esa-sceva/satcom-chunk-collection huggingface dataset:
# Control of Corner Separation with Plasma Actuation in a High-Speed Compressor Cascade
## 1 Introduction
Based on their research, it was concluded that, for the control of compressor corner separation, the required plasma actuator strength scaled approximately with the square of the Reynolds number. In summary, researchers have spared a lot of efforts to uncover the flow control law and the mechanism behind the control of compressor corner separations with plasma actuations. These results laid solid foundations for the engineering applications of plasma actuations in controlling compressor corner separations. However, all the studies illustrated above are conducted in low-speed environments, and the performances of plasma actuations in controlling the high-speed compressor corner separations still remain unclear. One critical reason for the limitation of plasma actuator to suppress the corner separation in high-speed compressors should be the limited induced body forces. Recently, plasma actuation has been used by [PERSON] et al. [23] to control the stall of a transonic compressor by focusing on the control of the tip leakage flow. They adopted multiple dielectric barrier discharge (DBD) type plasma actuators, which can induce high magnitude velocity in quiescent air, in the experiments and successfully improved the stall margin of the transonic compressor. Hence, multiple DBD type plasma actuators may also be an effective tool for the control of endwall corner separations in high-speed compressors. The main purpose of the present work is to uncover the flow control law and the mechanism of high-speed compressor cascade corner separation control with plasma actuations that consist of multiple DBD type plasma actuators. Both the suction surface and the endwall plasma actuations are used to control the corner separations in a high-speed compressor cascade. The main flow structures within the high-speed compressor cascade corner separation and the development of the corresponding flow loss are investigated firstly. Next, the performances of the plasma actuations in suppressing the high-speed compressor cascade corner separation are studied. According to the impact of plasma actuations on the flow structures within the high-speed compressor cascade corner separations, the mechanism behind the control effects of both the suction surface and the endwall plasma actuations are discussed in detail too.
# Pediatric Endoscopy, Update 2020
## 3 Indications of Pediatric Endoscopy
### Therapeutic Indications  
Esophageal structures in children are almost never malignant and mostly based on accidental ingestion of caustic substances. Other reasons are: Congenital webs and strictures, achalasia, a Schatzki-ring, eosinophil esophagitis, and problems after surgery (like esophageal atresia). [PERSON]'s disease can--although rarely--also be the cause of an inflammatory stricture of the esophagus.  
Endoscopic dilatation techniques include balloon and bougienage. The TTS balloon requires the use of a standard endoscope as it will not fit through the 2 mm working channel of the neonatal endoscope. If a standard endoscope cannot be used, the balloon should be placed over a guide wire under X-ray guidance. The perforation rate for endoscopic balloon dilatation is given in the literature somewhere in between 0.1%-0.4% [1; 2; 39; 40]. Gastrointestinal bleeding is rare in children. All common endoscopic hemostatic techniques can be applied, for example: Injection methods, clip methods, coagulation methods, ligature, and obliteration of varices. Experience in the application of hemospray is sparse.
## 2 Materials and Methods
### PDMS/Paper Device Fabrication  
The PDMS module was fabricated by conventional soft lithography methods [34; 35]. The device was first designed with Autocad 2013 (Autodesk) and a dark-field mask was printed (JD-Photo Data, UK). SU-8 2025 negative photoresist (MicroChem) was spin-coated onto a silicon wafer (diameter: 76.2 mm) at 500 rpm for 5 s and then ramped to 1000 rpm at an acceleration of 300 rpm s\({}^{-1}\) for 33 s. After spinning, the wafer was prebaked (2 min at 65 \({}^{\circ}\)C, then 6 min at 95 \({}^{\circ}\)C) and exposed to UV light through the acetate mask on a mask aligner at 200 mJ cm\({}^{-2}\) (MA6 BA6, Suss Microtech). After post-baking (1 min at 65 \({}^{\circ}\)C and 3 min at 95 \({}^{\circ}\)C) and development in the SU-8 developer (PGMEA, Sigma Aldrich), the master was hard-baked for 2 min at 170 \({}^{\circ}\)C. A final thickness of 60 \(\upmu\)m was obtained, as measured by using profilometry (KLA-Tencor). For the fabrication of the PDMS (PDMS, Sylgard 184) replicas, a mixture of the PDMS prepolymer and cross-linker (ratio 10:1, w/w) was poured on top of the master, degassed, and then cured for 1 h at 65 \({}^{\circ}\)C. The cured device was cut and peeled off from the master, and holes for tubing were made with a biopsy punch (diameter = 1.25 mm, Kai Medical). Lastly, for the preparation of the hybrid PDMS-paper devices, weighing paper and PDMS replicas were treated with oxygen plasma for 20 s. Afterward, the exposed surfaces of both PDMS and paper were bound. To guarantee the optimal sticking of all the features within the device to the paper, soft pressure was applied.
# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
###### Abstract  
Currently, deep reinforcement learning has been widely applied to energy system optimization and scheduling, and the DRL method relies more heavily on historical data. The lack of historical operation data in new integrated energy systems leads to insufficient DRL training samples, which easily triggers the problems of underfitting and insufficient exploration of the decision space and thus reduces the accuracy of the scheduling plan. In addition, conventional data-driven methods are also difficult to accurately predict renewable energy output due to insufficient training data, which further affects the scheduling effect. Therefore, this paper proposes a small-sample scenario optimization scheduling method based on multidimensional data expansion. Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed method is verified by arithmetic examples.  
Keywords:integrated energy system; deep reinforcement learning; photovoltaic output prediction; small-sample scenario; data expansion algorithm +
Footnote †: journal: _Algorithms_
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 3 Results and Discussion
### Results Comparison on Different Architectures  
The encoder-decoder architecture failed to achieve a good result when modelling the Blansko data with n_steps of 30; instead, it achieved the best performance when n_steps is set to 3. Otherwise, the network has to increase the complexity, such as adding more layers, more neurons or training for a longer time, in order to achieve a comparable performance to the other two architectures, which leads to a much higher computation cost. Therefore, the below demonstrated result based on encoder-decoder architecture was using n_steps of 3 (Figures 15 and 16).  
Figure 15: Model based on Architecture 3: encoder–decoder architecture.  
Figure 16 shows the result comparison of the models trained from these three architectures with testing data, in the aspects of prediction accuracy (Figure 16a) and computation efficiency (Figure 16b). It is noticeable that the prediction performances of Architecture 1 and 2 are very similar with an RMSE around 2.4 dB and an \(R^{2}\) around 0.68, slightly better than the performance of Architecture 3 with an RMSE around 2.7 dB and an \(R^{2}\) around 0.64, as shown in Figure 16a. However, as indicated in Figure 16b, the model training took a very long time with Architecture 1, almost 10 times longer computation time than Architecture 2; Architecture 1 trained 15 times more parameters than Architecture 2 by backpropagation, where the model based on Architecture 1 learned 590,430 parameters, and the model based on Architecture 2 learned 38,701 parameters. Architecture 3, in terms of both prediction accuracy and computation efficiency, does not have any privilege. Therefore, Architecture 2 was selected for training the recurrent neural network model.
# Hypoxia Modulates Regenerative Potential of Fetal Stem Cells
## 2 Materials and Methods
### Evaluation of Proliferation, Surface Markers, and Stemness Genes
#### 2.2.3 Reverse Transcription Quantitative Polymerase Chain Reaction  
TRIzol\({}^{\text{\textregistered}}\) reagent (MilliporeSigma, Burlington, MA, USA) was used to extract total RNA from each sample (\(n=4\)). Two \(\upmu\)g of RNA was used for reverse transcription with a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). Stemness genes for evaluation included _NANOG_, _SOX2_, _KLF4_, _BMI1_, _MYC_, _NOV_, _POUSF1_, and _NES_. Other genes for assessment included: chondrogenesis genes (_SOX9_, _ACAN_, _COLZA1_, _PRG4_, _COLZA1_, _COLA1_, _COLA1_, _FOXF1_, _FBLN1_, _CDH2_, and _FN1_), adipogenesis genes (_LPL_, _PPARG_, _FABP4_, and _CEBPA_), and osteogenesis genes (_RUNX2_, _SPP1_, _BGLAP_, and _SP7_). The relevant primers were obtained from Applied Biosystems (Thermo Fisher Scientific) as part of the Custom TaqManMen\({}^{\text{\textregistered}}\) Gene Expression Assays. _GAPDH_ was carried out as the endogenous control gene. Assay IDs of primers were listed in Table 1. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed using Applied BiosystemsTM 7500 Fast Real-Time PCR System (Thermo Fisher Scientific). Relative transcript levels were calculated as \(\chi=2^{-\Delta\Delta\text{C}}\), in which \(\Delta\Delta\text{C}t=\Delta\text{E}-\Delta\text{C}\), \(\Delta\text{E}=\text{C}_{\text{exp}}-\text{C}_{\text{GAPDH}}\), and \(\Delta\text{C}=\text{C}_{\text{ct1}}-\text{C}_{\text{GAPDH}}\).  
