esa-sceva/satcom-chunk-collection huggingface dataset:
# Reverse Circulation Drilling Method Based on a Supersonic Nozzle for Dust Control
## 6 Conclusions  
The suction performance of an optimized reverse circulation drilling method was investigated. The primary and secondary relations of various factors and flow characteristics were systematically discussed. The main findings are summarized as follows:  
1. The application of a supersonic nozzle can significantly improve the suction performance of reverse-circulation drilling. A high entrainment ratio up to 0.76 was obtained, which implied a strong suction performance for dust control.
2. The length of the divergent section (\(L_{\mathrm{s}}\)), spray angle (\(\theta\)), flushing nozzle diameter (\(D_{\mathrm{f}}\)), and center passage diameter (\(D_{\mathrm{c}}\)) had significant effects on entrainment and suction performance, while the effects of nozzle quantity and height can be ignored. The entrainment ratio decreased with \(L_{\mathrm{s}}\) monotonously. There existed an optimum value of \(\theta\) in the range of 32\({}^{\circ}\)-40\({}^{\circ}\) for better suction performance. The entrainment ratio increased with the center-passage diameter while presenting a negative relation with \(D_{\mathrm{f}}\).
3. The CFD result agreed well with experimental data with a maximum difference of 17%. The Standard \(k\)-e was not proper to describe the details of shockwave, however, it is reliable to predict the entrainment ratio to save time for numerous and complex calculations and is helpful to assisting analysis.
4. For the unequal-length divergent part, the flow with high speed tended to distribute along the longer side, which was easier to deflect. The shock train length presented a negative correlation with \(L_{\mathrm{s}}\) and a positve correlation with \(\theta\). A synthetic consideration of the effects of flow shear and mixture, flow collision, pressure, and the distributary situation is necessary.  
The combination of a supersonic nozzle and reverse circulation drilling method is likely to be a solution to the heavy dust problems caused by drilling processes. The findings above can also provide a reference to the design of other suction structures.  
The authors gratefully acknowledge the support of China Geological Survey Project (No. 12120113096900) for this work. The authors are also grateful to the reviewers for their helpful advices.  
[PERSON] and [PERSON] proposed this study. [PERSON] and [PERSON] performed the numerical simulations. [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiments. [PERSON] wrote the paper. [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
## 2 Optical Access-Metro Networks
### Scheduling and Resource Management
Two-stage buffers (tandem queue at ONU): in this scheme, a priority queue and a single first-in first-out (FIFO) queue are deployed in the first and second stage, respectively. At a given time slot, each ONU transmits traffic from the second stage, while the first stage forwards traffic to the second stage in a priority order. At the end of each time slot, the ONU reports the current occupancy of the second stage buffer to the OLT [13]. Although this scheme eliminates the light-load penalty problem, two-stage buffers increase delay for higher priority classes, _i.e._, EF traffic. 2. Credit-based slot size: in this method, the ONU predicts the amount of high-priority packets that are expected to arrive. The prediction is done for constant bitrate (CBR) flows (_i.e._, EF traffic), where the intra-ONU scheduler increases the size of requested bandwidth in its REPORT message based on the statistical history of previous cycles [14]. In [16], the so-called DBA with multiple services (DBAM) algorithm has been proposed to accommodate various types of traffic in EPONs. DBAM applies priority queuing to store EF, AF, and BE traffic. It adopts the limited bandwidth allocation scheme to arbitrate bandwidth allocation among ONUs and employs class-based traffic prediction of frames arriving during the waiting time in order to reduce delay and queue length in addition to overcoming the light-load penalty.
# Real-Time Circuit Breaker Availability Assessment in the Transmission Network
## 2 State Estimation Model
### Probabilistic Model  
The probabilistic model defines the state of CBs. Let \(X_{nm}\) be a discrete random variable that defines the state of circuit breakers n-m with the relation \(X_{nm}=1\)--breaker closed and \(X_{nm}=0\)--breaker opened.  
The probability function of \(X_{nm}\) is  
\[P(X_{nm}=1)=s_{nm} \tag{10}\]  
\[P(X_{nm}=0)=1-s_{nm} \tag{11}\]  
where \(P\) represents probability, and \(S_{nm}\) is a continuous variable ranging from 0 to 1.  
The expected variance of \(X_{nm}\) is given by  
\[Var(X_{nm})=s_{nm}(1-s_{nm}) \tag{12}\]  
If the circuit breakers status can be correctly assumed (\(S_{nm}=1\) or \(S_{nm}=0\) for closed and opened breakers, respectively), the following equation can be derived:  
\[(1-s_{nm})P_{nm}=0 \tag{13}\]  
\[(1-s_{nm})Q_{nm}=0 \tag{14}\]  
\[s_{nm}\delta_{nm}=0 \tag{15}\]  
\[s_{nm}V_{nm}=0 \tag{16}\]  
where \(P_{nm}\) and \(Q_{nm}\) represent active and reactive power flow and \(\delta_{nm}\) the n-m angle difference. If (13)-(16) are not true, the status of the breakers is not certain.
