esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies, and Human Health
## 5 Biomimetic Robotics
### Robots Inspired by Natural Organisms
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Description** \\ \hline Agility and Adaptability & Insect-inspired robots can manoeuvre through confined spaces, which is crucial in disaster scenarios where access is limited [82]. \\ \hline Cyborg Insects & These robots are equipped with electronic enhancements that improve their communication and sensing capabilities, facilitating survivor recovery in challenging environments [83]. \\ \hline Swarm Robotics & Groups of insect-like robots can autonomously collaborate, enhancing efficiency in search operations [82]. \\ \hline Locomotion Strategies & Research on insect locomotion has led to the development of robots capable of climbing and overcoming obstacles efficiently, utilising adhesion-based and multimodal movement strategies [84]. \\ \hline Vision-Based Navigation & Systems inspired by insect visual processing have laid the foundation for the development of bioinspired models for autonomous navigation, reducing computational demands and enhancing efficiency [85]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Insect-inspired robotics in rescue missions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Description** \\ \hline Biologically Inspired Fins & Miniature robotic fish utilise oscillating fins driven by Eccentric Rotating Mass (ERM) vibration motors, achieving speeds of 1.36 body lengths per second [86]. \\ \hline Fluidic Actuation & Soft robotic fish employ fluidic actuators that enable three-dimensional movement, enhancing manoeuvrability compared to traditional designs [87]. \\ \hline Adjustable Stiffness & Tail flexibility, optimised through adjustable stiffness, mimics real fish movements, improving propulsion efficiency [88]. \\ \hline Versatile Use Cases & These robots are suitable for various applications, including military operations, pollution detection, and underwater exploration [89; 90]. \\ \hline Energy Efficiency & Biologically inspired designs significantly outperform traditional robots in energy consumption, making them more sustainable for prolonged missions [91]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fish-inspired underwater robots. While fish-inspired underwater robots currently offer numerous advantages, challenges remain--particularly the durability of materials and the robustness of control systems--that must be overcome to enhance their operational capabilities in diverse marine environments.
# Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network
###### Abstract
To analyze the MEG signals with high temporal resolution, a CNN-based model was proposed that the experimental results demonstrated its efficiency [21]. Logistic regression with \(\ell 1\) penalization (termed Pool) was proposed to model MEG data of many subjects [4,22,23]. Inspired by deep learning  
Figure 1: Magnetoencephalography (MEG) decoding for visual information experiment [4,8]. methodology, two logistic regression layers were stacked that a new MEG decoding method termed stacked generation (SG) was proposed and achieved better performance than the Pool model [4; 24]. Additionally, inspired by deep learning models, logistic regression and random forest were combined in a deep structure (called linear regression and random forest--LRRF model) which achieved a good MEG decoding performance [25]. A deep network based on riemannian metric was proposed for decoding MEG signals evoked by the stimulation of face or scrambled face images [15; 26]. However, there are still many problems in the existing MEG decoding methods. We need to find a more effective approach to model numerous high-dimensional MEG signals, extract their features and further improve MEG decoding performance. The main contributions of this paper can be summarized as follows:  
1. A hybrid gated recurrent network (HGRN) was proposed for inter-subject visual MEG decoding. 2. The proposed HGRN is able to model high-dimensional MEG signal, extract its features, and distinguish the signal evoked by different visual stimulations. 3. Experimental results demonstrated that the proposed model is able to achieve a good MEG decoding performance. More importantly, the proposed method can be utilized as a new analyzing tool for MEG signals. The remainder of this paper is organized as follows. The proposed hybrid gated recurrent network (HGRN) is presented in Section 2. In Section 3, numerical inter-subject MEG decoding experiments are carried out. In addition, the performance of different MEG decoding methods are presented and compared. Some discussions are presented in Section 4. Conclusions of this paper are given in Section 5.
