esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The ANOVA conducted on the raw errors revealed the reference repetition \(\times\) number of sets interaction was also significant (F = 47.52 \(p\) < 0.001) as the overestimation of the predicted %Rep with the increase in the number of sets was only observed using the MV\({}_{\text{fastest}}\) of the first set. The remaining interactions failed to reach statistical significance (F \(\leq\) 1.87; \(p\) \(\geq\) 0.180). In addition, a significant main effect of the reference repetition (F = 125.8, \(p\) < 0.001; the predicted %Rep was overestimated using the MV\({}_{\text{fastest}}\) of the first set, but no meaningful overestimations were observed using the MV\({}_{\text{fastest}}\) specific to each set) and the number of sets was also observed (F = 4.03, \(p\) = 0.023; the overestimation of the predicted %Rep was greater as the number of sets increased), but not of the regression model (F = 0.02; \(p\) = 0.893) (Figure 3). Figure 2: Absolute errors between the actual percentage of performed repetitions in relation to the maximum number of repetitions performed until failure (%Rep) and the predicted %Rep from the generalized (**upper panels**) and individualized (**lower panels**) %Rep-%VL equations determined in set 1 when a velocity loss magnitude (%VL) of 20% is reached. The %VL was computed considering both the mean velocity of the fastest repetition specific to each set (MV\({}_{\text{fastest}}\): **left panels**) and the MV\({}_{\text{fastest}}\) of the first set (**right panels**). The dashed line represents the threshold for high accuracy (10%).
## 2 Materials and Methods
### Feature Selection Phase
#### 2.1.3 Two-Level Feature Selection Preprocessing Model  
When PSO-SVM makes a \"one-to-one\" feature selection, a variable number of redundant features are filtered in each iteration, resulting in an unpredictable number of residual features. In addition, the selection of SVM parameters substantially affects classification accuracy, frequently resulting in \"missing the mark.\" This study develops a two-level feature selection preprocessing model, PSO-SVM-RFE, to eliminate unanticipated mistakesproduced by parameters impacting the accuracy of feature selection. Initially, the original data features are filtered using PSO-SVM, and then the filtered features are further filtered by SVM-RFE to produce the final feature selection results. The process of this model is shown in Figure 1.
## 2 Mobile M2M Literature Review
### Energy Efficiency  
Resource usage efficiency is one of the most important requirements for mobile M2M communications when using radio technologies, due to lower available bandwidth, higher link failure, and higher energy consumption. The amount of devices and the requisite that they might have to operate for many years with the same battery, or consuming the least possible energy, demands for energy efficiency in M2M communications [12,22]. [PERSON] _et al_. [22] argue that M2M communications cannot be widely accepted as a promising communication technology, if energy efficiency is not met. In Section 7 we illustrate the importance of energy efficiency when using smartphones as mobile M2M gateways.  
The concept of using a mobile M2M gateway device as an intermediary node to collect and process data from neighbouring sensors is approached by [PERSON] _et al_. [25], who name it a smart M2M device, and [PERSON] _et al_. [12], who name it a cognitive gateway. Both works argue that connecting devices through a gateway should be preferred when they are sensitive to cost or power. The use of M2M gateways shall have a direct impact in the reduction of devices accessing and using the channels for communications, reducing interference and contention, and increasing the efficiency. Reducing the number of devices in networks also translates into easier to deploy and less complex transmission scheduling schemes, and eases the problem of the depletion of the pool of unallocated IP addresses.  
