esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User
## 4 Simulation Results and Analysis
Specifically, when the cancellation coefficient \(\kappa\) is 0.008, the power allocation factor \(\beta^{*}\) with \(R_{th}=1\) is greater than that with \(R_{th}=0.5\), and about 0.2 larger than that with \(R_{th}=0.1\). Figure 3: The optimal power allocation factor at D1 versus secrecy rate threshold. Figure 2: The optimal power allocation factor at cellular user (CU) under various secrecy rate thresholds. The curves of the optimal allocation factor \(\alpha^{*}\) versus various cancellation coefficient are plotted in Figure 6. The optimal power allocation factor \(\alpha^{*}\) is a monotonically increasing function with respect to the cancellation coefficient \(\kappa\). Figure 7 compares the optimal D2D link rates of our proposed FD scheme and the conventional HD scheme with various cancellation coefficient \(\kappa\). Obviously, the performance of the conventional HD scheme where the cancellation coefficient \(\kappa\) ranges from 0.005 to 0.01 is worse than the proposed FD scheme. When the cancellation coefficient \(\kappa\) is 0.005, the D2D link rate \(R_{th}=0.5\) in the proposed FD scheme is approximately 1.8 bps larger than that of the conventional HD scheme. Moreover, the D2D link rate \(R_{th}=0.5\) is better than that \(R_{th}=1\) in the proposed FD scheme. Figure 4: The D2D link rates of FD and half duplex (HD) modes. Figure 5: The optimal power allocation factor at CU versus cancellation coefficient. Figure 8 compares the optimal D2D link rates of two schemes, which concludes the scheme with the optimal power allocation factor \(\alpha^{*}\) at D1 and the scheme with fixed power allocation factor \(\alpha\) versus various power of D1. The fixed power allocation factor is set as 0.4. It can be seen that the optimal power allocation factor scheme can achieve a larger D2D link rate as compared to the scheme with the fixed \(\alpha\). In the proposed scheme, the D2D rate is about 6.9 bps, while in the fixed power allocation factor scheme, the D2D rate can only achieve approximately 4.2 bps. In addition, the optimal D2D link rates become good with the increase of the power of D1. Figure 6: The optimal power allocation factor at D1 under various cancellation coefficient. Figure 7: The D2D link rates under various cancellation coefficient.
# Spatiotemporal Evolution of Gas in Transmission Fluid under Acoustic Cavitation Conditions
## 5 Conclusions  
To accurately predict the spatiotemporal evolution of the gas in the cavitating flow of transmission fluid, the fluid state equation of the gas-liquid mixture is combined with the homogeneous flow model to obtain a gas void fraction prediction model. Considering the three cavitation effects, including air cavitation, vapor cavitation, and pseudo-cavitation, the unsteady cavitating flow is simulated using the Roe scheme decomposition and the Steger-Warming flux splitting method. The numerical calculation algorithm is relatively stable without obvious oscillation.  
1. When the initial gas content is 1.425%, the maximum gas void fraction in the whole cavitation process reaches 1.925%. The gas void fraction increases with the decrease in pressure, and the peak value of pressure fluctuation is delayed.
2. In the air cavitation zone, the lower the pressure is, the faster the air void fraction increases. The maximum air void fraction is about 1.425%. In the pseudo-cavitation zone, the gas void fraction decreases with the increase in pressure. In the vapor cavitation zone, cavitation mainly occurs within \(x\) = 0-0.51 m. The maximum vapor void fraction is about 0.5%, and the maximum cavitation duration is almost 0.04 s.  
Figure 10: Effect of initial fluid temperature on void fraction.  
Under the conditions given in this study, the fluid temperature change caused by the compression and expansion of transmission fluid is nearly 6 K.  
