esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Artificial Intelligence in Ultrasound in Symptomatic Carotid Plaque
### AI Based on Ultrasound Images for Automatic Carotid Segmentation  
Promising results have been reached in applying a CNN in a U-shaped structure (U-Net) for automatic segmentation. This model uses contracting or encoding layers followed by up-sampling or decoding layers and skip connections to increase the resolution of the output. U-Net has been used in one study by [PERSON] et al. to identify carotid stenosis in more than 60% using 3D ultrasound datasets, obtaining a high accuracy (Dice coefficient 0.9). However, the study was not accurate enough, considering the fewer subjects selected (13) [88].  
Dilated convolution networks represent another CNN architecture with the advantage of no need for down- and up-sampling. A combination of U-Net and dilated convolution networks offers the best carotid segmentation method [89; 90]. The application of dilated U-Net [91] has shown to be better in under-performing conditions and the case of complex plaques with a Dice coefficient of 0.88, compared to the value of 0.77 for U-Net alone, while no differences were reported in the better-performing situations. The low value of the Dice coefficient compared with the study reported by [PERSON] et al. could derive from the different methodologies used to detect US images. In fact, [PERSON] et al. used a 3D model that is more accurate than the 2D datasets generated from research data in the latter study. Moreover, the model could perform carotid segmentation in healthy people without arterial plaques, as confirmed by other studies [92; 93; 94].  
Despite the better performance of dilated U-Net, this model presents some limitations, such as the need to resort to a sonographer input via a bounding box on the plaque to achieve an acceptable segmentation performance (semi-automated segmentation) and the reduction of the accuracy in the case of the presence of acoustic shadowing. Therefore, new CNN models have been proposed to pose the bounding box correctly before the segmentation to reduce errors [95; 96].  
These data were confirmed in more recent studies using layered deep learning U-Net architecture for carotid wall segmentation using images from US studies. [PERSON] et al. conducted one study on two different demographic groups of patients (a Japanese cohort and a Hong Kong cohort) with atherosclerotic carotid disease. The study showed good accuracy, dice similarity, and correlation coefficient (>90, >75, and >0.80%, respectively) in detecting and classifying carotid plaques. However, including the noisy Hong Kong database in the system and the high-intensity zone of the media region of the plaques did not allow for performing the best partial volume in the region of interest [97].
# Preparation and Properties of Waterborne Polyurethane and SBS Composite-Modified Emulsified Asphalt
###### Abstract  
To address the issue of insufficient durability of traditional modified emulsified asphalt in the application of cold mix and cold paving anti-skid wear layers, this study utilizes cationic waterborne polyurethane (PU+) for composite modification to enhance adhesion and performance across a range of temperatures. Initially, composite-modified emulsified asphalt samples were prepared with varying dosages of PU+ according to a gradient method. Routine performance tests were conducted on the evaporated residues for analysis. Advanced rheological tests, including temperature sweep (TS), frequency sweep (FS), linear amplitude sweep (LAS), and multi-stress creep recovery (MSCR) tests, were performed using a dynamic shear rheometer (DSR). Surface free energy (SFE) tests were conducted with a fully automated surface tension meter (STM). A comprehensive evaluation of the high-temperature rheological properties, fatigue properties, adhesion properties, and water damage resistance of the modified emulsified asphalt residues was carried out. Chemical changes before and after modification were characterized using Fourier transform infrared spectroscopy (FTIR), and the distribution of polymers in the evaporated residue was observed using fluorescence microscopy (FM). The results demonstrated that cationic waterborne polyurethane significantly enhanced the fatigue and adhesion properties of SBS-modified emulsified asphalt, but it also weakened the water damage resistance of asphalt. MSCR tests revealed that the addition of cationic waterborne polyurethane might reduce the elastic recovery performance of modified asphalt, thereby weakening its resistance to rutting. Among the samples, the modified asphalt with a PU+ content of 6% exhibited good high-temperature shear resistance and elastic recovery performance, demonstrating the best anti-rutting performance.  
