esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
FIREFLY was designed to cover a spectral range that would facilitate SIF retrievals via direct or indirect methods, and utilizing oxygen absorption features, solar absorption features, or spectral fitting. For each SIF retrieval approach, different attributes of the FIREFLY instrument hold critical importance, and there are different requirements for those attributes. Some attributes need to meet a particular standard, whereas other simply need to be characterized to a particular standard. Additionally, each SIF retrieval approach requires different supporting information, whether from coincident measurements or modelling; requires different assumptions to be met; and begets different, and sometimes even conflicting, recommendations for retrieval conditions and data processing techniques. The subsections of Section 4.1 each explore a different potential SIF retrieval approach for an operational FIREFLY product, evaluating the specifications and characterizations of FIREFLY in the context of the retrieval approach, and identifying the assumptions, supporting information, and further studies that would be required to enact the approach.
# Electrically Small Water-Based Hemispherical Dielectric Resonator Antenna
###### Abstract  
Recently, water has been proposed as an interesting candidate for use in applications such as tunable microwave metamaterials and dielectric resonator antennas due to its high and temperature-dependent permittivity. In the present work, we considered an electrically small water-based dielectric resonator antenna made of a short monopole encapsulated by a hemispherical water cavity. The fundamental dipole resonances supported by the water cavity were used to match the short monopole to its feed line as well as the surrounding free space. Specifically, a magnetic (electric) dipole resonance was exploited for antenna designs with a total efficiency of 29.5% (15.6%) and a reflection coefficient of \(-\)24.1 dB (\(-\)10.9 dB) at 300 MHz. The dipole resonances were effectively excited with different monopole lengths and positions as well as different cavity sizes or different frequencies in the same cavity. The overall size of the optimum design was 18 times smaller than the free-space wavelength, representing the smallest water-based antenna to date. A prototype antenna was characterized, with an excellent agreement achieved between the numerical and experimental results. The proposed water-based antennas may serve as cheap and easy-to-fabricate tunable alternatives for use in very high frequency (VHF) and the low end of ultrahigh frequency (UHF) bands for a great variety of applications.  
dielectric resonator antenna; electrically small antenna; Mie resonances; tunable antennas; water-based +
Footnote †: journal: Applied Physics Letters
# Bioremediation of Persistent Organic Pollutant--Oxybenzene with _Pleurotus diamor_
###### Abstract  
Oxybenzene, a common sunscreen ingredient, has been widely detected in various environmental matrices, posing significant ecological and health risks. The present study demonstrates, for the first time, the capacity of _Pleurotus diamor_ to degrade oxygen-zone in in vitro cultures. After 14 days of mycelial incubation, oxygenzone concentrations in the medium decreased from 25 mg to 1.5394 \(\pm\) 0.095 mg. The final amount of oxygenzone in the mycelium after lyophilization was 6.2067 \(\pm\) 0.2459 mg. Furthermore, oxygenzone addition significantly reduced biomass growth from 2.510 \(\pm\) 0.6230 g to 1.4697 \(\pm\) 0.0465 g. The transformation products in the dry mycelium and medium were assessed and identified using UPLC-Q-tof based on monoisotopic molecular mass and fragmentation spectra. In processes initiated by _P. djamor_, mainly acylated derivatives of oxygenzone were formed. Additionally, compounds with thiol and amino groups were identified. Alterations in antioxidant profiles (L-tryptophan, 6-methyl-D,L-tryptophan, p-hydroxybenzoic acid, ergosterol, lovastatin, L-phenylalanine, and ergothioneine) in response to oxygenzone exposure were observed. Our findings reveal significant changes in the antioxidant levels and biomass growth inhibition, underscoring the potential toxicological risks associated with oxygenzone. The observed reduction in oxygenzone concentration highlights the potential of _P. djamor_ as an effective and environmentally friendly strategy for mitigating this pollutant.  
