esa-sceva/satcom-chunk-collection huggingface dataset:
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 6 Conclusions  
In this paper, we introduce a new framework based on the CSwin Transformer, which mainly takes into account the superiority of multi-scale vision Transformer to efficiently combine the architecture of UperNet for automatic remote sensing image segmentation.  
The main contributions are as follows: firstly, the proposed encoder of DCS-TransUperNet uses dual subnetworks to learn feature representation from different scale inputs, which avoids the limitation of the fixed scale patch. Coarse-grained features are more easily captured by large patches, while small scales have the faculty of obtaining fine-grained features. Secondly, to obtain enhanced features, we propose a specific FFM to fuse the coarse and fine-grained feature representations from the two encoder subnetworks. Thirdly, to alleviate the problems caused by sample imbalance, we propose a new mixed loss function, which maintains the stability of the gradient and prevents falling into the local optimum. Profiting from these refinements, the proposed DCS-TransUperNet can improve the performance of road segmentation.  
Experiments using the Massachusetts dataset and DeepGlobe dataset showed that the proposed DCS-TransUperNet could effectively solve the discontinuity problem and preserve the integrity of the road segmentation results, achieving a higher IoU (65.36% on Massachusetts dataset and 56.74% on DeepGlobe) of road segmentation, compared to other state-of-the-art methods. The considerable performance also proves the powerful generation ability of our method.  
In the next work, we will pay attention to building lighter models based on Transformer to enhance the speed of road segmentation. As we all know, Transformer's computing time is mainly spent on the attention mechanism. The memory occupied by attentionincreases with the square of the calculation. So, we plan to reduce the time complexity by designing new attention computing methods.  
Conceptualization, Z.Z. and C.M.; methodology, Z.Z. and C.M.; software, C.M.; validation, Z.Z.; formal analysis, C.L.; investigation, Q.T.; resources, Q.T.; data curation, C.M.; writing, Z.Z. and C.M.; original draft preparation, C.M.; visualization, Z.Z.; supervision, C.L. and Q.T.; project administration, C.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Fundamental Research Fund of Beijing Municipal Education Commission and North China University of Technology Research Start-up Funds.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Proposed System Model
### LSTM Channel Estimation
1: Randomly initialize the weights (\(W\)'s) and bias (\(b\)'s) values
2: The forget gate: \(f_{t}=\textit{sigmoid}\big{(}\sum_{t=1}^{2000}W_{f}H_{t-1}+W_{f}x_{t}+b_{f} \big{)}\)
3: The input gate: \(i_{t}=\textit{sigmoid}\big{(}\sum_{t=1}^{2000}W_{i}H_{t-1}+W_{i}x_{t}+b_{i} \big{)}\)
4: The candidate value: \(\widetilde{C}_{t}=\textit{tanh}\big{(}\sum_{t=1}^{2000}W_{c}H_{t-1}+W_{c}x_{t} +b_{c}\big{)}\)
5: Update the old cell state, \(C_{t-1}\), into the new cell state, \(C_{t}\), by \(C_{t}=(C_{t-1}\times f_{t})+\big{(}i_{t}\times\widetilde{C}_{t}\big{)}\)
6: Update the output of the LSTM by:  The output gate: \(o_{t}=\textit{sigmoid}\big{(}\sum_{t=1}^{2000}W_{o}H_{t-1}+W_{o}x_{t}+b_{o} \big{)}\)  The estimated channel matrix: \(H_{t}=\,o_{t}\times\tan\mathrm{h}(C_{t})\) Outputs: Estimated channel matrices, \(H_{t}\), and the equivalent SNR values. ```  
**Algorithm 2** LSTM Channel Estimation scheme  
Figure 5: Construction of recurrent neural networks.
