esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Stress-Strain Response  
Figure 4a-e present the stress (\(\sigma\))-strain (\(\epsilon\)) curves for all specimens, and Figure 4f demonstrates a typical characteristic of the \(\sigma\)-\(\epsilon\) curve. It can be observed that, at the initial stage of loading, the curves undergo a relatively short phase with increasing slope, reflecting the gradual enhancement of the specimen resistance to deformation due to being progressively compressed and densified. The slope of the curves gradually stabilizes as the load increases, representing the elastic phase of the specimens. Subsequently, the curves become nonlinear again and reach a peak. Finally, the curves begin to decline, indicating that the specimens have lost their load-bearing capacity, which is accompanied by the appearance of obvious shear cracks on specimens. Based on these characteristics, the _UCS_ of the specimens was determined as the stress corresponding to the peak of the curve; the elastic modulus \(E\) was calculated as the ratio of the change in stress (\(\Delta\sigma\)) to the change in strain (\(\Delta\epsilon\)) during the stable slope phase; and the ductility \(DI\) was taken as the ratio of the strain at failure (\(\epsilon_{\text{f}}\)) to the peak strain (\(\epsilon_{\text{u}}\)). The _UCS_, \(E\), and \(DI\) of test specimens are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & _UCS_ **(MPa)** & \(E\) **(MPa)** & _DI_ \\ \hline
1 & 0.45 & 63 & 1.26 \\
2 & 0.97 & 114 & 1.08 \\
3 & 0.43 & 76 & 1.28 \\
4 & 0.34 & 44 & 1.16 \\
5 & 1.14 & 127 & 1.09 \\
6 & 0.90 & 114 & 1.12 \\
7 & 0.85 & 89 & 1.09 \\
8 & 0.64 & 76 & 1.22 \\
9 & 0.43 & 50 & 1.23 \\
10 & 0.33 & 39.7 & 1.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _UCS_, \(E\) and _DI_ of test specimens.
# Transcriptome Landscape Variation in the Genus _Thymus_
## 1 Introduction  
_Thymus_ is one of the most consequential genera belonging to the _Lamiaceae_ family, and contains more than 336 species of aromatic perennial herbaceous plants with valuable medicinal properties [1; 2]. Most species originated in the Mediterranean region but have since have been distributed worldwide [3]. Several members of the genus are also cultivated as culinary herbs or for ornamentation [4].  
Many studies have investigated the chemical composition and biological properties of essential oils in different species of _Thymus_[5; 6]. The six major constituents showing variations among _Thymus_ species include thymol, carvacrol, \(\gamma\)-terpinene, geraniol, linalool, and p-cymene, [5; 6; 7]. In regard to previous research, the most important factors that strongly affect the quantities and chemical compositions of essential oils are species, environmental parameters, growth regions, cultivation practices, and plant growth stages [5; 6; 7]. Thymol has been identified as the major constituent in _Thymus_wulgaris_[7, 8]_Thymus daenensis_[9, 10], and _Thymus lancifolius_[10], while carvacrol is known to be major component in _Thymus pubescens_[11, 12] and _Thymus persicus_[13].  
Since the introduction of next generation sequencing technology (NGS), there has been a major transformation due to the possibility to extracting abundant genetic information from biological systems through the genome and transcriptome sequence data of any species. NGS has made it possible, in a single sequencing process, to generate millions of genomic DNA or transcriptomic fragments in record time and at ever-lowering costs. NGS has catalyzed a number of important breakthroughs in fields such as agriculture or evolutionary science [14, 15]. This tool provides a powerful method of developing molecular markers, studying evolutionary processes, and discovering natural biosynthetic pathways in plants, especially in species with unknown genomes. De novo transcriptome assembly is a critically important approach to exploring gene sequences in organisms without genome sequences [16], and recent progress in RNA-sequencing techniques has provided a fast, reliable, and affordable framework for transcriptomic studies [17].  
Since the molecular biology underlying the terpenoid pathway in the genus _Thymus_ has not been sufficiently investigated yet, RNA-Seq technology was applied to five different species to explore (a) the molecular basis and potential genes involved in terpenoid biosynthesis, with a focus on thymol and carvacrol biosynthesis and transcription factor (TF) families; and (b) comparative phylogenetic analysis based on single-copy orthogroups, to elucidate the origin of these species. This study will supply basic genetic resources for understanding the biology of the genus _Thymus_ and reinforce further research endeavors.
