esa-sceva/satcom-chunk-collection huggingface dataset:
## Working Principle  
### Sensor Structure  
The sensitive constituents of the sensor under consideration have been meticulously selected from hard ceramic material with piezoelectric properties, namely PZT (lead zirconate titanate) disc, which has a low dielectric loss factor and high mechanical quality factor. The PZT coating thickness measures 0.1 mm. The comprehensive parameters of the ceramic disc are presented in Table 1, whereas the mechanical dimensions are illustrated in Figure 1.  
The loss detection sensor comprises a total of eight parts arranged from top to bottom. These components include (1) an impact plate, (2) a piezoelectric ceramic vibration plate, (3) a buffer connecting mechanism, (4) counter-weight metal connecting mechanism, (5) a two-stage buffer damping mechanism, (6) a data processing module, (7) dust protection shell, and (8) fixing bolts. The sensor configuration is graphically presented in Figure 2. The PZT ceramic disc is glued with GSE T-8000 adhesive in the central position below the impact plate. The first stage vibration reduction buffer mechanism is situated immediately below the impact plate. The counterweight metal is positioned under the first-stage buffer structure, while the second-stage buffer vibration reduction mechanism is located below the counterweight metal. Finally, the sensor's dust protection shell is located at the bottom to ensure proper protection for the internal components of the sensor.  
When the impact plate is struck by moving grains, vibrations are transmitted to a piezoelectric ceramic-sensitive element situated below it. According to the positive piezoelectric effect principle, the piezoelectric sensitive element generates an electric charge, and the quantity of charge is dependent on both the impact force and inertial force of the impact plate. Due to the small amount of charge generated by piezoelectric sensing components, direct measurement is not possible. Consequently, a charge amplificatio  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Item** & **Parameter** \\ \hline
1 & Piezoelectric strain coefficient (P/N) & 300 \\
2 & Dielectric loss tg \(\delta\) & \(\leq\)0.5\% \\
3 & Resonance frequency (KHz) & 80 \(\pm\) 5\% \\
4 & Resonance impedance (\(\Omega\)) & \(\leq\)5 \\
5 & Diameter and thickness (mm) & 20 \(\pm\) 0.1; 1 \(\pm\) 0.05 \\ \hline \end{tabular}
\end{table}
Table 1: Piezoelectric ceramic disc parameter table.  
Figure 1: Piezoelectric ceramic disc size structure diagram.  
convert and amplify the charge signal into a voltage signal that can be measured. When different objects, (e.g., grain grains, straw, etc.) are affected by their mass, speed, and hardness. Additionally, the contact action time and force size between the grain and the impact plate varies. Loss detection can be achieved through either the action time or force size.
# Towards the Internet of Smart Trains:
## 2 Communication Systems in Railway Scenarios
### Infrastructure-to-Infrastructure  
Infrastructures are connected in real-time and require bidirectional links with high data rates and low latencies. The information is transferred between the cameras or the IoT infrastructure and the APs deployed on the trains, stations, platforms, and the wayside along rail tracks.  
Table 2 reviews the main characteristics of the technologies that are commonly used in the scenarios previously described.