UV filters; oxygenzone; Benzophenone-3; _Pleurotus diamor_; biodegradation products; mycoremediation +
Footnote †: journal: Applied sciences
## 3 Results  
### Accuracy Assessment of NPP Estimation  
The accuracy of three modeled NPP results (MODIS derived, the Miami model, and the Thornthwaite Memorial model) were assessed by a comparison with the in situ observations. Validation analysis indicated that the modeled NPP results were all very significantly (Figure 1; \(p\) < 0.01) correlated with the in situ NPP measurements. The \(R^{2}\) value for the MODIS derived NPP was 0.39, which was higher than that of Miami model (\(R^{2}\) = 0.27) and the Thornthwaite Memorial model (\(R^{2}\) = 0.28). Furthermore, the RMSE and MAE deceased to 0.19 kg C m\({}^{-2}\) a\({}^{-1}\) and 0.14 kg C m\({}^{-2}\) a\({}^{-1}\), respectively for the MODIS NPP data compared to 0.29 kg C m\({}^{-2}\) a\({}^{-1}\) and 0.23 kg C m\({}^{-2}\) a\({}^{-1}\) for the Miami model, as well as 0.31 kg C m\({}^{-2}\) a\({}^{-1}\) and 0.26 kg C m\({}^{-2}\) a\({}^{-1}\) for the Thornthwaite Memorial model, respectively. The results demonstrated that the NPP derived from MODIS had an improved performance of NPP simulation by increasing \(R^{2}\) by 46.18% and 40.46% when compared with the Miami model and Thornthwaite Memorial models, respectively. In addition, the NPP derived from MODIS decreased the relative RMSE and MAE by 34.01% and 38.34%, respectively, when compared with the Miami model, and by 39.71% and 45.88%, respectively, when compared with the Thornthwaite Memorial model.
## 2 Numerical Methodology
### Geometry  
As mentioned earlier, this model overlooked the influence of the intermediate principal stress on the assessment of material fracture. Moreover, as microwave simulation requires an extremely fine mesh division (one-tenth of the wavelength or less), this resulted in the number of grids being huge. Furthermore, the parameters were iteratively calculated after each time step. This resulted in a huge amount of computational workload. Additionally, the 3D model calculations encountered severe convergence problems and, therefore, could not be analyzed reliably. Therefore, the 2D model was selected as a more suitable and stable approach for characterizing failure behaviors. It produced satisfactory results in this study. The microwave oven model was developed with reference to the Institute of Applied Electromagnetic Research at Sichuan University in the literature [42]. The model consisted of three parts (Figure 3): a rectangular waveguide, a rectangular microwave cavity, and a coal sample model. The rectangular microwave cavity and waveguide were filled with air.  
The average sulfur content of anthracite from Guizhou (No. 5 coal seam of the Upper Permian Longtan Formation in the Xingyi Coalfield, Guizhou Province) was 5.75%, with pyritic sulfur accounting for 16.13% to 89.32%. In addition, the coal seam contained numerous aggregated pyrite blocks with particle sizes greater than 0.1 mm [43]. Therefore, a coal body model was created referencing this seam. Pyrite was randomly distributed in the coal, with a percentage of 1% and a radius of 0.8 mm (Figure 4). The coal sample was heated at the positions of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) in the static heating model (SM). The coal sample was heated with a rotational speed of \(\pi/3\) rad/s in the rotational heating model (RM). The rotation of the coal sample was achieved using a discrete method, which had previously been verified for accuracy [42]. The accuracy of the discrete method depended on the degree of each iteration and the accuracy of the assignment of grid nodes. The lower the degree set for each iteration, the higher the number of iterations required, and the greater the amount of calculation. In previous studies on the discrete method, when the iteration degree was below 30\({}^{\circ}\), the calculation results were accurate. In this study, a more refined 3\({}^{\circ}\) was used. The continuously rotating circle was divided into 60 discrete points: for instance, at 3\({}^{\circ}\), the coal sample changed position 60 times in 1 rotation. These models were labeled S\({}_{0}\)M, S\({}_{90}\)M, S\({}_{180}\)M, S\({}_{270}\)M, and RM, respectively. The coal sample remained stationary at one position and did not affect the electromagnetic field in the SM. In contrast, the coal sample maintained a uniform speed of rotation and continuously  
Figure 2: ETMD coupling relationship.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 3 Results and Discussion
### Discussion
#### 3.3.2 Suggestions for Future Research  
The operational state of the thruster was unknown apart from the rotational speed of the pinion shaft. In practice, the steering angle changes and the vessel may be in transit or performing a task using the dynamic positioning. These factors presumably have effects on the acceleration signals, and therefore, their correlations with the signals should be further analyzed in various operational circumstances. The utilization of such information could reduce the variation in inference, if the information was used to filter samples or to predict feature values, for example.  
