esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 3 System Architecture
### Packet Transmission Structure  
The main priority of C-OFSMA is increasing the rate of successful packet transmission. Multiple sensors can try to transmit duplicate data at a single time slot, and with the packet diversity principle, if at least one packet reaches the receiver end from \(d_{n}\) duplicate packet, it will be counted as a successful transmission. In Figure 3, a massive subcarrier channel is assigned to increase successful packet transmission. However, some of the sensors can occupy the same subcarrier channels and create collisions. These collided subcarrier channels cannot afford to transmit data. To achieve 99.999% reliability as in [12], massive subcarrier channels are required.However, our scheme is aimed at minimizing the usage of subcarrier channels which is analyzed in Algorithm 1. Then, the challenge is to achieve 99.999% reliability considering fewer subcarrier channels. In Figure 4, the C-OFSMA provides much better performance in terms of successful transmission. In Figure 5, the packet transmission behavior of a single cluster is shown. Here, packet transmission and collision are both performed. Collision is inevitable, only while multiple sensors from one cluster transmit the duplicate packet, occupying the same subcarrier channels and within a single time slot. Therefore, the probability of a collision is reduced as the system rigidly abides by all of these dependent conditions. For audio and video sensors, dedicated bandwidth \(B_{d}\) contains a Dedicated Access Channel (DCA) [12]. DCA provides fixed and unique subcarrier channels to each audio and video sensor to ensure 100% successful transmission. From Equation (6), the complex baseband signal is considered with 2 quarter cycles (180 degrees or \(\pi\) radians) for general sensors. The equation also shows the number of packet increase considering different cluster. So, \(Z_{x}\) clusters for sensors placement with \(d\) packet duplication, the total packet in a single slot will be:  
Figure 3: Collision scenario of OFSMA. \[P_{single-slot}=d\times Z_{x} \tag{9}\]  
However, the higher packet generation increases the chance of collision. Again from Equation (6), the complex baseband signal is considered with 2 quarter cycles (\(\pi\) radians) and considering the number of pilot subcarriers per symbol (\(N_{ref}=4\)) [46] and with \(T\) transmission latency and at \(\lambda\) arrival rate with the number of the packet will be \(4T\lambda\). Then to ensure the reliability of 99.999%, the minimum subcarrier channel \(N\) can be revealed as:  
\[N =e\Bigg{(}\frac{\frac{\pi+collision}{\sqrt{Z_{x}}}}{P_{single-slot} +N_{ref}T\lambda}\Bigg{)} \tag{10}\] \[=e\Bigg{(}\frac{\pi+collision}{d\sqrt{Z_{x}}}+4T\lambda\Bigg{)}\]  
Here, \(T\) is the latency of 0.2 ms and \(\lambda(pkt/s)\) represents different arrival rates.
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 3 Methodology for 5G NR Link-to-System Mapping  
5G K-Simulator [13] is a Korean collaborative research project consisting of several Korean universities and companies to develop a 5G NR simulator. At the start of the project, there was no widely accepted L2S mapping suitable for 5G NR system. Our role was to develop a L2S mapping method as an inter-connection between the LLS and the SLS in 5G K-Simulator. In this section, we deal with considerations for the 5G NR system in Section 3.1, and we describe the preliminary phase to be performed in the LLS simulation for L2S mapping in Section 3.2. Finally, we explain a L2S mapping method for 5G NR systems in Section 3.3.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 6 Experiment and Results  
The performance of the proposed approach is demonstrated in this section via a real-world experiment conducted in downtown Calgary.
