esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
## 3 Results
### 2D Infinite Serpentine Passage Flow
The performance of the turbulence models concerning predicting the re-circulation can also be illustrated using the separation and re-attachment points, as well as the re-circulation length, which are listed in Table 1. It shows that all models predicted the separation point earlier and the re-attachment point later. The original STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model and the STT \(k\)-\(\omega\)-CC model enlarged the re-circulation region dramatically; however, the two proposed models in the present paper obtained significantly improved results. It can be seen that, for the 2D infinite serpentine passage flow, compared with DNS data, the STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC1 model provided the best results in terms of the velocity profile, the friction coefficient, and the length of the re-circulation zone. The STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC2 model also improved the results significantly. These results demonstrate that, compared with the original STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model, the two proposed models in the present paper work well for this flow. The two proposed models showed good sensitivity to streamline curvature and successfully predicted turbulence augmentation near the concave wall and turbulence suppression near the convex wall, but the original STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model did not. In the region near the concave wall, generally \(\Omega<S\). For the STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC1 model, the Richardson number is negative, so that \(F_{2}>1\), the turbulence is enhanced due to reduction of turbulent dissipation. For the STT \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC2 model, the production of turbulent kinetic energy (Equation (13)) increases, leading to turbulence enhancement. In the region near the convex wall, the opposite situation happens. Because the turbulence near the convex wall is suppressed, the re-circulation zone becomes shorter. A notable phenomenon was that the predictions of the STT \(k\)-\(\omega\)-CC model were the worst. This result is strange, as this model has been shown to have a good ability for flows with strong streamline curvature by [PERSON] and [PERSON] (2018). The reason for the observed result is not clear, and further research is needed for clarification.
# SCS-JPGD: Single-Channel-Signal Joint Projected Gradient Descent
## 4 Experiments and Results  
### Experimental Setup  
#### 4.1.1 Data Description  
The CWRU (Case Western Reserve University) dataset is a widely used public dataset for mechanical fault diagnosis research. It mainly contains vibration signals from electric motors and bearings. The dataset was provided by Case Western Reserve University and includes vibration data collected under different fault conditions, covering various fault types such as inner race faults, outer race faults, and rolling element faults. The data were collected using accelerometers under different load and speed conditions, with a sampling frequency of 12 kHz. The CWRU dataset is widely applied in fault diagnosis, feature extraction, and the validation of machine learning and deep learning models. It plays a crucial role in the preventive maintenance of electric motors and mechanical components, vibration analysis, and the development of intelligent diagnostic systems.
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 5 Results and Discussions
### Hydrodynamic Performance in Calm Water
#### 5.1.3 Wave Pattern  
Figure 18 shows the wave patterns of monohull and pentamaran in calm water. It can be known from Figure 18 that  
(1) At a low speed of 5 kn, there were two obvious Kelvin wave patterns at the bow and stern of the monohull, and there was almost no wave interference in the middle section of the hull. The Kelvin wave generated by the side hulls of the pentamaran was fused and superimposed with the bow and stern, forming a longer scattered wave. At the speed of 10 kn, the Kelvin wave pattern of the pentamaran was significantly larger than that of the monohull. Wave peaks were generated between the front hulls and the main hull, and the wave peaks generated by the head of the rear hulls and the main hull were superimposed, which was unconducive to wave-making resistance. The wave trough at the stern of a catamaran is larger than that of a monohull, and the range of wave peaks after the stern is reduced.  
(2) At medium and high speeds of 15 kn, 20 kn, and 25 kn, there was almost no wave making in the inflow area in front of the bow of the two ship types, which was related to the special bow shape of the monohull, and its bow was thin and pointed. There was a cock-tail flow in the tail area, which was divided into two strands at the rear area, and the cock-tail was formed into an arrow shape. Due to the installation of the side hulls, the wave trough generated by the main hull at the position of the side hulls met the side hulls, which would cancel each other out with the wave crest generated by the side hulls, thereby causing the trough area to move backwards. In addition, the waves generated at the tail of the rear side hulls and the waves generated at the tail of the main hull were coupled to each other to form multiple wave valleys, which reduced the wave crest area behind the stern.  
