esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Limitations of Existing Representative Methods
### Analysis of Security Vulnerabilities in the Tsobdiou Protocol  
In 2021, [PERSON] et al. [28] proposed a lightweight authenticated two-party key-agreement protocol for mobile communication. In this protocol, the private information of the communication entities is protected by an ID and a password, and the key security is ensured by temporary random numbers that are generated by each party. Secure authentication of the two parties can be achieved under the premise of six scalar multiplication operations. However, in our analysis, we found that the protocol above could not withstand attacks based on known session-specific temporary information. A symbolic definition of the Tsobdiou protocol can be found in [28]. The security analysis of the Tsobdiou protocol is as follows:  
If the attacker obtains the temporary information n of the session during the execution of the Tsobdiou protocol, the session key \(K_{MC}\) of the session can be calculated using the following steps:  
1. Attacker \(C\) obtains the message by listening to the public channel \(M\).
2. Attacker \(C\) obtains the session key directly by calculating \(KMC=n\cdot M\).  
In summary, by leaking the temporary information \(n\) of a specific session, an attacker can calculate and obtain the session key of the current session using the above steps.
# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
## 3 Material and Methods
### Predictive Analytics
#### 3.2.4 SIR Epidemic Model for Spread Disease Model  
The SIR (Susceptible, Infected, and Removed) model is the simplest mathematical model that can developed for infectious diseases [26]. In this model, the total population is divided into three components (shown in Figure 6) as follows:  
* S: Susceptible cases--people who could potentially catch the disease; they either have low immunity system, or they still not infected.
* I: Infected--people who currently have the disease and can transmit it to others.
* R: Removed--people who have already caught the disease and have either recovered or have died.  
In this epidemic model, it is assumed that the epidemic is sufficiently short, so it does not last that long. In addition, the total populations remain constant for this short time period, which gives a reasonable justification for the decrement of the infected and susceptible rate, taking into consideration that all populations will get the disease. The second assumption is that the infected increment rate is propositional to the contact between susceptible and infected cases.  
The SIR model can be presented using Equations (3)-(5) [26]:  
\[\frac{ds}{dt}=-\frac{\beta IS}{N} \tag{3}\]  
\[\frac{ds}{dt}=-\frac{\beta IS}{N}-\gamma I \tag{4}\]  
\[\frac{dR}{dt}=-\gamma I \tag{5}\]  
\(t\) is the time, \(S(t),I(t),\) and \(R(t)\) is the number of susceptible cases, infected, and recovered cases at specific time \(t\). B is the contact rate, and \(\frac{1}{\gamma}\) is the average infection rate.  
We used the SIR model in this study to predict the number of COVID-19 susceptible, infected, and recovered cases for a new unseen time (future time). The prediction used data starting from 2 nd of March 2020 for 250 days. The number of days considered for the initial guess is 26 in the population for each country of the study. The results of applying this model on the three selected countries are shown and discussed in Section 4.3.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 4 Knowledge Gaps and Challenges
### Lighting and Shadow Conditions  
The camera is an optical sensor, so the image is extremely sensitive to light intensity. The quality of images collected under different lighting conditions varies greatly. Poor lighting results in blurry pictures, and the point cloud is inaccurate and noisy. Especially in the construction site, the similarity of the surface texture of many materials and adverse light conditions makes the appearance-based reasoning difficult. Furthermore, the constantly changing shadows during the day add a lot of messy lines to the image and make the same material show a completely different appearance. Various illumination, shadows, weather, and site conditions make it difficult to perform consistent image analysis on such imagery [3], as shown in Figures 10 and 11. In the face of this situation, using laser scanning is a more ideal solution, although there may be some problems such as cost and technical requirements.  
The above-mentioned various attempts to solve the occlusion problem were also applied to solve the problem of light intensity and shadow. Therefore, they will not be repeated here.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 4 Experiment
### Application Test  
To showcase the positive impact of image enhancement on improving downstream high-level visual tasks, we perform experiments using the Extremely Dark (ExDark) dataset [39]. Experiments are conducted on a randomly selected 10% of the ExDark dataset, applying our pre-trained SGPC as a pre-processing step. Subsequently, the YOLOv8 [43] model is applied for pre-training on the COCO dataset [44] for detection and segmentation.  