## 2 System Description
We find that the downlink geometry is favorable for the CV-QKD task. This is because the optical beam in the downlink geometry starts to propagate in a vacuum until it enters the atmosphere, while the atmospheric influence appears in the early stages of beam transmission in the uplink geometry. Note that the layer around the Earth with a thickness of 20 km contains, on average, 95% of the total mass of the atmosphere [7]. Therefore, the downlink geometry is more favorable for optical signal transmission. However, the uplink geometry is important for a satellite-mediated network between ground stations because the ground station first generates the keys and needs to send them to a satellite via the uplink. In addition, the uplink geometry has the following advantages: the simple design of the satellite, variability of quantum light sources, and accessibility for maintenance or repair.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 3 Method  
In this section, we present a low-light enhancement method for deep-space backgrounds based on multi-image fusion. Given the unique characteristics of deep-space background images and the variations in target composition, this study classifies deep-space background images into the following two categories: (1) isolated satellite images, which refer to images that contain only a single spacecraft body and have no interference from other celestial bodies; and (2) satellite images containing celestial bodies, which refer to satellite images that contain natural celestial bodies (e.g., Planet Earth). This categorization takes into account the differences in noise characteristics and target properties in various scenes, providing a theoretical foundation for designing the enhancement method. Figure 1 illustrates the architecture and processing flow of this enhancement method for deep-space background satellite images.
## 8 Conclusions  
Traditional methods in orthognathic and oral maxillofacial surgery come with inherent risks and complications such as infection, bleeding, nerve damage, issues with wound healing, unfavorable bone segment movement, and relapse. These methods rely heavily on the surgeon's skill and experience for precision, which can lead to variability in outcomes. Translating two-dimensional pre-surgical plans into three-dimensional surgical procedures can be challenging and may affect the accuracy of the operation.  
As medicine moves towards patient-centric care, the demand for personalized surgical methods increases. Surgeons need to tailor surgical plans to the individual patient's anatomy and desired outcomes. The need for greater surgical precision and predictable outcomes is paramount in improving patient satisfaction rates and reducing complications. Utilizing advanced technology can help achieve this by improving surgical planning, execution, and follow-up care. Incorporating 3D technology in surgical procedures can aid in better visualization of the surgical area, enhancing precision during surgery.  
The amalgamation of 3D printing and VSP serves to revolutionize surgical planning and implementation by providing tactile 3D models for visualization and planning, and accurately designed surgical guides for execution. The virtual environment allows for a meticulous preoperative plan, while 3D printing translates this plan into a tangible reality, serving as a roadmap during surgery. This convergence of digital planning and physical modeling facilitates a more predictable, personalized, and precise surgical process, paving the way for advanced patient care in orthognathic and oral maxillofacial surgery.  
Despite the immense potential of 3D printing and VSP in orthognathic and oral maxillofacial surgery, these technologies are not without their limitations. One of the major challenges in adopting these technologies is the need for extensive software training. Professionals need to acquire the necessary skills to operate the software used for designing and creating 3D-printed models and surgical guides. This training can be time consuming and may require additional financial resources. Moreover, the software itself may have a steep learning curve, especially for those who are not familiar with CAD programs.  
Conceptualization, J.-Y.K. and S.-G.K.; methodology, Y.-C.L.; software, Y.-C.L.; validation, S.-G.K., J.-Y.K. and U.G.; investigation, Y.-C.L.; resources, U.G.; data curation, Y.-C.L.; writing--original draft preparation, J.-Y.K. and S.-G.K.; writing--review and editing, Y.-C.L. and U.G.; visualization, S.-G.K. and Y.-C.L.; supervision, U.G.; project administration, Y.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 3 Condition Monitoring
### Comparison with Other Condition Indicators
#### 3.5.1 Crest Factor  
The crest factor is used as an indicator for various types of waveform analyses, including electrical and acoustic applications. As an indicator for mechanical vibration monitoring, it is commonly used for roller bearings, but also for transmission monitoring. It is defined as the ratio between the absolute peak value and the Root Mean Square (RMS) value of the signal. Defects in an early stage often show up as short impacts in the time signal. In this case, the RMS value will not change significantly like the peak value, and so the crest factor increases. When the defect develops, the duration of the impact increases while the amplitude of the peak shows no significant change. This increases the RMS value and the crest factor decreases again. Hence, an increase is expected during the early stage of the defect and decrease when the defect gets worse.
