esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Characteristics and Loss Models of the Transonic Radial Turbine
## 3 Flow Fields Characteristics  
Figure 6 shows the distribution of the dimensionless velocity divergence at different expansion ratios. The number of the velocity divergence is very large. The dimensionless velocity divergence is the ratio of the velocity divergence to \(10^{6}\). The dimensionless velocity divergence indicates the degree of expansion of the fluid. A positive velocity divergence indicates that the fluid flows from the inside to the outside, and the fluid is expanding; a negative velocity divergence indicates that the fluid flows from the outside to the inside, and the fluid is compressed. For the transonic flow in the turbine rotor passage, the compression processes occur at the shock waves mainly and the leading edge. Therefore, the locations of the shock wave can be determined by regions of negative value in the dimensionless velocity divergence.  
Figure 7 shows the distribution of the relative Mach number at different expansion ratios. Figure 8 shows the distribution of entropy at different expansion ratios. As can be seen from the above figures, with expansion ratio increases, the Mach number at the outlet  
Figure 5: The total to static efficiency vs. expansion ratio.  
Figure 6: The distribution of dimensionless velocity divergence at different expansion ratios.  
of the turbine blade becomes larger and larger. At a low expansion ratio, the flow in the turbine is subsonic, and the Mach number at the outlet is less than 1. As the expansion ratio increases, the flow in the turbine changes from subsonic to transonic, and the Mach number at the outlet of the blade is greater than 1.  
In the case of a low expansion ratio, the expansion effect of the airflow at the blade outlet is relatively weak, and the Mach number of the airflow in front of the oblique shock wave is relatively low, so the oblique shock wave is relatively weak. With the increase in the expansion ratio, the expansion effect of the airflow at the blade outlet increases, the Mach number in front of the shock wave increases, and the intensity of the corresponding oblique shock wave increases.  
Figure 8: The distribution of entropy at different expansion ratios.  
Figure 7: The distribution of relative Mach number at different expansion ratios.  
Under the condition of a certain expansion ratio, the oblique shock wave on the blade pressure surface occurs at the Mach reflection on the blade suction surface, and an obvious shock wave surface is formed near the outlet of the blade passage. Because of the obvious entropy increase in the flow field caused by the shock wave, flow loss increases, leading to a sharp decrease in turbine efficiency. As the expansion ratio further increases, the Mach number of the oblique shock front further increases, and the oblique shock waves on the blade pressure surface and suction surface intersect at the blade outlet and form a normal shock wave, and there is an obvious entropy increase region behind the shock wave, which induced a great number of losses.
## 2 Material and Methods
### Mathematical Formulation Part
#### 2.2.3 Evaluation Indexes  
The system is investigated thermodynamically in the present work, making reasonable assumptions about its real operation. The energy and the exergy efficiencies are the main evaluation criteria used.  
The system energy efficiency (\(\eta_{en,sys}\)) is defined as:  
\[\eta_{en,sys}=\frac{Q_{cool}+Q_{heat,m}+P_{el,out}}{P_{el,in}} \tag{30}\]The system exergy efficiency (\(\eta_{ex,sys}\)) is defined as:  
\[\eta_{ex,sys}=\frac{Q_{cool}\cdot\left(\frac{273.15+T_{gas}}{273.15+T_{cool}}-1 \right)+Q_{heat,m}\cdot\left(1-\frac{273.15+T_{gas}}{273.15+T_{heat,m}}\right)+P_ {el,out}}{P_{el,in}} \tag{31}\]  
At this point, it is important to note that the system energy efficiency, generally, takes values above 100%, while the system exergy efficiency is always lower than 100%. The high values of the energy efficiency are justified and reasonable because the present system acts as a heat pump. However, it is unable to use the term \"Coefficient of Performance\" because this system also has electricity as a useful output. Thus, the present system is not a \"heat pump\" but it behaves similar to one and therefore its energy efficiency can exceed 100%. Moreover, it is important to note that the system exploits the cooling input and the ambient source inputs in order to produce heating of medium and high-temperature levels. Therefore, the way that the system finds its energy inputs are reasonable and it does not violate any thermodynamic rule. This is also obvious based on the values of exergy efficiency which are usually in the range of 40-45%.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### Experimental Verification of Actual Scene Dataset
#### 3.6.1 Data Description and Preprocessing  
Modern injection molding machines play an indispensable role in industrial production, especially in the field of plastic products. The efficient and stable operation of these machines is crucial to ensure the stability of the production process and product quality [29]. Due to the harsh working conditions of high speed and high temperature, long rotation friction, and corrosion of materials, the surface of the screw and three small components (check ring, check valve, and cushion ring) [30] will be worn, as shown in Figure 14. The wear of mechanical parts may lead to performance degradation or even serious equipment failure, which makes it impossible to carry out production. This experiment uses the wear fault data in the actual scene of the injection molding machine for experimental case analysis.  
