esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper focuses on the control design of a differential boost inverter when used in single-stage grid-tied PV systems. The inverter performs both Maximum Power Point Tracking (MPPT) at the DC side and Power Factor Correction at the AC side. At first, the state-space time-domain averaged model of the inverter is derived and the small signal frequency domain model is obtained using a quasi-static approximation in which the inverter is treated as a DC-DC converter with a slowly varying output voltage. Then, the controllers are designed using a three-loop strategy in which the inverter inductor currents loop is used for suitable compensation, the DC Photovoltaic (PV) voltage loop is used for MPPT and the output grid current loop is used for Power Factor Correction (PFC) and active power control. The selection of the control parameters is based on a compromise among suitable system performances such as settling time of the input PV voltage, the sampling period of the MPPT, total harmonic distortion of the output grid current, power factor as well as suppression of subharmonic oscillation for all the range of the operating duty cycle. The resulting design ensures that the oscillations of the voltage, current and power at the DC side and the grid current at the AC side are effectively controlled. The validity of the proposed control design is verified by numerical simulations performed on the switched model of the system demonstrating its robustness and fast response under irradiance variations and MPPT perturbations despite the nonlinearity and complexity of the system.  
DC-AC boost inverter; photovoltaic (PV); grid-connection; maximum power point tracking (MPPT); power factor correction (PFC) +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussions
### Classification Result of Decision Tree  
#### 3.3.1 Rule Generation of CART  
In this paper, the CART Rule Generator toolbox embedded in ENVI 5.3 was set to produce the rule for decision tree classification. Nine features discussed in this investigation, i.e., four spectral features, NDVI, EVI, NDWI, NDMRI, plus the radar backscattering coefficient, were input into the training procedure.  
To obtain training samples for the classification and validation samples for an accuracy assessment, 280 photos taken from an onsite investigation and recorded with coordinates, along with the high-resolution GF-2 RGB composite images with varied acquisition time, were applied to manually recognize samples. All samples were randomly divided as 70% and 30% for training and validation, respectively.
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 2 Materials and Methods
### Classification Methods  
Support vector machine (SVM) and artificial neural network (ANN) are effective methods for spectral data analysis. SVM, based on the statistical learning theory and structural risk minimization, is a supervised classification method which can deal with both linear and nonlinear data efficiently with good generalization ability [25; 26]. Compared with other methods, SVM has such advantages as it does not need a large number of training samples for developing a model and is not affected by the presence of outliers [27] and has been proven as a reliable and efficient method for the spectral data analysis of agricultural products [28; 29; 30; 31; 32]. Artificial neural networks have been widely used in agriculture areas, including surface inspection of fruits [17; 33; 34; 35; 36; 37; 38], since regaining their popularity in the early 1980s. By using a 3-layer feedforward backpropagation ANN model, [PERSON] et al. demonstrated the feasibility of hyperspectral reflectance imaging technique for detecting cold injury of peaches [17], and [PERSON] et al. differentiated injured apples from normal ones [36].  
In this work, SVM and ANN were used for classification experiments performed on the hyperspectral data of the jujubes. The classification accuracy of SVM and ANN models trained by using HSI data obtained by linear and rotation scan were compared respectively. The Radial  
Figure 3: The graphic user interface for pre-processing hyperspectral image data of jujube specimens.  
Basis Function (RBF) was chosen as the kernel function of SVM, and grid search was used to select the optimal penalty parameter \(C\) and the kernel function parameter \(g\). Moreover, the three-layer feedforward backpropagation model was adopted as ANN model in this study. The input layer had 435 nodes, representing the spectral responses of each jujube sample point with 435 spectral bands. The hidden layer had 100 nodes and the output layer had 7 nodes defined primarily by number of skin features. A hyperbolic tangent activation function was used as a transfer function between the input and hidden layers, while the transfer function between the hidden and output layers was a softmax function. The training of SVM and ANN models and prediction of skin defects of jujube samples were performed in the Python environment with the use of scikit-learn and Keras packages. Pixels from the linear scan dataset and the rotation scan dataset were selected respectively from the 50 randomly chosen jujube samples. Each skin feature had the same number of data. The classification accuracy of models trained using data obtained from linear scan and rotation scan were compared. The selected data were divided into a training set (70%) and a validation set (30%). The training set was used to establish classification models, while the validation set was used for evaluating the performance of models. Classification models were evaluated based on the confusion matrices of the classification performed on the validation sets.
