esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Gas Foil Bearings in China
## 2 Applications of Gas Foil Bearings
### Application in High-Speed Gas Compressor  
Recently, high-speed compressors supported by gas foil bearings have become an indispensable part of fuel cell systems. Air compressors are mainly used to provide pressurized clean air. The performance of a fuel cell system is directly related to the pressure of the oxygen provided. When the air pressure is increased, the energy density of the fuel cell system can be improved and a higher output power and power performance can be obtained. Up to now, high speed air compressors with gas foil bearings have been put into use in some renewable energy automotive fuel cell systems, for example, Toyota and Honda in Japan, Garrett in the United States, etc.  
After nearly ten years of hard work, prototype compressor products can be produced in China. An air compressor with a maximum speed of 120 krpm (10 kW) was developed by the Institute of Refrigeration and Cryogenics of Xi'an Jiaotong University for automotive fuel cell systems. Subsequently, prototype trial production and preliminary tests were completed. The high-speed air compressor is shown in Figure 8. The test results showed that the air compressor operated stably, and the overall technology reached the leading level in China. In addition, the State Key Laboratory for Strength and Vibration of Mechanical Structures (Xi'an Jiaotong University) developed a 11-kW, 70-krpm high-speed experimental air compressor system supported by gas foil bearings. Experiments including loss, rotor transient temperature, and critical speed were carried out and a stable operation of the system under 70 krpm was realized [56; 57]. In addition, Hunan University processed and built an oil-free high-speed air compressor experiment test rig [58]. After many test cycles, a diagram of the rotational speed and the temperature characteristics of foil bearings were drawn. Under different experimental conditions, two time-domain waveforms were used to configure the orbit of the rotor system. Meanwhile, the Dalian University of Technology designed a high-speed two-stage air compressor with gas foil bearings [59; 60]. Using DyRoBes-Rotor software, the dynamic characteristics of the rotor bearing system were analyzed. The results showed that the rotor-bearing system performances had good repeatability. The maximum bearing force was 60 N, which was much lower than the bearing capacity of 100 N. For refrigerant compressors supported by foil bearings, a centrifugal compressor (630 kW) was produced by the GREE company with a rotor amplitude of less than 12 \(\upmu\)m. The unit energy efficiency coefficient of performance (COP) reached 6.35, and the integrated part load value (IPLV) reached 10.15. Meanwhile, the R134a high-speed refrigerant compressor with a rated speed of 80,000 rpm was developed by Xi'an Jiaotong University, and is shown in Figure 8. Its pressure ratio is \(2\times 2\), but its size is only one third that of conventional compressors.  
Figure 8: High-speed compressors and gas bearings by Xi’an Jiaotong University (**a**) air compressor; (**b**) refrigerant compressor.
## 4 Gestational Diabetes Mellitus  
Gestational diabetes mellitus (GDM) is a condition of glucose intolerance with onset or first recognition during pregnancy, and is characterised by insulin resistance and impaired insulin secretion [19]. In most women, GDM is asymptomatic and, depending on severity, it may be adequately controlled without medication (diet-controlled GDM), otherwise insulin and/or oral hypoglycaemic agents may be needed to achieve euglycaemia [68]. A diagnosis of GDM is generally made following an oral glucose tolerance test (OGTT) at 26-28 weeks gestation; however, the individuals targeted for screening as well as the timing and criteria used can vary. For instance, the American College of Obstetricians and Gynaecologists (ACOG) criteria requires two or more threshold values and use a 100 g glucose solution, while the Canadian Diabetes Association (CDA), the International Association of Diabetes and Pregnancy Study Groups Consensus Panel (IADPSG), and the World Health Organization (WHO) require only one threshold value and use a 75 g glucose solution [68; 69; 70; 71]. Similarly, diagnostic cut-offs also differ, with a fasting glucose \(\geq\)5.3 mmol/L for CDA and ACOG and 5.1 mmol/L for IADPSG and WHO. A 1-h plasma glucose cut-off \(\geq\)10.0 mmol/L is adopted by all except CDA, which uses 10.6 mmol/L; and a 2-h glucose cut-off \(\geq\)8.5 mmol/L is adopted by IADPSG and WHO, whereas the CDA cut-off is \(\geq\)9 and ACOG is \(\geq\)8.6 mmol/L. Only ACOG includes a 3-h plasma glucose, with a cut-off of 7.8 mmol/L [68; 69; 70; 71].  
