esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal
## 2 Experimental Apparatus and Procedure
### Uncertainty Analysis  
The errors for each of the major measuring instruments are shown in Table 2. Two types of thermocouples were used to measure the temperature. The thermocouple to measure the fluid temperature had a zero-contacting point and was tested to have a measurement error of 0.05 K. The ten thermocouples used to measure copper block A in the test section were not tested, and the measurement error was 0.2 K. The measurement results showed that there was an error of 0.02 mm in the depth of the hole in copper block A.  
Thus, the heat flux was calculated with the following equation.  
\[q=k\frac{T_{2}-T_{1}}{\delta} \tag{2}\]  
where \(k\) is the thermal conductivity, \(T_{2}-T_{1}\) is the differential temperature between the upper and lower thermocouples and \(\delta\) is the distance between these thermocouples. The uncertainty of the heat flux is evaluated as follows:  
\[\Delta q=\sqrt{\left(\frac{\partial q}{\partial k}\Delta k\right)^{2}+\left( \frac{\partial q}{\partial\delta}\Delta\delta\right)^{2}+\left(\frac{ \partial q}{\partial T_{1}}\Delta T_{1}\right)^{2}+\left(\frac{\partial q}{ \partial T_{2}}\Delta T_{2}\right)^{2}} \tag{3}\]  
In Figure 5, the errors of each heat flux are plotted for 0 and 1 mm thickness heat transfer surfaces in comparison with the experimental results. The heat flux error was about 3% at the 200 kW\(\cdot\)m\({}^{-2}\); however, the heat flux error was less than 1% at a heat flux higher than 500 kW\(\cdot\)m\({}^{-2}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Uncertainly** \\ \hline Mass flux & 0.2\% \\ Thermocouples at copper block A & 0.05 K \\ Thermocouple at copper block B & 0.2 K \\ Depth of the measurement point at copper block & 0.02 mm \\ Pressure page & 0.26 kPa \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uncertainty values of the important parameters.  
Figure 4: High-porosity sintered fiber metal. (**a**) Porous heating surface; (**b**) enlarged view of the porous material.
# En-Face Optical Coherence Tomography Angiography for Longitudinal Monitoring of Retinal Injury
###### Abstract  
A customized Optical Coherence Tomography Angiography (OCTA) algorithm and Orthogonal OCT (en-face and B-Scans) were used for longitudinal assessment of retina murine vascular and tissue remodeling comparing photoreceptor ablation and laser-induced Choroidal Neovascularization (CNV). In the mouse model, we utilized a combined OCTA/OCT technique to image and quantify morphological and vascular features of laser lesions over time. This approach enabled us to monitor and correlate the dynamics of retina vascular and tissue remodeling as evidenced by swelling, edema, and scarring. From the OCT B-Scans, three stages of inflammatory progression were identified: the early response occurring within hours to day 3, the transition phase from 3-7 days, and the late stage of 7-21 days entering either the resolving phase or chronic phase, respectively. For the case of CNV, en-face OCTA revealed a transient non-perfusion of inner retina capillaries, specifically Deep Vascular Plexus (DVP), which corresponded to growth in lesions of a height of 200 \(\upmu\)m or greater. Non-perfusion first occurred at 24 hours, persisted during edema and CNV formation days 7-14. In contrast, the acute inflammation induced photoreceptor damage, but no detectable alterations to the microvasculature were observed. We demonstrated that the en-face OCTA system is capable of visualizing capillary networks (\(\sim\)5 \(\upmu\)m) and the corresponding tissue remodeling and growth dynamics allowing for separating acute injury from CNV. For the first time, by using OCTA we observed the presence of the 5-10 \(\upmu\)m capillary non-perfusion present in DVP as part of CNV formation and the associated wound healing in the retina.  
