esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Performance Measurement  
The fabrication process of test samples is shown in Figure 1c. Each coating was cut into samples 1 and 2, perpendicular to the scanning direction by using a wire cutting machine tool (AQ400 Ls, Sodick, Yokohama, Japan). The microstructure observation and hardness measurement were conducted on Sample 1. The phase identification and wear tests were conducted on Sample 2. Pane 1 and 2 are polished with 240#-2000# SiC sandpaper, then polished with a polishing paste of 2.5 \(\upmu\)m, and cleaned with ultrasonic in ethanol for two minutes. Plane 1 was eroded in aqua regia for 10 to 20 s for the microstructure observation and the microstructure distribution at the indentation edge. An X-ray diffractometer (XRD) and scanning electron microscopy (SEM) equipped with energy dispersive analysis (EDS) were adopted for the composition and morphology of phases. A three-dimensional (3D) laser microscopy (OLS4100, Olympus, Kokyo, Japan) was used to obtain the optical microstructure. The microhardness of the coatings along the building direction was measured with a Vickers hardness tester (HV-1000, Shanghai, China), where a loading force and time were 300 gf and 10 s, respectively. Since the thickness of the 316L and composite coatings approached 1.0 mm, the microhardness was measured at 10 evenly spaced points from the bottom to the top of the coating. A multi-functional surface performance tester (MFT-4000, Lanzhou, China) was used to obtain the friction and wear properties on plane 2. The parameters for the wear test were as follows: a GCr15 ball with a diameter of 5 mm, a load of 15 N, a reciprocating speed of 200 mm/min, the test time of 60 min, and the movement distance of 3 mm. In addition, the coefficient of friction (COF) was recorded automatically during the test process. The two-dimensional (2D) and 3D morphologies of the worn tracks and GCr15 ball were obtained with an OLS4100 to reveal the wear mechanism of coatings.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Reactive Approach to Road Safety  
The reactive approach analyzes historical road accident data to identify high-risk areas. By leveraging detailed geolocated incident data, this method provides insights into accident patterns, allowing for targeted safety interventions. The key steps which have been followed are as follows:  
* The data collection and validation were performed by providing historical data that was sourced from local or national databases, such as Istat [4] in Italy, which now employs the \"GINO\" system for facilitated and accurate data reporting. Precise geolocation is critical for identifying black spots. Missing or repeated coordinates are corrected using semi-automated geocoding tools (i.e., API--Application Programming Interface), integrating address information with geographic coordinates;
* The spatial visualization was performed by importing the validated data into Geographic Information Systems (GIS) to create visual layers of accident points. Spatial distribution is analyzed using density maps to identify clusters of incidents (black spots), guiding further risk assessment and safety planning.  
Figure 2: Flowchart of: (**a**) the proposed methodology; (**b**) the proposed cost-benefit analysis.  
This approach emphasizes data accuracy and geolocation precision, ensuring reliable identification of critical areas in the road network for targeted interventions.
## 7 Trade-off Between Energy Efficiency and Data Transmission
Further, the adaptive decision-making within the proposed algorithm originally takes place based on the received signal conditions. This allows for the decision-making algorithm proposed for the switch to be readily implementable without any further adjustments. Thus, it can be stated that the manner in which the algorithm has been developed allows it to be capable of being implemented in any environment or application without requiring changes within the algorithm, and the changes are to be made only on the parameter initialization process. However, the switch mechanism proposedalso allows it to be maintained automatically, thereby requiring very low intervention from the users.
# Investigating the Physical Mechanisms of Quicksand Using a Custom-Designed Experimental Apparatus
###### Abstract  
Quicksand initiation in saturated sandy soils represents a critical geohazard with significant implications for geotechnical infrastructure stability. Despite its importance, the granular-scale mechanisms driving the physical state transitions during quicksand remain insufficiently understood. This study employs a custom-designed hydrodynamic seepage testing system to investigate these mechanisms, enabling precise regulation of hydrodynamic velocity and real-time monitoring of pressure variations. Through experiments on quartz sand specimens with varying particle gradations, we demonstrate that particle gradation primarily governs quicksand susceptibility, while hydrodynamic velocity controls its initiation timing and exhibits a linear correlation with seepage discharge. The quicksand process evolves through three distinct stages: self-consolidation, reorganization, and quicksand initiation, with the reorganization stage identified as the pivotal phase where particle rearrangement dictates system stability. These findings elucidate the intrinsic physical mechanisms of quicksand as a hydraulic failure phenomenon, offering valuable insights for predictive modeling and geohazard mitigation in granular media.  
quicksand initiation; particle gradation; hydrodynamic velocity; seepage discharge; reorganization 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), pp. 221-222. IEEE.