## 8 Performance Evaluation
### End to End Delay and Packet Success Ratio
It is observed that, at any time instant, the MN finds itself within the vicinity of MSP and there is a fix cost from MSP to the other MSP, or to the GW Moreover, unlike the case of our LoWMob scheme, the end to end delay and packet success ratio do not change significantly even at different speeds of the MN. Even though, when the MN is moving in a high speed  
Figure 19: Packet success rate vs. number of hops with variable MN’s speed (DLoWMob). Figure 18: Delay vs. number of hops with variable MN’s speed (DLoWMob). and incurring frequent handoffs, the number of hops between the MSP and the GW are reduced. The end to end delay for the DLoWMob scheme has been reduced up to twice of the delay as recorded in the LoWMob scheme. Similarly the packet success ratio is observed to be 0.79, i.e 79%, as compared to 0.4, 40%, as observed in the LoWMob scheme. This because the number of hops in the DLoWMob is less as compared to the original scheme. This decreases the delay and increases the packet success ratio in DLoWMob. Figures 20 and 21 show the end-to-end delay and the packet success ratio for DLoWMob with Route Optimization, respectively. In this case, both MNs send packets to each other while following random way point mobility model. It can be seen that the performance of our DLoWMob scheme with single MN and with multiple MNs is almost similar. At any speed and at any location within the network, both MNs are separated at most by a five hop count, and it is the matter of fact that with the lesser number of hops, the packet's reliability and QoS increases. High throughput is achieved by  
Figure 21: Packet Success Rate vs. number of hops with variable MN’s speed (DLoWMob with Route Optimization). Figure 20: Delay vs. number hops with variable MN’s speed (DLoWMob with Route Optimization). DLoWMob when MN's target MSPs communicate directly; this limits the number of hops between any source and any destination within the network. Also, as long as the source and the destination MNs do not change their MSPs, there is no need to make new paths between the source and the destination MSPs. This reduces the number of messages needed to establish the paths.
## 3 Methodology
### CCSS Method
Then, the threshold ranges (\(r_{n}^{i}\)) of different gradients were set for the cross-correlation coefficient average, and the satellites with \(\overline{r_{a}^{i}}\) larger than \(r_{n}^{i}\) were selected. Among them, the setting of \(r_{n}^{i}\) started from a value greater than 0.400 and increased at intervals of 0.1 each time. Moreover, in every screening process, it was necessary first to eliminate the satellites with \(\overline{r_{a}^{i}}\) smaller than \(r_{n}^{i}\), then continue to calculate \(r_{a}^{i}\) and \(\overline{r_{a}^{i}}\) for the remained satellites, and only later compare the updated \(\overline{r_{a}^{i}}\) with the newly set threshold \(r_{n}^{i}\). This way, the accurate selection of satellites with different precision was realized: \[\overline{r_{a}^{i}}=\frac{\sum_{n=1}^{m}r(\varphi_{a},\varphi_{n})}{m}\geq r _{n}^{i}\] (7) where \(\sum_{n=1}^{m}r(\varphi_{a},\varphi_{n})\) is the sum of the correlation values between satellite \(a\) and other \(m\) satellites. 4. Based on the satellites selected in step 3, effective satellites within different \(r_{n}^{i}\) ranges were obtained. We continued to select and process them, eliminating satellites with duplicate ascending and descending segments. For the same satellite, if there was no ascending segment (S), the phase of the descending segment (J) was used; if there was no descending segment, the phase of the ascending segment was used; if both the ascending and the descending segments existed, the satellite within the higher CCSS threshold range was selected.