modified emulsified asphalt; waterborne polyurethane; modifier; road performance
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 2 A Cellular Automaton Model for Urban Roads Considering Vehicle Heterogeneity
### Vehicle Update Rules for Road Segments
#### 2.2.1 Safe Distance Model
Similarly, the calculation process for the safe distance \(L_{sd}\) when a car decelerates is analogous to that for the safe distance \(L_{sa}\) when a car accelerates and the safe distance \(L_{sk}\) when a car maintains its speed. The difference lies in that, at time \(t\), the position of the following car is updated based on its speed \((v_{n}(t)-a_{n})\). By analogy to the calculation processes of \(L_{sa}(t)\) and \(L_{sk}(t)\), we can derive the expression for \(L_{sd}(t)\) as follows:  
\[L_{sd}(t)=f(L_{r},L_{b},L_{u};v_{n}(t)-a_{n},E_{n};v_{n-1}(t)-E_{n-1},E_{n-1}) \tag{12}\]  
Therefore, when the distance between the following vehicle and the leading vehicle satisfies the condition \(L_{sd}(t)\leq L_{n}(t)<L_{sk}(t)\), the following vehicle needs to decelerate, whereas when \(L_{n}(t)<L_{sd}(t)\), in order to avoid a collision, the following vehicle needs to perform emergency braking.
## 2 Acoustic Metasurfaces
### Phase-Modulating Metasurfaces
The proposed structure consists of a binary phase-adjustable membrane resonator array, with a two-dimensional metasurface morphology of 90 pixels. Each membrane has a radius of 27 mm and a thickness of 0.1 mm, with a magnetic disk with aradius of 6 mm mass 0.9 g attached to its center, and above each unit cell, an electromagnet with a diameter of 2.5 mm can be placed at intervals to fix or release the center of the membrane depending on the voltage application. As shown in Figure 6, the unit cell is designed to enable binary phase switching via electromagnetic control, and its vibration modes vary depending on the applied voltage state. Through experiments, it was confirmed that it is possible to form a hotspot that concentrates sound pressure as opposed to a quiet zone that reduces sound pressure by up to 21 dB at a specific location. This control is achieved through a feedback-based optimization algorithm, and SSM shows that complex echo sound fields can also be actively reconstructed. Figure 5: Three-dimensional focusing and acoustic vortex generation of ultrasound using gradient-index (GRIN) metasurfaces. Reproduced under a Creative Commons Attribution International 4.0 license [41], Nature Communications, 2019. Figure 6: (**a**) Structural diagram of a membrane-based unit cell capable of switching the phase state through electromagnetic control. (**b**) Simulation result of vibration mode according to the phase state transition of the unit cell. Reproduced under a CC BY-NC-ND 4.0 License [42], PNAS, 2018. [PERSON] et al. developed a tunable hybrid ultrasonic lens by infiltrating a solid phononic crystal (PnC) lens with a thermo-responsive hydrogel composed of PVA and PNIPAm [43]. The hydrogel exhibits a discontinuous phase transition near 32 \({}^{\circ}\)C, which causes a sharp increase in the speed of sound and refractive index. This thermal actuation enables the hybrid lens to dynamically shift its focal length from 40 mm to 50 mm. The focal shift mechanism enabled by the hydrogel's phase transition is schematically represented in Figure 7. The design leverages the contrast between the PnC structure and the thermosensitive polymer to modulate dispersion and transmission, allowing for temperature-driven focusing without electrical components.
## 2 Results and Discussion
### Profiling of Onion and Lemon Secondary Metabolites via UPLC-QTOFMS and Multivariate Data Analysis
#### 2.2.2. Flavonoids  
Flavonoids were detected as the most abundant class, represented by 18 peaks belonging to flavanone, flavone, and flavonoids subclasses, principally as _O_-glycos. In MS analysis, the nature of the sugars could be revealed by the elimination of the sugar residue, which would have a mass of 162 amu (hexose; glucose or galactose), 146 amu (rhamnose), or 132 amu (pentose; xylose or arabinose). The MS spectrum interpretation allowed for the annotation of hesperitin signals (\(m/z\) 301.27, C\({}_{16}\)H\({}_{3}\)O\({}_{6}\)-) in peaks 37 and 39, luteolin (\(m/z\) 285.21, C\({}_{15}\)H\({}_{9}\)O\({}_{6}\)-) in peaks 37 and 23, apigenin (\(m/z\) 269.21, C\({}_{15}\)H\({}_{9}\)O-) in peak 13, diosmetin (\(m/z\) 299.26, C\({}_{16}\)H\({}_{11}\)O\({}_{6}\)-) in peak 14, and limocitirin (\(m/z\) 345.21, C\({}_{17}\)H\({}_{13}\)O\({}_{8}\)-) in peaks 17, 18, and 26. The readily cleaved sugar moieties from aglycone yielded a fragment mass in these respective peaks. In contrast, several flavonoid peaks (8-12) showed intense molecular ion peaks, with \(\mathrm{[M-90-H]^{-}}\) and \(\mathrm{[M-120-H]^{-}}\) fragments exclusively in flavone peaks of the MSn spectra indicative of sugar cleavage in _C_-glycos peaks 8-12. The fragmentation pathways most characteristic of _C_-glycoslavonoids include dehydration (\(-18\) amu) and cross-ring cleavage (0,2 and 0,3) of the sugar moiety, i.e., \(-120\) amu and \(-90\) amu for the _C_-hexosides; \(-90\) amu and \(-60\) amu for the _C_-pentosides. Citrus fruits are rich dietary sources of flavonoids [43] and thus exhibit a wide range of pharmaceutical properties including anti-atherogenic, anti-inflammatory, antitumor, and antioxidant activities [44].
