esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Civil infrastructure (e.g., bridges and buildings) is highly susceptible to damage when subject to large external load events, such as high winds or earthquakes. Active structural control is an attractive method for mitigating this undesired response in such structures as it applies counterbalancing forces using an actuating device [1]. Such systems require a high level of integration between sensors that measure the response of the structure (e.g., displacement, velocity, and acceleration), computational nodes that determine appropriate reactions, and actuators that apply these counteracting forces. While active control methods have been successfully implemented in civil infrastructure [2, 3, 4], the successful coordination of communication between nodes and the necessary rapid response at the computational node have proved to be challenging. In order to increase the flexibility of these systems, the research community has turned to using low-power computing nodes equipped with wireless communication capabilities for the three control tasks (i.e., sensing, computing, and actuating) [5, 6, 7].  
Wireless sensing units (WSUs) are individualized data acquisition units that are typically comprised of several key components. In particular, they contain an on-board microcontroller which enables localized data processing and allows them to serve as the controller in the actuation network. They are equipped with analog-to-digital convertersthat allow them to seamlessly interface with transducers and serve as a sensing node. They also contain digital-to-analog converters or pulse width modulation output, such that they can interface with actuators and act as the actuation node. Finally, their on-board wireless transceiver enables peer-to-peer communication to allow for information exchange across the network. While the wireless technology does streamline communication, it also creates additional challenges such as a higher probability of data loss, which can degrade the control effectiveness [7]. Furthermore, while the on-board microcontroller does increase the flexibility of the unit, complex control algorithms can inhibit the real-time control capabilities of the system due to the limited computational capacity of the unit.  
This work focuses on addressing these challenges by using a simplistic weighted-sum control algorithm that is designed to shift the computational demand to the sensing nodes and enable real-time control, while also minimizing communication. To achieve this, a method for determining weights in the control algorithm is needed and it is proposed to pair the particle swarm optimization (PSO) method with pruning mechanisms to further streamline the control law. This paper is structured as follows: (1) in Section 2, a literature review is presented; (2) in Section 3, the particle swarm optimization algorithm and the weighted-sum control algorithm are introduced; (3) in Section 4, the benchmark structure is defined and the algorithm is applied; (4) in Section 5, a pruning technique with PSO retraining is applied; and (5) Section 6 concludes the paper with a discussion.
## 3 System Model and Detail Design
### Adaptive UE Aggregation
```
input : Threshold SNR \(\gamma_{\text{th}}\) and SNR of any two devices \(\gamma_{\text{UB},i}\)\(\gamma_{\text{UC},ij}\)\(\gamma_{\text{CB},j}\)\((i=1,2,\cdots,N_{\text{U}})(j=1,2,\cdots,N_{\text{C}})\) output : Overall transmission number \(T_{i}\) of \(i\)th UE \((i=1,2,\cdots,N_{\text{U}})\)
1 for\(i\gets 1\)to\(N_{\text{U}}\)do // calculate transmission number for UB link \(T_{\text{UB},j}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UB},i})\); for\(j\gets 1\)to\(N_{\text{C}}\)do // calculate transmission number for UC link \(T_{\text{UC},ij}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UC},ij})\); // calculate transmission number for CB link (only cooperation node transmits) \(T_{\text{CB},ij}^{1}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UB},i},T_{ \text{UC},ij})/\gamma_{\text{CB},j}\); // calculate transmission number for CB link (cooperation node and UE transmit together) \(T_{\text{CB},ij}^{2}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UB},i},T_{ \text{UC},ij})/(\gamma_{\text{CB},j},\gamma_{\text{UB},i})\); if\(\gamma_{\text{UB},j}>\alpha+\gamma_{\text{CB},j}\)then \(T_{i}\leftarrow T_{\text{UB},i}\); elseif\(\gamma_{\text{CB},j}>\beta+\gamma_{\text{UB},i}\)then \(T_{i}\leftarrow T_{\text{UC},ij}+T_{\text{CB},ij}^{1}\); else \(T_{i}\leftarrow T_{\text{UC},ij}+T_{\text{CB},ij}^{2}\)  end if  end for  end while
```  
**Algorithm 3**Adaptive UE aggregation.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 3 Research on Mechanical Performance of Porous Asphalt Mixture
### Research on Mechanical Performance of High-Viscosity Modified Asphalt Mixtures  
This study used SBS-modified asphalt and high-viscosity modified asphalt to prepare two types of porous asphalt mixtures. The porous asphalt mixture prepared with high-viscosity modified asphalt was designated as 10-A, while the mixture prepared with SBS-modified asphalt was designated as 10-B. High-temperature rutting tests, low-temperature beam tests, freeze-thaw splitting tests, and two-point fatigue tests were conducted on both mixtures to verify whether the mechanical performance of the porous high-viscosity modified asphalt mixtures meets the engineering requirements.
