esa-sceva/satcom-chunk-collection huggingface dataset:
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 4 Categorization of Bridgeless Boost Converters
### Totem Pole Topology  
Figure 15 depicts the conventional or asymmetrical topology based on boost converter [33; 41; 42; 43; 113; 114]. Figure 15a illustrates the conventional BBC totem-pole with line rectification done by diodes; this topology has switches (\(Q_{1}\) and \(Q_{2}\)) in the first branch of the converter and diodes (\(D_{1}\) and \(D_{2}\)) in the second branch. This converter can operate in the limit of CCM and DCM, including techniques for sampling (as explained in [115]). This allows for reducing reverse-recovery of diodes and \(THD_{i}\). Nonetheless, this operation mode produces a random ripple to consider in control designing. In addition, power losses can be mitigated with snubber circuits [92; 116; 117; 118; 119; 120; 121; 122]. This topology has commonly been used in low voltage and power applications; nevertheless, in medium power applications, it is suggested to build the converter with switches (\(Q_{1}\) and \(Q_{2}\)) using GaN technology [123]. GaN switches allow for converter operation using higher switching frequencies (greater than 100 KH z) with low power losses in comparison with IGBT power switches. It should be noted that the maximum switching frequency of power switches is a function of multiple factors such as structure dimensions, duty cycle, control, technology, etc. In this way, an IGBT switch can also operate at frequencies up to 100 KHz [124]; nevertheless, IGBTs are limited by their constructive features and working at high frequencies reduces their efficiency and operating current.  
Figure 14: Semi-bridgeless topology with an internal snubber circuit.  
Figure 15: Conventional BBC or totem-pole converter: (**a**) line rectification with diodes; (**b**) line rectification with switches.  
A modification to the totem-pole topology is shown in Figure 14(b) where diodes are removed and the line rectification is carried out by means of switches (\(Q_{3}\) and \(Q_{4}\)); additionally, it is suggested that \(Q_{1}\) and \(Q_{2}\) be GaN, so that it is possible to reach high power factor and efficiency with PI control outside the design operation point [125]. In [126], a BBC with GaN switches was implemented for low voltage and medium power applications. The experimental results show efficiency of 99% considering copper losses from the converter inductor and EMI filter. In the same way, in [127], a BBC totem-pole with GaN switching was implemented for low voltage and medium power applications. The authors showed that \(THD_{i}\) complies with the normative range with classical cascade PI control, improving the PWM modulation technique. This topology can reach a great reduction of switching power losses when used with a resonant circuit [76; 128; 129; 130; 131; 132].
## 3 Results
### Osteogenic Activity  
Finally, the ability of HNS and SM surfaces to impact the osteoblastic differentiation of MC3T3-E1 pre-osteoblasts was evaluated. The cells were grown in osteogenic medium for 21 days, and the calcium deposits were then measured by Alizarin Red S staining, and the activity of alkaline phosphatase was evaluated on both the culture medium and the cell lysate.  
As it is possible to appreciate in Figure 5A,B, the HNS surface is able to determine an impressive and significant increase in the calcium deposit compared to the SM surfaces.  
Figure 4: Cell proliferation assay measured using CellTiter-GLO at 24, 48, and 120 h, measuring the luminescence through a microplate reader. Values represent mean \(\pm\) SEM. The symbol (*) indicates the statistical significance vs. HNS surface, considering a \(p\)-value < 0.05.  
Figure 5: Osteogenic activity of MC3T3-E1 cultured for 21 days in osteogenic medium on HNS or SM surfaces. Representative picture of staining of calcium deposit on titanium samples through Alizarin Red S (**A**). Quantification of optical density at 405 nm of solubilized Alizarin Red S staining with 10% acetic acid (**B**). Evaluation of ALP activity on cell lysate (**C**) and culture medium (**D**) of MC3T3-E1 by using ALP Activity Assay Kit ab83369 (Abcam). Values represent mean \(\pm\) SEM. The symbol (*) indicates the statistical significance vs. HNS surface, considering a \(p\)-value < 0.05.  
Surprisingly, the cells grown on the SM surfaces show, on the contrary, a significant increase in the activity of alkaline phosphatase both in the cell lysate Figure 5C and in the culture medium Figure 5D.
## 3 Proposed Method
### 8-Bit Brightness Extraction
#### 3.2.1 Blackbody-Based Radiometric Calibration  
Blackbody radiation is the thermal electromagnetic radiation within or surrounding a body in thermodynamic equilibrium with its environment, or emitted by a black body (an opaque and non-reflective body) [32]. This has a specific spectrum and intensity that depends only on the body's temperature, which is assumed for the sake of calculations and theory to be uniform and constant [13].  