## 3 \(V_{oc}\) Losses Analysis
### \(V_{oc}\) Losses Calculation  
The theoretical maximum open circuit voltage achievable in a 20 \(\upmu\)m silicon solar cell is 767 mV, when both surface recombination velocities are 0 cm/s. However, the front n type surface and the rear p\({}^{+}\) emitter surface of the UTSi solar cell are passivated by SiO\({}_{x}\)N\({}_{y}\) and SiO\({}_{2}\) respectively, and neither SiO\({}_{x}\)N\({}_{y}\) nor SiO\({}_{2}\) are the perfect passivation layers; thus, there is surface recombination on each surface. This section calculates the \(J_{0}\) of the UTSi solar cell after introducing the surface recombination. According to Table 2, the SRV value of the SiO\({}_{x}\)N\({}_{y}\) passivated n front surface is less than 13 cm/s; According to Table 3, the SRV value of the SiO\({}_{2}\) passivated p\({}^{+}\) rear surface is 1551-1677 cm/s, and 1640 cm/s is chosen to calculate the new \(J_{0}\) for rear emitter (\(J_{0 es}\)) because it is very close to the mean value and also consistent with the value reported by King. The rest parameters in this calculation are the same as those in the calculation of Table 1. The \(J_{0}\) values for each layer and the corresponding \(V_{oc}\) are shown in Table 4. The \(V_{oc}\) decrease from 767.0 mV to 710.1 mV is caused by the recombination on the SiO\({}_{x}\)N\({}_{y}\) passivated surface. Since a maximum SRV is used in this calculation, 710.1 mV is a minimum value; in other words, a higher \(V_{oc}\) at this step is possible, such as when the front SRV is 5 cm/s, the corresponding \(V_{oc}\) is 725 mV. \(V_{oc}\) decrease from 710.1 mV to 687.0 mV is caused by recombination on the SiO\({}_{2}\) passivated p\({}^{+}\) surface.
## 2 Materials and Methods
### Communication Model  
Regarding the acquisition and the release of the information regarding the angle between two arriving signals (AOAI), on the condition of their containing little valid information in signals, we have formulated three functions for quantification: the monitoring function (\(FM\)), calculation function (\(FC\)), and persistence function (\(FP\)).  
\(FC\) is mainly used to record the switch of the action and distinguish the signal batch at the same time.  
\[FC(t)=\left\{\begin{array}{ll}0\lor FC(t-1)\wedge T_{d},&\text{no signal received}\\ 1\lor FC(t-1)\wedge T_{d},&\text{signal received}\end{array}\right., \tag{9}\]  
where \(\wedge\) is the logical operation AND, \(\vee\) is the logical operation OR, 0 represents the end of state, and 1 is the beginning of it. Among these, \(T_{d}\) is the specified cut-off time within which signals arriving are grouped into the same batch and its specific value is related to \(FP\) by  
\[T_{d}(t)=T_{d}(t-1)+FP(t)+t-t_{0}, \tag{10}\]  
where \(t_{0}\) is the time when listening state started and the function expression of \(FP\) is as follows:  
\[FP(t)=\
eg FC(t-1)\wedge FC(t)\cdot\triangle t, \tag{11}\]  
where \(\
eg\) is the logical operation NOT, and \(\triangle t\) is the longest span in time of the same batch of signals specified in advance. Then, let us quantify the specific \(FM\) by  
\[FM(t)=\theta_{s}(t)\cdot FC(t), \tag{12}\]where \(\theta_{s}(t)\) represents the angle of arrival of the signal at time \(t\). Finally, we use matrix \(AS\) to store the angle between signals:  
\[AS=\begin{pmatrix}|FM(1)-FM(1)|&\cdots&|FM(1)-FM(n)|\\ |FM(2)-FM(1)|&\cdots&|FM(2)-FM(n)|\\ \vdots&\ddots&\vdots\\ |FM(n)-FM(1)|&\cdots&|FM(n)-FM(n)|\end{pmatrix}, \tag{13}\]  
When in different environments, the signal will receive interference and attenuation of model strength. So, we add offsets to AS to simulate real-life errors:  
\[ASX=AS+EX, \tag{14}\]  
where \(EX\) is an equal-sized matrix with only 0 or 1 internal elements to represent the impact of the environment, in which 0 means the signal cannot be received correctly because its strength is attenuated or interfered with and 1 means a signal is received. Therefore, on the basis of obtaining AOAI, the batch calculation can be performed according to the above method.
