esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Tomato Juice as Influenced by Non-Thermal Air Plasma Treatment
## 1 Introduction  
Currently, consumers are eager to buy products that exert a positive effect on their health and wellbeing and protect their bodies against diseases. A healthy human diet should contain large amounts of raw or processed fruit and vegetables. NFC (Not From Concentrate) juices are part of the currently promoted so-called healthy lifestyle. They are made only from fresh raw material without the addition of water, concentrated juice, artificial ingredients; hence, they are definitely more valuable than \"From Concentrate\" juices. They provide the organism with substantially higher amounts of vitamins, minerals, dietary fiber, pectins, and many substances that are essential for health, e.g., antioxidants (flavonoids or carotenoids) [1, 2, 3]. Most of these juices are pasteurized to eliminate microorganisms present in the products. Elimination of bacteria protects the products against spoilage, thereby extending their storage stability over a long time. However, this treatmentprocess can destroy not only microorganisms but also many valuable biologically active substances [4; 5; 6].  
Therefore, there is a need to devise methods for juice preservation that will induce the smallest possible changes in the physicochemical properties of the material without compromising its microbiological safety. The application of cold atmospheric plasma (CAP), classified as a low-temperature treatment contributing to the preservation of thermolabile substances in juice, seems to be a promising approach. A properly designed experimental plasma system and properly selected process parameters can yield very high-quality products. Cold plasma has become a novel option as a disinfecting and preserving agent, which can also improve the quality of certain food parameters [7; 8; 9]. Many research groups reported that relatively low temperatures allowed the treatment of a variety of samples, including biological material, such as seeds and food, and including juices and nects [10; 11; 12; 13; 14; 15; 16]. Plasma is generated in electrical discharges, and depending on the operational parameters of the device, and many factors, such as free electrons and electromagnetic radiation, species such as RONS (reactive oxygen and nitrogen species) can be generated within plasmatrons and transported to the sample via working gas [17; 18; 19; 20].  
The aim of the current work was to investigate the impact of low temperature plasma on tomato juice. Air, as the most affordable option, was selected as a substrate gas.
# A Review of Multifunctional Antenna Designs for Internet of Things
## 1 Introduction
This review focuses on four primary operational fields: biomedical and implantable devices, smart homes, and smart cities. It is important to note that even within these categories, there are sub-categories with vastly different requirements (for example, the differences between implantable and wearable antenna specifications). This review represents the first attempt, to the authors' knowledge, to compare IoT antennas based on characteristics tailored to specific applications, providing a clear understanding of how antenna designs can meet the diverse needs of the IoT landscape.
## 4 Conclusions  
Acquiring sufficient image data for efficient training of CNNs for defect detection in gear parts is a challenging research problem. We used the ROS method to take the copied data as the real input to the D network, allowing DCGAN to generate high-quality synthetic image data for the purpose of training a defect detection CNN model. The proposed DCGAN in conjunction with traditional data augmentation techniques, was able to generate a robust training image set in our experiments. The synthetic images generated through the DCGAN achieved IS scores of 3.850 \(\pm\) 0.027 and an average accuracy of 97.67%. This proves that the images generated by DCGAN were clear and meaningful.  
We then introduced an improved CNN based on the state-of-the-art Vgg11 network which was trained on the aforementioned image training set. The proposed CNN uses the Leaky ReLU activation function and a Dropout layer and efficiently extracted features while alleviating the model overfitting problem. Compared with the reference network models in our experiments, the proposed model with GAN reached an optimal \(F1_{macro}\) score of 98.40%, which shows that the proposed model performed defect detection with a low error rate. It also shows that the model had a high check rate. The proposed model can effectively compress the network without affecting network performance.  
We finally performed t-SNE clustering to visualize and compare the classification capability of the proposed CNN and the Vgg11 network. t-SNE clustering reinforces our earlier findings which indicate higher detection accuracy in case of the proposed model with GAN.  
The above work achieved a high accuracy in identifying gear surface defects, but some shape defects (such as size) still need to be screened in the first step through a pre-set  
Figure 10: A 2D feature visualization using the t-SNE algorithm for the last layer of the network: (a) Proposed model with GAN; (b) Vgg11.  
program, and future work will also be committed to the combination of the two to form a complete algorithm for gear defect detection.  
