esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper's research and analysis help the construction team establish the allowable deformation range for loess tunnel design. The main conclusions are as follows:  
1. Through the three correlation analysis methods of Pearson, Kendall, and Spearman, it is revealed that soil type strongly affects arch crown settlement, followed by excavation span, while moisture content and cover depth have a weaker impact. There-fore, deformation allowance design should prioritize soil type and excavation span.
2. In grade IV surrounding rock sections of loess tunnels, for clayey loess and silty loess, vault deformation allowance can be limited to 10 cm, and tunnel side wall deformation  
Figure 17: Analysis of assurance rate of deformation allowance for peripheral.  
allowance to 4 cm. For sandy loess arches, consider a 15-20 cm crown deformation allowance and an 8-12 cm allowance at the tunnel side wall measurement.
3. In grade V loess tunnel sections with clayey loess, consider a 15-25 cm deformation allowance at the crown and 10-15 cm at both sides of the tunnel wall. In silty loess sections, limit deformation to 15 cm at the tunnel crown and 10 cm on both tunnel side walls. For sandy loess sections, allow for 35-47.5 cm deformation at the tunnel crown and 15-20 cm on both tunnel side walls.
4. Moisture content ranges from 6% to 26%, with crown settlement falling between 11% and 23%. Notably, moisture content has a more pronounced impact on sandy loess tunnels compared to cohesive and silty loess sections. Therefore, the deformation allowance for tunnels in sandy loess sections should exceed that in clayey loess and silty loess sections.
5. Finally, in line with the specifications, a recommended deformation allowance value is suggested. The deformation allowance suggested by the real monitoring data and statistical analysis greatly exceeds the standard value. Therefore, considering the specifications, it is advisable to raise the deformation allowance in the loess tunnel section.  
X.Q.: conceptualization, formal analysis, writing--original draft, software. J.Q.: funding acquisition, methodology, visualization. J.L.: resources, supervision, project administration. Y.L.: data curation, investigation, validation. All authors have read and agreed to the published version of the manuscript.  
This research work was jointly supported by the National Natural Science Foundation of China (No. 52208386), the Construction Science and Technology Project of Xi'an (No. SZJJ2019-23), and the Key R&D Plan of Shaanxi Province (No. 2020 SF-428).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We also acknowledge the editor for the valuable suggestions.  
Author [PERSON] was employed by the company China Railway First Bureau Group Urban Investment Operation Management Co. 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.
## Materials and Methods
### Characterization  
MWCNTs and hybrid materials synthesized for the current study were characterized using a number of chemical and physical analysis methods. XRD, TEM, SEM and the energy-dispersive X-ray spectroscopy (EDS) were conducted using equipment of Center \"Physics and technology of micro- and nanostructures\" at The Institute for Physics of Microstructures of RAS, and Raman Spectroscopy--using equipment of the Centre of Collective Use at the Institute of Geology of Komi Science Centre of the Ural Branch of RAS. All samples were studied by NEXAFS spectroscopy using synchrotron radiation from RGBL at BESSY II Storage Ring (Berlin, Germany) [44,45]. XPS studies were carried out using the equipment of the resource center of the Science Park of St. Petersburg State University \"Physical methods for surface research\". The features of experimental studies and the characteristics of used equipment are described in more detail in [42].  
Raman studies were conducted at room temperature using an Ar laser with the power of 1 mW and wavelength of 488 nm. Spectra were recorded by the grating of 600 g/mm in the range 100-4000 cm\({}^{-1}\). The spatial and spectral resolution was 1 \(\upmu\)m and 1 cm\({}^{-1}\), respectively. Each spectrum was the result of three accumulations with a 10 s exposure. The spectra flitting were performed by LabSpec 5.36 software.  
All NEXAFS spectroscopy data were obtained at the Berliner Elektronenspeicherring fur Synchrotronstrahlung (BESSY) using radiation from the Russian-German beamline (RGBL) [44,45]. All spectra were obtained using total electron yield (TEY) mode. For energy calibration the energy positions of the first- and second-order light-excited Au 4f\({}_{7/2}\) lines were used. The flux in arbitrary units was measured by the Au plate. The resolution of photon energy was less than 0.05 eV.  
XPS studies were carried out using AlK\(\alpha\) radiation (1486.6 eV). The C1, O1, W4f spectra were measured at the pass energy of 50 eV and the survey spectra at 100 eV. The studies were performed at room temperature in vacuum of 10\({}^{-10}\) mbar with using the system of an electron-ion charge compensation of the samples  
All Raman, NEXAFS, and XPS measurements were performed for a series of samples at different points for each sample, and their results had good repeatability.
