, normal nodes are deployed in zone 1, close to base station, while advanced nodes are deployed distant to BS in zone 2 and zone 3. We suppose that nodes are not mobile in the field and field dimensional is unknown. Assume \(m\) as a fraction of total nodes \(n\) with \(\alpha\) times more energy. Thus, \((1-m)n\) of normal nodes. Normal regular nodes transmit records to base station directly, whereas in advanced nodes hierarchical clustering is formed among nodes, where one sensor node is elected as a cluster head and other cluster members join and deliver data to this cluster head, and then this cluster head transmits aggregated data to BS as shown in Figure 2.
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 4 Experimental Results and Discussion
### Experiment Discriptions
#### 4.1.2 Semiphysical Simulation  
The true trajectory files from the hardware simulator were used to simulate the ideal sensor output of the inertial measurement unit (IMU). In order to reflect the performance of the navigation algorithm more properly in polar areas, semiphysical simulation experiments with the IMU were conducted to get the actual sensor output errors. Then, a set of semiphysical experiment sensor output data could be obtained by adding the actual sensor errors to the ideal sensor output.  
A turntable experiment was conducted to extract the actual IMU output errors. An IMU was installed on a high-precision three-axis turntable, as shown in Figure 4.  
The IMU actual output data, i.e., \(\widetilde{\mathbf{\omega}}_{ib}^{b}\) and \(\widetilde{f}_{ib}^{b}\), and the turntable's movement were collected. Meanwhile, the turntable's movement data (consider as the true trajectory) were used to generate the ideal IMU measurement data, i.e., \(\mathbf{\omega}_{ib}^{b}\) and \(\widetilde{f}_{ib}^{b}\). Then, the actual output errors of the IMU could be obtained from the difference between the IMU actual output data and the ideal IMU output data. The actual IMU output error can be obtained by  
\[\left\{\begin{array}{l}\delta\mathbf{\omega}_{ib}^{b}=\widetilde{\mathbf{\omega}}_{ib }^{b}-\mathbf{\omega}_{ib}^{b}\\ \delta\mathbf{f}_{ib}^{b}=\widetilde{f}_{ib}^{b}-\widetilde{f}_{ib}^{b}\end{array} \right.. \tag{33}\]  
The IMU error data \(\delta\mathbf{\omega}_{ib}^{b}\) and \(\widetilde{\mathbf{\delta}}_{ib}^{b}\) gained from the above turntable experiment can reflect not only the actual error characteristics of the gyroscopes and accelerometers but also the scale errors and installation errors. Finally, the actual IMU output errors were added to the ideal sensor output of the IMU from the hardware simulator to get a final semiphysical simulation.  
Table 3 shows the detailed parameters of the overall system and experiments.  
Figure 4: The high-precision three-axis turntable and the inertial measurement unit (IMU) in the temperature control box.  
Figure 3: Data collection process with GNSS hardware simulator.
## 2 Methodology
### Ionospheric Correction Evaluation Method
#### 2.2.1 SBAS Ionospheric Correction Calculation
\tag{19}\]  
where the determination of \(x_{IPP}\) and \(y_{IPP}\) is the same as Equation (16). The slant ionospheric correction can be computed with vertical delay divided by the mapping function  
\[SIC=\frac{VIC}{\sqrt{1-\left(\frac{R_{e}\cos E}{R_{e}+h_{I}}\right)^{2}}} \tag{20}\]  
where \(E\) is the elevation angle of the satellite with respect to the user's location, \(R_{e}\) is the approximate spherical earth radius and it is taken to be 6378.1363 km, \(h_{I}\) represents the height of the ionospheric layer, which is 350 km as mentioned above [1].
## 5 Ultimate Capacity Test Results and Discussion  
### Specim Failure and Its Analysis  
As can be seen from Figure 9, in the uniform load test, the load-stress curves of the glass panels showed a linear relationship even when the uniform load reached 5 kPa, indicating that the glass panels were still elastic. Consequently, the execution of the ultimate capacity test after the uniform load test became feasible, and the ultimate capacity test showed that with the ultimate load the glass panels broke suddenly without warning. The lower layer of the glass panels exhibited numerous cracks and glass particles, but these particles remained closely bonded to the interlayer, indicating that the SGP laminated glass was safer than traditional glass. Notably, the upper layer of the glass panels was in contact and undamaged.  
