esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Coding Metamaterials for RCS Reduction
### Development of Unit-Cell Metamaterial Structures
#### 2.1.3 Triangular Split-Ring Resonator  
Similar substrate materials and dimensions were also utilized for this triangular design. A comparison revealed unusual bistatic RCS peak values at 8 GHz when the thickness of the substrate material decreased. Design 2 manifested a peak point that increased at first when the substrate material changed from FR-4 to Rogers RT6002. However, the value slightly decreased to \(-60.3\) dBm\({}^{2}\) when the Rogers RO3006 substrate material was used (as demonstrated in Table 3). It showed that besides substrate material thickness, design structure also influenced the outcomes. Moreover, this design structure possesses monostatic RCS values similar to the circular design that range from \(-110\) dBm\({}^{2}\) to \(-40\) dBm\({}^{2}\), as shown in Figure 3.  
Figure 3: _Cont._Figure 3: Monostatic RCS values for triangular metamaterial unit-cell design with various substrate materials: **(a)** Design 1; **(b)** Design 2; and **(c)** Design 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Designs** & **FR-4** & **Rogers RT6002** & **Rogers RO3006** \\ \hline Design \(1\) & & -57 & -59.5 \\  & -64.3 & -63.1 \\  & -67.9 & -71.6 \\  & -75.8 \\  & -78.2 & -77.6 \\  & -88.2 \\  & -86.1 \\  & -98.3 \\  & -93.4 \\  & -97.6 \\  & -99.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bistatic scattering patterns of triangular structure with the various substrate materials.  
Figure 4: Monostatic RCS values for uniquely shaped metamaterial unit-cell design with various substrate materials: (**a**) Design 1; (**b**) Design 2; and (**c**) Design 3.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 2 Challenges in Cybersecurity-Driven Opportunistic Networks
* _Energy Efficiency_: Acknowledging the resource-intensive nature of blockchain operations, the research community is actively engaged in optimizing consensus algorithms and transaction validation processes within the OppNet framework [13]. The objective is to strike a balance between the security features of blockchain and the imperative of energy efficiency, thereby ensuring sustainable network operations [14]. This dual focus seeks to harness the potential of blockchain, while mitigating its environmental impact, fostering a harmonious integration within the OppNet ecosystem.
## 3 Applications of Single Molecule Measurement: Disease Diagnosis, Monitoring
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detection** & **Target Marker** & **Detection Method** & **Detection Device** & **Sample Source** & **Reference** \\ \hline Cancer (chronic lymphocytic leukemia) & DNA: TP53 gene & Sequencing & bionanopore (MinION) & peripheral blood & [67] \\ \hline Cancer (lung cancer) & miRNA (miR-155) & Binding by complementary probe & bionanopore (alpha-haemolysin) & blood & [68] \\ \hline Cancer (colorectal cancer) & miRNA (miR-21) & Binding by complementary probe & bionanopore & serum & [69] \\ \hline Cancer (lung cancer) & miRNA (miR-155, miR82-5p, miR210, miR21) & Binding by complementary probe & bionanopore & synthesized nucleotide & [70] \\ \hline Cancer/HIV & miRNA (left7b, miR155, miR21), protein (HIV-TAT) & Binding by complementary probe & bionanopore & synthesized nucleotide/peptide & [71] \\ \hline Cancer & IgG antibody (HED10) & Binding by poly (dT)\({}_{45}\) & bionanopore (alpha-haemolysin) & - & [72] \\ \hline Cancer & DNA:5-methylcytosine (5 mC) & Binding by MBD1 (MBD-1x) proteins & solid state nanopore & - & [73] \\ \hline Cancer & DNA: Target DNA extracted from cell & Binding by complementary probe & bionanopore (Aerolysin) & (Ramons, A549, Jurkat, MCF-7, Hela) & [74] \\ \hline Cancer (breast cancer) & Circulating tumor