## 1 Introduction
[24] provided an extensive evaluation of the RT-PPP performance of the PPP-B2b products in terms of matching characteristics, product accuracies, and availability. The results showed that the RMSE of the MEC satellite orbit in radial, along-track, and cross-track components were 6.8 cm, 33.4 cm, and 36.6 cm, respectively, and the accuracy of the clock was 0.2 ns. The availability of BDS-3 PPP-B2b products is over 80%. The PPP test results presented that the accuracies of BDS-3 dual-frequency kinematic RT-PPP after convergence were 11 cm and 17 cm in horizontal and vertical components. In addition, the positioning accuracy of the B1C/B2a IF combination PPP was better than that of the B1I/B3I IF PPP. [PERSON] et al. [25] evaluated the accuracy of PPP-B2b products and the corresponding RT-PPP, and the results showed that the real-time orbit accuracy is about 7.25, 24.79, and 25.87 cm in radial, along-track, and cross-track components for BDS-3 satellites, and 7.29, 30.98, and 21.93 cm for those of GPS satellites. The STD of the clock offset errors for BDS-3 and GPS are within 0.2 ns and 0.15 ns, respectively. The positioning accuracy of real-time BDS+GPS integrated static PPP was about 1.07 cm, 2.69 cm, and 2.25 cm in the N, E, and U directions, and the kinematic PPP of those were about 3.6 cm, 5.9 cm, and 9.4 cm in the N, E, and U directions. Earlier studies evaluated the PPP-B2b performance using the dual-frequency PPP with static and simulated kinematic mode. However, the PPP-B2b RT-PPP based on the practical kinematic data has hardly been studied, especially using the BDS-3 new signals \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Information** & **Message Type** & **Sample Rate (s)** & **Nominal Validity (s)** \\ \hline Satellite mask & 1 & 48 & - \\ Orbit correction and & 2 & 48 & 96 \\ User range accuracy & 3 & 48 & 86,400 \\ Differential code bias & 4 & 6 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The PPP-B2b message information. (B1C and B2b). Therefore, this contribution will present a comprehensive performance evaluation on the real-time orbit and clock products provided by broadcast ephemeris, BDS-3 PPP-B2b, CAS, DLR, GFZ, and WHU, as well as the ultra-rapid products from WHU. The performance of the kinematic dual-and single-frequency RT-PPP is analyzed with B1C+B2b observation data collected in Beijing, China. The paper is arranged as follows. The recovery method of real-time orbit and clock offset and the mathematical models of dual-/single-frequency PPP are described in the next section. Then, the accuracy of the orbit and clock offset provided by broadcast ephemeris, PPP-B2b, CAS, DLR, GFZ, and WHU are evaluated separately. The positioning accuracy with dual-/single-frequency real-time dynamic PPP is afterward assessed, and the conclusions about the accuracy of real-time satellite orbit and clock products and their impacts on dual-/single-frequency PPP are drawn.
# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
## 6 Conclusions  
In this study, the fracture zone in front of the tunnel face was recognized based on the seismic and electromagnetic wave characteristics. The method is used to identify the fracture zones in front of the tunnel face before tunnel excavation, which improves the safety of tunnel construction, allows for better planning of project progress, improves the work efficiency, and reduces the extra costs caused by unanticipated fracture zones. The following are the main contents of this study.  
The difference in the wave impedance of seismic waves in cracks and rock masses leads to changes in reflected waves during the propagation of seismic waves. When local seismic waves propagate to the fault zone of the surrounding rock, the amplitude of the superimposed reflection signal is used to locate the positive and negative strong reflection areas. The area of the fracture zone is determined according to the characteristics of the seismic waves. The reflection coefficient of electromagnetic waves propagating into the air is opposite to that of waves injected into the surface of the tunnel, and the fault zone area shows an increased amplitude in the A-scan signal, with a higher center frequency compared to the intact surrounding rock area. The fracture zones are identified based on the characteristics of electromagnetic waves. The identification results of seismic waves and electromagnetic waves in the fracture zones are consistent with the excavation results of the Liangwangshan Tunnel, achieving the advanced prediction of the fracture zones.  
