esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Edge Computing and Related Computing Paradigms
### Edge Computing
#### 2.4.4 Multi-Access Edge Computing  
Another concept merging edge computing and mobile network architecture was proposed by the newly created (2014) industry specification group (ISG) within the European telecommunications standards institute (ETSI) [75]. The solution outlined by ETSI is known as mobile edge computing (MEC), which has been supported by prominent mobile operators and manufacturers, such as NTT DoCoMo, Vodafone, TELECOM Italia, IBM, Nokia, Huawei, Intel, and other companies. At the MEC World Congress 2016, MEC ISG renamed mobile edge computing to \"multi-access edge computing\" to reflect the growing interests of noncellular operators and to include a wider scope of applications not only limited to mobile device-specific tasks.  
According to ETSI, MEC is a platform that offers application developers and content providers cloud computing capabilities and an IT service environment within RAN in 4G and 5G at the edge of the network [76].  
In MEC, edge computing functionalities can be appended to existing base stations using RAN operators. In MEC, data centers are small with virtualization capacity. Compared with cloud computing, available computing resources in MEC are moderate because of the underlying hardware.  
Furthermore, low-latency applications, as well as delay-sensitive critical applications [75], can be supported in MEC. A personalized and contextualized experience has been offered to mobile users using MEC applications because these applications profit from real-time radio and network information.  
Connectivity in MEC has been established via WAN, Wi-Fi, or cellular connections. The focus of MEC is on RAN-based network infrastructure providers. It is anticipated that MEC benefits significantly from the 5G platform [75] since a broad range of mobile devices with lower latency and higher bandwidth can be supported by 5G. In addition to 5G technologies, MEC also benefits from using software-defined networking (SDN) and network function virtualization (NFV) capabilities [77, 78].
## 4 Application to Plastic Facial Phantom
### Apparatus and Experimental Protocol  
CT was performed using medical imaging equipment (Brivo CT 385, General Electronics Medical Systems, Milwaukee, WI, USA) to acquire the location information of the phantom surface. Cross-sectional CT images were taken at 0.5 mm intervals (resolution, 512 \(\times\) 512 pixels). In addition, an optical tracking device and an electromagnetic tracking device, which are commonly used in clinical practice, were used to acquire the points in the patient space. For the optical devices, three optical cameras (Optitrack Flex 3, Natural points, Corvallis, OR, USA), like those that are generally used for motion capture systems [28; 29], and a passive surgical probe (Northern Digital Inc., Waterloo, ON, Canada) were used (Figure 6A). The 3D coordinates of four infrared markers attached to passive probes were obtained using the optical system. The optical system consisted of the three optical cameras, the data acquisition S/W, and the surgical probe. An Aurora electromagnetic tracking system (Northern Digital Inc., Waterloo, ON, Canada) was used to acquire the point cloud of the facial surface (Figure 6B). The system consisted of an electromagnetic field generator, a tracker attached to the surface of the forehead, and a pointer tool to extract the point cloud of the facial surface.  
The Medical Imaging Interaction Toolkit (MITK) library was used to acquire 2D tomographic images and the 3D shape of the facial phantom from CT DICOM files. The 3D point sets of the facial surface were extracted using the marching cube technique, and the acquisition range of the 3D points was limited in range from the lip area to the top of the forehead. Patient space coordinates were obtained by tracing the surface of the face using the optical and electromagnetic equipment. The tracing time did not exceed 20 s in order to accurately reflect real clinical situations.
## 4 Cavitation Effects  
### Pressure Distribution on the Stator Blade Surface According to the Two CFD Models  
Figure 11 shows the load distribution on the stator blade with and without cavitation. The torque resulted from the pressure difference between the suction surface and pressure surface of the blade. The fluid coming out of the turbine struck the leading edge of the stator, which caused a significant pressure drop at the suction side immediately downstream. Without cavitation, the predicted pressure on the suction side may even drop below zero, which indicates cavitation risk [30].The pressure difference between the pressure and suction sides was lower with cavitation than without cavitation, which was attributed to the negative pressure on the suction side being clipped by the vapor pressure. The pressure difference on the blade surface was over-predicted when cavitation was not considered, which explains the large prediction error for the hydrodynamic performance. It would be worth noting out that the vapor pressure is very small (about \(10^{-4}\) MPa) so it is effectively zero in the figures.  
