esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Digital Modulation Performance  
To assess the digital modulation performance, the proposed device is tested with pulse amplitude and quadrature amplitude modulations (PAM-2, QAM-4 and QAM-16). Pseudo-random binary signals varying from 10 Gbps to 400 Gbps with 100 samples per bit are generated (considering bitrate equal to twice the bandwidth of operation) and mapped to PAM-2, QAM-4 and QAM-16 to form modulating signals. Pulse shaping is performed using a raised cosine filter with a roll-off factor of 0.01, and the peak-to-peak voltage of 4 V is maintained for all the signals. These signals are passed through the traveling wave electrode, whose filter response is estimated from the obtained frequency response (see Appendix A.4) and then given to the proposed EOM, operating at the quadrature biasing point \((V_{\pi}/2)\). Biasing voltage is varied with regard to the bit rate from 3 V to 4.2 V. For PAM-2 modulation, a single MZI is used, while for QAM-4 and QAM-16 modulations, a dual-parallel Mach-Zehnder scheme is followed [30; 31]. An OSNR of 25 dB is considered while obtaining the output from the downsampled signal at the detector to generate the constellation diagrams shown in Figure 9. The figure shows the reference point (red), received signal constellation at the output of EOM (green) and received signal constellation at the output of detector (blue). A slight deviation and scattering in the constellation at the output of EOM (green) can be observed; this is due to the low-pass filtering effect induced by the traveling wave electrodes (which induce ripples in the waveform) and velocity and impedance mismatch. In this case, the filtering effect is the dominating factor for this deviation. The output signal is applied to a decision maker to recover the transmitted bits after eliminating eight samples from the start and the end. For BER estimation, bitrates equal to the bandwidth are considered and applied to the device. The BER of 0 is obtained at all the data rates for PAM-2 and QAM-4 modulation. BER for QAM-16 modulations is presented in Figure 10 (blue). The figure shows that the BER increases for lower data rates as well due to reduced \(V_{\pi}\), which causes an imbalance of applied voltage between the outer and the inner constellations. BER, in these cases, can be improved by manipulating the applied voltages for the inner constellation such that these constellations move towards the center while keeping outer constellations intact at their positions. Values of improved BER by scaling the voltages of the inner constellation by 0.83 are also plotted in the figure (orange). As observed in the figure, the BER increases rapidly as the device's bandwidth (136 GHz) is crossed, and at 150 Gbps (150 GHz), the BER increases to \(1.8\times 10^{-3}\). At the bitrate of 400 Gbps (100 GHz), a BER of \(1.8\times 10^{-3}\) is obtained, which is below the hard decision forward error correction (HD-FEC) threshold.  
Figure 8: (**a**) Fundamental, IMD2 and IMD3 RF output at the detector versus biasing angle and (b) fundamental, IMD3 and noise floor RF output versus RF input power.
## 2 The Proposed Method
### Feature Extraction  
For PV systems, the faulty conditions are reflected in the I-V curve, which gives sufficient information about PV functionality. However, the fault diagnosis methods based on the I-V curve have a major difficulty; finding a proper pattern for distinguishing between faulty and normal conditions could be very complicated. Figure 2 shows the I-V curves under normal conditions, an LL fault with 10% mismatch and impedance of R = 10 \(\Omega\), an LL fault with 20% mismatch and without impedance, and an LL fault with 30% mismatch and impedance of R = 10 \(\Omega\). As illustrated in Figure 2, the I-V curves cannot be directly adapted for fault classification. This is because of the following two reasons:  
1. I-V curves under normal conditions are close to LL faults at low mismatch levels with high impedances, which is a challenging task to identify the faulty conditions.
2. I-V curves related to the LL faults under low mismatch and low impedances are very close to LL faults at high mismatch levels with high impedances, which is another challenge to classify the faulty conditions.  
