esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we investigate methods to reduce inter-beam interference and intra-beam NOMA user interference in underwater optical multi-beam NOMA systems. We propose an adaptive transmit diversity strategy. The strategy makes an adaptive selection of LEDs for merging based on the interference level of the beams and utilizes the diversity technique to transform the beam interference into signal gain. At the same time, the OFDM-NOMA scheme is employed within the beam to pair NOMA users in groups. Users within a group are superposition coding in the power domain, and different subcarriers are assigned between user groups and multiplexed in the frequency domain to reduce intra-beam interference and reduce the difficulty of detecting the signal at the receiver. Simulation results show that more BER gains are obtained with the adaptive transmit diversity strategy than with the conventional scheme at the same SNR. In addition, we analyze the BER performance of the system with this strategy for different numbers of serving users and verify the feasibility of the adaptive transmit diversity strategy for multi-user communication in multi-beam NOMA systems.  
Figure 11: Sum rate of two strategies under high inter-beam interference.  
Figure 10: SNR = 20 dB, BER of users with the worst performance among different service users.  
**Author Contributions:** Conceptualization, Y.L. and Y.J.; methodology, Y.L. and Y.J.; formal analysis, Y.L., Y.J. and X.C.; investigation, Y.L., Y.J., X.C. and Y.H.; resources, Y.L., X.C. and Y.H.; data curation, Y.L., Y.J., P.J. and S.L.; writing--original draft preparation, Y.L. and Y.J.; writing--review and editing, Y.L., Y.J., P.J. and S.L.; visualization, Y.L., Y.J. and Y.H.; supervision, Y.H.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by National Natural Science Foundation of China under grant (62261009), Guangxi Natural Science Foundation (2022 GXNSFDA035070), and Innovation Project of Guangxi Graduate Education (YCBZ2022106).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest. The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
## 5 Discussion  
### Analysis of the Model Stability of the Anomalies and Lesions  
To support various post-processing computer-aided diagnosis models, the dataset we used in training the model includes MR images from patients with various brain diseases. Therefore,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **Original** & **The Proposed** & **Original Universal** & **Histogram Matching** & **Joint Histogram** \\
**Criteria** & **Images** & **Method** & **Method** & **Method** & **Registration Method** \\ \hline PSNR & 53.75 & 65.51 & 65.13 & 61.39 & 64.27 \\ SSIM & 0.9829 & 0.9992 & 0.9989 & 0.9968 & 0.9988 \\ HC & 0.2724 & 0.9273 & 0.8865 & 0.8049 & 0.8825 \\ GMSD & 0.1328 & 0.1026 & 0.1027 & 0.1291 & 0.1245 \\ MSE & \(18.50\times 10^{3}\) & \(1.222\times 10^{3}\) & \(1.353\times 10^{3}\) & \(1.652\times 10^{3}\) & \(1.559\times 10^{3}\) \\ AD & 30.8 & 4.505 & 1.093 & 13.54 & 8.467 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison results of the different methods.  
Figure 4: The tests of four standardization methods on five example slices from volunteers: (**a**) T2 FLAIR MR images from GE scanner treated as the target; (**b**) T2 FLAIR MR images from SIEMENS scanner treated as the reference; (**c**) standardization results with the proposed method; (**d**) standardization results with the original many-to-one method; (**e**) standardization results with the histogram matching method; and (**f**) standardization results with the joint histogram registration method.  
Figure 5: The logarithmic absolute error image of four standardization methods in Figure 4: (**a**) standardization results with the proposed method; (**b**) standardization results with the original many-to-one method; (**c**) standardization results with the histogram matching method; and (**d**) standardization results with the joint histogram registration method.  
the resized and cropped MR images used in the training may be either completely normal or contain various types of lesion/tumor areas. Therefore, the diversity in data can guarantee the robustness of the transformation model in one aspect. Meanwhile, on the other hand, since we use a method based on GANs, all regions of the image, including lesions and tumors, will tend to match the intensity distribution corresponding to the same tissue type of the reference image group during training to minimize the adversarial loss. Furthermore, the consistency-based loss functions including both the mutual information loss and the cycle consistency loss ensure the stability of the structure (including the type of tissue) before and after the transformation (standardization). These loss functions ensure that, although the transformation model has undergone regional intensity changes, such changes should also be stable for abnormal tissues.
# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 4 Results  
This section reports the procedures carried out and the results obtained regarding the classification of the fluorescence intensity in the positive/negative classes of the HEp-2 images with the fine-tuning method. For a better evaluation of the results, a comparison was made with the results obtained by the same CNNs used by feature extractors and finally a comparison with other state-of-the-art methods. For the four pre-trained CNN networks described in Section 3.4, the transfer learning technique applied to HEp-2 images was analyzed. The division of the available patterns was carried out with a 5-fold cross validation and for each of the 5 iterations, training, validation, and testing were performed. The training phase was optimized considering the AUC as a measure of merit. IIF images have been resized to 227 \(\times\) 227 (for AlexNet and SqueezeNet networks) and 224 \(\times\) 224 (for GoogLeNet and ResNet18 networks) to be provided as input to CNN; no preprocessing has been applied to the image. CNN networks want a 3-channel image input, for this reason, we have evaluated the results using both RGB IIF images and only the green channel and replicating it on R and B channels. The results favored the second choice, to which all of the analyses reported in this work were conducted. Moreover, we evaluated the application of the data augmentation using the rotation with angles of 20\({}^{\circ}\). Data augmentation is a very effective practice especially when the data set for training is limited, or as in our case, when some classes are not particularly represented in the set of examples. In this way an 18 times larger sample data set was obtained. The effect of this data augmentation was valued quantitatively in terms of performance. Table 5 shows the best results obtained, in terms of AUC, from the four CNNs analyzed:
## 3 Results and Discussion
### Optimization by Changing the Carrier Concentration
For AM1.5 solar spectral irradiance, bifaciality reaches the highest value for a concentration of \(6.22\times 10^{17}\) 1/cm\({}^{3}\), where it is marked with the red circle. When modeling is performed under the Atacama spectrum, bifaciality reaches the highest value for \(6.28\times 10^{17}\) 1/cm\({}^{3}\), marked with a red circle in Figure 10. Values of \(V_{oc}\) tend to increase at a higher acceptor concentration in the studied range, and short-circuit current density remains constant. Energy conversion efficiency and the fill factor of the two spectra studied tend to increase with concentration. Figure 11 shows the optimization of the hydrogenated amorphous silicon layer \(a-Si:H(n^{+})\) varying donors between \((1.0-3.0)\times 10^{18}\) 1/cm\({}^{3}\) above this range \(Efn<Ec\) in the \(a-Si\):\((n^{+})\) layer. Bifaciality comes closer to 1 at a higher donor concentration. The open-circuit voltage and the short-circuit current show a small initial increment followed by a constant value with increasingly higher donor concentration. Thus, for this study, when modeling under the standard spectrum, the chosen donor concentration is \(1.2\times 10^{18}\) 1/cm\({}^{3}\), which is marked with the red circle in the bifaciality curve. When modeling with the Atacama spectrum, the chosen concentration is \(1.4\times 10^{18}\) 1/cm\({}^{3}\). Energy conversion efficiency and the fill factor tend to remain constant at a higher donor concentration in the studied range.