## 2 Experimental Methodology
### Wind Tunnel Setup and Incoming Flow  
The experiments have been conducted in the atmospheric boundary layer wind tunnel of the Ruhr-University Bochum, Germany. The wind tunnel has a cross-section of \(1.6\times 1.8\) m\({}^{2}\) and a test section length of 9.4 m. Figure 2 shows the wooden models mounted on a rotating table in the wind tunnel. The reference velocity \(U_{ref}\) used to normalise the data was measured using a Prandtl tube mounted at the height of the high-rise building model and placed 1 m upstream of the model. Reynolds  
Figure 1: High-rise building model used in the investigation, (a) with a flat roof, (b) with a deck roof.  
number related to all measurements based on the mean velocity at the height of high-rise building and the side of the roof is \(Re\sim 1.4\times 10^{5}\). Horizontal buoyancy is found to be absent in the wind tunnel, as very low decay of the static pressure is measured throughout the measurement section [50].  
The boundary layer flow is generated in the wind tunnel using both spires at the tunnel inlet and roughness elements as shown in Figure 2a. The mean wind profile matches that of a power law with the exponent of 0.2 as shown in Figure 3a. This wind profile is found to be consistent in the lateral direction of the wind tunnel, covering the central portion of the test section. This spans over approximately 0.9 m at least (\(\pm 0.45\) m from the central position), which is outside of the influence of the walls [51]. This is representative of the terrain category II [52] simulating realistic conditions of the flow around isolated high-rise buildings. Further, measured data can be used as an approximation for the flow pattern in case of an urban area with a dominant high-rise building surrounded by low-rise buildings. This arrangement is common on the outskirts of large cities, university campuses displaced from city centres, etc., all representing potential locations for efficient urban wind energy harvesting. The mean stream-wise wind speed (\(U_{\textit{ref}}\)), the stream-wise turbulence intensity (\(I_{U}\)) and the vertical turbulence intensity (\(I_{W}\)) at the height of the model are 16 m/s, 13% and 11%, respectively.  
Figure 3: (**a**) Mean stream-wise wind speed (\(U\)/\(U_{\textit{ref}}\)), stream-wise turbulence intensity (\(I_{U}\)) and vertical turbulence intensity (\(I_{W}\)) profiles, measured from the floor of the wind tunnel (\(U_{\textit{ref}}\) is the mean wind speed at the model height), (**b**) longitudinal velocity spectrum at the height of the model.  
Figure 2: The two building models mounted in the wind tunnel, (**a**) flat roof and (**b**) deck roof.
## 3 Experimental Results
### Beam Performance
\begin{table}
\begin{tabular}{c c c c c} \hline
**Beam** & **Load (kN)** & **Pload (\%)** & **Deflection (mm)** & **Pdef (\%)** \\ \hline B1 & 49.02 & 58.2 & 11.70 & 73.2 \\ B2 & 49.23 & 56.3 & 11.38 & 69.4 \\ B3 & 46.83 & 77.1 & 14.11 & 92.0 \\ B4 & 44.51 & 91.0 & 12.81 & 84.0 \\ B5 & 50.72 & 42.8 & 13.31 & 87.6 \\ B6 & 48.50 & 63.0 & 13.98 & 91.4 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental test results for the bending beams tested. are shown in Figure 9 for both the first-order and total indices. It can be seen that both indices are practically similar, so the second-order indices can be discarded. According to these results, the most relevant parameter in the deflection model is the modulus of elasticity of the concrete, accounting for 47% of the total variance. On the other hand, the parameter with the least influence is the tensile strength of the concrete, with an index of 0.01 (1%), as it is only used for the calculation of the moment to cause cracking. It is also worth mentioning the high influence (39%) of the coefficient used to calculate the effective moment of inertia. Considering that this is a complex parameter to calculate experimentally, this may explain the lower accuracy of the model.
## 2 RPL in the Literature
### RPL Performance Evaluation in WSN  
The performance of RPL was studied in many ways as some authors build their implementations only on End-to-End (E2E) delay and Packet Delivery Ratio (PDR, while others try to measure the performance with respect to the DODAG construction issues like stability of DODAG and Trickle timer configurations.  
In [7], [PERSON] et al. measures RPL's behavior in a WSN composed of 100 nodes. Their simulation results find that RPL network can converge quickly but they experienced a great overhead that must be reduced.  
RPL's ICMPv6 control messages are studied in [8]. Generally, [8] mentioned that RPL control messages especially DODAG Advertisement message (DAO) messages may be result in network congestions. In [9], the authors investigated RPL's behavior in high density network: They find that RPL protocol guarantees a stable path, however, the physical network topology instability showed that RPL PDR is very low in dense networks. They used an indoor topology with 100 nodes on Lille SensLAB [10] testbed and they conducted experiments to prove that RPL protocol is an efficient routing protocol to find the shortest routes to destinations even in a large-scale network.  