optical coherence tomography; optical coherence angiography; ophthalmic imaging; laser injury; choroidal neovascularization +
Footnote †: journal: Applied  
1
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 5 Conclusions  
This paper proposed a low-cost and Bluetooth connected module to detect defects in rolling bearings. The acoustic noise delivered by the bearing to be monitored is acquired by means of a MEMS microphone and sampled by a microcontroller, which also evaluates the frequency spectrum of the signal exploiting an optimized version of the Fast Fourier Transform algorithm for real data. The detection of defects is based on the exceeding of given thresholds in certain frequency ranges. If a fault is detected, the module notifies the central controller, thus reducing at the minimum the amount of transferred data and occupying the wireless channel only when it is required. The configuration parameters of the acquisition process, as well as the frequency ranges to be monitored can be modified on-the-fly through the wireless link. A prototype of the proposed system was realized, and the relative errors affecting the evaluation of the frequency spectrum were measured and discussed. It resulted that the use of a top-flat windowing function allows for an amplitude error within \(\pm\)0.6% in the case of a single tone. Then, the wheel bearing of a vehicle was considered as a test case, and the environmental noise acquired in different driving scenarios. Such recorded noise was used as noise floor for the simulation of a faulty bearing, and the module successfully detected the damages, provided that the corresponding fundamental frequencies arose sufficiently from the environmental noise.  
Conceptualization, F.F.; methodology, E.R. and F.F.; software, E.R.; validation, E.R.; investigation, E.R.; writing, E.R. and F.F.; supervision, F.F. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 1 Introduction
Proposed Hierarchical Spatiotemporal Context Feature Learning Model for EEG-Based Emotion Recognition  
The proposed method for EEG-based emotion classification is a hierarchical neural network architecture with self-attention called the HSCFLM, which mainly consists of three key modules: electrode division module, regional brain-level encoding module, and global brain-level encoding and classification module, as shown in Figure 1. First, EEG channels are grouped by brain region according to the spatial location of the electrodes, because each brain region has a different function. Each grouped regional EEG signal is preprocessed and inputted into the regional brain-level encoding module to extract the spatiotemporal features of each region. After learning the regional deep features, the global brain-level encoding module is used to learn the global emotional EEG context features from local to global brain regions, which are then input into the emotion classifier. The remainder of this section introduces these three key modules.
## 2 Background and Related Studies
They performed an analysis review on QoS techniques developed in the literature for IoT applications and investigated current research trends. They found that the most popular QoS metrics are Network Usage, Throughput, Reliability, and Latency.
## 4 Results and Discussion
### Nozzle Optimization Results  
The T-100 nozzle optimization has evolved for 34 generations and 1020 CFD calculations are performed. The optimal solution has an increase in total-to-static efficiency (\(\eta_{ls}\)) by 0.1% and a decrease  
Figure 6: A Computational mesh of solid simulation.  
Figure 7: Convergence curves of flow simulation.  
in total pressure loss (\(\xi_{P}\)) by 0.3%, compared with the T-100 baseline design. The change of the mass flow rate (\(m\)) is less than 0.01% which is acceptable. Figure 8 and Table 2 show the comparison between the baseline design and the optimal design. It can be seen that the improvement is very small because the T-100 turbine is a well-developed design itself.  
The T-100 re nozzle optimization has evolved 30 iterations, and 600 CFD computations are involved. Though the CFD computational cost is lower than the simple accurate optimization, the ANN training time becomes longer as the database grows, and the training time even exceeds the CFD computation time at the later stage of the optimization. In general, the final cost of the metamodel-assisted optimization is nearly the same as the T-100 accurate optimization. Therefore, the rotor optimization will not use a metamodel anymore.  
However, the present metamodel training strategy does result in high precision ANN. Figure 9 shows the mean relative error of the ANN prediction and it can be seen that the error level is adequate to provide a high precision prediction. The computational cost of the present work is not high itself, therefore it is not cost-effective to use this GA-optimized ANN in this situation, but it is still useful for large-scale computational cases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**T-100** & \(m\) [kg/s] & \(\eta_{ts}\) [-] & \(\xi_{P}\) [-] \\ \hline T-100 baseline & 0.330 & 0.874 & 2.503 \\ T-100 optimized & 0.328 & 0.875 & 2.495 \\ T-100 re baseline & 0.329 & 0.883 & 2.814 \\ T-100 re optimized & 0.328 & 0.884 & 2.660 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between T-100 and T-100 re turbines after nozzle optimization.  