Referring to Figure 8, the bicycle in the second row, various types of targets in the third row, and boats in the fourth row demonstrate the way to use enhanced images significantly. This could reduce both the missed detection rate and missed segmentation rate compared to performing detection and segmentation directly on the low-light images. Enhanced images for segmentation tasks also improve segmentation accuracy. For instance, as shown in the first row of Figure 8e,f, the husky and the mat underneath it are incorrectly segmented as  
Figure 6: Ablation analysis of the regularization parameter for guided filtering. \(\epsilon=10^{-n}\) represents using a single regularization parameter for the whole image, while \(\mathcal{M}\) indicates using a regularization map with the same dimensions as the input image. Zoomed-in regions are used to illustrate the visual distinctions.  
the same target when segmenting the low-light image. This is solved when segmenting the enhanced image. In addition, the enhanced images also improve the confidence of the detected targets, such as the husky in the first row and the boat in the fourth row of Figure 8c,d.  
YOLOv8 model is also applied pre-trained on the ImageNet dataset [45] to analyze the ExDark dataset for the classification task. The classification accuracy is evaluated for only 11 categories (where the \"People\" category is absent from the ImageNet dataset). According to the statistics in Table 2, the classification accuracy is enhanced through the use of SGPC as pre-processing, improving from 37.25% (top-1) and 61.86% (top-5) to 37.79% (top-1) and 62.13% (top-5). Top-1 refers to the proportion of correct categories that the model predicts to be the most probable, while top-5 relaxes the condition as long as the correct category appears within the top five most likely categorie  
Figure 8: Application examples of low-light image detection and segmentation. (**a**) Low-light input image, (**b**) Image enhanced by SGPC, Parts (**c**,**d**) depict the object detection results of (**a**,**b**) respectively. Parts (**e**,**f**) show the instance segmentation results of (**a**,**b**) respectively.  
Figure 7: Ablation study on the influence of low-light image guidance. (**a**) Low-light image, (**b**) enhanced image without GPF, (**c**) denoising effect when using the enhanced image itself as the guidance image, and (**d**) the results obtained by using the low-light image as the guidance image.
## 2 Materials and Methods
### Study Area and Datasets Used
Furthermore, we integrated Indian Ocean Data Coverage (IODC) imagery from the visible 0.6 \(\upmu\)m channel of the \"High Rate Spinning Enhanced Visible Infra-Red Imager  
Figure 1: Global positioning of the locations used in this study. The locations’ labels are the short names of the sites, adopted from Table 1.  
(SEVIRI)\" on the MSG satellite, operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT); the data are available at ([[https://view.meteosat.int/](https://view.meteosat.int/)]([https://view.meteosat.int/](https://view.meteosat.int/)), accessed on 19 April 2024). Images captured before June 2022 were sourced from MSG-1 (Meteosat-8), while those from June 2022 onward were obtained from MSG-2 (Meteosat-9), following the conclusion of the MSG-1 mission on 1 July 2022. The VIS0.6 channel of MSG SEVIRI belongs to the visible and near-infrared spectrum and has a central wavelength of 0.635 \(\upmu\)m and a spectral band ranging from 0.56 to 0.71 \(\upmu\)m [35]. In this study, the full-disk images from the MSG satellite covered a range from 77\({}^{\circ}\) N to 77\({}^{\circ}\) S in latitude and from 31\({}^{\circ}\) W to 122\({}^{\circ}\) E in longitude. The images were updated every 15 min, with a spatial resolution of 3 km; the data are accessible at [[https://view.eumetsat.int/productviewer/productDetails/msg_iodc:vis006?v=default](https://view.eumetsat.int/productviewer/productDetails/msg_iodc:vis006?v=default)]([https://view.eumetsat.int/productviewer/productDetails/msg_iodc:vis006?v=default](https://view.eumetsat.int/productviewer/productDetails/msg_iodc:vis006?v=default)), accessed on 19 April 2024.