Figure 10: Isosurface of 0.1 vapor volume fraction distribution in the stator and turbine domains under various _SRs_. (**a**) cavitation distribution in stator at different _SRs_, (**b**) cavitation distribution in turbine at different _SR_s.  
To define the position of the monitoring points around the blade, \(x\) is the length of the curve between the points on the camberline and the leading edge of the blade. The intersection of the normal of the bisecting point on the camberline and the blade surface (the intersection point is the position of the blade surface \(y=0\)) is considered and the outer normal of the blade surface is taken as the \(y\) direction at this intersection point, and the height of the monitoring point from the blade surface is \(y\). c represents blade camberline length. The definition of camberline for blades can be found in Ref. [30]. The low-pressure region slowly shifted from \(x/\text{c}=-0.015\)-\(0.225\) (\(SR=0\)) to \(0.018\)-\(0.217\) (\(SR=0.2\)). The cavitation center shifted from \(x/\text{c}=0.106\) to \(0.142\). These results indicate that the cavitation zone moved downstream of the suction surface of the stator blade as the \(SR\) increased. The mass flow rate coming out of the turbine and striking the stator blade decreased, which caused the flow stagnation point to transfer from the pressure side to the suction side. This increased the pressure on the suction side and reduced the risk of cavitation.
###### Abstract  
The utilization of Low Earth Orbit (LEO) satellites is anticipated to augment various aspects of traditional GNSS-based Positioning, Navigation, and Timing (PNT) services. While the LEO satellite orbital products can nowadays be produced with rather high accuracy in real-time of a few centimeters, the precision of the LEO satellite clock products that can be achieved in real-time is less studied. The latter, however, plays an essential role in the LEO-augmented positioning and timing performances. In real-time, the users eventually use the predicted LEO satellite clocks, with their precision determined by both the near-real-time clock precision and the prediction time needed to match the time window for real-time applications, i.e., the precision loss during the prediction phase. In this study, a real-time LEO satellite clock determination method, consisting of near-real-time clock determination with ultra-short-term clock prediction is proposed and implemented. The principles and strategies of this method are discussed in detail. The proposed method utilized Kalman-filter-based processing, but supports restarts at pre-defined times, thus hampering continuous bias propagation and accumulation from ancient epochs. Based on the method, using Sentinel-3B GNSS observations and the real-time GNSS products from the National Center for Space Studies (CNES) in France, the near-real-time LEO satellite clocks can reach a precision of 0.2 to 0.3 ns, and the precision loss during the prediction phase is within 0.07 ns for a prediction time window from 30 to 90 s. This results in a total error budget in the real-time LEO satellite clocks of about 0.3 ns.  
LEO; real-time; satellite clock; clock determination; clock prediction +
Footnote †: journal: remote sensing  
Acticle Real-Time LEO Satellite Clocks Based on Near-Real-Time Clock Determination with Ultra-Short-Term Prediction
## 3 Results and Discussion
### Modeling of Leakage Rate  
Figure 8 depicts the pressure-dependent leakage rates in closed state of the introduced microvalve designs based on the analytical leakage model derived in Section 2.3. The depicted standard deviation is a result of the calculated propagation of manufacturing tolerances and uncertainties in material properties.  
The analytically calculated leakage rates show a clear linear dependency on the inlet pressure. The highest leakage of (171.5 \(\pm\) 93.4) \(\upmu\)L/min at 20 kPa is expected for the basic design. The high force design shows no significant change in leakage, where increased contact pressure of the valve diaphragm results only in a slightly reduced leakage of  
Figure 8: Analytically modelled pressure-dependent leakage rates for the three microvalve variants. Error bars show standard deviations based on calculated propagation of uncertainty of manufacturing and material tolerances.  
Figure 7: (**a**) Flow field for a width of the trenches of 100 \(\upmu\)m. (**b**) Flow field for a width of the trenches of 180 \(\upmu\)m, where no vortex generation is observed.  