# Structural Performance of a Precast Concrete Modular Beam Using Bolted Connecting Plates
## 4 Experimental Results and Analysis
### Load-Strain Behavior  
To investigate the flexural behavior of the test specimen, the strain of concrete, rebar, and connecting plate were analyzed as shown in Figure 12. In the MCF specimen, it was confirmed that the strain of the concrete reached the ultimate compressive strain, 0.003. In the PCF specimen, the strain at failure was not measured because the concrete gauge was damaged. It was considered that the concrete reached ultimate strain because the concrete in the compressive zone was crushed. In addition, it was confirmed that the concrete strain distributions of the MCF and PCF were similar with increasing load. The rebar reached the yield strain of 0.002 in both specimens. It was indicated that the rebar effectively resisted tensile strength and splicing performance was confirmed. According to Figure 12b, tension failure occurred in both specimens due to the yielding of the rebar. The tension failure is a desired failure type in the beam design process. The bolted connecting plate inserted into the PC module exceeded the yield strain of 0.00138. Therefore, the stress between each PC module was transferred effectively through the joint, indicating that this component could resist the tensile strength as well.  
Figure 12: Cont.  
To evaluate the shear behavior, the strain of the stirrup was analyzed as shown in Figure 13. The strain distributions are depicted in Figure 13 when the initial cracks occurred, cracks developed, and load reached approximately peak shear force. In the MCS and PCS specimens, the strain of the stirrup near the loading point, which is located 1050 mm from the end of the beam, increased the most rapidly, as they reached the maximum load; this stirrup yielded at the maximum load. In addition, it was confirmed that the strain of the stirrup located closer to the loading point was greater than that of the stirrup close to the support in both specimens. This is because, in RC beams, shear cracks progressed from the support to the loading point and, generally, strains show a rapid increase as the load approaches the peak shear resistance [40]. According to these results, it was confirmed that the bolted connecting plate could transfer stress to each PC module effectively. This means that corrosion did not occur due to the moisture penetration in the module and non-shrink mortar filled the connecting region tightly. Therefore, it was indicated that the PC modular method using the bolted connecting plate can ameliorate corrosion problems that occur in typical steel modules and difficulties related to injection into grouted splice sleeves used in typical PC module methods.  
Figure 12: Strains of flexural test specimens: (**a**) strain of concrete; (**b**) strain of rebars and plate.  
Figure 13: _Cont._
## 2 Numerical Simulation Method
### Mesh Generation  
The influence of grid quality on the accuracy of numerical simulation is very significant, so the hexahedral grid was generated for the whole computational domain extracted from the 3D geometry model, as shown in Figure 2. The O-topology method and local mesh refinement were used for the impeller blade, front guide vane, and rear guide vane. Due to the high velocity and complex flow pattern in the backflow clearance, there were at least 20 grid nodes in the clearance to accurately capture the flow characteristics. Figure 3 shows the grid independence analysis of the full tubular pump device. The hydraulic efficiency of the full tubular pump device decreases with the increase of grid node. When the number of grid nodes exceeds 8.82 million, the calculation deviation is less than 0.82%. Therefore, the number of grid nodes in the inlet passage, front guide vane, impeller, motor rotor, post guide vane, and outlet passage were finally determined as 1,151,280, 1,157,940, 1,837,080, 1,699,840, 1,395,000, and 1,579,100 million, respectively.  
Figure 1: (**a**) Three-dimensional model and (**b**) impeller section of full tubular pump device.
## 4 Discussion
### Limitations  
The first limitation is that, for each test problem, the MGRIT algorithm must be run with an appropriate initial subinterval partitioning to achieve computational stability. Figure 14 illustrates this phenomenon. This characteristic can be seen as analogous to the choice of the initial step size in sequential algorithms (see [4], Chapter II.4, and [32], Section 4.2).  
The second limitation may be that our current results have provided satisfactory quality only for nonstiff (or at most mildly stiff) problems. This naturally suggests a research direction focusing on stiff problems. Due to their much larger variation in step sizes, they pose a more significant challenge. Based on the structure of MGRIT methods, it is clear that the analogy of PI- and PID-type controllers (4), which are commonly used in sequential algorithms (for details, see [7]), needs reconsideration in this setting. However, from this theoretical background, we also know that for stiff problems, PI controllers of a different parametrization are needed. An example of this can be seen in Figure 15, which illustrates the case of the stiff van der Pol Equation (8) with \(\mu=30\) and initial value \([1.5,3]\) on the time interval \([0,10]\). Using heuristics, we were able to significantly improve the step-size sequence.
