esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 3 Results
### Numerical Simulation of Large-Scaled Rigid Wings at High Altitudes

Table 7: The periodic-averaged aerodynamic performance of the eight states in Table 6.  
Figure 16: Comparison of transient lift and thrust: different altitudes (**a**,**b**), different flight states at 4000 m altitude (**c**,**d**). The legend of Figure 16c,d, for example, V7 AOA10f4, represents the calculated case, with a wind speed of 7 m/s, an angle of attack of 10\({}^{\circ}\), and a flapping frequency of 4 Hz. As can be seen from Figure 16c,d, the increment in the angle of attack can boost the transient lift throughout the cycle, but this is at the cost of increased drag and a tendency to stall. The angle of attack affects the drag mainly in the downstroke, and the trend of the transient drag can be found in the fact that when the angle of attack increases from 10\({}^{\circ}\) to 15\({}^{\circ}\), the sudden drop at the beginning of the downstroke is eliminated. The same problem is presented in terms of the time-averaged value in Table 7, and the increase in the absolute value of the pitching moment implies an increase in power consumption. A 1/3 reduction in the angle of attack results in a 27.19%, 62.34%, and 26.56% reduction in lift, drag, and pitching moment coefficients, respectively. The increase in flight speed will significantly increase the peak positive lift during the downstroke, when the peak value of drag also increases sharply. The change in flight speed, on the contrary, has less effect during the upstroke. From the time-averaged value, the absolute values of force and pitching moment coefficients are reduced. After dimensioning, the absolute values of force, moment, and consumed power increase obviously. In order to compensate for the decrease in thrust performance due to the reduced density, the flapping frequency of the wings needs to be increased. The adjustment of the frequency brings about a larger absolute magnitude of the peak value in the cycle. The time-averaged results obtained for the two selected frequencies (i.e., 4 and 5 Hz) are presented for comparison in Table 7. It appears that a 25% increase in frequency and the Strouhal number produce an 11.38% increase in the averaged lift coefficient and a 9.53% decrease in the averaged drag coefficient. At the same time, the pitching moment coefficient increases by 12.31%. In general, when flapping-wing vehicles with the same weight fly at high altitudes, it is necessary to improve the flapping frequency to realize trim flight at higher flight speeds. In addition, it is worth noting that compared with sea level, the increase in flapping frequency at high altitudes will increase power consumption, so how to adjust its abnormal state at high altitudes needs further flight tests. This paper does not consider the flexible deformation of real flapping wings and the gap between the numerical model and the real model, which will affect the aerodynamic force and power consumption in forward flights to a certain extent.
## 5 Validation of the Numerical Method
### Comparison with the Analytical Solution  
Figure 6 shows a comparison between the analytical solution (solid line) and the numerical solution (symbols) for mesh \(M_{3}\) with \(Re=10^{-3}\), \(Wi=0.1,0.2,\cdots,1.0\) and \(\varepsilon=0.25\) (\(\xi\) was set to \(0\)). In Figure 6a we have \(\alpha=0.5\) and \(\beta=0.5\) and in Figure 6b\(\alpha=1.5\) and \(\beta=1.5\).  
It can be seen that an excellent agreement is obtained between the analytical and numerical solutions for all the considered values of \(Wi\), which underlines the robustness of the numerical method and the meshes.  
For lower values of \(\alpha\) and \(\beta\), we obtain a higher destruction rate of the junctions in the gPTT model. Note that the typical viscoelastic velocity profile is more flattened for lower values of \(\alpha\) and \(\beta\). In this case, the different values of \(Wi\) have a stronger impact on the model's behaviour. This result is similar to those found in the literature comparing linear and exponential functions of the trace of the stress tensor.  
Figure 5: Mesh with refinement in the centerline region.  
Figure 6: Comparison between the analytical solution (solid line) and the numerical solution (symbols) for mesh \(M_{3}\) with \(Re=10^{-3}\), \(Wi=0.1,0.2,\cdots,1.0\), and \(\varepsilon=0.25\) (\(\xi\) was set to \(0\)). (**a**) \(\alpha=0.5\) and \(\beta=0.5\). (**b**) \(\alpha=1.5\) and \(\beta=1.5\).
## 3 Experimental Results and Analysis
### Experimental Platform  
The hardware for the experiments in this paper includes a CPU of Intel i5 1040F and a GPU of MSI GeForce RTX 3060 GAMING X TRIO 12G, software environment for Ubuntu18.04, CUDA 11.1, cuDNN--8.0.5, python 3.7, and deep learning framework Pytorch--1.8.  