(147.9 \(\pm\) 82.6) \(\mu\)L/min at the same fluidic pressure applied. All microvalve design variants exhibit large standard deviations of about 60% due to high manufacturing tolerances of the steel actuator foils, propagating to high relative uncertainty in the calculated leakage rates. Coated microvalves show significantly reduced absolute values of leakage rates. The effective Young's modulus resulting from the Parylene-C-steel interface leads to a lower value of the average surface separation (see Equation (2)) and, consequently, leakage is expected to be as low as (37.9 \(\pm\) 23) \(\mu\)L/min for an inlet pressure of 20 kPa, and, hence, is much lower than for the two other design variants. The theoretically determined leakage rates are compared to measured values of fabricated microvalves of the three designs; results are presented in Section 3.3.
# Critical Factors behind Construction Delay in Petrochemical Projects in Saudi Arabia
## 5 Identifying Factors Causing Construction Delay in Petrochemical Projects
### Weather Effects  
Bad weather conditions usually affect the construction project. For example, heavy rains, sandstorms, extremely hot weather, humidity, and so forth may affect the labor productivity and eventually delay the project schedule.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{12}{c}{**Potential Factors**} & \multicolumn{12}{c}{**Resources Reference Number**} \\ \cline{2-12} \multicolumn{12}{c}{} & **[1]** & **[2]** & **[3]** & **[4]** & **[5]** & **[6]** & **[7]** & **[8]** & **[9]** & **[10]** & **[11]** & **[12]** & **[13]** & **[14]** & **[15]** \\ \hline
1. & Poor site management and supervision by contractors & X & X & X & X & & & X & X & X & X & X & X & & & X & X & X \\
2. & Conflict between the main contractor and subcontractor & & X & & & & X & & & & & & & & & & & & & & \\
3. & Inadequate planning and scheduling of projects by the contractor & & & & & & X & X & X & & & & & & & & & & & \\
4. & Delays of material or equipment & & & & & & & & & & & & & & & & & & & \\
5. & Delays in handing over the construction site to the contractor & & & & & & & & & & & & & & & & & & & \\ \hline X & & & & & & & & & & & & & & & & & & & & & \\
6. & Shortage of material or equipment & & & & & & & & & & & & & & & & & & & & \\
7. & Shortage of skilled laboreers by the contractor & & & & & & & & & & & & & & & & & & & & \\
8. & Weather effects & & & & & & & & & & & & & & & & & & & & \\
9. & Changes of the scope/design during construction & & & & & & & & & & & & & & & & & & & & \\
10. & Delays in getting work permits & & & & & & & & & & & & & & & & & & & & \\
11. & lack of effective communication between the parties involved & & & & & & & & & & & & & & & & & & & & \\
12. & Inaccurate design drawings/documents by the designer & & & & & & & & & & & & & & & & & & & \\
13. & Lack of experience and knowledge of contractor staff & & & & & & & & & & & & & & & & & & & \\
14. & Rework due to errors during construction & & & & & & & & & & & & & & & & & & & \\
15. & Delays in progress payments by the owner & & & & & & & & & & & & & & & & & & & \\
16. & Government and political related issues & & & & & & & & & & & & & & & & & & & \\
17. & Delays in contractor payment to sub-contractors and suppliers & & & & & & & & & & & & & & & & & & & \\
18. & Long time for approval and decision making by owner & & & & & & & & & & & & & & & & & & & \\
19. & Unforensen (unexpected) site conditions & & & & & & & & & & & & & & & & & & & & \\
20. & Inappropriate construction methods & & & & & & & & & & & & & & & & & & & & \\
21. & Inflation and escalation of material prices & & & & & & & & & & & & & & & & & & & \\
22. & Delays related to sub-contractors’ work & & & & & & & & & & & & & & & & & & & \\
23. & Low productivity by contractor labor & & & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary of the factors drawn from the literature and local experts.
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 2 Materials and Methods
### Model Architecture
#### 2.1.3 Barcode Detector  
Barcode detection was implemented using the Computer Vision algorithm adapted from [26]. The detector does not work with all existing barcodes, but it works well with those with a striped spectrum as ones present on identity cards and maritime documents. The input image is converted in grayscale and filtered using Scharr operator (with a \(3\times 3\) kernel) to detect the second derivative in the horizontal and vertical direction. The gradient image is filtered with a \(9\times 9\) blurring filter and a binary thresholding algorithm is applied in order to create a black and white image, where the white region contains the barcode. Morphological operations are also applied to make the candidate region more regular. Finally, if a detection exists, the boundaries of the barcode region are determined and the detector returns the coordinates of the bounding box.