Conceptualization, C.L. and H.W.; methodology, C.L., H.W. and L.W.; software, H.W.; validation, X.Y. and Y.W.; formal analysis, H.W.; investigation, L.W.; data curation, X.W., Y.W. and X.Y.; writing--original draft preparation, H.W.; writing--review and editing, C.L.; project administration, C.L. and X.W.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Photograph of the fracture zones during tunnel excavation.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 62263015).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality requirements of the project.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] were employed by the company Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15  
C-V2X has its foundation in the enhancement applied to LTE, as specified in 3 GPP Rel-14, to support vehicle connectivity for mostly safety related use cases. This involves use of both short-range direct connectivity and long-range connectivity via existing mobile network infrastructure. Network communications operate over licensed spectrum and messages are transmitted to user equipment (UE) via Evolved Node B (enodeB) using the Uu interface. In contrast, direct communications allow vehicles to directly exchange information using the PC5 interface [1]. 3 GPP Rel-15 introduced a set of enhancements for Rel-14 LTE-V2X PC5 and 5G New Radio (NR) technology for the Uu interface. The use of 5G NR leveraged the benefits of increased capacity, reduced latency and improved reliability enabling the environment for more advanced use cases in autonomous mobility that typically require timely and reliable transfers of vast amount of data. However, 3 GPP Rel-15 is only an introductory point for 5G NR in C-V2X; specifications of further enhancements are ongoing or were almost complete in subsequent releases. In this section, we highlight some of the new or continued enhancements in 3 GPP releases that further strengthen 5G NR's robustness as a C-V2X solution for advanced automotive use cases in various contexts, including cross-border scenarios.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 5 The \(D^{3}\)-Tree
### Fault Tolerance  
Searches and updates in the \(D^{3}\)-Tree do not tend to favour any node and in particular nodes near the root, which are therefore not crucial, and their failure will not cause more problems than the failure of any node. However, a single node can be easily disconnected from the overlay, when all nodes with which it is connected fail. This means that four failures (two adjacent nodes and two children) are enough to disconnect the root. The most easily disconnected nodes are near the root since their routing tables are small in size.  
When a node \(w\) discovers that \(v\) is unreachable, the network initiates a node withdrawal procedure by reconstructing the routing tables of \(v\), in order \(v\) to be removed smoothly, as if \(v\) was departing. If \(v\) belongs to a bucket, it is removed from the structure, and the links of its adjacent nodes are updated.  
In case \(v\) is an internal binary node, its right adjacent node \(u\) is first located, making use of Lemma 1, in order to replace \(v\). More specifically, we assume that node \(w\) discovered that \(v\) is unreachable during some operation.  
Considering all possible relative positions between \(w\) and \(v\), we have the following cases, in which we want to locate the right child \(q\) of \(v\) that will lead to \(u\). First, if \(w\) and \(v\) are on the same level, by Lemma 1(i) we locate \(q\) and thus the right adjacent node \(u\) of \(v\) is the leftmost leaf in the subtree of \(q\). To be more clear, if \(w\) is connected to \(v\) by the \(i\)-th link of its routing table, then its right child is connected to \(q\) by the \((i+1)\)-th link of its routing table.  
Second, if \(w\) is the father of \(v\), by Lemma 1(i) its left (right) child \(p\) has a link to the missing node \(v\) and the right child of \(p\) has a link to \(q\), so \(u\) is located. Third, if \(w\) is the left (right) child of \(v\), then \(u\) is easily located.  
If \(v\) is a leaf, it should be replaced by the first node \(u\) in its bucket. However, in \(D^{3}\)-Tree predecessor, if a leaf was found unreachable, contacting its bucket would be infeasible since the only link between \(v\) and its bucket would have been lost. This weakness was eliminated in \(D^{3}\)-Tree, by maintaining multiple links towards each bucket, distributed in exponential steps (in the same way as the horizontal adjacency links). This way, when \(w\) is unable to contact \(v\), it contacts directly the first node of its bucket \(u\) and \(u\) replaces \(v\).  
In any case, the elements stored in \(v\) are lost. Moreover, the navigation data of \(u\) (left adjacent of \(v\)) are copied to the first node \(z\) in its bucket, which takes its place, and \(u\) has its routing tables recomputed.