However, to distinguish the normal cases from the LL faults and also to classify LL fault events from each other, the detail processing of I-V curves is required. This processing includes efficient extracting of the features and also selecting the features for learning process of any type of faulty conditions. In this study, a PV array has been modeled in MATLAB software in order to simulate the faults and the normal events under various scenarios, see Figure 1. The PV array consists of 3 strings  
Figure 2: Current–Voltage (I–V) curves under normal and line-to-line (LL) fault conditions at standard test condition (STC).  
with 10 modules in each string. The detailed parameters of the PV modules (at STC) are listed in Table 1. The output voltage and current of the PV array are recorded by controlling the output value of the voltage source. Later, these values are transmitted to the MATLAB workspace to capture the I-V curves. In order to detect and classify accurately the faulty conditions, ten features have been extracted (see Table 2) from the I-V curves via analyzing the I-V characterization under normal and fault events based on three points, namely: short circuit current (A), Maximum Power Point (MPP) (B), and open-circuit voltage (C), (see Figure 2).
## 4 Discussion
### The Impact of \(\varepsilon_{max}\) for GPP Estimation  
The maximum light energy utilization rate (\(\varepsilon_{\max}\)) of the remote sensing GPP model is an important parameter and has an important impact on the accuracy of GPP modeling. [PERSON] revealed that the MODIS GPP algorithm may cause \(\varepsilon_{\max}\) to be underestimated [49]. MODIS GPP products in the midwestern U.S. croplands were underestimated due to the inaccuracy of \(\epsilon_{\text{max}}\) estimation. Therefore, the LUE values measured in the field should be used consistently in large-scale GPP modeling [50]. [PERSON] conducted a monthly parameterization of the one-leaf LUE model and the two-leaf LUE model of eighteen typical plant function types. The results indicated that the \(\epsilon_{\text{max}}\) of most plant function types change with the season and had a significant positive correlation with the leaf area index and environmental temperature changes [17]. [PERSON] developed a data-driven RFR-GPP model using random forest regression method, which showed LUE varied largely with latitude [51]. [PERSON] examined different sources of uncertainty in GPP simulated by LUE models and found that model structures (e.g., FPAR, Ws, and Ts) influenced both model parameters and model performance [52]. The \(\epsilon_{\text{max}}\) in alpine meadows at different altitudes on the Tibetan Plateau estimated to be 1.83 g C/MJ and 1.33 g C/MJ [47; 53]. The \(\epsilon_{\text{max}}\) of this study was equal to 1.6 g C/MJ in alpine meadows [39]. \(\epsilon_{\text{max}}\) also vary with spatial and even in different year. Using a constant \(\epsilon_{\text{max}}\) for a certain biome may cause errors in LUE-oriented GPP modeling [54]. Therefore, obtaining reasonable \(\epsilon_{\text{max}}\) is vital to LUE models.
## 3 Unscented Particle Filter with Adaptive Sample Size
### Adaptive Sample-Size Strategy
\[\overline{X}_{k}\triangleq\int_{\chi}f(X_{k}|X_{k-1})p(X_{k-1})\mathrm{d}X_{k -1}. \tag{11}\]7  
The experimental results are shown in \[\overline{X}_{k}\triangleq\int_{\chi}f(X_{k}|X_{k-1})p(X_{k-1})\mathrm{d}X_{k -1}. \tag{11}\]8 For further verification, the experimental results of the KL-sampling particle filter (KL-PF), known as a classical adaptive algorithm, is added for comparison. It is found that the estimation accuracy of the PF could be improved by increasing the sample size, but this improvement is limited. When sample size is increased from 500 to 1000, there is only a small improvement in accuracy, and the running time is positively correlated with sample size. For KL-PF and APF, their estimation accuracy and running time are both stable with different initial sample sizes. In terms of accuracy, both of them are close to the PF with 1000 samples. However, in terms of running time, under the same accuracy, APF is about 76% of PF, while KL-PF is higher than PF, and is about 1.5 times of APF. Since KL-PF requires us to calculate the information distance between samples, the computational complexity is increased to some extent. On the contrary, APF adaptively adjusts the sample size according to the state variance at each time step, therefore as initial sample size changes, the estimation accuracy could be maintained, while the running time is reduced. Figure 2 shows changes of particle number caused by adaptive sample size strategy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Initial Sample Size** & **RMSE** & **Running Time (s)** \\ \hline \multirow{3}{*}{PF} & 100 & 4.0561 & 0.2152 \\  & 500 & 3.7749 & 1.0601 \\  & 1000 & 3.7458 & 2.4824 \\ \hline \multirow{3}{*}{KL-PF} & 100 & 3.7436 & 2.7982 \\  & 500 & 3.7428 & 2.8630 \\  & 1000 & 3.7441 & 2.8258 \\ \hline \multirow{3}{*}{APF} & 100 & 3.7447 & 1.8856 \\  & 500 & 3.7419 & 1.8919 \\ \cline{1-1}  & 1000 & 3.7423 & 1.9136 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of simulation results in the univariate non-static growth (UNGM) model.