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 7 Conclusions and Future Work  
In this study, the experimental analysis of ultrasonic multiple scattering was performed to obtain the scattering attenuation triggered by fine dust particles in air. We proposed a signal processing approach based on an independent scattering approximation. Based on the results obtained, the following conclusions were drawn:  
1. The developed ultrasonic scattering hardware enables the acquisition of meaningful signal data through air with fine dust (PM 10). The applied ultrasonic wavelength was approximately 68 \(\upmu\)m in air and a total of 120 different time series data were obtained per case of the experiment.
2. The proposed signal processing approach (including ensemble averaging and Fourier analysis) enables the calculation of scattering attenuation, and the results obtained indicate a correlation between the scattering attenuation and the dosage of fine dust until 0.008 g.
3. The range of scattering cross-sections of actual fine dust particles is required to estimate the number of fine dust particles per unit volume. Numerical investigation presented the effect of irregular particle shape on the number density estimation.  
In future work, the estimation of number density will be conducted to validate the proposed scattering attenuation approach.  
Conceptualization, H.S. and H.C.; Methodology, H.S. and H.C.; Investigation, U.W.; Data Curation, U.W.; Writing--Original Draft Preparation, H.S. and H.C.; Writing--Review and Editing, H.C.; Supervision, H.C.; Funding Acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Agency for Infrastructure Technology Advancement (KALA) grant, funded by the Ministry of Land, Infrastructure, and Transport (Grant 20 CTAP-C151907).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 1 Introduction
### Epidemiological Evidence Linking Periodontal Disease and Alzheimer's Disease  
Several epidemiological studies have explored the relationship between periodontal disease and cognitive decline, with mixed but increasingly supportive findings. Observational studies indicate that individuals with chronic periodontitis are at a higher risk of developing Alzheimer's disease and other forms of dementia [70; 71; 72; 73; 74; 75; 76; 77; 78]. For instance, longitudinal studies have demonstrated that markers of periodontal inflammation, such as elevated serum levels of C-reactive protein (CRP) and pro-inflammatory cytokines, are associated with an increased risk of cognitive impairment. Furthermore, retrospective analyses have found that tooth loss, a surrogate marker for severe periodontal disease, correlates with a heightened incidence of dementia [79; 80; 81; 82; 83; 84; 85; 86; 87; 88]. The connection between oral health and systemic diseases, including Alzheimer's, is also supported by studies investigating oral microbiota [89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100]. Advances in metagenomics and microbiome research have revealed that specific periodontal pathogens, such as _Porphyromonas gingivalis_, _Treponema denticola_, and _Fusobacterium nucleatum_, may play a role in neurodegenerative processes. _P. gingivalis_, in particular, has been implicated in the pathogenesis of Alzheimer's disease through the detection of gingipains--virulence factors produced by this pathogen--in the brains of AD patients. These findings suggest a potential causal link, although further research is needed to establish direct pathways and mechanisms [101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111].
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 2 Environmental Vibration Model of Metro Depot
### Soil Model
According to [34], when \(\Delta x<\lambda_{s}/6\) (\(\Delta x\) is the mesh size and \(\lambda_{s}\) is the wavelength of the shear wave), satisfactory results can be obtained at most locations except near the wave source 0.5\(\lambda_{s}\). Due to the rapid attenuation of high-frequency vibrations in the soil and the gradual increase in shear wave velocity with soil depth, a locally refined mesh strategy is adopted for the soil model. The mesh size near the track vibration source is set to 0.4 m. Moving away from the vibration source toward deeper soil and the sides of the track, the mesh size is gradually increased, reaching 1.5 m at the edges of the soil region. The soil elements are modeled using C3D8 elements, with a total of 384,652 elements. Figure 5 shows the finite element model of the track-soil-building established in this study. \begin{table}
\begin{tabular}{c c c c c} \hline
**Soil Layer** & **Thickness** & **Density** & **Elastic Modulus** & **Poisson’s** \\  & **(m)** & **(kg/m\({}^{3}\))** & **(MPa)** & **Ratio** \\ \hline Artificial fill & 2 & 1950 & 210 & 0.31 \\ Clayey silt & 5.6 & 2040 & 219 & 0.25 \\ Fine sand & 8.5 & 1980 & 364 & 0.3 \\ Silty clay & 7.2 & 2120 & 402 & 0.35 \\ Sandy silt & 16.7 & 2150 & 401 & 0.25 \\ \hline \end{tabular}
\end{table} 
Table 4: Soil parameters.