# Achievement Prediction and Performance Assessment System for Nations in the Asian Games
## 2 Materials and Methods
### Back-Propagation DEA Algorithm
Furthermore, correctly and effectively leveraging national resources for mega-sports events is an objective for which each Asian nation must strive. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Observations** & **Max** & **Min** & **Mean** & **SD** \\ \hline GDP & 126 & 17,734,060 & 1700 & 1,186,524 & 2,594,255 \\ population & 126 & 1,430,000,000 & 429,152 & 208,000,000 & 422,000,000 \\ athletes & 126 & 1956 & 17 & 419.80 & 306.86 \\ ZSG & 126 & 161.84 & 0.17 & 24.57 & 34.54 \\ efficiency & 126 & 1.95 & 0.04 & 0.74 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics of 14 Asian nations’ input and output in the 2023 Asian Games. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **GDP** & **Population** & **Athletes** & **ZSG** & **Efficiency** & **Gold** & **Silver** & **Bronze** \\  & & & & & & **Medals** & **Medals** & **Medals** \\ \hline
1990 & 121,400 & 20,546,664 & 288 & 6.11 & 0.22 & 0 & 10 & 21 \\
1994 & 224,000 & 21,298,930 & 268 & 11.26 & 0.36 & 7 & 13 & 23 \\
1998 & 308,000 & 21,908,135 & 384 & 22.36 & 0.73 & 19 & 17 & 41 \\
2002 & 386,000 & 22,548,009 & 359 & 14.33 & 0.34 & 10 & 17 & 25 \\
2006 & 386,450 & 23,036,087 & 391 & 12.39 & 0.37 & 9 & 10 & 27 \\
2010 & 444,281 & 23,083,083 & 397 & 18.10 & 0.52 & 13 & 16 & 38 \\
2014 & 535,332 & 23,422,513 & 416 & 14.22 & 0.38 & 10 & 18 & 23 \\
2018 & 609,251 & 23,726,185 & 588 & 19.94 & 0.72 & 17 & 19 & 31 \\
2022 & 761,400 & 23,923,276 & 524 & 17.18 & 0.59 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example for inputs and outputs of Chinese Taipei from the 1990 to 2022 Asian Games. The confusion matrix, which combines predicted versus actual class examples, is central to the algorithm performance evaluation. The Asian nations' prediction results must fit the decision threshold to reach the confusion matrix. To determine whether the predictions for gold, silver, bronze, and total medals won are ideal, we can evaluate them based on different needs and contexts [47; 48]. The following are some possible considerations (Figure 2):

1. Accuracy: The prediction accuracy for gold, silver, bronze, and total medals won ranges between 0.825 and 1.000, with the lowest accuracy for bronze medals at 0.825. Higher accuracy is generally considered ideal, depending on the specific application context. 2. Precision: Precision indicates the accuracy of optimistic predictions. Prediction precision for gold, silver, bronze, and overall medals ranges between 0.824 and 1.000, varying depending on the type of medal. Higher precision is crucial to ensure prediction accuracy, while low precision might lead to misjudgments. 3. Recall: Recall represents the proportion of correct predictions among positives. Prediction recall for gold, silver, bronze, and overall medals ranges from 0.966 to 1.000. Figure 2: The 2023 Asian game medal prediction results of confusion matrix.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 4 Technical Advantages of Our Solution
### High-Precision Ranging Based on 5G Millimeter-Wave Technology  
Fifth-generation (5G) millimeter-wave technology offers higher frequency wireless signals that enable more accurate ranging. This solution fully utilizes the high bandwidth and low latency characteristics of 5G millimeter-wave technology, enhancing the precision of signal collection, especially in complex indoor or urban environments. Millimeter-wave signals are more sensitive to signal attenuation, but their high frequency also provides more  
Figure 3: An overview of the UE environmental sensing workflow.  
usable information, which better meets the needs of high-precision ranging. By combining millimeter-wave signals with the powerful computing capabilities of MEC, the system can accurately calculate the distance between devices and base stations, significantly improving the accuracy of signal matching and ranging.