In this paper, the experiment is based on the Pytorch deep learning framework to perform building on the basis of the CrossVIS network. In the training process, the data loader is input into the network with a uniform size of 360 \(\times\) 640. A total of 23,000 iterations are used fortraining, the number of loop iterations for the complete dataset is 12 epochs, the batch size is set to 4, and the initial learning rate is set to 0.005. The weight decay mode is linear.
## 4 Material and Methods
### Identification and Isolation of trans-IDS Genes  
To identify putative _P. trichocarpa trans_-IDS genes, a thorough search of the Phytozome database ([[http://www.phytozome.net/poplar](http://www.phytozome.net/poplar)]([http://www.phytozome.net/poplar](http://www.phytozome.net/poplar))) was conducted using the amino acid sequences of AtFPPS1 (At5G47770), AtSS1 (At1G78510), and AtGGPPS2 (At2g18620) from _Arabidopsis thaliana_ and HIGPPS.SSUI (FI455406) from _Humulus lupulus_ as an input for BLASTP analyses. One of the resulting sequences was tentatively annotated as FPP synthase (PtFPPS1 (Potri.006g003400)) and four sequences were tentatively annotated as large and small subunits of GPP synthases (PtGPPS1.LSU (Potri.007g031100), PtGPPS2.LSU (Potri.005g127100), PtGPPS.SSU1 (Potri.015g043400), and PtGPPS.SSU2 (Potri.009g139600)).  
The full-length ORF of _PfFPPS1_ and the N-terminal truncated ORFs of _PfGPPS1.LSU_, _PtGPPS2.LSU_, _PfGPPS.SSUI_, and _PtGPPS.SSU2_ lacking the putative signal peptides predicted with the programs ChloroP ([[http://www.cbs.dtu.dk/services/ChloroP/](http://www.cbs.dtu.dk/services/ChloroP/)]([http://www.cbs.dtu.dk/services/ChloroP/](http://www.cbs.dtu.dk/services/ChloroP/))), TargetP ([[http://www.cbs.dtu.dk/services/TargetP/](http://www.cbs.dtu.dk/services/TargetP/)]([http://www.cbs.dtu.dk/services/TargetP/](http://www.cbs.dtu.dk/services/TargetP/))), and PREDOTAR ([[https://urgi.versailles.inra.fr/Tools/Predotar](https://urgi.versailles.inra.fr/Tools/Predotar)]([https://urgi.versailles.inra.fr/Tools/Predotar](https://urgi.versailles.inra.fr/Tools/Predotar))) (Figure S2) were amplified from cDNA made from herbivore-damaged _P. trichocarpa_ leaves using the primers listed in Table 1 and cloned into the expression vectors pASK-IBA33+ (IBA-GmbH, Gottingen, Germany, _PfGPPS1.LSU_, _PfGPPS2.LSU_, _PfGPPS.SSUI_, and _PfGPPS.SSU2_) or pASK-IBA37+ (_PfFPPS1_). The constructs were introduced into the _E. coli_ strain TOP10 (Thermo Fisher Scientific, Erlangen, Germany) and fully sequenced to check for amplification errors.
## 4 Discussion
### Heatwave Detection, Its Magnitude, and Characteristics  
#### 4.2.1 Ground-Observed Air Temperature Assessment  
In examining the spatial and temporal patterns of heatwaves, our findings highlight a distinct trend across various regions. During daytime heatwaves, peri-urban areas such as Pathum Thani and urban locations like Don Muang in Bangkok are particularly affected, showing significant increases in HWF, HWD, HWM, and HWA. This phenomenon is largely attributed to the urban environment, characterized by construction materials such as concrete, asphalt, and steel, and unique morphological features that inherently possess heat retention properties. These factors contribute to longer, more frequent, and intense heatwave conditions [123]. The study of [124] found that the cumulative hours of extreme heat waves increased significantly with the proportion of urban land and decreased significantly with the proportion of forested land and water. Additionally, the analysis highlights the years 1997, 2016, and 2019 as having the highest instances of each heatwave index, indicating an upward trend in the occurrence of heatwave events. The peri-urban station in Pathum Thani showed the most significant increases in daytime HWN, HWF, and HWD, while the most pronounced nighttime HWN increases occurred in rural areas near Saraburi. This pattern highlights the growing susceptibility of peri-urban areas to heatwaves, exacerbated by rapid urbanization, diminishing green spaces, and local climate factors that enhance heatwave sensitivity. This observation is consistent with the research of [125, 126, 127, 128, 129, 130, 131, 132], which associated severe heatwaves with the strongest El Nino-Southern Oscillation (El Nino) years on record, including 1998, 2010, and 2016. In opposition to nighttime heatwave, examining heatwave detection through \(T_{\text{min}}\) unveils unique patterns and trends distinct from those associated with \(T_{\text{max}}\). Notably, urban areas such as Bang Na, Klong Toei, and Don Muang in Bangkok exhibit the most pronounced heatwave activities, particularly in terms of frequency and duration, while peri-urban regions like Pathum Thani also experience a high incidence of events. The years 2013 and 2019 are marked as having significant impacts, with the highest values recorded for various indices of nighttime heatwaves. Across both day and night, an increase in heatwave metrics was observed in 1997, 2013, 2016, and 2019, coinciding with severe global heatwaves during the most intense El Nino events recorded so far regarding to [133, 134, 135, 136]. These observations align with broader research efforts in the field, such as those by [26, 137], which note significant climatic shifts impacting heatwave patterns, particularly in urban settings. The findings underscore the critical importance of understanding regional heatwave trends and their interactions with biophysical changes, human activities, and land use shifts. The results of this study are particularly concerning in light of the ongoing expansion of urban areas into rural landscapes, which could further exacerbate heatwave conditions.