The data used for system identification are based on the normal operation of the azimuth thrusters and can only be relied on during such operations. Therefore, the identified system cannot fully describe the behavior of the physical one when it encounters high domain oscillations and vibrations inflicted by extreme conditions. The identification of the entire operational domain is limited by the associated costs and the lack of facilities to gather the ideal data sets for that. Therefore, alternative approaches should be developed to improve the system identification procedure.  
In addition, the uncertainty of inference should be studied by focusing on the effects of feature values and the parameters shown in Table 2. Furthermore, the sample size requirements [55] for the model training should be studied to enhance the robustness of the models while the amount of training data is limited in practice. Additionally, the testing of different hypothesis tests for normal distribution could provide alternative views to the operational area selection.  
As shown by the overlapping classes in Figures 13 and 14, the feature sets used in this study were not optimal for the fault cases studied. Undoubtedly, clearer difference between classes could be obtained by using wrapper methods [32] on large feature sets during classifier training or by using other criteria [34]. However, these approaches give feature sets that are specific to the data sets analyzed, and their benefits for new data sets remain unclear. In addition, the various methods for signal processing could increase the sensitivity to defects, which can be inferred from the success in previous industrial applications [24].  
Finally, a change detected in the operation is not necessarily inflicted by a defect in the monitored system. Therefore, reliable references should be identified for the implemented model to verify its performance. In the development stage, this could be done based on human expertise, but in the long run, automated adaptation mechanisms [56] may be required, where the use of synchronized data from redundant sensors could be useful.
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 2 Materials and Methods
### Randla-Spall
#### 2.3.2. Dynamic Enhanced Dual-Branch Structure
The dynamically enhanced branch and the original branch are configured in parallel as input layers to the backbone network, with each branch directed to separate encoders. Four encoding layers are employed to reduce the size of the point clouds from large scenes while simultaneously increasing the feature dimension of each sampled point. Assuming that the initial number of points in the point cloud input to the network is N, the size of the points after passing through the four data encoding layers becomes \(\left(N\rightarrow\frac{N}{4}\rightarrow\frac{N}{16}\rightarrow\frac{N}{64} \rightarrow\frac{N}{256}\right)\). At the same time, the feature dimension of the sampled points in each layer gradually increases to retain more information from the point clouds, following the sequence \(\left(8\to 32\to 128\to 256\to 512\right)\). Subsequently, the decoding layer in the network comprises four stages designed to reduce the feature dimensions of each point and up-sample the point cloud. After passing through the four layers, the number of points is \(\left(\frac{N}{256}\rightarrow\frac{N}{64}\rightarrow\frac{N}{16} \rightarrow\frac{N}{4}\to N\right)\), while the feature dimensions of the sampled points are progressively reduced as \(\left(512\to 256\to 128\to 32\to 8\right)\). The network concludes with a fully connected layer whose function is to map the extracted features to the sample label space. In this architecture, the fully connected layer connects the hidden and output layers, and to prevent overfitting, a dropout layer is added. It is worth mentioning that, inspired by self-supervised learning [34], the Jensen-Shannon divergence is utilized as a constraint on the predictions of the outputs from the dynamically augmented and original branches. This approach strengthens the prediction consistency between the augmented and original branches. In RandLA-Spall, the robustness of the model is effectively enhanced by introducing a dynamically enhanced dual-branch structure. The addition of the channel attention mechanism enhances the focus on key features and reduces the sensitivity to noise and data variability, thereby significantly enhancing the overall performance and stability of the model.