cells (CTC) & Binding by breast cancer (CTC) & bionanopore (MspA) & blood: tumor cells (CTC) from blood, cell (MCF-7, HpeG2, U87) & [75] \\ \hline Cancer & Cancer marker proteins (carincoembryonic antigen, \(\alpha\)-fetoprotein antigen, and human epidermal growth factor receptor-2) & & bionanopore (engineered antibody mice blood serum & [77] \\ \hline Cancer (colon cancer) & EpCAM antibody & Cancer EpCAM Antigen peptide ligand & bionanopore (engineered antibody mice blood serum & [77] \\ \hline Cancer (prostate, colon, lung, liver, breast cancer) & Mehtylated CpG DNA & Binding by methyl-binding protein ligand & solid-state nanopore (SiN) & synthesized DNA & [78] \\ \hline Cancer (prostate cancer) & Cancer biomarkers: Prostate-specific antigen (PSA) & Binding by DNA aptamer probe & solid-state nanopore (SiN) & PSA in serum & [79] \\ \hline Cancer & Vascular endothelial growth factor (VEGF), matrix metallopeptidase-9 & Binding & bionanopore (\(\alpha\)-haemolysin) & - & [80] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations
### LiCSAR for Volcanic Applications
#### 4.2.2 Value-Added LiCSAR-Based Products for Volcanic Studies  
LiCSAR unwrapped interferograms can be used to produce time series of ground deformation to track the long-term dynamics of magmatic systems. For example, InSAR time series from the Campi Flegrei caldera (Italy) reveal a persistent uplift signal of about 5.7 cm/yr for the period 2015-2019, consistent with GNSS results [64] (Figure 14). We also observe variations in the associated rates of displacement with period of deceleration (late 2016) and period of acceleration (early 2018).  
Figure 14: LiCSBAS InSAR time series derived from LiCSAR products showing (**a**) the LOS velocity map generated for the Campi Flegrei area (frame ID 022D_04826_121209) and (**b**) the displacement time-series for point A, centered on the caldera, with respect to the reference area located \(\sim\)20 km north of the caldera.  
Figure 13: Coherence time series at Erta Ale showing the emplacement of the lava flow during the early stage of the 2017 eruption, from InSAR combinations (having 36-days temporal difference) between: (**a**) 20170128–20170305, (**b**) 20170305–20170410, (**c**) 20170410–20170516, (**d**) 20170516–20170621. The red triangle locates the volcanic edifice and the red arrows indicate the direction of propagation of the lava flow for each period.  
InSAR time series derived from LiCSAR products have already been successfully used to better understand the dynamics of magmatic systems during the 2017 eruptions at Mt. Agung (Indonesia) [26] and Erta Ale (Ethiopia) [72].
# Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision
## 5 Validation of the Monitoring Results  
We used a Leica P50 Scan-Station to validate the monitoring results. The scanning accuracy is set to the highest, the scanning resolution is 0.3 mm/10 m, the target acquisition accuracy is 0.5 mm/50 m, and the noise accuracy is 0.2 mm/10 m. We obtained a three-dimensional model of the beam through three-dimensional laser scanning, as shown in Figure 21, and further obtained the deformation chromatograms of the beam model under various load conditions, as shown in Figure 22.  
Figure 21: Three-dimensional laser-scanned model of the beam.  
Figure 22: Deformation chromatograms of the beam under various load conditions. (**a**) Beam’s deformation chromatography (100 kN). (**b**) Beam’s deformation chromatography (200 kN). (**c**) Beam’s deformation chromatography (300 kN). (**d**) Beam’s deformation chromatography (400 kN).  
Using the pasted code marks on the beam, as previously shown in Figure 8a, we compared the displacement of the code marks' positions in Figure 22 with those in Figure 20. The results are shown in Table 5, using a load condition of 400 kN as an example.  