New applications and business opportunities will come along with mobile M2M communications. For example, the innovative idea of using the already existing scheduled airliners as relays between ground devices and satellites, providing a new and complementary M2M infrastructure, is presented in [34]. Mobile M2M devices in airplanes can act as M2M gateways by forwarding data received from M2M ground terminals to satellites, and vice versa. With this approach, there is no need for M2M terminals in satellites to have a very powerful amplifier or large dish antennas to send or receive messages, and, thus, the operational costs should be lower. Furthermore, this solution addresses the challenge of connectivity, eventually relieving traffic from cellular networks. However, one should expect important challenges originated from transmission scheduling and mobility.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
###### Abstract
[23] proposes a non-orthogonal OBF system, where the number of beams is larger than that of transmit antennas. The interferences among the beams is minimized with the non-orthogonal beamforming matrix based on Grassmannian. In [24], a superimposed code is applied to multiplex the data of different users, and the approximate expression of sum rate is derived. The power domain non-orthogonal multiple-access (NOMA) method of multibeam OBF is considered in [25], and then a joint optimal resource allocation algorithm is presented to maximize the sum SE. It is found that multiuser OBF systems can offer the satisfactory quality of service (QoS) to multiple users, simultaneously. In [26], the authors propose a superimposed multiuser shared access (MUSA) scheme based on NOMA method for a massive machine-type communications (mMTC) system. Moreover, the generalized frequency division multiplexing (GFDM) technique is combined with MUSA-NOMA to reduce the latency with high overloading ratio. However, most of the previous works concentrated on flat fading channels, although few considered frequency selective fading channels. This paper proposes a downlink multiuser OBF system, where OFDM and NOMA schemes are applied to deal with frequency selective fading channels and achieve multiple-access, respectively. Then we derive the closed-form expressions of equivalent channel and SE. Moreover, a joint iterative algorithm is provided to maximum the SE of the proposed system. The main contributions of this paper consist of three aspects. 1. A downlink multiuser opportunistic beamforming with OFDM-NOMA (OBON) system is proposed in frequency selective fading channels, which can obtain both multiuser diversity and multiplexing gains. 2. The improvement of spectrum efficiency caused by OBF weights is taken into consideration. Then, we put forward an optimization problem to maximize the spectrum efficiency with the users' QoS requirements. 3. Since the optimization problem is non-convex and hard to solve, it is decomposed into two suboptimal problems. According to the analyses of the two suboptimal problems, we propose a joint iterative algorithm is to obtain the solution of the original optimization problem. The rest of this paper is organized as follows. In Section 2 a multiuser downlink OBON system is introduced. Section 3 formulates an optimization problem to maximize the spectrum efficiency. We propose a joint iterative algorithm to solve the optimization problem, and analyze the convergence and complexity of the algorithm in Section 4. In Section 5, numerical results are provided. Finally, we draw a conclusion in Section 6.
## 6 Conclusions and Future Work  
This paper presents a comprehensive design approach for series-tuned Class-E RFPAs, focusing on determining the bounds of the quality factor \(Q_{s}\) and implementing advanced harmonic control and suppression techniques. By establishing these bounds on \(Q_{s}\) and integrating harmonic control circuits, this study achieved a balance among high efficiency, bandwidth (up to 30%), and maximum output power capability. Simulations of the nonlinear device model demonstrated a bandwidth of approximately 27%, which is in close alignment with the predictions. Future work will involve incorporating matching networks directly into the design equations, followed by experimental validation through the fabrication and testing of the RFPA prototype.  
Conceptualization, P.K.G.; Methodology, P.K.G.; Software, A.S.; Validation, P.K.G.; Formal analysis, P.K.G.; Investigation, P.K.G.; Resources, P.K.G.; Data curation, A.S.; Writing--original draft, P.K.G.; Writing--review and editing, A.S. and J.V.; Visualization, A.S.; Supervision, J.V. and J.L.; Project administration, J.L.; Funding acquisition, J.V. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Delft University of Technology, The Netherlands.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Special thanks are extended to [PERSON] and [PERSON] for their valuable suggestions regarding the structure of this paper. The authors would also like to thank the editor and the anonymous reviewers for their valuable reviews and comments regarding this paper.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RFPA & radio-frequency power amplifier \\ HB & harmonic balance \\ MRI & magnetic resonance imaging \\ MF & medium frequency \\ HF & high frequency \\ VHF & very high frequency \\ ZVS & zero voltage switching \\ ZVDS & zero voltage derivative switching \\ \(V_{DD}\) & supply voltage \\ \(V_{BB}\) & biasing voltage \\ \(c_{p}\) & maximum output power capability \\ \(Q_{2\,2.1}\) & quality factor for topology 2.2.1 \\ \(Q_{s}\) & quality factor of the resonator \\ \(Q_{sat}\) & saturation quality factor for the upper limit \\ \(Q_{min}\) & minimum quality factor for the lower limit \\ \(L_{0}\) & original inductor \\ \(L_{nn}\) & inductor obtained by modifying the original inductor for the \(n^{th}\) harmonic \\ \(L_{pn}^{L},C_{pn}^{L}\) & inductor-capacitor pair obtained by modifying \(L_{0}\) for the \(n^{th}\) harmonic \\ \(C_{0}\) & original capacitor \\ \(C_{nn}\) & capacitor obtained by modifying the original capacitor for the \(n^{th}\) harmonic \\ \(C_{pn}^{C},L_{pn}^{C}\) & inductor-capacitor pair obtained by modifying \(C_{0}\) for the \(n^{th}\) harmonic \\ \(f_{3 dB}\) & frequency at 3 dB level \\ \end{tabular}
## 3 Comparison with Existing Schemes
Therefore, the total conduction losses of two transistors and two diodes in one PWM switching transition are realized as:  
\[P_{cond}=2v_{f}I_{o(avg)}+2I_{o(rms)}^{2}\left(R_{f}+R_{tr}\right) \tag{19}\]  
The switching losses of the two transistors and diodes of converters reported in [17; 18; 19] are computed as:  
\[P_{sw([17-19])}=V_{o}I_{off}(t_{on}+t_{off})+2f_{s}V_{o}Q_{rr} \tag{20}\]  
The switching losses of diodes of the proposed topology are negligible as they operate in low frequency (input frequency), so here switching losses only come from switching transistors and are computed as:  
\[P_{sw(\text{Proposed})}=V_{o}I_{o}f_{s}(t_{on}+t_{off}) \tag{21}\]  
In summary, the comparison of the suggested circuit with the existing circuits is given in Table 2.  