## 2 Materials and Methods
### Data Proceeding  
#### 2.3.1 Flux Data  
Eddypro (7.0.7, Li-COR Inc.) software was used for calibration and quality control of the 10 Hz raw data and generated 30 min data. The correction of the turbulent flux data included planar fit correction, sonic temperature correction, density fluctuation correction, and spectral correction. The calculated fluxes data were quality controlled by deleting those data with a quality flag of \"2\". The friction velocity (\(u^{*}\)) thresholds for this site across the years, ranging from 0.12 m s\({}^{-1}\) to 0.26 m s\({}^{-1}\), were calculated using the REddyProc R package based on the moving point test method ([[https://cran.rstudio.com/web/packages/REddyProc/index.html](https://cran.rstudio.com/web/packages/REddyProc/index.html)]([https://cran.rstudio.com/web/packages/REddyProc/index.html](https://cran.rstudio.com/web/packages/REddyProc/index.html)), (accessed on 9 March 2022)). Furthermore, 46% of the NEE, 42% of the LE, and 32% of the H values were rejected because of quality control, \(u^{*}\) filtering, and equipment failure. The energy budget closure of the post-processed 30 min data was 80%. Gap filling was conducted using the marginal distribution sampling method, and the gross primary production (GPP) was calculated using NEE partitioning based on the night-time flux partitioning method in the REddyProc R package. The valid original data were used for statistical analyses at the 30 min time scale, and the filled data were used at other time scales.  
Figure 2: General trends in (**a**) annual solar radiation (S\({}_{r}\)), (**b**) annual mean air temperature (T\({}_{a}\)), and (**c**) annual precipitation (P) in our study area during 1960–2019. The \(p\) and s values are the significance levels and slopes of the red lines.
## 2 Experimentation  
### Test Materials  
In this study, to remove any possible influence of particle size and grading [17; 18; 19; 20], sieved calcareous sand (300-425 \(\upmu\)m) was adopted as the base sand, and rubber chips with the same range of particle sizes were used as the additive. To produce a sequence of rubber-sand mixtures, the quantity of rubber chips was varied from 0% to 10% by mass. For consistency, in the following sections, the numbers in the parentheses are used to denote the rubber content in the mixtures. As an example, the \"sieved (5)\" means sieved calcareous sand mixed with 5% of rubber chips, and the \"sieved (0)\" stands for pure calcareous sand. Calcareous sand originates from broken coral reefs, transported by the sea water. A careful examination using energy dispersive spectroscopy (EDS) indicated that the calcareous sands used in this study were composed primarily of calcium oxide. Micro-scale images of the test materials using a scanning electron microscope (SEM) are presented in Figure 1. Several unique features of the calcareous sand (Figure 1a) at the micro-scale level can be recognized, including the irregular particle shape (i.e., high angularity and low sphericity) and the presence of intraparticle voids, which could potentially result in a high void ratio for the calcareous sand [21; 22; 23]. On the other hand, the rubber chips (Figure 1b) were mainly composed of natural rubber. As seen in this Figure, some particles were prone to agglomerate and also to yield irregular particle shapes.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 5 Results
Thus, blocking and non-blocking simulations were conducted with the optimum load parameter for the blocking case. This facilitates comparisons. Figure 6 illustrates the throughput in blocking and non-blocking conditions. In the case of blocking requests, every blocking request is issued in an independent thread. In this way, many simultaneous blocking requests can be simulated. In the case of non-blocking requests, each non-blocking socket opens a connection to Orion every 50 ms. As the graph indicates, Orion has an initial range in which throughput increases almost linearly up to a maximum of 133.2721 requests or kilobytes per second at a concurrency of 9. As from this value, Orion enters overload reducing its throughput as concurrency increases. The simulation of 30 non-blocking sockets is a clear indication of how this state affects throughput (83.4611 req/s). If we compare the maximum throughput measured when traffic is generated by means of the two testbed methods, we see that the value obtained with the non-blocking method is significantly lower than that obtained with the blocking method. We would note that in the blocking context generation method, each NGSI client generates an _updateContext_ request once the previous one is completed. Figure 6: Throughput obtained using the blocking and non-blocking methods. Clients remain in sync with the server and the rate of requests generated never exceeds the capacity of the Orion server.
## 3 Experiments and Analysis
### Model Evaluation Metrics
#### 3.3.1 Accuracy  
The accuracy is calculated by dividing the number of correctly identified samples by the number of all samples. It is expressed as:  
\[Accuracy=\frac{\textit{The number of correctly identified samples}}{\textit{The number of all samples}} \tag{9}\]  
Although accuracy can reflect the overall performance of the identification network, it cannot be used as a good metric to measure the results in the case of unbalanced samples. Therefore, other metrics are needed to jointly conduct the experimental evaluation.