It can be observed from Table 5 that the displacement errors of the coded marks' positions were within 9%, indicating that the calculated multi-point displacement can accurately reflect the structural displacement.
# UAV Formation Trajectory Planning Algorithms: A Review
## 5 Local Trajectory Planning Algorithm
### Dynamic Window Approach  
The dynamic window method (DWA) is a classic UAV local trajectory planning algorithm. It determines a sampling velocity space that satisfies the hardware constraints of the UAV in the velocity space according to the current position state and velocity state of the mobile UAV and transforms the local trajectory planning problem into a motion constraint problem in space. Then, it calculates the UAV trajectory of the drone moving for a certain period of time under these speed conditions and evaluates the trajectory through the evaluation function. It selects the trajectory with the best evaluation and the corresponding speed as the movement speed of the UAV; finally, through the motion constraints, it selects the locally optimal trajectory and so on until the UAV reaches the target point. Figure 26 is a schematic diagram of the DWA velocity vector space.  
[PERSON] et al. [143] proposed a multi-UAV consistent formation trajectory planning algorithm based on an improved dynamic window method (DWA). The algorithm introduces a new rotation cost evaluation function, A* algorithm and azimuth-related variable weight factors to improve DWA and finally adds a leader-following UAV model for UAV formation trajectory planning. It solves the problems of frequent large-angle rotation and low search efficiency in the traditional DWA algorithm.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 2 Background
### Four Types of Vulnerabilities in Smart Contracts
#### 2.2.2. _Timestamp Dependency_  
Timestamp-dependency vulnerability happens when smart contracts utilize the value of _block.timestamp_, which is a block built-in variable, as a part of the call condition to execute critical operations (e.g., token transfer). The value of _block.timestamp_ is generated by the node executing the smart contract (e.g., anonymous miners), which makes it manipulable and vulnerable to attacks.
## 4 Engineering Application
### Influence of the Cohesion on the Tunnel Surrounding Rock  
The instability probability curve of the tunnel's surrounding rock is shown in Figure 6b. When the tunnel passes through the slightly weathered dolomite strata, with the tunnel depth of 50 m, the tunnel diameter of 6.7 m and the cohesion of the rock-soil mass of 400 kPa, the instability probability of the M\(-\)1 region in the PH\(-\)1 failure mode and the instability probability of the P\(-\)1 zone and the P\(-\)2 zone in the PH\(-\)2 failure mode is 9.82% and 14.26%, respectively. And the difference in the instability probability calculated for the two damage modes is 4.44%. When the cohesion of the rock-soil mass is 200 kPa, the instability probability of the M\(-\)1 region in the PH\(-\)1 failure mode and the instability probability of the P\(-\)1 zone and the P\(-\)2 zone in the PH\(-\)2 failure mode is 13.18% and 42.57%, respectively. And the difference in the instability probability calculated for the two damage modes is 29.39%. The curve slope of the instability probability for the tunnel's surrounding rock in the slightly weathered dolomite strata suddenly becomes steeper after the cohesion of the rock-soil mass is less than 400 kPa. This is due to the fact that after the cohesion is reduced to 400 kPa, the energy dissipation rate of the slightly weathered dolomite in the PH\(-\)2 failure mode is less than the external power under the self-weighting effect of the geotechnical body. The curve slope of the instability probability for the tunnel surrounding rock in the moderately weathered dolomite strata also suddenly steepens after the cohesion of the rock-soil mass is less than 200 kPa.  
Figure 6: Parameter analysis: (**a**) the tunnel diameter; (**b**) the cohesion of the rock-soil mass; (**c**) the medium pressure of the karst cave; and (**d**) the clear distance between the karst cave and the tunnel; The curve in the green area is denser. In order to see more clearly, the enlarged curve is in the upper right corner of Figure 6a. The curve in the yellow area is denser. In order to see more clearly, the enlarged curve is in the upper right corner of Figure 6b. Note: the hard plastic red clay strata—Layer A, the moderately weathered dolomite strata—Layer B, the slightly weathered dolomite strata—Layer C, \(H_{1}\)—the clearance between the karst cave and the tunnel, and \(D_{1}\)—the diameter of the karst cave.  
