esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Object and Numerical Analysis Model  
### Research Object  
Under the influence of a gas turbine blade profile, the internal cooling channel often presents characteristics of section variation along cooling fluid flow direction. In addition, for better cooling effect, the channel surfaces are usually arranged with enhanced heat transfer structures, such as dimple and protrusion. In this study, the variable cross-section serpentine channel is taken as the baseline model, which is from a real gas turbine blade, and protrusion structure is arranged on its surface. The turning vanes are arranged in both of the first and second turning area, as shown in Figure 1a. The diagram from different direction of the channel is shown in Figure 1b. There are three straight passes and two turning regions in the model. The cross-sectional area of the first pass (pass1) and third pass (pass3) shrink along the flow direction, while the second pass (pass2) expands. The two straight passes are connected by turning regions. The inner side of the two turning regions is an arc. The outer side of the first turning region includes two right angles, while the second turning region is an arc. The pressure surface (PS) of the three passes are all cambers as well as the suction surface (SS).  
Figure 1c shows the model size. The radial distance in two turning regions are 14.38 mm and 18.61 mm, respectively. The rotation axis is 1138.85 mm from the channel inlet. There are eight protrusion structures on the pressure and suction surfaces of each pass, as shown in Figure 2. The protrusion can be represented by radius \(r\) and depth \(\delta\). In this paper, the radius \(r\) is selected as 60% of the suction width in the middle of each pass, and the depth-radius ratio (\(\delta\)/\(r\)) is 0.2. The specific parameters of protrusion structure in PS and SS are shown in Table 1, where the subscript represents the number of passes. A total of 6 channels are constructed in this paper. Table 2 shows the value of turning vane thickness in each channel, where \(d\) is the thickness of the turning vane and \(D\) is the minimal width of the turning region. In this study, the comprehensive performance of five channels with different turning vane thickness are discussed and compared with the baseline case1 under stationary conditions (\(Re\) = 10,000-50,000). The performance of six channels is also analyzed when rotating (\(Re\) = 10,000, \(Ro\) = 0-0.5).  
Figure 1: The serpentine channel model studied in this paper. (**a**) Three-dimensional model; (**b**) diagram from different direction; (**c**) structure and size of the channel.
# A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition
## 3 Background and Problem Formulation
### Preliminaries  
#### 3.2.1 Problem Formulation  
This paper explores the topic of the multi-objective QoS-aware WSCP. \(QoS\) attributes were mapped to fitness values using fitness functions. The heuristic algorithm was iterated through these fitness values and evolved to obtain Pareto optimal solutions. The formula for the problem is as follows:  
\[\left\{\begin{array}{c}\text{Minimize }f(L)=(f_{1}(L),f_{2}(L),\dots,f_{n}(L))\\ \text{With }L=(S_{1},S_{2},\dots,\ S_{n})\\ f_{i}(L)=W_{i}\cdot QoS_{i}\text{, for }i=1,\ 2,\ \dots,\ 6\\ \text{subject to }S_{j}\in CS_{i}\text{, for }j=1,\ 2,\ \dots,N_{SR}\end{array}\right. \tag{1}\]  
Figure 1: An example of web service composition.  
where \(L\) is a composition result constructed by different services \(S\), \(f_{i}\) is an objective function that captures \(QoS\), respectively, for every composition result \(L\), \(QoS_{i}\) is an objective in \(QoS\), \(W_{i}\) is the weight value for \(QoS_{i}\), \(CS\) denotes the set of all candidate services, and \(N_{CS}\) is the size of \(CS\).  