An estimated 7% of pregnancies in the US were complicated by diabetes in 2009 and roughly 86% of these cases were women with GDM [72]. In Australia, GDM affects approximately 10-13% of pregnancies depending on the diagnostic criteria used [73]. GDM increases the risk of macrosomia, birth trauma, pre-eclampsia and neonatal hypoglyaemia [68]. The prevalence of GDM varies by ethnicity and is directly proportional to the prevalence of type 2 diabetes in the population [68]. Vitamin D has been implicated in both GDM and type 2 diabetes via its reported effects on insulin resistance and inflammation, and given the similar pathophysiology of these conditions [74; 75]. However, the potential links between vitamin D status and GDM remain contentious, with several clinical trials attempting to clarify whether supplementation may reduce the risk of GDM or ameliorate its clinical presentation.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 5 Results And Discussion
### Performance Analysis
#### 5.3.1 Example Program of FEI1 Dataset under Occlusion Scenarios  
Figure 10 displays an example program evolved by ITACIE-GP for the FEI1 dataset in occlusion scenarios, along with its two visualization examples. This program achieves an accuracy of 99.33% on the training set and can reach an accuracy of 98% on the test set.  
In the left branch of the example program, the _Prewitt_ operator is first applied to process the image to highlight edge information, and the mouth region with significant discriminative ability is extracted from it. Then, the extracted image region undergoes  
Figure 10: Example program evolved from the FEI1 dataset through ITACIE-GP in occlusion scenarios, along with its two visualization examples. (**a**) Example program, (**b**) Visualization example1, (**c**) Visualization example2.  
a downsampling process. Next, fused features are generated by combining the pixel features after downsampling with the LBP features of the original region. Based on these fused features, as well as the information transmitted from the downsampling layer, new features are further constructed. In the right branch of the example program, median fusion is performed on two different extracted image regions to construct new image regions with a higher discriminative. Subsequently, the new image region undergoes a downsampling process, resulting in the generation of fused features based on pixel and SIFT features. Ultimately, the features of the left and right branches of the example program are concatenated for the final classification decision.  
It is worth mentioning that, in occlusion scenarios for \(Happy\) category example images (as visualized on the left side of Figure 10), the ITACIE-GP method effectively extracts key regions in the images and performs efficient feature extraction and construction. This enables the effective classification of images obstructed in the same location. However, for \(Nature\) category example images in occlusion scenarios (as visualized on the right side of Figure 10), the region extracted by the left branch might lead to ineffective features due to the obstruction. Despite this, the fused region obtained by the right branch still contains valid information, making the classification decision based on these features effective. This demonstrates the strong generalization ability of the ITACIE-GP algorithm, capable of extracting multiple sets of discriminative features, enabling effective classification decisions even in obstructed or complex scenarios.
## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Livestock and Aquaculture Monitoring
#### 4.5.2 Aquaculture Monitoring
[256] proposed an atrous convolution-based segmentation model for fish in complex underwater scenes, addressing multi-species entanglement. [PERSON] et al. [257] introduced a marine intelligent netting monitoring system, and [PERSON] et al. [258] extended CV applications to sheep herding using a vision-based module integrated with robotic shepherds. CV systems are also contributing to species identification and behaviour modelling in wild and lab conditions. [PERSON] et al. [259] explored visual cues in social recognition among mangrove fish, while [PERSON] et al. [260] used aerial video surveys and deep learning to map eagle ray populations. [PERSON] et al. [261] refined pattern prediction in aquaculture using deep learning for intelligent decision support systems. Figure 5: Illustration of the significant domain gap in fish imagery across different environments [239]. [PERSON] et al. [262] provide comprehensive overviews of CNN-based CV applications in animal farming, emphasising the need for adaptable, real-time, and cost-effective monitoring systems to meet global demands for sustainable and ethical animal farming practices. Despite these advancements, challenges remain, including the high cost of deploying underwater or aerial sensors, limitations in data annotation, and the need for generalised models that function across species and farm conditions. Environmental variability in livestock barns or open aquatic spaces can also degrade detection accuracy. Integrating GenAI holds promise in several areas: it can enhance image resolution, simulate rare behaviours to expand training datasets, and improve model robustness when data is scarce. In conclusion, CV continues to redefine livestock and aquaculture monitoring through enhanced behaviour analysis, health tracking, and yield optimisation. With the ongoing integration of multispectral sensors, generative models, and edge AI hardware, future monitoring systems are expected to become more autonomous, reliable, and scalable, catering to both smallholder farmers and industrial operations. Table 5 summarises selected representative studies that demonstrate the diversity of CV techniques applied to livestock and aquaculture monitoring tasks, highlighting a spectrum of animal species, deployment environments, and technical approaches.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 3 Conventional Two-Stage Carrier Tracking Strategy
### Fine Tracking Design  
When the dynamic stress of the received IF signal is large, coarse tracking can substantially weaken the dynamic stress. The FLL-assisted-PLL tracking loop outperforms the PLL tracking loop under dynamic signal conditions, however, the measurement accuracy of the FLL is worse than that of the PLL. Hence, the fine tracking stage should be employed to improve the tracking accuracy further.  
