esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The predictive model neglects the most crucial parameters associated with sCO\({}_{2}\), such as the density and viscosity dependency on pressure and temperature. The authors compared oil- and sCO\({}_{2}\)-lubricated TPJBs and discovered that using sCO\({}_{2}\) as a TPJB lubricant requires increased bearing size to have dynamic coefficients comparable to those of oil-lubricated TPJBs. [PERSON] and [PERSON] [18] developed a thermoelastohydrodynamic model for CO\({}_{2}\)-lubricated externally pressurized GFTB. The lubricant, CO\({}_{2}\), was treated as a real gas. The authors compared laminar and turbulent flow models for CO\({}_{2}\)- and R245 fa-lubricated bearings. [PERSON] [19] analyzed hybrid journal and thrust bearings lubricated with sCO\({}_{2}\) and calculated the load capacity using the rule of thumb. To calculate the rotordynamic coefficients, the author used a commercial tool developed by [PERSON] [20] for water-lubricated journal bearings and reported that the stiffness coefficient increases with an increase in the bearing diameter for angled injection hybrid journal bearing. [PERSON] et al. [21] reported a detailed dynamic analysis of sCO\({}_{2}\)-lubricated plain journal bearings. The turbulent flow model employs the look-up table method to extract the thermodynamic properties of sCO\({}_{2}\) in Ref. [12], similar to that in Ref. [11]. [PERSON] et al. [22] conducted theoretical and experimental studies on the rotor dynamic instability of magnetic journal bearings operating in an sCO\({}_{2}\) ambiance. To understand the unstable shaft levitation at high speeds, sCO\({}_{2}\) pressure forces developed between the shaft and the magnetic bearings were calculated using the one-dimensional Reynolds equation. Herein, we developed a computational model for hybrid (hydrostatic/hydrodynamic) TPJB lubricated with sCO\({}_{2}\) and predict its static and dynamic performances. In the hybrid TPJB, pressurized sCO\({}_{2}\) is supplied through a hole located at the center of the pad's recess to enhance the load capacity and dynamic characteristics, as reported for water- and air-lubricated bearings [20; 23; 24]. The Reynolds equation for turbulent compressible fluid flows solves hydrodynamic pressure generation. The mass flow balance between the orifice flow and the Poiseuille and Couette flows at the pad recess boundaries calculates the recess pressure. The fluid inertia effects at the recess boundaries, which cause a pressure drop,were modeled using the average velocities of film flows. The real gas model is used for sCO\({}_{2}\), whose thermophysical properties are functions of pressure and temperature.
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 3 Background
### Malicious Behavior in Vishing Apps  
Vishing, a combination of \"Phishing\" and \"Voice\", refers to a type of mobile cyber crime that uses voice calls to financially exploit victims [20, 21]. Attackers lure victims into installing vishing applications and subsequently impersonate public institutions or acquaintances through these applications. Consequently, victims are misled by insidious fraud and inadvertently provide sensitive information, potentially leading to financial harm [22]. The malicious behaviors identified in the vishing applications we examined are as follows:
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
## 3 Model Test Device
### Measurement System  
The measurement system included a vortex flowmeter, air-pressure transducer, pore water pressure (PWP) transducers, electronic balance and PIV device. The parameters of the abovementioned transducers, including their measuring range, resolution and sampling frequency, are summarised in Table 2. The PIV technique was originally developed in the field of experimental fluid mechanics [17]. It works by using a high-resolution digital camera to photograph tracers, which are evenly spread onto the inner wall of the glass window before the model box is filled with slurry. Silicon grease was smeared onto the inner wall of the transparent glass to hold the initial positions of the tracers. When slurry particles move under vacuum or air pressure, the tracers move with them. The movement is recorded by the PIV camera, which takes photographs at a high frequency. The PIV software divides the images into a grid of test patches according to the image pairs of each tracer. The displacement vector of each patch during the interval between photographs is found by locating the peak of the autocorrelation function of each patch. This peak indicates that the two images of each tracer captured during the flashes overly each other. Then, the displacement vector is obtained as the correlation offset [18]. Due to the resolution  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Width (mm) & 100 \(\pm\) 3 \\ Thickness (mm) & 4.2 \\ Equivalent pore diameter (\(\upmu\)m) & 75 \\ Tensile strength (kN/cm) & 0.2 \\ Water flux capacity (cm\({}^{3}\)/s) & 40.9 \\ Permeability coefficient (cm/s) & 5 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of PVDs.  
requirements, as shown in Figure 3, a rectangular observation window (height = 150 mm and width = 200 mm) with a booster plate located along its central axis is set for the PIV camera. The silicon grease should minimise the negative influence of friction at the slurry /wall boundary.  
