esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation  
In this section, the performance of the proposed personalized HRTF estimation method was evaluated in terms of both objective and subjective tests. For objective tests, the RMSE and LSD between the reference and estimated HRTF were measured. For the subjective test, a sound localization experiment was performed, and the distance perceived by listeners after applying the estimated HRTF to a sound source was measured. Subsequently, the performance of the proposed method was compared to those of the following other HRTF estimation methods: (1) an HRTF estimation method using average HRTF, referred to as \"Average HRTF\"; (2) the estimated HRTF by a DNN trained with anthropometric measurements in Section 2 [11], referred to as \"DNN(37) HRTF\" because there were 37 anthropometric measurements including ear measurements; (3) the estimated HRTF by a DNN trained with only 17 head and torso measurements, referred to as \"DNN(17) HRTF.\" Henceforth, the proposed method is referred to as \"CNN-DNN HRTF.\" Note that since it was difficult to obtain anthropometric measurements of the pinna for a test subject, the performance of the DNN in Section 2 was only evaluated using objective measures.  
All methods were implemented in MATLAB with a version of R2013b using Tensorflow whose version was r1.1.0 with Python 3.5.2. In this paper, 30 subjects and one subject of the CIPIC database were used for training and testing each neural network, respectively. Then, 31 cross-validations were performed and measurements were averaged over all cross-validations. Note here that the average HRTF method took an average over the HRTFs of all 31 subjects. In addition, since there were 1250 different environments (25 azimuths and 50 elevations), both the proposed method and the DNN-based methods were constructed in each different environment, resulting in 1250 CNN-DNN HRTFs, 1250 DNN(37) HRTFs, and 1250 DNN(17) HRTFs.
## 6 Conclusions  
In the present work, a conception of using an ECTMD to control a subsea pipeline structure was introduced. The feasibility experiment was performed to demonstrate that eddy current damping can be used in a seawater environment. To obtain the optimal parameters of a TMD for a damped structure, a numerical optimization method was adopted. Then the attenuation experiments were carried out to show the performance of the ECTMD in a seawater environment.  
The results of this study show that eddy current damping can be employed in a seawater environment. The additional damping by eddy currents in the seawater environment is almost the same as in the air environment. As shown from the vibration suppression experiment in the seawater environment, the damping of the submerged pipeline model is increased from 2.4% to 5.5% and the ECTMD can significantly reduce the peak response of the pipeline in a wide frequency range.  
This research work was supported by the National Key Basic Research Program of China (\"973 Project\") (Grant Number 2015 CB057702) and the Intergovernmental Innovation and Corporation Program of China (Grant Number 2016 YFE0127900). The support from the National Natural Foundation of China (Grant Numbers 51422806; 51378203) is also greatly acknowledged. In addition, the first author's research stay at the University of Houston is sponsored by the China Scholarship Council (CSC) and the second author is sponsored by the Summer Undergraduate Research Fellowship at the University of Houston.  
Figure 12: Vertical response of the pipeline model in seawater environments: (**a**) Free vibration; (**b**) resonant vibration.  
Figure 13: Vertical response of the pipeline model in a large frequency domain.  