Conceptualization, X.L. and C.X.; methodology, X.L.; software, Y.C., Y.W. and J.Z.; validation, Y.W., Y.C., W.W. and J.J.; formal analysis, Y.C., Y.W. and F.O.; investigation, Y.W., Y.C., X.L. and C.X.; resources, X.L.; data curation, Y.W., Y.C., J.Z. and J.J.; writing--original draft preparation, Y.C., Y.W. and X.L.; writing--review and editing, X.L., C.X. and F.O.; supervision, X.L.; project administration, X.L. and C.X.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Zhejiang Province, grant numbers LZ23E060002 and LY14E050023.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company ZOOMLION Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice
## 3 Case Study and Analysis
### Data Collecting and Pre-Processing
#### 3.2.4 Predicted Navigation Data  
Amap (also known as Gaode Maps) is a leading provider of digital map content, navigation and location-based solutions in China. It is known for its accuracy and good route suggestions. According to the Amap technology annual in 2020 [34], Amap proposes a hybrid spatiotemporal map convolutional network and implements intelligent algorithms that can infer future road conditions, resulting in a 15% reduction in severe deviations. Amap is a high quality source of basic data for accessibility analysis.  
When it is difficult to obtain large-scale, long-term real travel times, it is essential to use more and more accurate navigation prediction times for accessibility analysis. The predicted navigation data is obtained by calling the Amap path planning API in real time, which includes the actual distance and predicted travel time from each neighborhood to each hospital at each moment.  
Travel time is one of the main factors that influence people's daily choice of medical care, and it will indirectly affect the actual accessibility of medical facility services. In order to use more accurate travel time to analyze the actual accessibility of medical facilities, this paper obtained the predicted travel time from each community to each hospital at different time periods through Amap, which was used as the input information of the distance decay function.  
First, the API of Amap was called every two hours starting from 0:00 every day to obtain the real-time traffic data from the community to the hospital. The data, including traffic time and traffic distance, were obtained continuously for 30 days from September 1 to 30 September 2021, with a total of 7,552,440 records.  
Next, the traffic distance and traffic time were summarized, while the shortest traffic distance was extracted and the average of the traffic time at each moment (0, 2, 4, 6,..., 22) in the 30 days was calculated. The total traffic distance was divided by the total traffic time to get the average speed, which was determined as 40 km/h in this paper.  
Third, the average tolerable time was calculated using the questionnaire results and used as a reference to determine the time threshold \(d_{l}\). In this study, the average tolerable traffic time was calculated as 15 min. The tolerable traffic time and the average speed were multiplied to obtain the distance threshold \(d_{s}\), which was 10 km in this study.
## 1 Introduction  
Adenosine, also known as adenine nucleoside, is an endogenous nucleoside with significant physiological functions in human cells [1]. It can directly enter myocardial tissue, where it is phosphorylated to form adenosine monophosphate (AMP), thereby contributing to myocardial energy metabolism [2]. Clinically, adenosine is used in the treatment of coronary vascular disorders, myocardial infarction, arteriosclerosis, sequelae following stroke, primary hypertension, and angina pectoris [3]. Additionally, adenosine serves as an important pharmaceutical intermediate for synthesizing various antiviral and antibacterial agents,including pharmacologically relevant derivatives, such as 8-chloroadenosine, puromycin, homocitrullinyladenosine, lysylaminoadenosine, S-methyladenosine, and vidarabine, as well as biologically important molecules, like cyclic adenosine monophosphate (cAMP) and adenosine triphosphate (ATP) [4]. Due to its wide range of applications and substantial demand, adenosine offers broad prospects and significant development potential.  
Microbial fermentation is the predominant method for adenosine production due to its advantages of low production cost, mild reaction conditions, and the wide availability of raw materials [5]. Microbial strains commonly used for adenosine production include _Escherichia coli_[6], _Cordyceps cicadae_[7], and _Bacillus subtilis_[5, 8], the last of which is the most widely employed in adenosine biosynthesis.  
_Bacillus subtilis_ is a preferred strain in industrial fermentation owing to its well-characterized genetic profile and diverse modification strategies [9]. During adenosine biosynthesis, _Bacillus subtilis_ converts glucose into 5-phosphoribibose via the pentose phosphate pathway, followed by multiple enzymatic steps to synthesize adenosine. Moreover, glutamine and hypoxanthine exert reciprocal effects on adenosine synthesis, where elevated concentrations of one substrate enhance the biosynthesis of the other [10, 11]. By combining molecular biology and bioinformatics approaches with clarified metabolic pathways, strain improvement strategies have enabled enhanced adenosine biosynthesis [12]. As a result, yields from modified strains have significantly increased, reaching levels ranging from 14.39 g/L to 23.30 g/L [8, 13], providing valuable microbial resources for industrial-scale adenosine production. However, engineering the strain for targeted adenosine production still remains challenging due to the complexity of its metabolic pathways.  
