esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cellulose, Hemicellulose and Lignin Content of Paprika Samples  
Lignin is a natural phenolic polymer found in higher plant tissues and the second most abundant polymer after organic cellulose [22]. In all samples, cellulose, hemicellulose and lignin contents were identified. The lignin content varied in the following order (%): P1 (15.2 \(\pm\) 1.1) > P4 (13.1 \(\pm\) 1.0) > P8 (13.0 \(\pm\) 0.89) > P7 (12.8 \(\pm\) 0.85) > P2 (11.3 \(\pm\) 1.1) > P5 (11.2 \(\pm\) 1.0) > P6 (8.3 \(\pm\) 0.62) > P3 (7.2 \(\pm\) 0.52). A high concentration of lignin was identified in red Kapia pepper. Our results are in accordance with other researchers' results regarding lignin determination from pepper [23]. [PERSON] et al. [23] reported 4-9% lignin from the fruit of _Capsicum annuum_ pepper species and reported the variation of lignin during the maturation period. It was concluded that the maturation process caused the lignin content to decrease. The process can be explained by the rearrangement of cell structure by plant maturation and the interaction between lignin-like substances derived from phenyl propanoid precursors [23]. Celluloses and hemicelluloses are carbohydrates that provide the taste of the pepper and can create links with protein, lipids and biomolecules in the paprika powder [24].  
The cellulose content varied in the following order (%): P2 (32.2 \(\pm\) 2.1) > P4 (28.6 \(\pm\) 1.8) > P6 (25.5 \(\pm\) 2.0) > P1 (23.1 \(\pm\) 1.9) > P5 (23.0 \(\pm\) 1.3) > P7 (21.1 \(\pm\) 1.6) > P8 (19.2 \(\pm\) 1.2) > P3 (19.1 \(\pm\) 1.0), whereas hemicellulose contents were (%) P6 (13.5 \(\pm\) 0.8) > P4 (12.1 \(\pm\) 1.0) > P2 (11.1 \(\pm\) 0.9) > P8 (8.9 \(\pm\) 0.5) > P1 (8.1 \(\pm\) 0.5) > P5 (7.2 \(\pm\) 0.6) > P7 (5.4 \(\pm\) 0.3) > P3 (5.2 \(\pm\) 0.4). The highest content of cellulose was found in spicy varieties. According to [PERSON] et al., in Serbian paprika, the following sugars were identified: glucose, fructose, arabinose, xylose, mannose, disaccharides (trehalose and maltose), trisaccharides and sugar alcohols [24]. The results show a good agreement between the thermogravimetric analysis and the presence of cellulose, hemicellulose and lignin in paprika samples.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 1 Introduction
Here, RSUs are defined as a preselected node (chosen by the Department of Transportation and the consortium members) placed at the network's edge. Moreover, we use the PBFT algorithm for the consensus mechanism as it is more suitable for consortium blockchain [14]. PBFT is presented to audit publicly, store data shared, and record the whole consensus process. It can also defend against system failures with or without symptoms to reach an agreement among consensus participants. Furthermore, we allow the schema of the vehicle's data reputation based on its credibility assessment process to prevent irrelevant data information. Further, an incentive mechanism is proposed to motivate vehicles to contribute honestly and share their data to maintain and improve system reliability. In short, this paper's contribution can be summarized as follows. 1. We propose a decentralized trust system on vehicular edge computing network by utilizing a consortium blockchain and smart contracts. 2. We aim to enhance reputation-based data management's security using blockchain to encourage vehicles to contribute honestly to VECNets data sharing transactions. 3. We conduct the simulation to show that our proposed architecture increases the vehicular network's general parameter performance. The remainder of this paper is organized as follows. In Section 2, we give background knowledge related to vehicular edge computing networks and blockchain technology. Then, we present the design architecture of blockchain-based VECNets, a secure and decentralized framework in vehicular networks, in Section 3. In Section 4, we discuss the relevant security feature and performance evaluation of our proposed system. Finally, Section 5 concludes this paper.