**Author Contributions:** All authors discussed and agreed upon the idea, and made scientific contributions. [PERSON], [PERSON] and [PERSON] designed the experiments and wrote the paper, [PERSON] and [PERSON] collected the experimental and analyzed the data, [PERSON] and [PERSON] performed the optimum design of ECTMD and revised the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Classroom Expression Recognition Based on Deep Learning
## 2 Related Works
### Expression Recognition Methods
#### 2.2.2 Expression Recognition Method Based on Deep Learning  
With the increasing application of deep learning in expression recognition, many scholars have devoted themselves to studying and improving deep learning algorithms for expression recognition. Among them, [PERSON] et al. [19] extracted expression-specific features from facial images based on convolutional neural networks and explored the influence of sample presentation order. In 2014, [PERSON] et al. [20] proposed a boosted deep belief network (BDBN). The joint fine-tuning process proposed by the network can enhance the discrimination ability of the selected features. Since expression is a short-term action, [PERSON] et al. [21] proposed a three-dimensional facial expression recognition method based on the Riemann manifold kernel method. This method uses 3D data to process the inter-class and intra-class variability of human facial expressions and focuses on the variation factors in the data by focusing on the manifold-based classification. In addition, [PERSON] et al. [22] proposed a CNN method for facial expression recognition based on real-time landmarks in 2022. Since wearing a mask is indispensable during the COVID-19 pandemic, this method uses landmarks on the upper part of the face to extract facial expression features from key points of the face, uses Haar-Cascade for face detection, and completes expression recognition through the media pipeline face mesh model. In order to solve the problem of redundant information, [PERSON] and [PERSON] [23] proposed a generative adversarial network (WGAN), which extracts expression features irrelevant to user information through adversarial learning and can complete occluded expression images. In general, the expression recognition process is shown in Figure 2.  
Although the accuracy and speed of deep learning methods in facial expression recognition tasks are constantly improving, and the transfer ability is also constantly increasing, compared with traditional expression recognition technology, its accuracy has also been greatly improved after the feature extraction method has been improved. However, in the facial expression recognition task, there are still challenges such as lack of data, poor network feature extraction capabilities, and low utilization rate of extracted features. In addition, in the real classroom environment, there are still problems such as light influence and foreign object occlusion. Therefore, the problem of deep learning expression recognition is still a very meaningful research topic, which requires scholars to continue to explore and improve.  
Figure 2: Expression recognition process.
# Small Magnus Wind Turbine: Modeling Approaches
## 4 Discussion  
The goal of MPPT techniques is to achieve maximal power production at the given wind speed. An MPPT can be achieved with different control methods, including variable blade pitch, yaw control, torque control, and aerodynamic brakes. Variable blade pitch allows changing \(C_{p}\) by adjusting one of the function's parameters. This method allows for simple control; however, it also requires more complex blades with pitch control drives. Yaw control aims to reduce the rotor swept area by rotating the nacelle against the flow direction. However, this control method increases fatigue of rotor blades due to variation in load, and therefore it is rarely used for large-scale wind turbines. Torque control is one of the most straightforward and affordable techniques. It is based on varying torque on the generator, and it does not require additional mechanical components inside the rotor or nacelle to control yaw or pitch. Meanwhile, aerodynamic brakes are used to stall the turbine. This is a relatively complex control technique that requires the mechanization of rotor blades.  
In the case of MWT, the most feasible conventional control technique is torque control. However, due to the fact that lift in the rotor of MWT is generated by cylinders and depends on their rotational speed, it becomes possible to control the wind turbine by adjusting the angular speed of Magnus cylinders. This approach has a potential advantage of over-torque control because it allows for the running cylinders at optimal rotational speed to limit energy consumption.  
The model developed in this research work was used to study the power characteristics of the experimental generator. For that reason, the mechanical output power was obtained at wind speeds from 2 to 10 m/s, and results are presented in Figure 10. Thus, the power curve shown in the figure represents the maximum mechanical power extracted from the wind at a given wind speed.  
In the first region (wind speed 1-4 m/s), the wind turbine operation is not feasible due to low power output. In the second region (4-10 m/s), MPPT techniques should be used to achieve optimal mechanical output. With the increase in wind speed, the MWT reaches the third region (10+ m/s), and the rated power production should be achieved. The turbine has to be kept at a lower rotational speed in this region to ensure the rated power generation and avoid mechanical damage.  
Figure 9: Comparison of maximum torque points for different simulation techniques.
