esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Kinetics of the Acid Hydrolysis and Auto-Hydrolysis Step
Values are reported as mean values (\(n=3\)) \(\pm\) sd. Regarding the sugar degradation products, [PERSON] and [PERSON] [62] reported that under severe pretreatment conditions, such as long reaction time, high temperature or acid concentration, HMF is further degraded to levulinic and formic acids. Moreover, as HMF, furfural can further degrade to formic acid. These aspects are in good agreement with the data reported in Table 6. Indeed, furfural increases until 30 min and then remains constant, while 5-HMF increases and then decreases in cases of acid hydrolysis, because of its conversion into levulinic acid and formic acid (which, in fact, appears after half of the reaction time) or because of its reduction to 5-MF which constantly increases until 30 min. The phenolic components showed different behaviors depending on the hydrolysis type and the compound. For all the detected compounds (vanillin, syringaldehyde, ferulic acid and p-coumaric acid), the auto-hydrolysis allowed for higher concentration, in agreement with the results from total phenols and antioxidant power. Since the liquid to solid ratio was double in the auto-hydrolysis, the yields were even higher suggesting a higher straw delignification. As for the sugar degradation products, the concentrations of all the detected phenolic compounds of the auto-hydrolyzed liquor increased over time, in contrast to acid hydrolysis, showing the progress of all the involved reactions: lignin solubilization, hemicellulose hydrolysis, sugars degradation. In the auto-hydrolysis, the concentrations increased over time indicating a progressive release of the compounds, while in the acid hydrolysis, it looks like a certain amount is released immediately and then the compounds degrade over time (or the degradation rate is higher than the release rate). Vanillin is the only exception, showing a slight increase also in the acid hydrolysis, probably linked to progressive solubilization of the acid soluble lignin fraction, since it is one of the most relevant lignin degradation products. Finally, acetic acid, which comes from the hydrolysis of the acetyl groups of hemicellulose, is another acid found in the liquor [62]. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Time [min]**} & \multicolumn{2}{c}{**Acid Hydrolysis**} & \multicolumn{2}{c}{**Auto-Hydrolysis**} \\ \cline{2-5}  & **D-Glu [g/L]** & **D-Xyl [g/L]** & **D-Glu [g/L]** & **D-Xyl [g/L]** \\ \hline
0 & 1.64 \(\pm\) 0.24 & 12.92 \(\pm\) 0.17 & 0.06 \(\pm\) 0.2 & 0.02 \(\pm\) 0.01 \\
10 & 2.02 \(\pm\) 0.10 & 13.51 \(\pm\) 0.25 & 0.11 \(\pm\) 0.2 & 0.43 \(\pm\) 0.05 \\
20 & 0.86 \(\pm\) 0.01 & 15.11 \(\pm\) 0.49 & - & - \\
30 & 0.93 \(\pm\) 0.06 & 15.76 \(\pm\) 0.57 & - & - \\
45 & 0.96 \(\pm\) 0.02 & 15.99 \(\pm\) 0.54 & - & - \\
180 & - & - & 0.14 \(\pm\) 0.3 & 0.32 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table} 
Table 5: Xylose and glucose concentration variation during the conventional acid hydrolysis and the auto-hydrolysis. Values are reported as mean values (\(n=3\)) \(\pm\) sd.
# Research on Influence of Tank Sloshing on Ship Motion Response under Different Wavelengths
## 4 Simulation of Pitching Motion of Liquid Carrier in Head Sea Condition
### Influence of Liquid Loading Rate on Pitching Motion of Ship  
Cases 1-4 in Table 3 explore the effect of the liquid loading rate on the pitching motion of the ship under the same incident wave wavelength (\(\lambda/L=1.15\)). Figure 8 compares the time history of the ship's pitching motion when the liquid loading rate is 0, 0.8, 0.9, and 0.98.  
It can be seen from Figure 8 that under the condition of the same incident wave wavelength, the changes of the pitch angle of the ship with different liquid loading rates are basically the same. By observing the calculation results of a certain period, it can be seen that with the increase of the liquid loading rate, the pitch phase of the hull slightly shifts to the right. This is mainly due to the superposition of the moment generated by the sloshing of the tank with the external wave moment, which changes the phase of the original moment. However, due to the small sloshing moment of the tank, the contribution to the phase offset is limited, so that the phase of the pitch motion of the ship is only slightly offset. Figure 9 shows the liquid level change in the tank during pitching motion of the ship during one cycle when the liquid loading rate is 0.8. Figure 10 shows the pressure change in the tank.  