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
###### Abstract  
Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance--usually, the best performance--but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.  
artificial neural networks; phase transmittance radial basis function; massive MIMO; MIMO decoding; 5G +
Footnote †: journal: _Sensors_  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON] Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers. _Sensors_**2021**, _21_, 8200. [[https://doi.org/10.3390/s21248200](https://doi.org/10.3390/s21248200)]([https://doi.org/10.3390/s21248200](https://doi.org/10.3390/s21248200))
## 4 Satellite Images Processed  
The study area of the SUHI pattern is Milan, North Italy, with city center located at 45.47\({}^{\circ}\) N and 9.18\({}^{\circ}\) E. The summer is generally hot, with some urban areas affected by high temperatures and humidity [11]. In this study, data acquired by the MODIS instruments on the Terra and Aqua satellites were employed, passing over the Milan area four times a day. The LSTs extracted from the MOD11A1 and MYD11A1 products, with a 1-km spatial resolution and with an accuracy better than 1.0 K [12], were used to compute the SUHI of the city. Although the 1-km spatial resolution of MODIS may represent a limitation for a finer scale monitoring, the four daily passes of the sensor allow an investigation of the urban heat island at different temporal scales.  
While low-resolution satellite data are adequate for studying the large-scale SUHI characteristics, it is useful for urban planners to use also high-resolution thermal data to monitor the heat island intensity on a smaller scale, with the aim to control or mitigate the effects of the SUHI in relation to population density and land cover.  
For the high resolution image on the same area (pixel of 30 m), data provided by the Thematic Mapper (TM) instrument onboard Landsat-5 platforms were used. This satellite passes over the same area every 16 days, and therefore has a lower frequency of revisit time. The Landsat TM sensor is composed by seven bands, six of them in the visible and near infrared (TM1-5, TM7), and one band in the thermal infrared region (TM6). TM has a native spatial resolution of 30 m for the six reflective bands and 120 m for the thermal band. TM6 is also delivered at 30 m, resampled with a cubic convolution by the US Geological Survey (USGS) Center website [13]. TM data were processed according to the calibration technique proposed by [PERSON] [14], applying an atmospheric correction using the Chavez COST method [15]. The land surface temperature was retrieved from the Landsat TM thermal channel by inversion of the radiative transfer equation applied to the thermal infrared region [16].  
Then, SUHI intensity maps were produced from each LST image, computing the difference in temperature between the urban locations and the surrounding rural background. For this purpose, pixels ascribed to the urban area were temporarily masked out using the land cover map of the MODIS products, producing an image consisting entirely of rural pixels. Each rural temperature image was fit to a planar surface, and then subtracted from the LST image, obtaining the SUHI pattern.
## 2 Feasibility Analysis of Related Work
However, in the harsh acoustic environment, the method is completely ineffective, and the frequency centers of each modal component are concentrated on the main frequency components of the noise. Some scholars use neural networks to extract sound features, such as auto-encoder [19; 20], convolutional neural network (CNN) [21; 22], recurrent neural network (RNN) [23; 24], etc. However, these methods require larger training data to learn, and it is difficult to obtain significant features from the learned neural network model. Therefore, such methods are not considered in this study. At present, the Mel Cepstrum Coefficient (MFCC), wavelet transform, wavelet packet transform and LPCC linear cepstrum coefficient are widely used in trunk borer sound detection. However, these methods are less robust to noise, that is, when the acoustic environment includes interference such as additive noise, channel distortion and reverberation, the recognition accuracy of the algorithm is significantly reduced. Considering that the duration of the vocal behavior of a trunk borer is about 15 ms (as described in Section 3.1), which is about the same as the duration of a frame signal, a short-time frame should be preferred. Some state-of-the-art cepstrum coefficients are usually based on a short time frame. In recent years, the most commonly used cepstrum coefficients are PNCC and constant Q cepstral coefficients (CQCC). Both of them are new methods in the field of speech processing. CQCC focuses on speaker recognition, while Power Normalized Cepstrum Coefficient (PNCC) focuses on speech recognition in noisy environments.Therefore, PNCC is more suitable for this study. Power Normalized Cepstrum Coefficient (PNCC) [25] is inspired by MFCC and can be regarded as an improved method based on MFCC. Considering the achievements of generalized MFCC in this area, we firmly believe that this method will be suitable for this study. The results in Section 5 show that its performance is better than that of MFCC in most noise reverberation environments. However, we believe that the detection ability of this method should not be limited to this, because all the parameters and processing processes of this method are completely designed around the characteristics of human speech, not a trunk borer. Therefore, we improve this in Section 3.