In the experiment, an injection molding machine model was selected as the research object, which was divided into three types: screw wear fault data, three-piece wear fault data, and normal operation data. The labels are set to 0, 1, 2, respectively. Five parameters were selected for analysis, primarily influenced by wear-related faults. These parameters include actual melt time, screw position, screw actual velocity, actual melt velocity, and actual melt torque. Partial data visualizations are shown in Figure 15.  
The data preprocessing is consistent with the above public dataset. Since the data have five features, the input feature dimension C is set to 5, while the remaining parameters remain unchanged. The experiment used a sliding window to capture samples, with a sliding window of 105 and a step size of 5, which means partial repeated sampling. The test set accounted for about 0.2 of the total samples. There are 4749 training set samples and 1188 test set samples in this experimental dataset.  
Figure 14: Three damaged small components (check valve, check ring): (**a**) check valve; (**b**) check ring.  
Figure 15: Visualization of fault data.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 5 Experiments and Results
### Research Questions and Findings
#### 5.3.1 RQ1: Motivation  
There are myriad limitations in existing approaches for processing static analysis warnings; for example, they often rely on manually crafted rules or patterns that may not capture all possible issues or may generate false positives due to the complexity of modern software systems. On the other hand, DL techniques could be used to automatically learn complex patterns and relationships in software code and can be trained to recognize a wide range of issues with high accuracy. By leveraging the power of DL to learn from large amounts of data, one can overcome the limitations of existing methods and improve the accuracy and efficiency of alarm processing. For example, DL models can be trained on large datasets of ground-truth to automatically identify and classify alarms with high accuracy. Additionally, DL-based approaches could be used to generate more precise and actionable alarms, reducing the time and effort required for manual analysis and reducing false positive rates.  
Figure 7: Distribution diagram of token sequence lengths.
## 3 Results  
Based on the results of the X-ray fluorescence spectroscopy (XRF), the amounts of the main components follow an order of SiO2 > Al2O3 > Fe2O3 > CaO > Na2O > K2O > LOI > MgO > TiO2 > P2O5 > MnO (Table 1). Since the standard deviation of each major component is less than 0.42 wt.% (Table 1), the columnar joints of Mudeungssan have a somewhat homogeneous chemical composition. According to the total alkali-silica diagram, all tuff rocks are displayed in the area of dacite (Figure 3) [10]. According to the geological map of this area (Figure 1), dacite was reported to the southwest of the Mudeungssan area, and the study area can also be expanded with the same rock distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \({}_{\text{w}}\)**R\({}_{\text{p}}\)** & **Quartz** & **Plagioclase** & **Sanidine** & **\(\alpha\)-Cristobalite** & **Chlorite** & **Biotite** & **Zeolite** & **Sum** \\ \hline
**ISD-1** & 5.8(1) & 26.8(1) & 62.7(1) & 9.2(1) & 0.6(1) & & 0.6(1) & & 99.9(1) \\
**ISD-2** & 11.7(1) & 29.0(1) & 58.2(1) & 9.9(1) & 0.7(1) & 0.9(1) & 1.1(1) & 0.2(1) & 100.0(1) \\
**IR-1** & 5.7(1) & 25.1(1) & 56.9(1) & 15.8(1) & 1.1(1) & 0.5(1) & 0.5(1) & & 99.9(1) \\
**IR-2** & 5.4(1) & 33.6(1) & 49.3(1) & 14.2(1) & 2.7(1) & & & 0.1(1) & & 99.9(1) \\
**BN-1** & 5.7(1) & 22.3(1) & 61.5(1) & 11.6(1) & 2.6(1) & 2.0(1) & & & 100(1) \\
**BN-2** & 4.3(1) & 32.5(1) & 47.7(1) & 19.3(1) & 0.4(1) & 0.1(1) & & & 100(1) \\
**NT-1** & 4.3(1) & 21.9(1) & 64.4(1) & 13.0(1) & 0.6(1) & & 0.1(1) & & 100(1) \\
**Anyang** & 4.8(1) & 29.9(1) & 55.7(1) & 11.7(1) & 0.1(1) & 1.1(1) & 0.4(1) & 1.1(1) & 100(1) \\ \hline \end{tabular}
\end{table}
Table 2: Quantitative phase analysis results using Rietveld refinement (in%).  