## 2 Related Work and Technologies
### Analysis and Decision  
Several authors present comparative studies of different technologies, namely [47; 48], who contributed to the decision regarding the technology to be used in our work. Moreover, in [49], a technical comparison of LoRaWAN and NB-IoT can be found, explaining that LoRaWAN is an open LPWAN system architecture developed and standardized by LoRa Alliance, a non-profit association of more than 500 member companies that operates in the unlicensed spectrum, while, in opposition, the NB-IoToperates in the licensed spectrum. While both technologies can compete on QoS, IoT applications that require more frequent communications are better served by NB-IoT, which has no duty cycle limitations operating in the licensed spectrum, at the expense of higher TCO relative to LoRaWAN.  
We elaborate on our analysis of the options offered by the two main long-range technologies--with the use of mobile networks vs. without the use of mobile networks--in Table 1, which summarizes the main characteristics of the two best options. When there is no mobile network coverage, LoRa technologies were considered the best option due to the several advantages over other technologies, the wide use, robustness, low cost, great ease and availability of equipment and also because they allow total customization and the system can be built entirely from scratch, and integrated into The Things Network. Alternatively, when using mobile networks, it is understood that NB-IoT technology is the one that can best meet the requirements, considering that it can be operated on the future 5G network, when globally available. However, this is not our focus in the present research work, since the studied areas are remote, rural and either do not have mobile network coverage or have poor signals.  
The need for a project based on LoRa networks of low consumption, low acquisition cost and long reach is precisely related to the absence of mobile communications networks in the targeted regions, excluding any solution that implies the use of mobile networks. The existence of mobile network coverage would make possible other solutions. The possibility of using TVWS seems to be a distant reality; nevertheless, the results obtained in [30; 31; 32; 33; 34] are promising, as long as guaranteed commitment from the agents involved and the regulatory entities can be provided.  
Advocated in this analysis, as well as in the works presented in [47; 48; 49], the authors conclude that LoRa technology supported by the LoRaWAN architecture is the one that best meets the requirements.
## 4 Results and Analysis
### Technical Theme Content Evolution
Some technologies have been inherited and developed in the second stage after integrating the technologies of the previous stage, such as battery management system (B-Topic 0, C-Topic 13), garbage cleaning (B-Topic 2, C-Topic 2), solar power generation ( B-Topic 8, C-Topic 1), path planning (B-Topic 10, C-Topic 3). Some technologies appear in the third phase after integrating technologies from the previous phase, such as speed control (C-Topic 5) and simulation technology (C-Topic 14). In order to verify the accuracy of the results of this paper, unmanned boat patent data were collected for the year 2023 to validate the results of this paper. The unmanned ship results derived above were validated by collecting unmanned ship patent data for the study. The feasibility of the technical opportunity to verify the results is obtained in the patent (CN117193321A) for an autonomous decision-making system for unmanned ship navigation and autonomous navigation method. The combination of path planning and autonomous navigation technologies may accelerate the development of intelligent navigation systems in the future. The feasibility of water quality monitoring in the future is verified in the patent (CN116991097A) for a water information intelligent monitoring system based on the unmanned ship technology, and in the patent (CN219565417U) for an intelligent river cleaning boat device, the driving force of the boat is used to automatically collect rubbish on the river surface so as to improve salvage effect. The patent (CN117614310A) for a composite nano power generation device for realizing self-driven power positioning of a ship was verified. The use of renewable energy sources such as solar energy may be a new development direction for unmanned ship energy technology in the future. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage 1 (2013–2015)** & **Stage 2 (2016–2019)** & **Stage 3 (2020–2022)** \\ \hline