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 5 TMS Measures in the Diagnosis and Evaluation of MS
### TMS Measures in Longitudinal Assessment of MS  
Previous work has demonstrated the potential for TMS to be used as a tool in the longitudinal monitoring of disease progression and efficacy of treatments and therapies. Indeed, TMS has been put forward as an important outcome measure in therapeutic trials of demyelinating therapies, a key unmet need in MS. An early study by [PERSON] et al. [64] utilized TMS to monitor a group of 27 MS patients over a 3 month period. Patients who had experienced a relapse were administered steroids, and the study found a significant correlation between shortened CMCT and those who had a decrease in EDSS score. A similar study by [PERSON] et al. [65] monitored 23 MS patients who were treated with methylprednisolone, examining them at baseline, after treatment, and 2 months later. They reported a significant association between CMCT and EDSS at each examination interval. A study by [PERSON] et al. [46] also examined a group of 30 MS patients, finding that patients after 3 months who improved clinically on the EDSS by one or more points from baseline had a statistically significant improvement in CMCT. Additionally, a recent study by [PERSON] et al. [59] followed a group of 20 MS patients over 48 weeks, where 9 of which were in the treatment group using mesenchymal stem cell (MSC) therapy as a putative treatment to suppress MS inflammation and promote remyelination. While MSC therapy failed to improve neurophysiological and functional outcomes in patients, it was found that MEP latency and CMCT had a strong, positive correlation with impaired dexterity as measured using the 9-Hole Peg Test, although only in the left hand. Overall, the longitudinal association between TMS measures (in particular, CMCT and MEP latency) and changes in disability or dexterity level in MS patients demonstrates the potential of TMS as an objective and dynamic functional correlate. As an increasing number of potential remyelinating treatments are investigated, TMS may form important endpoints in clinical trials and perhaps in clinical practice.
# Raman Spectroscopy to Monitor the Delivery of a Nano-Formulation of Vismodegib in the Skin
## Materials and Methods
### VDG Extraction from Capsules and Its Quantification  
The extraction and quantification of VDG from commercially-formulated capsules were conducted according to a previously described method [11]. Briefly, the capsules' contents were extracted using methanol (1 mL of solvent per 4 mg of Eivedge(r) through vortexing for 1 min, followed by centrifugation, yielding a recovery rate of 78.8 \(\pm\) 7.2% in the supernatant. VDG determination was performed with a Shimadzu UV-1800 UV-Visible spectrometer, using 270 nm as absorption wavelength, and a calibration curve was generated from triplicate measurements of the standard, covering a concentration range of 0.24-24 \(\upmu\)M (Y = 0.019 \(\times\) X \(-\) 0.0238; R\({}^{2}\) = 0.9917).
The contributions of this study can be listed as follows:  
Figure 1: RIS working scenario for wireless communication system blockage. * We propose a new TCN-based machine learning framework for recovering end-user data using RIS-based signal reception. We consider multiple user signals transmitted from a single base station reflected by a RIS. * We evaluate the performance of the proposed model in terms of the bit error rate (BER) and symbol error rate (SER) with two different modulation schemes, such as quadrature phase-shift keying (QPSK) and binary phase-shift keying (BPSK). * Different optimizers are employed to observe the performance variation for the model. In addition, a different system configuration is considered for testing the effectiveness of the TCN model. * For benchmarking, long short-term memory (LSTM) and without ML are taken into account. The rest of this paper is organized as follows. Section 2 represents the RIS-based communication scenario for MIMO communication, Section 3 describes the construction of the TCN model and the different features, Section 4 describes the simulation results of the RIS-enhanced communication demodulation system, and finally, Section 5 represents the conclusions.
# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 4 Shooting Results Prediction  
We conducted 55 shooting experiments, including 31 successful shots and 24 missed shots. To verify the superiority of our method, we divided the original dataset into two groups. The first set contains trajectory coordinate data for 16 successful and 12 missed shots. These data make up the training set to train the classifier. The second set contains trajectories coordinate data for 15 successful shots and 12 missed shots. These data constitute the test set to test the prediction of shooting results.  
We divided the data into training set and test set after obtaining the basketball trajectory data. Then, we preprocessed the basketball trajectories in the training set. We interpolated the Y coordinate and time t separately. We first selected different feature combinations and then trained the predictive model through a machine learning classification algorithm. We fed the data from the test set into a prediction model to draw conclusions about whether to shoot or not. Finally, we compared the results with the actual results to obtain the prediction accuracy.  