The primary stress on the upper layer of the glass panel was compressive stress, while the primary stress on the lower layer of the glass panel was tensile stress. In addition, the glass is a brittle material [24], meaning the tensile strength was much smaller than the compressive strength, leading to breakage of the lower layer of the glass panel with the ultimate load. The SGP laminated glass is less dangerous than traditional glass. However, in daily use, the stress on the tension face of cold-bent SGP laminated glass should be controlled with utmost priority to prevent damage. Moreover, concentrated loading on cold-bent SGP laminated glass should be avoided.
## 3 Results
### Fitting Corrosion Test  
The polarization curves for two groups of samples are presented in Figure 9. Differences in pitting corrosion resistance were found between the tested groups of samples--as shown in Table 4. Samples after mechanical polishing had higher corrosion resistance. In the case of samples after sandblasting, heat treatment at 1020 \({}^{\circ}\)C resulted in a decrease in corrosion potential and an increase in polarization resistance, which is confirmed by the authors' research [38].  
The greatest resistance to pitting corrosion for sandblasted specimens is characterized by the specimens after heat treatment at 1020 \({}^{\circ}\)C. The lowest current density and the highest values of corrosion potential and polarization resistance were determined for this group. To confirm the results of the pitting corrosion resistance test, macroscopic observations of the surface of the samples were carried out at 221 \(\times\) magnification--as show in Figures 10 and 11. Few pits were found on the surfaces of sandblasted specimens (Figure 10c) for specimens  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **I [mA/cm \({}^{2}\)]** & **E\({}_{\text{cor}}\) [mV]** & **R\({}_{\text{p}}\) [k\(\Omega\cdot\)cm \({}^{2}\)]** \\ \hline S & 818 \(\pm\) 139 & \(-\)226 \(\pm\) 87 & 29 \(\pm\) 1.5 \\ \hline S\_800 & 940 \(\pm\) 487 & \(-\)115 \(\pm\) 30 & 32 \(\pm\) 0.1 \\ \hline S\_910 & 766 \(\pm\) 145 & \(-\)118 \(\pm\) 6.4 & 31 \(\pm\) 0.9 \\ \hline S\_1020 & 164 \(\pm\) 79 & \(-\)51 \(\pm\) 54 & 89 \(\pm\) 29 \\ \hline MP & 215 \(\pm\) 116 & \(-\)58 \(\pm\) 16 & 95 \(\pm\) 43 \\ \hline MP\_800 & 289 \(\pm\) 69 & \(-\)63 \(\pm\) 133 & 95 \(\pm\) 32 \\ \hline MP\_910 & 615 \(\pm\) 88 & \(-\)233 \(\pm\) 45 & 63 \(\pm\) 48 \\ \hline MP\_1020 & 150 \(\pm\) 53 & \(-\)207 \(\pm\) 66 & 67 \(\pm\) 17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pitting corrosion resistance results for samples from groups I and II.  
Figure 9: Polarization curves in logarithmic form for samples from two groups.  
with less resistance to this type of corrosion. This is confirmed by the results obtained by the authors [38], who found pits on the surface after testing for pitting corrosion resistance. The number was smaller for heat-treated samples [39].  
In the case of the group I samples, sand grains on the surface of the test material can also be observed. On the other hand, the black images obtained (for the second group of samples) are related to the polished surface of the sample. Mechanical polishing provides a high degree of surface smoothness, significantly increasing its ability to reflect light bidirectionally. The appearance of scratches on the surface of the samples after testing for resistance to pitting corrosion is the result of placing them in an electrochemical cell. The pitting observed occurs only in the case of sample \"S\". According to standard [29] if a material exhibits corrosion resistance up to 2 V, it can be considered corrosion-resistant across the entire measurement range.