# Electromagnetic Field Variation of ELF Near-Region Excited by HED in a Homogeneous Half-Space Model
## 3 Analytical Approximate Expression of the Field Component
### Simplification of Field Component Expression  
Let the electric moment be \(ldl=1\) A \(\cdot\) m; substituting Equations (25) to (29) into (5) to (10), respectively, we obtain the approximate expression of each EM field component. Taking the EM field components \(E_{z}\) as an example, this paper gives the process of simplifying the near-field approximate expression of the vertical magnetic field component. For \(k_{2}\rho<<1\), the expressions of \(E_{z}\) can be given as follows:  
\[E^{\prime}{}_{1z}=\frac{j\omega\mu_{0}\rho}{4\pi k_{1}}\cos\varphi\left[\mp \frac{d_{0}}{r_{0}}\left(\frac{k_{1}}{r_{0}^{2}}+\frac{3j}{r_{0}^{3}}-\frac{3}{k _{1}r_{0}^{4}}\right)e^{jk_{1}r_{0}}-\frac{d_{1}}{r_{1}}\left(\frac{k_{1}}{r_{1 }^{2}}+\frac{3j}{r_{1}^{3}}-\frac{3}{k_{1}r_{1}^{4}}\right)e^{jk_{1}r_{1}}\right] \tag{30}\]  
In the equation number, \"\(+\)\" is taken when \(z\geq d\), and \"\(-\)\" is taken when \(0\leq z\leq d\). According to the quasistatic assumption, the distance relationship between the field source point and observation point is \(\rho>>d\), \(\rho>>z\), and \(r_{0}\approx r_{1}\approx\rho\). Therefore, the approximate expression of the vertical EM field components \(E_{z}\) satisfying the quasistatic assumption can be obtained as follows:  
\[E^{\prime}{}_{1z}=\frac{j\omega\mu_{0}\rho}{4\pi k_{1}}\cos\varphi\left(\frac{k _{1}}{\rho^{2}}+\frac{3j}{\rho^{3}}-\frac{3}{k_{1}\rho^{4}}\right)e^{jk_{1}\rho }(\mp d_{0}-d_{1}) \tag{31}\]  
The second approximation of \(e^{jk_{1}\rho}\) was expanded using [PERSON]'s progressive formula. In the process of simplification, the attenuation rate of EM waves in air was considerably less than that in seawater; thus, it meets the requirements of Im(\(k_{2}\)) \(<<\) Im(\(k_{1}\)). The quasistatic approximate expression can be further simplified as follows:  
\[E^{\prime}{}_{1z}=\frac{j\omega\mu_{0}}{4\pi k_{1}}\cos\varphi\left(\frac{k_{1} }{2\rho^{2}}+\frac{jk_{1}^{2}}{2\rho}\right)(\mp d_{0}+d_{1}) \tag{32}\]  
In the same way, the near-field approximate expressions of the EM field components in the remaining \"air-seawater\" half-space can be obtained.