To elucidate the key genes in the adenosine biosynthesis pathway, this study investigated a mutagenized _Bacillus subtilis_ strain with enhanced adenosine production. Transcriptome sequencing analysis was performed on bacterial cells under varying adenosine yield conditions to identify genes associated with adenosine biosynthesis and validate the expression patterns of critical genes. This research provides genetic resources and a theoretical foundation for the targeted improvement of adenosine-producing microbial strains.
# Mesh Twisting Technique for Swirl Induced Laminar Flow Used to Determine a Desired Blade Shape
## 3 Results and Discussion  
### Mesh Size and Twisting Sensitivity  
Twisting the mesh affects the orthogonality of the cells and can increase skewness. Furthermore, the mesh faces deviate slighly from being flat in the twisting region, thus inducing minor errors in area and volume calculations.  
Figure 8 shows the error in axial velocity, compared to Equation (4), for three different mesh densities when the mesh is not twisted. The average cell sizes are 0.1, 0.05 and 0.025 diameters for the three different mesh densities. It is clear that the discretization errors decay relatively fast when a finer mesh is used and are of the order of magnitude \(10^{-3}\) for the finest mesh (for a mean velocity of 1 m/s). This result also indicates which magnitude of error is acceptable for a twisted mesh.  
It is important to verify that twisting the mesh does not affect the calculation greatly before implementing it on numerical simulations with guidance blades. Using a mesh twisted according to Equation (7), laminar flow with no guidance blades was compared to a simulation on an equal but untwisted mesh. The velocity profile from those two meshes is also compared to the theoretical solution for laminar flow.  
Figure 9 shows the maximum error in velocity for the three mesh densities discussed above. The error grows steadily with twisting angle, indicating the effect of skewness and face distortion in the mesh. However, the effects are moderate and the finest mesh outperforms the coarsest one by a factor two even though it is twisted by \(180^{\circ}\). In general, twisting by \(180^{\circ}\) increases the error roughly by a factor of five, but the overall error is still relatively small for the finest mesh.  
Finally, a mesh sensitivity study is shown for a case with actual blades present in the pipe, thus giving rise to swirling flow. Figure 10 shows the effect of \(180^{\circ}\) twisting on the swirl number, along the axial direction of the pipe. The coarsest mesh deviates clearly from the other two and it can be argued that the finest mesh is sufficent for an accurate simulation of the swirling flow, using the mesh twisting technique.  
Figure 8: Absolute error of the axial velocity for a mesh with no twist. (**a**) coarse mesh (left); (**b**) intermediate mesh (center) and (**c**) fine mesh (right) are shown.
## 2 Tunnel Overview  
### Karst Cave Overview  
As shown in Figure 1, the tunnel in the Chengdu-Guiyang Railway is located in the section between Xingwen county and Weixin county, within Yunnan Province. The design speed is 250 km per hour. There are double tracks in a single tunnel. The longitudinal slope of the line is a single upward slope of 30%, with a total length of 6306.28 m and a maximum buried depth of 350 m.  
The tunnel mainly passes through coal measure strata and soluble rock strata, of which 71 m of section passes through the coal-bearing stratum of the Permian Changxing Formation (P2C), which is a high gas section; 439 m of section crosses the coal-bearing stratum of the Longtan Formation (P2I), which is a gas outburst section, and 795 m of section crosses the limestone of the Qixia Maokou Formation (P1m + Q) of the Lower Permian system, and karst is strongly developed there.  