## 4 The TRAJ-Q-GPSR Algorithm
### Traj-Q-GPSR Algorithm
#### 4.3.1 Definition of State and Action Spaces  
In this algorithm, each UAV node functions as an agent whose state representation comprises both physical attributes and network context. Specifically, the state includes the node's current position, velocity, and acceleration, along with two-hop neighbor information obtained from shared trajectory data. Additionally, the queue status is incorporated to reflect the current load of the node. The state space is formally defined as follows:  
\[s=\Big{(}p_{i},v_{i},a_{i},\mathcal{N}_{i}^{(2)},Q_{i}\Big{)} \tag{22}\]  
where \(Q_{i}\) indicates the current queue length reflecting the load of the UAV. The action space \(A\) defines the decision options available to the UAV in each state. Given the high dynamism of UAV networks, conventional GPSR, which relies solely on the current positions of one-hop neighbors for greedy forwarding, often suffers from local optima and routing voids. Therefore, this study extends the action space to include two-hop neighbor information, allowing UAVs to make next-hop selections with broader topological awareness and achieve improved global routing performance. The action space is formally defined as  
\[A=\{a\mid a\in\mathcal{N}_{i}^{(2)}\} \tag{23}\]
### Objectives  
The paper at hand focuses on item providers that pose requirements for fair item recommendations in terms of the coverage and diversity of users to whom each provider's items are recommended. The notion of fairness here implies that the items of each provider are recommended to a similar number of users as items of other providers, as well as that the users to whom a provider's items are recommended are as diverse as the users that items of other providers are recommended. More specifically, provider fairness (p-fairness) [6] requirements in terms of coverage and diversity are introduced as constraints that need to be fulfilled for each item provider. Hence, average user coverage and diversity per item for each provider should reach at least a certain target, while the expected satisfaction from the provided recommendations should remain as high as possible.  
To this end, the presented recommender system aims at maximizing the total predicted rating of the recommendation lists, as well as satisfying the requirements for fair user coverage and diversity per item provider. Specifically, the problem is formulated as a mathematical optimization problem as proposed in the literature [7]. Moreover, the proposed methodology for the solution of the problem is followed. As the problem is not linear and hence it is not computationally feasible to be solved, and due to the fact that in the literature the solution for diversity is not adequately described, a heuristic algorithm which finds solutions that are very close to the optimal one and also fulfill the diversity constraints is introduced. As a next step, the results and the limitations of the proposed approach are presented in brief, and several weaknesses of the proposed formulation are identified. Moreover, two new mathematical definitions for provider diversity are proposed to address the identified weaknesses of the planned approach by the literature, and a high-level methodology is proposed for the solution of the new optimization problem. Finally, several limitations of the newly proposed approach are concisely presented, along with future extensions for the proposed framework.
## 4 Fatigue Life Estimation  
### Multibody Dynamics Analysis  
To obtain force load history needed in fatigue life estimation of welded A-type frame, a multi-body dynamic model for the whole vehicle was constructed using the ADAMS software for simulating real operating conditions. The truck model and the horizontal surface are shown in Figure 14. A complete description of this modeling can be found in Refs. [1; 13; 14]. To verify the accuracy of this multi-body dynamic model, the acceleration time history of wheel center at the speed 30 km/h obtained from the simulation was compared with the tested results, displayed in Figure 15. The acceleration time history and its frequency spectrum matched were well-matched between them. However, there were several peaks in the tested acceleration frequency spectrum around 30 Hz. It could be caused by the external interferences or operational errors. The differences of the root mean square acceleration for the wheel center at different speed between the test and simulation were small, as shown in Figure 16.  
Figure 14: Multi-body dynamics model.  
Figure 13: Low-cycle fatigue fracture morphology.  
When the precision of multi-body dynamic model was guaranteed, the downhill and turning road surface was built to simulate bad working conditions. The starting speed of the truck was defined as 30 km/h, and the turning radius was 60 m, shown in Figure 17. The simulation time was 16.5 s. Then, the force time history of welded A-type frame at connection sites was acquired from multi-body  
Figure 16: Root mean square acceleration of wheel center at different speed.  
Figure 15: Acceleration time history and its frequency spectrum of wheel center at the speed 30 km/h: (**a**) tested results; (**b**) simulated results.  
dynamics analysis, shown in Figure 18, which was considered as cyclic load required for fatigue life prediction.