### Related Works
Based on the convolution neural network, classification and recognition of various fault images such as lightning strikes, cracking, and corrosion were achieved. [PERSON] et al. [17] used convolutional neural network VGG16-RCNN for wind turbine blade surface damage detection. [PERSON] et al. [18] evaluated the performance of the currently popular YOLOv3, YOLOv4, and Mask R-CNN deep learning algorithms for detecting and classifying defects by type of wind turbine blades and proposed an image-enhancement-based Mask R-CNN algorithm (IE Mask R-CNN). Deep learning was applied by [PERSON] et al. [19] to the recognition of wavelet transform blade defects. For the rapid detection of wavelet transform blade defects, a residual network structure based on the fusion of multiple features was proposed. Based on YOLOv3, [PERSON] et al. [20] proposed a wavelet transform blade damage recognition model for UAV images. The Super-Resolution CNN (SRCNN) method was used to identify low-resolution UAV images and convert them to high-resolution images. YOLOv3 was then applied to aerial images to detect damage to turbine blades. [PERSON] et al. [21] proposed a novel algorithm by enhancing RNN to achieve rapid warning of icing faults in wind turbine blades. First, the algorithm selects the features that have a significant impact on wind turbine blade icing faults based on the mean impact value (MIV) index, and then the T-distributed Stochastic Neighbor Embedding (TSNE) algorithm reduces the dimensions of the selected features, which drastically decreases the processing time of the algorithm. Finally, the processed data are inputted into the RNN model for wind turbine blade icing prediction. [PERSON], [PERSON] et al. [22] proposed a new real-time hybrid fault detection strategy for wind turbine blades. The suggested fault detection system employed an integrated Generalized Regression Neural Network Ensemble for Single Imputation (GRNN-ESI), Recursive Principal Component Analysis (RPCA), and wavelet-based Probability Density Function (PDF) methods to detect incipient faults in an early stage. As a result of extensive research conducted by academic researchers, the structure of convolution neural networks has been continuously improved, and the efficiency of the task used for damage detection of wind turbine blades has gradually increased. However, when a network performs a convolutional operation, the loss of feature information details of the image is unavoidable. Such damage characteristics of the blade as the length and depth of the crack and the size and shape of wear are variable. As a consequence, lost information detail frequently leads to errors in identification, so the identification accuracy has room for improvement.
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 4 Experiment and Results
### Results and Discussion
#### 4.2.2 Prediction with Different Features  
To investigate the impact of different types of features on the experimental results, we conduct feature ablation experiments using the NOA-XGBoOST algorithm. This involved removing one type of feature at a time and observing the changes in results. The outcomes are shown in Table 6 and Figure 10.  
As shown in Table 6, removing any one of the features such as airline, aircraft, restriction status, or time results in only a minor decrease in the metrics. This indicates that these features have relatively small impacts on the prediction of the ATT. However, removing the traffic flow features causes a significant drop in performance. When using the traffic flow features, MAE, RMSE, R\({}^{2}\), \(\pm\)3 min accuracy, \(\pm\)5 min accuracy, and (+5, \(-\)10) min accuracy are improved by 16.3%, 19.47%, 18.46%, 8.45%, 5.62%, and 3.19%, respectively. Similarly, removing the structure-related features proposed in this study also results in a noticeable drop in performance. MAE, RMSE, R\({}^{2}\), \(\pm\)3 min accuracy, \(\pm\)5 min accuracy, and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Based** & **MAE** & **RMSE** & **R\({}^{2}\)** & **\(\pm\)3 min Accuracy** & **\(\pm\)5 min Accuracy** & **(+5, \(-\)10) min Accuracy** \\ \hline NOA-XGBoOST & 2.06 & 2.73 & 0.77 & 0.77 & 0.94 & 0.97 \\ XGBoOST & 2.15 & 2.85 & 0.75 & 0.76 & 0.93 & 0.96 \\ RF & 2.25 & 2.95 & 0.73 & 0.73 & 0.92 & 0.95 \\ SVR & 2.27 & 3.19 & 0.69 & 0.73 & 0.92 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of four prediction models.  
Figure 9: Prediction results of different models.  
(+5, \(-10\)) min accuracy can be improved by 7.62%, 6.51%, 10%, 4.05%, 3.30%, and 3.19%, respectively, by applying the newly proposed features.  
Figure 10 shows the comparison of the absolute prediction errors for flights numbered 1050 to 1150, with and without the structure-related features. The blue area represents the errors without the new features, while the red area represents the errors with all features. It is obvious that the blue area is larger than the red area, which indicates that the proposed features are effective in improving the prediction performance.