Methodology, H.G.; supervision, D.W.; validation, W.L. and J.Y.; writing--original draft, H.G.; writing--review and editing, Y.Z. and T.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Key Research and Development Program of Zhejiang Province, grant number 2022C01139 and 2019C01134, and in part by the Key Research and Development Program of Jinhua, grant number 2021-1-001a.  
Not applicable.  
The authors declare no conflict of interest.
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 5 Discussion
### Limitations and Improvements of the Model  
Although our study provides valuable insights, it is subject to certain limitations that outline pathways for future refinement and investigation. Primarily, the quality of the data employed requires enhancement, particularly concerning its spatiotemporal resolution and spatial coverage. To address this limitation, we propose extending the application of our model to larger geographical domains and exploring innovative approaches to represent spatiotemporal dynamics more effectively. Additionally, our reliance on interpolation methods to impute missing data introduces a layer of uncertainty, as the assumptions embedded within these models may diverge from the actual patterns exhibited by the data. This misalignment could introduce biases into the estimation of PM\({}_{2.5}\) concentrations. Furthermore, although our analysis distinguishes between spatial and temporal effects, it does not account for the possibility that spatial patterns may exhibit temporal variability. Future research should prioritize a more comprehensive examination of the spatiotemporal continuity associated with PM\({}_{2.5}\) concentration changes, as well as a nuanced consideration of the heterogeneity inherent across both spatial and temporal dimensions.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
###### Abstract  
As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (\(R^{2}\)), and root mean square errors (\(RMSE\)). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (\(R^{2}\) = 0.66 (CI, 0.63-0.69), \(RMSE\) = 2.46 (2.33-2.58) g Cm\({}^{-2}\) day\({}^{-1}\)) for the three grassland biomes. GPPMOD had varied performances with \(R^{2}\) values of 0.72 (0.68-0.75) (temperate), 0.64 (0.59-0.68) (alpine), and 0.40 (0.27-0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (\(\varepsilon_{max}\)) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in \(\varepsilon_{max}\), FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.  
GPP; MOD17; grassland ecosystem; grassland types; FluxNet 26 September 2018; Accepted: 5 November 2018; Published: 8 November 2018
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 4 Solar Irradiance Forecasting Based on Deep Learning Techniques
### Solar Irradiance Forecasting Model Based on Deep Hybrid Model
The suggested approach was applied to six solar farms in Queensland, Australia, at daily temporal horizons in six different time increments. The proposed forecasting model outperformed two Deep Learning (Deep Neural Network, CNN-LSTM) and three Machine Learning (Artificial Neural Network, Random Forest, Self-Adaptive Differential-Evolutionary Extreme Learning Machines) models in all six selected solar farms. The results \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **LSTM (s)** & **CNN–LSTM (s)** \\ \hline Training time & 70.490 & 983.701 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of LSTM vs. CNN–LSTM [94]. showed good performance metrics (r > 0.9 in all cases) and improvements of more than 10% over daily, monthly, and seasonal periods. [PERSON], et al. [174] suggested a hybrid approach that combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) to predict Global Sun Radiation (GSR). The proposed method involved utilizing a CNN-LSTM model to extract optimal topological and structural characteristics from the predictive features, followed by an MLP-based predictive model for generating GSR forecasts. Meteorological features were used to predict GSR in six solar farms located in Queensland, Australia. To enhance the efficiency of the proposed CMLP model, a Hybrid-Wrapper feature selection technique based on a Random Forest-Recursive Feature Elimination (RF-RFE) scheme was employed to eliminate redundant predictor features. The performance of the CMLP model was compared to those of seven temperature-based deterministic models based on artificial intelligence. The evaluation demonstrated the excellent performance of the CMLP model across all tested solar energy study sites, spanning daily, monthly, and seasonal scales.
## 4 The Proposed 2 FAKA-C/S Protocol
### System Setup Phase  
The aggregated server \(S_{j}\) independently chooses a random number \(x\in Z_{p}^{*}\) and sets an abelian group \(G\) in the elliptic curve, where \(P\) is a generator of \(G\). Further, \(S_{j}\) selects a one-way hash function \(H(\cdot)\). Finally, \(S_{j}\) publicizes the parameter \(H(\cdot)\) and reserves a secret long-term key \(x\).