## 6 Simulation Results and Discussion  
As described in the previous section, our model translates real event sequences into CPN transitions using the rules that we have developed. The transitions extracted can be enabled by tokens that can contain different values. Because of the lack of similar modeling approaches, we tested the accuracy of our modeling approach, by comparing our simulation results against the real statistical data. We set up our simulations by assuming that the shipping orders arrive following the Poisson distribution with mean inter-arrival rate from 5 to 25 min. The service times follow exponential distribution with mean service time equal to 5 min for the officers, 5 to 60 min for short distance shipping, and 60 to 240 min for long distance shipping. The break times are also exponential, with mean value of 110 min for experienced drivers that ship products by Lorry, 50 min for inexperienced drivers that ship products by Lorry, 60 min for all the drivers that take a car, and 15 min for inexperienced drivers that use the motorbike. The figures shown in this section are average values from a set of 20 simulations. To test the validity of our model, we compare the results with the corresponding results obtained from the actual data. These results were averaged for a period of one month. Generally, we found that there is a divergence between the results of our model and the average results. However, this divergence is not so significant and can be explained by the model and the simulation parameters.
## 3 Results and Discussion
### Comparison with Available Studies  
The proposed model was compared with the extant literature on, and different methodologies applied to, the Chile, California, and Hindukush regions for earthquake or seismic prediction.
## 2 Results and Discussion
### Application for Survey of Fungicides in Fruits Samples
As shown in Figure 6, for different varieties of fruits, the application of fungicides in grapes should be concerned with both the high detection ratio and high concentration result. Furthermore, the mixed use of the target fungicides in grapes is a universal phenomenon. For example, high concentrations of three types of fungicides such as 1060 \(\upmu\)g/kg of proymidone, 618 \(\upmu\)g/kg of pyrimethanil, and 321 \(\upmu\)g/kg of dimethomorph were simultaneously detected in one grape sample. The main fungicide in apple samples was found to be carbendazim, the detection ratio of which was up to 93.3%. However, it was gratifying that most of them were at a low level (<100 \(\upmu\)g/kg). In addition, lower concentrations of pyrimethanil and dimethomorph could also be detected in apple samples. Very small doses of fungicides, which were under 10 \(\upmu\)g/kg, could be detected in pear samples. The levels of fungicides in orange and yangtao are both low; only proymidone was detected in one yangtao sample  
Figure 5: (**a**) Selected ion monitoring (SIM) chromatogram and (**b**) dd-MS\({}^{2}\) spectra of a positive grape sample. RT: retention time. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Frequency (\%)**} & \multicolumn{2}{c}{**Concentration Detected (\(\upmu\)g/kg)**} \\ \cline{3-4}  & & **Range (min.–max.)** & **Mean** \\ \hline
**Carbonazim** & 79.0 & 0.41–314.0 & 22.6 \\
**Thiophanate-methyl** & 39.5 & 0.32–45.2 & 3.35 \\
**Pyrinethanil** & 34.6 & 0.56–618 & 57.1 \\
**Metalaxyl** & 3.7 & 0.53–2.4 & 1.2 \\
**Prochloraz** & 49.4 & 0.40–693.0 & 56.9 \\
**Proxylindone** & 6.2 & 6.17–1060 & 287.7 \\
**Dimethomorph** & 32.1 & 0.41–321.0 & 56.9 \\
**Triadimefon** & 0 & - & - \\
**Definoconazole** & 42.0 & 0.42–121.6 & 13.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Frequency, mean, minimum (min. ), and maximum (max.) levels of the nine target fungicides detected in the analyzed fruit samples. above 100 \(\mathrm{\SIUnitSymbolMicro g}\)/kg. The types and dosages of fungicides in watermelon were the most satisfactory because all of them were lower than 60 \(\mathrm{\SIUnitSymbolMicro g}\)/kg.