## 2. Mature Technologies for Water and Wastewater Remediation
### Chlorination
#### 2.1.3. Mechanism of Chlorination  
The reactions of chlorine in water are presented in Equations (1) and (2) [11]. Chlorine forms hypochlorous acid in the presence of water. This is demonstrated in Equation (1), where:  
\[\mathrm{Cl}_{2}+\mathrm{H}_{2}\mathrm{O}\rightleftharpoons\mathrm{HOCl}+ \mathrm{H}^{+}+\mathrm{Cl}^{-} \tag{1}\]  
HOCl then partially decomposes, as shown in Equation (2):  
\[\mathrm{HOCl}\rightleftharpoons\mathrm{H}^{+}+\mathrm{OCl}^{-} \tag{2}\]  
These compounds initially react with inorganics (such as metals) in the water, usually forming insoluble compounds (such as Fe(OH)\({}_{3}\)), which can be filtered out. Thereafter, organic compounds (such as phenol) and pathogens are oxidised, which generally renders them harmless.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 1 Introduction
This work demonstrates that the unintended EM radiation from Ethernet cables, commonly used in ICSs and other networked devices, can be leveraged to build a covert channel to leak critical data from the system. Malicious code injected into a critical component of an ICS can cause specific network traffic patterns to intentionally modulate critical data into the EM radiation of the Ethernet cables. The demonstrated attack is performed by sending two carefully-chosen network traffic patterns through the Ethernet cable by a malicious code running on the victim device, i.e., ICS. A software-defined radio hardware along with a decoding software running on the attacker's computer captures the EM radiation of the Ethernet cable and successfully recovers the data modulated into the network by the malicious code. This work makes the following contributions:  
* Demonstrates and experimentally evaluates the design of a covert channel protocol that can exfiltrate data through the EM radiation of Ethernet cables. * Introduces and experimentally evaluates a methodology to automatically detect information-leaking EM frequencies of Ethernet cables. * Explores the potential of increasing reliability in EM-based covert channels through error correction codes. * Discusses the impact of the identified attack vector on the large-scale industrial control systems. The rest of the paper is organised as follows. Section 2 provides an overview of the state of the art literature on the domain of covert channels in general, and EM-based covert channels in particular. Section 3 details the specific covert channel attack vector that is utilised by the presented work. Section 4 provides a comprehensive coverage on the algorithms to detect information-leaking EM radiation frequencies on Ethernet cables. Sections 5 provides the details of the design and the implementation of the introduced covert channel, followed by its experimental evaluation in Section 6. Finally, Section 7 discusses the findings and their implications before Section 8 concludes the paper.
###### Abstract  
Both the frequency domain Nyquist curve of electrochemical impedance spectroscopy (EIS) and time domain simulation of DC equivalent first principle linear circuit (\(FPLC_{DCqu}\)) are some of the fundamentals of lead-acid batteries management system design. The Nyquist curve is used to evaluate batteries' state of health (SoH), but the curve does not distinguish charging/discharging impedances on electrode-electrolyte interfaces in the frequency domain. \(FPLC_{DCqu}\) is used to simulate batteries' terminal electrical variables, and the circuit distinguishes charging/discharging impedances on electrode-electrolyte interfaces in the time domain. Therefore, there is no direct physical relationship between Nyquist and \(FPLC_{DCqu}\) This paper proposes an AC equivalent first principle linear circuit (\(FPLC_{ACqu}\)) by average switch modeling, and the novel circuit distinguishes charging/discharging impedances on electrode-electrolyte interfaces in Nyquist. The novel circuit establishes a physical bridge between Nyquist and \(FPLC_{DCqu}\) for lead-acid batteries management system design.  
state of health; state of charge; lead-acid battery; electrochemical impedance spectroscopy; battery management system; charge-transfer resistance; double-layer capacitance; first principle circuit; behavioral circuit; power electronics; average switch modeling +
Footnote †: journal: Applied Soft. 2020, 10, 1907; doi:10.3390/app10061907
# Quantum Computing for Healthcare: A Review
## 1 Introduction
### Quantum Computing for Healthcare  
QC is particularly well-suited to numerous compute-intensive applications of healthcare [1], especially in the current highly connected Internet of Things (IoT) digital healthcare paradigm [2; 3], which encompasses interconnected medical devices (such as medical sensors) that may be connected to the Internet or the cloud. The massive increase in computational capacity is not only beneficial for healthcare IoT but can allow quantum computers to enable fundamental breakthroughs in this domain. When we leap from bits to qubits, it could improve healthcare pharmaceutical research [4], which includes analyzing the folding of proteins, determining how molecular structures, for instance, drugs and enzymes, fit together [5], determining strengths of binding interactions between a single biomolecule, for example, protein or DNA, and its ligand/binding partner such as a drug or inhibitor [6], and accelerating the process of clinical trials [7]. A few potential applications are briefly described next for an illustration. A quantum computer can perform extremely fast DNA sequencing, which opens the possibility for personalized medicine. It can enable the development of new therapies and medicines through detailed modeling. Quantum computers have the potential to create efficient imaging systems that can provide clinicians with enhanced fine-grained clarity in real time. Moreover, it can solve complex optimization problems involved in devising an optimal radiation plan that is targeted at killing cancerous cells without damaging the surrounding healthy tissues. QC is set to enable the study of molecular interactions at the lowest possible level, paving the pathway to drug discovery and medical research. Whole-genome sequencing is a time-demanding task, but with the help of qubits, whole-genome sequencing and analytics could be implemented in a limited amount of time. QC can revolutionize the healthcare system through modern ways of enabling on-demand computing, redefining security for medical data, predicting chronic diseases, and accurate drug discoveries.