At Berkeley universities, an implementation of RPL is introduced using TinyOS [11]. In [11], authors measured the RPL performance using simulations and real data and their results observed that the control messages overhead at the beginning of RPL process increased linearly with the number of nodes participating in the RPL network and then become stable as RPL process is about to be finished. Previously, many experimental evaluations investigated RPL with small network scenarios and nowadays many researchers are about to use testbeds to test large-scale network. In [12], authors used two hardware platforms, namely WSN430 Sensor Board and the MSB430 Scatter web mode to implement RPL protocol. The results of [12] showed that RPL performed well and didn't depend on the running network topology.  
Authors of [13] used the discrete event simulators Castalia/ OMNET++ to propose an evaluation of RPL protocol performance depending on various network topologies with real data. In [14], Authors using the NS2 simulator proposed a new RPL multipoint to point mechanism and RPL broadcast mechanism.  
In [15], RPL performance is conducted using a Contiki operating system [16] and java based network simulator Cooja. Results showed that a few RPL nodes experienced high packet loss rate and suffered from unreliability issues while average performance was reasonable for AMI networks. In [17] authors had investigated RPL performance using a wireless sensors testbed, they conducted experiments using 100 nodes with uniform distribution through IOT-LAB testbed [18]--this study was the first phase of RPL performance evaluation. The results of [17] stated that the RPL protocol works well in medium dense networks and is considered to be the first IPv6 wireless sensor routing protocol.
###### Abstract  
Electromagnetic wave-absorbing materials (EMAMs) and structures are crucial in aerospace and electronic communications due to their ability to absorb electromagnetic waves. The development of materials that are lightweight, sustainable, and cost-effective, exhibiting high-performance absorption across a broad frequency spectrum, is therefore important. However, homogeneous electromagnetic absorbing materials require assistance to meet all these criteria. Therefore, developing multi-layer absorbing coatings is essential for enhancing performance. The present study uses 21 different composites of varying weight fractions of polypropylene, graphene nanoplatelets, and multiwall carbon nanotubes nanocomposites to develop multi-layer absorbing materials and optimize their performance. These multi-layer carbon polymer nanocomposites were meticulously constructed using evolutionary algorithms like Non-sorted Genetic Algorithm-II and Particle Swarm Optimization to achieve ultra-broadband electromagnetic wave absorption capabilities. Among the designed electromagnetic absorbing materials, a two-layer model, i.e., 1.5 wt% MWCNT/PP/epoxy with a thickness of 1.052 mm and 2.7% GNP/PP/epoxy with a thickness of 4.456 mm totaling 5.506 mm, was identified as optimal using NSGA-II. The structure has exhibited exceptional absorption performance with a minimum reflection loss of \(-\)21 dB and a qualified bandwidth extending to 4.2 GHz. PSO validated and optimized this structure, confirming NSGA-II's efficiency and effectiveness in quickly obtaining optimal solutions. This broadband absorber design combines the structure design and material functioning through additive manufacturing, allowing it to absorb well over a wide frequency range.  
nanocomposites; X-band; multi-layered structure; NSGA-II; PSO; microwave absorption Article
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Field Measurement  
To study the reliability of cellular communication aerial links, we conducted a comprehensive measurement campaign in a 4G network within the National University of  
Figure 1: Schematic diagram of the considered path planning problem.  
Figure 2: General flowchart of the methodology.  
Malaysia (UKM), Bangi campus, Malaysia. The campus environment can be considered a suburban metropolitan area with a geographic terrain of undulating hills. The dataset is publicly accessible on [48]. To measure the required cellular key performance indicators (KPIs), the G-NetTrack Pro application [49] was installed on a Huawei STK-L22 smartphone, and the smartphone was mounted on the developed drone in [50]. During the measurement, the drone flew at different altitudes and routes, the smartphone was served by the 4G mobile network, and G-NetTrack Pro measured the 4G-related parameters, such as reference signal received power (\(RSRP\)) and reference signal received quality (\(RSRQ\)). For details on the development of the drone and the method of conducting the aerial drive test, readers are referred to [50; 51], respectively.  
\(RSRP\) is a key measurement parameter indicating the average received signal power of a single resource element in an LTE Resource Block (RB) and can be calculated as [52]  
\[RSRP[W]=\frac{1}{N}\sum_{n=1}^{N}P_{n}\, \tag{1}\]  
where \(N\) is the number of received reference signals and \(P_{n}\) is the received power of the \(n^{th}\) reference signal. However, \(RSRP\) alone does not fully reflect the quality of the received signal because it also picks up the energy of interfering signals in the corresponding frequency range.  