## 4 Results
### CovidDetNet Experimental Setup
#### 4.3.2 Performance Evaluation on Train/Validation/Test Split  
This experiment aims to validate further the COVID-19 detection (three-class classification) performance of the proposed CovidDetNet framework using chest radiograph images. For this experiment, the dataset is divided into three parts, i.e., training, validation, and testing sets. We used 70% of the input data for training, 10% for validation, and 20% for testing the proposed CovidDetNet approach. More specifically, we used all the 15,153 radiograph images (3616 data instances of COVID-19 patients, 1345 pneumonia radiographs, and 10,192 instances of healthy individuals) of the dataset named COVID-19 Radiography database, where 10,606 images (2531 images of COVID-19 individuals, 941 pneumonia radiographs, and 7134 images of healthy individuals) are used for training. In contrast, 1515 radiograph images (362 images of COVID-19 individuals, 134 pneumonia radiographs, and 1019 radiograph images of healthy individuals) are used for validation. The remaining 3032 images (723 images of COVID-19 individuals, 270 pneumonia radiographs, and 2039 images of healthy individuals) are used for testing. The training set trains the proposed CovidDetNet framework for COVID-19 detection and classification with the same parameters mentioned in Table 2. The training of the proposed CovidDetNet model consumed 3510 min and 36 s for COVID-19 detection and classification. This time, however, is proportional to the maximum number of epochs and iterations per epoch. The total number of iterations in the training stage for CovidDetNet is 4920 (82 iterations per epoch), and the number of epochs is 60. At epoch 60, the model achieved average validation
# Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form
## 3 Grey Decision-Making for Product Form Driven by Fuzzy Relationship between Kansei Images
### Identification of Kansei Image Weight Based on Variable Weight Theory
#### 3.1.2 Calculation of Kansei Image Variable Weights  
To address the fuzziness of a product's Kansei image, this study introduces a penalty-incentive variable weight function [18] to correct the Kansei image weight and reduce the error caused by a constant weight. The expression is:  
\[W^{0}(X)=\frac{W\cdot S(X)}{\sum\limits_{j=1}^{n}\bigl{(}w_{j}S_{j}(X)\bigr{)}} \tag{6}\]  
where \(n\) is the number of Kansei images, \(S(X)\) is the equilibrium function, and \(W\cdot S(X)=(w_{1}S_{1}(X),w_{2}S_{2}(X),\ldots,w_{n}S_{n}(X))\) is the Hadamard product.  
As the focus of variable weight theory, the equilibrium function \(S(X)\) adjusts the Kansei image weight by analysing the changes in the Kansei image evaluation to make it more suitable for the actual decision-making process [23]. Its equation is:  
\[S_{j}(X)=\left\{\begin{array}{l}\frac{1-\beta}{\beta^{2}}\big{(}X_{j}-\delta \big{)}^{2}+\beta\dots\dots,X_{j}\in(0,\delta]\\ \beta\dots\dots\dots\dots\dots\dots,X_{j}\in(\delta,\gamma]\\ \frac{1-\beta}{(1-\gamma)^{2}}\big{(}X_{j}-\gamma\big{)}^{2}+\beta\dots,X_{j} \in(\gamma,1]\end{array}\right. \tag{7}\]  
where \(X_{j}\) is the normalised value of the \(j\)-th Kansei image and \(\delta\) is the penalty threshold. When \(X_{j}\leq\delta\), the penalty effect is higher than the incentive effect. \(\gamma\) is the incentive threshold. When \(X_{j}>\gamma\), the incentive effect is higher than the penalty effect. \(\beta\) is the weight adjustment parameter.  
Based on the variable weight, combined with the description of comprehensive evaluation in reference [24], a variable weight comprehensive evaluation can be obtained:  
\[Z(X)=W^{0}(X)\cdot X \tag{8}\]
## 5 Performance Evaluations
### Accuracy  
One of the most important criteria for evaluating any proposed model is the accuracy of localization. Therefore, this criterion is considered the primary criterion used to compare different path planning models. Accuracy is measured by the number of localization errors in the nodes. In this study, two methods are used to calculate each model's localization error: the average localization error and the std of the localization error.
## 6 Practical Application
### Calculation of NSCC Star-Connected Compensator  
The negative-sequence currents of the NSCC compensator are determined from Equation (44). The values of \(\underline{I_{aP-}}\) have been obtained in the previous section, and those of \(\underline{I_{zL-}}\) are listed in Table 2. Therefore, the values of \(\underline{I_{zF-}}\) are as follows:  
\[\underline{I_{aF-}}=25.41e^{-j172.79}\;\mathrm{A}\;\;\underline{I_{bF-}}=25.4 1e^{-j52.79}\;\mathrm{A}\;\;\underline{I_{cF-}}=25.41e^{j67.21}\;\mathrm{A}.\]  
Considering Equation (46), the imbalance powers caused by the positive-sequence voltages and negative-sequence currents are as follows:  
\[Q_{aF+}^{-}=255.36\;\;Q_{bF+}^{-}=-4567.60\;\;Q_{cF+}^{-}=4312.24.\]  
Substituting the values of \(\underline{I_{zF-}}\) and \(Q_{zF+}^{-}\) in (47), the values of reactances in the NSCC compensator are obtained as:  
\[X_{aF}=-2\frac{255.36}{25.41^{2}}=-0.7907\;\Omega,\]  
\[X_{bF}=-2\frac{-4567.60}{25.41^{2}}=14.1435\;\Omega,\]\[X_{cF}=-2\frac{4312.24}{25.41^{2}}=-13.3528\ \Omega.\]  
From its sign, \(X_{aF}\) is a capacitor, \(X_{bF}\) is a coil, and \(X_{cF}\) is a capacitor.
