esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrast Enhancement for Topographic Imaging in Confocal Laser Scanning Microscopy
## 3 Setup  
The setup used for the experiment is based on an in-house built stage-scanning confocal microscope in reflective geometry, which is shown in Figure 2. The laser source is a cost-effective laser diode with a wavelength of \(\lambda=$405\,\mathrm{nm}$\), which is typically implemented in blu-ray players. It is coupled into a single-mode fiber and thus enables a point-source beam profile. The light is collimated by a large beam reflective collimator (LBC) and reflected by a beam splitter (BS). To focus and collect the light from the sample, an aspheric lens (L1) with added correction glass and a numerical aperture (NA) of 0.6 was chosen. A computer controlled movable xyz-stage is used to transport the sample. The back reflected light transmits through the beam splitter and is focused through a pinhole onto a silicon photodiode (PD). A transimpedance amplifier (FEMTO) is used to amplify the signal from the PD. The entire setup is controlled using an XPS motion controller (Newport) and a PC. Point-by-point-scanning enables an intensity recording for single points of the sample.  
According to the Sparrow criterion, the system provides a resolution of 317 nm [27]. By modifying the Sparrow criterion with the improvement factor of \(\sqrt{2}\) used for confocal microscopes, the diffraction limited resolution is 224 nm [28]. Thus, according to theory, the resolution limit of our system should be in the range of 224-317 nm.  
Figure 2: Schematic drawing of the reflection confocal laser scanning microscopy (CLSM); Laser: laser diode \(\lambda=$405\,\mathrm{nm}$\), SMF: single-mode fiber, LBC: large beam collimator, BS: 50% beam splitter, L1: aspheric lens, L2: plan achromat objective, PD: photodiode, Controls: transimpedance amplifier, Newport XPS with A/D converter and PC with Matlab.  
During the experiment and for the simulation, different pinhole sizes are used. The equivalent pinhole size in Airy units (AU) can be calculated via:  
\[D=\frac{1.22\lambda}{NA} \tag{1}\]  
using an \(NA_{L2}=0.1\) and resulting in D = 4.94 \(\upmu\)m which corresponds to 1 AU [29]. We used different pinholes, mainly with diameters of 50 \(\upmu\)m, 25 \(\upmu\)m and, for comparative measurements, 10 \(\upmu\)m and 5 \(\upmu\)m. Corresponding AU sizes are about 10 AU, 5 AU, 2 AU and 1 AU.
## 3 Construction of the Model
### Feature-Extraction Optimization Based on BiFormer  
Aiming at the problems of multiple anchors and small samples in a single frame image, the sample contour and features are easily ignored under the influence of the environment, resulting in poor recognition results. The attention mechanism is added to the YOLOv7 target-detection network to strengthen the critical feature information. For the backbone, the BiFormer attention mechanism is added to the top layer, that is, the final extraction feature layer, to make up for the deficiency of the original model in small target-detection ability. The structure is shown in Figure 4. By embedding BiFormer's Bi-Level Routing Attention (BRA) module into YOLOv7, the vector of each position can interact with the vector of other places, and different weights are assigned according to the relative importance between them [24; 25]. In BRA, each encoder contains multiple Transformer [26; 27] attention heads. Through parallel computing, it captures different scales and types of information, learns the correlation between other image regions, pays attention to the union between areas, and uses sparsity to skip the most irrelevant computing regions, further reducing the computational pressure and difficulty.  
Figure 4: BiFormer attention mechanism structure.  
Figure 3: CLAHE algorithm flow chart. The red circle in the figure shows the part before and after the algorithm processing as a comparison.
## 3 Connection Design  
For a complete composite interaction, steel and concrete must work together, facilitated by the stud shear connection in a composite section. The assumption of a linear relationship between shear force and slip for composite beams with partial shear interaction was proposed by [PERSON] et al. [22] and subsequently evaluated and discussed by other authors [23; 24].  
The connection can be designed by considering the linear behavior of the studs, with the shear demand dependent on the shear distribution along the beam. This suggests designing variable spacing with smaller steps where the shear is higher, such as at the ends of a simply supported beam under a distributed load. If ductile behavior is assumed for the studs, the spacing can be uniform along the beam. The number of studs required depends on the global horizontal shear that must be transferred between significant sections of the beam, such as the support and the most stressed section.  