###### Abstract  
Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species and electromagnetic emissions generated through gas plasma jets have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets or approaches within machine learning, such as reinforcement learning and deep learning. This review emphasizes the significant application of advanced algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of machine learning techniques in plasma medicine requires research efforts, data sharing, and interdisciplinary collaborations. Unraveling the complex mechanisms, developing real-time diagnostics, and optimizing advanced models will be crucial to harnessing the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.  
machine learning; reinforcement learning; deep learning: Gaussian process; artificial neural networks; real-time diagnostics +
Footnote †: journal: Medical  
plasma are dependent on factors such as temperature, pressure, energy supply, nature of the gas, electron density ranges, the device used, and operational conditions utilized to generate the plasma [12; 13]. The most common type of low-temperature atmospheric pressure plasma is cold atmospheric plasma jets (CAPJ), which have been well established in the fields of biology and medicine [14].  
Atmospheric plasma can self-organize into different patterns with modified plasma compositions in response to various operational parameters and interactions with the target environment [15; 16]. This transition from a spatially homogeneous state to self-organized patterns in response to various parameters like discharge voltage [17; 18; 19] and the electrical permittivity of target cells [20] can play an important role in the therapeutic outcome of plasma treatment for cancer [21; 22]. Self-organization might allow an adaptive and self-adaptive plasma system that can dynamically adjust and optimize plasma treatment conditions based on real-time feedback [16]. It aims to maximize the therapeutics on cancer cells while ensuring safety and efficacy.  
This review delves into the pivotal role of mathematical modeling and machine learning (ML) approaches in achieving distinctive adaptive and self-adaptive features of CAP. CAP, with an adaptive feature, adjusts treatment parameters based on external feedback, while self-adaptive plasma autonomously optimizes treatment parameters using internal data and algorithms. These elements propel the application of CAP in precision oncology/personalized cancer treatment, where CAP doses are tailored to the unique characteristics of individual patients' cancers.
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 5 Experimental Results and Analysis
### Robustness Under Noisy Conditions  
To evaluate the performance of the MMCSNN in complex acoustic environments, we conducted a noise augmentation experiment. By simulating real-world usage scenarios, various types of background noise were introduced, including white noise, cafe background chatter, and electrical interference--common disturbances in audio inputs. Each type of noise was tested under three different signal-to-noise ratio (SNR) levels: 20 dB, 10 dB, and 5 dB.  