## 3 Results and Discussion
### Clustering of Edible Mushrooms Based on Their Biological Properties  
The results of PCA analysis revealed two main groups of edible mushrooms which were clustered on the bases of metabolite content, total phenol content, antioxidant capacity, and ability to inhibit enzymes visualized as therapeutic targets (Figure 9A). One group (group 1) contained _R. brevipes, C. squannulosa_, and _A. jacksonii_, whereas the other (group 2) was comprised of _R. delica_, _C. subclavipes, C. nuda_, and _L. indigo_. This model explained 81.4% of total variability, while OPLS-DA confirmed the presence of the two main groups produced by PCA (Figure 9B). The OPLS-DA model was validated with 999 permutations and values of R\({}^{2}\) = 1 and Q\({}^{2}\) = 0.997 (Figure 9C). These results suggested that the differential consumption of the different groups might produce similar biological effects. Group 1 had outstanding amounts of _p_-hydroxybenzoic acid, _p_-coumaric acid, vitamin C, and substantial levels of phenols (Figure 9D). Group 2 had remarkable anti-lipase activity as well as substantial levels of Mg, thiamine, riboflavin, and oleic acid (Figure 9D). Interestingly, the variable important plot in each fungal species reveal that lipase activity fat, Mg, and _p_-coumaric, _p_-hydroxybenzoic acid, thiamine, and vitamin C contents influenced the results of OPLS-DA model and should be considered as the most relevant properties of the edible mushrooms studied (Figures S3-S9).  
\begin{table}
\begin{tabular}{c c c c c c c}  & **LI 1** & **CN 1** & **CS 1** & **RD 1** & **RB 1** & **CQ 1** & **AJ 1** \\ \hline _Helyobacter pylori_ (ATCC53504) & 156.8 & 135.9 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & 156.8 \\ _Escherichia coli_ ATCC25922 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & 356.8 & 458.7 & \(>\)500 \\ _Enterococcus faecalis_ (ATCC 29212) & 130.4 & 158.9 & 197.8 & 238.2 & \(>\)500 & \(>\)500 & 135.4 \\ _Salmonella typhi_ (ATCC 6539) & 98.7 & 217.9 & \(>\)500 & 318.7 & \(>\)500 & \(>\)500 & 106.5 \\ _Saccharomyces cerevisiae_ (INV5C1) & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 \\ _Lactobacillus reuteri_ (ATCC55730) & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 \\ _Lactobacillus acidophilus_ (ATCC4356) & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 \\ _Bifidobacterium bifidum_ (ATCC 29521) & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 & \(>\)500 \\ \hline \end{tabular}  
* 1: Acronym of Lactarius indigo (LI), Clitocyte nuda (CN), Clitocyte subclavipes (CS), Rusula delica (RD), Russia breevipes (RB), Clitocyte squannulosa (CQ), and Amanita jacksonii (AI).  
\end{table}
Table 2: Minimum inhibitory concentration (\(\mu\)g mL\({}^{-1}\)) of the hydroalcoholic extracts from seven edible mushrooms consumed in the northeastern highlands of Puebla, Mexico.
## 4 Experiments
### Singing Voice-Separation Experiment
#### 4.1.3 Separation Results

Table 2: Signal-to-distortion ratio (SDR, dB) comparison with other methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ArchitectureStatistics** & **SDR** & **SIR** & **SAR** & **SDR** & **SIR** & **SAR** \\ \hline A1 (MDenseNet) & 4.93 & 12.98 & 6.22 & 11.48 & 16.74 & 13.45 \\ A2 (MMDenseNet) & 5.61 & 13.43 & 6.57 & 11.97 & 16.84 & 13.96 \\ A3 (MMDenseLSTM) & 5.78 & 13.57 & 6.87 & **12.03** & 16.44 & **14.22** \\ A4 (A1 + MB) & 5.31 & 13.51 & 6.30 & 11.62 & 16.24 & 13.38 \\ A5 (A3 + TDConv) & 5.51 & 14.27 & 6.47 & 11.73 & 16.86 & 13.50 \\ A6 (A3 + FDConv) & 5.26 & 13.50 & 6.48 & 11.71 & **17.82** & 13.21 \\ A7 (A3 + 2 DConv) & 5.46 & 13.82 & 6.42 & 11.81 & 17.67 & 13.07 \\ A8 (A3 + T-FDConv) & **5.90** & **14.56** & **6.90** & **12.03** & 17.16 & 13.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Separation performance (dB) on DSD100 dataset. As a result, the proposed method, which effectively expands the receptive field at DenseNet, showed the highest performance next to the MMDenseLSTM with 6.25 dB SDR in _vocals_ and 12.58 dB SDR in _acompaniment_.