For ductile studs, the number of shear connectors defines the level of interaction. A full shear connection is achieved when an increase in the number of shear connectors (N\({}_{\ell}\)) does not further increase the design bending resistance of the member. Otherwise, the shear connection is considered partial [20], resulting in a reduction in the section resistance in bending with the minimum value of the steel beam. [PERSON] et al. [25] evaluated full and partial shear connections in composite beams using the finite element (FE) method for simply supported beams subjected to either concentrated or uniformly distributed loads.  
The partial shear connection leads to reduced strength and increased ductility in the beam, but slips in serviceability conditions increase, and the deformability of the beam becomes too high. However, in Eurocode 4, the degree of interaction is limited to not less than 0.4 of the full interaction.
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
###### Abstract  
Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry; the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R2 > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.  
additive manufacturing (AM); fused deposition modelling (FDM); machine learning; energy use; Gaussian process regression Article
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method
### Turbulence Model  
The RNG \(k\)-\(\epsilon\) turbulence model is adopted in this work, which considers the influence of rotation and swirl flow in the fluid and modifies the turbulent viscosity. Meanwhile, the RNG \(k\)-\(\epsilon\) turbulence model adapts well to the VOF model. The turbulent kinetic energy and dissipation rate equations are as follows [17]:  
Figure 6: Mesh of bearing chambers. (**a**) Mesh of Chamber 1; (**b**) mesh of Chamber 2; (**c**) mesh of Chamber 5.  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{i}}= \frac{\partial}{\partial x_{j}}\left(\alpha_{k}u_{eff}\frac{\partial k}{ \partial x_{j}}\right)+G_{k}-\rho\epsilon \tag{5}\]  
\[\frac{\partial(\rho\epsilon)}{\partial t}+\frac{\partial(\rho\epsilon u_{i})} {\partial x_{i}}=\frac{\partial}{\partial x_{j}}\left(\alpha_{t}u_{eff}\frac{ \partial\epsilon}{\partial x_{j}}\right)+\frac{C_{1\epsilon}\epsilon}{k}G_{k}- C_{2\epsilon}\rho\frac{\epsilon^{2}}{k} \tag{6}\]  
Among them,  
\[\mu_{eff}=\mu+\mu_{t} \tag{7}\]  
\[\mu_{t}=C_{\mu}\rho\frac{k^{2}}{\epsilon} \tag{8}\]  
where \(k\) is turbulent kinetic energy; \(\epsilon\) is turbulent dissipation; \(u_{i}\) is time-averaged velocity; \(G_{k}\) is the generation of turbulent kinetic energy due to mean velocity gradients; \(\mu_{eff}\) is effective dynamic viscosity; \(\mu_{t}\) is turbulent viscosity; \(C_{1\epsilon}\), \(C_{2\epsilon}\) and \(C_{\mu}\) are constant terms, where \(C_{1\epsilon}=1.42\), \(C_{2\epsilon}=1.68\), \(C_{\mu}=0.0845\).
## 4 Conclusions  
The experimental results highlighted that the type and level of active principles in the analyzed extracts differed depending on the species, the method of processing the plant material, and the extraction process. The processing in the form of powder (FG) leads to a higher content of active principles compared to the processing in the form of shredding (CS). The comparative analysis of the 3 extraction methods--conventional solvent extraction (CES), ultrasound-assisted extraction (UAE), and microwave extraction (MWE)--led to the conclusion that the optimal extraction method for the extraction of active principles from most medicinal plants is the microwave method, followed by ultrasound extraction and extraction with solvents. Regarding the antimicrobial activity, the effectiveness of the extracts was observed, especially on the strains of _H. influenzae_, _C. albicans_, _C. parasilopsis_, _S. aureus_, and _S. flexueri_, while reduced antimicrobial activity was recorded on the strains of _S. pyogenes_ and _P. aeruginosa_.  
The information presented in the paper has an innovative and applicative character, representing a useful tool in establishing the optimal conditions for processing/extracting medicinal plants to obtain a maximum content of compounds with antioxidant or antimicrobial activity specific to a strain, depending on the intended destination.  