In conclusion, the wave patterns of the monohull and pentamaran in calm water show that the waves between the side hulls and the main hull of the pentamaran superimposed each other at low speeds, resulting in unfavorable interference and wave resistance. At medium and high speeds, the range of the wave crest at the stern of the pentamaran was reduced, and the range of the wave trough was increased. The wave-making of the rear side hulls was able to offset part of the waves of the main hull. The impact of the front hulls on the wave-making of the main hull was small. The influence of the rear hulls on the wave-making was mainly reflected in the wave valley area.  
Figure 17: Heave and pitch in calm water.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 4 Lessons Learned
### Hybrid Approach  
Simulation results showed that, in a dense environment, decreasing the rate after failed transmissions leads to longer transmission times and a decrease in the throughput, which, in turn, increases the occupation time of the channel affecting all of the nodes in the network. One of the possible approaches to fix this may be using a sliding window that will help predict the future channel conditions and act accordingly [24].  
Designing an efficient hybrid approach is a challenging task on which few existing works have concentrated. The simulation results showed that the selected hybrid algorithm (namely Mutfed) gave worse performances than the other algorithms, this is mainly due to the way Mutfed decreases its bit rate, which, in turn, decreases throughput and increases interference and frame loss. Ideally, a hybrid algorithm could be constructed on the top of an explicit algorithm by including additional metrics, such as collecting statistics about FLR that can be used to adapt the number of MPDUs in an A-MPDU.
## 1 Introduction  
The following tests are commonly used across the world to identify the type of mineral and organic soils in the ground of a designed object: a Standard Static Test (SPT), a Pressuremeter (PMT), a Cone Penetration Test (CPT) or a Marchetti dilatometer (DMT).  
In recent years, static probing by \"dilatometer penetration tests\", commonly known as DMT (Figure 1), is a widely-used method of in situ investigation of the ground to provide information needed by civil engineers for design, construction, permissions, and operation control. The results of the DMT field tests, complemented by well-established experience, need to be considered to derive the characteristic values (\(X_{m}\)) and to later design values (\(X_{d}\)) of geotechnical parameters [1; 2; 3; 4]):  
\[X_{d}=X_{m}/\gamma_{M} \tag{1}\]  
where \(\gamma_{M}\) is the partial factor of a material property.  
Figure 1: Marchetti dilatometer: (**a**)—flat blade, 1—electric wire, 2—pneumatic tubing, 3—steel membrane, (**b**)—test stages, 4—pushing, 5—contact stress \(p_{0}\), 6—expansion stress \(p_{1}\), 7—pressure \(p_{2}\), c: view of DMT blade actually used in soil in situ tests.  
This paper presents a review of the methods used in the identification of soils from DMT test results. Next, the analysis of the results from field test studies on six sites is presented in [5]: The DMT testing was carried out in the frame of the project of SGGW campus development [6], an experimental embankment (Antoniny site), embankment dams (Nielisz, Koszyce and Mielimaka), \"Plocka subway station\" and Stegny site in Warsaw. Finally, a new diagram for the identification of mineral and organic soils from DMT tests is offered.
## 3 Effect of Incident Angle of the Light on the Measurement Sensitivity  
As shown in Figure 1, the laser beam is obliquely incident on the stylus shaft; therefore, we now discuss the relationship between incident angle and measurement sensitivity both experimentally and theoretically. First, the enhancement sensitivity is confirmed by using the basic experimental apparatus. Next, the relationship between the incident angle of the laser beam and the sensitivity is theoretically examined using the 3D ray-tracing method. Finally, the factor of the sensitivity enhancement is analyzed using same method.
# Use of Data Augmentation Techniques in Detection of
## 2 Data Augmentation
### Data Augmentation in NLP  
While using augmentation techniques in computer vision and image classification applications is very popular [26; 27; 28; 29], text data augmentation techniques in NLP applications are still relatively less used mostly due to the complexity of natural language. However, more recently, methods to extend textual training data for NLP tasks have been the subject of several studies [30; 31; 32]. In general, augmentation methods in NLP can be divided into four groups based on the level at which the augmentation procedure works. Character-level augmentation is based on adding noise to the training samples (e.g., switching the letters) on a character level. Such an approach can be used to simulate the natural noise in text (e.g., spelling mistakes), common in written text. Models trained on character-level enhanced data should be more suitable for text processing tasks in some specific domains, e.g., classification of texts from social networks [33; 34]. Word-level augmentation is based on adding noise or the replacement of words. Very common are vocabulary-based approaches, where replacements are based on different lexicons [35; 36; 37]. One of the most frequently used ones is Wordnet [38]. A very popular word-level augmentation technique is easy data augmentation (EDA) [39], which we will describe later. Besides the replacement of the lexical units, there are more advanced methods based on embedding replacement which (instead of synonym substitution) replace words based on textual context [40] or replacement methods based on language models [41]. Phrase- or sentence-level augmentation methods perform the replacement of multiple words or parts of the text (e.g., semantic text exchange [42]). In the last group, document-level methods are used to generate entire text documents. In this case, mostly generative approaches[43] or translation models [44; 45] are used.
