esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The above information outlines the relevance of developing and implementing modern intelligent technologies for training ATCos. Modern technologies based on the automatic assessment of the ability of ATCos to respond to simultaneous tasks should analyze the speed and accuracy of their decisions as well as the effectiveness of managing aviation systems in stressful conditions. To assess the level of practical knowledge of ATCos based on their reactions in multitasking situations, it is advisable to use the tools of fuzzy set theory and neural network machine learning technologies to analyze their behavior in various situations. This will make it possible to objectively assess the level of ATCo training and improve the training and the training process, providing a personalized approach to training and correcting identified deficiencies. Adequate assessment will increase flight safety and optimize the time and resources for ATCo training.  
The main goal of the research is to develop artificial intelligence technology for assessing the practical knowledge of air traffic controller students based on their reactions and answers in multitasking situations using the proposed neuro-fuzzy model verified within the experiments.  
The following research questions are formulated from the above.  
1. Will the proposed artificial intelligence technology make it possible to identify problematic topics or simulated practical tasks to increase the quality of individual support for students in acquiring the necessary skills, knowledge, and competencies?
2. Will the developed artificial intelligence technology make it possible to automate the process of objective assessment of future air traffic controllers during training?  
These main results are useful for an individual approach to the students for understanding and mastering the correct solutions to achieve the desired educational results. The developed technology will enable the integration of automated knowledge and skills assessment systems into the real educational process and the identification of problematic topics and tasks in the training of individuals.  
The rest of this article is organized as follows. Section 2 provides an overview of the current research on the issue under consideration. Section 3 describes the formal formulation of the problem and the artificial intelligence technology used to assess [PERSON]' level of practical knowledge. In Section 4, the neuro-fuzzy model is verified and adjusted. An example of the evaluation of accurate data is given. Section 5 discusses the results of the conducted research, the advantages of the presented technology, and the research limitations. The first scientific results and ideas for future research are presented in Section 6, which concludes the study.
# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 2 Related Research  
### Previous Studies on Acoustic Abnormality Detection  
Earlier studies in acoustic abnormality detection primarily relied on conventional signal processing and statistical techniques. For example, [PERSON] et al. [8] analyzed the frequency characteristics of cough sounds by computing power spectral density (PSD) and used the extracted vectors as input for a classification algorithm. Their method involved calculating the Euclidean distance between PSD vectors and reference data, followed by fuzzy clustering to classify sounds as either coughs or non-coughs. While these traditional approaches performed well on small datasets, they exhibited limitations in scalability and robustness when applied to larger and more complex acoustic data.  
To address these issues, several studies have adopted machine learning techniques such as support vector machines (SVMs). [PERSON] et al. [5], for instance, analyzed poultry vocalizations to detect stress conditions, distinguishing between physical stress (e.g., temperature changes) and psychological stress (e.g., fear). Their work utilized features such as RMS, pitch, jitter, shimmer, PSD, and formants, and achieved a detection accuracy of 96.2% and a classification accuracy of 96.7%. These findings demonstrated the potential of using non-invasive acoustic monitoring to assess animal welfare. However, the reliance on manually selected features in SVM-based methods imposes limitations on performance and generalization, particularly when compared to deep learning models capable of automatic feature learning [13].
## 4 Analysis of Shape Factor of Wind Load
### Comparison of Shape Factors of Wind Load for Different Sizes
#### 4.3.2 Front Roof  
(1) At a wind angle of \(0^{\circ}\), the shape factor of wind load of the solar greenhouse front roof registers below 0, indicating a wind suction force in the upper region significantly surpassing that in the lower area. The absolute magnitude of the upper wind load shape factor experiences a slight reduction with increasing size by Method 2, ranging from 1% to 18% on the front roof. In contrast, for the lower front roof, the absolute value of the shape factor of wind load undergoes a slight increment with an increase in size, changing from \(-2\)% to 19%.  