This comparison validates the effectiveness of the proposed circuit topology as it requires fewer switching transistors, gate driving circuits, isolated dc supplies, and simple control schemes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Components** & **Proposed Topology** & **Topology in [17]** & **Topology in [18]** & **Topology in [19]** \\ \hline Transistors & 4 & 8 & 6 & 2 \\ \hline Diodes & 4 & 8 & 6 & 4 \\ \hline Driving circuits & 4 & 8 & 6 & 2 \\ \hline DC supplies & 4 & 8 & 6 & 2 \\ \hline Voltage sensing circuits & 0 & 1 & 0 & 0 \\ \hline Center tap transformer & 0 & 0 & 0 & 1 \\ \hline Shoot-through issue & No & No & No & Yes \\ \hline Output voltage gain & Bipolar & Bipolar & Bipolar & Bipolar \\ \hline RMS voltage rating of the diodes and transistors & \(V_{o}\) & \(V_{o}\) & \(V_{o}\) & \(V_{o}\) \\ \hline RMS current rating of the diodes and transistors & \(I_{o}\) & \(I_{o}\) & \(I_{o}\) & \(I_{o}\) \\ \hline Conduction losses & \(2v_{f}I_{o(avg)}+\) & \(2v_{f}I_{o(avg)}+\) & \(2v_{f}I_{o(avg)}+\) & \(2v_{f}I_{o(avg)}+\) \\  & \(2I_{o(rms)}^{2}\left(R_{f}+R_{tr}\right)\) & \(2I_{o(rms)}^{2}\left(R_{f}+R_{tr}\right)\) & \(2I_{o(rms)}^{2}\left(R_{f}+R_{tr}\right)\) & \(2I_{o(rms)}^{2}\left(R_{f}+R_{tr}\right)\) \\ \hline Switching losses & \(V_{o}I_{o}f_{s}(t_{on}+t_{off})\) & \(V_{o}I_{o}f_{s}(t_{on}+t_{off})\) & \(V_{o}I_{o}f_{s}(t_{on}+t_{off})\) & \(V_{o}I_{o}f_{s}(t_{on}+t_{off})\) \\  & \(+2f_{s}V_{o}Q_{rr}\) & \(+2f_{s}V_{o}Q_{rr}\) & \(+2f_{s}V_{o}Q_{rr}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of the suggested and the recently-developed topologies.
## 5 Conclusions and Future Work  
In this paper, an NMT model X-Transformer was designed for translation, improving the original state-of-the-art Transformer. First, stacking more self-attention mechanisms before the feed forward layer allowed the model to significantly improve the understanding of the input sequence during training. Second, we reduced the number of useless encoder and decoder layers to improve the inference speed of the model. For the WMT 2014 EN-DE and EN-FR translation corpora, the X-Transformer improved the BLEU score by about 20 points.  
Based on the above experimental results, the following considerations will be taken forward for our future work. First, the decoder will still be an important aspect to be discussed. Its main function is to generate the target sentence with the assistance of the understanding of the input sentence by the encoder, which is different from the functionality encoder. We will attempt to find a better algorithm to clarify the relationship between the input and output sequences and to generate an appropriate translation. Second, we will build a Chinese-based translation corpus of the medical field for the NMT system. In machine-learning-based NMT systems, the training corpus is one of the key elements to improve the quality of translations. The frequent international communication that is the result of business, education and travel drives the need for international medical care. The need for bilingual or even trilingual communication using medical field terminology is an urgent and essential issue to be solved. These will be the two directions of our future work to continue to improve machine-learning-based NMT systems.  
