esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### Collection and Maintenance of Seagrass Seedlings  
Fruits of _E. acoroides_ were collected on 12 January, 2017 on the southwest side of Barrang Lompo Island, South Sulawesi, Indonesia (S 5\({}^{\circ}\)03\({}^{\prime}\)05, E 119\({}^{\circ}\)19\({}^{\prime}\)37) where _E. acoroides_ is abundant in a depth range of 1-3 m. Annual seawater temperature range in the dry season varies in this area between 26 and 32 \({}^{\circ}\)C [40], and light intensity ranges between 100 and 250 \(\mu\)mol photons m\({}^{-2}\) s\({}^{-1}\)[41]. The ripe seagrass fruits were opened, packed in a Styrofoam box with wet cotton, and then transported to the Marine Experimental facilities (MAREE) at the Leibniz Centre for Tropical Marine Research (ZMT) in Bremen (Germany) in less than 24 h. Once at the MAREE, seeds were planted directly in polypropylene trays previously filled with silicate sediment of at least 10 cm in depth.  
The trays were kept in 250-1 aquaria filled with artificial seawater (Red Sea Salt, Red Sea Europe Company, 27130 Verneuil d'Avre et d'Iton, France) under controlled fluorescent light (200 \(\pm\) 30 \(\mu\)mol photons m\({}^{-2}\) s\({}^{-1}\), 12:12 h light:dark), temperature (26 \(\pm\) 1 \({}^{\circ}\)C), and salinity (35) conditions. Seagrass seeds were maintained under these controlled conditions for approximately 8 weeks until the experiment began. During this time, seeds developed into seedlings with both short leaves and roots (see below for average size).
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
###### Abstract  
Cylindrical multi-degree-of-freedom (multi-DOF) ultrasonic motors have the potential to significantly reduce motor size compared to other ultrasonic motors. They find applications in various systems, including micro-robot joints and space probes. This paper proposes a 3-DOF cylindrical ultrasonic motor with hybrid vibration modes. Hybrid vibration modes encompass non-standard longitudinal and bending vibrations. The structure and operating principle of the motor are described first. COMSOL Multiphysics models the stator's vibration modes, frequency response, and 3-DOF motion. A motor prototype is manufactured and characterized to demonstrate the output characteristics of the motor. The results indicate that the motor has a no-load speed of 37 rpm along the \(x\)- and \(y\)-axes and up to 77 rpm along the \(z\)-axis. The maximum output torque of the motor is 25 Nm. The motor is low in height and compact, providing a method for further reducing the stator length of motors of the same type.  
multi-degree-of-freedom; hybrid modes; cylindrical ultrasonic motor; high load capacity +
Footnote †: journal: Applied Sciences
# Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
## 4 Results and Discussion
### Implementation Configuration  
Our model outcomes are represented using the Accuracy and F1-score metrics on the CMU-MOSEI dataset and the WA and WF1-score metrics on the MELD dataset, owing to the inherent disparity among different emotions [34,36].  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{(\text{TP}+\text{TN}+\text{FP}+ \text{FN})} \tag{4}\]  
\[\text{F}_{1}=\frac{2\text{TP}}{(2\text{TP}+\text{FP}+\text{FN})} \tag{5}\]  
\[\text{WA}=\frac{\text{TP}\times\text{N}/\text{P}+\text{TN}}{2\text{N}} \tag{6}\]  
The weighted accuracy, which is identical to the mean recall across all emotion categories, provides an understanding of the model's performance considering the imbalance between different classes.  
To evaluate our model fairly and objectively using the MELD and CMU-MOSEI datasets, we employed a robust training procedure, as explained in [37]. This involved restructuring the original datasets and subsequently dividing the data into training and testing subsets (Table 1), allocating 80% of the data for training and 20% for testing.  