In general, since the sloshing moment of the tank is much smaller than the external wave moment, the change of the liquid loading rate has little effect on the pitching motion of the hull. Therefore, it can be said that the effect of tank sloshing on the pitching motion of the hull is not significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Case**} & **Wavelength** & **Wave Height** & **Degree of Freedom of** & \multirow{2}{*}{**Loading Rate**} \\  & **\(\lambda\)/m** & **H/m** & **Hull Movement** & \\ \hline
1 & 7.015 & 0.092 & pitch & 0 \\
2 & 7.015 & 0.092 & pitch & 0.8 \\
3 & 7.015 & 0.092 & pitch & 0.9 \\
4 & 7.015 & 0.092 & pitch & 0.98 \\
5 & 2.0 & 0.092 & pitch & 0.8 \\
6 & 42.52 & 0.092 & pitch & 0.8 \\ \hline \end{tabular}
\end{table}
Table 3: Calculation condition settings in head sea condition.
## 1 Introduction
[33], which can predict and generate emotions, further demonstrates the broad application prospects of multimodal sentiment analysis. In the process of delving deeper into the field of multimodal emotion recognition, we have discovered that integrating information from different modalities often leads to more precise and comprehensive analysis results. Especially in the current flourishing landscape of deep learning technologies, some advanced model architectures provide powerful tools for processing both speech and text data. Based on this, we propose a multimodal MMER-TAB model focusing on two modalities: speech and text. Building upon the previous research, we have made improvements to the existing MMER model. While MMER has achieved significant results in some respects, it also has some limitations that motivate our work. Firstly, the MMER model faces challenges in handling long-term dependencies and global context. To address this issue, we introduce a new structure consisting of three layers of TAB modules combined with attention mechanisms to enhance the modeling capability of the model. These improvements enable our model to better capture the long-term dependencies and global contextual information in speech signals, thereby improving its performance and robustness. The main contributions of our study are as follows:  
* We particularly focus on two Transformer-based feature extraction methods, namely Wav2 vec and BERT (Bidirectional Encoder Representation from Transformers) [34]. Additionally, we investigate the TIM-Net [35] model and make corresponding improvements to better adapt it to our application scenario in multi-modal emotion recognition tasks. * We introduce a multi-head attention mechanism, which accurately captures significant emotional features from both speech and text, thereby enhancing the model's sensitivity and capability to capture emotional information. This innovation gives our model a competitive edge in emotion recognition. * To validate the effectiveness of our proposed model, we conduct extensive experiments on the IEMOCAP [36] and MELD [37] datasets. The experimental results demonstrate that our MMER-TAB (Multi-Modal Emotion Recognition-Temporal-Aware Block) model exhibits outstanding performance in multi-modal conversation emotion analysis tasks. This not only confirms the effectiveness of the feature extraction methods and model improvements we have adopted but also provides new insights into and methods for research in the field of multi-modal emotion recognition. The rest of the paper is structured as follows: Section 2 introduces the feature extraction methods for the speech and text modalities, respectively. Section 3 presents the TIM-Net network architecture and attention mechanism. Section 4 elaborates on the proposed model's structure. Section 5 discusses and analyzes the experimental results. Finally, Section 6 provides a summary of the paper.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 4 Discussion  
This section introduces a comparative study of three temporal modeling techniques--RNN, DRRN, and DBRRN--applied to three different datasets: Vid 4, SPMCS, and UMD10. We also presents the trade-off between runtime and accuracy in Table 1.  
The gathered quantitative and qualitative results are summarized in Table 1. In this table, we also highlight the balance between the execution time and accuracy of the models. Notably, the DRRN and DBRRN models demonstrate superior computational efficiency relative to the RRN-based methodologies, attaining pleasing outcomes with fewer parameters. The runtime of the DRRN is 15 ms longer than the RNN.  
Figure 3 provides a visual comparison of the impacts of RNN, DRRN, and DBRRN. The comparison vividly illustrates that both DRRN and DBRRN effectively capture the prominent image features, yielding superior outcomes in contrast to their low-resolution equivalents. The outcomes detailed in Table 1 demonstrate that although DRRN and DBRRN do not exhibit a substantial performance improvement when compared directly to RNN, they do significantly reduce the computational requirements and execution duration.  
Figure 4 illustrates the PSNR and Loss values of the three models on the Vid 4 dataset. A slight improvement of DBRRN over DRRN is noticeable, although the difference from RNN is marginal. However, as discernible from Figure 3, this minor difference does not significantly affect the perceptual quality of the output.  