## 3 On-Site Measurements
### Embankment Section
#### 3.2.1 Admittance Measurements Using the Impact Tester  
During a scheduled maintenance closure, the impact tester (falling mass 50 kg, drop height 1.25 m) was placed at 11 positions directly on the ballasted track. Multiple impacts were induced on the ballast between the concrete sleepers. In this survey, the use of a vibration generator was inhibitive due to practical and budgetary limitations.  
During excitation, the generated vibration velocities were recorded at all geophone points marked in Figure 12, at distances ranging from 13-75 m to track no. 3. Using the measured forces and impressions, the point transfer admittances (average over up to 15 impacts) were determined for each receiver point and integrated to obtain the line transfer admittances. A comparison between the line transfer admittance, \(Y_{line}\), computed through integration of measured \(Y_{point}\) and the line transfer admittance obtained through the simplified approach described in Section 2.1.1 is shown in Figure 13. The diagram shows that a very good approximation of \(Y_{line}\) can be obtained from a single point transfer admittance measurement, suggesting that the number of survey points can be reduced significantly without information loss.  
Figure 11: Force density spectra of different train categories, depending on their velocity.
## 2 Distributed PV System and Proposed MPPT Algorithm
### Distributed PV System
#### 2.1.3 Partial Shading and Its Effects  
Under ideal operating conditions, the characteristic curve of photovoltaic cells is shown in Figure 3a. However, the operation of photovoltaic cells under actual working conditions will be affected by various external factors. PV cells are usually partially or completely covered by adjacent buildings, trees, and clouds. Under PSCs, the PV curve of photovoltaic cells will become very complex and have multiple peaks. One peak is called the global MPP (GMPP), while the other peaks are called local MPPs (LMPP) [26]. The photovoltaic cell JY-210-A parameters are shown in Table 3.  
In this simulation, a PV consisting of four rows and three columns is built, and each PV module is composed of four PV cells. The PV model and P-V curves are shown in Figure 5. PV model temperature is set to either 25 \({}^{\circ}\)C or 30 \({}^{\circ}\)C. Each photovoltaic module is inserted into the anti-counterattack diode to ensure the stable operation of the battery.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Value** \\ \hline Frequency of switching & 50 Hz \\ C & \(202.55\times 10^{-6}\) F \\ L & \(8.578\times 10^{-3}\) H \\ R & 20 Ohm \\ \hline \hline \end{tabular}
\end{table}
Table 2: DC/DC boost converter parameters.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **PV Modules** \\ \hline Maximum Power & 213.15 W \\ Peak power voltage (Vmpp) & 29 V \\ Peak power current (Impp) & 7.35 A \\ Open circuit voltage (Voc) & 36.3 V \\ Short circuit current (lsc) & 7.84 A \\ Current temperature coefficient (ki) & \(-0.36099\)\%/\({}^{\circ}\)C \\ Voltage temperature coefficient (kv) & 0.102 \%/\({}^{\circ}\)C \\ No. of cells & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PV cell JY-210-A parameters.  
In order to verify the proposed method, constant irradiance (pattern 1) and variable PSCs (pattern 2, pattern 3, and pattern 4) were set. The irradiance settings for three patterns are shown in Table 4. By setting different irradiance levels for each photovoltaic module (G11, G12, G13, etc.), sudden changes in irradiance were controlled through step functions.  