All authors contributed to the study's conception and design. Conceptualization original draft preparation, N.M.H., A.C. and E.A.; methodology: D.O., I.C., D.L., M.-A.P., E.A., A.C. and C.B.; formal analysis: N.M.H., D.O., I.C., D.L., M.-A.P., E.A., A.C., I.M.I, G.P. and C.B.; review, editing and validation: N.M.H., A.C. and E.A.; Supervising: I.M.I. and G.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the research project no. 2381/31.03.2022, \"Obtaining and characterizing natural extracts with an antioxidant and antimicrobial role for use as feed additives and in the food industry,\" financed by S.D.E.A. Lovrin, Romania, and the project \"Increasing the impact of excellence research on the capacity for innovation and technology transfer within USAMVB Timisoara,\" project code 6 PFE Program 1--Development of the national system of research--development, institutional development projects--development projects of excellence in R.D.I.  
Not applicable.  
The reports of the analyses performed are available at the Interdisciplinary Research Platform (PCI) belonging to the University of Life Sciences \"[PERSON]\" from Timisoara.  
The authors declare no conflict of interest.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 7 Data Reduction Techniques  
The experimental evaluation used all the different data reduction techniques identified in the taxonomy illustrated in Figure 1. From the examples in Table 2, we selected: Random Projection, Quantization, Linear Regression, Principal Component Analysis, K-Means, Simple Random Sampling, Delta Encoding, and BZip2. The foundation for this choice was their popularity and implementation simplicity.
# Monitoring the Capillary Jet Breakage by Vibration Using a Fast-Video Camera
## 3 Theoretical Approach  
[PERSON] [11] analyses the instability of the liquid jet and its tendency to break into droplets due to axial symmetric disturbances. He observed that the breakage occurs when the jet length is similar to the jet circumference. For Newtonian and low viscosity liquid, the optimum breakage wavelength, \(\lambda_{opt}\), to obtain mono-dispersed droplets was given by:  
\[\lambda_{opt}=\pi\sqrt{2}\,d_{j} \tag{1}\]  
Figure 2: Monitoring of a jet capillary breakage.  
where \(d_{j}\) is the jet diameter. The optimum frequency, \(f_{opt}\), is equal to:  
\[f_{opt}=\lambda_{opt}\;u_{j} \tag{2}\]  
where \(u_{j}\) is the linear velocity of the jet. According to [PERSON] and [PERSON] [16], uniform droplets may be produced in a range of wavelengths defined by:  
\[3.5\;d_{j}\;<\lambda_{opt}<7\;d_{j} \tag{3}\]  
This provides some freedom in selecting the conditions for droplet production. In 1931, [PERSON] [17] extended the [PERSON] equation considering the impacts of the liquid physico-chemical properties:  
\[\lambda_{opt}=\pi\sqrt{2}\;d_{j}\;\sqrt{1+\frac{3\eta}{\sqrt{\rho\sigma d_{j}}}} \tag{4}\]  
where \(\eta\) is the fluid viscosity, \(\rho\) is the liquid density, and \(\sigma\) is the surface tension. Generally, the jet diameter, \(d_{j}\), is assumed equal to the internal nozzle diameter, \(d_{n}\)[13]. However, at the exit of the nozzle, the jet is submitted to a pressure relaxation, leading to a jet expansion that could be expressed by [8]  
\[\frac{d_{j}}{d_{n}}=4.33\;We^{-0.337} \tag{5}\]  
where \(We_{n}\) is the Weber number related to flow inside the nozzle:  
\[We_{n}=\frac{\rho d_{n}\;u_{n}^{2}}{\sigma} \tag{6}\]  
The liquid flow rate, \(F\), is the product of the jet section times the jet velocity:  
\[F=\frac{\pi}{4}d_{j}^{2}u_{j} \tag{7}\]  
The volume, \(\
u\), of the droplets may be predicted by:  
\[\
u=\frac{F}{f_{opt}}=\frac{\pi}{6}\;d^{3} \tag{8}\]  
and, combining Equations (1), (2), (7) and (8), the droplet diameter, \(d\), is given by:  
\[d=\sqrt[3]{\frac{2}{3}d_{j}^{2}\lambda_{opt}}=1.89d_{j} \tag{9}\]
## 4 Validation and Calibration  
Wavelength modulation spectroscopy signal (whether the WMS-1\(f\) or the WMS-2\(f\) signal) depends on the laser characteristics (such as emitted wavelength, applied current, laser temperature, modulation amplitude and modulation depth of 2.2 on which the signal was optimized) and on the absorbing molecule parameters (concentration, temperature, pressure). Therefore, the need for a well-controlled and automated environment is highly recommended. From one side it is interesting to test the electronic board in some extreme conditions such as very low pressures and temperatures. From the other side it is also important to monitor any influence of temperature and pressure variation on the quality of the spectra and/or the precision of the spectrometer.