Figure 8: Comparison of T-100 nozzle profiles.  
Figure 9: Mean relative error of the ANN prediction. ANN: artificial neural network.  
The optimal solution of T-100 re nozzle optimization has an increase in total-to-static efficiency by 0.1% and a decrease in total pressure loss by 5.4%, compared with the T-100 re baseline design. The change in the mass flow rate is less than 0.01%. Figure 10 shows the comparison of T-100 re nozzle profiles between the baseline and the optimized. From Table 2, it can be seen that the total-to-static efficiency increases by 1%, which is the same as the growth after the preliminary design optimization in Part I [20]. Both designs are improved after the nozzle aerodynamic optimization.
## 3 Experiments and Results
### Gaining Performance by Fine-Tuning  
As the silver-standard mask was computed by combining three other automatic segmentations, the CNNs were not initially fit to the manual segmentation protocol. Instead, the CNN models were replicating the segmentation resulted from the STAPLE method. To make the CNN model replicate the manual segmentation (ground truth), fine-tuning was conducted with manually annotated data, presenting a substantial improvement (Table 3).  
Fine-tuning was performed by freezing the encoder component of U-Net and training solely the decoder component. Conceptually, the feature extraction of the CNN model was kept intact, and the reconstruction of the segmentation was adjusted to the manual label. The same training hyper-parameters from the training phase were used during the fine-tuning phase.  
During the fine-tuning phase, 40 subjects with manual segmentation as ground truth were used (Table 2). The input configuration (input channels and their order) was kept identical.
## 3 Seal-Material Compatibility Testing
### Test Methods  
Accelerated tests thus provide a basis for selecting the most suitable candidates from a wide range of seal material compounds. To a person with some experience in sealing technology, such testing can give a good enough indication of how well a seal made of or from the tested rubber material or similar compound will actually perform in use when in contact with the fluid being tested and/or the fluid used later.  
For the purpose of testing the compatibility of seal materials, there are quite a few tests that can also be performed fairly easily. In order to achieve credible and comparable results, it is definitely reasonable to use standardised testing methods. For this purpose, there are several Standards, e.g., ASTM D471, ASTM D4289, ASTM D1414 and ASTM D2240, etc. (e.g., [5; 6; 7; 8]), where the testing procedures, and also the necessary equipment, are defined precisely. Brief descriptions of the testing procedures for individual properties can also be found in the relevant professional literature, e.g., [9], on the basis of which it is easier to decide on the suitability of a certain test. The results obtained after testing are certainly helpful in assessing the use of a particular seal material in combination with a liquid and for a specific application.  
Also, the fluid must be the same as that used in the actual system, and the test temperature must be the highest temperature to which the seal will be exposed during operation. Test samples should be analysed after different testing times, e.g., 24, 70, 100, 250, 500 and 1000 h, to assess the compatibility of the seal over the minimum service life of the fluid. The test is concluded when the property changes exceed the established limits [9].  
Also, the shape of the seal test sample should correspond in shape to the actual seals used. Thus, samples close to the actual sizes of O-seals are used as shapes, e.g., 2-021, -120, -214 or -320 to Standard AS568 [10], which is the specification that defines the actual sizes of O-rings. The most commonly used test specimens have an approximate inside diameter of 1 inch, which is the most common dimension used in industrial systems. Test specimens must be made of the same compound as the actual seal.
# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
## 3 Experiments and Results  
### Experimental Settings  
Our proposed algorithm was trained and evaluated on the TIMIT speech database [32] corrupted by the noises from Noisex-92 noise database [33]. Six noises were selected to generate the noisy speech database. Each selected noise file was split into three non-overlapping sections used for training (60%), validation (20%), and test (20%). From the TIMIT training set, 4620 utterances were mixed with four noises (babble, factory1, destroyer engine, and destroyer operation noises) to generate the 12.5 h noisy speech training database. A total of 280 utterances from the TIMIT test set were mixed with the validation section of the same four noise types to generate the noisy speech validation set. The mixed SNR levels followed the uniform distribution in the range of \(-5\) to \(15\). For the denoising performance evaluation of the DNN model, 320 unseen utterances from the TIMIT test set were mixed with four seen noises and two unseen noises (pink, factory2) to generate the noisy speech test set. The SNR levels of each test noisy speech were fixed at \(-5\), \(0\), \(5\), \(10\), and \(15\) dB to obtain insights into performance at different degradation levels. The perceptual evaluation of speech quality (PESQ) [34] and the short-time objective intelligibility (STOI) [35] were adopted as two metrics to evaluate the enhanced speech quality and intelligibility.  
All the aforementioned clean speech and noise signals were resampled to 16 kHz before mixture generation. For the input of the proposed model, 514-dimensional noise-aware features were extracted by performing the short-time Fourier transform (STFT) on noisy signals using a 512-point Hamming window with 50% overlap. During model training, first, the classification network was trained, for five epochs, in each mini-batch with 1024 frames, by following the Adam optimization method [36] with a learning rate of 0.0001. Then, the denoising network was trained for 30 epochs under the guidance of the well-trained classification network. It was also optimized, by the Adam method with the learning rate of 0.0002, in each mini-batch with 1024 frames. To reduce the influence of overfitting, batch normalization [37] and a dropout strategy [38] (0.2 dropout rate) were applied in the hidden layers of the classification and denoising networks.
## 4 Discussion  
The rockburst mechanism is very complex, and it is also very difficult to predict accurately. Therefore, researchers need to explore scientifically by multiple methods and perspectives. Many researchers have found that the Hilbert spectrum can reflect the frequency characteristics and energy characteristics of a waveform at a certain time, and can more comprehensively display the characteristics of an MS waveform to feedback the MS instantaneous activity characteristics inside surrounding rocks.  
Rock failure is a very complex process that contains a lot of information, so we need to constantly explore and identify. For MS signals or rockburst signals, due to their different energy released, damage types, and others, there are also some differences in their waveforms and frequency characteristics. Due to different factors inducing rockbursts, the precursor information of rockbursts is also different. According to the field monitoring, many MS events were captured before a rockburst occurred. These MS events contained different information. Some were typical MS events, and some events contained some rockburst precursor information. Therefore, we need to use certain technical means to analyze, identify, and accurately extract the precursor information of rockbursts to accurately predict the occurrence of rockbursts and even predict rockbursts in time and intensity.  
The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics at a certain time and reflected the change laws of signal instantaneous energy and local abrupt change information. For complex tunnel construction conditions, the HHT time-frequency analysis technology can realize a new idea of using a single-channel signal to predict rockbursts, which was very meaningful. The HHT instantaneous frequency analysis technology was introduced to analyze rockbursts that occurred at the main powerhouse and the access tunnel in the main powerhouse. It was found that before the rockbursts, the frequency of some events had a significant downward trend over time. From the HHT spectrum, the frequency points of some events had good continuity, the energy distribution of some events was in the low-frequency range, the FD range of some events gradually narrowed, and there was some other abrupt information. Rock failure information or rockburst precursor information was very complex. Therefore, rockburst precursor information may be partially or completely abnormal in frequency indicators. For some events, although the frequency has no obvious downward trend over time, the overall FD of events was relatively low. For the longitudinal comparison of multiple events, the energy distribution of some events increased in the relatively low-frequency band over time.
# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 2 Materials and Methods
### Calculation of the Tool Rotation
The reduction of the \(\overset{\rightarrow}{\mathbf{m}}\) vector prevents both situations. Thanks to this, smoother path-following is achieved, which, in turn, results in better path detection effectiveness. The only drawback of this solution is that in the case where the path has a low radius bend, the tool cannot be turned to the proper orientation in one step. In this case, the bend is negotiated in a few subsequent steps. The trigonometric relationships and the Pythagorean theorem result in the following:  
\[\tan\gamma_{n}=\frac{w_{a,y}}{w_{a,x}}, \tag{18}\]  
Figure 6: The \(\overset{\rightarrow}{\mathbf{w_{a}}}\) vector with its components, \(w_{a,x},w_{a,y},w_{a,z}\), and two rotation angles that can be determined on the basis of this vector: \(\beta_{n}\) and \(\gamma_{n}\). \[\tan\beta_{n}=\frac{-w_{a,z}}{\sqrt{w_{a,x}x^{2}+w_{a,y}z^{2}}}, \tag{19}\]  
where \(\gamma_{n}\)--new rotation angle around the \(Z\) axis (yaw); \(\beta_{n}\)--new rotation angle around the \(Y\) axis (pitch). The minus sign in Equation (19) results from the use of a right-handed coordinate system. In such a system, the positive angle \(\beta_{n}\) (pitch) corresponds to the counterclockwise rotation around the \(Y\) axis. In case the \(w_{a,z}\) component of the vector is positive, it corresponds to a clockwise rotation. For this reason, the expression must include a minus sign to conform to the convention. As the result, using the arctangent function, new tool rotation angles can be determined:  
\[\gamma_{n}=\text{atan}\frac{w_{a,y}}{w_{a,x}}, \tag{20}\]  
\[\beta_{n}=\text{atan}\frac{-w_{a,z}}{\sqrt{w_{a,x}x^{2}+w_{a,y}y^{2}}}, \tag{21}\]  
and a constant value assigned to the angle of rotation around the \(X\) axis (roll):  
\[\alpha_{n}=0. \tag{22}\]  
Figure 7 presents the summary of combining calculations of both tool rotation and the next tool position.
## 2 Materials and Method
### Simulated Data  
In this study, soil moisture simulations from the Phase 2 of the North American Land Data Assimilation System (NLDAS-2) are utilized over the CONUS from 1983 to 2014 with an 8-day temporal resolution and spatial resolution of \(1/8^{\circ}\) (about 12 km). The data is available over the north America from 1979 to present [67]. Soil moisture states are simulated using the semi-distributed grid-based model Variable Infiltration Capacity (VIC) [68; 69] which is a macroscale hydrologic model that ingests meteorological forcing data and solves for full water and energy balances. A Soil-Vegetation-Atmosphere Transfer (SVAT) scheme controls the moisture and energy fluxes within VIC and in comparison with most SVATs, it reproduces the runoff characteristics more accurately [70]. In the NLDAS-2 dataset, VIC model was run at a spatial resolution of \(1/8^{\circ}\) with full energy balance mode at hourly time step. It also represents sub-grid variability of vegetation and runoff generation [71]. The version of the VIC model used for the NLDAS-2 is VIC-4.0.3. The vadose (unsaturated) zone in each grid cell is partitioned into three layers with a depth of 10 cm for the top layer and varying depths for other layers. Table 1 represents a summary of all the data products used in this study and their characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Original Spatial** & **Temporal** & **Unit** & **Type** \\  & **Resolution** & **Resolution** & & \\ \hline Gross Primary & & & & \\ Productivity (GPP) & 1 km & 8 days & kgC/m\({}^{2}\) & Remotely sensed \\ (MOD17A2) & & & & by MODIS \\ Evapotranspiration (ET) & 1 km & 8 days & mm & Remotely sensed \\ (MOD16A2) & & & by MODIS \\ Land Cover (MCD12Q1) & 500 m & monthly & — & Remotely sensed \\ Soil Moisture (NLDAS-2) & \(1/8^{\circ}\) (\(\sim\)12 km) & 8 days & cm/cm & Simulated by VIC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the data used in this study and their characteristics.