This methodology ensured a comprehensive and detailed evaluation of the performance of the proposed model. This procedure yielded 10,676 training samples and 2674 testing samples from the MELD dataset. Similarly, for the CMU-MOSEI dataset, 22,851 training  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Categories**} & \multicolumn{6}{c}{**Emotions**} & \multirow{2}{*}{**Percentage**} \\ \cline{3-3} \cline{5-10}  & & **Happy** & & **Angry** & **Disgust** & **Sadness** & **Surprise** & **Neutral** & **Fear** \\ \hline \multirow{2}{*}{MELD} & Train & 1846 & 1285 & 288 & 801 & 1308 & 5148 & - & 80\% \\ \cline{2-10}  & Test & 462 & 322 & 73 & 201 & 328 & 1288 & - & 20\% \\ \hline CMU- & Train & 8601 & 3680 & 3004 & 4480 & 1644 & - & 1442 & 80\% \\ \cline{2-10}  & Test & 2151 & 920 & 751 & 1121 & 411 & - & 361 & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the MELD and CMU-MOSEI datasets.  
Figure 5: Emotion data distribution of the CMU-MOSEI dataset.  
and 5715 testing samples were obtained. Unlike the methodology outlined in [38], we did not implement 10-fold cross-validation in our study. This choice was driven by the practical complications associated with deploying cross-validation on deep-learning models, considering the extensive time and computational resources required.  
The proposed model was subjected to an extensive training and testing regimen of over 300 epochs using batches of 32. To execute these deep learning tasks efficiently we used an Nvidia GeForce RTX 3090, Nvidia, Santa Clara, CA, USA, 24 GB graphics processing unit coupled with an Intel Core i7-13700K 10-Core Processor, Intel, Santa Clara, CA, USA. The Ubuntu platform was complemented by 128 GB of RAM, and this provided a robust and high-performance computational environment for model training and evaluation.
## 1 Introduction
It should be noted that good signal performance is the prerequisite of reliable precisions of Positioning Navigation and Timing (PNT). The effective analysis of signal performance provides a reference for the signal selection of high-precision data processing. In addition, atomic clocks are the key components of satellites, which provide frequency standards for ranging signals. The indicator, frequency stability, can reveal the performance of atomic clocks. Thus, this paper focuses on the signal performance, Precise Orbit Determination (POD) and atomic clock's frequency stability issues of BD3 FOC satellites, aiming at estimating BD3 performance as comprehensively as possible. The paper is organized as follows. First, the availability and collection of data are described in Section 2. In Section 3, the signal performance is analyzed in detail. POD and the atomic clock's frequency stability performance are presented in Section 4. The main conclusions are provided in the final section.
## 4 Conclusions  
In this article, river ice in the ice cover reason of the Yellow River was taken as the research target, and a definition and determination method for the RVE of the microscopic calculated strength of the river ice was proposed. A mesoscopic calculation model for river ice was constructed, and its corresponding macromechanical properties were simulated. The RVE of the calculated strength of river ice was defined considering two aspects: sample discretization and overall similarity. Based on the established RVE of the calculated strength of Yellow River ice, the uniaxial compressive strength of the river ice was simulated, calculated, and compared with existing research results. The conclusions were as follows:  
1. By summarizing and analyzing methods for determining RVE, a quantitative determination method for the RVE of the calculated strength of Yellow River ice was proposed. With the Voronoi algorithm and the ANSYS parameterized programming language, a computational microscale numerical model of the river ice was established. The corresponding macromechanical properties were simulated and the RVE size of the macromechanical strength of the Yellow River ice was quantified to be 250 mm.
2. Based on the determined RVE of the calculated strength of the river ice, the uniaxial compressive strength of the river ice under different working conditions was simulated and analyzed, and the accuracy and effectiveness of the RVE of the calculated strength of river ice were verified.
3. The determination of the RVE of the calculated strength of river ice provides another way of bridging the macroscopic and micromechanical properties of river ice and develops the basic theory and a method for analyzing the cracking process of river ice at multiple scales, from the macroscale to the microscale, thereby providing a basis and reference for analyzing the fracture process of ice covers at the macroscale.  
Conceptualization, methodology, investigation Y.D. and J.W.; methodology, data curation, validation J.Z.; software, J.Z.; formal analysis, Y.D. and J.Z.; writing--original draft preparation, writing--review and editing Y.D. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Special Funds for Basic Scientific Research of the Yellow River Institute of Hydraulic Research (HKY-JBYW-2022-08) and the National Natural Science Foundation of China (Grant Nos. 51979024 and 51879116).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Application of the Developed Classical Computer Vision Method to the Bubble Dynamics Problem
### Bubble Projection Area Estimation  
The bubble projection area is calculated by the formula \(S=\pi Rr\), where \(R\) and \(r\) are the radii of the major and minor semi-axes, obtained as a result of applying the elliptic regression described above. It should be noted that henceforth the procedure for area determining might be refined in a number of different ways, e.g., average areas between successive frames. Note that the bubble surface area can not be fully estimated from a single projection; thus, the simplest assumption would be to consider the bubbles as ellipsoids of revolution. Thus, the total contact area of the phases is calculated by approximate formulas and is either a function of the projection semi-axes for ellipses or a function of the area of a pixel cluster for irregularly shaped bubbles.  