## 2 Materials and Methods
### Antioxidant Activity of Protein Hydrolysates (LPH)
#### 2.4.2 ABTS Radical Scavenging Capacity  
The analysis was performed according to [24], with some modifications: The solutions of 7 mM ABTS (in 0.1 M acetate buffer, pH 4.5) and 2.45 mM potassium persulfate (in distilled water) were prepared. Equal amounts of the two solutions were mixed and the mixture was left in the dark at room temperature for 12-16 h, during which radical cation ABTS was generated. Before analysis, 4.0 mL of the ABTS solution was diluted to 140 mL with methanol to obtain an ABTS working solution with an absorbance of 0.700 \(\pm\) 0.025 at 734 nm. A fresh ABTS working solution was prepared for each analysis.  
First, 0.250 g of LPH from all experimental groups were dissolved in 10 mL of distilled water. The aliquots of the solutions were diluted 10 times with 80% methanol. After 20 min incubation at room temperature, the samples were centrifuged at 8240\(\times\) g, for 10 min at 10 \({}^{\circ}\)C (Beckman J2-21M, Brea, CA, USA). The precipitate was discarded and the supernatant was collected and analyzed.  
In addition, 2850 mL of ABTS working solution was mixed with 150 \(\mu\)L of the sample (supernatant) and allowed to stand in the dark for 60 min. Absorbance was measured at 734 nm against methanol. The control was developed in the same way but the sample solution was replaced with 150 \(\mu\)L of methanol. Standard solutions of Trolox (concentration from 6.25 to 125 \(\mu\)g/mL) were used as a positive control.  
The following formula is used to calculate the ABTS radical inhibition percentage (%RSA):  
\[\%\text{RSA}(\text{ABTS})=\frac{(\textit{Acontrol}-\textit{Asample})}{ \textit{Acontrol}}\times 100 \tag{2}\]
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 2 Receivers  
In the literature [14; 15; 16; 17] we can find the description of very efficient and appropriate receivers for use in MIMO systems, able to be extended for mMIMO. This section summarizes their main properties.  
* ZF: Zero Forcing (ZF) [21] is a simple receiver technique that applies the inverse of the channel frequency response to the received signal to equalize the communications channel. It allows for a perfect separation of the different users as well as the removal of the ISI, however, it cannot be used in most practical applications as when the received signal is very weak, that is when the Signal-to-Noise ratio (SNR) is very low, it compensates by amplifying the received signal and completely deteriorates the overall SNR when trying to invert values close to zero. In terms of complexity it is not very efficient as well, since it requires the inversion of matrices;
* IB-DFE: The IB-DFE receiver is an iterative receiver where each stream is detected one at a time and the interference is canceled with the help of the streams already detected. When the number of iteration increases, the interference cancellation is improved, improving the overall performance. As with the ZF, this receiver also has the disadvantage of using matrix inversions;
* MRC: The MRC receiver combines the different received branch signals in order to maximize the received SNR ratio. The main advantage is that there are no matrices inversions;
* EGC: The EGC receiver only involves phase rotations, combining all received signals with unitary weights to achieve a high SNR. Like MRC, the EGC does not need matrices inversions.  
Table 1 presents a summary of the BER performance achieved by the previous receivers, as well as the number of required iterations to achieve it, where applicable, based on the work presented in [15].  
As we can observe, the referred ZF and IB-DFE receivers have the same BER performance as the MFB, for very similar values of \(E_{b}/N_{0}\), remarkably for the IB-DFE. In this table, only the non-iterative versions of MRC and EGC receivers were considered.