###### Abstract  
A nitrogen deficiency always causes bog bilberry syrup wine to have a poor sensory feature. This study investigated the effect of nitrogen source addition on volatile compounds during bog bilberry syrup wine fermentation. The syrup was supplemented with 60, 90, 120 or 150 mg/L dibasic ammonium phosphate (DAP) before fermentation. Results showed that an increase of DAP amounts accelerated fermentation rate, increased alcohol content, and decreased sugar level. Total phenol and total flavonoid content were also enhanced with the increase of DAP amounts. A total of 91 volatile compounds were detected in the wine and their concentrations were significantly enhanced with the increase of DAP. Ethyl acetate, isoamyl acetate, phenethyl acetate, ethyl butanoate, ethyl lexanoate, ethyl octanoate, ethyl decanoate, isobutanol, isoamyl alcohol, _levo_-2,3-butanediol, 2-phenylethylanol, _meso_-2,3-butanediol, isobutyric acid, hexanoic acid, and octanoic acid exhibited a significant increase of their odor activity value (OAV) with the increase of DAP amounts. Bog bilberry syrup wine possessed fruity, fatty, and caramel flavors as its major aroma, whereas a balsamic note was the least present. The increase of DAP amounts significantly improved the global aroma attributes, thereby indicating that DAP supplementation could promote wine fermentation performance and enhance the sensory quality of bog bilberry syrup wine.  
b  
Bog bilberry syrup wine; dibasic ammonium phosphate; volatile compounds; odor activity value; aroma attributes
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 2 Method  
We propose a denoising method for single-channel seismic signals based on generative adversarial networks (GANs). The method takes the short-time Fourier transform (STFT) time-frequency spectrum of a seismic signal as input and employs a generator network to denoise the signal, while a discriminator network is used to evaluate the denoising performance. The denoised spectrum \(\hat{s}(t,f)\) can be obtained by  
\[\hat{s}(t,f)=Y(t,f)\cdot M(t,f) \tag{1}\]  
In Equation (1), \(Y(t,f)\) represents the noisy spectrum, \(M(t,f)\) is a function that maps \(Y(t,f)\) to the desired noise-free spectrum \(\hat{s}(t,f)\). [PERSON] and [PERSON] [18] showed that this mapping can be carried out via a simple thresholding in a sparse representation where the thresholding value can be estimated from noise level assuming a Gaussian distribution. Here, we cast the problem as a supervised learning problem. As shown in Figure 1, the generator part of the proposed method will learn a sparse representation of the input data to produce a mask with the same size as the input spectrum, ranging from (0,1), and the clean signal can be obtained by multiplying the mask with the noisy signal.  
The generator is built upon the U-Net architecture and incorporates a residual shrinkage structure with a soft thresholding mechanism, allowing the network to focus on signal-relevant features. The discriminator consists of a feature extraction module and a discrimination module. The feature extraction module extracts multi-scale features from the time-frequency spectrum and employs a lightweight convolutional attention module for feature fusion, generating a perceptual feature map. The discrimination module adopts a PatchGAN architecture, evaluating each local region of the perceptual feature map and ultimately outputting a score map. The overall network structure is as follows.  
As shown in Figure 1, the proposed method can be divided into two parts: the generator and the discriminator. The generator adopts U-Net architecture, taking the real and imaginary parts of the STFT as input and outputs two masks ranging from 0 to 1. The denoised spectrum is obtained by multiplying the masks with the input. The discriminator consists of two modules: the feature extraction module and the discriminator module. The feature extraction module extracts high-level features from the signal spectrum and calculates the perceptual loss. The discriminator module differentiates the denoised result and the true noise-free signal.