As shown in Figure 1, the switch between coarse tracking and fine tracking depends on whether the bit synchronization is successful. Once bit synchronization is achieved, the receiver enters the fine tracking stage.  
For fine tracking, a third-order PLL is utilized to track the carrier phase accurately. The estimated carrier phase error between the locally generated signal and the received signal in the fine tracking can be obtained by the two-quadrant arctangent discriminator.  
Owing to the success of the bit synchronization, the limitation of the data bit sign has been eliminated. CIT can be increased to track weak signals. Nonetheless, the limitation, \(B_{n}\!\cdot\!T\ll 0.5\), should also be considered. Therefore, the design of the CIT should balance the sensitivity and the normalized loop bandwidth, namely, \(B_{n}\!\cdot\!T\), as presented in [16].
## 2 Materials and Methods
### Fatty Acid Profile  
The fatty acid profile was determined following ISO-12966-2 [23] and using C130 (0.04 mg mL\({}^{-1}\)) as internal standard for later quantification. After extraction and methylation, for separation and quantification, a gas chromatograph (GC) Shimadzu GC-2030 coupled with a flame ionization detector (FID) an automatic injector AOC-20i was used. Helium was used as carrier gas and nitrogen as a make-up gas (40 mL min\({}^{-1}\)). FID used hydrogen and air, at rates of 35 mL min\({}^{-1}\) and 350 mL min\({}^{-1}\), respectively. The GC system used a Supelco SP\({}^{\infty}\)-2380 capillary column (length: 60 m, internal diameter: 0.25 mm and film thickness: 0.20 \(\upmu\)m). The injector and detector temperatures were 250 and 260 \({}^{\circ}\)C, respectively and a 1:20 split ratio was used. A helium lineal flow velocity of 28.4 cm s\({}^{-1}\) was also used. The oven temperature started at 70 \({}^{\circ}\)C and increased up to 250 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C min\({}^{-1}\). Methyl fatty acids were identified as compared with retention times with FAME Supelco MIX-37 standards (Supelco Company, Bellefonte, PA, USA). Results were calculated as percentage of each fatty acid in the total fatty acid profile.  
Additionally, atherogenic index (_AI_) (Equation (2)) and thrombogenic index (_TI_) (Equation (3)) were calculated according to [PERSON] et al. [24].  
\[AI=(4\times[14:0]+[16:0])\ /\ \left[\sum\
olimits_{PUFA\ (n-3)}+\sum \
olimits_{PUFA\ (n-6)}+\sum\
olimits_{MUFA}\ \right] \tag{2}\]  
where [14:0] and [16:0] are the contents (%) of myrist and palmitic acids, respectively, and _MUFA_ and _PUFA_ are the contents of monounsaturated and polyunsaturated fatty acids, respectively.  
\[TI=([14:0]+[16:0]+[18:0])/\left[0.5\times\sum\
olimits_{MUFA}+0.5\times\sum \
olimits_{PUFA\ (n-6)}+3\times\sum\
olimits_{PUFA\ (n-3)}+(n-3)/(n-6)\right] \tag{3}\]  
where [18:0] is the content (%) of stearic acid.
## 3 Results and Discussion
### Effect of Compression Pressure
At a 130 MPa compression pressure, the electrical resistivity was the lowest at 61.2 \(\upmu\)\(\Omega\)m.  
This trend of decreasing electrical resistivity with higher compression pressure aligns with that observed in the porosity measurements, as shown in Figure 3a. The electrical resistivity of carbon blocks is known to be significantly affected by their porosity. [PERSON], [PERSON] [26] explained that pores within carbon blocks inhibited the movement of electrons, thereby resulting in increased electrical resistivity. Additionally, [PERSON], [PERSON] et al. [27] reported that electrons within carbon blocks moved along the solid regions, composed of fillers and binders, but their movement was disrupted when they collided with pores. The observations made in this study can also be interpreted similarly. Figure 4 presents OM images of the carbon blocks fabricated under different compression pressures. In the images, white particles and black pores are visible. The white particles are kneaded particles after carbonization, which are larger than the kneaded particles observed in Figure 2. The increase in size is attributed to the pressure applied to the kneaded particles during compression molding, which causes them to connect and coalesce, forming large granules. Most granules are 100 \(\upmu\)m or smaller, despite a few larger exceptions. In the carbon block fabricated under a compression pressure of 70 MPa, the proportion of granules was the smallest. As the compression pressure increased, the proportion of granules in the microstructure increased. At a compression pressure of 70 MPa, pores were the largest both in terms of size and quantity. These pores exhibited significant variations in both shape and size; some were about 100 \(\upmu\)m, while others were 200 \(\upmu\)m or larger. As the compression pressure increased, both the size and quantity of pores decreased. In general, the shapes of pores and granules are affected by the compression pressure applied. An increase in compression pressure causes kneaded powder particles to come closer to each other, resulting in the protrusion of pores from the kneaded powder. The observed decrease in pore size can be explained by this mechanism. This explains the observation that, at the highest compression pressure of 130 MPa, pores were the smallest in size and the distance between granules was smaller. Figure 5 shows SEM images of the carbon blocks fabricated under different compression pressures. The size of granules and pores varied depending on the applied pressure, with the distinction becoming more pronounced at a magnification of 5000\(\times\). At a compression pressure of 70 MPa, the number of pores was the largest and they existed separately without being connected to others. At 130 MPa, the number of pores was smallest, with granules being connected to each other. These variations in the size of pores and the connectedness of granules were considered to have affected the mechanical and electrical properties of the resulting carbon blocks.