# A Mixed Clustering Approach for Real-Time Anomaly Detection
## 7 Conclusions, Limitations and Lines for Future Works
### Limitations and Lines for Future Works  
Although our algorithm performs well in many cases, it still has some limitations. First of all, it has a limitation in finding anomaly from high-dimensional data with continuous attributes. Secondly, as it is based on \(k\)-means algorithm, it faces issues such as the centroid can be dragged by anomalies or anomalies may have their own clusters instead of being ignored. Finally, though it works nicely on time-stamp data, it lacks efficacy in dealing with temporal interval data.  
Future works may proceed in the following directions:  
* An efficient algorithm can be designed to find real-time anomalies in high-dimensional, heterogeneous data with continuous attributes.
* An efficient algorithm can be designed to find anomaly from temporal interval dataset.
* An approach other than partitioning and hierarchical viz. density based approach can be employed for the real-time anomaly detection.  
Conceptualization, F.A.M.; Methodology, F.A.M.; Software, F.A.M., M.S.; Validation, F.A.M., M.S.; Formal Analysis, F.A.M.; Investigation, F.A.M., M.S.; Reounce, F.A.M., M.S.; Data Curation, F.A.M., M.S.; Writing--original draft preparation, F.A.M., M.S.; writing--review and editing, F.A.M., M.S.; visualization, F.A.M.; supervision, F.A.M.; project administration, F.A.M., M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
On behalf of all the authors the corresponding author states that this research has received no funding from any external agency. The co-author [PERSON] has paid the APC.  
The data, code and other materials can be made available on request.  
All the authors have consented to gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the quality of the work.  
There is no conflict of interest or competing interest among the authors.
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 3 Proposed Community Detection Framework  
This section proposes a novel unsupervised model for end-to-end community detection. The process has three steps: (1) user topology construction; (2) communication content and feature matrix computation; (3) community detection and visualization. Figure 1 illustrates the flow chart of the community detection model.  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Reference** & **Disadvantages** & **Performance** & **Possible Improvements** \\ \hline [PERSON] (2006 [17]) & Focused mainly on modularity and may miss non-topological community attributes. & Revealed unexpected structural features. & Included a broader set of community-detection attributes. \\ \hline [PERSON] et al. (2013 [42]) & LogAnalysis may not dynamically adapt to evolving crime tactics or understand larger contextual data groupings. & Popular in criminal network analysis for identifying influential members. & Integrate dynamic learning capabilities to adapt to evolving crime tactics. \\ \hline [PERSON] et al. (2013 [31]) & Overly focuses on direct connections only. & More accurate online community detection results. & Incorporate broader network perspectives beyond direct connections. \\ \hline [PERSON] et al. (2018 [33]) & First GNN-based model might not be optimized fully for performance. & First to propose a GNN-based community detection model. & Optimize and update model frameworks based on subsequent research and feedback. \\ \hline [PERSON] et al. (2019 [8]) & ClusterNet might not account for non-topological data features sufficiently. & Achieves optimal modularity of 0.55 on multiple datasets. & Adapt model to include non-topological features and improve versatility. \\ \hline [PERSON] et al. (2020 [43]) & CISMO focuses primarily on semantic tools; could miss critical structural insights in social network analysis. & Assists law enforcement with crime prevention by visualizing user connections. & Enhance structural analysis capabilities alongside topic extraction methods. \\ \hline [PERSON] et al. (2023 [37]) & Does not evaluate performance on unstructured datasets like Enron. & Effective use of attention mechanisms to capture dynamic community structures. & Test and validate on diverse, including unstructured, datasets. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the discussed studies.
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attacks on Various Network Layers  
#### 5.3.1 Denial of Service Attack  
Restricted memory and less computational capacity of wireless sensor network make them vulnerable of denial of service attack [71; 72; 73; 74; 75; 76; 77; 78; 79; 80]. In this attack, network resources are made inaccessible by network congestion [70; 71; 72; 73].  