## 1 Introduction
Influenced by the generalization ability of deep neural networks, RoNIN [10] proposed a new concept of motion model-based RIDI and constructed multiple neural network models to successfully predict high-precision trajectories. To reduce the cumulative error caused by continuous double integration, IONet [11] partitioned the IMU data into independent windows and used long short term memory (LSTM) [12] algorithm to regress the rate of change of pedestrian speed and heading for each window. TLio [13] merged relative state measurements of neural network outputs into a stochastic cloning Kalman filter to further reduce pedestrian attitude, velocity and sensor bias. To solve the problem of inaccurate smartphone pose estimation, IDOL [14] proposed a two-stage neural network framework that uses a recurrent neural network (RNN) to estimate device pose in the first stage and a combination of RNN and extended Kalman filter to estimate device position in the second stage. [PERSON] [15] combined neural networks and ZUPT to recognize pedestrian walking patterns using multihead convolutional neural networks, and adaptively adjusted the step detection threshold based on the walking pattern recognition results. However, the existing data-driven methods still suffer from the problem of low navigation accuracy due to the simplicity of their network models. Taking inspiration from [PERSON], we have designed a new deep neural network model, called RBCN-Net (see Figure 1), to improve the ability of the network to regress pedestrian motion features. We achieve this by combining ResNet18 [16], bidirectional long short term memory (BiLSTM) [17], a convolutional block attention module (CBAM) [18], and a normalization-based attention module (NAM) [19]. By adding the attention mechanism, we address the problem of the network's inability to distinguish the importance of features. To demonstrate the effectiveness of RBCN-Net, we conduct comparative experiments by constructing multiple network models using the VOIMU dataset to verify its ability to improve navigation accuracy. The main contributions of this work are summarized as follows:  
1. To enhance the network's ability to learn channel and spatial features, the NAM is added to each Basicblock of ResNet18, and the CBAM is added after the maximum pooling layer of ResNet18. Meanwhile, in order to enhance the network's capability in long-range learning modeling, we add a BiLSTM network after the 8 th Basicblock-NAM structure. 2. A dataset called VOIMU which contains 80 sets of IMU data of various walking routes with a total path length of 18.53 km and a time of 5.65 h. The ground truth trajectories are based on ocular inertial odometry. We will share all the datasets and codes for further research. The rest of the paper is organized as follows: Section 2 presents the network model; Section 3 presents the training data collection method, the data preprocessing steps, and the network model implementation details; Section 4 presents the experimental results and analysis; finally, Section 5 concludes the paper.
# Effect of Slag Particle Size on Fracture Toughness of Concrete
## 5 Conclusions  
The following conclusions were made based on the experimental results and the findings of the study:  
1. The filling effect and the reactivity of slag can be improved by reducing its particle size. Incorporating finer slag into concrete may lead to larger early strength gains and larger strength increments of the concrete at later ages. The compressive strength of slag concrete was found to increase in conjunction with the fineness level of the slag incorporated into the mixture.
2. The use of finer slag presents a beneficial effect on the fracture energy (\(\mathrm{G_{F}}\)) of concrete, even at early ages (14 days), due to superior filling effect. Increasing the fineness level of the incorporated slag leads to an increase of the \(\mathrm{G_{F}}\) value of concrete or an enhanced fracture toughness.