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 3 Materials and Methods
### Network Overview  
The proposed SCSAEN network architecture for FSC is illustrated in Figure 1. The network comprises three main components: the backbone, a similarity feature extraction and processing module, and a density map prediction module. The core lies in the similarity extraction and processing module. This module receives features from the backbone ResNet and feeds them into the SSA module, which initially extracts multi-angle similarity features across spatial dimensions. These features \(M_{N}\), after undergoing the optimal fusion operations determined through extensive experimentation, are passed into the CAE module. The CAE module enhances critical channel information to produce a more precise similarity map \(C_{f}\). Finally, the \(C_{f}\) is fed into the density map prediction module, where simple convolutional operations are applied to generate the predicted density map.  
Figure 1: Schematic of the overall architecture of the spatial and channel similarity-aware attention enhancement network. \(f_{Q}\) and \(f_{S}\) represent the features obtained by the input images through the backbone, respectively. After passing through the spatial similarity-aware module, the fused similarity feature \(f_{RQ}\) is obtained. The channel attention enhancement module processes \(f_{RQ}\) to obtain the  
similarity map \(C_{f}\). Finally, the output density map is obtained through the density map prediction module. Here, NL stands for layer normalization, SA represents the similarity-aware attention mechanism, max() indicates the global maximum pooling, ga() represents the global average pooling, and sigmoid() represents the sigmoid function.
## 2 Results and Discussion
### Determination of the Antioxidant Activity of Samples  
For a better interpretation of the results, antioxidant activity was evaluated by three different methods. There has still not been established one, single method for the determination of the antioxidant capacity of specific food sample and, therefore, it is recommended to combine at least two or more different methods to provide comprehensive results of the total antioxidant capacity of a food samples [28]. Moreover, all methods used were based on different mechanisms, and this is also the reason why the results from these various methods can be biased. The antioxidant activity of tested samples evaluated by DPPH, ABTS, and FRAP method is shown in Table 2.  
The antioxidant activity of all samples measured by DPPH assay varied between 6.09 \(\pm\) 0.01 g Trolox equivalents (TE)/kg in blend chocolate with 80% cocoa solids and 6.19 \(\pm\) 0.00 g TE/kg in blend chocolate with 100% cocoa solids. Especially when talking about blend chocolates, there are different cacao cultivars as well as processing parameters used, and all these factors affect the antioxidant capacity of the final products [1]. Our results are in consonance with the results of [PERSON] et al. [26], where DPPH showed lower results than ABTS test because ABTS radical reacts with both hydrophilic and lipophilic antioxidants while DPPH reacts only with lipophilic ones. In addition, DPPH radical does not react with phenolic acids and, therefore, the antioxidant capacity determined by DPPH and ABTS methods are partially different. The main advantage of the ABTS radical is its high reactivity and, thus better ability to react with a wider range of antioxidant compounds [29].  
The highest antioxidant activity measured by ABTS assay was shown by the sample of blend chocolate with 90% of cocoa solids, and the lowest activity was found in the sample of 100% cocoa solids' blend chocolate. These results positively correlated (R\({}^{2}\) = 0.94) with the total polyphenol and phenolic acids' content. Chocolate is a very complex matrix consisting of various components, different than polyphenols that could influence the final antioxidant activity of the samples. Moreover, different types of phenolic compounds respond differently in these assays [1].  
The lowest and the highest activity measured by the FRAP method was found in the same samples as ABTS. Antioxidant capacities of single-origin chocolates evaluated by ABTS and FRAP method were in both cases higher than 100% blend chocolate and comparable to other high-percentage blend chocolates.
# Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
## 2 Materials and Methods
### Construction of Quantitative Curve  
The determined optimum solid-phase antibodies were coated in the enzyme-labeled plate. Then, 100 mL PPI with the concentrations of 82.5 pg/mL, 41.25 pg/mL, 20.65 pg/mL, 10.31 pg/mL, 5.16 pg/mL, 2.58 pg/mL, 1.29 pg/mL, 0.645 pg/mL and 0 pg/mL were added, respectively. They were placed at 37 degC for 60 min and washed with TBST three times. Next, 1 mg/mL of optimum detection antibody was added at 100 mL/pore, and the double-antibody sandwich ELISA was performed according to the method described in 1.2.2 with three multiple holes per PPI concentration. With the average OD450 value of each concentration as the ordinate and the PPI concentration as the abscissa, the standard curve was fitted with Skanlt RE (Thermo, Varioskan LUX, Waltham, MA, USA) to determine the linear range and quantitative equation of the detection.
## 5 Results and Discussion  
### Quantitative Results  
#### 5.1.1 Shot Boundary Detection  
In this study, to evaluate shot boundary detection, we have compared our framework with state-of-the-art CNN-based fast shot boundary detection[20]. We have used 10 random Internet Archive videos from the RAI dataset. Table 1 compares the precision, recall, and F-score of our pipeline with this state-of-the-art algorithm. These experimental results show that the state-of-the-art model performs extremely well on normal transitions, while performing comparatively poorly on complex transitions. Our approach, on the other hand, has obtainedsimilar precision values for both complex and normal transitions. On average, our approach has outperformed the state-of-the-art with an f1 measure of 0.92.
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 2 Seismic Anisotropy
The Delta parameter controls the near-vertical anisotropy and does not relate to the horizontal velocity, so it can be positive or negative. Assuming weak anisotropy, the Delta parameter refers to the stiffness components by the following equation:  
\[\delta=\frac{(C_{13}+C_{44})^{2}-(C_{33}-C_{44})^{2}}{2C_{33}(C_{33}-C_{44})} \tag{7}\]  
where \(\delta\) is the anisotropy parameter Delta, while \(C_{13}\), \(C_{44}\), and \(C_{33}\) are the stiffness components of the elastic tensor. The Gamma parameter is the fractional difference between the vertical and horizontal S-wave velocities and can be given as follows:  
\[\gamma=\frac{V_{SH}-V_{SV}}{V_{SV}}=\frac{C_{66}-C_{44}}{2C_{44}} \tag{8}\]  
where \(\gamma\) is the anisotropy parameter Gamma, \(V_{SH}\) is the horizontal S-wave velocity, \(V_{SV}\) is the vertical S-wave velocity, and \(C_{66}\) and \(C_{44}\) are the stiffness components of the elastic tensor. The P- and S-wave velocities can be defined in terms of the anisotropy parameters based on the weak anisotropy assumption [53; 61], as shown below:  
\[V_{P}(\theta)=V_{P0}\big{[}1+\delta sin^{2}(\theta)cos^{2}(\theta)+\epsilon sin ^{4}(\theta)\big{]} \tag{9}\]  
\[V_{SV}(\theta)=V_{S0}\big{[}1+\bigg{(}\frac{V_{P0}}{V_{S0}}\bigg{)}^{2}( \epsilon-\delta)sin^{2}(\theta)cos^{2}(\theta)\big{]} \tag{10}\]  
\[V_{SH}(\theta)=V_{S0}(1+\gamma sin^{2}(\theta)) \tag{11}\]  
where \(V_{P}(\theta)\) and \(V_{SV}(\theta)\) are the vertical P-wave and S-wave velocities, respectively, \(V_{SH}(\theta)\) is the horizontal S-wave velocity, \(V_{P0}\) and \(V_{S0}\) are the zero-offset P-wave and S-wave velocities, respectively, at \(\theta\) = 0, and \(\theta\) is the incident angle.