## 4 Evaluation
### Evaluation Methodology
#### 4.1.1 Simulation Set-Up  
In order to simulate a city-wide scenario that portrays a relatively high degree of spatial locality as compared with the Helsinki scenarios presented in Section 2.2.2, we utilize the Skudai simulation area, which as presented in Figure 7, roughly covers a 21 \(\times\) 11 km\({}^{2}\) terrain consisting of 32 districts. Static nodes are placed in popular locations to represent sensors and gateways in a city. Specifically, 64 source nodes are placed in home locations (representing sensors in residential areas) and along roads (for environmental sensors), while 32 destination nodes are placed in office and meeting spot locations (representing gateways in work places and shopping malls). The remaining 1253 nodes represent handheld devices carried by 1070 pedestrians, 118 bus drivers and 65 users that move in the background to simulate delivery men, police men and taxi drivers. Each source generates a message to a randomly-chosen destination on an hourly basis, resulting in a total of 4608 messages in the 3-day period. All other configurations are according to the settings in Section 2.2.2.
## 5 Proposed Scheme
### Channel Model  
In order to determine the user's connection behavior, a path-loss model for an urban environment is considered in this paper. We apply different path-loss models with zero mean and unit variance log-normal fading to D2D users and cellular users. The distance-dependent path-loss models for line of sight (LOS) communications for D2D users and cellular users are expressed as [25]:\[PL_{i} = 49+40\ log_{10}\Big{(}\frac{l_{i,j}}{1000}\Big{)}+30 log_{10}\Big{(} \frac{f_{c}}{1000}\Big{)}\] \[PL_{i^{\prime}} = 49+40\ log_{10}\Big{(}\frac{l_{i^{\prime},j^{\prime}}}{1000}\Big{)} +30 log_{10}\Big{(}\frac{f_{c}}{1000}\Big{)}\ \ \Bigg{\}} \tag{20}\]  
\[PL_{k} = 40.9+36.7\ log_{10}(l_{k})+\ 26\ log_{10}\Big{(}\frac{f_{c}}{5}\Big{)} \tag{21}\]  
where \(l_{i,j}\), \(l_{i^{\prime},j^{\prime}}\), and \(l_{k}\) are the distance from D2D-Tx \(i\) to D2D-Rx \(j\), from D2D-Tx \(i^{\prime}\) to D2D-Rx \(j^{\prime}\), and from cellular transmitter to receiver, respectively, and \(f_{c}\) is the carrier frequency in GHz.
## 3 Results and Discussion
In addition, differences between varieties in polyphenolic composition and extractability of those compounds impact the final signature of the yeast on wine. The addition of catechin in wines has a strong impact on the direction of condensation reactions by yeasts [18]. The highest concentration of epigallocatechine, proanthocyanidin B1, B3, and B4, and flavan-3-ols was in N wines. The significant reduction in epigallocatechin was in S wines, while catechin and epicatechin were the same as in N wines. _S. cerevisiae_ strain S16 was previously reported to produce higher concentrations of catechin, epicatechin, and epigallocatechin in wines of Plavac Mali [23]. PCA Analysis of Arona and Polyphenolic Plavac Mali Wine Composition Produced by Sequential L, Conventional S and Spontaneous N Fermentation  
The contribution of F1 was 75.08%, and of F2, 18.85% (Figure 2). According to the PCA, wines of different treatments were clearly separated. Wine sample replicates grouped together for three fermentation treatments. S and N wines were located on the positive side of PC1 and were characterized by five aroma compounds: linalool, \(\beta\)-damascenone, hexanoic acid, 3-methyl-1-butanol, and 2-phenylethyl acetate. L wines were located on the negative side of PC1 and correlated with TDN and isobutanol aroma compounds. The seven compounds were selected as the most important contributors to floral and fruity aromas (linalool, \(\beta\)-damascenone, 2-phenyl acetate) and as important enhancers and suppressors of fruity aromas (\(\beta\)-damascenone, 3-methyl-1-butanol, isobutanol), with only two from green and petroleum class, hexanoic acid and TDN [33; 36; 37]. The data for one non-flavonoid (gallic acid) and six flavonoids (myricetin-3-O-glucoside, quercetin-3-O-glucose, catechin, epigallocatechin, B1, B4) variables were processed using principal component analysis (Figure 3) to differentiate among Plavac Mali wines obtained by three different fermentations (sequential (L), conventional (S) and spontaneous (N)). The first two axes represent 95.94% of the initial variability. The first eigenvalue equals 5255 and represents 77.67% of the total variability explained by PC1. PC2 explains 18.27% of the total variability. According to the PCA, wines produced by different yeasts were clearly separated. Wine sample replicates grouped together for all three treatments. N and S wines were grouped on the positive side of PC1, in the first and fourth quadrants, respectively, and characterized by all nine variables. Sequential inoculation L wines were grouped on the negative side of PC1. Figure 2: PCA analysis of aroma compounds of Plavac Mali wines produced by sequential L, conventional S and spontaneous N fermentation. PCA confirmed that the fermentation treatment affected the volatile and polyphenic profiles of the wine samples.