Collecting information on the target's blackbody through the IR sensor is important for calculating the temperature value, as shown in Figure 3. First, thermal energy is collected through a thermal detector in the IR sensor. The collected thermal energy is converted to a digital count value by an the A/D converter. (In the case of a FLIR IR camera, the A/D converter with a 14-bit dynamic range is used to generate a count value between 0 and 16,383.) Second, the generated digital count is converted to a radiant energy. Finally, the blackbody temperature measurement can be obtained by applying a radiometric calibration, temperature versus radiant energy model, and emissivity of the object or scene to the radiant energy measurements.  
We can get calibration parameters (A, B) as Equations (4) and (5) from the value of \(L_{cold}\), \(L_{hot}\), \(G_{cold}\) and \(G_{hot}\) in Equations (2) and (3). In Equation (6), \(h\), as defined by the Plack Constant, is set to \(6.625\times 10^{-34}\)\([J\cdot S]\). \(K_{B}\), as defined by Boltzmann Constant, is set to \(1.380649\times 10^{-23}\)\([J/K]\). And, \(c\), as defined by the speed of light, is set to \(3\times 10^{10}\)\([cm/s]\). \(L_{cold}\), \(L_{hot}\) can be calculated through \(T_{cold}\), \(T_{hot}\) and constant values. Based on the Equations (4) and (5), A and B can be calculated through \(L\) and \(X\). \(L_{target}\) corresponding to \(G_{target}\) can be calculated based on the A and B. Finally, \(T_{target}\) is obtained in Equation (6).  
\[L_{cold}=AG_{cold}+B \tag{2}\]  
\[L_{hot}=AG_{hot}+B \tag{3}\]  
\[A=\frac{L_{hot}-L_{cold}}{G_{hot}-G_{cold}} \tag{4}\]  
\[B=L_{cold}-AG_{cold}=L_{hot}-AG_{hot} \tag{5}\]  
\[L_{T}=\int_{\lambda_{cold}}^{\lambda_{hot}}L(\lambda)d\lambda,\qquad\text{where} \quad L(\lambda)=\frac{2 hc^{2}}{\lambda^{5}}\frac{1}{e^{\frac{hc}{kT_{B}T}}-1}. \tag{6}\]  
Figure 4: Digital count versus Planck radiance.  
Figure 3: Remote hot/cold blackbody measure.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 4 Experiments
### Baseline Models  
1. GMM: The Gaussian mixture model consists of multiple Gaussian distributions as its components. Each image is associated with a Gaussian distribution, making it suitable for data spaces with multiple central distributions. As long as the model has a sufficient number of components, it can approximate any complex distribution.
2. STM: The Student's t mixture model, similar to the GMM, employs Student's t-distribution as its components. It is particularly well suited for modeling data distributions with heavy tails, as seen in natural images.
3. PixelCNN Base Model: This is a typical autoregressive model that works well for data with spatial structure. By stacking multiple masked convolutional layers, the receptive field can be expanded. In this context, the baseline model includes one type A masked convolutional layer with a kernel size of 7 \(\times\) 7, two type B masked convolutional layers with kernel sizes of 3 \(\times\) 3, and one type B masked convolutional layer with a kernel size of 1 \(\times\) 1.
## 7 Result and Discussion
### Performance Comparison  
Figure 13 shows the execution time comparison for different strategies and different numbers of agents. Execution time is the measure of how the strategy performs the service dataset, where a minimum execution time means a perfect performance. From the results,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Agents Number**} & \multicolumn{8}{c}{**Number of Generations**} \\ \cline{2-11}  & **50** & **100** & **150** & **200** & **250** & **300** & **400** & **500** & **700** & **1000** \\ \hline
20 & 0.799 & 0.774 & 0.761 & 0.746 & 0.792 & 0.905 & 0.766 & 0.771 & 0.859 & 0.779 \\
40 & 0.774 & 0.811 & 0.849 & 0.834 & 0.845 & 0.820 & 0.851 & 0.880 & 0.847 & 0.782 \\
60 & 0.881 & 0.934 & 0.876 & 0.895 & 0.874 & 0.872 & 0.910 & 0.869 & 0.849 & 0.905 \\
80 & 0.931 & 0.875 & 0.915 & 0.91 & 0.977 & 0.941 & 0.912 & 0.941 & 0.977 & 0.905 \\
100 & 0.808 & 0.909 & 0.939 & 0.939 & 0.972 & 0.925 & 0.977 & 0.888 & 0.946 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NSGA-II without Agents in different Population Sizes and Generation Sizes.  