When the tunnel passes through the hard plastic red clay strata and the cohesion of the rock-soil mass is 100 kPa, the instability probability of the M\(-\)1 region in the PH\(-\)1 failure mode and the instability probability of the P\(-\)1 zone and the P\(-\)2 zone in the PH\(-\)2 failure mode is 17.82% and 16.42%, respectively. And the difference in the instability probability calculated for the two damage modes is 1.4%. It is illustrated that the method for calculating the instability probability in tunnel construction based on the two failure modes is applicable when the tunnel is constructed in the hard plastic red clay stratum with the cohesion of 100 kPa.
## 5 Experimental Results  
In order to verify the performance of the proposed backscatter methods and low complexity demodulation schemes, the system was designed by using MATLAB. The distance between a Wi-Fi helper and a Wi-Fi device (reader) was set to five meters based on the experimental environments of the existing paper [9]. The channel in the simulation followed the model of 802.11 TGn [13]. The 802.11 TGn channel model C and D considered in this paper were both indoor and outdoor environments for wireless LAN (local area network) systems working in the 2 GHz frequency band. Model C is for a large open space (indoor and outdoor), non-line-of-sight (NLOS) conditions, and a 150 ns root-mean-square (rms) delay spread. Model D is for a wide area which is the same environment as the model C, line-of-sight (LOS) conditions, and a 140 ns rms delay spread. The first delay of model D had a Ricean K-factor of 10 dB because of the LOS environment.  
There were two different environments for the channel in Wi-Fi backscatter communication systems. The first channel environment was that there were a Wi-Fi helper and a reader without a tag's reflection. Model C was used for this channel, since a tag sensor did not reflect Wi-Fi RF radio waves and there was a communication channel only between the Wi-Fi helper and the reader. The other environment was that the tag sensor backscatters Wi-Fi RF radio waves while the Wi-Fi helper communicated with the reader. Since LOS existed between the reader and the tag sensor, model D was appropriate for the channel between the reader and the tag sensor with additionally considering the channel effects between the helper and the tag sensor. Therefore, the channel model from the helper to the reader with the backscattering tag sensor used both the delay profiles of models C and D. In addition to that, this paper evaluated the performance of systems with varying the distance between the reader and the tag sensor from one meter to five meters.
## 3 Results and Discussion
### High-Risk Zones Created by Cruise Ships' Routes
#### 3.1.7 Lastovo Island  
Longitudinal northwesterly and souteasterly cruise ship routes often pass along the south coast of Lastovo island. Cruise ships on a northeasterly longitudinal route from Kotor and Dubrovnik to northern Adriatic ports do not use the Central Adriatic Separation Scheme. They proceed in the east coast outer island route, heading along the south coast of Lastovo island and Susac island until they reach the Northern Adriatic Separation Scheme. Cruise ships on souteasterly routes from the north Adriatic ports, after leaving the Northern Adriatic Separation Scheme, proceed in east coast outer island route along Lastovo south coast to Dubrovnik and Kotor.  
During the research period, 113 cruise ships passed less than 3 M from the south shores of the island with average retention periods of 21.2 min, 281 cruise ships passed with 3 M to 6 M distances from the south shores of Lastovo, while 19 cruise ships kept distances of 6 M or more from the south island shores (Table 15). Traffic monitoring showed that two cruise ships met within 3 M from the shore six times in a 90 min period.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
###### Abstract  
Low-light image enhancement is crucial for accurately interpreting images captured under low-lighting conditions. Existing low-light enhancement methods based on diffusion models have demonstrated effectiveness; however, they suffer from slow training processes and less structured guidance during optimization. We propose a novel patch-wise-based diffusion model, which introduces the low curvature reverse trajectory to ensure stable parameter optimization and uncertainty guidance in the diffusion training process. Specifically, we randomly select patches of varying sizes from the entire image and apply patch-wise optimization between the generated image and the ground truth to enforce a stable optimization path in the diffusion model. Additionally, within each patch-wise region, an uncertainty network estimates the uncertainty, which is then integrated as a weighting factor in the diffusion process to balance areas of abrupt change in the image. Experimental evaluations on various datasets demonstrate that our method achieves significant improvements, particularly in experiments with real-world images. These results indicate that the proposed patch-wise-based diffusion model enhancements are effective.  