Figure 3: Total alkali–silica (TAS) diagram [10] using chemical analysis results (Table 1).  
rite and zeolite coincide with minor phases. The quantitative phase analysis results of the selected minerals using the Rietveld refinement method are summarized in Table 2. For the ISD-2 sample, the d\({}_{(001)}\) reflection of biotite (at ca. 0.6 A [1]) appears in the preferred orientation, in contrast to the homogeneous results of the XRF analysis. Therefore, this reflection is not considered in the Rietveld analysis stage to avoid overestimation of the amount of biotite. The samples are mainly composed of quartz (21.9 to 33.6%) and plagioclase (47.7 to 64.4%). Sanidine are observed as a major component, ranging from 9.2 to 19.3%. Mudeungsan is characterized by the presence of cristobalite (0.1 to 2.7%). Small amounts of chlorite, biotite, and zeolite are found. It is expected that biotite can be converted into chlorite by changing two layers of biotite to one layer of chlorite through hydrothermal reaction. Zeolite is known to occur in volcanic ash and rocks with alkaline water at an appropriate temperature. These minerals therefore may indicate that the Mudeungsan tuff is slightly altered.
## 2 Problem Statement  
### Network Model  
Considering the scenario in which a single UAV provides services to users in a hotspot area, the system model is shown in Figure 1. Mobile users are randomly distributed in the geographical area of \(L_{x}\times L_{y}\), and the UAV acts as a mobile base station to provide communication and charging services for these users. In the UAV communication system, the relationship between the UAV, the content providers, and the users can be formulated as follows. The UAV acts as a flying base station to actively cache files from multiple content providers during off-peak network traffic hours and pays the corresponding content providers. In the user request stage, the UAV delivers the requested file to the user and obtains the corresponding communication service and charging service fee from the user.  
Regarding the issue of user mobility, in our model, to address the dynamic challenges posed by user mobility, we adopt a time-slot optimization strategy. Specifically, during the optimization process, we set a relatively short time window \(T\). Within this time window, we assume that the user's location remains constant, allowing the optimization to be conducted in a relatively stable scenario. We recognize that user mobility is inevitable. Therefore, we consider the occurrence of user movement in the next time slot. This approach enables our model to re-optimize at the beginning of each time slot based on the user's new location, thereby ensuring the accuracy and effectiveness of the optimization results. The service cycle of the UAV \(T\) is divided into two stages, which provide users with information transmission and energy propagation services, as shown in Figure 2. In the previous stage \(\mu T\), the UAV sends the cache file to the requesting user until all file transfers are finished.  
Figure 1: UAV caching system model.  
In the latter stage \((1-\mu)T\), the UAV sends an energy signal to the user via WPT, where \(0<\mu<1\).