The basic parameters considered for the WSCP in this paper can be described as follows:  
* Web services (services for short) \(S=\left\{I_{s},O_{s},QoS_{s}\right\}\);
* For each service, a set of service inputs \(I_{s}\) and a set of service outputs \(O_{s}\);
* The set of all candidate services \(CS\);
* SWCP task with input \(I_{t}\) and required output \(O_{t}\);
* Two special nodes, start node \(S_{start}=\left\{\varnothing,I_{t},\varnothing\right\}\) and end node \(S_{end}=\left\{O_{t},\varnothing,\varnothing\right\}\);
* A group of web services with the same ontology called service responsivity \(SR_{i}\);
* Ontology \(OT_{i}=\left\{SR_{i},OT_{subj}\right\}\). \(OT_{subj}\) is the sub-semantic of ontology, which connects the hierarchical relationship of ontology.
## 4 Results
### User Results  
In this chapter, the satellite clocks and biases as generated by the network are used at the user level. The focus is put on analyzing the convergence time and accuracy of the user coordinate solution. Regarding the time stability of the user coordinates, two different assumptions are being made: user coordinates are assumed to be constant or epoch-wise parameters. When coordinates are assumed to be constant, this is referred to as the static coordinate solution. The case of epoch-wise parameters is referred to as the kinematic coordinate solution. The processing at the user level is done for multi-frequency observations in Section 4.2.1 and for dual frequency observations in Section 4.2.2 to assess the advantages of the multi-frequency approach regarding the convergence time of the coordinate solution.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Discussion
#### 4.3.1 GRU Model  
Figure 7 shows that the accuracy achieved with GRU at 20 epochs in the training set is (99.07%), while, in the validation set, it is (99.65%). Although there is a slight difference between the accuracy of the training set and the validation set (0.58%), this gap is minimal, indicating effective generalization of the model. The overall trend shows a steady increase in accuracy and a decrease in loss across epochs, supporting the effectiveness of the training and the ability of the model to learn and generalize to unseen data.  
The F1-score, which combines precision and recall, performed highly in all classes, with values ranging from (96.00%) to (100%). Specifically, classes 1 and 3 had a perfect F1-score of 1.00, while classes 0 and 2 had very high F1-scores of (97.00%) and (96.00%), respectively. The precision per class was (100%), indicating no false positives for any class. On the other hand, recall varied slightly between classes, with values ranging from (93.00%)  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (s)** & **GPU Utilization (\%)** \\ \hline \multirow{4}{*}{LSTM} & \multirow{4}{*}{0.9963} & Class 0: 1.00 & Class 0: 0.94 & Class 0: 0.97 & \multirow{4}{*}{389} & \multirow{4}{*}{33} \\  & & Class 1: 1.00 & Class 1: 0.99 & Class 1: 0.99 & & \\ \cline{1-1}  & & Class 2: 1.00 & Class 2: 0.88 & Class 2: 0.94 & & \\ \cline{1-1}  & & Class 3: 1.00 & Class 3: 0.99 & Class 3: 1.00 & & \\ \cline{1-1}  & & Class 4: 1.00 & Class 4: 0.98 & Class 4: 0.99 & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
Figure 7: GRU accuracy.  
to (99.00%). This suggests that, although the model correctly classified the vast majority of instances of all classes, some instances of classes 0 and 2 needed to be correctly classified, as indicated by the slightly lower recall for those classes. These results indicate exceptionally high model performance on the multiclass classification task.  
The training time of the model was 339 s. During the training process, (28.00%) of the GPU was used, suggesting an efficient utilization of the available hardware resources to speed up the training process. This moderate GPU usage also indicates that the model did not require an intensive workload regarding computational resources, which benefits the scalability and efficiency of training in resource-constrained production environments.
# Quantitative Electroencephalographic Analysis in Women with Migraine during the Luteal Phase
###### Abstract  
Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photopobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the hormonal effect of estrogen is significant, it is not clear how hormonal phases affect brain excitability and EEG patterns in women with migraine. The objective of this research was to study the effect of migraine on the resting-state EEG activity of women during the luteal phase. This work compares electroencephalographic (EEG) absolute power in different frequency bands and scalp areas between young women who suffer from migraine and had a migraine attack within 24 h prior to EEG recording (experimental) and ten age-matched young healthy women (controls), all with normal menstrual cycles. For women with migraine, we found a significant decrease/increase in alpha power in the occipitoparietal/frontcentral area, significant decrease in beta power for all areas, significant decrease in delta power in the temporal area, and significant decrease in theta power in the frontcentral and occipitoparietal area. We concluded that women with migraine have a distinct electroencephalographic pattern during the luteal phase in comparison with control women. A possible explanation might be an intermittent rhythmic activity linked to pain.  