Figure 12: Scores of different strategy agents (Ring, Master/Slave, and Hybrid).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Agents Number**} & \multicolumn{8}{c}{**Number of Generations**} \\ \cline{2-11}  & **50** & **100** & **150** & **200** & **250** & **300** & **400** & **500** & **700** & **1000** \\ \hline \multirow{3}{*}{Ring} & 2 & 0.78 & 0.884 & 0.895 & 0.911 & 0.921 & 0.943 & 0.971 & 0.973 & 0.975 & 0.976 \\  & 4 & 0.913 & 0.94 & 0.939 & 0.971 & 0.967 & 0.97 & 0.973 & 0.972 & 0.975 & 0.975 \\  & 6 & 0.912 & 0.941 & 0.949 & 0.969 & 0.971 & 0.972 & 0.972 & 0.973 & 0.975 & 0.977 \\ \hline \multirow{3}{*}{M/S} & 2 & 0.796 & 0.821 & 0.811 & 0.856 & 0.861 & 0.849 & 0.851 & 0.859 & 0.964 & 0.975 \\  & 4 & 0.931 & 0.957 & 0.968 & 0.969 & 0.972 & 0.974 & 0.976 & 0.976 & 0.976 & 0.977 \\  & 6 & 0.937 & 0.945 & 0.966 & 0.973 & 0.975 & 0.977 & 0.977 & 0.977 & 0.977 & 0.977 \\ \hline \multirow{3}{*}{Hybrid} & 2 & 0.856 & 0.895 & 0.932 & 0.868 & 0.948 & 0.975 & 0.977 & 0.977 & 0.974 & 0.977 \\  & 4 & 0.946 & 0.957 & 0.977 & 0.972 & 0.977 & 0.977 & 0.976 & 0.977 & 0.977 & 0.977 \\ \cline{1-1}  & 6 & 0.961 & 0.945 & 0.977 & 0.977 & 0.975 & 0.977 & 0.977 & 0.977 & 0.977 & 0.977 \\ \hline \hline \end{tabular} \it appears that the hybrid strategy has the minimum execution time. A practical increase of agents increased the volume of exchanged messages.
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
## 2 Deep Learning-Based Denoiser  
Driven by the easy access to the large-scale dataset and the advances in deep learning methods, there have been several attempts to handle the denoising problem by deep neural networks [30; 31; 32; 33]. In this section, we present the design of the DnCNN used for denoising and the associated training process.  
The overall framework of DnCNN is shown in Figures 2 and 3. We modify the ResNet [34; 35] to make it suitable for the reshaped signal image array denoising as shown in Figure 3. Unlike the residual network that uses many residual units (i.e., identity shortcuts), our DnCNN employs three single residual units to predict the latent clean signal. With such a residual learning strategy, the network can be easily trained and help ease optimization [35; 36].  
Generally, DnCNN has a layer structure that includes an input layer, more hidden layers, and a regression output layer. The input of DnCNN is a real mono-component chirp signal blurred by additive white Gaussian noises. We cut the observation sequences \(y(n)\) into equal-length segments at regular intervals and splicing them form lift to right in chronological order to form a two-dimensional image matrix, denoted as \(Y^{0}\in\mathbf{R}^{m\times n}\). The regression output layer computes the half-mean-squared-error loss for regression problems. The optimization goal of training is to minimize the half-mean-squared-error loss between  
Figure 3: The framework of DnCNN, Conv is the convolutional layer, BN is the batch normalization layer, FC is the fully connected layer, Tanh is the hyperbolic tangent activation function of the nonlinearity layer.  
Figure 2: Overview of DnCNN.  
regression output \(y_{1}^{\prime}(n)\) and the clean labels \(x_{i}(n)\), then the loss function for training is shown in Equation (1).  
\[loss=\frac{1}{2}\sum_{i=1}^{M}\big{(}y_{i}^{\prime}-x_{i}\big{)}^{2}, \tag{1}\]  
where M is mini-batch size, \(y_{i}^{\prime}(n)\in\mathbf{R}^{N\times 1}\ x_{i}\in\mathbf{R}^{N\times 1}\), N is the length of observation. During the training process, we use mini-batch gradient descent with Adam optimization algorithm [37] to evaluate the gradient of the loss function and backpropagate to update DnCNN weights. To effectively remove noises of observation, we need to train the DnCNN with a diverse set of training signals with different parameters and SNR conditions that cover the signal of interest. One important factor in generating the training data is the SNR. As the SNR of the test signal is unknown, DnCNN has to be trained with a range of SNR values.