Table 2 provides an overview of the experimental outcomes when using the DBRRN model on the SPMCS and UMD10 datasets. It is noticeable that DBRRN's performance varies across different categories. Specifically, the model performs well on 'car', 'hk', and 'jvc' categories, while demonstrating suboptimal results on 'hsdclub' and 'hitachi_see'. This discrepancy might be associated with the original image's clarity. f. Therefore, considering the quality of the original video dataset is crucial when applying this model.  
Figure 3: Qualitative comparison of the SPMCS datasets.  
In summary, all three models exhibit competent performance in super-resolution tasks. DRRN and DBRRN models offer a promising compromise between accuracy and computational efficiency, making them ideal for applications with limited computational resources. Further investigation into these models may yield ways to enhance their performance.  
In order to compare the difference in user experience between three models, we invited more than thirty people to experience games with different models. Table 3 shows that users in the DBRRN score higher. Compared with RRN, the average score is 0.2, but the visual subjective evaluation is not much different.
## 4 Discussion
### Changes in Muscle Strength Due to Hypoxia  
Strength training in hypoxic conditions is a new horizon for training programmes to improve strength capacity, as proposed in some studies [21]. Furthermore, some contrasting evidence suggests that strength training under hypoxic conditions has or has not greater benefits than under normoxia [2]. Consequently, these systematic review results are inconclusive, regardless of suggesting a significantly greater benefit of hypoxia than normoxia. Anecdotally, compared to hypertrophy variables, in strength, almost all the studies reported a positive change, and only one study presented a slightly greater improvement in the control group [21]. Furthermore, some significant improvements (\(p<0.05\)) were found in the hypoxia group in some studies [6; 16; 18; 20; 23; 32].  
Methodological consistency was found in the hypertrophy-selected variables. Regarding strength variables results, in some studies, a higher percentage of change was found [15; 18; 22; 23; 30; 32]. These studies shared the duration of the intervention (4-8 weeks) [15; 18; 22; 23; 30] and reported a load intensity of 50-70%, considering a low-moderate load. Additionally, increased strength was reported when a low training load was applied under hypoxic conditions [22]. This can also be noted when intensities of 85-90% of 1 RM were used [21].  
The physiological mechanisms caused by the low load in strength training are not entirely understood. Type I muscle fibres are thought to fatigue earlier in a low-load, hypoxic environment and are highly recruited. The recruitment of type II fibres can cause growth hormone alterations and testosterone because of hypertrophy and muscle strength [19; 28].  
Finally, regarding the training volume, in those studies reporting the highest percentage of change, a total of three to four series and six to fifteen repetitions were programmed Falta algo. [15; 18; 22; 23; 30]. Another study used three sets to failure, and the results had a small impact on the hypoxia group compared to the control group [19]. In addition, there was a small impact on the percentage of change between groups, using >25 repetitions [17]. Concerning the body segments used, the highest rate of change was found in those studies where different body segments were involved [15; 18; 22; 23; 30; 32]. There is no clear methodological consistency among studies, and no evidence-based recommendation can be given based on this review at this time. Based on particular data, strength training under hypoxia could be performed using three to four series, six to fifteen repetitions and large muscle groups.