Figure 1: Schematic of Experimental Design. Both fetal NPCs and SDSCs were expanded in T175 plastic flasks for 7 days followed by either two phases of chondrogenic differentiation (D0-D10, D10-D21) in a pellet culture system, or one phase of either adipogenic or osteogenic differentiation (D0-D21) in T25 plastic flasks. (**A**) The pretreatment culture regimen was designed to subject each chondrogenic cell type to four treatment conditions that vary in oxygen tension (N-N-N, L-N-N, N-L-N, and L-L-L). Adipogenic and Osteogenic cells were subject to hypoxia or normoxia during expansion followed by differentiation induction in normoxia (LN and NN). (**B**) The differentiation treatment regimen was designed such that chondrogenic cells were expanded in normoxia and then subjected to three treatment conditions varying in oxygen tension (N-N-N, N-N-L, and N-L-L). Adipogenic and osteogenic cells were subjected to normoxia in cell expansion followed by hypoxia or normoxia during differentiation (NL and NN). Incubators were set to N = normoxia (21% O\({}_{2}\)) incubation and L = hypoxia (5% O\({}_{2}\)) incubation.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 4 Results and Discussions
### Plotting IOU vs. Validation IOU  
Analyzing the Intersection over Union (IoU) and validation IoU of the SA-SC U-Net model provides crucial insights into its segmentation accuracy. IoU measures the overlap between predicted and true masks, while validation IoU extends this assessment to unseen data. Elevated IoU values, especially in the validation set, signify the model's proficiency in accurately delineating object boundaries. Consistent improvement in both IoU and validation IoU during training indicates effective learning and robust generalization. Monitoring these metrics for the SA-SC U-Net ensures a comprehensive understanding of its segmentation performance, guiding adjustments to enhance accuracy and reliability in real-world applications. A higher IoU indicates better alignment between predicted and actual segmentation, signifying the model's ability to precisely identify and delineate objects in unseen data. Monitoring the IoU and IoUv trends throughout training offers insights into the model's generalization capability, ensuring its effectiveness in real-world scenarios. These metrics serve as valuable benchmarks for evaluating and fine-tuning the model's segmentation performance during the development and validation phases.
## 3 Results and Discussion
### Comparisons of Deposition Efficiency Predictions Using DPM Approach
#### 3.2.2 Tidal Breathing  
As shown in Figure 9, it is clear that different breathing treatment periods have noticeable effects on the deposition of smaller particles. This figure shows 1 and 5 \(\upmu\)m two-way particles deposition for different breathing periods. In this part of the study, we investigate how the particle deposition efficiency is affected by breathing time periods. According to a prior study [9], the impactions between particles and walls of alveoli or deposition phenomenon may be obtained when a particle's computed trajectory causes it to pass out of the fluid streamline domain. We have computed the effect of particle diameter and breathing period (\(\upl_{\text{TB}}\) = 5, 7.5, 11.25, 15 s) for 18-22 generations with gravity acting along the \(z\)-axis [0, 0, -1].  
The results show that small particles, i.e., 1 \(\upmu\)m, have a greater dependence on breathing period than larger particles, i.e., 5 \(\upmu\)m, which experience more sedimentation transport than small particles. In addition, during slower breathing (higher \(\upl_{\text{TB}}\)), smaller particles are affected more significantly than during rapid breathing (lower \(\upl_{\text{TB}}\)). This is attributable to the decreasing convection forces during slower breathing (increasing \(\upl_{\text{TB}}\)); thus, the particles are carried to the deformable wall as a result of diffusion forces.  
Thus, an important consequence of these results is noticeable when the smaller particles remain suspended for a long time under study conditions; the particle transport dynamics can be affected significantly by breathing patterns. These trends are shown for 1 and 5 \(\upmu\)m particles in Figure 8.  
Figure 8: Particle deposition efficiency vs. various tissue visco-elasticity values for 1 \(\upmu\)m and 5 \(\upmu\)m, two-way coupling particle diameter and (**a**) 18 st, (**b**) 19 st, (**c**) 20 st, (**d**) 21 st and (**e**) 22 st generations.  
The value \(\lambda_{\text{TB}}\)= 5 (s) is selected as the value of tidal breathing period in the investigation of other deposition parameters [9].