EEG; migraine; luteal phase; absolute power Article
## 3 Simulation of Slot-Waveguides  
To investigate the modal and confinement properties of a slot-waveguide we built a 2D finite element method (FEM)-based model in COMSOL Multiphysics(r). The electromagnetic wave frequency domain (ewfd) has been chosen as the physics interface and the modal analysis was added to the study. For meshing the profile we used well over 3000 triangular vector-elements in a total area of \(A_{\text{tot}}=$5.0\,\mathrm{\SIUnitSymbolMicro m}$\times$2.5\,\mathrm{ \SIUnitSymbolMicro m}$\), which was proven to give reliable and reproducible results. By calculating the eigenvalues and eigenfunctions of the Helmholtz equation  
\[\
abla\times(\
abla\times\mathbf{E})-k_{0}^{2}n^{2}\mathbf{E}=0, \tag{3}\]  
where \(k_{0}\) is the wave number in free space and \(n\) is the refractive index of each material. Solving this equation gives the effective refractive index \(n_{\text{eff}}\) of the waveguide structure. The effective refractive index \(n_{\text{eff}}\) is an important property in modal analysis, because it describes impact of the waveguide on the optical mode in a single figure, without the need to resort to the electric fields, which cannot be measured directly anyway [21].  
The slot-waveguide design and the denotation of its parameters is presented in Figure 4. Table 1 lists the parameters and their standard values, which are then consecutively varied to investigate the change in the effective refractive index and the field confinement factor. These values are widely used in literature due to their compatibility to typical SOI fabrication processes. The slot width \(s\) usually is the independent variable in this study.  
During the design of the structure, only one half of the model has been drawn and afterwards mirrored on its vertical axis in a post-process. The waveguide wires are fully parameterized pentagonal Bezier-polygons, enabling a quick and flexible manipulation of the geometry. For the cladding material beneath, SiO\({}_{2}\) was used, while the wire waveguides and slabs consist of Si. For the cladding and slot material the dispersion curves of PMMA+DR1 were important into the program. All materials chosen from the database were measured at 293 K. The telecommunication wavelength \(\lambda=$1550\,\mathrm{nm}$\) was always fixed during the simulation.
## 2 Framework of Surface Reconstruction for Freehand 3D US Using VSR  
The framework of surface reconstruction for freehand 3D ultrasound using the VSR method [40] is shown in Figure 2.  
First, we perform a spatial transformation to convert the 2D pre-segmented ultrasound contours into a 3D point cloud. Thus the orientation of the contours will become irrelevant and the non-parallel contours can be disregarded.  
Second, we define constraint conditions for the surface reconstruction. All the boundary points of the contours are defined as on-surface constraints. Moreover, additional off-surface constraints are defined to indicate which points should be located inside the surface.  
Third, the variational interpolation is invoked to find a function that has the minimum bending energy and satisfies the defined constraints. The solution to the variational interpolation is a single 3D implicit function and is at least \(C^{1}\)-continuous, _i.e._, smooth.  
Finally, we perform an iso-surface extraction step using the Marching Cubes algorithm proposed by [PERSON] _et al._[19]. The implicit function of VSR is then evaluated to extract the zero-valued surface as the reconstruction result.  
To assess the quality of the reconstructed surface, an extra assessment step is performed. Quantitative metrics [47], e.g., the Hausdorff distance, are calculated to compare the quality of reconstructed surface by four point-based surface reconstruction methods: Ball Pivoting, Power Crust, Poisson, and VSR.