When the guide gallery was excavated to d3k279 + 948, a huge karst cave was revealed, in the shape of a dome, about 100 m along the line direction and about 200 m perpendicular to the line. The height of the karst cave is 50-120 m. It is high on the right side of the line and low on the left side. The tunnel is located near the top of the karst cave. Most of the extent of the karst cave is stable. However, under the influence of cracks, joints, groundwater and blasting, local block falling from the surface of the cave wall has occurred. The thickness of the bottom accumulation layer is 30-90 m, and its composition is complex, including  
Figure 1: Map of the Chengdu-Guiyang high-speed railway.  
soft clay, gravelly soil and medium coarse sand. An underground river has developed at the foot of the slope on the left side of the line in the karst cave. The river is 5-15 m wide and flows from the right side of the line to the left. The flow of the underground river during the dry season was observed to be 4-15 m\({}^{3}\)/s, and the maximum flow during the rainy season is about 20.16 m\({}^{3}\)/s. According to the drilling and geophysical data, it is very likely to be unstable and damaged due to the influence of branch pipeline water at the bottom and in surrounding areas, the scouring and erosion of the underground river and unbalanced loading on the upper part of the later backfill body. See Figure 2 for details [23].
## 2 Enzymatic Defensive Mechanisms
### Glutathione Reductase (GR)  
This enzyme (GR, EC 1.6.4.2), also known as flavoprotein oxidoreductase, is the last in the ascorbate-glutathione cycle that is found predominantly in chloroplasts (GR\({}_{2}\)) and partially in the cytosol (GR\({}_{1}\)), mitochondria (GR\({}_{2}\)) [11; 26; 159] and peroxisomes (GR\({}_{1}\)) [11]. The proper enzymatic activity of GR mainly springs from its possession of triple domains, including NADPH-binding domain, FAD-binding domain, and a dimerisation domain [160]. The above-mentioned isozymes of GR are involved in tolerance to high light (GR\({}_{1}\), GR\({}_{2}\)) [161; 162], salt stress (GR\({}_{1}\)) [163; 164], chilling stress (GR\({}_{2}\)) [165; 166], methyl  
Figure 1: Schematic model showing the reactive oxygen species (ROS) generation in plants as well as the ascorbate-glutathione (ascorbic acid/AA-GSH) and the guaiacol peroxidase (GPX) cycles. AA is oxidised by ROS and converted into monoidehydroascorbate (MDHA). A set of three enzymes, including FAD-dependent monoidehydroascorbate reductase (MDHAR), GSH-dependent dehydroascorbate reductase (DHAR) and glutathione reductase (GR), catalyse the recycling of ascorbate. Superoxide dismutase (SOD) converts O\({}_{2}\)\({}^{\bullet-}\) into H\({}_{2}\)O\({}_{2}\). Ascorbate peroxidase (APX) (1), catalase (CAT) (2), and GPX (3) act as the main H\({}_{2}\)O\({}_{2}\) detoxifying enzymes. AA and GSH are antioxidants. Abbreviations: ascorbate peroxidase (APX), oxidised glutathione (GSH), reduced glutathione (GSSG).  
viologen (MV\({}^{2+}\))-induced oxidative stress (GR\({}_{2}\)) [167], and toxic metals (GR\({}_{1}\)) [168; 169]. Moreover, GR\({}_{2}\) has been proven to interfere with developmental processes involving embryo development [170], seed germination [171], root growth, and apical meristems maintenance [172]. Thus, it is follows from the above that abiotic [173; 174] and possibly biotic [175] stress factors induce the activity of GR in plants.  
GR mediated plant tolerance to stress conditions often refers to its role in ROS detoxification through GSH (reduced glutathione) regeneration [176]. GR has been proven to mediate the conversion of oxidised glutathione (glutathione disulfide-GSSG) to GSH, using the electron donor (reductant) NADPH. Moreover, most of the synthesised GSH is involved in the regeneration of ascorbate (AA) from DHA under DHAR mediation (Figure 1) [26], and the activation of several CO\({}_{2}\)-stabilizing chloroplast enzymes [4]. Also, GR has the potential to maintain a high GSH/GSSH cellular ratio by catalysing the formation of glutathione disulfide [26], signifying that the pool of GSH consumed by the DHAR reaction is replenished by GR [11]. Increasing GR activity increases the NADP\({}^{+}\)/NADPH ratio, thus elevating the amount of available NADP\({}^{+}\) as the last electron receptor in photosynthetic light reactions, and eventually reducing the likelihood of electrons being transferred to O\({}_{2}\) for superoxide radical production [177; 178; 179].