(2) At a wind angle of \(180^{\circ}\), the lower section of the front roof experiences wind suction at its eastern and western boundaries, while the central region encounters predominantly wind pressure. On the other hand, the upper segment of the majority of the upper roof area was wind suction, resulting in a negative shape factor. An examination of the shape factors derived through the application of Method 2 for roof zoning reveals the following trends: The shape factors of wind load for the upper portion of the front roof remain relatively consistent across various sizes. In contrast, the shape factors of wind load for the middle area of the lower roof (from QX2 to QX4) exhibit an increase corresponding to the size increment. Markedly, the increment in shape factors of wind load for the 16 m span, compared to that of the 8 m span, reaches a significant range of 82% to 108%.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 2 Materials and Methods
### PD Data Acquisition  
A custom-made HFCT was employed to gauge the high-frequency current signal generated by the cable's PD [39]. The sensor's frequency range extended from 1 MHz to 25 MHz, and it exhibited a transmission impedance of up to 16.4 mV/mA. Figure 2 illustrates the physical HFCT along with its transmission characteristics.  
Considering that the sampling rate of the STM32F7 MCU with its own ADC that was used in this paper was up to 2.4 MHz, a signal-conditioning circuit was designed to detect and amplify the high-frequency signal output from the HFCT, so that the sampling rate requirement could be greatly reduced while basically maintaining the signal peak and phase characteristics. As shown in Figure 3, a calibrated pulse generator conforming to the IEC60270 standard [40] and modeled as a JZF-9 calibrator(Makers: Shanghai Songbao Technology Development Co; Source City: Shanghai, China) was used to output a charge of 500 pC; the raw waveform is shown in Figure 4a. After the signal-conditioning circuit, the discharge pulse signal could be broadened to the \(\upmu\)s level and the microcontroller could use its own ADC to sample it; the conditioned waveform is shown in Figure 4b.  
Figure 1: Physical diagram of the three designed discharge models. Note: (**a**) represents tip discharge, (**b**) represents flat plate discharge, and (**c**) represents ball-plate discharge model. The black disc in the middle of the top and bottom electrodes is the polyvinyl chloride insulation.  
Figure 2: Physical diagram and transmission characteristics curve of the homemade HFCT.  
To ascertain distinct characteristics of various PD types, a 0-10 KV high voltage was applied to both terminals of the discharge model, inducing PD by breaching the insulating medium. The resulting PD pulse current signal along the ground wire was then captured using the HFCT. Subsequent to signal conditioning, the signal was sampled using the STM32F769 microcontroller's integrated ADC.  
The microcontroller was equipped with three ADCs, each with a single ADC sampling rate set at 1.8 MHz. In a three-fold ADC mode cyclic-sampling configuration, the cumulative sampling rate reached 5.4 MHz, enabling the collection of 108,000 data points within a single cycle. The schematic for data acquisition is depicted in Figure 5.
# Data Traffic Modeling in RPAS/UAV Networks with Different Architectures
## 7 Conclusions  
The presented research is devoted to obtaining quantitative characteristics of RPAS/UAV traffic parameters for radio access technology to cellular networks. Models of RPAS/UAV integration are based on the ICAO recommendations. Base station data transmission channels have different architectures, for which the RPAS/UAV traffic parameters were first obtained. The dependencies of the average channel utilization on the transaction size, the effect of different bandwidth, and the effect of the bit error rate were obtainedand analyzed. A quantitative comparison of traffic characteristics in models with different topologies was carried out.  
It was discovered that the AU values practically do not change for the considered models when varying the TS parameter (from 10 bits to 10 Kbits), which means messages of bigger size can be transmitted with the same efficiency. However, for reliable data transmission the one should use a bandwidth not lower than T3 (44.736 Mbps) for all models (to avoid network load higher than 10%). At zero BER all considered models has AU values less than 8%. However, with the BER increase, the situation becomes the most unfavorable for Model 4. The obtained data allow us to judge about the provision of the required quality of service with heavy traffic and about the choice of the necessary data transmission modes for this. With appropriate communication strategies, an RPAS/UAV platform can provide a viable relay or broadcast node for high-bandwidth communications.  
Conceptualization, A.G., V.K. and S.I.; methodology, S.I. and A.G.; validation, S.I., A.G. and V.K.; investigation, A.G. and S.I.; resources, V.K.; writing--original draft preparation, A.G.; writing--review and editing, S.I.; supervision, project administration and funding acquisition, V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
###### Abstract  
Since remote sensing of ocean colour began in 1978, several ocean-colour sensors have been launched to measure ocean properties. These measures have been applied to study water quality, and they specifically can be used to study algal blooms. Blooms are a natural phenomenon that, due to anthropogenic activities, appear to have increased in frequency, intensity, and geographic distribution. This paper aims to provide a systematic analysis of research on remote sensing of algal blooms during 1999-2019 via bibliometric technique. This study aims to reveal the limitations of current studies to analyse climatic variability effect. A total of 1292 peer-reviewed articles published between January 1999 and December 2019 were collected. We read all the literature individually to build a database. The number of publications increased since 2004 and reached the maximum value of 128 in 2014. The publications originated from 47 countries, but the number of papers published from the top 10 countries accounted for 77% of the total publications. To be able to distinguish between climate variability and changes of anthropogenic origin for a specific variable is necessary to define the baseline. However, long-term monitoring programs of phytoplankton are very scarce; only 1% of the articles included in this study analysed at least three decades and most of the existing algal blooms studies are based on sporadic sampling and short-term research programs.  