Three types of denial of service attacks are described in the literature [70; 71; 72; 73; 74].  
* Physical devastation/Node capture of wireless sensor nodes  
Wireless sensor nodes are deployed and distributed at various places for various applications and there are possibilities of physical damage or destruction of nodes by attackers. Node capturing may result into alteration of hardware and software of WSN node [75; 76; 77].  
* Utilization of network resources by intruders and making the scarce resources unavailable  
Attackers or intruders can exploit the limited network resources and make them unavailable for actual users.  
* Alteration of configurations of wireless sensor network by attackers  
The encryption and other WSN configuration aspects must be confidential and any alterations made by intruders make them unapproachable for genuine users. Jamming of network, camouflaging the wireless sensor network ambiance and physical attack on sensor nodes are common threats.
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
## 4 Simulation  
To evaluate the accuracy of the analytical results, we performed computer simulations using RAPTOR [23]. As shown in Figure 4, a UE first turns on the power in cell 1 and moves to other cells until it receives another incoming call.  
The simulation was carried out with the following procedures.  
1. Every UE turns on the power in cell number 1.
2. The call arrival time (incoming or outgoing) to the UE follows an exponential distribution with a mean of \(1/\lambda_{c}\) (\(T_{c}\sim\text{Exp}[\lambda_{c}]\)).
3. The time spent in a cell follows a gamma distribution with a mean of \(1/\lambda_{m}\)[25] (\(T_{m}\sim\text{Gamma}(\alpha,\,\beta)\), \(\text{E}(T_{m})=\alpha\beta=1/\lambda_{m}\)).
4. If the call occurrence time is less than the time spent in a cell, the UE does not require registration as the system knows the UE's location without any additional registration cost.
5. Otherwise, when the call arrival time is greater than or equal to the time in a cell, the UE moves to the direction with a probability of \(q\). Note that the moving direction is  
Figure 4: RAPTOR flowchart-based simulation.  
randomly chosen among six directions. The number of registrations can be updated whenever the UE crosses the new TA.
4. If the call arrival time is less than equal to the sum of time spent in a cell, the simulation procedure of the UE stops.
5. Finally, the paging cost (number of paged cells) is evaluated. Note that the paging cost is 7 in an urban environment and 10 in a rural environment.  
In the simulation model, the process for 1000 UEs was repeated. After 10 simulations, final results for 10,000 UEs were evaluated to compute average number of registrations.
## 2 Materials and Methods  
### Sample Preparation and Hyperspectral Image Collection  
The commercial whole wheat flour (JIN FEIXUE QUAN MAI FEN, FEI XUE LIANG YOU SHI PIN Co. Ltd., Dongying, China) was obtained from a local supermarket, and was made of winter wheat harvested in Shandong province, China. Cooked peanut and walnut powders were procured from a commercial food-processing factory (Wotelaisi Biological Technology Co. Ltd., Lanzhou, China). The particle sizes of the three types of powders--flour, cooked peanut and walnut powders--were all less than 180 um. Peanut powder and walnut powder were mixed into flour, respectively, with contaminant levels of 0.01%, 0.05%, 0.1%, 0.5%, 1%, 3%, 5%, and 10% (_w/w_). The samples were fully mixed to be homogeneous. Pure flour, peanut, and walnut powder samples were also prepared. Mixed flour of each proportion filled up three plastic square Petri dishes (100 mm width, 100 mm length, and 15 mm height) as three replicates. The hyperspectral images were collected via a pushbroom HSI system (SPECIM SisuCHEMA, Spectra Imaging Ltd., Oulu, Finland) with a spectral range of 936-1720 nm and spectral resolution of 3.45 nm. The hyperspectral camera (SPECIM FX 17, Spectra Imaging Ltd., Finland) combined with indium gallium arsenide (InGaAs) detector acquired hyperspectral images by scanning line by line, yielding the three-dimensional hypercube data with dimensions of 640 (pixels) \(\times\) 972 (lines) \(\times\)224 (bands). Each hyperspectral image comprised the three plastic square Petri dishes filled up with powder of the same mixed proportion. In total, 19 hyperspectral images (eight for peanut-flour mixtures with different contamination proportions, eight for walnut-flour mixtures, as well as each for pure flour, peanut powder, and walnut powder) were collected. The image resolution was about 0.32 mm/pixel.