###### Abstract  
The current study aimed to develop a relationship between surrogate safety indicators and human judgement of severity. It has been demonstrated that human observers frequently display excellent agreement when asked to assess traffic incidents by their level of danger. Therefore, this research examines, in depth, how surrogate safety indicators might be used to represent human judgement of the severity of traffic incidents. This study analyzed 1141 traffic incidences of various vehicle categories according to their behavior during an interaction. Furthermore, ordinal logistic regression was used to develop a model for evaluating the most significant objective indicators relevant to people's perceptions of danger. The findings indicated that the most crucial data for determining the severity of a traffic event is found in its earliest conditions, which are defined as the beginning of an evasive action. Moreover, factors affecting both closeness and collision consequences are significant and should be included in severity metrics.  
surrogate safety measures; vehicle-vehicle interaction; heterogenous traffic conditions; behavior analysis; logistic regression; time to collision; post encroachment time
# A Comprehensive Failure Risk Assessment Method of
## 2 Comprehensive Risk Assessment of the Machining Center Component Based on Topological Analysis
### Comprehensive Failure Risk Assessment of Machining Center Component Based on Topological Analysis  
#### 2.3.1 Calculation of Propagation Failure Risk of Machining Center Components  
There is a certain failure propagation relationship between the components of the machining center. A component may be affected or even fail due to the failure of the upstream node. Therefore, the influence of the propagation failure risk on the component cannot be ignored. The calculation equation of the propagation failure risk of the component is as follows:  
\[R_{P_{i}}(t)=\sum_{k\in D_{i}}R_{k_{k}}(t)\cdot P(k,i), \tag{17}\]  
where \(R_{P_{i}}(t)\) represents the propagation failure risk of component \(i\) at time \(t\). \(R_{I_{k}}(t)\) represents the independent failure risk of component \(k\) at time \(t\). \(D_{i}\) represents the set of upstream components connected to component \(i\). \(P(k,i)\) is the failure propagation influence of the connection edge \(k\to i\), and \(P(k,i)\) is calculated as follows:  
\[P(k,i)=F_{k}(t)\cdot I(k,i), \tag{18}\]  
where \(F_{k}(t)\) is the failure probability of component \(k\) at time \(t\). \(I(k,i)\) indicates the importance of connecting edge \(k\to i\).
## 3 Instantaneous Multi-Constellation Relative Positioning: Methodology and Performance
### Performance Assessment of Precise Positioning with Focus on E14 and E18 Galileo Satellites
#### 3.2.2 Geometry and Configuration of the Satellites during the Experiment  
Figure 5 depicts the number of tracked satellites and corresponding PDOP values at Olsztyn during three days of processed data. The values are given for GPS and Galileo, as well as for a combined solution. As can be seen from the figure, the number of visible GPS satellites varied from five to 11; however, most of the time their number was in the range of 8-10. As a consequence, the PDOP values in most cases did not exceed the level of 2.5. This indicates relatively good conditions in terms of the geometric distribution of satellites. In the case of a combined solution, the PDOP values were obviously lower, in most cases below 2. On the other hand, judging on the basis of the number of Galileo satellites, one can expect a significant deterioration of the solution based solely on this constellation. With the current Galileo constellation, the number of satellites most of the time did not exceed six, but some periods accidentally reached seven or eight (Figure 5 solid green line). However, there were time intervals when the number of satellites equalled five or less; this accounted for 37% and 28% of the time, respectively. Thus, PDOP reached a significantly higher level than in the case of the GPS constellation. For 25% of the time, PDOP for the Galileo system was higher than 5 (Figures 5 and 6). Therefore, we can conclude that the geometric distribution of the Galileo satellites above the area of interest was not favourable for positioning, especially regarding the single-epoch mode.  
Figure 7 presents sky plots of the Galileo and GPS constellations over the area of interest. We can clearly see from the figure the deviation of E14 and E18 altitudes from the  
Figure 5: Number of tracked satellites (solid lines) and PDOP (dotted lines) corresponding to GPS-only constellation—red, Galileo-only constellation—green, GPS + Galileo—magenta).  
Figure 6: Histogram of PDOP values during experimental period. GPS constellation—red, Galileo constellation—green.  
lines). While the nominal altitude of Galileo satellites reaches over \(\sim\)23,200 km, E14 and E18 satellites were tracked at a significantly lower height above the Earth (below 18,000 km). Moreover, when the E14 and E18 satellites are close to their minimal orbital height, they are observed at high elevation angles at experiment localisation. As is well known, GPS orbits with altitude lower than Galileo equal \(\sim\)20,200 km, hence in Figure 7 they are marked with azure.