## 3 Spherical Robotic Positioning Device
### Inverse Kinematic Analysis  
A base coordinate and a moving coordinate, \(\mathsf{O}(x,y,z)\) and \(\mathsf{P}(u,v,w)\), are assigned to the ground and the stimulator, respectively, as shown in Figure 2. In this study, for analyzing inverse kinematics, two vector loops are employed. The first vector loop, which passes through the points \(\mathsf{O}\), \(\mathsf{P}\), and \(\mathsf{Q}\), is the transformation (\(T_{1}\)) including information related to the desired posture. The second vector loop is the transformation (\(T_{2}\)) through the actuators from the origin (\(\mathsf{O}\)) to the point \(\mathsf{Q}\). When the position vector of the stimulator (\(\mathsf{P}=\left[P_{x}\,P_{y}\,P_{z}\,\right]^{T}\)) and its rotation matrix \(R_{\mathsf{P}}\) are given, the transformation matrix \(T_{1}\) is obtained as follows:  
\[\mathbf{T}_{1}=\mathbf{D}_{\mathsf{P}}R_{\mathsf{P}}\mathbf{D}(-u,r_{s}), \tag{1}\]  
where \(\mathbf{D}_{\mathsf{P}}\) is the transformation matrix of translation from the origin to point \(\mathsf{P}\), and \(\mathbf{D}\) (axis, value) is the transformation matrix of translation along a particular axis. Then, the transformation matrix (\(\mathbf{T}_{2}\)), which is composed of the input values (\(\theta_{i}\)) of the revolute joints and the input value (\(d\)) of the prismatic actuator, can be expressed as  
\[\mathbf{T}_{2}=\mathbf{R}(x,\theta_{1})\mathbf{R}(y,\alpha)\mathbf{R}(x,\theta_{2})\mathbf{R}(y, \beta)\mathbf{D}(-x,r_{d})\mathbf{D}(x,d)\mathbf{R}(x,\theta_{3})\mathbf{D}(z,\theta_{4})\mathbf{R }(y,\theta_{5}), \tag{2}\]  
where \(\mathbf{R}\) (axis, value) is the transformation matrix of rotation along a particular axis. In Equations (1) and (2), \(r_{d}\) and \(r_{s}\) are the distances between point \(\mathsf{O}\) and the initial position of the prismatic joint and between point \(\mathsf{Q}\) and the stimulation focus (\(\mathsf{P}\)), respectively. \(\alpha\) and \(\beta\) are the design angles of the spherical frames. Because each component of \(\mathbf{T}_{1}\) and \(\mathbf{T}_{2}\) should be equal, the desired values (\(\theta_{i},\ d\)) of each joint are derived as follows:  
\[ \begin{array}{l}\theta_{1}=atan2\Big{(}\frac{P_{x}-r_{s}\mathbf{R}_{\mathsf{P}} (3,1)}{\mathbf{R}},\frac{P_{x}-r_{s}\mathbf{R}_{\mathsf{P}}(1,1)}{\mathbf{R}}\Big{)}-atan2 \Big{(}g_{\beta}g_{0_{2}},c_{\beta}s_{\alpha}+s_{\beta}c_{\alpha}c_{\theta_{ 2}}\Big{)},\\ \theta_{2}=acos\Big{(}\frac{Kc_{\alpha}c_{\beta}-r_{s}\mathbf{R}_{\mathsf{P}}(2,1) +P_{y}}{s_{\alpha}s_{\beta}\mathbf{K}}\Big{)}d=r_{d}-K,\ \theta_{3}=acos(A),\\ \theta_{4}=-asin(B),\ \text{and}\ \theta_{5}=asin(C),\end{array}  \tag{3}\]  
where \(c\) is \(cos\) and \(s\) is \(sin\), \(K=\sqrt{\left(P_{x}-r_{s}\mathbf{R}_{\mathsf{P}}(2,1)\right)^{2}+\left(P_{y}-r_{s} \mathbf{R}_{\mathsf{P}}(2,1)\right)^{2}+\left(P_{x}-r_{s}\mathbf{R}_{\mathsf{P}}(3,1) \right)^{2}},\)  
\[
## Materials and Methods
### Method Validation
#### 2.5.2 Linearity, Limit of Detection (LOD), and Limit of Quantitation (LOQ)  
Calibration curves of berberine, palmatine, geniposide, and paeoniflorin stock solutions were plotted over the concentration ranges 2.50-25.00 ug/mL, 2.50-25.00 ug/mL, 2.50-25.00 ug/mL, and 2.50-25.00 ug/mL, respectively. Solutions were analyzed in quintupplicate, and calibration curves were obtained by plotting peak areas (\(\Upsilon\)) _versus_ compound concentrations (x, ug/mL). Correlation coefficients (\(r^{2}\)) obtained from calibration curves were used to evaluate linearity, and all \(r^{2}\) values exceeded 0.999. LOD and LOQ were calculated using the standard deviations of y-intercepts (\(\sigma\)) and calibration curve slopes (S), as follows:  
\[\text{LOD}=3.3\times\frac{\sigma}{S} \tag{1}\]  
Figure 1: Chemical structures of standard compounds.  