## 1 Introduction  
Images taken on rainy days usually contain a large number of rain streaks, which have a significant impact on image quality as the intensity of rainfall increases. Many vision systems require high input image quality, so image deraining has become an important research topic in the field of computer vision, aiming at restoring a clear background, free from rain streaks. The task of image deraining is divided into video image deraining and single image deraining [1, 2, 3, 4, 5, 6]. The latter is unable to utilize inter-frame differences and is more difficult to research, which is of great academic and practical significance. Existing single-image rain removal algorithms are mainly classified into traditional methods [7, 8, 9, 10, 11, 12] and deep learning methods [13, 14], the latter of which have made significant progress on synthetic datasets, but mostly focus on architectural innovations, ignore the multi-scale features of rain streaks, and are trained on artificial synthetic rain scenes, resulting in a poor ability to generalize to real scenes. Despite their improved performance on real images, semi-supervised learning methods [15; 16; 17; 18] still face problems in rain streak recognition and background detail recovery.  
In order to solve the above problems, considering that the rain marks in rainy images have obvious vertical downward or oblique downward directional characteristics, this paper proposes a semi-supervised single-image rain removal network based on multi-scale wavelet transform, which is divided into supervised and unsupervised network branches sharing the same network parameters, and utilizes the wavelet transform to decompose the rainy image into components of different scales and designs networks of different scales accordingly. The network is designed for different scales by using wavelet transform to decompose the rainy image into differently scaled components and design different scale networks for it, which are co-trained on both labeled synthetic images and unlabeled real images to improve the rain removal effect and enhance the generalization ability of the model, so as to achieve a better restoration of the background detail information of the image while removing the rainwater.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Simulation Environment and Setup
#### 4.1.4 Non-Cross-Slot Boundary  
Transmissions at the end of the RAW slot pose problems for bidirectional communication when non-CSB is used. As the transmission would go beyond the RAW slot, it would become impossible to receive the IEEE 802.11 ACK frame from the AP. Consequently, the device has to re-transmit anyway the next time channel access is granted. To prevent such unnecessary re-transmissions, we pass the RAW slot duration information to the DCA queue so that it can determine, based on the calculated transmission time and remaining RAW slot duration, whether or not a transmission would cross the boundary of the RAW slot. If the calculated transmission time is larger than the remaining RAW slot duration, the station will not contend for the medium and defer the transmission.
## 5 Physical Layer: Five Decades of Evolution (from 45 Mb/s to 100+ Tb/s)
### Five Decades of Physical Layer Evolution in Backbone
#### 5.2.2 Main Achievements per Generation
Finally, the use of full-band optical amplifiers, such as EDFAs, gave the opportunity to concurrently restore the power of multiple channels, allowing not only to decrease the overall cost per bit but also to attain a transported capacity of up to 10 Tb/s, which could be transmitted transparently beyond 4000 km. _Fifth Decade (2010-2020)_  
The fifth decade further expanded the usability of DSP components, introducing algorithms that were able to counterbalance, even partially, the impact of various performance constraints, such as dispersion, polarization mode dispersion, nonlinearity, etc. At the same time, the flexible spectrum exploitation significantly increased the spectral efficiency compared with the 50 GHz fixed grid. In addition, the probabilistic shaping was designated as another key advance of this decade, which could attain higher performance compared with PM-QAM equivalents. Finally, the advances on optical amplifiers, allowed to exploit multiple amplification bands such as O, S, C and L and to transmit more than 100 channels within a single fiber, achieving an overall rate of up to 100 Tb/s, which is marked as the capacity limit for modern commercially available fiber-optic communication systems. The limitations and key advances for the five decades are summarized in Table 10. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Novelies** & **Limitations** & **Key Technology** \\ \hline
1 st Decade (1970–1980) & increased capacity compared with co-axial cables & mainly attenuation attenuation & introduction of fiber-optics for transmission lasers for 2 nd–3 rd transmission windows \\
2 nd Decade (1980–1990) & extended rates to up to 10 Gb/s & dispersion @ 1550 nm, single channel only & transmission windows \\
3 rd Decade (1990–2000) & extended transparent length and capacity & transmission limited in part of C-band & optical amplifiers and DCMs, WDM, new fibers, advanced MFs \\
4 th Decade (2000–2010) & extended number of channels, use of dynamic optical switches & limited spectral efficiency & dynamic optical switching, full C-band amplification, coherent detection \\
5 th Decade (2010–2020) & extended reach and spectral efficiency & capacity crunch of 100 Tb/s & expansion of coherent systems, DSP, flexgrid, constellation shaping \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the key advances and limitations in the physical layer for each decade.