## 6 MODIS LE product
### Auxiliary Data  
The monthly gridded precipitation dataset provided by National Tibetan Plateau Data Center was used in this study [52]. The dataset was generated based on the Climatic Research Unit (CRU) dataset and the climatology dataset of WorldClim. The precipitation data has a spatial resolution of 0.008333\({}^{\circ}\) (\(\sim\)1 km). Validation results from the 496 weather stations across China showed that this product was reliable in China [53; 54].  
We also used the monthly runoff dataset provided by the Global Land Data Assimilation System (GLDAS) in this study. We choose the GLDAS-2.1 dataset (GLDAS_NOAH025\(M\)2.1) [55], which has a temporal resolution of monthly and a spatial resolution of 0.25\({}^{\circ}\). To evaluate six products, we resampled spatially all gridded dataset into 0.05 degree using bilinear method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Products** & **Spatial Resolution** & **Temporal Resolution** & **Study Domain** & **Time Span** & **References** \\ \hline GLASS & 0.05\({}^{\circ}\) & 8-day & Global & 1982–2018 & [PERSON] et al., 2014 \\ FLUXCOM & 0.0833\({}^{\circ}\) & 8-day & Global & 2000–2015 & [PERSON] et al., 2019 \\ PML\_V2 & 0.05\({}^{\circ}\) & 8-day & Global & 2002–2019 & [PERSON] et al., 2019 \\ GLEAM & 0.25\({}^{\circ}\) & daily & Global & 1980–2020 & [PERSON] et al., 2017 \\ BESS & 0.01\({}^{\circ}\) & 8-day & Global & 2000–2015 & [PERSON], 2016 \\ MODIS & 500 m & 8-day & Global & 2001–2021 & [PERSON] et al., 2011 \\ \hline \end{tabular}
\end{table}
Table 2: Detailed information of LE products used in this study.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 7 Performance Assessments
### Assessment Metrics
#### 7.1.4 User Satisfaction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The First Level** & **The Second Level** & **The Third Level** \\ \hline \multirow{6}{*}{Coverage} & \multirow{6}{*}{Coverage rate} & Coverage rate of single satellite \\  & & Coverage rate of double satellites \\  & & Coverage rate of multiple satellites \\ \cline{2-3}  & & Maximum outage time \\  & & Average outage time \\  & & Outage probability \\ \cline{2-3}  & & Elevation & Minimum elevation \\  & & Average elevation \\ \hline \multirow{6}{*}{Capacity} & Capacity of single channel & \\  & Capacity of single beam & \\  & Capacity of single satellite & \\  & System capacity & \\  & Congestion probability & \\ \hline \multirow{2}{*}{Access and control} & Access capacity & \\  & Access delay & \\  & Access success probability & \\ \hline \multirow{6}{*}{Quality of service} & Bit error rate & \\  & Transmission rate & \\  & End-to-end delay & \\  & Call drop rate & \\ \hline \multirow{6}{*}{Adaptability} & \multirow{6}{*}{Terminal availability} & Usability \\  & & Learnability \\  & & Human-machine interaction \\  & & Response speed \\  & & Stability \\  & & Security \\ \cline{2-3}  & & Degree of connectivity \\  & Reliability & Redundancy \\  & & Connectivity probability \\ \cline{2-3}  & & Scalability \\ \cline{2-3}  & & Maintainability \\ \cline{2-3}  & & Networking flexibility \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assessment indicators.