## 3 Experiments and Results
### The Influence of the Separation Factor on the Images  
The separation factor \(r\) is a constant value in [0, 0.5]. The smaller the value, the better the separability between classes. In contrast, the bigger \(r\), the better the pixel separability within the class and the better the distance-preserving properties that are obtained, meaning that the visual sense of the color of each pixel in the image will be closer to the relational characteristics between each pixel of the original hyperspectral data. When \(r_{max}~{}=~{}\Delta h/4,r~{}=~{}n\times r_{max}/20\), \(n\subset N\), the relationships between the value of \(r\) and the class separability and the correlation within the class are shown in Figure 10.  
Figure 10: The relationship between \(r\) and (**a**) \(\delta\), (**b**) \(\rho\), or (**c**) \(\lambda\).  
Figure 9: Average running time for each manifold algorithm.  
Figure 11 displays six results for the Indian Pines data with different \(r\) values. To avoid the visual confusion of the expression between classes, the biggest \(r\) value is \(r_{max}~{}=~{}\Delta h/4\). As shown in Figures 10 and 11, the smaller the \(r\), the easier it will be to distinguish the difference between the classes, and the bigger the \(r\), the better the separability and details of the image. When \(r\) is relatively high, this can be problematic, such as shown in Figure 11f. Some color differences in one category are larger than those between different categories. If this value is exceeded, the color expression of pixels will cause confusion and lose significance. Therefore, the value of \(r\) should be determined according to the demands of the observers.
## 3 Path Loss Models
### Machine Learning Models  
Machine learning encompasses a collection of methods for making predictions by utilizing datasets and modeling algorithms. These techniques find application across numerous domains [49]. In the realm of path loss modeling for prediction, machine learning techniques have gained prominence. Some researchers are exploring the idea that path loss models based on machine learning may outperform traditional empirical and analytical models.  
Machine learning models utilize algorithms and techniques to automatically learn and extract patterns and relationships from a large dataset. These models can analyze vast amounts of empirical or measured data to identify complex patterns and dependencies that may not be easily captured by deterministic or statistical models. Machine learning models can be trained using historical data to accurately predict path loss in various environments based on input parameters like transmitter-receiver distance, frequency, antenna heights, and other relevant factors [35].  
Empirical models are based on observed measurements; deterministic models use physical principles and equations; statistical models consider statistical properties; and machine learning models learn patterns and relationships from large datasets.
## 3 Results
### Release Rate  
The selected WVM bait shows a similar behavior, independent of the model, in terms of the release of compounds over time and subject to the average temperature and relative humidity conditions of the area during the cherry season, which runs from September to December (average temperature of 21.2 \({}^{\circ}\)C with 75% relative humidity) (Figure 3). Both models, Korsmeyer-Pepas (r\({}^{2}\) = 0.97) and Higuchi (r\({}^{2}\) = 0.99), indicated that the release of compounds from the WVM bait reached 20% at 120 h. By projecting the bait's durability under the conditions mentioned, it is estimated that the complete loss of encapsulated compounds will occur within 25 days.  