## 4 Classification of Auto-Configuration Protocols  
The auto-configuration protocols may be classified according to address management:  
* _Stateful_: The nodes know the network state, _i.e._, they keep tables with the IP addresses of the nodes.
* _Stateless_: The IP address of a node is managed by itself. Generally they create a random address and perform a process of duplicated address detection steps to verify their uniqueness.
* _Hybrid Protocols_: They mix mechanisms from the previous ones to improve the scalability and reliability of the auto-configuration. Their algorithms have a high level of complexity.
# Ultrafast Optical Signal Processing with Bragg Structures
## 2 The Bragg-Grating (BG) Structure  
Bragg structures are basically 1D photonic crystals (PCs) [19; 20] comprising materials with periodic refract index modulation along one direction, taken as:  
\[n(z)=n_{0}[1+a_{1}\cos(2k_{c}z)], \tag{1}\]  
with constant \(k_{c}\), \(a_{1}\), and \(n_{0}\) values. The fabrication, characterization, and optical properties of PCs have been thoroughly investigated since the original works of [PERSON] and [PERSON] [19; 20]. It is well known that the BG structure gives rise to a bandgap, with its central wavelength located at \(\lambda_{0}=\frac{2\pi n_{0}}{k_{c}}\) and a bandwidth of \(\Delta\lambda=\frac{2\pi n_{0}a_{1}}{k_{c}}\). Strong dispersion and velocity reduction occur at the photonic band edges [21]; in some materials, losses can also be significant. Both a low group velocity and low dispersion can be obtained by designing the structures of BG. In Reference [22], a low velocity of \(0.02c\) (\(c\) is light speed in vacuum), in combination with a 10 nm-bandwidth and low dispersion, was demonstrated by changing the structure of a PC's waveguide [22].  
A frequently studied 1-D Bragg structure is the fiber Bragg grating, which can be fabricated by interference lithography in fibers [23]. Fiber Bragg gratings are widely used in sensors [24], optical telecommunications [25], and for dispersion compensation [26]. 1D Bragg structures can also be fabricated in other solid materials, such as silicon [27] and AlGaAs [28], for optical switching and limiting operations [29]. Besides such artificial Bragg structures, self-assembled Bragg structures can also be found in liquid-crystal materials, such as cholesteric liquid crystals, whose optical properties can be modulated by the light field [30]. Cholesteric liquid crystals can also be used as temperature sensors [31], due to their temperature-dependent pitch.  
By introducing nonlinear optical effects, many interesting discoveries have been made in one-dimension (1D) fiber Bragg grating. These include multistability, a zero velocity, and the creation of wobbling or oscillating solitons [32; 33; 34; 35; 36]. Using Kerr nonlinearity to balance the strong dispersion near the bandgap's edge, BG solitons with speeds of 0.5 \(c\)[35; 37] and as low as \(c/7\)[33] were observed in experiments. Furthermore, it was predicted that standing light can be created using BG fibers with defects [38; 39; 40], Bragg reflectors combined with resonant nonlinearity [41], and the collision of BG solitons [42; 43]. However, high input power densities (\(>\)10 GW/cm\({}^{2}\)) and long propagation lengths are required to achieve strong nonlinear effects, which may pose serious problems in experiments and applications.  
Several Bragg structures have been evoked to address the power and interaction length issues, as detailed in the following sections.
###### Abstract  
In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of \"circles of friends\" in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the \"circles of friends\". We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the \"circles of friends\" of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms.  
mobile opportunistic network; routing; clustering; social relationship; circles of friends Vol. 1, 2017