The execution of the classification algorithm determines the accuracy of the model predictions. To explore the best classification algorithm, this paper used four different machine learning algorithms to predict shooting results, including K-NearestNeighbor (KNN) [23; 24; 25; 26], naive Bayes (NB) [23; 24; 25], linear discriminant analysis (LDA) [27; 28], and classification and regression trees (CART) [29; 30; 31].  
The purpose of shooting prediction is to predict the effect of shooting in advance based on the shooting information at the current moment. This can provide basic guidance for basketball players when they practice shooting. We need to achieve faster predictions by using as few trajectory data points as possible while maintaining the accuracy of shot predictions. Therefore, we need to balance the two key factors of accuracy and trajectory length. This paper discussed the prediction results under three feature combinations: time t and coordinates (x, y), time t and distance D, and time t and altitude Y.
# Plant Growth Regulators Improve the Production of Volatile Organic Compounds in Two Rose Varieties
## 4 Conclusions  
The best sampling time was 4 weeks after application of the two PGRs. Eighty-one and 76 VOCs were obtained from flowers from both varieties after the application of BA and NAA, respectively. Of these, twenty VOCs were produced at significantly higher concentrations than the controls. The application of both NAA and BA at 200 mg/L was the optimum concentration and superior to the control and 100 mg/L treatments. Plant growth regulators play an important role in regulating plant responses, such as cell division, cell elongation and plant physiological and biochemical processes. To our knowledge, this is the first study on BA and NAA to improve the production of aromatic volatile compounds emitted from fresh rose flowers. These findings will benefit industries where there is an interest in increasing the quantity and quality of volatile compounds from plant products. In addition, four VOCs known to be precursors of fragrant compounds were increased significantly after BA and NAA treatments, these were cis-muurola-4(14),5-diene, \(\gamma\)-cadinene, premyl acetate and \(\gamma\)-muurolene. All of these compounds play important roles in aromatic plants. Consequently, BA and NAA at 200 mg/L can be used to increase and improve the quantity and quality of VOCs produced from roses and potentially other floricultural plants. Future research could explore using concentrations higher than 200 mg/L for both BA and NAA. Furthermore, future research could look at applying both BA and NAA together; this has not been done before. Together they might enhance the quality and abundance of VOCs produced.  
[PERSON] and [PERSON] conceived the project and assisted in editing; [PERSON] conducted the research, analyzed the results and wrote the manuscript; [PERSON] helped in the analysis and interpretation of results, including statistics; [PERSON] helped in the planning of the experiment, interpretation of the results and the discussion and editing of the manuscript; [PERSON] and [PERSON] contributed to the interpretation of the results; [PERSON] assisted in the writing, review and editing.  
This research received no external funding.  
We thank the Higher Committee for Education Development (HCED) of Iraq for the scholarship to the senior author. We also thank [PERSON], from Murdoch University, for the technical assistance and [PERSON] for assistance with statistical analysis.  
The authors declare no conflicts of interest.  
Figure 6: Principal component analysis (PCA) model obtained from the classification of volatile organic compounds emitted from the flowers of Floribunda (**A**) and Hybrid Tea (**B**), based on VOCs according to NAA treatments. F and H represent Floribunda and Hybrid Tea, respectively. While C (control) (red), T1 (100 mg/L) (green) and T2 (200 mg/L) (blue) represent 0, 100 and 200 mg/L NAA, respectively. Three dots in each group represent \(n=3\) biological replicates.
# A Comprehensive Review of Multimodal Analysis in Education
## 4 Applications in Education
### Monitoring Emotional and Cognitive States  
This group of papers focuses on the detection and interpretation of learners' emotional and cognitive states using multimodal signals. Applications typically involve the recognition of engagement, confusion, frustration, or cognitive overload, using facial expressions, body posture, speech, or physiological signals such as EDA. The insights derived from these data streams are used to inform adaptive interventions, improve instructional strategies, or encourage emotional awareness in educational settings.  
For example, [PERSON] [8] developed a recognition model for classroom state monitoring in music education, combining depth-separable convolution and LeNet-5 networks to identify students' engagement levels based on facial expressions and behavioural data. The model achieved a recognition accuracy of 94.12%, with a significant reduction in computational complexity compared to traditional convolutional approaches, making it particularly suitable for real-time classroom analysis. Similarly, [PERSON] [90] proposed a multimodal emotion recognition framework for English translation teaching, integrating speech and expression recognition through convolutional neural networks. This approach demonstrated a high recognition accuracy of 86.4%, supporting the design of more responsive and personalised teaching interventions based on real-time emotional feedback.