# Adaptive Segmentation of Streaming Sensor Data on Edge Devices
## 2 Related Work
### Data Stream Compression  
The cloud computing is an indispensable part of the Internet of Things (IoT). However, using it gives us many problems including transmission latency, bandwidth constraints, and high energy consumption [2]. Micro-controller, transceiver, and sensor units are the parts of smart devices that consume most of the energy, and data transmission is the most power-hungry task [22; 23]. Energy efficiency can be improved by moving computation tasks from the cloud to edge devices, and by reducing the amount of data transferred from IoT devices to the edge (which additionally conserves the edge devices' storage space) [1; 2; 24].  
The proposed approaches for data reduction during transmission between IoT devices and edge computing devices were based on adaptive sampling [25; 26; 27], aggregation and compression [28; 29; 30; 31; 32], and Compressive Sensing [33; 34; 35; 36]. The main disadvantage of the above approaches is the low compression ratio of non-stationary data coming from multiple sensors [2].  
To address the above issue, in [2] a lightweight version of fast error-bounded lossy compression algorithm [37] was proposed. The authors showed that the proposed approach was able to reduce the amount of data transmitted from the wearable device to the edge device by approximately 103 times, simultaneously not worsening the results of data analytics.  
None of the above-mentioned compression algorithms pre-process data into a form that would potentially accelerate the operation of machine learning algorithms on edge devices, for example by segmenting or smoothing the data stream before sending it from sensors or IoT devices.  
Our algorithm segments the data stream (sampled signal belonging to the continuity class \(C^{2}\)) in an online way. Segmentation allows for a significant degree of compression (we have observed the data size reduction of 135 to 208 times) and the \(C^{2}\) class of the signal helps in this process since three out of four coefficients of each spline segment (apart from the first one) can be calculated from the continuity conditions. The omission in the segmentation process of the known (in advance--because we know what we are measuring) continuity class of the processed signal could potentially allow for a slightly better compression ratio, but at the cost of the accuracy of signal reproduction (and in many cases it is unacceptable). It is primarily about recreating the qualitative characteristics of the signal (continuity/smoothness class), and less about the accuracy of approximation (quantitative feature, measured, for example, with the mean square error).
## 2 Photogrammetric Processing Chain
### Camera Calibration Models  
Camera calibration estimates the parameters of a given camera, including considerations for lens distortion, that are required to model how scenes are represented within images. Camera self-calibration is an essential component of photogrammetric measurements. As outlined in reference [34], self-calibration involves recovering the properties of the camera and the imaged scene without any calibration object but using constraints on the camera parameters or on the imaged scene. A perspective geometrical model allows computing exterior orientation and calibration by means of SfM. Non-linear co-linearity equations provide the basic mathematical model, which may be extended by additional parameters.  
Both PhotoScan and MicMac propose models for frame cameras, spherical or fisheye cameras. In PhotoScan, all models assume a central projection camera. Non-linear distortions are modeled using [PERSON]'s distortion model [30]. The following parameters can be accepted as input arguments of this model:  
* f: focal length
* cx, cy: principal point offset
* K1, K2, K3, K4: radial distortion coefficients
* P1, P2, P3, P4: tangential distortion coefficients
* B1, B2: affinity and non-orthogonality (skew) coefficients  
In MicMac, various distortion models can be used, the distortion model being a composition of several elementary distortions. Typical examples of basic distortions are given in MicMac's user manual [32]. For instance, the main contribution to distortion can be represented by a physical model with few parameters (e.g., a radial model). A polynomial model, with additional parameters, can be combined with the initial model to account for the remaining contributions to distortion. A typical distortion model used in Apero module is a [PERSON]'s radial model [35] with decentric and affine parameters and 12 degrees of freedom (1 for focal length, 2 for principal point, 2 for distortion center, 3 for coefficients of radial distortion, 2 for decentric parameters and 2 for affine parameters). [15] presents the last evolutions of MicMac's bundle adjustment and some additional camera distortion models, in particular \"F15P7\", specifically designed to address issues arising with UAV linear photogrammetry. In our study, the \"F15P7\" refined radial distortion model is used according to the description in reference [15]. It consists of a radial camera model to which is added a complex non radial degree 7 polynomial correction.
# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 3 Improved CEEMD-WPT Noise Reduction Algorithm
### Improved CEEMD-WPT Reconstruction Method  
After CEEMD is improved, the crack acoustic emission signal is decomposed into a series of IMF components and residue. According to the decomposition principle, the high-frequency part of the signal (containing a large number of random noise) is decomposed into the IMF component at the front, while the low-frequency part (effective signal) is in the rear component, and the component can be distinguished by the mutual relation number.  
The correlation number is an indicator used to measure the degree of similarity between two signals.  
\[C_{i}=\frac{cov(IMF_{i},x(t))}{\sqrt{Var(IMF_{i})Var(x(t))}} \tag{9}\]  
In Equation (9), \(IMF_{i}\) is the \(i\) IMF component and \(x(t)\) is the original signal.  
It is feasible to use the correlation number as an indicator to distinguish the effective signal content in the IMF component, i.e., the effective signal has a greater correlation with the original signal, and the correlation number is larger. In this case, the IMF with a greater correlation number with the original signal is called the strongly correlated component. On the contrary, it has been shown that IMF components contain more noise components, which are called weak correlation components. In addition, the mean of the correlation number between all IMF components and the original signal is calculated as the index to judge the strong and weak correlation components. In addition, in order to prevent the IMF component of some signals from being decomposed too much and thus dragging down the average, we set the mean lower limit here at 0.3, and if it is below this lower limit, it is calculated as 0.3.  
\[\overline{C}=\frac{1}{n}\sum\limits_{i=1}^{n}C_{i} \tag{10}\]  
\[T=\begin{cases}\overline{C}&\overline{C}>0.3\\ 0.3&\overline{C}\leq 0.3\end{cases} \tag{11}\]Because the weakly correlated portion contains a small amount of effective signal and a large amount of noise, it needs to be re-denoised via WPT. However, the strong correlation component has a high content of effective signals, and reprocessing the signal will have the risk of the loss of effective signals. Therefore, the strong correlation component and the weak correlation component after WPT processing are directly reconstructed.
## 2 Materials and Methods
### Oil Spill Model Establishment  
According to the characteristics of tide-affected rivers, a two-dimensional hydrodynamic model of the Taipu River was established using the FM module of the MIKE21 software package, which provided the hydrodynamic basis for the two-dimensional oil spill model. In combination with the oil particle calculation model, a two-dimensional oil spill model of the Taipu River was established with the help of the SA module in the MIKE21 software package, which can simulate the dynamic transport processes of oil spills, such as expansion, drift, and diffusion, under the joint actions of wind and hydraulic forces after the oil enters the water body. The simulation can project various oil spill parameters, such as oil film drift trajectory, time to reach sensitive waters, and attribute changes in oil products, which will assist in emergency decision making and damage assessment of oil spill accidents. Figure 3 is the flow chart of oil spill model construction in this paper.  
The bed level of the river was determined by combining the relevant mapping data provided by the hydrological department and field-measured data from May 2015. According to the measured data from March 22 to March 24, 2015, the hydrological conditions of the Taipu River reach were calculated. The actual flow and water level of the Taipu River in the same period were taken as the upper and downstream boundary conditions, and the change process of the upper and downstream boundary hydrological elements of the two-dimensional hydrodynamic simulation in the study area was obtained through the simulation of a one-dimensional hydrodynamic mathematical model. The changes were taken as the boundary conditions of tidal flow volume and tidal level to simulate the hydrodynamic characteristics of the tidal flow process.  
Figure 2: Schematic diagram of the heat balance of an oil film. 1 = heat transfer between the atmosphere and the oil film; 2 = heat radiation between atmosphere and oil film; 3 = solar radiation; 4 = evaporative heat loss; 5 = heat transfer between oil film and water.