\(RSRQ\) is another key measurement parameter that indicates the received signal quality level in the 4G network and the effect of interference from adjacent BSs. \(RSRQ\) can be calculated as [52]  
\[RSRQ=N\times\frac{RSRP[W]}{RSI[W]}, \tag{2}\]  
where Reference Signal Strength Indicator (\(RSSI\)) is the power measured over the entire bandwidth of occupied RBs, including intracell power, interference, and noise. Note that \(RSRQ\) is dimensionless and usually written in dB.  
Figure 3 depicts the different examined routes and the location of the serving BSs inside the campus. The drive tests were conducted on three different routes and at four different elevations (65, 85, 105, and 125 m). The measured data are statistically analyzed in this step to identify reliable aerial communications coverage for cellular-connected drones. The output of this step is used as the input to define the objective function of the path planning problem in Section 3.2.2.  
Figure 3: Illustration of the conducted aerial drive test on the UKM campus in three different routes.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Threats
#### 4.1.1 Security-Based Threats  
The security threats to smart grid infrastructure are diverse and evolving. It is crucial for smart grid operators to regularly assess their security posture and implement appropriate measures to mitigate these risks [27]. To address these security threats, smart grid operators must implement a comprehensive security plan, including encryption, access control, intrusion detection, and monitoring. Periodic security audits and employee training are essential to keep a smart grid secure. Regular security audits and employee training are critical to maintaining a secure smart grid. Cyberattacks, such as ransomware, malware, and denial-of-service (DoS) attacks, pose a significant security risk to smart grids as they can disrupt operations, cause blackouts, and compromise data privacy and security. The security of sensitive data and the dependability of the grid are further threatened by physical attacks on equipment as well as flaws in communication protocols.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Qualitative Evaluation  
The detectability and isolability of the faults are essential bases for reflecting the fault diagnosis performance. Therefore, it is necessary to analyze the diagnosability before designing a diagnostic system in order to determine whether the faults can be diagnosed and the difficulty of the diagnosis. As a basis for the design of fault diagnosis systems, structural analysis is an essential theoretical analysis tool for diagnosability analysis due to its methodological characteristics [30]. It does not require numerical operations or the establishment of an exact analytical model; this allows the system to have a significant degree of design freedom [31].  
Figure 3: Diagnosability structural model based on structural analysis.
## 3 Materials and Methods
### Problem Statement  
Manufacturing companies rely on many suppliers. Thus, hundreds of entities compose their SCs, and suppliers deliver thousands of components daily. In this context, the on-time delivery of each component is fundamental for guaranteeing a smooth production flow for the final manufacturers. It has thus become essential to know in advance if future deliveries will be made on time. However, knowing whether a delivery will be late or not is sometimes not enough. Material planners should have precise daily information about each component's expected delivery delay or advance to avoid excessive inventory or stockout. In addition, with the increase of globalization, SCs have become more exposed to global dynamics and as a result, the local delivery performance of suppliers could be affected by the macroeconomic conditions of different countries and sectors. Overall, the problem faced by an increasing number of industries is thus how to effectively predict the amount of delay or advance in the delivery of each component from their suppliers by leveraging the collection of publicly available macroeconomic indicators.
## 2 Materials and Methods
### Agronomic Sensors  
To allow for real-time monitoring, several sensors were connected to wireless nodes (see Table 1):  
Figure 1: WSN infrastructure of the experimental orchard.  
* A total of four sensors per replicate (twelve in total) deployed as follows: two volumetric soil water content sensors (10 HS, Decagon devices, Inc., Pullman, WA, USA) and two matric potential of soil water sensors (MPS-6, Decagon devices, Inc., Pullman, WA, USA), installed at 23 cm from each dripper and 25 and 45 cm depth located under the canopy and along the tree drip lines.
* A total of two linear variable displacement transducer sensors (LVDT, Model DF \(\pm\) 2.5 mm, Solartron Metrology, Bognor Regis, UK) per replicate (six in total) were installed on the main tree branch for measuring micrometric branch diameter variations.
* A total of two infrared radiometer sensors (IR120, Campbell Scientific Inc., USA) per treatment were mounted on wooden poles and installed at 0.5-1.0 m over the trees canopy for monitoring canopy temperature.
* A total of four climatic sensors (VP4, Decagon devices, Inc., Pullman, WA USA) were installed to record air temperature, relative humidity and vapor pressure deficit.