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 3 The Proposed Method
### RetinaNet Based on Rotation Anchor  
RetinaNet is one of the most advanced one-stage detection frameworks, which can be applied for real-time inference. RetinaNet uses the structure of ResNet and Feature Pyramid Network (FPN) as the backbone to generate a multi-scale convolutional feature pyramid. Two subnetworks are connected to the backbone subsequently to classify anchor boxes and regress anchor boxes to ground truth target boxes.  
In the traditional RetinaNet framework, the area anchored on the pyramid P\({}_{3}\) to P\({}_{7}\) layers is 322 to 5122 respectively, where P\({}_{i}\) represents the pyramid level, and P\({}_{i}\) has resolution 1:2 lower than P\({}_{i-1}\). At each pyramid level, we use anchors of different scales and sizes. In the RetinaNet based on rotation anchor, orientation parameters are added to each anchor box to propose the RRoI. Therefore, in each pyramid level from P\({}_{3}\) to P\({}_{7}\), Rotation RetinaNet uses anchors with five different aspect ratios, three different sizes and six different angles.  
However, we found that in Rotation RetinaNet, the confidence of rotation target with large aspect ratio detection is very sensitive to the preset angles of anchor boxes, as shown in Figure 4. The same object with different orientations can achieve remarkably different  
Figure 3: The schematic diagram of our method comparing the traditional matching method to this matching method. This figure shows a simplified process of matching an anchor with a target when the IoU threshold is 0.7. This method configures two auxiliary anchors for an anchor, calculates three IoUs with the target frame and then takes its maximum value.  
confidence. The two objects have the same image features, while the angle enhancement during the training process is also random without specificity. So, the different results are not caused by data or network structure.  
In fact, this is not a coincidence but a necessity. The target which achieves high confidence is oriented with an angle the same as the anchor angle. For a target with a large aspect ratio, if we add an anchor oriented at the same angle of target 2 in the network, the confidence of target 2 will also be improved. Therefore, it can be assumed that if we set more anchors with different orients in the network, the network will learn more positive examples, and the recall rate of the network will definitely be higher.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.8 Hop Count  
We include a hop count field in the header of the data packet. The hop count indicates how many UAVs the packet has traveled through during its journey from the sender to the destination. Whenever a UAV receives a packet, it increases the hop count number by one. It is initialized as one at the sender. Furthermore, we put the maximum limit for the hop count as the maximum number of UAVs participating in the mission. This maximum limit avoids infinite tries if the packet fails to reach the destination.
## 2 Materials and Analytical Methods
### Statistical Approach in the Experimental Design of Extraction Optimization  
Optimization of the extraction process was performed as a two-step experiment. In the first step, the effects of different conditions on the extraction process were assessed. The influence of organic solvent concentration (% organic solvent in water solution (\(v/v\))), extraction time, solvent-matrix ratio (SMR), ultrasound assistance, and solvent type on antioxidant activity and total phenolics content (TPC) was evaluated using a randomized half-fraction factorial design (25-1) with four central points. The statistical model included aliases defined as follows: organic solvent concentration (A), extraction time (B), SMR (C), ultrasound assistance (D), and solvent type (E). The extraction factors and their levels used in the experiment are presented in Table 1. The quality assessment of the model was based on the coefficient of determination (R\({}^{2}\)). Central points were used to evaluate the curvature of the model-testing adequacy of the first-order model.  
According to the most desirable parameters found in the first stage, the second stage of the experiment was constructed to optimize the extraction process further. The influence of organic solvent concentration and SMR on antioxidant activity measured by means of ABTS and FRAP assays, TPC, caffeine and chlorogenic acids content, and browning index (BI) was estimated with the use of a randomized 2-factor central composite design. Table 2 summarizes values of the parameters included in the second stage of the experiment.  
All of the extraction runs and analyses were performed in triplicate, and the results are reported as means \(\pm\) standard deviation. The experimental models were simplified by the reduction of statistically non-significant terms. Statistical analyses were performed using Minitab 21 software. The statistical significance of the operational extraction process variables was determined at the significance level of \(\alpha\) = 0.05. The predictability of the developed models was verified to ensure model adequacy for approximation to the real values by means of statistical parameters, including different types of R\({}^{2}\) (the coefficient of determination), the model \(p\)-value, and the lack-of-fit test.