3. The increment of the fracture energy of all the slag concretes measured in this study from 14-56 days was attained by 18-24%, which is found much higher than that of reference concrete (10.1%), and accordingly, the \(\mathrm{G_{F}}\) of the slag mixtures at 56 days almost exceeds that of the reference mixture.
4. An increase in compressive strength of slag concrete of 10% resulted in a fracture energy increase of around 18%. This raise rate is significantly higher than that previously found in high-strength concretes without slag, indicating that use of the finer slag can have a unique effect on the enhancement of the fracture resistance of concrete.
5. The related tendency of the critical stress intensity factor (\(\mathrm{K^{S}_{IC}}\)) of the slag concretes is similar to that of the fracture energy. At early ages (14 days), the \(\mathrm{K^{S}_{IC}}\) values of slag concrete are less than that of the reference concrete (R0) but exceed that of R0 after 56 days.
6. Concretes incorporating finer slag exhibit larger \(\mathrm{K^{S}_{IC}}\), and the \(\mathrm{K^{S}_{IC}}\) increases in conjunction with the fineness level of the slag. This also implies an increase in the fracture resistance of the concrete.  
Conceptualization, C.-H.W. and T.Y.; Methodology, C.-H.W.; Validation, C.-H.W., C.-H.H. and S.-K.L.; Formal analysis, C.-H.W. and C.-H.H.; Investigation, C.-H.W. and C.-H.H.; Resources, C.-H.W; Data curation, C.-H.W. and S.-K.L.; Writing--original draft preparation, C.-H.W.; Writing--review & editing, C.-H.W. and T.Y.; Visualization, C.-H.W. and S.-K.L.; Supervision, T.Y.; Project administration, C.-H.H.  
Figure 8: Critical stress intensity factor versus compressive strength for slag concretes.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Case Study: Product Development
### Modeling  
#### 6.2.1 Localization Method  
The proposed PDR method for the indoor localization applies the measurements from the three-axial accelerometer gathered with a sampling frequency of 90 Hz. In the case study, the accelerometer's normal working position is vertical; Figure 2 shows the orientation of the accelerometer axes. The person's localization is based on the information about the previous position, number of steps, their length and their direction.  
The previously estimated position is stored in the device memory or in the case of the first use of a device, it is set manually at the calibration point. The number of counted steps \(S_{c}\) is estimated using three-axial accelerometer data consisting of acceleration readings in the \(x\), \(y\) and \(z\) directions, which define the Signal Magnitude Vector (\(SMV\)) calculated as:  
\[SMV=\sqrt{x_{i}^{2}+y_{i}^{2}+z_{i}^{2}} \tag{1}\]  
where \(x_{i}\), \(y_{i}\), \(z_{i}\) are the \(i\)-th sample of acceleration in the \(x\)-, \(y\)- and \(z\)-axis, respectively. The step is detected when \(SMV\) exceeds the empirically chosen threshold. The threshold has to be adjusted to the walking manner of the monitored person.  
The step length \(S_{l}\), approximately unalterable, due to the walking manner of an individual, should be set as fixed and also has to be determined individually. Using such data, the \(M\) factor is determined as:  
\[M=S_{l}\times S_{c}. \tag{2}\]  
Figure 2: Accelerometer \(x\)-, \(y\)- and \(z\)-axis orientation.  
In the last stage of PDR, the magnetometer along with gyroscope readings are used to estimate the direction \(\theta\) of the step [26]. Finally, the person's position can be calculated as:  
\[\left[\begin{array}{c}\hat{x}_{k}\\ \hat{y}_{k}\end{array}\right]=\left[\begin{array}{c}\hat{x}_{k-1}\\ \hat{y}_{k-1}\end{array}\right]+M\left[\begin{array}{c}\cos\theta\\ \sin\theta\end{array}\right] \tag{3}\]  
where \(\hat{x}_{k}\) and \(\hat{y}_{k}\) are the coordinates of the estimated position, \(\hat{x}_{k-1}\) and \(\hat{y}_{k-1}\) are the coordinates of the previously estimated position, \(\theta\) is the heading direction and \(M\) is the factor from (2).  
Foremost, occasionally, the system has to be calibrated by activating the device in a known location of the apartment; for instance, while the person is sitting in an armchair or while standing on the clearly marked place in the middle of the antechamber.