The dynamics were investigated, namely how the number of identified bubbles and their total area change with time. In order to proceed not a single frame was considered, but the entire clip: since the video was recorded with high frequency, the adjacent frames were quite similar; therefore, not all frames of the video were considered, but every 30 th. As a result, the dynamics of the number of bubbles, although they have a large spread, are stationary. At the same time, the dynamics of the total area show a declining trend. However, there is a correlation with the number of bubbles. This might indicate that over time there are more smaller bubbles or the shooting conditions have developed in such a way that the smaller ones have started to be detected better (Figure 14).  
The dynamics represented in Figure 14 also show frames with the maximum and minimum number of bubbles. These frames are shown in Figure 15. We note that visually the first image of Figure 15 (left) has generally more medium-sized bubbles, while the second frame (Figure 15 (right)) is darker and represents bigger bubbles. This fact might cause a difference in the estimated number of bubbles, while this in its turn together with bubble motion (bubbles appear in front of the viewing window and disappear, collide and diverge, etc.) cause fluctuations as seen in Figure 14.
## 3 Proposed Scheme  
The proposed scheme for bearing anomaly identification and crack size detection in bearings using a robust smooth sliding digital twin is shown in Figure 4. Bearing anomaly identification and crack size detection using the proposed digital-twin-based algorithm consists of three parts: (a) resampling and featuring, (b) designing the robust smooth sliding digital twin, and (c) bearing anomaly identification and crack size detection using SVM. In the first step, the AE signals extracted from the bearing are resampled and its RMS characteristic is extracted. The second part is the main section of this research. At this stage, the proposed robust smooth sliding digital twin is designed. This part consists of two main sub-sections: modeling and estimation. The first sub-section focuses on modeling the indirect AE bearing (resampled RMS feature) signals using the N. The proposed autoregressive fuzzy Gauss-Laguerre algorithm for normal feature signal modeling is designed using a combination of the autoregressive algorithm, Gaussian methods to improve the power of nonlinearity modeling, a Laguerre filter to overcome the uncertain conditions, and a fuzzy algorithm to modify the power of nonlinearity signal modeling. After modeling the normal resampled RMS signal using the proposed autoregressive fuzzy Gauss-Laguerre algorithm, the signal estimation algorithm is designed using the proposed robust smooth sliding digital twin. The proposed robust smooth sliding digital twin is designed based on the combination of the proposed autoregressive fuzzy Gaussian-Laguerre algorithm for signal modeling, a high-order sliding mode technique to improve the robustness, and a fuzzy approach to overcome fluctuations arising in unknown conditions. After modeling and estimation of the AE feature signals using the robust smooth sliding digital twin, in the Section 3, the residual signals are generated and the SVM technique is used for bearing anomaly identification and crack size detection.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
## 4 Discussion  
### Chemical Composition  
In general, the DM content increased gradually with increasing MW treatment time in this study, which agrees with previous studies [14; 19]. The inside-out heating mechanism associated with MW treatment facilitated steam generation in the cell wall, which induced rapid heating, which caused, ultimately, the disruption of the cell wall leading to quick evaporation in the plant hay material [20].  