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
## 1 Introduction
When Gaussian process and measurement noises were considered, the authors of [23] presented a framework for constructing control barrier functions for stochastic systems, based on which safety control schemes were provided. To deal with the situation in which the relative degree of the control barrier function with respect to the targeting dynamic system is greater than one, high-order control barrier functions have been constructed, and corresponding safety control can be designed based on such functions [24; 25; 26]. Facing the complicated external environment, it is necessary to develop safety control schemes for satellite formation flying to avoid collisions by using the control barrier function, which can effectively deal with the complex safety constraints. Inspired by advances in control barrier functions and the need to ensure satellite safety, this paper investigates the safety control problem of satellite formation flying. A control barrier function-based safety control scheme is devised to strictly ensure the safety of satellites. The main contributions of this paper can be summarized as follows. (1) A high-order control barrier function is constructed for a dynamic model of satellite formation flying. The safe set is defined based on the constructed control barrier function. (2) An optimization problem, where the Lie derivative of the high-order control barrier function is added as the constraint, is clearly formulated. The safety controller can be obtained by solving the optimization problem in a real-time manner. The remainder of this paper is organized as follows. Section 2 describes the problem formulation and preliminaries. The safety control design results are provided in Section 3. Section 4 presents the simulation results to show the effectiveness of the proposed safety control scheme, and the paper is concluded in Section 5.
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 3 Experimental Results Analysis  
### Influence of Plant Root Systems on Soil Shear Modulus  
Torsional resonance testing provides resonance frequency and resonance voltage of the specimen. The following formulas can be employed to calculate shear wave velocity, shear modulus, shear strain, and other relevant parameters [29].  
Rotational inertia:  
\[I=\frac{md^{2}}{8} \tag{2}\]  
Fundamental equation for fixed-free resonant column:  
\[\frac{I}{I_{0}}=\beta\tan(\beta) \tag{3}\]  
Shear wave velocity calculated according to the following formula:  
\[V_{s}=\frac{2\pi fl}{\beta} \tag{4}\]  
Shear modulus can be calculated from shear wave velocity according to the following formula:  
\[G=\rho V_{s}^{2} \tag{5}\]  
In the provided expressions, the variables represent key parameters in the context of the experiment. \(d\) stands for the outer diameter of the solid specimen, \(m\) denotes the mass of the specimen, \(I\) signifies the rotational inertia of the soil sample, \(I_{0}\) represents the rotational inertia of the column drive system, \(f\) is the natural resonance frequency of the specimen obtained from the resonant column test, \(l\) indicates the height of the specimen, and \(\rho\) represents the bulk density. These variables play crucial roles in characterizing and analyzing various aspects of the experimental setup and outcomes.  
Under the influence of dynamic loading, the dynamic shear modulus is a crucial dynamic parameter characterizing soil behavior. It serves as a key indicator for describing the stress-strain relationship within the soil [30]. In this study, the dynamic shear modulus test results were fitted using the Hardin-Dmreich hyperbolic model. The hyperbolic formula [8] is as follows:  
\[G=G_{\text{max}}\cdot\frac{\gamma_{r}}{\gamma_{r}+\gamma} \tag{6}\]  
Figure 5: Procedure of test.  
In the equation, the following terms are defined as follows: \(G_{max}\) denotes the maximum dynamic shear modulus, \(\gamma\) represents shear strain, and \(\gamma_{r}\) is the reference shear strain.
## 3 Results  
### Paired Comparison Results  
The average \(\pm\) SD CoCs for AMS-SD and AMS-LD were 0.778 \(\pm\) 0.083 (range: 0.589-0.946) and 0.753 \(\pm\) 0.090 (range: 0.589-0.973), respectively. Thus, all SMEs met the a priori-defined cut-off of 0.35 and contributed to the PC analysis. The main findings of the PC are shown in Figure 1 (numeric results for calculating AMS-SD and AMS-LD can be found in Table 1).  
Figure 1: Results of the paired comparison analysis for short- (up to 6 months; blue bars) and long-duration (>6 months; red bars) space missions. Asterisks indicate statistically significant differences  
at \(p<0.05\) after adjustment for multiple testing (N = 14) between short- and long-duration missions for the proportion in which each of the items was considered more relevant. Items are sorted in ascending order from left to right based on the average proportion of short- and long-duration ratings. Error bars reflect 95% confidence intervals (CI).  