Keywords:phytoplankton; ocean colour; chlorophyll a; SeaWiFS; CZCS; MODIS; MERIS; VIIRS; Sentinel; GOCI +
Footnote †: journal: Applied Sciences
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 2 System Model
### Energy Collection  
In the first stage with that interval of \(\rho T\) seconds, \(S\) transmits energy and information to \(R\), and \(R\) receives energy from the RF signal transmitted by \(S\) with a TS receiver. Then, the energy collected at \(R\) can be described by  
\[E=\eta\sum_{j=1}^{N_{\rm S}}\left|h_{\rm S,R}\right|^{2}R_{\rm S}\rho Td_{\rm SR }^{-m}. \tag{1}\]  
where \(\eta\in(0,1)\) is the energy conversion efficiency (Although the non-linear model is more practical and accurate, it has been stated that when the distance between the energy transmitter and the receiver is relatively far or the input energy of the EH module at the receiver is relatively small, the EH circuit may still work in the linear region [34]. Therefore, the research based on the linear EH model is still meaningful in many wireless scenarios. Thus, similar to many existing works [34; 35; 36], the linear EH model is also adopted to analyze the outage performance of the system in this paper.), \(N_{\rm S}>=2\) is the number of source node antennas, and \(P_{\rm S}\) is the transmit power of each antenna in the source node  
Figure 1: Two-hop relay network model with SWIPT.  
Figure 2: Time frame of the SWIPT-enabled relaying with TS protocol.  
(we assume that the transmit power of each antenna is the same, i.e., \(P_{\text{S}}=P_{\text{sum}}/N_{\text{S}}\), with \(P_{\text{sum}}\) being the total transmit power of the source node), \(h_{\text{S},\text{R}}\in[1,N_{\text{S}}]\) denote the channel coefficient of the link from each antenna of \(S\) to \(R\), and \(d_{\text{SR}}\) denotes the distance from \(S\) to \(R\), and \(m\) is the path loss index.
## 4 Discussion  
To effectively reduce NOx emission at mid- to low-load working points, an electric heater was applied at SCR upstream. During the entire WHTC bench test, the exhaust was heated before entering the EAT system to improve the activity of the SCR catalyst at a lower exhaust temperature. The simulation result is shown in Figure 13. Increased temperature has improved the activity of cold-start SCR catalyst, and the effect of thermal management on NOx emission is significant. During 200-500 s of WHTC test, after electrical heating, the average NOx emission of the LO-SCR system is 61.5 ppm, a reduction of 45% compared to the value without electrical heating before. The effect of upstream DOC is also moderately positive, as the NOx emission decreased by 63%, reaching 41.9 ppm. However, the effect of DOC will inevitably hinder the temperature increase of LO-SCR during the early stage of cold start. With higher exhaust temperature, the DOC before LO-SCR may generate NO2 in a larger proportion, which will increase the possibility of N2O generation. The moderate positive effect is insufficient to compromise these two concerns.  
Figure 11: DOC NO2 conversion rate.  
Figure 12: NOx Conversion rate with different EAT system schemes.  
From 1500 to 1800 s, there was a significant difference in nitrogen oxide emissions between the EAT system with LO-SCR and the conventional arrangement system of 1500-1600 s. The increase in average exhaust temperature caused by thermal management increases the reaction rate between ammonia and oxygen, resulting in the increase in the ammonia chemical loss. If the urea input is not adjusted after thermal management, the ammonia storage is insufficient at 1500 s, which, in turn, leads to significant NO\({}_{\text{x}}\) emission. The problem can be largely avoided by proper urea input adjustment.