###### Abstract  
In water-related projects, the application of steel sheet pile cofferdams is becoming more and more widespread, and the influence of tunnel construction on the mechanical properties of adjacent cofferdams is important. In this study, the object of research was the mechanical properties of large-span steel sheet pile cofferdams. The open-cut tunnel project was located in Suzhou Yinshan Lake, China. According to the actual construction steps of the tunnel foundation pit, assuming that the soil was a small strain hardening soil model, combined with on-site monitoring data, a three-dimensional elastoplastic finite difference model was established. The results show that during tunnel construction, the maximum settlement of the cofferdam appeared at 0.27\(\sim\)0.53 m on the side of the foundation pit; the maximum horizontal displacement of the steel sheet pile occurred at the pile bottom of foundation pit side, and the seepage gradually increased during construction, eventually resulting in water gushing at the bottom of the foundation pit. After the completion of tunnel construction, the settlement value of the cofferdam presented a pattern that first increased and then decreased from the side of the foundation pit to the side of the adjacent lakeside; the steel cofferdam tilted toward the side of the foundation pit, with a maximum inclination angle of 3.37\({}^{\circ}\). It should be pointed out that as the construction progressed, the axial force of the tie rods in the steel cofferdam changed from a U-shaped distribution to a V-shaped distribution. This study could provide a reference for the impact of tunnel foundation pit construction on adjacent steel cofferdam and could also provide a reference for the safety research of open-cut tunnel construction.  
cut-and-cover tunnel; double sheet pile cofferdam; mechanical properties; finite difference method +
Footnote †: journal: Applied
# Study on Surface Roughness of Sidewall When Micro-Milling LF21 Waveguide Slits
## 2 Materials and Methods  
### Experimental Procedure  
#### 2.1.1 Experimental Device  
Experiments were conducted on a self-developed, three-axis and vertical micro-milling machine (Figure 1). The body size of the machine was 194 mm (X) \(\times\) 194 mm (Y) \(\times\) 400 mm (Z), and the workspace size was 50 mm (X) \(\times\) 50 mm (Y) \(\times\) 102 mm (Z). The spindle speed ranged from 40,000 rpm to 140,000 rpm. The absolute-position accuracy of the feed system was 1 \(\upmu\)m, and the repeat-position accuracy was 0.2 \(\upmu\)m. The Tungsten-Carbide-coated, flat-end mill made by the Nisshin Scientia company was adopted with a diameter of 300 \(\upmu\)m (MSE 230). The material compositions of the LF21 are shown in Table 1. The size of the LF21 workpiece was 15 mm (X) \(\times\) 40 mm (Y) \(\times\) 0.5 mm (Z). The surface roughness of the micro-milled surface was measured by a confocal laser microscope, made by the Keyence company (VK-X200). The optical resolution of the instrument was 0.5 nm min. The 3D surface topography and the 2D surface roughness of the LF21 sidewall micro-milling are shown in Figure 2a,b, respectively. The arithmetic means deviation of the contour of the Ra was used to evaluate the surface roughness of the sidewall surface.  
Where x and y represent the feed and the depth of cut directions. The averages of the surface roughness values, measured at 4 equidistant locations on each slot, were used for the analysis.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### Analytical Assessment of Impact Bending Performance
#### 4.4.2 Bending Stiffness Result  
Generally, composite specimens with thicker cores exhibit higher impact stiffness, as demonstrated in [32]. For the single impact loading regime (Figure 11), the results showed that before seawater exposure, GSV samples with a thinner core had a marginally higher stiffness (43.7 N/mm) compared to the GSH specimen (42.1 N/mm). This was mainly due to the vertical adhesive layers providing additional rigidity in the through-thickness direction in conjunction with the top and bottom facesheets. After seawater exposure, all specimens recorded reduced impact bending stiffness by 8.9%, 45%, and 19.2% for GSP, GSH, and GSV, respectively.  