Figure 4A shows the following values at different time points: 293,305,351 at 24 h; 292,312,663 at 48 h; 276,440,152 at 72 h; 192,811,652 at 96 h; and 216,009,183 at 120 h. Despite these fluctuations, statistical analysis revealed no significant differences between the groups (\(p\) = 0.998), indicating that the observed variations were not large enough to be considered statistically significant. This suggests that the passage of time did not have a substantial impact on the measured variable. In addition, Figure 4B shows the relative abundance percentage of each compound at different time points. For ethanol,  
Figure 2: The olfactometric response of female _Drosophila suzukii_ to three different attractive baits in a Y-tube olfactometer. SAG I (apple cider vinegar with 95% ethyl alcohol); SAG II (sugar, yeast, wheat flour, apple cider vinegar, and water); and WVM (Merlot wine, apple cider vinegar, and sugarcane molasses). * Indicates a statistically significant difference between treatment and control, according to Poisson distribution.  
the abundance fluctuates slightly, with values of 14.93% at 24 h, 15.52% at 48 h, 14.64% at 72 h, 11.68% at 96 h, and 12.99% at 120 h (\(p\) = 0.0768), indicating no significant difference in its relative abundance over time. Acetic acid, with percentages of 28.60% at 24 h, 28.54% at 48 h, 27.78% at 72 h, 32.51% at 96 h, and 31.06% at 120 h (\(p\) = 0.121), also shows small fluctuations, and no significant difference is observed. For 1-butanol, the abundance starts at 36.95% at 24 h, decreases slightly to 36.26% at 48 h, and further declines to 34.90% at 72 h before rising to 40.99% at 96 h and then dropping back to 36.92% at 120 h (\(p\) = 0.342), suggesting no significant change in its relative abundance. Lastly, phenylethyl alcohol shows a more pronounced variation, starting at 4.21% at 24 h, increasing to 4.89% at 48 h, peaking at 9.17% at 72 h, dropping to 0% at 96 h, and rising again to 5.72% at 120 h (\(p\) = 0.342), indicating no significant difference in its relative abundance across time, despite the clear temporal fluctuations.
## 2 Equipment Group Maintenance Planning Model Based on Service Age Regression Model  
### Problem Description and Model Assumptions  
Much research on maintenance planning often ignores the constraints of maintenance resources, such as maintenance time and maintenance team, and believes that multiple resources required by maintenance tasks are adequately available. However, maintenance tasks in the real world are often constrained by the limited resources [24]. Therefore, the problem of maintenance decision under limited maintenance resources has become the focus of research in the field of system support in recent years. Under the premise that various maintenance resources are subject to task constraints, this paper pays attention to the clustering characteristics of the fleet under maintenance, carries out research on the selective maintenance decision making of a fleet considering imperfect maintenance and provides an optimized maintenance strategy under the mission constraints.  
In the equipment group maintenance planning model involved in this paper, it is considered that the equipment group and maintenance tasks meet the following assumptions:  
**Assumption 1**.: _The equipment cluster has several devices of the same model, all of which have the same system structure, are series-parallel systems and have the same number and type of components._  
**Assumption 2**.: _The maintenance cost of each component is related to the initial and postmaintenance state of the component, which can be measured by the component maintenance grade and replacement maintenance cost. The replacement maintenance cost of all components of the same model is the same._  
**Assumption 3**.: _The probability of the fleet to satisfy the requirement in the next mission given by the model is a value predicted according to the component state, without considering the influence of other environmental factors during the task._  
**Assumption 4**.: _Each process requires different maintenance team and equipment, and each group of maintenance team and equipment can only maintain one subsystem of the equipment at the same time._
## 2 Materials and Methods
### Methodology
#### 2.3.4 Standardized Anomaly Indices  
In order to monitor the interannual anomalous variations in the climate variables, NEP, WUE, and CUE, their standardized anomaly indices (SAI) were calculated to analyze their anomalous variations and their correlations within the study timeframe. The formulas are given below:  
\[SAI=\frac{(Y(i,j,t)-\overline{Y}(i,j))}{std(Y(i,j))} \tag{9}\]  
where \(Y(i,j,t)\) is the original value of image element \((i,j)\) at moment t, \(\overline{Y}(i,j)\) is the mean value of image element \((i,j)\) from 2001 to 2022, and \(std(Y(i,j))\) is the standard deviation of image element \((i,j)\) from 2001 to 2022.