## 4 Simulated and Measured Results
### Total Radiation Efficiency and Gain  
The measured results have confirmed the validity of the simulated total radiation efficiency and peak gain at both the Mw and MMw bands. Figure 16a shows the results for the sub-6 GHz Mw frequency band, where a small discrepancy between the simulated and measured results can be observed, but the proposed open-loop antenna (Port 5) has a total radiation efficiency between 80-85% and a corresponding peak gain of about 1.8 dBi over the frequency band. Figure 16b displays the results for the MMw frequency bands at 28 GHz and 38 GHz, where only the results for the Port 1 antenna are presented, since  
Figure 14: Scattering parameters at 0.13 THz. (**a**) Simulated reflection parameters using CST and HFSS. (**b**) Simulated mutual coupling parameters using CST and HFSS.  
Figure 15: Simulated versus measured reflection coefficient of Port 5 at 5.5 GHz.  
all ports have similar performance. The difference between the simulated and measured results is larger and is caused by the extended-length connectors. However, both the simulated and measured results show similar trends. The MIMO antenna elements have a measured total efficiency of over 73% at 28 GHz and over 62% at 38 GHz with a peak gain of 4-4.5 dBi and 4.3-4.5 dBi at the 28 GHz and 38 GHz frequency bands, respectively. Using proper connectors to excite the MMw antenna could bring the measured results to the same level as the simulated ones. The results at 0.13 THz were carefully calculated using two reliable full-wave simulators, FDTD in CST and FEM in HFSS. Figure 16c shows that both techniques produced similar results, with the total efficiency of antenna ports 1 and 3 ranging from 78-85% and 72-85%, respectively, the peak gain of FDTD results ranging from 6.5 to 6.6 dBi, and the FEM results varying from 6.35 dBi to 6.42 dBi. This demonstrates the reliability of the results at 0.13 THz.
## 3 Results and Discussion
### Calculation of Total Impulse Measurement Uncertainty
In the specific case presented in this article, and shown in Figure 3, the numerical integration yields a total impulse \(I_{t}=354.93\) N\(\cdot\)s, for \(n=11,430\), \(f_{s}=25\)\(k\)Hz, and \(u_{c\text{ } thrust}=2.9466\) N. Thus, \(u_{c\text{ } impulse}=0.0126\) N\(\cdot\)s, corresponding to 0.0035% of the total impulse. Using the same methodology as thrust to calculate the effective degrees of freedom (\(v_{eff}\)) of the combined uncertainty of total impulse, the following is discerned:\[v_{eff\ impulse}=\frac{u_{c\ impulse}^{4}}{v_{eff\ x_{1}}^{4}+\frac{X_{2}^{4}}{v_{ eff\ x_{2}}^{4}+\ldots+\frac{X_{n}^{4}}{v_{eff\ x_{n}}}}} \tag{23}\]  
For the calculated degree of freedom, and according to the table in JCGM (2008) [8], it is possible to establish a coverage factor equal to 2 for a confidence level of 95.45%. Therefore, the combined uncertainty is multiplied by the coverage factor k to obtain the estimated expanded uncertainty (\(U_{exp}\)) for the numerical integration of the thrust signal. \[U_{\exp impulse}=k\times u_{c\ impulse}=0.0252\ \text{N}\cdot\text{s}=0.007\ \% \tag{24}\]
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 5 Discussion
### Technical Challenges
#### 5.1.2 User Experience  
* Teachers' Adaptation to Multimodal Technology Multimodal technology has changed traditional teaching methods, and thus teachers have to learn new tools and platforms. Some instructors struggle to leverage these tools to their full potential because they have not been trained in them. This inhibits their ability to fully benefit from multimodal systems [70]. Hardware failures and computer glitches lead to added workloads for instructors and compromised teaching effectiveness [71,72].
* Students' Acceptance of Personalized Learning Pathways Multimodal technology facilitates the development of personalized learning pathways by adapting instructional content to students' individual learning styles and progress. However, students' acceptance of such personalized learning approaches varies. While some learners prefer self-directed learning and customized educational content, others may be more inclined toward traditional teacher-led instruction [73]. Furthermore, designing personalized learning pathways necessitates a balance between self-regulated learning and teacher guidance to ensure an optimal and structured learning experience [64].
* Complexity of Human-Computer Interaction Multimodal systems support different input modes beyond speech, including touch interfaces, gestures, and facial recognition. Users must switch between these modes to interact with the system. Some users face problems with usability. Common issues include complex interface design, system delays after input, and low recognition accuracy [74]. High error rates also reduce user experience. Inaccurate speech recognition can cause unintended actions, and poor gesture recognition can disrupt classroom teaching. A major challenge in multimodal instructional systems is maintaining a good user experience while keeping the system easy to use [27].
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 4 Analysis of Discharge Risk Coefficients of Overhead Conductors Under Different Icing Conditions
### Effect of Uneven De-icing on Safety Distances  
External factors, such as temperature and wind direction combined with the structural characteristics of transmission lines, can lead to localized de-icing. This localized de-icing causes oscillations and complicates the line due to the traction effect of the de-iced area. Using finite element simulation, this study modeled uneven de-icing by sequentially removing ice loads from various sections, adjusting de-icing positions, and analyzing changes in parameters and risk intervals (Tables 5 and 6).  