Boredom (22.1%), monotony (23.1%), and sleepiness (35.6%) were chosen least often by SMEs over other items, while health (74.3%), depression (76.5%), and crew conflicts (77.9%) were chosen most often (percentages reflect averages across scorings for short- and long-duration missions). Six out of the fourteen items differed statistically significantly between PCs for short- and long-duration missions: sleepiness, tiredness, energy level and mental status were considered more relevant by SMEs for short-duration missions, while monotony and loneliness were considered more relevant for long-duration missions (Table A1).
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Data Collection Modalities
#### 3.1.3 Physiological Modalities  
Physiological sensors enrich MMLA by capturing internal states such as stress, attention, or cognitive load. EDA, heart rate variability (HRV), and EEG are the most commonly used signals [50; 51]. Wearables like the Empatica E4 wristband have been used to measure arousal and engagement [41; 50; 52]. Some platforms even infer physiological signals from webcams (e.g., heart rate via skin tone changes) [53]. These data provide a window into affective states and are often triangulated with behavioural data to enrich the interpretation [4; 54; 55].
## 3 Residue Number System and Multiple Error Correction
### Modular Projection Method with MLD  
The paper [19] improved the classical modular projection method to reduce the number of projections by changing their construction. When constructing a modular projection, the idea is to delete not \(r\) residual bits of the \((n,\,k)\)-RRNS instead of \(t\) ones, where \(t=(n-k)/2\) is the maximum multiplicity of an error corrected by the \((n,\,k)\)-RRNS, and \(r=n-k\) is the number of redundant moduli. The algorithm for constructing modular projections was described in detail in [29].  
Such an approach to constructing modular projections allows for reducing their number to \(C_{n/2}^{t}\)[29]. However, some incorrect projections fall into the legitimate RRNS range. To separate correct projections, we propose using maximum likelihood decoding (MLD) [19]. According to the MLD concept, an additional step is to calculate the Hamming distances between the erroneous number in the RRNS representation and each modular projection. In the RNS, the Hamming distance is defined as the number of distinct corresponding remainders. Let \(X=(x_{1},\,x_{2},\,\ldots,\,x_{n})\) and \(Y=(y_{1},\,y_{2},\,\ldots,\,y_{n})\) be numbers written in the RNS with bases \(m_{1},\,\,m_{2},\,\ldots,\,m_{n}\). Then the Hamming distance is given by  
\[h(X,Y)=\sum_{i=1}^{n}f(x_{i},y_{i}),\,\,\text{where}\,\,f(a,b)=\left\{\begin{array} []{l}1\text{ if }a\
eq b,\\ 0\text{ if }a=b.\end{array}\right. \tag{8}\]  
Therefore, we should find the remainders of the moduli deleted when constructing each projection.  
Two conditions determine a correct projection:  
* The modular projection \(X_{i}^{\prime}\) in the WNS (its weighted characteristic \(F\big{(}X_{i}^{\prime}\big{)}\)) is smaller than the RRNS dynamic range \(M_{K}\) (its weighted characteristic \(F(M_{K})\)).
* The Hamming distance between the distorted number \(X/X^{\prime}\) and the modular projection \(X_{i}^{\prime}\) in the RRNS does not exceed the value of the maximum multiplicity \(t\) of errors corrected by this RRNS.  
The following formula combines these conditions:  
\[X=X^{\prime}_{i}:\left\{\begin{array}{l}F\big{(}X^{\prime}_{i}\big{)}<F(M_{K})( \text{or }X^{\prime}_{i}<M_{K}),\\ h\big{(}X^{\prime},X^{\prime}_{i}\big{)}\leq t,\end{array}\right.\text{ where }t=(n-k)/2. \tag{9}\]  
The remainders of the moduli deleted when constructing each projection can be calculated by extending the RNS moduli system [32] or restoring the number in the WNS and calculating the remainders of these moduli. These operations are resource-consuming and significantly increase the total time for correcting errors and restoring the number in the WNS.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 4 Numerical Simulations
### Single-Factor Numerical Simulation Analysis
#### 4.4.1 Depth of Pit Excavation  
For the depth of the foundation pit excavation, five levels were set: 5, 10, 15, 20, and 25 m. Numerical simulations were performed for each condition to compare and analyze the vertical and horizontal displacements of the pipelines. As evident from Figure 8a, with increasing excavation depth, the pipeline settlement gradually increased. The vertical displacement of the pipeline within 0-60 m from the \(y\)-axis showed significant variation, leading to substantial uneven settlement. This could cause tearing or cracking in the pipelines, warranting special protection in this area, which aligns with the results obtained from the analysis of the actual data. Figure 8b shows that the pattern of change in the horizontal pipeline displacement was essentially consistent with the vertical displacement. Within 70-140 m from the \(y\)-axis, the horizontal displacement of the pipelines was relatively uniform. However, significant variation in horizontal displacement occurred near the pit corners. The combined effect of horizontal and vertical displacements could accelerate pipeline damage. Therefore, special protection should be applied to pipelines in these areas.  