**Theme Content** & **Theme Content** & **Theme Content** \\ \hline (A-Topic 0) Remote control & (B-Topic 0) Power Battery & (C-Topic 0) Sampling technology \\ (A-Topic 1) Sensor Technology & (B-Topic 1) Image Processing & (C-Topic 1) Solar Battery \\ (A-Topic 2) Water monitoring & (B-Topic 2) Garbage removal & (C-Topic 2) Garbage removal \\ (A-Topic 3) Autonomous navigation & (B-Topic 3) Water quality testing & (C-Topic 3) Path planning \\ (A-Topic 4) Measurement technology & (B-Topic 4) Hull structure & (C-Topic 4) Intelligent Aquaculture \\ (A-Topic 5) Hull structure & (B-Topic 5) Attitude control & (C-Topic 5) Speed control \\ (A-Topic 6) Drive System & (B-Topic 6) Target Detection & (C-Topic 6) Hull structure \\ (B-Topic 7) Remote control & (C-Topic 7) Remote control \\ (B-Topic 8) Solar battery & (C-Topic 8) Movable platform \\ (B-Topic 9) Advancing technology & (C-Topic 9) Water quality testing \\ (B-Topic 10) Path planning & (C-Topic 10) Propulsion equipment \\ (B-Topic 11) Surveillance system & (C-Topic 11) Surveying and mapping \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thematic content of unmanned ship technology at different stages.
# Similarity Model Test on Rainfall Scouring Mechanism of High-Speed Railway Subgrade Slope
## 1 Introduction
In this study, indoor rainfall erosion model tests were conducted on railway roadbed slopes with different rainfall intensities, slope gradients, and soil particle sizes, clarifying the erosion mechanism of small particle sizes starting first and large particle sizes starting later on the slope surface. The erosion process on the slope was studied by analyzing factors such as the initial velocity and the amount of erosion. This analysis allowed us to quantify the process and validate the theoretical formula for the incipient velocity of soil particles on the slope. Additionally, this study revealed the impact of rainfall intensity, slope gradient, and soil particle size on slope scouring erosion. The relationship between rainfall intensity and particle size under different working conditions was verified, providing a basis for the analysis of slope scouring erosion stability and the slope protection design of the high-speed railway subgrade slope.
## 4 Description of the Field Measurements  
RFD was measured at 156 points distributed along 15 profiles (Figure 4). The principal studies were conducted within and in the immediate vicinity of the known boundaries of the Volchya kimberlite pipe. Furthermore, two profiles were investigated outside the kimberlite pipe, one to the northeast and one to the southwest, at a distance of 0.5 km from the pipe. The distance between the measurement points was 30 m. Meteorological parameters, namely atmospheric air temperature and relative humidity, were recorded during the installation and removal of the accumulation chambers.  
Figure 3: Measuring complex for radon monitoring CAMERA-01 (**a**) (photo taken from the official site of the equipment manufacturer [[https://www.niton.su](https://www.niton.su)]([https://www.niton.su](https://www.niton.su)), accessed on 7 January 2025) and accumulation chamber with carbon sorbent for the following RFD measurement (**b**).  
To implement the passive interferometry method, the signal was accumulated at five points. Four--at the edges of the study area. One--in the center. The distance between the stations was from one-half to one kilometer. The microseismic signal was recorded simultaneously by all stations during the day.  
For the MSM methods, measurements were made along the seventh profile. Profile 1 is located to the south of the pipe. Profile 2 crosses the Volchya southern pipe. Profile 3 passes between the southern and northern pipes. Profile 4 crosses the Volchya northern pipe. Profiles 5 and 6 are located to the north of the pipe. Profile 7 crosses the pipe in the  
Figure 4: Scheme of field measurements.  
meridional direction (Figure 4). Profiles 1-5 coincided with radon profiles but went out at a greater distance from the pipe. At each point, microseisms accumulated over three hours, and the reconciliation time was five hours. The distance between the points was 30 m. The registration of microseisms was carried out simultaneously by the mobile and reference stations. The reference station was located near the intersection of profiles 3 and 7.