The vortex flow meter and air-pressure transducer were connected to the booster pipe so they can record the real-time air flux and air pressure, respectively, during the pressurisation operations. There were six PWP transducers arranged symmetrically on both sides of the booster plate, i.e., three transducers on each side located 2.5 cm, 5 cm and 10 cm laterally away from the booster plate. All PWP transducers were 10 cm above the plate bottom. The electronic balance automatically recorded temporal variations in the water discharge during the treatment.
## 3 Conventional GS Pre-coding  
For expressing the conventional GS pre-coding, gram matrix \(\mathbf{W}\) is represented using (1) as follows,  
\[\mathbf{W}=\mathbf{H}\mathbf{H}^{H}=\left[\begin{array}{c}\mathbf{h}_{1}^{T }\\ \mathbf{h}_{2}^{T}\\ \vdots\\ \mathbf{h}_{N_{u}}^{T}\end{array}\right]\left[\begin{array}{cccc}\mathbf{h }_{1}^{*}&\mathbf{h}_{2}^{*}&\cdots&\mathbf{h}_{N_{u}}^{*}\\ \end{array}\right]=\left[\begin{array}{cccc}\mathbf{h}_{1}^{T}\mathbf{h}_{ 1}^{*}&\mathbf{h}_{1}^{T}\mathbf{h}_{2}^{*}&\cdots&\mathbf{h}_{1}^{T}\mathbf{ h}_{N_{u}}^{*}\\ \mathbf{h}_{2}^{T}\mathbf{h}_{1}^{*}&\mathbf{h}_{2}^{T}\mathbf{h}_{2}^{*}& \cdots&\mathbf{h}_{2}^{T}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{h}_{N_{u}}^{T}\mathbf{h}_{1}^{*}&\mathbf{h}_{N_{u}}^{T}\mathbf{h}_{2}^ {*}&\cdots&\mathbf{h}_{N_{u}}^{T}\mathbf{h}_{N_{u}}^{*}\end{array}\right] \tag{3}\]  
In (3), \(\mathbf{W}\) is Hermitian positive definite and \(\mathbf{W}\) can be decomposed as follows,  
\[\mathbf{W}=\mathbf{D}+\mathbf{L}+\mathbf{U}=\mathbf{D}+\mathbf{L}+\mathbf{L}^ {H}, \tag{4}\]  
where \(\mathbf{D}\), \(\mathbf{L}\), and \(\mathbf{U}\) are diagonal, strictly lower triangular matrix, and strictly upper triangular matrix of \(\mathbf{W}\), respectively. With the number of iterations \(i\), the \(i\)-th GS solution \(\mathbf{s}^{(i)}\) is calculated as follows,  
\[\mathbf{s}^{(i)}=(\mathbf{D}+\mathbf{L})^{-1}\Big{(}\mathbf{x}-\mathbf{L}^{ H}\mathbf{s}^{(i-1)}\Big{)},\ i=1,2,\cdots, \tag{5}\]  
where \(\mathbf{x}=\left[\begin{array}{cccc}x_{1}&x_{2}&\cdots&x_{N_{u}}\end{array} \right]^{T}\) is \(N_{u}\times 1\) column vector for the modulated symbols and \(\mathbf{s}^{(0)}\) is an initial solution which is variously defined according to the system requirement. The final downlink transmit symbols vector \(\mathbf{s}\) is calculated with matched filter and normalization factor as follows,  
\[\mathbf{s}=\frac{1}{\sqrt{\beta}}\mathbf{H}^{H}\mathbf{s}^{(i)}, \tag{6}\]  
where \(\beta\) is the normalization factor to prevent the variation of transmit power from the pre-coding. Again, the BS does not know the number of required iterations for obtaining target error performance and it should be estimated accurately prior to signal transmission.
# Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
###### Abstract  
It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz.  
5G; mmWave; 28 and 38 GHz; Propagation; Path Loss; IOT; D2D +
Footnote †: journal:  
Article
## 4 Results and Discussion  
The test environment was barren land and tests were conducted on a clear day with mild winds. To view the targets in a wide perspective, the device was positioned on a higher hill than the targets' positions. We pointed our LRF to three different targets at different distances. As reference, those distances were measured with a commercial long distance LRF of 1064 nm laser with 8 mj energy and resolution of \(\pm\)5 m.  