Figure 7: Distribution of pipeline settlements parallel to the pit. \(\delta_{\text{vm}}\) denotes the maximum settlement value of the pipeline; \(H_{\text{m}}\) denotes the excavation depth.
## 5 Experimental Results and Analysis
### Dataset  
The dataset used in this study is from 223 GB of Open Sourcing driving data [50], which was collected by driving a Lincoln MKZ car in Mountain View under various weather conditions for 70 min with more than 150 variables. The ECU messages of the car were collected and recorded by the robot operate system (ROS) automatically. We extracted the in-vehicle ECU data in the ROS and eliminated the pictures and other non-significant data. The data variables used in this study included vehicle speed (denoted as signal 1), wheel speed (signal 2), GPS speed (signal 3), acceleration (signal 4), and torque of wheel (signal 5). The reason for choosing these in-vehicle sensor variables is that we tend to pay more attention to the variables related to vehicle speed, acceleration, and torque because these variables are crucial to a vehicle. If they are attacked or at fault, the safety of vehicles and people will face great threats. Moreover, there are existing works which have illustrated that these sensors are vulnerable to attacks and faults [51; 52].
# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
## 5 Conclusions  
In this research, we trained a model capable of detecting birds and flocks in drone footage. The models we used were YOLOv4 and YOLOv4-tiny. The footage was recorded in suburban environments at an altitude of 30-50 m. Using this footage, we developed a dataset and applied various techniques in order to increase the quantity and quality of our dataset.  
During the development of our dataset, we applied image enhancement, labelled each image, and applied various techniques to increase the overall mAP of our model. The highest mAP we reached for our testing dataset was 91.28% with an average IoU of 65.10%. One crucial technique we evaluated our models with and applied to our dataset was image tiling [16], in order to detect small objects and maximise our model's potential. Image tiling played an important role in improving the overall mAP, as proven in Figure 11, and dramatically increased the overall mAP of the model. Additionally, we enhanced our dataset using gamma correction to further improve the mAP of our model, as well as the prediction results. Both models performed superbly, in the detection of small objects such as flocks and birds in drone footage.  
Despite YOLOv4-tiny's results containing less precision, we benefited from its overall increase in performance and how lightweight the model is. During the evaluation, the model was able to detect objects in images by up to 10\(\times\) faster than YOLOv4. Whilst YOLOv4's evaluation results were high, at 91.28% mAP and 65.10% avg IoU (Table 2), YOLOv4's drawback for its high accuracy was its overall performance in terms of its computational speed. Each frame took up to a second for the model to detect whilst using image tiling [16].  
The results produced from our dataset proved to perform excellently in terms of accuracy. This high accuracy was likely to be due to bias toward the dataset, which may result in false or missing detections in the footage the model was not trained on. It is essential to consider that different methods often utilise different datasets, making direct comparisons challenging. The goal of our presented methodology was to test deep learning methods such as the state-of-the-art YOLO detection algorithm (and the tinyYOLO model) from drone footage. We must point out that the detection of birds was a challenging problem since the drone video was captured at a relatively high altitude (30-50m above sea level) avoiding sea disturbance. Overall, we can conclude that, with the current dataset, the models performed excellently, and they effectively accomplished the task of object detection based on the given dataset.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Evaluation Metrics
#### 3.4.2 Root Mean Squared Error (RMSE)  
RMSE quantifies the standard deviation of prediction errors:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
It is sensitive to large errors and reflects the overall prediction accuracy.