diffusion model; low light; uncertainty guidance; patch-wise +
Footnote †: journal: Applied sciences
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues
### Tumour Tissues
#### 5.1.2 Breast Cancer  
The process of freezing or H&E staining tissue sections is time-consuming under the current pathology detection methods. During breast-conserving surgery, surgeons analyse and remove a small part of the biopsy tissues to shorten the surgery time. The THz real-time imaging technologies can win treatment time for breast cancer patients. In 2011, [PERSON] et al. found areas in each of 46 different breast cancer specimens whose absorption coefficient was higher than 9 mm\({}^{-1}\) based on the imaging system showed in Figure 6[83], and the absorption coefficients of sections without breast tumours were all under 9 mm\({}^{-1}\). Although the breast tumours could be distinguished from normal tissues using the THz imaging system, the different types of breast cancer could not be identified. In 2013, [PERSON] al. used the reflective THz raster scanning imaging approach on the breast cancer tissue samples with thicknesses greater than 3-5 mm [84]. They located cancerous areas in excised human breast tissue samples at 1.89 THz and obtained the absolute refractive index values of the breast cancer. In 2018, [PERSON] et al. distinguished the connective fibrous tissues from normal tissues of an ex vivo breast using CW THz SI microscopy [54; 85]. The working principle of the imaging method has been explored in the Section 2.5. Here, the THz image clearly revealed sub-wavelength single fat cells and the ex vivo connective fibrous tissues of the breast, as shown in Figure 13. The observed contrast was caused by the water content, THz refractive index, and absorption coefficient, which were lower in the fatty cells compared to those in the dense fibrous connective tissues.  
Figure 13: THz solid immersion microscopy of soft biological tissues with the spatial resolution of 0.15 \(\lambda\): (**a**) Photo of an ex vivo human breast tissue specimen; (**b**) THz SI microscopy image of human breast tissue; (**c**) The diagnosis of the breast tissue specimen as confirmed by histology [54]. Copyright © 2018, AIP Publishing. All rights reserved.
## 5 Experiments and Analysis of Results  
### Basic Experiment  
Table 8 presents the experimental results of the SimCSE algorithm and the EdaCSE algorithm. The models named after the algorithm itself represent the experimental results of combining the model with the SimCSE algorithm, serving as the baseline experiments.  
From Table 8, it can be observed that using the EdaCSE algorithm, there is an improvement of 1.67 and 0.89 for the \"bert-base\" and \"roberta-base\" models, respectively, and 1.08 for the \"bert-large\" model. This suggests that the use of the EdaCSE algorithm is also effective in improving the generalisation ability of the model. Generally speaking, for models such as BERT, adding and deleting characters is a sensitive transformation that is easy to damage the model performance, and EdaCSE has a favourable impact on the model performance, and in a way, the algorithm improves the robustness of such models. Next, around the EdaCSE method, experimental analyses are carried out on Chinese punctuation, English punctuation, types of punctuation, and the number of punctuation insertions.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Core Network
#### 3.1.3 SDN, NFV, MEC
This method incorporates automatic rule checking for arising conflicts in the network and can act automatically and accept or decline actions based on security measures and risk tolerances. SDN is and will continue to make a growing impact in 5G networks, making it even more important for up-to-date security measures to be in place along with routine network audits.