The de-icing area of the conductor does not affect the maximum sag of the ground line. Due to energy conservation, the maximum sag of the conductor also remains constant. As the de-icing area increases from 150 to 300 m, the conductor's bounce height rises from 5.455 to  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**De-icing Area**} & \multicolumn{1}{c}{**Risk Range**} \\ \hline \hline \multirow{4}{*}{\(\begin{array}{c}\text{D-}\text{L}  
16.140 m, and the minimum spacing between the conductor and ground line decreases from 9.262 to 2 m. At 300 m de-icing, a risk zone of 115 m (accounting for 28.75% of the total length of the line) emerges, concentrated on the left side of the line. When de-icing occurs under other conditions, if the minimum spacing of the guide wire is 2.708 m, there is no discharge risk. Generally, a de-icing area closer to the line results in a higher bounce height and a smaller minimum spacing of the guide wire.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### CharBiGRU Embedding  
After the character tokenization process, each of the characters is then mapped to embedding vectors using a lookup table or embedding layer, capturing the semantics of individual characters. The character embeddings are passed through a BiGRU layer, which processes the character sequence in both forward and backward directions to capture contextual dependencies among the characters. CharBiGRU embedding can be highly effective for URL classification due to their ability to capture both the structural and contextual nuances at a character level. URLs often contain strings that do not follow standard language patterns, such as abbreviations, concatenated words, numbers, special characters, and domain-specific jargon. Traditional word-based models may struggle with this kind of input since URLs often contain many rare or out-of-vocabulary tokens and its ability to process text at the character level, combined with its deep contextualunderstanding at the word level, allows it to generalize better and handle diverse URL formats. It commonly used in natural language processing tasks, particularly when dealing with out-of-vocabulary (OOV) words or when fine-grained representations of words are required. The BERT embedding has the limitation of OOV and sub-word embedding. Therefore, to alleviate this issue, we used CharBiGRU embedding to capture sub-word-level and char-level information and create more expressive char representations. Instead of treating words as atomic units, CharBiGRU embedding breaks down each word into its constituent characters and represents them using character-level embedding. This allows the model to capture morphological and structural information within words.
## 3 Results and Discussion
### Impact of Agricultural Droughts on Grain Yield
Some studies projected a progressive increase in climate threats in Europe, mainly driven by a trend toward more likely and severe extreme soil drought events in central Europe under future warming scenarios [16; 42]. The detrimental effect observed for wheat and barley yields in Spain due to the decrease in SM may be an indication of what may happen to areas where soil water content was not previously a crop constraint. In Germany, an area where SM was not historically a limiting factor, an observed and significant reduction trend in SM reported in recent years is starting to cause reductions in crop yields that, according to future projections, could increase. In fact, this likely hypothesis is reinforced by a recent study [143] conducted for the period 1980-2100, in which a widespread shift in the ecosystem from energy- to water-limited conditions was found, which the authors attributed to global warming. This finding warns against a possible increase in the frequency of agricultural drought events and consequent yield loss in cereals, such as that already detected in this study for Germany in the last decade.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### China Satellite
The precision of its XCO\({}_{2}\) measurements is expected to be less than 1 ppm, which effectively improves the spatial and temporal coverage and global detection capability. It is intended to verify satellite data using ground-based measurements from the Total Carbon Column Observing Network and EM27/SUN measurements for optimizing parameters in satellite sampling. The summary of typical shortwave infrared satellites and sensors is presented in Table 2 below.
## 5 Conclusions  
This study developed a quaternary industrial solid waste (ISW)-based cementitious material composed of steel slag (SS), granulated blast-furnace slag (GGBFS), phosphorus slag (PS), and carbide slag (CS) for the stabilization of aeolian sand in desert regions using a four-factor simplex lattice experiment (SLM) based on the three chemical moduli (TCM) theory referenced standard cement. The optimal proportion of ISWs is determined, and the solidification effects are evaluated in terms of mechanical properties, hydration product levels, and microstructure. The main conclusions obtained are as follows:  
1. The results of the simplex lattice point experiment indicate that an increase in the dosage of GGBFS has a significant positive effect on the _UCS_ of stabilized aeolian sand due to its highest pozzolanic reactivity. Conversely, higher SS incorporation reduced _UCS_. Considering the workability of the cementitious material in conjunction with the TCM theory, the optimal proportion of ISWs was determined to be 5:35:20:40, and the _UCS_ of aeolian sand stabilized using ISWs with optimal proportion was comparable with that stabilized by OPC.