# A Rectangular Toroidal Current-Based Approach for Lung Biopsy Needle Tracking
## 1 Introduction
The XYZ coordinates of the biopsy needle relative to the emitted magnetic field, along with the pitch and pendulum angles, were calculated using the least squares method. Unlike traditional electromagnetic tracking methods, our approach utilizes a rectangular toroidal current distribution to improve spatial accuracy while maintaining computational efficiency. This novel configuration, combined with optimized signal processing algorithms, enables high-precision tracking of biopsy needles, making it a viable alternative for real-time medical guidance.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
###### Abstract  
Radiometric calibration adjusts the measured pixel intensity to a physical property, the radar cross section (RCS). After calibration, this relationship is defined over the entire backscatter range: from low image power (near noise) up to high reflections (below saturation). Based on a proper radiometric calibration, the measured radar backscatter for the Sentinel-1 synthetic aperture radar (SAR) satellite constellation is validated over a wide backscatter range using different target types. Therefore, the RCS derived from point targets and radar brightness from distributed targets are compared between Sentinel-1A (S-1A) and Sentinel-1B (S-1B) acquisitions over the same observation area for regions where a stable target backscatter is expected for a certain period of time. Low differences (in the order of 0.3 dB) are found between S-1A and S-1B for high and medium backscatter derived from point targets or rainforest regions, but higher differences for low backscattering regions like ice areas and lakes. For comparing radar brightness containing low backscatter targets, an accurate derived noise level has to be taken into account. In addition to the measured lower noise equivalent beta zero (NEBZ) level, higher transmit power was detected for S-1B compared to S-1A. The higher antenna gain of S-1B leads finally to a higher sensitivity for low backscattering areas of S-1B compared to S-1A and explains the found differences.  
synthetic aperture radar (SAR); Sentinel-1; SAR system constellation; radar backscatter; radiometric accuracy; radiometric noise; NESZ; SAR system calibration 12 February 2020; Accepted: 5 March 2020; Published: 6 March 2020
# Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems
## 2 Dynamic Model of Liquid Level and System Description
Thus, it is possible to translate the dynamic equations of Equations (7) and (8) as follows:  
\[\dot{L}_{1}(t)=-\frac{a_{1}}{A_{1}}\sqrt{2g(L_{10}+L_{11}(t))}+\frac{K_{p}}{A _{1}}(u_{1}(t)+u_{2}(t)), \tag{12}\]  
\[\dot{L}_{2}(t)=-\frac{a_{1}}{A_{2}}\sqrt{2g(L_{11}(t)+L_{10})}-\frac{a_{2}}{A _{2}}\sqrt{2g(L_{21}(t)+L_{20})}, \tag{13}\]  
The liquid level system's dynamic equations are nonlinear due to the characteristics of the pump and valve, as well as variations in the system's parameters, as shown by Equations (12) and (13). In order to execute the suggested cascade for the connected tank system, it is necessary to linearize the linked tank system model around the operational point \((L_{10},\ u(t))=(L_{10},u_{1}(t))\). Applying the first-order Taylor series approximation at the points (\(L_{20},\ L_{10}\) and \(u_{1}(t)\)) to Equations (12) and (13) results in the following [42]:  
\[\dot{L}_{11}(t)=\alpha_{1}L_{11}(t)+\beta_{1}u_{2}(t), \tag{14}\]  
\[\dot{L}_{21}(t)=\alpha_{2}L_{21}(t)+\beta_{2}L_{11}(t), \tag{15}\]  
where  
\[\begin{array}{l}\alpha_{1}\cong-\frac{a_{1}}{A_{1}}\sqrt{\frac{\delta}{2L_{ 10}}},\ \ \beta_{1}\cong\frac{K_{p}}{A_{1}},\\In this particular context, \(L_{21}\) and \(L_{11}\) symbolize the output and input of the subsystem, respectively, as mentioned in Equation (15).  
\[G_{1}(s)=\frac{K_{1}}{\tau_{1}s+1}, \tag{17}\]  
\[G_{2}(s)=\frac{K_{2}}{\tau_{2}s+1} \tag{18}\]  
In addition, the subsystems in Equations (14) and (15) are modelled as transfer functions, and the Laplace transforms of these equations are given in Equations (17) and (18), where \(K_{1}=\frac{K_{p}\sqrt{2 gL_{10}}}{a_{1}g}\), \(K_{2}=\frac{a_{1}\sqrt{L_{20}}}{a_{2}\sqrt{L_{10}}}\), \(\tau_{1}=\frac{A_{1}\sqrt{2 gL_{10}}}{a_{1}g}\), and \(\tau_{2}=\frac{A_{2}\sqrt{2 gL_{20}}}{a_{2}g}\). It is evident that Equations (17) and (18) can be applied to both cascade and traditional PI. Table 1 displays the parameters of the connected tank system.