## 1 Introduction
The fusion of machine learning with time-series phenology modeling allows for dynamic yield adjustments as the growing season progresses, updating yield potential prediction insights before harvest [42]. Due to the aforementioned research gap and lack of knowledge on the effectiveness of phenological modeling in crop yield potential prediction using machine learning, the aim of this study was to evaluate the combinations of seven vegetation indices from Sentinel-2 images and seven phenology metrics for the yield potential prediction of maize and soybean. This approach also provided evaluation of frequently used machine learning algorithms for yield potential prediction and additional observations on crucial phenological stages in that prediction, providing guidance for future studies.
## 2 Materials and Methods
### Field Measurements  
#### 2.2.1 Eddy Covariance CO\({}_{2}\) and H\({}_{2}\)O Fluxes  
The measurement of CO\({}_{2}\) and H\({}_{2}\)O fluxes in the study area utilized an open-path eddy covariance (EC) system, which mainly consisted of an open-path CO\({}_{2}\)/H\({}_{2}\)O analyzer (LI-7500A, LI-COR Inc., Lincoln, NE, USA), a soil heat flux plate (HFT-3, Campbell Scientific Inc., Logan, UT, USA) at a depth of 0.05 m, a three-dimensional ultrasonic anemometer (GILL-WM, Gill Instruments Ltd., Limington, UK), a net radiometer (CNR1, Kipp and Zonen, Delft, Holland), a data collector, and power supply equipment. The instrument was mounted 10 m above the vegetation canopy and measured the molar concentrations of CO\({}_{2}\) and water vapor (H\({}_{2}\)O), as well as latent heat flux (LE, W m\({}^{-2}\)), sensible heat flux (H, W m\({}^{-2}\)), soil heat flux (G, W m\({}^{-2}\)), wind speed, wind direction, and net radiation (Rn, W m\({}^{-2}\)).  
The post-processing of the carbon and water flux measurements was conducted using Eddypro 7.0.4 software. The process included w-boost error correction [31], coordinate rotation [32], Webb-Pearman-Leuning (WPL) correction [33], the removal of turbulent fluctuations, and time lag compensation [34]. Spectral correction and mass filtering were also needed. Steady-state tests and refined turbulence tests [35] were used to check the quality of the remaining data. Following the implementation of quality control measures, the standardized approach of [PERSON], et al. [36] and [PERSON], et al. [37] was employed to address data gaps resulting from adverse micrometeorological conditions or instrument malfunctions. The EddyPro R software bundle provided by the Max Planck Institute for Biogeochemistry [38] utilized the aforementioned procedure.  
GPP and ecosystem respiration (R\({}_{\text{eco}}\)) were partitioned by the measured net exchange of CO\({}_{2}\) flux (NEE) between the wetland and the atmosphere, based on the daytime data-based flux partitioning algorithm considering the vapor pressure difference (VPD) limitation of GPP and the temperature sensitivity of respiration [39; 40; 41]. The GPP data used in this study are consistent with those of [PERSON], et al. [42]. ET is obtained by dividing the latent heat of vaporization of water (\(\lambda\) = 2.45 MJ kg\({}^{-1}\); \(\lambda\) is not a constant; it is dependent on barometric pressure and temperature, but the value of 2.45 MJ kg\({}^{-1}\) is widely used in calculations) by the LE measured by the EC system.
## 4 Experimental Results
### Performance Indices  
For objective evaluation, several performance indices are used to assess the performance of image segmentation. The recall and precision rates are widely used in many applications such as object detection, shot change detection, etc. We utilized them to evaluate the performance of the proposed scheme. The recall rate is the ratio of correct detections to the total number of pixels in the \(i\)-th class (\(C_{n}\), \(C_{p}\), and \(C_{b}\)), the precision rate is the ratio of correct detections to total number of detected pixels in the \(i\)-th class, and F1_score is the weighted mean of recall and precision for the \(i\)-th class [14; 34; 35; 36]. The definitions of the performance indices are described as follows:  
\[Recall=\frac{TP}{TP+FN}, \tag{6}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{7}\]  
\[F1\_score=2\times Precision\times\frac{Recall}{Precision+Recall}, \tag{8}\]  
where _TP_, _FP_, _TN_, and _FN_ represent the true positive, false positive, true negative, and false negative for classifying pixels into the \(i\)-th class. Theoretically, if an image segmentation scheme achieves high recall and precision rates, its performance is considered effectively. In addition, we also utilize the accuracy rate to evaluate the proposed scheme [14; 37]. The accuracy rate can be calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
As we can see in Equation (9), the higher the value of the accuracy rate, the better the scheme's performance.  