## 6 Monitoring and Ensuring System Reliability
Proactive approaches, such as unsupervised learning for anomaly detection and predictive maintenance models, can anticipate failures and enable targeted interventions to prevent downtime [154; 155]. [PERSON] et al. [156] presented an innovative low-cost irrigation system employing deep learning to analyze anomalies in water usage, with autoencoders achieving 90-97% accuracy in detecting anomalies in soil moisture, air temperature, and humidity. Figure 7 illustrates various fault tolerance techniques that work together to improve the reliability of precision irrigation systems. A truly robust automated irrigation system requires a multi-faceted approach encompassing physical redundancies (e.g., sensor duplication and communication channel backup), intelligent self-management (e.g., algorithmic diversity and adaptive control strategies), decentralized architectures (e.g., controller replication and edge computing), and predictive capabilities (e.g., anomaly detection and predictive maintenance using AI/ML). By integrating redundancy, diversity, reconfiguration, and graceful degradation strategies across the hardware, software, sensing, and control domains, irrigation systems can achieve high levels of fault tolerance and reliability even in the face of unexpected disruptions. Ongoing research into resilience evaluation methods will further enable quantitative assessment and optimization of these techniques for context-specific requirements [157]. Figure 7: Fault tolerance techniques for improving reliability in precision irrigation systems. A holistic reliability framework for automated irrigation should combine component-level hardening, system-level resilience architecture, and data-driven predictive analytics. This will enable self-awareness and self-adaptation in the face of both acute faults and gradual degradation, ultimately delivering robust and uninterrupted service over the full lifecycle of these mission-critical systems in agriculture. 7 Case Studies of Application of IoT-Based Automated Solutions in Irrigation Management Applications  
To better understand the real-world implementation of autonomous irrigation, in this section, four case studies are analyzed, evaluating their effectiveness, cost, ease of use, and security considerations. These implementations demonstrate the practical application of the IoT architectures, devices, and protocols discussed in Section 3, showing how theoretical frameworks translate into functional irrigation systems. The case studies showcase the evolution of IoT-based irrigation from basic monitoring to sophisticated real-time control, providing concrete evidence of both capabilities and challenges in field deployments. Each case study is examined through the lens of the core components of an automated irrigation system, from data collection and processing to irrigation scheduling and application, highlighting both the benefits and limitations observed in practical deployments.
# High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus
## 2 Research Purpose and Research Questions  
The general objective of this work is to propose a sophisticated methodology to create a dataset of human-annotated tweets that can then be used to train machine learning models to detect online anti-immigrant speech in Mexican Spanish. This can be seen as the first stepping stone in the research project briefly presented in Section 1.1 and described in a previous communication [26]. Thus, the dataset must not only be adequate for machine learning purposes to achieve internal statistical accuracy, but it also must represent \"real\" data that can be used to conduct an extensive analysis of Mexican anti-immigrant online speech produced on Twitter between 2018 and 2022.  
More specifically, this work aims to overcome one of the limitations of a human-labeled corpus: the significant amount of time required by expert annotators to label a large corpus of examples by hand. Since our solution involves supplementing expert annotations with non-expert annotations, we raise the following question: How does implementing a control mechanism based on the kappa coefficient value affect the quality and temporal efficiency of annotations for data compared to relying solely on expert annotators? (RQ1)  
Another way to assess non-expert labeled data's reliability would be to look at their performance in the task by revising the data that they produce. Given that each of them had the same guidelines used by the experts and that they had to label a small sample containing tweets previously classified by the experts, we asked the following: Would the guidelines be enough for non-experts to recognize all categories of anti-immigrant speech identified by the experts? (RQ2) If not, what are the mislabeled categories? (RQ2A).  
It is also relevant to inquire as to whether the data obtained from a mixture of experts and non-experts represent a valid input for any learning algorithm so it can serve any machine learning practitioner to train their model (RQ3).  
Lastly, we expect that the annotation process can highlight those semantic aspects of the corpus that allude to Mexican anti-immigrant HS. In this regard, an arising question is as follows: How can we determine the existence of these aspects, and to what extent are they related to the context of our study? (RQ4).