In consideration of forage type and MW treatment interaction, it was observed that CP content of canola and oat hay after 20 s and 40 s MW treatment increased by 25% and 15% compared with the control, respectively. The causes of this improvement are unknown, while other hays did not show any changes in the CP content. One possible reason could be due to the increase of detection of unavailable cell wall-bound CP during the determination of CP [21], which might become available for detection due to cell wall destruction in this study (Figures 1 and 2). Further, other feed studies suggested that exogenous protein factors (structure, content of antinutritional factor, starch and non- starch polysaccharides) and endogenous factors (cross-linkage, molecular structure, hydrophobicity) [22] could be altered by heat treatment [23], which could in turn impact on protein availability and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Equation \#** & **Sample Size** & **r\({}^{2}\)** & **SE** & **\(p\)-Value** \\ \hline \(\text{MME}_{1}=-941.2+126.1\) CP \({}^{1}\) g/kg & 15 & 0.79 & 242 & \textless{}0.001 \\ \(\text{MME}_{2}=1932-26.9\) NDF \({}^{2}\) g/kg & 15 & 0.37 & 423 & 0.01 \\ \hline \hline \end{tabular} \# Equation has been predicted based on multiple linear regression model. \(\text{MME}_{1}=\text{CP}\): MW energy input for crude protein; \(\text{MME}_{2}=\text{NDF}\): MW energy input for neutral detergent fiber.  
\end{table}
Table 2: Relationships between the minimal MW energy (MME) input for five tested forage hay to achieve maximal dry matter digestibility and organic nutrients (crude protein and neutral detergent fiber) from control hay.  
Figure 3: Relationship between crude protein (CP; g/kg) of control hays with DMD changes (%) due to MW treatment (**a**) and minimum MW energy input (MME; kJ kg\({}^{-1}\)) to achieve maximal dry matter digestibility and CP g/kg (**b**).  
utilization in animals [13]. Future work is needed to better understand how and why CP content change in response to MW treatment.  
Most of the previous research focused on MW treatment on concentrate feed (grain and meal) and hardly reported fiber fraction change of the feed. In the current study, the ADF and NDF content of different forage hays showed no difference when the interaction of MW treatment and forage type was considered. Further, the NDF and ADF content in wheat hay were not impacted by the MW treatment alone. This result agrees with [14], who found no difference in ADF content and NDF content of wheat straw treated with MW (750 W) for 240 s and 480 s.
## 4 Conclusions  
Our results show that ageing on ultrasound-treated lees improved the red base wine quality with a greater favourable impact when the ultrasound treatment was more intense. The ageing on sonicated lees at the highest amplitude enhanced the concentration of neutral polysaccharides and some volatile compounds in the wine, mainly acetates, esters and terpenes with floral and fruity aromatic notes, and reduced the chemically-assessed astringency. Regarding the sensory analysis, although no significant differences were observed among the different wines elaborated, it should be remarked that the wines aged on sonicated lees at the highest amplitude showed high scores on layer intensity, odour and flavour intensity, and low on astringency. Taken together, the application of ultrasound-treated lees from _Saccharomyces cerevisiae_ during the ageing of wines could be a promising alternative to enhance the quality of red sparkling base wines. Further research is needed to confirm these results, either using other red grape varieties or non-_Saccharomyces_ yeast lees, as well as increasing the concentration of lees in the wines.  
Conceptualization, methodology, validation and formal analysis C.B.-H., J.V.-C., J.M.R.-N., E.F.-F., R.M. and V.R; software, J.M.R.-[PERSON]; investigation and data curation, [PERSON], [PERSON], [PERSON], [PERSON] and V.R.; writing--original draft preparation, [PERSON], [PERSON], [PERSON], [PERSON] and V.R.; writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON] and V.R.; supervision and project administration, [PERSON], [PERSON], [PERSON] and V.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
We are very grateful to [PERSON] and [PERSON] for their contribution to the analytical work of this study. We also thank PROCEREALTech Group of the University of Valladolid for the ultrasound equipment and Lallemand BIO S.L. for its donation of oenological products.  
The authors declared no conflict of interest.  
Figure 3: Principal component analysis of wines and sensory data. C: Control; L: Ultrasound-untreated lees; 30, 60, 90: Ultrasound-treated at an amplitude of 30%, 60%, and 90%, respectively. Code of sensory parameters (see Table 3).