## 5 Technical Results
### Simulation Results  
To predict the maximum load that their design would be able to withstand before fracture/yielding, each group performed a linear static load simulation. When doing so, the same structural constraints and loads were applied to the designs as were used in their GD setup. Similarly, the predictions were calculated using the same PLA material properties as those used in the GD setups. All the simulations regarded the material properties of the PLA as isotropic. Whilst this is known to be unrealistic, it provides a representation of the relative strength of each design's structure. The maximum load prediction for each design can be found in Table 2.  
There is a 341 N standard deviation across the predicted maximum loads for all the designs. The spread is around a mean of 504 N. The ratio of standard deviation to the mean indicates the broad range of possible outcome performance created by different GD approaches.
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
###### Abstract  
Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects.  
pulsed thermography; deep learning; defect detection; depth estimation Article  
A Dataset of Pulsed Thermography for Automated Defect Depth Estimation  
2023  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 2 Factors That Affect HER Performance
### Influence of the Electrolyte  
The kinetics of Pt, Ir and Pd are two orders of magnitude slower in an alkaline medium (1 M KOH) than in an acidic medium (0.5 M H\({}_{2}\)SO\({}_{4}\)). Under the same overpotential, a higher load of precious metal is required on the negative electrode in an alkaline environment [48]. Therefore, the regulation of the HER catalytic activity can be achieved by optimizing the catalyst environment, namely by selecting the appropriate electrolyte.  
[PERSON] et al. [15] studied the relationship between the HER catalyst activity and pH value of four supported platinum group metal catalysts (Pt/C, Ir/C, Pd/C and Rh/C) using cyclic voltammetry and the rotating disk electrode method in a wide pH range (0-13) (Figure 3b). The exchange current density (\(j_{0}\)) of the four catalysts decreased with the increase in pH. Their HER activity decreased with the increase in pH, while their slope was similar, indicating that the effect of pH on the HER activity was independent of the catalyst composition. In addition, the HER activity has a small change in different buffer solutions with similar pH values (phosphoric acid/phosphate, citric acid/citrate, acetic acid/acetate, carbonate/bicarbonate and boric acid/borate), suggesting that the properties of anions and adsorption had no significant effect on the HER activity.  
[PERSON] and co-works [49; 50] reported that the HER activity on the surface of platinum-based catalysts could be adjusted by changing the type of alkali metal cations in the electrolyte. Figure 3c shows the effect of metal cations (Li\({}^{+}\), Na\({}^{+}\), K\({}^{+}\), and Cs\({}^{+}\)) on the HER activity in a 0.1 M H\({}_{2}\)-saturated alkaline electrolyte using NiFe cluster-modified Pt (111) as the model catalyst. When KOH was used instead of CsOH, the HER activity of this electrode could be increased by nearly 3 times [43], confirming that cations regulating electrolytes contribute to the improvement of catalytic activity [51]. KOH solution is usually used to study the HER performance of catalysts under alkaline conditions because K\({}^{+}\) has better molar ionic conductivity than Na\({}^{+}\) (0.0735 vs. 0.0501 S L mol\({}^{-1}\) cm\({}^{-1}\)) [52].
## 5 Conclusions  
The design of a frequency selective band-pass filter with small dimensions and second harmonic suppression using an electromagnetic resonator has been presented. A prototype has been manufactured at 6.4 GHz and characterized based on transmission loss measurements. Second harmonic suppression was obtained using a DGS topology. The filter presents a high selectivity with a sharp passband to stop band transition. The compact size, low cost, simple fabrication and integration with other components in the system make it appropriate for satellite/terrestrial communication and wearable applications.