# Automatic Refactoring Approach for Asynchronous Mechanisms with CompletableFuture
## 4 Empirical Research
### Mapping from AST to Jimple IR  
The specific code refactoring process involves converting the source code into an AST, modifying AST nodes, and then converting the AST back into the refactored source code. Program analysis is based on Jimple IR because it has only 15 types of statements. A statement involves at most three local variables or constants, and the conversion process  
Figure 4: Refactoring Method Framework.  
from bytecode to Jimple IR eliminates redundant code, making it more suitable for static program analysis. Therefore, specific nodes on the AST need to be mapped to Jimple IR before performing program analysis.  
In our method, the syntax tree nodes that need mapping include MethodInvocation expressions, instanceof type comparison expressions, and CastExpression type casting expressions. On the AST, expressions might correspond to multiple Jimple IR statements. For example, in the method invocation a.b().c(), the invocation of c() includes b(), with the AST placing the c() node as the parent of the b() node. In the three-address code Jimple IR, this expression is represented as two expression statements. In our mapping method, we do not consider the child nodes of the current expression node on the AST. Below is our detailed mapping method.  
\"MethodInvocation\" represents a method invocation. We traverse all method invocation statements in the corresponding block of Jimple IR code, comparing the names of the method invocations to identify the unique corresponding Jimple IR statement.  
\"Instanceof\" type comparison expression is used to check whether a variable or expression's type is the reference type or a subtype of the reference type on the right side of the instanceof operator. We traverse all Jimple statements in the corresponding block, matching whether they contain the string instanceof.  
\"CastExpression\" forces the type conversion of a variable or expression to a subtype of its original type. By traversing all definition and assignment statements in the corresponding block, we determine whether the right side of the statement includes a type-casting expression to identify the corresponding statement.  
Using the aforementioned methods for matching, if more than one statement is matched within a block, they are differentiated by line number. If the line numbers are also the same, the expressions in the AST are sorted from smallest to largest based on line and column numbers, following the left-to-right, top-to-bottom execution order of Java code. Simultaneously, the Jimple IR block is converted into an intraprocedural control flow graph, traversing from entry to exit, matching the corresponding Jimple IR statements by sequence number.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 2 Related Literature
### Machine Learning Techniques for Network Slice Creation  
An in-depth examination of all machine learning technologies is presented in [10; 11; 12], along with a list of prospective domains where machine learning techniques may be advantageous. A deep learning strategy is used to classify modulation, according to the authors in [13], which provides a more accurate solution than other conventional methods of classification. Using a Neural Network technique, the previously mentioned problem was subsequently addressed in [14], and the findings demonstrate that as the network size grows, the approach quickly converges to its optimal performance. A hybrid learning algorithm for network slicing was proposed [15], which combines approaches from Machine Learning and Deep Learning to achieve optimal results. In particular, this method optimised the weight function by using Glowworm Swarm Optimisation and the Peer Hunting Optimisation algorithm together. A multi-slice Deep Neural Network (DNN)for dynamic end-to-end slicing was proposed in [16], which incorporates two service level agreements (SLAs), namely, violation rate-based SLA and resource bounds-based SLA, in addition to other factors. To prepare the performance dataset for training, the method made use of a live cellular network with traffic analysis on the gathered data. It was demonstrated in [17] that two reinforcement learning-based algorithms could be used to optimise mode selection in fog-radio access networks (F-RANs). The mode selection is determined by the method, which is based on the immediate reward provided by the surroundings. In the paper [18], the authors discuss the deep learning-based work that has been proposed to improve the 5G RAN portion of the network, as well as how these works can be integrated with the AI-enabled Open-RAN architecture.