Conceptualization, H.-IL. and W.-LC.; methodology, H.-IL. and W.-LC.; software, W.-L.C.; validation, W.-L.C.; formal analysis, H.-IL. and W.-LC.; investigation, H.-IL. and W.-L.C.; resources, H.-IL.; data curation, H.-IL. and W.-LC.; writing--original draft preparation, H.-IL. and W.-LC.; writing--review and editing, H.-IL.; visualization, H.-IL. and W.-LC.; supervision, H.-IL.; project administration, H.-IL. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The WMT-2014 machine translation dataset is publicly available and has been used by many researchers in the area of machine translation.  
The authors declare no conflict of interest.
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 4 Performance Evaluation
### Core Network Attacks
Considering these six characteristics, we conducted Z-score normalization, which uses the mean and standard deviation of the dataset so that the data have zero mean and unit variance (i.e., the mean is subtracted by each value, and the result is divided by the standard deviation). We then applied ten-fold cross-validation, considering these six features as input for four different ML algorithms. For comparison purposes, we considered the following models: (1) SVM with linear kernel, (2) Random Forest (RF) with 100 individual trees, (3) Naive Bayes, and (4) LR. In each of the 10 folds, the complete dataset is divided into a 90% training set and a 10% testing set. The vast majority of the samples (i.e., the training set) are used to build the model, whereas the testing set samples are used to assess the model performance on unseen data. Thus, for each fold, we obtained different model accuracy scores (percentage of correctly classified samples). The final accuracy score of each ML algorithm was calculated as the average across the ten-fold-specific accuracy scores, as depicted in Figure 5. Evidently, the RF algorithm, being an ensemble technique, outperforms the other three ML algorithms by combining multiple models and showing an attack detection ratio of about 98.5%. Notably, the RF model includes a collection of individual tree models that are used to make anomaly predictions, which can generally increase the overall model accuracy. On the contrary, the other ML schemes are single-model, showing less than 82% attack detection accuracy. To further improve the RF performance in attack classification, we also tested the accuracy using a higher number of trees without significant performance gains. Figure 4: ANOVA F-scores for each feature (core network attack case). Figure 5: Attack type classification accuracy for various ML models (core network attack case).
###### Abstract  
This study predrains the encapsulation of bioactive compounds, specifically phenolic compounds and lycopene, extracted from olive and tomato by-products via oil-in-water (O/W) nanoemulsions and their potential application in functional beverages. The effect of various edible oils (olive pomace oil (OPO), sunflower oil (SFO), corn oil (CO), fish oil (FO), and canola oil (CLA)) in the lipid phase and antioxidants (ascorbic acid and phenolic extracts) in the aqueous phase on the physicochemical properties of oil-in-water (O/W) nanoemulsions enriched with lycopene was evaluated, along with the bioaccessibility of the encapsulated bioactive compounds using the static INFOGEST in vitro simulation protocol for gastrointestinal food digestion. All examined edible oils led to nanoemulsions with uniform droplet sizes (droplet size \(<\) 300 nm, droplet distribution \(<\) 0.3) and high stability during storage at 4 \({}^{\circ}\)C, with FO being the smallest, at 259.3 \(\pm\) 9.1 nm, and OPO the largest, at 286.6 \(\pm\) 10.0 nm. Ascorbic acid increased the droplet size by 5-8%, improved droplet distribution, and led to a lower deterioration rate (\(-\)0.014 d\({}^{-1}\)) when compared to the \"control\" counterparts (\(-\)0.037 d\({}^{-1}\)). Lycopene bioaccessibility was significantly affected by the lipid phase, with OPO exhibiting the highest percentage (53.8 \(\pm\) 2.6%) and FO the lowest (40.1 \(\pm\) 2.1%). The OPO nanoemulsion was selected for the development of a functional beverage, showing excellent long-term stability. The phenolic compound concentration remained consistent during storage, and the lycopene degradation rate was minimal, at \(-\)0.0088 d\({}^{-1}\), resulting in an estimated shelf life of 165 days at 4 \({}^{\circ}\)C, based on a 50% reduction in lycopene content. Similarly, phenolic compounds demonstrated high bioaccessibility, without a significant dependence on the lipid phase, and stability during shelf life, enhancing the beverage's overall antioxidant profile. These results indicate that O/W nanoemulsions are effective delivery systems for functional beverages, offering improved stability and bioaccessibility of lycopene.  