# A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
## 1 Introduction
During the evaluation, several methods of feature selection were applied. The dataset used in the experiment was gathered in accordance with the self-developed data acquisition procedure. The mentioned classification models were chosen after initial testing of classification accuracy. Such models, such as linear, quadratic discriminant analysis and logistic regression were discarded due to the low values of accuracy, obtained applying these models. Moreover, the literature shows extensive use of classifiers applied in the current research: SVM [40; 41; 42; 50], kNN [50; 51; 52], decision trees [50; 51]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{2}{c}{**Workload Class**} & \multicolumn{1}{c}{**Subject**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Approach**} \\  & **Number** & & & & & & \\ \hline
[45] & 3 & 8 & Artificial Neutral & accuracy & 0.74 & \multirow{2}{*}{si} \\ \cline{2-5}  & 2 & & Network & & 0.87 & \\ \hline
[33] & 3 & 8 & Artificial Neutral & \multirow{2}{*}{accuracy} & \multirow{2}{*}{0.83} & \multirow{2}{*}{si} \\  & \multicolumn{2}{c}{} & & Network & & & \\ \hline
[37] & \multicolumn{2}{c}{} & cognitive & \multirow{2}{*}{10} & \multirow{2}{*}{Linear Regression} & \multirow{2}{*}{coefficient between predicted and actual difficulty level} & \multirow{2}{*}{0.84} & \multirow{2}{*}{si} \\  & \multicolumn{2}{c}{} & workload level & & & & \\ \cline{1-1}  & \multicolumn{2}{c}{} & was defined as real number & & & & \\ \cline{1-1}  & \multicolumn{2}{c}{} & from the range & & & & \\ \cline{1-1}  & \multicolumn{2}{c}{} & [0.6; 7.2] & & & & \\ \hline
[46] & 6 & 10 & Linear Regression & \multicolumn{2}{c}{correlation coefficient between predicted and actual difficulty level} & \multirow{2}{*}{0.82} & \multirow{2}{*}{si} \\  & \multicolumn{2}{c}{} & & & & \\ \hline
[34] & 2 & 16 & SVM & accuracy & 0.94 & sd \\ \hline
[38] & 2 & 13 & SVM, ELM & accuracy & 0.99 & sd \\ \cline{1-1} \cline{3-6}  & \multicolumn{2}{c}{} & & 0.89 & si \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of cognitive workload classification research. Two approaches were regarded: subject independent (si) and subject dependent (sd). The rest of the paper is structured in the following way. Section 2 presents the experiment structure, where the data acquisition protocol and the equipment applied are described in detail. Section 3 covers the whole process of data processing, starting with preprocessing, including signal filtering and artifact removal, through feature extraction and feature selection, ending with a description of the classification models applied. Section 4 contains the obtained results, whereas the discussion concluding the paper is presented in Section 5.
## 1 Introduction
In this respect, the extremely high input impedance of the LNAs ensures the isolation of the two helical antennas for balanced amplitudes and phases during operation [9]. The beamformer was engineered to mitigate phase difference errors inherent in the fixed distance between the two helical antennas at varying operating frequencies, thus optimizing the front-to-back ratio of the array. Introducing this beamformer effectively reduced the spacing between the two adjacent helical antennas to \(\lambda/8\), compared to the conventional quarter-wavelength two-element array with a 90\({}^{\circ}\) phase difference feed, which typically has a spacing of \(\lambda/4\). In Section 2, we delve into the design considerations and evaluation of the active normal-mode helical antenna. In Section 3, we discuss the development of the helical phased array as well as the beamformer design and provide the corresponding test results. Finally, in Section 4, we conclude our work.
## 4 Results
### Results on the Phase Dataset  
Despite the challenges posed by noise, measurement errors within the dataset, and its limited size, the results obtained in this study are highly promising, strongly suggesting the practical utility of the proposed method in real-world WPT applications. The NN  
Figure 19: Prediction of the capacitance value \(C_{3}\) compared with original ones. The error is very low, with an MSE = 0.18. The output of the neural network was the capacitance value; the inputs were the impedance value and the distance between two coils of the WPT system.  
Figure 18: Prediction of the capacitance value \(C_{2}\) compared with original ones. The error is very low, with a MSE = 0.18. The output of the neural network was the capacitance value; the inputs were the impedance value and the distance between two coils of the WPT system.  
demonstrated a remarkable ability to generalize, evidenced by its excellent learning curve and consistently low prediction errors, even with the small dataset.  
In more detail, the training and validation losses converged to approximately 0.02, while the mean absolute error (MAE) on both training and validation sets reached approximately 0.04 (Figure 20).  
Finally, on the test dataset, the NN achieved a mean absolute error of just 1.65 %. Furthermore, a key strength of the proposed approach is the consistent reliability of its predictions: all predictions on the test set exhibited errors below 5 %, a crucial requirement for real-world control system implementations. This is because the frequency far away from the goal frequency produces a bigger phase angle, decreasing the power efficiency and current stress for the circuit. This level of accuracy ensures that the NN can be confidently integrated into a practical control loop. The detailed prediction results, showcasing this high level of performance, are presented in Figure 21. These findings highlight the potential of the proposed NN-based approach to significantly improve the performance and robustness of WPT systems in realistic operating environments.  
Figure 21: Prediction results of the frequency in red, in blue the measured original results. Near each point, there is the relative error reported.  
Figure 20: Training history of the NN for the prediction of the best frequency that minimizes the phase.