## 1 Introduction
Therefore, in the proposed strategy we successfully demonstrated how artifacts of potential value can be extracted from an encrypted network traffic analysis of the Signal app. The motivation behind choosing the Signal app is that it is a popular cross-platform encrypted messaging service developed by the Signal Foundation (previously known as Open Whisper Systems) powered by the open-source Signal Protocol (2013) and is available for android, IOS, and desktop. It consists of common features such as audio calls, video calls, voice messages, text, media messages, stickers, typing indicators, and many more. Each conversation has a safety number that can be verified among the communicating parties. Signal application encrypts all the messages using the Signal Protocol that is based on secure cryptographic algorithms. Recently, well-known IM apps, such as WhatsApp, Facebook Messenger, and Skype (in private conversation mode) employed the Signal Protocol to achieve secrecy and privacy. The protocol consists of a Double Ratchet algorithm, XEdDSA, and VXEdDSA, X3 DH, and Sesame. XEdDSA creates a single key pair that is used in the elliptic curve Diffie-Hellman and signatures. VXEdDSA is an extension of XEdDSA to make it a verifiable random function (VRF) [22]. X3 DH (extended Diffie-Hellman) is used to mutually authenticate the users and provides forward secrecy [23]. After sharing a common secret key, the messages are communicated between users by using the Double Ratchet encryption algorithm [24]. For session management of messages, Sesame (session management for asynchronous) is used with the Double Ratchetalgorithm and X3 DH [25]. Owing to the provision of security services, the app has been endorsed by technologists and cryptographers [26]. We performed an extensive traffic analysis of the Signal app while incorporating the firewall approach for the investigation. The firewall helps to understand the pattern of connectivity and communication activities. We thus forced the Signal client to connect to its server in a controlled environment and this arrangement revealed the obscured design of Signal app. Further, we monitored the live traffic to dissect the bytes, and payload-based patterns to identify the different activities of the Signal app, i.e., calls, text, typing indicator, etc. The major steps of the proposed strategy that are employed to analyze the encrypted network traffic on IM-based apps are shown in Figure 1.  
Figure 1: The flow diagram of proposed strategy.
## 2 Materials and Methods
### RT-qPCR Assays  
Human hepatoma HepG2 cells (treated by WST extracts for 48 h) were inoculated into six-well plates (1 \(\times\) 10\({}^{5}\) cells/well) and treated with different concentrations of WST extracts. Total RNA from the HepG2 cells was isolated using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's recommendations, and the concentration and purity of the RNA were determined at 260 nm using a micro ultraviolet visible spectrophotometer. The total RNA concentration of each group was adjusted to the same level. According to the manufacturer's suggestion (RevertAid First Strand cDNA Synthesis Kit; Thermo Fisher Scientific, Inc., Waltham, MA, USA), 1 \(\mu\)L of oligo (dT)18 primer (500 ng) and 1.0 \(\mu\)L of total RNA (1.0 \(\mu\)g) were added to 10.0 \(\mu\)L of nuclease-free water and heated at 65 \({}^{\circ}\)C for 5 min on a gradient PCR instrument. Then, the mixed reagent containing 4.0 \(\mu\)L of 5\(\times\) Reaction Buffer, 1.0 \(\mu\)L of Ribolock RNase Inhibitor (20 U), 2.0 \(\mu\)L of 10 mM dNTP Mix, and 1.0 \(\mu\)L of ReverAid Reverse Transcriptase (200 u/\(\mu\)L) were added into the mentioned total RNA system, respectively, and reverse transcribed into cDNA under the condition of 60 min at 42 \({}^{\circ}\)C and 5 min at 70 \({}^{\circ}\)C. The total reaction system (20 \(\mu\)L) consisted of 1.0 \(\mu\)L of cDNA, 1 \(\mu\)L of forward and reverse primers (10 \(\mu\)M), respectively, 10.0 \(\mu\)L of premix (SYBR@Select Master Mix; Thermo Fisher Scientific, Inc., Waltham, MA, USA), and 7.0 \(\mu\)L of sterilized double-steamed water, which was mixed and reacted on an automatic thermocycler (StepOnePlus; Thermo Fisher Scientific, Inc., Waltham, MA, USA). The amplification conditions were as follows: denatured at 95 \({}^{\circ}\)C for 3 min, annealed at 60 \({}^{\circ}\)C for 30 s, and extended at 95\({}^{\circ}\)C for 1 min, which was cycled 40 times. The expression levels of Caspase-3, -7, -8, -9, Bax, Bcl-2, NF-\(\kappa\)B, COX-2, p21, and p53 were respectively assessed by RT-qPCR. The sequences of the related primers are shown in Table 1. The cDNA samples for each gene were amplified three times in parallel, and the mean value of Ct was taken. The housekeeping gene GAPDH was used as an internal reference gene, and the levels of the related genes were calculated according to 2\({}^{-\Delta\Delta\Delta\Gamma}\)[19].