\[\text{LOQ}=10\times\frac{\sigma}{S} \tag{2}\]
## 4 Data Diversity of UAV
### Methods Applied to the UAV Dataset
#### 4.3.1 Machine Learning and Deep Learning
The ERA dataset establishes a benchmark for event recognition in aerial videos by utilizing pre-existing deep learning models like the VGG models (VGG-16, VGG19) [29], Inception-v3 [65], the ResNet models (ResNet-50, ResNet-101, and ResNet-152) [30], MobileNet, the DenseNet models (DenseNet-121, DenseNet-169, and DenseNet-201) [35], and NASNet-L [66]. Traditional computer vision algorithms, alongside advanced machine learning and deep learning methods, have shown considerable effectiveness in UAV applications. For instance, ORB (Oriented FAST and Rotated BRIEF) and FLANN (Fast Library for Approximate Nearest Neighbors) have been employed for efficient object recognition and feature matching in UAV rescue systems, enabling real-time processing under hardware constraints [67]. Similarly, the SIFT (Scale-Invariant Feature Transform) algorithm has proven to be highly effective in image mosaicking tasks, ensuring high accuracy and computational efficiency, particularly in agricultural monitoring [68]. These algorithms provide efficient alternatives in resource-constrained environments, proving essential for tasks including object detection, feature matching, and image alignment in UAV applications. Algorithms like ORB, FLANN, and SIFT, now utilized in object detection and mosaicking, could be further refined and incorporated into future UAV datasets to tackle developing issues. In the domain of deep learning, ensemble methods play a crucial role. They not only assess model performance but also boost accuracy while keeping the model's equilibrium intact, such as the following:  
* In the VRAI dataset, ensemble techniques were utilized such as Triplet Loss, Contrastive Loss, ID Classification Loss, and Triplet + ID Loss, and multi-task and multi-task + discriminative parts were introduced. These ensemble methods performed better than the state-of-the-art methods in their claim.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 4 System Model
### Problem Formulation  
For the sake of simplicity, we set the following equation:  
\[MS_{f}=\max\,(PF_{f},\,FM_{f}). \tag{1}\]  
We recall that the main aim of this paper is to dynamically and efficiently allocate resources in a way that (1) guarantees QoS requirements of different applications; and (2) proactively prevent congestion and resource waste. To this end, the routing matrix can be obtained such that it minimizes packet loss subject to QoS (i.e., Equations (3) and (4)) and flow conservation constraints (i.e., Equations (5)-(8)). The formulation is as follows:  
\[\min\max_{\forall i,j\in\{1,\ldots,n\}}\frac{\sum_{f=1}^{p}\mathbf{A}_{ij}^{f} \cdot MS_{f}}{\tau\cdot\mathbf{B}_{ij}}, \tag{2}\]  
subjected to:  
\[\sum_{i=1}^{n}\mathbf{A}_{is_{f}}^{f}\,+\,\sum_{i=1}^{n}\mathbf{A}_{d_{f}i}^{f} =1-\sum_{i=1}^{n}\mathbf{A}_{s_{f}i}^{f}=1-\sum_{i=1}^{n}\mathbf{A}_{d_{f}i}^{ f}=0,\quad\forall f\in\{1,\ldots,p\}, \tag{3}\]  
\[\sum_{f=1}^{p}\mathbf{A}_{ij}^{f}\mathbf{C}_{f2}\leq\mu\mathbf{B}_{ij},\quad \forall i,j\in\{1,\ldots,n\}, \tag{4}\]  
\[\sum_{i=1}^{n}\sum_{j=1}^{n}\mathbf{A}_{ij}^{f}\mathbf{D}_{ij}\leq\mathbf{C}_ {f1},\quad\forall f\in\{1,\ldots,p\}, \tag{5}\]  
\[\sum_{j=1}^{n}\mathbf{A}_{ij}^{f}=\sum_{j=1}^{f}\mathbf{A}_{ji}^{f},\quad \forall f\in\{1,\ldots,p\},\,\forall i\in\{1,\ldots,n\}-\{s_{f},d_{f}\}, \tag{6}\]  
\[\sum_{j=1}^{n}\mathbf{A}_{ij}^{f}\leq 1,\quad\forall i\in\{1,\ldots,n\},\, \forall f\in\{1,\ldots,p\}, \tag{7}\]  
\[\mathbf{A}_{ij}^{f}\in\{0,1\},\quad\forall f\in\{1,\ldots,p\},\,\forall i,j\in \{1,\ldots,n\}, \tag{8}\]  
where Equation (3) prevents flows from returning to the source switches while it makes the flows stay on the destination switches. Furthermore, it forces the flows to leave the origin switches and enter the destination ones. In other words, Equation (3) is the flow conservation constraint. Inequality (4) guarantees the link load to be smaller than the maximum target utilization \(\mu\). Equation (5) states delay requirement of flows. To this end, it calculates the delay of the selected path for each flow. This calculated value must be less than the maximum tolerable delay of the flow. When a switch is not the source or destination of a specified flow, the flow must leave that switch if it moves in. This limitation is met by Equation (6). Equation (7) makes sure that there is no loop in the routing matrix. Since the optimization problem is in form of Binary Linear Programming, it is a branch of integer linear programming that can be easily solved by the MATLAB (MATLAB version: R2016b (9.1.0.441655) 64-bit (win64) 7 September 2016, MathWorks Company, USA, 1984-2016, Professional License) convex programming language (CVX) toolbox.