# Innovative Acoustic-Hydraulic Method for High-Performance Fine Liquid Atomization
## 3 Mathematical Model of the Acoustic-Hydraulic Nozzle
### Ultrasonic Exposure: Acoustic Cavitation Threshold
According to the Boyle-Mariotte law:  
\[D_{c}^{3}\sim\frac{1}{p}. \tag{7}\]  
Thus, the critical volume of bubbles will be proportional to the ratio of the critical value of intensity to the power of \(3/2\) and pressure:  
\[D_{cr}^{3}\sim\frac{I_{cr}^{3/2}}{p}. \tag{8}\]  
This means that, depending on the hydrostatic pressure, a noticeable cavitation effect from ultrasonic exposure will be observed at different values of ultrasound intensity: the higher the pressure, the higher the ultrasound intensity required for developed cavitation and vice versa. As follows from dependencies (6) and (7), the diameter of the cavitation bubble is proportional to the square root of the ultrasound intensity and inversely proportional to the ultrasound frequency and the cube root of the hydrostatic pressure:  
\[D_{c}=\frac{2}{\omega}\sqrt[3]{\frac{p_{a}}{p}}\sqrt[]{\frac{I}{\rho c}}. \tag{9}\]  
We will consider the critical diameter \(D_{cr}\) to be a free parameter of the model. In accordance with it, at a given hydrostatic pressure, the critical (threshold) intensity of developed cavitation is determined:  
\[I_{cr}=\frac{D_{cr}^{2}\omega^{2}\rho c}{4}\bigg{(}\frac{p}{p_{a}}\bigg{)}^{2 /3}. \tag{10}\]  
Note that, in accordance with Equation (10), the critical intensity will be proportional to the hydrostatic pressure to the power of \(2/3\). The higher the pressure in the liquid, the higher the required ultrasound intensity for the onset of developed cavitation. The number of bubbles will be determined as the ratio of the gas volume to the volume of one bubble, calculated according to formula (9):  
\[N=\frac{4W_{g}}{\pi\text{D}^{3}}=\frac{\text{K}W_{w}}{2\pi}\bigg{(}\frac{p}{p _{a}}\bigg{)}\omega^{3}(pc)^{3/2}I^{-3/2}. \tag{11}\]  
As will be shown below, the size of the droplets depends significantly on the size of the bubbles and their number.
###### Abstract
A CAC with one inductance and two capacitances is proposed in [19]. The CAC can be used in different ITLDCs, and also in the four-switch ITLDC, as shown in Figure 1a. However, this CAC will not perform well when the ITLDC is used as an EV charger. According to [20; 21], the constant-current and constant-voltage charging strategy is used to charge lithium-ion batteries. In the first charging stage, the batteries are charged with a constant current and, then, the charging process will switch to a constant-voltage stage. During the constant-current stage, the duty cycle of the ITLDC will increase with the increase in battery voltage. During the constant-voltage stage, the output current of the ITLDC will decrease with the charging process time. The maximum value of the auxiliary current \(i_{\text{A}}\) is \(i_{\text{A\_peak}}\), which will change with the duty cycle. The maximum value \(i_{\text{A\_peak}}\) can be expressed as (1) [17] and the change in \(i_{\text{A\_peak}}\) with duty cycle is shown in Figure 1b. \[i_{\text{A\_peak}}=\frac{D(1-D)V_{\text{in}}T_{\text{s}}}{4L_{\text{A}}} \tag{1}\]  
where \(D\) is the duty, \(T_{\text{s}}\) is the period, \(V_{\text{in}}\) is the input voltage, and \(L_{\text{A}}\) is the auxiliary inductor of the CAC. As shown in Figure 1b, to achieve ZVS at a small duty cycle, the auxiliary inductor \(L_{\rm A}\) should be designed with a small value. However, the auxiliary current will be large when the duty cycle is large. The auxiliary current will result in high conduction losses and high current stress of the main switches. Moreover, during the constant-current charging stage, the output current is large enough to realize ZVS naturally, and then the CAC is unwanted. A controllable CAC is required to generate a controllable commutation auxiliary current, which does not change with the duty cycle and can be controlled to zero when the ITLDC realizes ZVS naturally. This paper proposes an active commutation auxiliary circuit (ACAC) with controllable commutation auxiliary current, which will realize a full load range ZVS of the ITLDC. The ACACs can be left unused when the ITLDC realizes natural ZVS, which will result in high efficiency. Since the ACACs will not work when the current of the ITLDC is large enough to realize natural ZVS, the ACACs do not increase the current stress of the main switches. The auxiliary switch achieves zero-current turn-on and zero-voltage turn-off and the switching loss of the auxiliary switch is quite small. The structure and operating principle of the ITLDC with ACACs are introduced and the performance of the proposed ITLDC is experimentally verified on a 1.5 kW prototype converter.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Baseline Schemes  
#### 5.2.1 Deep JSCC  
Deep JSCC [41] is a scheme that leverages deep learning to jointly optimize source coding and channel coding. Unlike traditional communication systems, which typically treat these two processes separately, Deep JSCC employs convolutional neural networks (CNNs) for end-to-end learning. This approach enables the simultaneous optimization of both processes, leading to improved communication efficiency and reliability. However, it should be noted that this method may not perform well in rapidly changing environments.