## 9 Numerical Simulation Example of POD Reduced-Order Extrapolation Galerkin Spectrum Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Figure** & **Calculation Time of Central** & **Calculation Time of POD** & **Time** \\
**Difference Galerkin** & **Reduced-Order Extrapolation** & **Multiple** \\  & **Spectrum Method (s)** & **Model (s)** & **Model (s)** \\ \hline Figure 2 & 28564.41 & 62.44 & 458 \\ Figure 3 & 199.66722 & 0.732 & 273 \\ Figure 4 & 115.70 & 0.451 & 257 \\ Figure 5 & 30.832 & 0.131 & 253 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of average calculation time between the two methods. To sum up, we can see that under different time steps, the contribution of the principal components energy is almost 100 or more, and the cumulative energy contribution is above 97.5%. This shows that the numerical solution of the iterative scheme of the central difference Galerkin spectral method is in good agreement with the approximate solution of the reduced dimension extrapolation central difference Galerkin spectral method. At the same time, the feasibility of using the central difference Galerkin spectral method and the POD dimension reduction spectral method to solve the heat conduction partial differential equation is objectively examined. In addition, the iterative algorithm in any step of the central difference Galerkin spectral method includes H\({}^{2}\) = 10\({}^{4}\) degrees of freedom. However, in the process of solving (32) and (33) by the iterative format of the reduced-order extrapolation Galerkin spectral method, it can be found that there are only **e** = 6 degrees of freedom at each time level. It can be seen from the numerical simulation process that the calculation speed of the POD low order model is at least 235 times faster than that of the central difference Galerkin spectral method, and the maximum temperature error does not exceed 0.03 \({}^{\circ}\)C. It can not only reduce the phase error of the iterative algorithm, but also greatly improve computational efficiency. From the figure, we can see that the error becomes smaller and smaller with the refinement of the time interval, thus realizing the fast calculation of the numerical analysis of the transient nonlinear heat conduction equation. This means that the POD reduced-order extrapolation algorithm is not only simpler in calculation but can also produce better results.
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
###### Abstract  
We investigated the light coupling to waveguide modes in nanophotonic thin-film solar cells exhibiting a tailored disordered grating texture by rigorous optical simulations. Such disordered nanophotonic light trapping concepts have demonstrated enhanced short-circuit current density compared to ordered nanophotonic thin-film solar cells. This observation is commonly explained by a spectral broadening of the resonant light coupling to waveguide modes. In this work, we investigated the origin of this spectral broadening. We identified two basic mechanisms that lead to a spectral broadening of the light coupling to waveguide modes: (1) the broadening of the wave vector transferred by the disordered interface texture and (2) the broadening of the waveguide mode due to the distortion of the wave guiding absorber layer. Depending on the type of disorder, the contribution from each of the mechanisms varies.  
Keywords:Light trapping; thin-film solar cells; waveguide modes; optical simulation; FDTD; disordered gratings +
Footnote †: journal: Applied sciences
## Materials and Methods
### Volatile Analysis  
The headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used to analyze the volatile compounds in the yogurt samples on day 0 of storage. A 20 mL head-space vial was used to mix 5 g of sample with 10 \(\upmu\)L of 1,2-dichlorobenzene as internal standard (I.S, 800 ppm), then tightly covered by a silicon septum. The extraction was done by the fiber made from StableFlex divinylbenzenecarboxen polydimethylsiloxane (DVB-CAR-PDMS) in 50/30 \(\upmu\)m (Supelco, Bellefonte, PA, USA), in a water bath at 70 \({}^{\circ}\)C for 60 min. Then, the absorbed volatiles were desorbed in injection port of GC-MS at 250 \({}^{\circ}\)C for 5 min in splitless mode.  