This large variation in the GSH samples could be attributed to the limited role the horizontal layer plays in improving stiffness under fracture load, which was exacerbated by the moisture uptake over a larger region of area compared to the other arrangements. Thus, the effects of moisture are more drastic on thicker samples as stiffness is significantly reduced, resulting in severe plasticization and its adverse effects on adhesive layers. Furthermore, the low reduction in stiffness of the GSP samples indicates that while the overall damage resistance is enhanced by the addition of adhesive layers in the core (Figure 5), it has an adverse effect on the bending stiffness at fracture loads. For the multiple-impact scenario (Figure 12), a similar trend is observed as all specimens recorded a reduction in stiffness with an increase in the number of impacts. It is worth noting that GSH exhibited the largest reduction in stiffness while GSP recorded the least, with GSV samples having values in between. This showed the limited role of the adhesive layers in the core of FRPSS in improving the bending stiffness of sandwich structures for marine applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Impact No.**} & \multicolumn{2}{c}{**GSP**} & \multicolumn{2}{c}{**GSV**} & \multicolumn{2}{c}{**GSH**} \\ \cline{2-7}  & **Energy (J)** & **Diff (\%)** & **Energy (J)** & **Diff (\%)** & **Energy (J)** & **Diff (\%)** \\ \hline
1 & 0.34 (0.28) & 17.9 & 0.30 (0.13) & 56.7 & 0.36 (0.29) & 19.4 \\
2 & 0.30 (0.33) & \(-9.1\) & 0.31 (0.15) & 51.6 & 0.37 (0.33) & 10.8 \\
3 & 0.34 (0.32) & 6.3 & 0.33 (0.16) & 51.5 & 0.38 (0.36) & 5.3 \\
4 & 0.38 (0.35) & 7.9 & 0.37 (0.17) & 54.1 & 0.38 (Figure 11: Bending stiffness of FRPSS—single impacts.  
Figure 10: Normalised theoretical prediction of FRPSS under impact bending; (**a**) facesheet fracture failure, (**b**) indentation failure, and (**c**) shear core failure (orange arrows denote the reduction in the property after the exposure, a green arrow denotes the increase in the property).
## 2 Research Method
### Numerical Analysis Modeling  
#### 2.2.1 Additional Flow Path MR Damper Design  
To analyze the damping performance of the MR damper with an additional flow path, a numerical analysis model was designed based on the mathematical model described in Section 2.1, which can evaluate the change in damping performance by simulating the pressure drop and magnetic field intensity change of the MR damper. Prior to designing the damper, a popular and widely used MR fluid was chosen. MRF-132 DG, the MR fluid used in this study, is recognized for its usefulness in many fields. In addition, because vehicle dampers must be designed within the operating range required by the vehicle, the quarter-car model of the vehicle on which the damper will be installed was modeled using AMESim 4.3.0.  
Based on this, the length of the piston rod that can be used in the magnetic circuit was determined, and an electromagnetic analysis was performed using ANSYS Maxwell software 2024 R3. The main variables of the electromagnetic analysis were the number of coil turns, current range, and the diameter and length of the main flow path, which were repeated until each operating point was located in the linear section of the B-H graph. Because the main flow path has a significant influence on both electromagnetic and flow analyses, and its design range is closely related to saturation of the magnetic circuit, the optimal value was selected through electromagnetic analysis prior to flow analysis. The above process is a modified design guide provided by the L Company and is shown in Figure 9; the manufacturer of the MR fluid and its effectiveness have already been proven.  
Conversely, because the additional flow path was almost unaffected by the magnetic circuit, the optimal value was selected based on a theoretical fluid dynamics equation. Subsequently, the anti-rod stress of the MR fluid was derived using the optimal value selected through electromagnetic analysis. Through the above process, the damping force model of the optimally designed additional passage-type MR damper was transferred to AMESim, and the performance of the MR damper was analyzed by applying road surface excitation modeled with bumps and random frequencies. The above processes and the result designed according to the processes are expressed in Figures 10 and 11.  
Figure 10: Flow chart to analyze the characteristics of an MR damper with an additional flow path.  
Figure 9: Magnetic circuit design procedure (L-Company).
# Fuzzy MCDM Methodology for Analysis of Fibre Laser Cutting Process
## 3 Results and Discussion
### Practicality and Feasibility of the Approach  
The practicality of the proposed fuzzy MCDM methodology in industrial laser cutting settings is reflected in its ability to consider multiple issues, including the following:  
* Merging qualitative (linguistic) and quantitative data from multiple decision makers (engineers and operators), i.e., cross-functional teams, with the possibility of handling imprecise judgments and decision makers' opinions (e.g., \"moderate dress\", or \"acceptable kerf geometry\"), as well as data variability and uncertainty (due to the actual cutting process or measurement system).