## 3 Results
### Case 5 (8 July 2015)  
On 8 July 2015, it was a geomagneticly quiet day with a minimum Dst index of \(-\)11 nT (daily SSN:123). Figure 6a-f provide a detailed depiction of this case study. At approximately 23:20 UT, the ionosphere over Dourbes and Roquetes experienced nighttime irregularities, leading to FSF ionogram signatures observed at Dourbes. In contrast, the Roquetes Digisonde detected several westward-propagating oblique traces, indicative of MSTIDs, as illustrated in Figure 6b. GNSS-derived d-TEC maps further confirmed that the association of SF is consistent with typical MSTID activity over Dourbes and Roquetes.  
At 23:24 UT, Swarm B passed over Dourbes. The corresponding latitude Ne profile of Swarm B, shown in Figure 6e, reveals fluctuations over Roquetes, whereas no notable fluctuations were observed over Dourbes. This event, where bottomside irregularities are not reflected in the topside, has also been reported by [PERSON] et al. 2020 [35], who observed equatorial ionospheric irregularities confined to the bottomside without accompanying topside signatures. This could be attributed to the rapid onset and dissipation of small-scale irregularities, which typically have a spatial extent of only a few tens of kilometres and exist for brief periods (e.g., [47; 48]). Additionally, small-scale irregularities might remain constrained to lower altitudes (e.g., [49; 50]).  
Figure 6f displays dNe, revealing the topside irregularity patterns. Over Roquetes, a significant dNe fluctuation of approximately 1 \(\times\) 10\({}^{4}\) elec/cm\({}^{3}\) was noted. The ROTI map, presented in Figure 6c, demonstrates a strong association between Swarm Ne fluctuations and ROTI variations (>0.15 TECU/min) over Roquetes with an hmF2 at 379 km. This agreement aligns with previous studies by [PERSON] et al. 2016 [34] and [PERSON] et al. 2019 [44], who demonstrated the link between Swarm Ne disturbances and ground-based ROTI measurements.
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
The challenges associated with mandibular bone segmentation can be categorized as follows:  
* The presence of anatomically complex bony structures in the scans. As the examples show in Figure 1a, a normal H&N scan includes other bony structures, with a complex anatomy and a similar density. Determining the correct boundaries and separating the mandibular bone from the other bones may be challenging. * Artifacts. When X-rays pass through high-density structures or materials, including teeth, postoperative metal implants, etc., the signal on the detectors will change, which will lead to attenuation calculation errors in the (cone beam) computed tomography (CBCT/CT) reconstruction process and consequently cause high noise and strong artifacts in the visual impression of the scans [16]. The mandible boundaries nearby teeth tend to be blurred and hard to detect. In particular, the boundaries of mandible rami are difficult to be identified when dental braces and metal implants badly affect the image quality [17], as shown in Figure 1b. Furthermore, the fact that the superior and the inferior teeth are at the same slice and even overlapping that makes segmentation methods challenging, as shown in Figure 1c. * Low contrast. Due to the tomography process and the thinness of condyles, the slices in condyles commonly have low contrast [18], as shown in Figure 1d. Especially in cone-beam CT (CBCT) scans, the condyles in the scans are more blurred than in CT because of its intensity inhomogeneity [19; 20]. * Variation in the appearance. As the examples show in Figure 1e, mandibular shapes vary in shape and size between individuals [18]. Additionally, the shape of the mandibular condyles and body is extremely variable among patients and different age groups [21]. * Annotation Bias. The manual mandible segmentation often leads to inter-observer variability (Dice score of 94.09% between two clinical experts) [22], which directly influences the quality of treatment planning. In the present article, a review has been conducted, where the literature available in PubMed and Web of Science databases relating to these studies was considered. In the literature, a variety of segmentation methods, based on statistical shape model, atlas and machine learning, are proposed for head and neck scanning. The selected publications were divided into several categories depending on the type of method used and discussed extensively.