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry
### Biofilm Control Methods in Food Industry
#### 3.2.2 Natural Compounds as Biofilm Inhibitors  
Various antimicrobial agents were assessed to prevent the adhesion of bacterial cells onto stainless steel surfaces via interrupting their metabolic pathways that lead to membrane damage, protein and cell wall binding, enzyme inactivation etc. [115]. It is important to say that antimicrobial and anti-biofilm formation properties must be distinguished. It was previously described that tea polyphenols did not affect planktonic growth of _Shewanella putrefaciens_ but were more effective in the inhibition of their initial attachment and the metabolic activity of the biofilm [116]. On the contrary, the most potent compounds towards planktonic cells were not always the most potent towards biofilm [117]. The efficiency of chemical compounds against biofilm formation varies depending on other environmental factors (pH, velocity of liquid, concentration etc.) and targeting bacterial species. Plant-based extracts and essential oils [118; 119], as well as individual phenolic constituents [120], are very attractive as antibiofilm-forming agents. The mode of action against bacterial cells can be antimicrobial (disrupting cell membrane), anti-QS activity (downregulate the transcription of genes involved in various metabolic pathways), altering the hydrophobicity of cells' surface, or their combined effect.
## 4 Experimental Results and Performance Analysis
unbalanced (the number of samples in one class is significantly greater than the number of samples in the other classes), accuracy becomes unreliable since it delivers an overoptimistic estimate of the classifier's skill in the majority class. The MCC provides an excellent approach for resolving the class imbalance issue. A receiver operating characteristic (ROC) curve plots the true positive rate (sensitivity) against the false positive rate (100-specificity) at various parameter cut-off points. Each point on the ROC curve corresponds to a pair of sensitivity/specificity values associated with a specific decision threshold. The ROC Area measures a parameter's ability to discriminate across distinct classes. Precision-recall curves (PRC) are frequently employed in binary classification to analyze a classifier's output. It is essential to binarize the output to extend the precision-recall curve and average precision to multiclass or multi-label classification. While one curve can be produced for each label, a precision-recall curve can also be drawn by treating each label's indication matrix element as a binary prediction (micro-averaging). Table 4 represents the precise accuracy by class using a single feature selection technique combined with LL, and its graphical representation is shown in Figure 6.
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 3 Results
### Evaluation Metrics  
We employed two key metrics to quantitatively assess our model's performance: accuracy and F1 score. This combination provides a comprehensive analysis of the model's effectiveness.  
Accuracy reflects the proportion of images correctly classified by the model out of the total number of images in the dataset. The F1 score considers both precision (proportion of true positives among predicted positives) and recall (proportion of true positives among actual positives) to provide a more balanced view of the model's performance.  
Additionally, we consider running time as a metric to assess speed. Running time refers to the average time taken by the classification model to process individual input images and generate classification outcomes. It's a critical performance metric, especially for real-time applications where efficiency is crucial.
# Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection
## 3 Mathematical Model
### Guidance Model of CAVs
The expression is:  
\[s_{\text{a}}=\frac{s_{0}+vT}{\sqrt{1-\left(v/v_{0}\right)^{4}}} \tag{13}\]  
If the calculated time of arrival is during a red light period (the downstream vehicles regard the green light period when the upstream vehicles pass as the red light period), the total section transit time is updated to the time between the arrival of the lead vehicle in the guidance area and the start of the next green light. The average target speed is calculated based on the total transit time and the length of the guidance area, and then the speed guide curve is obtained by performing the distance compensation planning calculation. The key to determining speed guidance is distance compensation planning. After the leader vehicles enter the guidance area, their distance and duration to reach the intersection are fixed. Therefore, when the CAV's speed is lower or higher than the target average speed when the lead vehicle enters the guidance area, the driving distance is compensated for by accelerating and decelerating the guidance. The target speed is determined by calculating the distance and the acceleration or deceleration duration, where the target speed must satisfy the speed limit constraint. The following assumptions were made in establishment of the tram and CAV model:  
1. The TSP strategy only considers the green extension, early green and phase insertion schemes. 2. All vehicles are connected to the Internet. The connected vehicles choose the road before the speed guidance area and only adjust their speed in the speed guidance area. Ignoring the communication delay, both trans and connected vehicles strictly obey the speed guidance scheme. 4. The connected vehicles with different driving directions travel the same distance through the intersection. 5. The influence of the slope of the road surface on the trans and the connected vehicles, as well as the additional resistance of the trans, is not considered.