Figures 8-10 show the average of raw data and the processed data which was obtained with our LRF with a picture of the target taken with a monocular mounted on the top of our LRF (see the Figure 2). Figure 8 shows the first target which has the size of 2 by 2 m of a black painted wooden plane and measured distance is 1840 m. The second target in Figure 9 is a solitary tree in the barren land and measured distance is 2940 m. Our third target in Figure 10 is the small hill in the ground with measured distance of 3385 m. Those distances are measured with our reference laser rangefinder. Blue colors are the averaged raw data and the red colors are the processed data of the blue one using the algorithm shown in Figure 5. Although any number of averaging could be chosen, the number of averaged data in Figure 8 is 2000 and in Figures 9 and 10 is 4000.  
For all cases, IFOV corresponding to those distances and the size of the laser spot are larger than the size of the target. This causes reduced SNR due to incoming unwanted radiation.  
As the averaging increases, signal becomes more visible as the SNR increases with square root of N. Calculated SNR of raw data for Figures 8-10 are 3.7 dB, 4.2 dB and 4.3 dB, respectively, then after signal processing, SNRs become 7.7, 7.3 and 7.7 decibels, respectively. Using the Wiener filtering algorithm, 3 dB enhancement in SNR for Figure 8 and 2.5 dB enhancement in SNR for other figures are obtained. Our LRF data after the algorithm correctly found the position of the signal with an average error of approximately 30 m. This can be due to the delay which can be corrected by the proper calibration.
## 4 Algorithm Design
### Conflict Resolution Mechanisms for Limited Charging Resources  
Existing studies on conflict resolution for charging tasks predominantly adopt the first-in-first-out (FIFO) principle, postponing subsequent energy replenishment tasks based on real-time conditions [11; 23; 35]. However, this mechanism exhibits a degree of myopia, failing to globally optimize the utilization of limited charging resources to serve the maximum number of vehicles. In this study, we abstract the vehicle charging process as a resource-constrained activity and leverage the critical path method (CPM) from project scheduling. By incorporating path \(T_{max}\) constraints, we propose a charging sequence feasibility theorem and a dynamic rescheduling strategy to eliminate conflicts, thereby enhancing the operation efficiency of capacity-limited CI.
## 2 Power Quality Disturbance Feature Extraction
### Feature Extraction Based on Discrete Wavelet Transform
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Wavelet Coefficients**} & \multicolumn{8}{c}{**Statistical Features**} \\ \cline{2-9}  & \(\mu\) & \(\sigma\) & **RMS** & **KT** & **Ent** & **SK** & **RG** \\ \hline D1 & \(\mu\_1\) & \(\sigma\_1\) & RMS\_1 & KT\_1 & Ent\_1 & SK\_1 & RG\_1 \\ D2 & \(\mu\_2\) & \(\sigma\_2\) & RMS\_2 & KT\_2 & Ent\_2 & SK\_2 & RG\_2 \\ D3 & \(\mu\_3\) & \(\sigma\_3\) & RMS\_3 & KT\_3 & Ent\_3 & SK\_3 & RG\_3 \\ D4 & \(\mu\_4\) & \(\sigma\_4\) & RMS\_4 & KT\_4 & Ent\_4 & SK\_4 & RG\_4 \\ D5 & \(\mu\_5\) & \(\sigma\_5\) & RMS\_5 & KT\_5 & Ent\_5 & SK\_5 & RG\_5 \\ D6 & \(\mu\_6\) & \(\sigma\_6\) & RMS\_6 & KT\_6 & Ent\_6 & SK\_6 & RG\_6 \\ D7 & \(\mu\_7\) & \(\sigma\_7\) & RMS\_7 & KT\_7 & Ent\_7 & SK\_7 & RG\_7 \\ D8 & \(\mu\_8\) & \(\sigma\_8\) & RMS\_8 & KT\_8 & Ent\_8 & SK\_8 & RG\_8 \\ A8 & \(\mu\_9\) & \(\sigma\_9\) & RMS\_9 & KT\_9 & Ent\_9 & SK\_9 & RG\_9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Original feature set. Standard deviation (\(\sigma\)), mean (\(\mu\)), root mean square (RMS), kurtosis (KT), entropy (Ent), skewness (SK) and range (RG). Entropy:  
\[Ent_{i}=-\sum\
olimits_{j=1}^{N}\left\{A_{ij}^{2}\log\left(A_{ij}^{2}\right),D_{ ij}^{2}\log\left(D_{ij}^{2}\right)\right\} \tag{4}\]  
Standard deviation:  
\[\sigma_{i}=\left(\frac{1}{N-1}\sum\
olimits_{j=1}^{N}\left\{\left(A_{ij}-\mu_{ i}\right)^{2},\left(D_{ij}-\mu_{i}\right)^{2}\right\}\right)^{\frac{1}{2}} \tag{5}\]  
Mean:  
\[\mu_{i}=\frac{1}{N}\sum\
olimits_{j=1}^{N}\left(A_{ij},D_{ij}\right) \tag{6}\]  
Kurtosis:  
\[KT_{i}=\left(\frac{E\left(A_{ij}-\mu_{i}\right)^{4}}{\sigma_{i}^{4}},\frac{E \left(D_{ij}-\mu_{i}\right)^{4}}{\sigma_{i}^{4}}\right) \tag{7}\]  
Skewness:  
\[SK_{i}=\left(\frac{E\left(A_{ij}-\mu_{i}\right)^{3}}{\sigma_{i}^{3}},\frac{E \left(D_{ij}-\mu_{i}\right)^{3}}{\sigma_{i}^{3}}\right) \tag{8}\]  
Root mean square:  
\[RMS_{i}=\left(\frac{1}{N}\sum\
olimits_{j=1}^{N}\left(A_{ij}{}^{2}\right), \left(D_{ij}{}^{2}\right)\right)^{\frac{1}{2}} \tag{9}\]  
where \(i=1,2,\ldots,1\) represents the number of wavelet decomposition at level 1. Here \(N\) is the number of coefficients in each decomposed data. The PQD waveforms are decomposed into up to eight levels which provide eight detail coefficients (D1, D2, D3, D4, D5, D6, D7, D8) and one approximation coefficient (A8).
## 3 Evaluation Results and Discussion
### Data on Product Delivery Information  
Using the SLS tool, the system administrator can easily identify products entered into the system, products for which transport routes are planned, and products that have been delivered to end users. Table 5 indicates that, out of the recorded 104 products, route planning was tested for 54%, while actual product delivery of 16% was confirmed according to the data captured by the smart system.
## 2 Materials and Methods
### General Synthesis of 9,10-aryl-2-(3-(trifluoromethylomethyl)phenyl)anthracene (**1-3**)
#### 2.2.3 Data for 9,10-di(naphthalene-2-yl)-2-(3-(trifluoromethylomethyl)phenyl)anthracene (**3**)  
Pale yellow solid (0.31 g, 46%). \({}^{1}\)H NMR (CDCl\({}_{3}\)): 8 8.11 (d, \(J\) = 7.78 Hz, 2H), 8.04 (m, 4H), 7.94 (m, 3H), 7.86 (d, \(J\) = 8.0 Hz, 1H), 7.74 (m, 3H), 7.66 (m, 3H), 7.61 (m, 4H), 7.54 (dd, \(J\) = 8.2, 7.6 Hz, 1H), 7.50 (d, \(J\) = 7.6 Hz, 1H), 7.42 (t, \(J\) = 8.4 Hz, 2H), 7.32 (q, \(J\) = 8.2 Hz, 2H). \({}^{13}\)C NMR (CDCl\({}_{3}\)): 8 141.84, 137.62, 137.12, 136.31, 136.17, 133.44, 133.42, 132.86, 132.82, 131.20, 130.88, 130.65, 130.42, 130.33, 130.25, 130.00, 129.47, 129.45, 129.33, 129.24, 128.20, 128.16, 128.15, 128.11, 127.96, 127.94, 127.14, 127.10, 126.54, 126.50, 126.33, 125.48, 125.44, 125.09, 124.73, 124.08, 124.04, 123.93, 122.70, 77.20 (CF\({}_{3}\)). \({}^{19}\)F NMR (CDCl\({}_{3}\)): 8-62.6. Anal. Calcd. for C\({}_{41}\)H\({}_{25}\)F\({}_{3}\): C, 85.70; H, 4.39. Found: C, 85.21; H, 3.98.  