Analysis by GC (Agilent 7890B, Santa Clara, CA, USA) coupled with MS (Agilent 7200, Santa Clara, USA) was performed using a semi- non-polar column (HP-5 MS, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technology, USA) with helium as the carrier gas at a flow rate of 1 mL/min. The initial oven temperature was kept at 60 \({}^{\circ}\)C for 2 min, then ramped at 7 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C and maintained to 30 min. The MS detector was operated in an electron ionization (EI) voltage of 70 eV under a mass scan range of 33 to 450 amu (m/z). The temperature of both ion source and line transfer was set at 250 \({}^{\circ}\)C.  
Quantitation analysis was done by using the internal standard method, where the peak area for the identified volatiles was compared to their corresponding peak area of the internal standard (i.e., 1,2-dichlorobenzene). The concentration of each compound was calculated by the following equation:  
\[\text{Concentration of volatiles}=(\text{peak area of the compound/peak area of the internal standard})\times\text{concentration of the internal standard}. \tag{1}\]  
All the samples were analyzed in triplicate.
## 3 Phenolic Compounds of Sweet Cherry By-Products
### Flavonoids
#### 3.2.5 Flavan-3-ols  
Flavan-3-ols can be found in many plant foods and supplements such as cacao, red wine, tea, and fruits, including cherries and other berries [62]. These phenolics have a non-planar structure and a hydroxyl group in C\({}_{3}\) of the C-ring and are characterized by the absence of the pyran-4-one structure and the lack of the 2,3-double bond in the C-ring [19]. These flavonoids can range from simple monomers (e.g., catechins) to oligomeric and polymeric forms (e.g., condensed tannins) [19]. Additionally, they do not show a moiety residue in foods.  
Condensed tannins are usually present in foods in connection with catechins. They are responsible for giving flavor and asringent character to certain foods and beverages due to the formation of complexes with salivary proteins [19]. Moreover, there are other classes of condensed tannins such as hydrolyzable and complex tannins [63]. These compounds have a polyol (generally D-glucose) as a central core, and the hydroxyl groups are partially or totally esterified with phenolic acids such as gallic acid (gallotannins) or ellagic acid (elagitannins). Mild acids and bases easily hydrolyze this type of tannins, originating sugars and phenolic acids [19]. In the literature, it has been reported that flavan-3-ol levels are significantly influenced by genotype and environmental and agronomical conditions [64], which explains the significant differences obtained by different authors [7; 12; 18].  
Concerning _P. avium_ by-products, different authors have reported the presence of flavan-3-ols in cherry stems [7; 10; 12; 28]. [PERSON] et al. [28] described the presence of catechin (159.1 \(\pm\) 0.4 ug/g dw) and epicatechin (87.3 \(\pm\) 0.2 ug/g dw) (Table 3), corresponding to 12.5% of the total polyphenols identified in stems (Table 3). More recently, [PERSON] et al. [7] described the epigallocatechin (950.6 \(\pm\) 59.8 and 951.2 \(\pm\) 57.2 ug/g dw) and catechin derivatives (197.0 \(\pm\) 4.0 and 179.3 \(\pm\) 20.0 ug/g dw) in both the hydroethanolic extract and infusion, respectively (Table 3). Procyanidin dimer B type 2 was another flavan-3-ol found in cherry stems, corresponding to about 55% of the phenolic compounds quantified in stem extracts (Table 3) [10].  
For the first time, catechin hexoside was found in _P. avium_ leaves by [PERSON] et al. [10]. With respect to the flowers of _P. avium_, there are no reports about the presence of these phenolics.  
These compounds in _P. avium_ fruits represent less than 11.29% of the total non-colored compounds (ca. 4.48 ug/g dw) [18]. According to [PERSON] et al. [18], catechins and procyanidins were the flavan-3-ols found in sweet cherry fruits. Their presence has already been reported in other studies [6; 11; 55]. Furthermore, procyanidins were identified in sweet cherry fruits from different cultivars (values ranging from 6.2 to 290.6 ug/g dw), representing less than 18% of the total non-colored phenolics [18].