## 4 Autonomous Trajectory Planning and Optimization  
In this section, the trajectory planning and optimization methods for coverage sample observations and ocean feature tracking are discussed. Coverage sample observation is an observation task with multiple marine vehicles working together to sample an ocean regional area uniformly. From the coverage sample, rough information observations can be obtained. After that, we focus on an ocean feature of interest, such as upwelling, or ocean front, and propose multiple vehicles combined with the NRT observation data to track the origin and boundaries of the feature.
# Malware Variants Detection Model Based on MFF-HDBA
## 2 Related Research
### Vision-Based Malware Recognition Using Machine Learning  
Malware detection methods based on machine learning contain two main parts: feature extraction and classification. Texture feature extraction algorithms are used to implement feature extraction, such as histogram of oriented gradient (HOG), PCA, scale-invariant feature transform (SIFT), and local binary pattern (LBP). Moreover, machine learning classifiers are used for classification, such as KNN, support vector machine (SVM), and naive Bayes. [PERSON] et al. [18] designed a novel feature descriptor which was more discriminating and robust. The descriptor was composed of a multi-layer LBP and dense SIFT descriptor for extracting more effective features of malware variants. Compared with the performance of state-of-the-art classification, their descriptors were outstanding. [PERSON] et al. [19] developed a novel feature descriptor named LGMP which could extract more abundant features. They extracted the local features of malware images using D-SIFT (dense scale-invariant feature transform) descriptor and the global features of the malware images via GIST descriptor. The LGMP feature vectors were generated by fusing the local feature vectors and the global feature vectors. They used LGMP descriptors for feature extraction and the KNN classifier for malware identification, achieving lower response times and better performance for malware classification. [PERSON] et al. [20] presented a novel malware classification method named SPAM (signal processing for analyzing malware). SPAM is a type of fusion model that combines vision-based and signal-based features. The method achieved good performance in both response time and classification accuracy. [PERSON] et al. [21] provided a robust, vision-based anti-malware solution against similar characteristics in malware variants. They made use of layer ensemble technology in deep random forest to improve the classification accuracy, and ultimately achieved high efficiency with low complexity.  
Above vision-based malware detection methods using machine learning is feature extraction, which is separate from classification; it results in low efficiency when faced with a large number of malware variants. Moreover, the extraction of malware image texture features relies on feature engineering, which consumes significant computational resources.
## 3 Numerical Applications  
The performance of the model is evaluated through the numerical response simulations of two real footbridges: the Pedro e Ines footbridge [18] and the Lardal footbridge [21]. Full-scale crowd pedestrian tests were performed in both, making it possible to compare the predictions of the proposed method with the results of the tests. Additionally, the lateral response of the two structures is calculated using the Hivoss guideline [12] and also compared with the proposed method.
###### Abstract  
Aim: The aim was to assess the effect of antimicrobial photodynamic therapy (a-PDT) on the oral health-related quality of life (OHRQoL) of denture stomatitis patients. Methods: Forty patients were randomly selected to participate. _Candidial_ proliferation was confirmed by using a CHROMagar culture and Gram staining. The denture surface and palatal mucosa were sprayed with a methylene blue photosensitizer prior to the photobiomodulation application. Laser therapy was applied two times a week at 72 h intervals for a period of 8 weeks. The OHIP-EDENT questionnaire was used to analyze the improvement in the OHRQoL. A Wilcoxon test was used to perform the _candidial_ colony-forming unit's count and comparison. A \(t\)-test was applied to evaluate the OHRQoL responses. Results: The overall CFU/mL values were higher in the dentures of the patients compared to a palatal mucosa swab. For instance, the CFU count was reduced from 5.56 \(\pm\) 2.15 (baseline) to 3.17 \(\pm\) 2.77 CFU/mL on day 60 on the palates. Similarly, the a-PDT application on the intaglio surface of the denture showed a reduction from 38.83 \(\pm\) 14.71 to 29.05 \(\pm\) 15.52 CFU/mL. A significant difference (\(p<\) 0.05) was found in function improvement as well as a reduction in physical pain, psychological discomfort, physical disability, and social interaction among the participants after photobiomodulation treatment. Conclusions: The OHRQoL was significantly improved in the DS patients. The _Candidia albicans_ abundance was radically reduced after the a-PDT application.  
Keywords:Gentulous; _Candidia albicans_; complete denture; low-level laser therapy; oral health impact profile; quality of life +
Footnote †: journal: Applied Sciences