As shown in Figure 8, the MMCSNN still performs well under all the noise conditions. Even at 5 dB, the Macro F1-score remains above 77%. It is worth noting that, starting from 10 dB, white noise caused a significant performance drop. This may be due to its broad spectral coverage, which can interfere with the extraction of audio attribute features.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variant** & **Accuracy (\%)** & **F1 (\%)** & **Macro F1 (\%)** \\ \hline MMCSNN (All) & **86.88** & **88.20** & **86.17** \\ Only High-Level (HNR, F1, F2) & 85.05 & 86.02 & 84.67 \\ Only Low-Level (F0, shimmer, jitter) & 84.52 & 85.44 & 83.92 \\ Without HNR & 84.36 & 85.13 & 83.81 \\ Without F0 & 83.94 & 84.62 & 83.45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of audio attribute subsets and individual feature removal on MMCSNN performance. **Bold values indicate the best results in each column.**
## 2 Related Work
Although these models optimize performance through mining information dissemination structure features, they rely too heavily on the feature extraction performance of graph neural networks and ignore the fact that information quality is uneven in propagation, which makes semantic representations unreliable. As the complexity of a model increases, the decision-making process within the model becomes more difficult to explain and verify. Interpretable machine learning. Our study was also correlated with interpretable machine learning, which mainly focuses on two aspects: the explanation of models and the explanation of results. The explanation of models primarily relies on probability graph models or knowledge graphs technologies. For example, [PERSON] et al. [37] proposed a KG-based method to verify facts through predicate paths. [PERSON] et al. [38] hypothesized that the shortest path between concept nodes could be determined by defining appropriate semantic proximity indicators on a knowledge graph, which can effectively approximate human fact checking. Most automatic fact-checking methods require a knowledge base, and must be updated regularly to ensure that the knowledge base remains current. However, it is difficult to handle new topics, such as symptoms of COVID-19, at an early stage. The explanation of results is primarily dedicated to visualizing the attention distribution in the model decision process. For example, [PERSON] et al. [39] found that words reflecting negative emotions have higher attention weights than words related to events through visualizing the attention weights of their model. [PERSON] et al. [21] depicted semantic interactions between high-quality comments and claims with a co-attention mechanism, and found that the attention weights of evidence-related words were higher than that of other words. Compared to previous studies, we detected fake news in more realistic social media scenarios. To overcome the fact that information quality is uneven in propagation, we calculated the credibility of nodes in an information dispersion network from the perspectives of user authority and communication relationships, and filtered highly informative evidence from comments. To fully leverage spatial and temporal structures in combination, we designed an evidence representation module based on spatiotemporal structure to aggregate the spatiotemporal neighborhood characteristics of message propagation. To enhance the transparency of the model reasoning process, a capsule network was used to model the implicit bias of evidence relative to the source information in a transparent manner.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 5 Model Ongoing Training
### Computational Benchmarking Results  
To further assess the computational efficiency of the proposed RL-based phishing detection framework, several benchmarks were conducted during model training and evaluation. Table 6 below summarizes the comparative results detailed as follows:  
* The RL-based DQN model required approximately 35 min of total training time across 10,000 episodes, with a peak memory consumption of 420 MB.
* The CNN-based phishing detection model required approximately 2.3 h of training and 1.6 GB of memory.
* The BERT-based phishing detection model required 5.5 h of training and 3.8 GB of memory during fine-tuning.
* The RL agent achieved convergence after approximately 7500 episodes, while CNN and BERT models required multiple epochs (25+ epochs for CNN; 5+ epochs for BERT) over the entire dataset.
* These findings demonstrate that the RL-based model achieved strong phishing detection accuracy with substantially reduced computational resource requirements compared to deep learning counterparts.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 5 Conclusions  
To acquire beneficial structural prior to guide the super-resolution process effectively, a gradient-guided and multi-scale feature network for image super-resolution (GFSR) is proposed in this paper. Firstly, to fully utilize the image structure information, a novel network with a dual-branch structure is constructed, and the gradient feature map obtained from the gradient branch is employed as the structural prior of the high-frequency details. Secondly, two different multi-scale feature extraction modules are designed to acquire the luxurian and effective features. Finally, the adaptive channel attention mechanism is incorporated into our GFSR model to highlight the significant channel features by adaptively exploring the dependence between channel features. Extensive experimental results demonstrate that the proposed GFSR achieves superior super-resolution results in comparison with state-of-the-art methods. However, the proposed GFSR model is slightly complex, we will focus on compressing the model while maintaining super-resolution performance to effectively promote computational efficiency in our future work.  