## 4 Discussion
### CO\({}_{2}\) Exchange Flux Comparation of Study Area and the World Rivers
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water Type** & **Name** & **Country** & **Climatic Zone** & **CO\({}_{2}\) Flux/mmol/(m\({}^{2}\)-d)** & **Reference** \\ \hline \multirow{8}{*}{River} & Heilongtan H1–H2 & China & Subtropic & 5.97 & This study \\  & Long Chuan & China & Subtropic & 6.87 & [51] \\  & Lower Xijing & China & Subtropic & 8.32–15.67 & [55] \\  & Yangtze (Datong) & China & Subtropic & 1.69–6.51 & [12] \\  & Hudson & USA & Temperate & 0.70–1.63 & [26] \\  & St. Lawrence & Canada & Temperate & 1.05–3.62 & [61] \\  & Eastmain, Quebec & Canada & Boreal & 0.71 & [53] \\  & Lowet Mekong & & Tropic & 8.56 & [56] \\  & Amazon & Brazil & Tropic & 15.19 & [57] \\  & Tigris & Turkey & Continental & 4.74 & [54] \\  & York & USA & Warm & 1 & [62] \\ \hline \multirow{8}{*}{Reservoir} & Heilongtan Q1 & China & Subtropic & 14.86 & This study \\  & Curua–Una & Brazil & Tropic & 65.91 & [63] \\  & Tucuri & Brazil & Tropic & 192.61 & [64] \\ \cline{1-1}  & Hongfenghu Reservoir & China & Subtropic & 20.2 & [65] \\ \cline{1-1}  & Hongjaidu Reservoir & China & Subtropic & 6.14 & [66] \\ \cline{1-1}  & Wan’an Reservoir & China & Subtropic & 12.74 & [60] \\ \cline{1-1}  & Laforge–1 & Canada & Temperate zone & 52.27 & [33] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of CO\({}_{2}\) flux of Heilongtan under different climatic conditions. sink enhancement in karst water bodies and helping to improve the accuracy of CO\({}_{2}\) flux accounting in rivers.
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation
## 3 Results and Discussion
### Transcriptional Profile Analysis
BoA is a transcriptional regulator involved in regulating cell morphology related genes, and is known to be involved in general stress response and has been reported to be overexpressed under osmotic stress [71]; however, the mechanism of how it is involved in osmotic tolerance remains unclear. HdeA is a periplasmic acid stress chaperone which plays a role in resistance to low pH [72; 73], and it is not known to be related to osmotic tolerance. As stated previously, the K370_A396 dup mutation is close to the proposed ppGpp binding site on RpoC [60; 61; 62]. PpGpp is known to be involved in stringent response upon nutrition starvation or other stress conditions, and has global impact on gene expression [63; 74]. It regulates transcriptional initiation directly by binding to an interface between the \(\beta^{\prime}\) and \(\omega\) subunit of the RNA polymerase, or indirectly by altering sigma factor availability, and it also indirectly affects translation and DNA replication [75; 74]. It has recently been reported that the supplementation of serine hydroxamate (SHX), which induces production of ppGpp, increased osmotic tolerance of ppGpp-proficient _E. coli_, which was not observed in ppGpp-deficient cells, demonstrating that ppGpp is involved in osmotic tolerance [76]. Furthermore, an RpoC \(\Delta\)312-315 mutation was previously found to suppress the ppGpp deficiency phenotypes [77]. Since the _rpoC_ K370_A396 dup mutation falls outside the amino acid residues known to impact RpoC interaction with ppGpp, any relationship between the _rpoC_ K370_A396 dup mutation and ppGpp remains to be investigated.