# A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands
## 3 Results  
The proposed antenna was modeled and optimized using the finite element method-based full-wave electromagnetic solver Ansys HFSS 2022. Based on the simulation results from HFSS, a prototype of the antenna was built. The measured results from the antenna prototype are compared below with the simulated results.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 3 Background
### Overview of LTE Networks  
The LTE system is based on a flat architecture, known as the SAE, with respect to the 3G systems [36]. LTE provides high speed delivery of data and signaling and a seamless mobility support. As depicted in Figure 2, it is mainly composed of two parts, the evolved packet core, also called the core network and the Evolved-Universal Terrestrial Radio Access Network (E-UTRAN). The evolved packet core is connected to the IP multimedia subsystem (IMS). The Home Subscribe Server (HSS) is the main IMS database which also acts as database in Evolved Packet Core (EPC). EPC has three main functional units: the Mobility Management Entity (MME), the Serving Gateway (SGW), and the Packet Data Network Gateway (PGW). The MME is responsible for handling user mobility, intra-LTE handover, and tracking and paging procedures of User Equipment upon connection establishment. The main function of the SGW is to route and forward user data packets among LTE nodes, and to manage handover among LTE and other 3 GPP technologies. The PGW has the function of interconnecting the LTE network with the rest of the Internet. The LTE access network can host only two kinds of node: the UE (that is, the end-user) and the eNB. Note that eNB nodes are directly connected to each other through the X2 interface and to the MME gateway. The eNB is the only device responsible for radio resource management and control procedures at the radio interface, in contrast to other cellular network architectures such as 3G.  
Figure 1: Model of Radio Link Control (RLC) entity in the Long-Term Evolution (LTE) network.
## Appendix A The LoRaWAN Communication Solutions  
The aim of this appendix is to provide an overview of the LoRaWAN solution. Currently, LoRaWAN is recognized as the most diffused and well-accepted example of LPWAN radio technology in both the consumer and the academic worlds.  
Differently from other LPWANs, LoRaWAN is an open standard, supported by an alliance in charge of drafting standards, with many manufacturers offering development solutions. Not surprisingly, very different application scenarios have been addressed in the market and in the literature, including precision agriculture [47; 48], electric vehicles management [49; 50], industry [51; 52], buildings [53; 54], and finally, smart campus [55; 56].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spreading Factor** & \(\sum T_{\text{OA}}\) **(ms)** & **Cell Capacity per Channel \({}^{*}\)** & **Cell Capacity per Channel \({}^{**}\)** \\ \hline
7 & 1058 & 10,206 & 1837 \\
8 & 1983 & 5446 & 980 \\
9 & 3454 & 3126 & 562 \\
10 & 6375 & 1693 & 304 \\
11 & 12,669 & 852 & 153 \\
12 & 23,372 & 462 & 83 \\ \hline \hline \end{tabular}  
* under perfect synchronization, ** pure ALOHA access.  
\end{table}
Table 8: Capacity of a single LoRaWAN base station (reported as number of allowed motes per channel) in a realistic scenario, corresponding to twelve consecutive uplink messages of 39 B each, and one downlink message of 35 B, with CR = 4/5 and BW = 125 kHz. CR: Coding Rate, BW: Bandwidth, \(\sum T_{\text{OA}}\): Sum of the Time-on-Air per each node over the considered refresh period \(T\).
## 2 Classification of LVRT Techniques  
External retrofit techniques can effectively solve these problems and enhance the LVRT capability by installing devices to DFIG-WTs. Different device topologies and installed locations determine the  
Figure 3: Reactive power requirement as per E.ON grid code.  
way external retrofit techniques work. For example, the rotor-side crowbar circuit is used to restrain rotor current increase and protect the RSC [17], while a DC-chopper can limit the overcharge during grid voltage dips [18]. According to the above factors, external retrofit techniques are classified into two parts: rotor-side external retrofit techniques and stator-side external retrofit techniques. Internal control techniques are prioritized choices for new installations of wind turbines [19]. Through properly designed control strategies, internal control techniques make full use of the DFIG's own capability to enhance the LVRT capability of DFIG-WTs. Thus, they are more economical by avoiding extra hardware devices. Many advanced control techniques are proposed to provide better dynamic performances and enhance the LVRT capability of DFIG-WTs.