As for object detection, Mean Average Precision (mAP) [22; 23] was utilized to assess whether a scheme can simultaneously detect several kinds of objects successfully. In addition, intersection over union (IoU) [22; 23] was also adopted as a performance index to evaluate whether the proposed scheme can locate objects precisely. We expected that the higher the mAP and IoU, the better the performance.  
Figure 6: Examples of augmented psoriasis images for model training: (**a1**,**a2**) Original, (**b1**,**b2**) brightness adjustment, (**c1**,**c2**) flipping + brightness increasing + translation, and (**d1**,**d2**) flipping + brightness decreasing + translation.
## 3 Results
### Laser Power Modulation  
For a more detailed look at laser power modulation, Figure 7 displays laser power data and the melt pool size response for both beads of layer 35 for each wall. Layer 35 was selected because it contained the maximum of three step changes, and each was sufficiently long to allow the system to achieve the desired melt pool size at setpoints. In Wall 1, bead one did not contain any site-specific step changes. Figure 7 shows that the melt pool was controlled at the nominal size (2450 pixels) throughout the bead. Some increased error is evident as the print head entered the bead termination routine (at ~24 s on the figure), at which point laser power was significantly modulated to control melt pool size in a highly transient thermal condition that tested the disturbance rejection capabilities of the controller, rather than setpoint tracking performance. In bead two, three step changes were present, and laser power modulated over a range of approximately 1.5-2 kW to achieve the higher melt pool size of 3063 pixels. It is noteworthy that the laser power required to achieve the nominal melt pool size was lower for bead two as compared with bead one, by approximately 0.5 to 1 kW. This result is not  
Figure 6: Toolpath with melt pool size overlay for bead one of Wall 2.  
unexpected, in that the residual heat present in the wall after the deposition of bead one contributes to the deposition of bead two, in what can be thought of as a secondary heating or insulating effect; the inter-bead time, or the duration from laser-off of bead one to laser-on of bead two, was only 16 s. After the deposition of bead two, however, there was a longer cooling time before the deposition of bead one of the subsequent layer; the interlayer time, which included the built height scan, was 89 s.  
Figure 7: Laser power and melt pool size response for layer 35.  
In Wall 2, both beads contained site-specific step changes in melt pool size. The secondary geometry was shifted +10 mm in the \(Y\)-axis for Wall 2 to better position the leaf within the wall; this is evident as a slight shift in time of the step changes for Wall 2 as compared with Wall 1. The laser power magnitude required to achieve the nominal melt pool size in Wall 2 was remarkably similar to that of Wall 1, and again, bead two laser power trended lower than bead one laser power. Where differences arise are in the step changes. Larger modulations in laser power, in the order of 2 to 3 kW, were required to achieve the increased melt pool size. Interestingly, the largest and most distinct difference between bead one laser power and bead two laser power occurred at the two inter-step returns to nominal melt pool size, at which point bead two laser power was approximately 1 kW lower than bead one. It is believed that the residual heat contributed to the larger reductions in laser power that were required to return the melt pool to the nominal size.