Cyclic voltammetry measurements of **1-3** were performed by a three-electrode cell configuration system consisting of platinum working and counter electrodes and a Ag/AgNO\({}_{3}\) (0.1 M in CH\({}_{3}\)CN) reference electrode at room temperature. The solvent was acetonitrile (CH\({}_{3}\)CN) and 0.1 M tetrabutylammonium hexafluorophosphate was used as the supporting electrolyte. All solvents were sufficiently degassed for 1 h before use. The oxidation potentials were recorded at a scan rate of 100 mV/s and reported with reference to the ferrocene/ferrocenium (Fc/Fc\({}^{+}\)) redox couple.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 3 Model Introduction and Problem Analysis
### Mapping Analysis
#### 3.4.2 Path Mapping  
After each subtask node is completely mapped to the corresponding satellite node, the weighted directed edge \(e_{ij}\) between subtasks can be converted to the shortest path, \(Path_{end(v(o_{i}))start(v(o_{j}))}\), between mapping nodes. \(m\left(o_{i},v_{j}^{n}\right)=1\) guarantees the node assignment of subtasks, and each subtask may span multiple time slots from the start of transmission to the completion of the computation. \(start\left(v(o_{j})\right)\) signifies the satellite node assigned by subtask \(o_{j}\) in the beginning time slot, and \(end(v(o_{i}))\) denotes the satellite node in the time slot when the subtask \(o_{i}\) computation is completed. The shortest path \(Path\) is obtained by [PERSON]'s shortest path algorithm with the route table \(SatTEG\) as input. Similarly, we can also obtain the minimum hop, \(Hop_{end(v(o_{i}))start(v(o_{j}))}\).
## 4 Health Benefits of Blackcurrant Anthocyanins and Related Molecular Mechanisms
### Anti-Postprandial Hyperglycemic and Anti-Diabetic Effect  
Postprandial hyperglycemia refers to the rapid rising of blood glucose level following a high carbohydrate meal. The regulatory effect of BCA on postprandial hyperglycemia has been previously reported. Both one-time consumption of BCA before a high-carbohydrate meal and 8-day supplementation of BCA reduced postprandial glycemia in humans [71; 72]. The \(\alpha\)-amylase and \(\alpha\)-glucosidase inhibitory activities of BCA were proposed as major reasons [73; 74]. The hydrolysis of starch by the digestive enzymes, including \(\alpha\)-amylase and \(\alpha\)-glucosidase, is a major cause of postprandial hyperglycemia [75]. Besides the effect on postprandial hyperglycemia, BCA also showed an anti-diabetic effect. Dietary BCA significantly improved glucose tolerance in type 2 diabetic mice [76]. Consumption of BCA for 8 weeks alleviated body weight gain and improved glucose metabolism in both low- and high-fat diet-fed mice [77].  
The preventive effect of BCA on dyslipidemia and hepatic steatosis, which are strongly associated with type 2 diabetes, was also observed in OVX rats [78]. The studies on the molecular mechanisms underlying the anti-obesity effects suggested that modulation of adenosine monophosphate-activated protein kinase (AMPK) and lipid metabolism-associated genes may contribute to these effects [76]. AMPK pathway, as a part of the energy sensing network, is critical at controlling energy expenditure. The activation of AMPK by BCA was observed in multiple animal models, including diabetic mice, nonalcoholic steatohepatitisitis (NASH) mice, and high-fructose fed rats [76; 79; 80]. BCA alleviated metabolic syndrome in those mice. It is noteworthy that an intact gut microbiome was found essential for BCA exerting those effects [77]. Therefore, this study suggested the interactions between BCA and the gut microbiome is critical for the anti-diabetic effect of BCA.
## 4 Interpretation of the Results  
The analysis clearly demonstrates that incorporating Nolpal Points into the supply chain significantly enhances the sustainability of both direct and deferred exchange systems. Specifically, Scenarios C and D show a 35% reduction in CO2-eq emissions compared to the traditional. This improvement is primarily attributed to Nolpal's extensive network of collection points across Italy, which enables lorries to travel significantly shorter distances.  
Figure 6: GWP 100—Sharing of emissions of the system stages for all scenarios.  
Figure 7: Percentage contribution of the stages of the systems for Scenario A, Scenario B, Scenario C and Scenario D.  
The reduced travel distance decreases emissions associated with diesel consumption, lorry maintenance, and other contributors to environmental impact.  
It is important to note that emissions cannot be reduced beyond certain limits. For example, during the initial stage of goods delivery from the trademark industry to the retailer, emissions remain constant across all four scenarios. However, other stages of the supply chain show significant reductions, particularly in the return trips for pallet recollection (UP4). These results highlight how Nolpal effectively concentrates most of its emissions in the first stage of the cycle, making the rest of the supply chain substantially more sustainable.