Figure 5: Partial shading analysis of the model PV system. (**a**) Shading patterns, (**b**) P\(-\)V curves under PSC.  
Pattern 1 has only one GMPP and no LMPP. It is worth noting that there are three LMPPs and one GMPP in pattern 2, three LMPPs and one GMPP in pattern 3, and three LMPPs and one GMPP in pattern 4.
# Molecular Dynamics-Based Study of Graphene/Asphal Mechanism of Interaction
## 2 Models Establishment and Simulation Methods
### Molecular Dynamics Simulation Methods
#### 2.2.3 Binding Energy  
Interfacial energy is essential for evaluating the mixing ability and compatibility between components in a mixing system [25]. Generally speaking, the total energy of a mixed system is always lower than the sum of the components' energies used to maintain the system's stability; the interfacial binding energy and the interaction energy are opposite [22; 23]. The interfacial binding energy in this study refers to the energy barrier that needs to be overcome to separate different types of graphene from asphalt. The larger the interfacial binding energy, the higher the interaction strength between the graphene and the asphalt system. The interfacial bind energy is calculated as shown in Equation (3).  
\[\Delta E_{binding}=E_{Asphalt}+E_{Modifier}-E_{Modifier/Asphalt} \tag{3}\]  
where \(\Delta E_{binding}\) is the interfacial binding energy; \(E_{Asphalt}\) is the total energy of the system containing only asphalt components; \(E_{Modifier}\) is the total energy of the system containingonly modifier; \(E_{Modifier/Asphalt}\) is the total energy of the system containing both asphalt components and modifier.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 6 Conclusions  
Based on the measurement principle of LDV, this paper proposes a dynamic measuring method for the laser beam incident angle of LDV, establishes a high precision dynamic measuring device, and analyzes the errors of the proposed dynamic measuring method and device from four key aspects: environmental noise, measurement point position error, roll angle, and pitch angle of the rotating disk. The main research conclusions are as follows:  
Figure 17: Velocity curves corresponding to different \(\alpha_{x}\).  
1. The traditional method can measure and calibrate the incident angle of the LDV laser beam with high precision but requires an extensive spatial range. In contrast, the dynamic measuring method proposed in this paper utilizes the vibration measurement principle of LDV, overcomes the shortcomings of the traditional method to a large extent, and can also achieve a high precision measurement of the laser beam incident angle of the space-limited LDV;
2. Affected by many factors, the actual measured value of LDV is not constant but has a high signal-to-noise ratio. Using the average value instead of the actual measured value of LDV can reduce the measurement error caused by the environmental noise and the roll angle \(\alpha_{y}\) of the rotating disk;
3. The position error of the laser spot will significantly impact the measuring results of the incident angle. When the ratio of the error radius of the actual laser spot to the expected radius is less than or equal to 0.026, the error of the measuring result can be guaranteed to be less than 0.05\({}^{\circ}\);
4. The pitch angle \(\alpha_{x}\) of the rotating disk directly determines the component of the circumferential velocity of the measuring point along the laser beam, and is the most critical factor affecting the measuring result of the incident angle. A high-precision inclinometer can be used to correct the measurement results, or an angle fine-tuning device can reduce the angle between the rotating disk and the horizontal plane to improve the dynamic measuring results.  
Conceptualization, Y.L. and G.J.; Funding acquisition, Y.L. and G.J.; Investigation, Y.L., X.L. and H.A.; Methodology, Y.L. and G.J.; Software, X.L. and H.A.; Supervision, G.J.; Validation, Y.L., X.L. and H.A.; Visualization, H.A. and Y.L.; Writing--original draft, Y.L. and H.A.; Writing--review and editing, Y.L. and G.J. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Fundamental Research Funds for Central Public Welfare Research Institutes of China under Grant 2022-9045, the China Postdoctoral Science Foundation under Grant 2022M721428, and the Pilot Program for Advanced Transportation Systems of China under Grant QG2021-4-20-1.  