# Echo Analysis in Iberian Bullfighting Arenas Through Objective Parameters and Acoustic Simulation 1
## 4 Results and Discussion
### Echo Criterion EK in the Study Cases
#### 4.2.4 Results in the Bullring of Campo Pequeno  
In this bullring, in a first approximation of the 59 source-receiver combinations in the audience area with the oculus closed in the roof, there is a risk of echo for speech in only 2 combinations: S2-R9 and S4-R1. Moreover, in a first approximation of the 24 source-receiver combinations in the audience area with the oculus open, there is a risk of echo for speech in only 1 combination: S2-R9 (Table 9). Furthermore, according to the  
Figure 16: Impulse responses in two source–receiver combinations of Las Ventas bullring (**a**) S1-E1 without the audience; (**b**) S1-E1 with the audience; (**c**) S1-R11 without the audience; and (**d**) S1-R11 with the audience. Figure 15: Speech echo: (**a**) in S1-E1, (**c**) in S1-E2. Echo music: (**b**) in S1-E1, (**d**) in S1-E2. Energy decay curve and Schroeder curve: (**e**) in S1-E1, and (**f**) in S1-E2 combinations in the bullring of Las Ventas. inspection of the time-energy curves for the combinations with the sound source on the side of the bullring S2, the strong reflection occurs within the reinforcement range of the direct sound and is not an echo. In the case of the combination for the sound source in the trumpet position, the strong reflection also occurs very close to 50 ms but is not of much interest given that it is a musical source and not a spoken source. It can therefore be observed that, in the spectator area for the performance taking place in the bullring, there are very few locations where echoes can be perceived for speech and in no case for music. In a first approximation, of the 24 source-receiver combinations with both sound-source and receiver in the ring, 12 of which are with the roof oculus closed, there is a risk of echo in 8 combinations: S1-R1, S1-R2, S1-R7, S1-R8, and S1-R13; and in S2-R1, S2-R7, and S2-R8. Of the 12 source-receiver combinations with the source-receiver both in the ring with the roof oculus partially open, there is a risk of echo for speech in 7 combinations: S1-R1, S1-R7, S1-R8, and S1-R13; and in S2-R1, S2-R7, and S2-R8, see Table 10. A detailed analysis of the strong reflection times of the signals reveals that in the combinations of the source S2 with both the closed and partially open oculus, the strong reflections occur within the 50 ms time interval relative to the direct sound and are therefore not echoes but reflections that reinforce the direct sound. Consequently, an echo occurs only when the source is located in the centre of the bullring, S1.
# A New Approach to Image-Based Estimation of Food Volume
## 2 Method
### Size/Ground Reference Localization
This allows the algorithm to estimate the checker size and, consequently, the scale factor, which is to be applied to estimate food volume. Additionally, the checkerboard can be assumed as a reference for the ground surface, whose orientation is represented by the plane having the smallest squared distance from the checkerboard corners. If we term \(n\) the vector normal to the ground plane and \(P=[p_{1},...,p_{n}]\) a \(3\times n\) matrix in which the columns are the detected corners' coordinates, the plane can be found by calculating the Singular Value Decomposition (SVD) of \(A=[p_{1}-c,p_{2}-c,...,p_{n}-c]\), where \(c\) is a point belonging the plane [26]. We set \(c\) equal to the centroid of the columns of \(P\) and set \(n\) as the left singular vector of the SVD [26]. This use of the checkerboard is valuable during 3D-modeling to extract more information about the lower parts of the object, which could be invisible in the video taken from above, as well as to remove noise. The approach requires that the checkerboard be at the same height as the food, which might raise some concerns about the practicality of the method. However, the procedure could still be useful, especially since one can estimate the average thickness of the dishes where users commonly eat and print the pattern on top of a box, in order to lift it at more or less the same level and compensate for  
Figure 5: Checkerboard model. An optimization algorithm evaluates the key points in five steps, in each of which different point sets (**a-e**) are matched to find the region which maximizes the similarity degree between the projected model and the image, as described in [12]. the thickness of the dish. The use of the checkerboard as ground-reference mainly helps to eliminate noise and outliers from the model's point-cloud. These outliers are usually located below the surface, at a very large distance from it. Therefore, the method only requires that the checkerboard be 'almost' on the same level as the dish bottom surface.