# A Novel Tool for Biodiversity Studies: Earthworm Classification via NGS and Neural Networks
###### Abstract  
Earthworms are important in agriculture in the process of soil fertilization and influence its physicochemical properties. The taxonomic classification of earthworms using morphological characteristics requires experts, is difficult, and can require specimen dissection to extract detailed anatomical studies. Molecular techniques are time-consuming and expensive. The objective of this study was to distinguish earthworms belonging to different genera, Eisenia, Dendrobaena, and Lumbricus, using an innovative approach involving machine learning models built based on image texture parameters from individual color channels R, G, B, L, a, b, X, Y, Z, U, V, and S. The earthworms _Eisenia fetida, Dendrobaena_ ssp., and _Lumbricus terrestris_ were used as research materials. Image acquisition was performed using a flatbed scanner on a black background. In the case of each earthworm, 2172 texture parameters from images in individual color channels R, G, B, L, a, b, X, Y, Z, U, V, and S were extracted. Textures after selection were used to develop classification models using machine learning algorithms. The earthworms _Eisenia fetida, Dendrobaena_ ssp., and _Lumbricus terrestris_ were distinguished with the accuracy reaching 100% for models built using Logistic, Ensemble, and Narrow Neural Network. All earthworms were correctly classified. Also, in the case of other models, earthworm classes were distinguished with high accuracies, such as 99% (Naive Bayes, Random Forest, SVM, KNN), 97% (Simple Logistic), and 94% (KStar). For the most important _species, E. fetida,_ the correctness of the species identification was confirmed by direct RNA sequencing. The application of image analysis and machine learning turned out to be a non-destructive, inexpensive, and objective approach to distinguishing earthworms belonging to different genera.  
earthworms; image processing; image textures; classification models +
Footnote †: journal: Applied Sciences  
Article
## 3 Packet Detection Algorithms
### Preamble-Based Algorithms
#### 3.2.2 Cross-Correlation Algorithm  
The cross-correlation algorithm also uses correlation calculation of the preamble. The difference from the former one is that it uses a template instead of the received signal itself [15; 17; 22; 23]. Therefore, completely knowing the preamble structure is the premise of using this algorithm. The flowchart is shown in Figure 11. In this approach, one symbol from the short preamble is used as a template, \(T_{n}\). The cross-correlation is performed between the received signal \(r_{n}\) and the stored template \(T_{n}\). Window \(C\) is used to calculate the correlation between the two signals by Equation (9). Only when the two parts match is the calculated result large, and the rest of the results small. There will be 10 peaks in the results since STF contains 10 repeated symbols. These peak points are very easy to identify. The correlation result is also normalized by the template signal power calculated in window \(P\) by Equation (10). \(m_{n}\) is the same as Equation (8).  
\[c_{n}=\sum_{k=0}^{16}r_{n+k}T_{k}^{*} \tag{9}\]  
\[p_{n}=\sum_{k=0}^{L-1}T_{n+k}T_{n+k}^{*} \tag{10}\]  
Figure 10: The response of auto-correlation algorithm.  
Figure 9: Auto-correlation algorithm.  
Simulation. The experiment result is depicted in Figure 12. There are 10 peak points during the index from 101 to 260, which are much higher than others. Especially when there is only ambient noise at the beginning, the matching values are very small. Because the template in this algorithm is fixed, the received signal can only match the template in the specific positions, but for the auto-correlation algorithm, it can be considered that its template is moving along the signal, so \(m_{n}\) is high during the entire STF period.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 2 The 26 May 2016 Eruption of Piton De La Fournaise Volcano  
Piton de la Fournaise is a highly active basaltic volcano located on the eastern part of Reunion Island (France), in the Indian Ocean (Figure 1). The summit cone, at an elevation of 2631 m, lies in an uninhabited caldera-shaped depression called the Enclos Fouque caldera. InSAR revealed a seaward motion (1.4 m) of the eastern flank [32] of this volcano during the major April 2007 eruption [33; 34]. GNSS stations installed in this area after the eruption, combined with InSAR monitoring, indicate an ongoing eastward motion of about \(\sim\)2 cm\(\cdot\)year\({}^{-1}\)[35]. A new eruption cycle started in June 2014 [36; 37]. Since then 16 eruptions have occurred on the flanks of the cone, inside the caldera-shaped depression including the May 2016 eruption studied here. After 10 days of minor inflation and a low seismicity rate, a seismic crisis started on May 25 at 19:40 (all times are U.T.C) accompanied by rapid deformation, which corresponded to magma transfer towards the surface [16]. At 04:05 on May 26, a fissure opened, 2800 m to the southeast of the summit cone, at an elevation of 1890 m producing lava fountaining and a 0.5 Mm\({}^{3}\) lava flow, which is an unusually small volume for a Piton de la Fournaise eruption [8]. The eruption stopped after 27 h. InSAR acquisitions and GNSS measurements provide an extensive dataset which can be used to understand the magma transfer during this eruption [16].  