O 2024 14 10495 10.3390/appl42210495 10.3390/appl42210495 10.390/appl
# Dynamic Investigation of a Solar-Driven Brayton Cycle with Supercritical CO2
## 3 Results and Discussion
### Yearly Performance and Discussion  
The yearly performance of the STP can be expressed using four efficiency indexes. The first efficiency index is the optical efficiency of the solar field. The second important efficiency index is the thermal solar, and the third is the efficiency of the thermodynamic cycle. The last and most important index of the system is the total efficiency of the STP. Table 9 shows the annual average efficiencies of the STP. It also provides values for these  
Figure 16: Daily variation in thermal efficiency (\(\eta_{\text{th}}\)) and system efficiency (\(\eta_{\text{sys}}\)) for the 25 th of March.  
Figure 17: Daily variation in thermal efficiency (\(\eta_{\text{th}}\)) and system efficiency (\(\eta_{\text{sys}}\)) for the 25 th of August.  
indexes from the literature, allowing the comparison between these values. The reported results of the present work are close to the reported results of the literature. Thus, the conclusions of the present work are reasonable, and the developed model can be accepted as a reliable one.  
Moreover, it is useful to add that the incorporation of the recuperator enhances the system and the thermodynamic cycle efficiencies. Specifically, the operation without the recuperator leads to 16.1% system efficiency, while the use of a recuperator leads to 18% presenting and an increase of about 11.8%. Moreover, the use of the recuperator enhances the thermodynamic efficiency from 34.2% to 42.9%.
## 2 Results
### Viable Plate Counts  
The results of viable plate counting are reported in Table 2. As a general trend, mesophilic aerobic bacteria, spore formers, and Enterobacteriaceae occurred at comparable or even higher levels in the dried sea fennel samples than in fresh sea fennel samples, irrespective of the drying method used. The only exception was the sea fennel samples subjected to MD, which were characterized by significantly lower levels of mesophilic aerobic bacteria and Enterobacteriaceae than fresh sea fennel. In more detail, MD resulted in a strong reduction in Enterobacteriaceae, yeasts, and molds, with the final loads below the detection limit (1.0 Log CFU g\({}^{-1}\)). Yeasts and molds exhibited different trends depending on the drying method and the batch. Overall, a significantly lower yeast load was observed in oven-dried samples than in fresh sea fennel. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Drying**} & \multicolumn{2}{c}{**Duration of the**} & \multicolumn{2}{c}{**Weight Loss (\%)**} & \multicolumn{2}{c}{**a\({}_{\mathrm{w}}\)**} \\ \cline{2-7}  & **Drying Method** & & & & & & \\ \cline{2-7}
**Treatment** & **Batch 1** & **Batch 2** & **Batch 1** & **Batch 2** & **Batch 1** & **Batch 2** \\ \hline RTD & 16 d & 16 d & 84.68 \(\pm\) 0.26 b\({}^{\mathrm{A}}\) & 82.69 \(\pm\) 0.34 a\({}^{\mathrm{B}}\) & 0.58 \(\pm\) 0.09 a\({}^{\mathrm{A}}\) & 0.49 \(\pm\) 0.02 a\({}^{\mathrm{A}}\) \\ OD & 4 d & 4 d & 85.73 \(\pm\) 0.22 a\({}^{\mathrm{A}}\) & 82.94 \(\pm\) 0.09 a\({}^{\mathrm{B}}\) & 0.21 \(\pm\) 0.06 b\({}^{\mathrm{A}}\) & 0.28 \(\pm\) 0.02 b\({}^{\mathrm{A}}\) \\ MD & 15 min & 8 min & 85.67 \(\pm\) 0.26 a\({}^{\mathrm{A}}\) & 82.63 \(\pm\) 0.66 a\({}^{\mathrm{B}}\) & 0.20 \(\pm\) 0.03 b\({}^{\mathrm{B}}\) & 0.40 \(\pm\) 0.06 a\({}^{\mathrm{A}}\) \\ FD & 24 h & 24 h & 85.34 \(\pm\) 0.39 a\({}^{\mathrm{B}}\) & 83.15 \(\pm\) 0.50 a\({}^{\mathrm{B}}\) & 0.26 \(\pm\) 0.05 b\({}^{\mathrm{A}}\) & 0.16 \(\pm\) 0.04 c\({}^{\mathrm{B}}\) \\ \hline \hline \end{tabular}  
Weight loss and water activity are expressed as mean value \(\pm\) standard deviation. Values labeled with different small letters in the same column are significantly different (\(p<0.05\)), whereas, for each parameter, values labeled with different capital letters in the same row are significantly different between the two batches (\(p<0.05\)). RTD: room-temperature drying; OD: oven drying; MD: microwave drying; FD: freeze drying.  
\end{table} 
Table 1: Drying parameters, weight loss, and water activity of dried sea fennel for each treatment.