# RCM: A Remote Cache Management Framework for Spark
## 4 Framework Implementation
### Cache Placement Module
Then, the augmenting path of \(G^{*}\) is searched to try to find the best matching (line 4 to line 1). Because of lacking enough edges, it is possible that there is not perfect matching in \(G^{*}\). In order to add more edges, the gap of items is narrowed between \(TX\) and \(SY\) (line 1 to line 2). After the adjustment, some edges will be added to \(G^{*}\) while promising all the items are feasible. Then, the augmenting path of \(G^{*}\) is searched repeatedly until the perfect matching is found. The process of searching is shown in Algorithm 4. FindPath (_i_) visits the vertices, \(i\), to find the matching of meeting \(TX\) (\(i\)) + \(SY\) (\(j\)) = \(weight_{ij}\) (line 1 to line 6). If that is met, FindPath (\(j\)) is run to try to search the augmenting path of \(G^{\star}\) (line 7 to line 9). When the augmenting path meets \(\sum weight_{ij}=\sum_{i\in X}TX(i)+\sum_{j\in Y}SY(j)\), the perfect matching is found in \(G^{\star}\). ```
1:vistTX[i] = True;
2:for\(j=1\) to N do
3:ifistDY[j] then
4: continue;
5:endif
6:\(\phi=TX[i]+SY[j]-efficiencyList[i][j];\)
7:if\(\phi==0\)then
8:vistSY[j] = TRUE;
9:if match[j] == \(-1\) or findpath (match[j]) then
10: match[\(-1\)] = \(i\);
11: return TRUE;
12:endif
13:elseif slack[j] >\(\phi\)then
14: slack[j] = \(\phi\);
15:endif
16:endfor
17:return FALSE;
```  
**Algorithm 4** Find Path. In the end, we analyze the complexity of CPL. According to Algorithm 3, CPL has three nested loops. Therefore, it has a time complexity of O (\(N^{3}\)). The scale of time complexity depends on N. Due to the limited remote cache servers, N will not be very large. So, there will be no time efficiency explosion. The space occupation of CPL is the two-dimensional cooperation efficiency table. It has a space complexity of O (\(N^{2}\)).
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 8 Biomedical Applications of PLA-Based Nanofibrous Structures
### Tissue Engineering
#### 8.3.1. PLA Electrospun Structures for Musculoskeletal Tissue Engineering
(**a\({}_{5}\)**–**a\({}_{8}\)**) SEM images of AN, AM, RN and RM, Reproduced with permission from [130] (License number: 5232540107019). SEM images of (**b\({}_{1}\)**) SiNPs, (**b\({}_{2}\)**) porous PLLA membrane after DOP surface modification and porous PLLA/DOP/SiNP membranes with different SiNP concentrations (**b\({}_{3}\)** = 0.05, **b\({}_{4}\)** = 0.10) Reproduced with permission from [132] (License number: 5232550661164), SEM image of (**c\({}_{1}\)**) nanoHA-coated nanofibers, (**c\({}_{2}\)**) liposome-loaded scaffolds (liposcalfolds), Reproduced with permission from [133] (License number: 5232550143142). SEM images of the internal morphological structures of (**d\({}_{1}\)**) PLA/GEL, (**d\({}_{2}\)**) nHA/PLA/GEL, and (**d\({}_{3}\)**) nHA/PLA/GEL-PEP 3D nanofibrous scaffolds, respectively, Reproduced with permission from [134] (License number: 5232541047315). SEM images of surface morphology of the P3C1 scaffolds after biomineralization for (**e\({}_{1}\)**) 7 days, (**e\({}_{2}\)**) SEM image of mineral clusters after 7 days mineralization in SBF with high magnification, reproduced with permission from [140] (License number: 5232551100684). SEM images of electrospun fibers prepared at different temperatures (**f\({}_{1}\)** = 35 \({}^{\circ}\)C), (**f\({}_{2}\)** = 50 \({}^{\circ}\)C), (**f\({}_{3}\)** = 60 \({}^{\circ}\)C) using a PLA/CS 70:30 weight ratio; Reprinted (adapted) with permission from [141]. Copyright (2022) American Chemical Society. A core\(-\)shell and island-like structure scaffold composed of a bicomponent was developed by [PERSON] et al. [141] to improve cell biocompatibility of the PLA membrane through designing nanosized topography with highly bioactive chitosan onto PLA electrospun fiber surface (Figure 16f). This method in which bioactive modification and topographic effects at the interface between cells and materials happens is based on automatic phase separation of two incompatible polymers during the electrospinning; indeed, by rapid solvent evaporation under a high temperature in jets, the chitosan molecular chains did not have enough time to migrate to the surface of electrospun jets to cover the fibrous shell. The structure of the intermittent island was therefore formed, which results in a controllable balance of the hydrophilicity and hydrophobicity of the surface, desirable surface roughness for cell attachment, and cell recognition sites.