## 1 Introduction
The stability problems of underground caverns and tunnels have also been studied by numerical calculation methods with good results, such as the finite element method (FEM) [30; 31] and the discrete element method (DEM) [32; 33]. Nowadays, a large number of underground caverns of hydropower projects are excavated intensively and on a large scale, and are faced with complex geological structure features such as high ground stress, broken and weak rock, and large burial depth and high water pressure, which means that the existing research results cannot meet the research on the stability of the surrounding rock of large underground cavern groups under complex geological conditions, nor can they systematically carry out engineering geological research on the rock structure of the surrounding rock of the underground cavern groups of broken rock, from the rock structure surface. It is of great theoretical and engineering significance to systematically carry out the engineering geological research on the rock structure of the surrounding rock of the underground cavern group in the fractured rock and to study the deformation and damage mechanism of the excavation process of the surrounding rock of the fractured rock and the response characteristics of the mechanical effect of the surrounding rock, so as to provide an important scientific basis for optimizing the excavation method and support method in the surrounding rock of the fractured rock. In this paper, in order to describe and evaluate the blockiness structural characteristics of the fractured rock mass more accurately, the field structural surface data are transformed into a three-dimensional distribution model by image software Rhino3D, and the concepts of fracture degree and occlusion degree are proposed. Based on this concept, a multi-factor analysis method is applied to propose the quality evaluation method and damage mode of the fractured rock mass and will use the discrete element numerical simulation method, the characteristics of rock mechanical parameters of different fractured rock masses and the deformation evolution characteristics of fractured surrounding rock after underground cavern excavation are simulated and analyzed.
# Comparative Analysis of Fatty Acids Concentration in Liver and Muscle Tissues of Rats and Mice
## 2 Materials and Methods
### Estimation of Saturated, Monounsaturated and Polyunsaturated Fatty Acid Fractions by GC-MS Approach  
Initially, samples were separated using a solid-phase extraction system on silica gel (Baker, CA, USA) with subsequent extractions using chloroform, acetone, and methanol [17; 18]. Nitrogen-dried lipid samples were transesterified according to [PERSON]'s method, which consists of several steps: mixing with methanol, toluene, KOH, and acetic acid, phase separation (centrifugation), drying and dissolving of separated fractions in 70 \(\mu\)L of hexane [17; 18]. Fatty acids were identified by gas chromatography. GC/MS analysis was performed using an HP 6890(II) gas chromatograph coupled to an HP 5972 mass spectrometer with electron impact ionization (70 eV). An Rt-2560 capillary column (100 m, 0.25 mm ID, 0.20 \(\mu\)m) (Hewlett-Packard 6890 (Agilent-HP, Santa Clara, CA, USA), Rt\({}^{\circledotimes}\)-2560 biscyanopropyl polysiloxane column (Restek, Centre County, PA, USA)) was used. The column temperature was programmed to rise from 50 to 240 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\). The carrier gas was helium at a flow rate of 1.2 mL min\({}^{-1}\). The split ratio was 60:1. The scan time and mass range were 1 s and \(m/z\) 40-300, respectively. The content of individual fatty acid fractions was converted into the wet weight of the tissue used for the study.  
Nineteen fatty acid species (Sigma-Aldrich, St. Louis, MO, USA) were used as standards: Caproic Acid (C6:0); Caprylic Acid (C8:0); Undecanoic Acid (C11:0); Lauric Acid (C12:0); Myristic Acid (C14:0); Pentadecenoic Acid (C15:0); Palmitic Acid (C16:0); Palmitic Acid (C16:1); Heptadecenoic Acid (C17:0); cis-10-Heptadecenoic Acid (C17:1); Stearic Acid (C18:0); Oleic Acid (C18:1n9c); Linoleic Acid (C18:2n6c); Arachidic Acid (C20:0); cis-11-Eicosanoic Acid (C20:1); Linolenic Acid (C18:3n3); cis-8,11,14-Eicosatrienoic Acid (C20:3n6); Tricosanoic Acid (C23:0); cis-4,7,10,13,16,19-Docosohexaenoic Acid (C22:6n3).
# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 5 Proposed Algorithm  
The optimization problem is non-convex due to the coupled variables \(b_{k,i}\) and \(p_{k,i}\) in (20) and (21). To find a simplified solution, the proposed scheduler in this section exploits the convexity property of the sub-problems [27] for the main non-convex problem (17). In addition, the proposed algorithm makes use of the resource element structure in NOMA which takes one subcarrier in frequency and one time symbol known as transmission time interval (TTI) [28], as demonstrated in Figure 3. Hence, the proposed algorithm operates in two domains, as illustrated in Figure 4. The first is delay minimization to guarantee the delay requirement of each IoT device which is performed by a time domain packet scheduler (TDPS). The second domain is rate maximization to increase the spectral efficiency and maximize the system sum rate which is performed by a frequency domain packet scheduler (FDPS).  
The optimum height of the UAV should be calculated to solve constraint C7. The optimal UAV elevation angle \(\theta_{optimum}\) was derived for each environment in [29] to cover a circular cell of certain radius in A2G communication independent of the multiple access technique used. Thus, according to [30]; the values of \(\theta_{optimum}\) given by (25) for UAV placement corresponding to each environment either suburban, urban, dense urban or high-rise urban environments are illustrated in Table 1. Thus, the optimum UAV altitude \(h_{optimum}\) to cover a cell of radius \(R_{c}\) can be given by  
\[\theta_{optimum}=\tan^{-1}\left(\frac{h_{optimum}}{R_{c}}\right) \tag{25}\]
## 3 Results
### Optimization of PLE Extraction by a Simplex-Centroid Experimental Design
Optimizing the PLE Temperature According to the Recovery of Antioxidant and Anticolinergic Phenolic Compounds  
The optimal extraction condition achieved in Section 3.1 was used to optimize the PLE extraction temperature. For that, extracts achieved by PLE at 40 \({}^{\circ}\)C using 100% 2-MTHF were compared with PLE extracts obtained at 100 and 160 \({}^{\circ}\)C in terms of TPC, antioxidant capacity determined by DPPH\({}^{\bullet}\) and ORAC assays, and anticholinergic capacity evaluated by AChE and BChE assays. Moreover, the efficiency of the PLE technique was assessed by comparison with maceration (at room temperature for 24 h). As can be observed in Figure 2, the results indicated that the PLE technique was more effective in the recovery of phenolic compounds with antioxidant and anticholinergic capacity than the maceration process. In addition, a gradual increase in the antioxidant capacity of the extracts was observed with the PLE extraction temperature. In fact, PLE at 160 \({}^{\circ}\)C provided the extracts with the highest TPC values and the highest antioxidant capacity determined by DPPH\({}^{\bullet}\) and ORAC assays (see Figure 2A,B, respectively). However, the anticholinergic capacity of the extracts increased from 40 \({}^{\circ}\)C to 100 \({}^{\circ}\)C PLE extraction temperatures, but it reduced from 100 \({}^{\circ}\)C to 160 \({}^{\circ}\)C (see Figure 2C). Figure 2: **(A)** TPC determined by Folin–Ciocalteu assay, **(B)** antioxidant capacity evaluated by DPPH\({}^{\bullet}\) and ORAC methods, and **(C)** anticholinergic capacity determined by AChE and BChE assays from PLE burdock extracts obtained at 40, 100, and 160 \({}^{\circ}\)C, and their comparison with SLE extracts. \({}^{\text{a,b,c}}\) Letters show the significant differences among extracts (\(p\leq 0.05\)). In addition, it was also noted that the extraction temperature had a significant impact on the total peak area measured in the HPLC-DAD analysis of the PLE extracts (see Figure S1). Specifically, an extraction temperature of 100 \({}^{\circ}\)C produced extracts with the highest total peak area (35,724 \(\pm\) 5,683). In comparison, at 160 \({}^{\circ}\)C, the total peak area decreased to 30,893 \(\pm\) 3629, and at 40 \({}^{\circ}\)C, it further dropped to 22,013 \(\pm\) 7877 (see Figure S1). This suggests that 100 \({}^{\circ}\)C is the optimal temperature for maximizing compound extraction under these conditions. Thus, due to the high antioxidant and anticholinergic capacities, and the high peak areas of the compounds detected in the HPLC-DAD analysis exhibited by the PLE extract obtained at 100 \({}^{\circ}\)C, this temperature was selected as the optimum to recover the highest content of bioactive phenolic compounds from burdock roots, particularly from the SPLE ecotype.