2. The _UCS_ of stabilized aeolian sand exhibited a linear positive correlation with its elastic modulus but a negative correlation with ductility. The failure mode of stabilized aeolian sand specimens was related to their strength: specimens with higher strength exhibited pronounced inclined shear cracks upon failure, while specimens with lower strength mostly displayed splitting cracks.
3. The abundance of hydration products such as C(-A)-S-H gel and ettringite was the primary reason for the high strength of stabilized aeolian sand with the optimal proportion. A higher content of CS can increase the level of Ca(OH)\({}_{2}\) in the system, leading to an increase in calcite formed through carbonation. However, an unreasonable proportion results in insufficient gel in the system, causing an incomplete particle encapsulation and weak connections between particles, which can be disrupted by the expansion of crystals such as ettringite and calcite, leading to a porous and loose microstructure and thus reducing the strength of stabilized aeolian sand.  
**Author Contributions:** Conceptualization, Z.X., B.Y. and Z.Q.; methodology, Z.X. and Z.Q.; software, Z.Q.; validation, H.W. and Y.Q.; formal analysis, Z.X.; investigation, Z.X., H.W. and Y.Q.; resources, Z.Q.; data curation, B.Y.; writing--original draft preparation, Z.X.; writing--review and editing, Z.Q. and B.Y.; visualization, Z.X.; supervision, Z.Q. and H.W.; project administration, Y.Q.; funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42372318).  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
## Materials and Methods  
### Reagents and Honey Samples  
#### 2.1.1 Reagents  
The ionic liquid 1-ethyl-3-methylimidazolium dicyanamide (EMIMDCA, purity \(>\) 98%) and bovine skin gelatin type B were acquired from Sigma Aldrich (St. Louis, MO, USA). Tetrahydrate iron (II) chloride (FeCl\({}_{2}\cdot\)4H\({}_{2}\)O, purity \(>\) 99%) was obtained from Vetec (Duque de Caxias, RJ, Brazil). Iron (III) chloride hexahydrate (FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, purity \(\geq\) 99%) was obtained from Acros Organic (Geel, Antwerp, Belgium). Ammonium hydroxide and organic solvents were purchased from Labsynth (Diadema, SP, Brazil). The interdigitated electrodes were manufactured by Micropress S.A. (Sao Paulo, SP, Brazil) with a 0.6 cm\({}^{2}\) interdigitated area, 200 um of spacing between the copper digits, and 100 um of spacing between the nickel digits covered with 0.05 um of gold.
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 2 Materials and Methods  
### Database  
The SVD, which was developed by the Phonetics Research Institute at Saarland University, Germany [12], contains more than 2000 voiced samples of sustained /a/, /i/, and /u/ vowels and the sentence \"Guten Morgen, wie geht es Ihnen?\". All recordings are sampled at 50 kHz, and their resolutions are 16 bits. The lengths of the speech files with sustained vowels are between 1 and 3 s. However, the dataset consists of recordings of the /a/, /i/, and /u/ vowels from 687 normal voices and 1354 pathological voices with one or more of 71 different pathologies. Table 1 shows the dataset information regarding the SVD utilized in this study. Although the numbers normal and pathological voice samples are imbalanced, they are balanced after oversampling proposed in this paper. Then, the pathological samples are the same for the two datasets, and the 687 normal samples appear also in the balanced set.
## 5 Discussion
News articles encompass a vast array of subjects, from politics and economics to sports and entertainment. Efficient topic categorization acts as a navigational aid, enabling readers to access articles of interest. By categorizing news articles into distinct topics, users can easily locate and access relevant information, streamlining the process of information retrieval amidst the influx of available content. In addition, topic categorization offers a structured framework for organizing news articles, enabling media outlets and platforms to present content in an organized and coherent manner. Thus, analyzing trends and patterns within news articles is essential for understanding public sentiments, societal shifts, and emerging issues. Recently, machine and deep learning-based approaches for topic categorization have been widely used, and their performance is comparable to that of human understanding [32]. This study used machine learning approaches rather than deep learning-based ones to understand how each morphological analyzer extracts meaningful information step by step. This study also investigated how each morphological analyzer affects the categorization performance for news articles. The results revealed relatively comparable performance for the categorization of news articles across the five different morphological analyzers, and it was determined which morphological analyzer works best for topic categorization in news articles, especially for those written in Korean. As with any scientific study, this study has limitations. First, only the KLUE dataset was tested for the topic categorization task; thus, future work should consider different datasets to verify the findings of this study. Second, only the classification task was performed; thus, other NLP tasks, such as text summarization or question and answer, should be tested in future work. Third, this study focused on the dataset of Korean news articles. Such a narrow scope can limit the generalization to a broader range. In future work multiple languages will be incorporated into the research to facilitate a more comprehensive understanding of linguistic features and cultural differences. Overall, this study suggests how each morphological analyzer is used for Korean text and what news topics are classified well, regardless of the morphological analyzer.