## 3 Materials and Methods
### Maturity Status Estimator
#### 3.6.2 Fuzzy Logic
It uses 81 inference rules  
Figure 14: Membership functions of RAROI input. Figure 13: Membership functions of OAROI input. obtained from using fuzzy values of interest regions. The transforming fuzzy values of classification output was carried out using Equation (13). The final output is determined by rules using \(Z_{i}\) levels of output and weight \(w_{i}\) of the rule. \[\textit{Final Output}=\frac{\sum_{i=1}^{N}w_{i}Z_{i}}{\sum_{i=1}^{18}w_{i}} \tag{13}\]  
Five fuzzy classification models were designed using the Matlab R2014a Fuzzy Logic Toolbox. Its training used a set of 50 vectors consisting of areas the interest regions in shades of green, yellow, orange, and red and their label corresponding to their maturity. The training used 10 epochs. The main difference between the models is the number of membership functions. Table 4 shows the number of membership functions used by each model and their training error. Three models were proposed where their main difference is the number of neurons in the hidden layer, as shown in Table 5; the precision of each model to predict the B \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \begin{tabular}{c} **Number of GROI** \\ **Membership** \\ **Functions** \\ \end{tabular} & \begin{tabular}{c} **Number of YROI** \\ **Membership** \\ **Functions** \\ \end{tabular} & \begin{tabular}{c} **Number of OROI** \\ **Membership** \\ **Functions** \\ \end{tabular} & \
\begin{tabular}{c} **Number of RROI** \\ **Membership** \\ **Functions** \\ \end{tabular} &
\begin{tabular}{c} **Training Error** \\ **RMSE \(\mathbf{1\times 10^{-6}}\)** \\ \end{tabular} \\ \hline Model 11 & 2 & 2 & 2 & 2 & 930.46 \\ \hline Model 12 & 3 & 2 & 2 & 3 & 479.86 \\ \hline Model 13 & 2 & 3 & 3 & 2 & 19.466 \\ \hline Model 14 & 3 & 3 & 3 & 3 & 9.8679 \\ \hline Model 15 & 4 & 4 & 4 & 4 & 2.0339 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification models. Figure 15: Operation of Takagi–Sugeno rules to classify maturity of bell peppers. 88% in identifying the four stages of maturity corresponding to the shades of green, yellow, orange, and red. The models with ANN have 100% precision to identify samples of green and red color similar to the results reported by [PERSON] et al. [41]. Of the results obtained, Model 8 was the one that presented a correlation of R = 0.79543 between the green, yellow, orange, and red regions of interest and \({}^{\circ}\) Brix. This result is similar to that reported by [PERSON] [42] and is slightly superior to those reported by [PERSON] et al. [46] (R = 0.788) and [PERSON] et al. [47] (R = 0.74). The advantage is that our proposal uses a visible RGB camera and they used a high-cost multispectral camera. Figure 18 shows the estimation.
## Materials and Methods
### Parameters Derived from Spectroradiometric Measurements
According to [36], APAR calculated this way corresponds to the PAR radiation absorbed by the entire canopy including photosynthetic vegetation (PV) and non-photosynthetic active vegetation (NPV). We consider uncertainties caused by NPV is less influential for the reliability of our results, since we expect almost no variation of NPV-PV fractions over the course of one day. However, this variation is important for seasonal studies. In addition to the fluorescence parameters, the PRI was calculated according to [8]. The PRI is a relative measure of the actual (de-)epoxidation state of xanthophylls. Theoretically, PRI values can vary between \(-1\) and \(1\) with lower values presenting an activation of non-photochemical energy dissipation (NPQ). To facilitate an easier inclusion of the PRI in our modeling framework, PRI values were linearly scaled to values ranging around 0.5 and calculated as:  
\[\mathrm{PRI}=\frac{1}{2}\left[\frac{\rho_{531}-\rho_{570}}{\rho_{531}+\rho_{5 70}}\right]+1 \tag{8}\]  
where \(\rho_{531}\) is the reflectance at 531 nm, which is strongly affected by the de-epoxidation of violaxanthin to zeaxanthin, and \(\rho_{570}\) is the reflectance at the reference wavelength of 570 nm unaffected by the de-epoxidation state. The normalized difference vegetation index (NDVI) is strongly correlated with the canopy chlorophyll content [37; 38] and was used as a proxy of the amount of vegetation that is intercepting radiation. NDVI was derived as:  
\[\mathrm{NDVI}=\frac{\rho_{780-785}-\rho_{680-685}}{\rho_{780-785}+\rho_{680-68 5}} \tag{9}\]  
where \(\rho_{680-685}\) represents the mean reflectance value in the red spectral domain between 680 nm and 685 nm and \(\rho_{780-785}\) is the mean reflectance value in the near infrared spectral domain between 780 nm and 785 nm.