Not applicable.  
The authors thank the anonymous reviewers and editors for their constructive comments on this manuscript.
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
###### Abstract  
The querying of large XML data over wireless broadcast channels can reduce bandwidth utilization, cause significant latency, and produce inefficient energy usage. This paper proposes a scheme to improve XML querying over wireless broadcast channels in order to address the issues mentioned above. Various techniques, including partitioning, load balancing, and query routing, have been combined into one approach. The proposed scheme partitions the XML data stream into several partitions based on criteria like document size, type, or content. Each partition is routed to a separate channel to balance the load on each wireless broadcast channel. A query routing mechanism that directs queries to the right channel or combination of channels that hold the relevant XML data partition was implemented. This study simulates, evaluates, and compares the proposed scheme's performance. The results from the comparison study with existing schemes demonstrate a considerable reduction in the access time for XML querying via wireless broadcast channels.  
XML; querying; wireless broadcast channels; partitioning; load balancing; query routing; result aggregation; caching; access time +
Footnote †: journal: Accepted: 7 November 2024
## 3 Results and Discussions
### Theoretical Analyzation of SEE Processes in a Double-Layer Structure
\(A_{1}\) and \(A_{2}\) are defined as the primary electron absorption constant, which represents the ability of the material to prevent the primary electron movement in its internal, and this parameter is directly positively correlated with  
Figure 7: Sketch map of SEE physical processes for an ideal double-layer structure. the physical density of the material. The parameter \(n\) is the power exponent used for SEE semi-empirical model fitting. The value of \(n\) given by [PERSON] in 1956 was 1.35 [31]. Later, other scholars proposed that the value of \(n\) was different due to the shape of SEY curve based on experimental fitting [32]. \(d_{\rm m1}\) and \(d_{2}\) respectively represent the maximum penetration depth of initial electrons in the top layer film and the bottom substrate, which is positively correlated with the energy of initial electrons when they just enter the material, and negatively correlated with the parameter \(A\). The formula of \(d_{\rm m1}\) is as follows:  
\[d_{\rm m1}=E_{\rm p}^{n}/A_{1}n \tag{5}\]  
The calculation of parameter \(d_{2}\) needs to consider the remaining energy when the primary electron reaches the interface #2. Therefore, according to Equation (5), the calculation formula of \(d_{2}\) can be expressed as follows:  
\[d_{2}=(E_{p}^{n}-A_{1}nd_{1})/(A_{2}n) \tag{6}\]  
In addition, according to the energy loss process of the primary electrons, we are able to derive the formula of SEY contributed by the top layer film and the bottom substrate respectively for the primary electrons that can penetrate the film. Here, we utilize \(\delta_{1}\) and \(\delta_{2}\) to represent the SEY induced by the top layer film and the bottom substrate respectively, \(\delta_{\rm t}\) equals to the sum of \(\delta_{1}\) and \(\delta_{2}\). The calculation formulas of these two parameters are:  
\[\delta_{1}=C_{1}-C_{1}\exp(-d_{1}/\lambda_{1}) \tag{7}\]  
\[\delta_{2}=C_{2}B_{1}[1-\exp(-d_{2}/\lambda_{2})]\exp(-d_{1}/\lambda_{1}) \tag{8}\]
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 2 Instrument Description
### Antenna Cylinder Assembly Description  
In the same way as MTP [15; 17], MTHP performs continuous multi-incidence angle scanning of the atmosphere and its internal calibration targets (i.e., RF absorbent) using a servo motor that is connected to a rotating cylinder (cylinder assembly) that houses the main receiving antenna. The scanning antenna is a paraboloid that directs the signal to a linearly polarized horn antenna. However, as the MTHP is dual-band, two paraboloids and two horns are used, one per band (i.e., 60 GHz and 183 GHz). The rotor moves the paraboloids 360\({}^{\circ}\) in the incident angle direction, allowing one to scan the entire atmosphere from nadir to zenith and back. As the antenna rotates, it 'observes' two loads located internally. These loads are used for calibration purposes. Figure 2 shows the different blocks composing the cylinder assembly, servo motor, paraboloids, and horn feeds. Note that the two paraboloids are pointing with an offset of 180\({}^{\circ}\) with respect to the cylinder assembly revolution axis one from the other, and the feeds are mounted on opposite sides of the instruments, as shown in Figure 2. The rotor performs one rotation every 1.62 s or 225\({}^{\circ}\)/s.  