## 3 Results and Discussion
### Mechanical Properties of EP/BCI/BSEA Composites
#### 3.2.2 Fracture Toughness  
As exhibited in Figure 9a and Table S2, the \(K_{\text{IC}}\) of EP/BCI/BSEA-4 reached a peak value of 2.09 MPa\(\cdot\)m\({}^{1/2}\) at RT and 2.97 MPa\(\cdot\)m\({}^{1/2}\) at 77 K, which showed increases of 127.17% and 81.10% compared with pure EP (0.92 MPa\(\cdot\)m\({}^{1/2}\) at RT and 1.64 MPa\(\cdot\)m\({}^{1/2}\) at 77 K), respectively. This was mainly ascribed to the fact that the plastic deformation capability of modified EP was improved and the microcrack surface due to internal stress was blunted with the addition of BSEA. Moreover, as exhibited in Figure 9c,d, the introduction of BSEA could lead to deflection and disproportionation of the microcrack propagation path compared with pure EP, thus dissipating more fracture energy and enhancing \(K_{\text{IC}}\). In addition, the \(K_{\text{IC}}\) values at 77 K were all higher than those at RT in the same components. This was due to the molecular spacing becoming smaller and the intermolecular force becoming stronger due to thermal contraction under ultra-low temperatures. Furthermore, in addition to intermolecular forces, more major chemical bonds in the crosslinking network  
Figure 8: (**a**) Bending strength and (**b**) bending deflection of the EP composites at RT and 77 K.  
needed to be overcome during crack propagation with the introduction of BSEA, which might have resulted in the enhanced \(K_{IC}\) values.  
SEM was performed to observe the fracture morphology near the cured EP pre-crack, as described in Figure 10. As presented in Figure 10a-f, the cracking surface topography of pure EP was very smooth at RT, which indicated that pure EP had weak resistance to crack propagation. The cracks became more continuous and rougher than those of pure EP, which was accompanied by stress dissipation streaks, with the introduction of BSEA. In fact, very rough, rodlike cracks appeared in the fracture surface with the further addition of BSEA, as shown in Figure 10d-f. This phenomenon indicated that adding BSEA could improve the crack resistance of EP. As shown in Figure 10g-l, the fracture morphology of pure EP, with obvious linear stripes, was highly smooth at 77 K. The overall appearance of pure EP exhibited obvious brittle fracture characteristics, revealing that the resistance to crack propagation was extremely weak under cryogenic conditions. With the increase in BSEA, the crack section became coarser, indicating that the capability to resist crack propagation was improved at 77 K. Moreover, the cracks at 77 K were shorter than those at RT, indicating that the free volume of the EP/BCI/BSEA crosslinking network under cryogenic conditions was decreased compared with that under RT and the intermolecular bonding force was stronger; thus, the \(K_{IC}\) values at 77 K were enhanced compared with those at RT.  
Figure 9: (**a**) \(K_{IC}\) and (**b**) impact strength of EP composites at RT and 77 K; schematic diagram of crack propagation for fracture toughness tests of (**c**) pure EP and (**d**) modified EP.
# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 5 Conclusions  
In this paper, we propose a deep transfer learning approach to effectively and efficiently identify slope surface cracks for the early monitoring and warning of geohazards, such as landslides. The essential idea is to employ transfer learning by training (a) the large sample dataset of concrete cracks and (b) the small sample dataset of soil and rock masses' cracks. In the proposed approach, (1) pretrained crack identification models are constructed based on the large sample dataset of concrete cracks; (2) refined cracks identification models are further constructed based on the small sample dataset of soil and rock masses' cracks. To evaluate the effectiveness of the proposed approach, the accuracy of deep learning models is tested. The results show that (1) AlexNet has the highest accuracy and computational efficiency, proving that the classic AlexNet model is effective in identifying slope cracks. (2) Each model achieved much higher accuracy by employing transfer learning (for example, the accuracy of LeNet is approximately 23% higher than using the small sample dataset), and the massive amount of data required for the identification of slope surface cracks is significantly reduced.  
Future work is planned to achieve better results for a sufficiently sized dataset. The deep transfer learning approach can be deployed on UAV aerial surveys, providing the detection of surface cracks on high and steep slopes. The deep transfer learning approach has important engineering significance to realize timely monitoring to prevent geological disasters.  
Conceptualization, Y.Y. and G.M.; methodology, Y.Y. and G.M.; writing--original draft preparation, Y.Y. and G.M.; writing--review and editing, Y.Y. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research was jointly supported by the National Natural Science Foundation of China (Grant No. 11602235)), the Fundamental Research Funds for China Central Universities (2652018091), the Major Program of Science and Technology of Xinjiang Production and Construction Corps (2020 AA002), and the 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (ZD2021 YC009).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the editor and the reviewers for their helpful comments.  
The authors declare no conflict of interest.
## Appendix B The Time Frequency Distribution of the SC-FDMA Signal
### The Equivalence of RF and Baseband Analysis
There are two methods to solve the non-equivalence problem between the time frequency distribution of the baseband signal and the RF signal:  
(1) Let the window length be integer multiples of the carrier cycle, so that there is no carrier leakage problem. In this case, the time frequency analysis of the baseband signal and that of the RF signal are equivalent. For example, Figure 8a is the time frequency distribution of the baseband complex signal when the window length equals to the carrier cycle, i.e., \(L_{h}=30\), and Figure 8b is the time frequency distribution of its corresponding RF signal when the window length is \(L_{h}=30\). It is obvious that the time frequency distributions of them are equivalent. In Figure 8c,d, the window length is two multiples of the carrier cycle, i.e., \(L_{h}=60\), It also shows the equivalence between the time frequency distribution of the baseband signal and that of the RF signal when the window length is integer multiples of the carrier cycle. Figure 7: The effect of carrier leakage. (**a**) Baseband complex signal, \(L_{h}=40\); (**b**) RF signal, \(L_{h}=40\); (**c**) Baseband complex signal, \(L_{h}=50\); (**d**) RF signal, \(L_{h}=50\). (2) Pad zeros before and after the truncated signal before Fourier transforming. The immediate benefit is the improvement of the analysis resolution in frequency domain. Fortunately, by zero padding, the equivalence between the time frequency distribution of the baseband complex signal and that of the RF signal is maintained even if the analysis window length is not integer multiples of the carrier cycle. Figure 9a is the STFT of the baseband complex signal when the truncated length is \(L_{h}=40\), and Figure 9b is its corresponding STFT of the RF signal. It validates the equivalence of the time frequency distribution of the baseband signal and the RF signal, even if the truncated length is not integer multiple of the carrier cycle. Figure 9c,d are the Choi-Williams distribution of the baseband and RF signal, respectively, which also validates the equivalence with zero padding, even if the truncated length is not integer multiples of the carrier cycle. In what follows, the Zero padding method is adopted to generate the signal's Choi-Williams time frequency distribution. Figure 8: Realizing time-frequency equivalent analysis of baseband and RF by controlling the window length. (**a**) Baseband complex signal, \(L_{h}=30\); (**b**) RF signal, \(L_{h}=30\); (**c**) Baseband complex signal, \(L_{h}=60\); (**d**) RF signal, \(L_{h}=60\).