Seismic data are also available. However, in this case earthquakes being too small and shallow, precise relocation does not allow to track magma at the depth indicated by InSAR and GNSS data [16; 17].
## 2 Model Problem of the Allen-Cahn Equation and Finite Difference Approximation
### Pod-Deim Reduced System
After obtaining \(\mathbf{Z}=[\mathbf{z}_{1},...,\mathbf{z}_{m}]\in\mathbb{R}^{n\times m}\) and \(\mathbf{P}=[\mathbf{e}_{\varrho_{1}},...,\mathbf{e}_{\varrho_{m}}]\in\mathbb{ R}^{n\times m}\), we are ready to apply the DEIM approximation for the nonlinear term in (9):  
\[\mathbf{N}(\mathbf{u}(t_{j}))=\mathbf{V}_{k}^{T}\mathbf{A}^{-1}\mathbf{F}\left( \mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j})\right). \tag{10}\]  
Suppose that \(\mathbf{f}(t_{j})=\mathbf{F}(\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))\) and define the approximation from projecting on the span of \(\mathbf{Z}\) as  
\[\mathbf{f}(t_{j})\approx\mathbf{Z}\mathbf{c}(t_{j}) \tag{11}\]  
where \(\mathbf{c}(t_{j})\) can be determined by selecting \(m\) rows from (11), i.e., \(\mathbf{c}(t_{j})\) can be computed by solving the system  
\[\mathbf{P}^{T}\mathbf{f}(t_{j})=(\mathbf{P}^{T}\mathbf{Z})\mathbf{c}(t_{j}), \tag{12}\]  
where multiplying by \(\mathbf{P}^{T}\) is equivalent to selecting \(m\) rows in this system (13). Since \(\mathbf{P}^{T}\mathbf{Z}\) is nonsingular [51], the approximation of (11) is  
\[\mathbf{f}(t_{j})\approx\mathbf{Z}\mathbf{c}(t_{j})=\mathbf{Z}(\mathbf{P}^{T} \mathbf{Z})^{-1}\mathbf{P}^{T}\mathbf{f}(t_{j}). \tag{13}\]  
From \(\mathbf{f}(t_{j})=\mathbf{F}(\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))\), (13) can be written as  
\[\mathbf{F}(\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))=\mathbf{Z}(\mathbf{P}^{T} \mathbf{Z})^{-1}\mathbf{P}^{T}\mathbf{F}(\mathbf{V}_{k}\mathbf{\tilde{u}}(t_ {j})). \tag{14}\]  
Since \(\mathbf{F}\) has to be evaluated for the original dimension before being interpolated by the matrix \(\mathbf{P}\), we use the fact that \(\mathbf{F}\) is a componentwise function, which implies \(\mathbf{P}^{T}\mathbf{F}(\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))=\mathbf{F}( \mathbf{P}^{T}\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))\) and  
\[\mathbf{F}(\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))\approx\mathbf{Z}(\mathbf{ P}^{T}\mathbf{Z})^{-1}\mathbf{F}(\mathbf{P}^{T}\mathbf{V}_{k}\mathbf{\tilde{u}}(t_ {j})). \tag{15}\]  
Therefore (10) can now be approximated by  
\[\mathbf{N}(\mathbf{u}(t_{j}))\approx\mathbf{V}_{k}^{T}\mathbf{A}^{-1}\mathbf{ Z}(\mathbf{P}^{T}\mathbf{Z})^{-1}\mathbf{F}(\mathbf{P}^{T}\mathbf{V}_{k}\mathbf{ \tilde{u}}(t_{j})). \tag{16}\]  
From the POD reduced system (9), we obtain the following POD-DEIM reduced system  
\[\mathbf{\tilde{u}}(t_{j+1})=\underbrace{\mathbf{C}}_{\mathbf{precupputed}:k \times k}\underbrace{\mathbf{\tilde{u}}(t_{j})}_{k\times 1}+\underbrace{ \mathbf{M}}_{\mathbf{precupputed}:k\times m}\underbrace{\mathbf{F}(\mathbf{P}^{T }\mathbf{V}_{k}\mathbf{\tilde{u}}(t_{j}))}_{m\times 1}. \tag{17}\]  
where \(\mathbf{M}=\mathbf{V}_{k}^{T}\mathbf{A}^{-1}\mathbf{Z}(\mathbf{P}^{T}\mathbf{ Z})^{-1}\) and \(\mathbf{M}\in\mathbb{R}^{k\times m}\) can be precomputed and used in each iteration. The dimension \(n\) of the nonlinear term in (9) decreases to \(m\) in (17) where \(m\ll n\), which can reduce computational cost for obtaining the approximate numerical solution.