* Existence of multiple conflicting processing and cut quality characteristics that characterize the laser cutting technology (e.g., cutting costs, kerf width, kerf taper, productivity, dress existence, surface roughness, etc.), the absence of a single optimal cutting condition and the necessity to determine the 'as good as possible' trade-off solution(s) for a given case study (sheet thickness and material type, required cut specifications, type of laser cutting method, etc.).
* Screening the key input parameters that are of the greatest importance among numerous input parameters to adequately consider their adjustments and guide further experimental hyper-space exploration in order to achieve more favourable cutting results.  
Although practical usage would require engineers' and operators' training or support from domain experts, at least in the beginning, it is believed that the approach discussed, with all its advantages and disadvantages, has a certain potential for practical application in industry. With the existence of available and accessible tools for fuzzy MCDM methods and applications, and their integration and adaptation with existing control and decision-support systems and Industry 4.0 technologies, it is believed to be a feasible solution that can be accepted by manufacturers using laser cutting technology.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 1 Introduction
### Pathophysiological Mechanisms Connecting Periodontal Disease to Alzheimer's Disease
#### 1.2.1 Systematic Inflammation and Neuroinflammation  
Periodontal disease is characterized by chronic local inflammation, which can spill over into systemic circulation, contributing to a sustained inflammatory state. Pro-inflammatory cytokines, such as interleukin-1\(\upbeta\) (IL-1\(\upbeta\)), interleukin-6 (IL-6), and tumor necrosis factor-\(\upalpha\) (TNF-\(\upalpha\)), are elevated in individuals with PD and have been implicated in the pathogenesis of Alzheimer's disease [122; 123; 124; 125; 126; 127; 128; 129; 130; 131]. These cytokines can cross the blood-brain barrier (BBB) and activate microglia, the resident immune cells of the central nervous system. Chronic microglial activation leads to neuroinflammation, which is a hallmark of [PERSON]'s pathology [132; 133; 134; 135; 136; 137; 138; 139; 140; 141].
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 5 Results  
### Delay-Doppler Plots for Selected Activities  
In a way similar to in [7], the information about the sensed environment is first presented in Figure 9 in the form of max-hold delay-Doppler plots for the four scenarios described above. Even in the case of the static person, slight leakage of the channel tap due to the delay-Doppler grid mismatch is visible. In all three moving scenarios, the reflections from static objects at similar distances from the sensing receiver can be identified. Note that a delay of \(\approx 29\) ns is in good agreement with a monitored area width of 4.5 m, see Figure 8. This static reflection usually happens in the situation when the person is out of the range of the used horn antennas, e.g., at the beginning or end of the measurements. In the scenario of the random walk of the person in the monitored area (scenario 2), we can notice that the movement of the person was permanent, as the grid element corresponding to zero velocity has negligible magnitude (not equal to zero as a result of leakage due to the grid mismatch). In the plot representing the running person (scenario 3), we can easily identify three phases of movement--acceleration, running with a constant speed, and deceleration  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \(N_{g}\) Cells & \(N_{t}\) Cells & \(P_{fa}\) & \(z\)-Score Threshold & **Time Complexity [ms]** \\ \hline \multicolumn{4}{l}{**Conventional CFAR**} \\ \hline
3 & 8 & \(1\times 10^{-6}\) & N/A & 10.0 \\ \hline
2 & 10 & \(1\times 10^{-6}\) & N/A & 9.9 \\ \hline \multicolumn{4}{l}{**Persistent homology applied on observations**} \\ \hline N/A & N/A & N/A & 6 & 264.6 \\ \hline N/A & N/A & N/A & 6 & 243.2 \\ \hline \multicolumn{4}{l}{**Persistent homology applied on SNR map**} \\ \hline
3 & 8 & N/A & 6 & 269.5 \\ \hline
2 & 10 & N/A & 6 & 249.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters and time complexity per one delay-Doppler image for three investigated detectors. N/A stands for ”Not applicable.”(the person had to stop prior to reaching the room wall). The Delay-Doppler plot for the last scenario--the dancing person (scenario 4)--contains important Doppler shifts due to the substantial movements of the body and the limbs, but the distance from the sensing receiver remains limited.