Figure 1: MTHP main block diagram. On the left-hand side of the diagram is the MTHP canister, mounted under the wing of the airplane. On the right-hand side is the control computer that controls and collects telemetry of the 60 GHz and 183 GHz back-end modules, mounted inside the aircraft cabin.  
The MTHP's calibration targets are at two different temperatures to provide two calibration points. One of the targets is thermally controlled to a temperature of 333 K, and the second target is left at an ambient temperature and is continuously monitored using thermocouples.
# Dual-Polarization Conversion and Coding Metasurface for Wideband Radar Cross-Section Reduction
## 1 Introduction
One approach to generating multiple coding elements with significant phase changes is by altering the structure's size or deploying different components. However, this approach increases design complexity and requires considerable time and effort [38]. In contrast, the Pancharatnam-Berry (PB) phase development enables the necessary phase difference to be achieved simply by rotating an anisotropic structure, without the need for a wide range of isotropic elements or changes in specific structural dimensions. Numerous studies have utilized this idea to reduce the RCS, as reported in the literature [33; 34; 35; 36]. Based on the above discussions, we propose a simple circular ring MS design capable of achieving linear polarization conversion within the central frequency band while accomplishing linear-to-circular conversion at lower and higher frequency ranges. In pursuit of this objective, our initial step involves designing a structure endowed with wide-angle and polarization insensitivity attributes. Later, employing the PB phase, numerous meta-atoms arranged in a randomized array sequence were utilized to fabricate a 1-bit coding metasurface for reducing the radar cross-section. Numerical simulations, complemented by experimental measurements, validate that the 1-bit CM structure can attain a reduction of over 10 dB in the RCS for a single wideband at the central frequency, along with LP-CP conversion within the two sidebands' window. Our design is ideally suited for deployment in stealth applications across various \(K\)-bands.
## 5 Conclusions  
In this paper, a mesoscale computational approach for SRMs with the consideration of the breakage of rock particles is proposed. The proposed computational approach adopts a random SRM geometry generation algorithm, which can take into account the shape content, gradation, and fractal dimension in the generation process. Based on the generated SRM structure, the simulation area is discretized to calculate the material distribution matrix. The material distribution matrix is used for the coupling calculation of Matlab and COMSOL. Using this approach, we carried out a numerical simulation of a uniaxial compressive test and a triaxial compressive test. Based on the analysis conducted as part of this study, the following conclusions were drawn:  
1. In contrast to other related studies, the proposed computational approach in this study not only employs randomly generated geometric models but also takes into account the breakage of rock particles under loadings;
2. The numerical simulation results of the uniaxial compressive test show that, in the SRM structure, the rock particles bear the majority of the load, and the stress concentration phenomenon is observed at the edges of the rock particles. Due to the stress concentration, large rock particles break down into several smaller rock particles and the stress is redistributed;
3. The stress-strain curve of the numerical simulation is in good agreement with the results from the experiment. This result indicates that the proposed computational approach can accurately characterize the mechanical behavior of SRM material.  
Figure 8: Comparison between the numerical simulation and experimental data of the triaxial compressive tests under different confining loadings.  
**Author Contributions:** The authors confirm contribution to the paper as follows: study conception and design: [PERSON], H.Y. and Z.Z.; data collection: Y.X. and G.L.; data analysis: Z.L. and G.L.; draft manuscript preparation: Z.Z., Z.L. and Y.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The experiments described and the resulting data presented herein, unless otherwise noted, were funded under the Natural Science Foundation of Hebei Province (Grant No. E2021508026) the Fundamental Research Funds for the Central Universities (Grant No. 3142023013).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data supporting this study are included in the paper.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.