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 2 Background
### Lossy Wireless Links  
Due to factors like channel noise, multi-path effects, link asymmetry, etc., low-power wireless links often suffer from a high packet loss rate [11]. We conduct extensive experiments to test the spatial characteristic of wireless links using a pair of STM32W108 chips in our teaching building corridor. Results demonstrate that wireless links can be represented by three different communication regions, the connected region, the transitional region and the disconnected region [24], as shown in Figure 1, which depicts the variation trend of packet reception rate versus the communication distance. In most cases, wireless data transmission occurs in the transitional region. Therefore, to ensure the reliability of communication, we need to take some error correcting measures, such as Automatic Repeat reQuest(ARQ), which retransmits the lost data packets, and Forward Error Correction (FEC), which adopts some coding schemes to improve the robustness of transmission. However, both of the methods above are imperfect because of the additional energy consumption and transmission delay.  
Thanks to the inherent sparsity of monitored signals, compressive sensing has been widely used in WSNs. Many research works have utilized it to deal with data loss in wireless communication to improve transmission reliability. However, the emphasis was put on the accuracy of signal recovery, rather than the information acquisition in specific application scenarios.
## 3 The Proposed Multi-Scale Demodulation Method
### Results and Discussion
Figure 15: Enhanced Kurtograms under (**a**) 100 rpm, (**b**) 300 rpm and (**c**) 500 rpm working conditions and the corresponding demodulation spectra under (**d**) 100 rpm, (**e**) 300 rpm and (**f**) 500 rpm working conditions. In order to validate the superiority of the proposed method to the other three methods, the mean-peak ratio (\(MPR\)) index [35] was applied to measure the fault-related components in the demodulation spectrum. A higher \(MPR\) value indicates a better demodulation performance. \(MPR\) is defined as follows,  
\[MPR=20\lg\frac{\sum_{i=1}^{N_{h}}(P_{i}-A_{s})}{A_{s}} \tag{18}\]  
\[A_{s}=\frac{\sum_{k=a}^{b}S_{k}}{b-a} \tag{19}\]  
where \(N_{h}\) indicates the number of harmonics of the fault characteristic frequency needed to compute an \(MPR\), which is set as 5 in this study, and \(P_{i}\) is the amplitude of the spectral peak around the \(i\)th harmonic of the fault characteristic frequency. As the average value of the spectral components is in the range from \(a\) Hz to \(b\) Hz, \(S_{k}\) is the amplitude of the \(k\)th spectral component. Figure 17 illustrates the \(MPR\) values of the four methods in different working conditions. It can be observed that the proposed method always achieved higher \(MPR\) values under the three working conditions. It yielded an average gain of 27.48 dB compared to the fast Kurtogram method, an average gain of 16.33 dB compared to the enhanced Kurtogram method and an average gain of 33.01 dB compared to the traditional envelope analysis method, respectively. This is mainly because the proposed method retains more fault-related components in the entire frequency range scale and the weighted-EMD de-noising technique effectively removes the noise components and interference components from the raw vibration signals. Moreover, the effect of the given threshold in the weighted-EMD de-noising technique on the demodulation performance of the proposed method was discussed, as shown in Figure 18. It was found that the \(MPR\) peaks were reached when the thresholds equalled 2.5\(\times\)\(\rho m\), 1.5\(\times\)\(\rho m\) and 2\(\times\)\(\rho m\) under the three working conditions, respectively. However, it was also observed that there was a sudden drop of the \(MPR\) value when the given threshold was too large. This was because some fault-related components were recognized as noise components when the threshold was too large. Figure 16: Demodulation spectra based on traditional envelope analysis under (**a**) 100 rpm, (**b**) 300 rpm and (**c**) 500 rpm working conditions. Figure 17: Mean–peak ratio (\(MPR\)) values of the four methods under 100 rpm, 300 rpm and 500 rpm rotating speed conditions. Validation of the Proposed Method by the Fault Diagnosis of a Fixed Shaft Gearbox with a Seeded Tooth Root Crack Fault