Conceptualization, J.C.; formal analysis, X.Z.; funding acquisition, D.H.; methodology, J.C. and X.Z.; software, F.C.; supervision, J.C. and D.H.; validation, J.C., X.Z. and F.C.; writing--original draft, J.C.; writing--review and editing, D.H. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 61901183 and Grant 61976098, in part by the Fundamental Research Funds for the Central Universities Grant ZQN-921, in part by the Natural Science Foundation of Fujian Province under Grant 2019J01010561, in part by the Foundation of Fujian Education Department under Grant JAT170053, and in part by the Science and Technology Bureau of Quanzhou under Grant 2017G046.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: Comparison of performance and number of parameters of different methods. Results are evaluated on Urban100 with scaling factor x2.
# On the Alkalinity of Solid Catalysts for Transesterification of Dimethyl Carbonate and Ethanol
## 3 Results  
### Catalysts Performance  
As shown in Table 1, in the transesterification reaction of DMC and ethanol, the selectivity of EMC and DEC is greater than 99.80%, with essentially no other side reactionsoccurring. This is consistent with the relevant calculations of the kinetics of the transesterification reaction between DMC and ethanol [10], and the products basically contain no other substances. The experimental results indicated that the reaction of DMC and ethanol was negligible in the absence of a catalyst, with a DMC conversion rate of 0.57%. While Al\({}_{2}\)O\({}_{3}\) and ZnO exhibited catalytic activity compared to the blank control, their performance remained suboptimal. MgO, Mg-Al, and Mg-Al-Zn as catalysts were all able to achieve more than 20% conversion of DMC, with the Mg-Al catalyst having the best catalytic effect. Therefore, only the cycling stability of MgO, Mg-Al, and the Mg-Al-Zn catalyst was investigated. For MgO and Mg-Al, the DMC conversion rate declined to below 6.00% after 6 cycles, prompting their exclusion from further cycling experiments. As illustrated in Figure 1, the Mg-Al-Zn catalyst exhibited an initial DMC conversion rate of 41.84%, which decreased to approximately 34.00% in the second cycle. Subsequently, the catalyst was stabilized in recycling, indicating good reusability of the catalyst. In addition, increasing the catalyst loading to 7.0 wt.% enhanced the DMC conversion rate to 56.53% (detailed information can be found in the Supporting Information).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Catalyst** & **DMC Con. (\%)** & **EMC Sel. (\%)** & **DEC Sel. (\%)** \\ \hline Blank & 0.57 & - & - \\ MgO & 23.49 & 95.25 & 4.67 \\ Al\({}_{2}\)O\({}_{3}\) & 7.08 & 96.42 & 3.45 \\ ZnO & 5.25 & 95.02 & 4.86 \\ Mg-Al & 51.79 & 91.23 & 8.47 \\ Mg-Al-Zn & 41.84 & 91.58 & 8.32 \\ \hline \hline \end{tabular} Reaction conditions: DMC/C\({}_{2}\)H\({}_{5}\)OH = 1, catalyst dosage: 3.0 wt.%, reaction temperature = 78 \({}^{\circ}\)C, reaction time: 3 h. DMC: dimethyl carbonate, EMC: ethyl methyl carbonate, DEC: diethyl carbonate. \({}^{\circ}\)-\({}^{\circ}\): No catalyst was added during the reaction process.  
\end{table}
Table 1: Effect of catalyst on the synthesis of EMC.  
Figure 1: Catalytic performance and durability of the catalysts (reaction conditions: DMC/C\({}_{2}\)H\({}_{5}\)OH = 1, catalyst dosage: 3.0 wt.%, reaction temperature = 78 \({}^{\circ}\)C, reaction time: 3 h). Since the conversion rate of DMC dropped below 6.00% after the sixth use of MgO and Mg-Al as catalysts, there was no need to continue the repeated experiments. The experiment was repeated three times for each data point. The error bar is the error range of the data point obtained based on the calculated standard deviation, and the upper and lower horizontal lines represent the upper and lower limits of the 95% confidence interval of the mean.