esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Test Setup and Simulation Environment
### Simulation Framework Validity and Simulation Workflow  
It is worth reiterating here that we used the well established, validated and commonly accepted simulation framework to simulate the V2I communication, consisting of the OMNeT++, SUMO and Veins, which has been deployed by more than 1260 scientific studies [35] so far. To simulate the LTE-specific communication protocols, we used the SimuLTE simulation framework developed by the Computer Networking Group (CNG) of the University of Pisa and built for the OMNeT++. For the simulation of the 5G-specific communication protocols, we used the Simu5G simulation framework jointly developed by the CNG and Intel Corporation, again built for the OMNeT++. Those simulation frameworks were also verified and validated, see [28] for the SimuLTE and [36] for the Simu5G for more details, and are well accepted by the community, see [37; 38] for more details. Moreover, the simulation workflow is, in detail, described in [28] for the LTE-based communication technology, i.e., the SimuLTE framework, and in [29] for the 5G-based communication technology, i.e., the Simu5G framework.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 4 Discussion
### Current Trends in Technologies Powered by Artificial Neural Networks  
The most exploited issues, which prove their importance but also the significantly deepened state of knowledge, include the following:  
1. Energy efficiency is improved by adjusting data collection and processing [8; 9; 28; 29; 30; 31; 32].
2. Hardware implementation of neural networks allows for their acceleration as well as improvements in energy efficiency. It also allows for their use in embedded systems [28; 29; 30; 34; 36; 37; 38; 39; 40; 41].
3. Image quality improvement allows images with much higher resolution and quality to be obtained than with simple interpolation [4; 5; 31; 38; 41; 49].
4. Infrared image analysis uses images invisible in visible light for in-depth analysis of reality [6; 58; 59; 60; 61; 62; 63; 64; 65; 66].
5. Detection of human diseases by analysing images allows changes to be recognised for various diseases, including anthropometric, dermatological, and diabetic foot [58; 59; 60; 61; 65; 67; 68; 69; 70; 71].  
Figure 8: Publications by technology in Research Methodology.  
Object recognition, including road signs, route markers, and obstacles on the road [78; 87; 88; 89; 90; 98; 99; 102; 104].
* [7] Combining multiple data sources through multi-source analysis has been used to identify hidden dependencies [79; 90; 91; 92; 93]
* [8] Mobile robot navigation is very widely analysed due to its potentially wide application, including special-purpose robots that need to be able to navigate on their own to solutions that can be used in autonomous cars [79; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 101].
* [9] Convolutional Neural Networks (CNNs) are the most frequently mentioned type of neural network in image processing [109; 112; 113; 114; 115; 116; 117; 118].
* [10] Pneumonia and COVID-19 were the most frequently studied infections in images due to the recent SARS CoV-2 coronavirus pandemic. This is mostly due to their similarity and the need for their distinction and rapid diagnosis [111; 113; 114; 115; 116; 117; 118].
## 4 Experiments and Results
### Implementation Details  
Our network was implemented using Python 3.8.10 and PyTorch 1.11.0. For the training phase, we utilized a single NVIDIA RTX A5000 graphics card (PNY, Parsippany, NJ, USA), equipped with 24 GB of memory, in conjunction with an AMD EPYC 7551P processor (AMD, Santa Clara, CA, USA). As detailed in Table 1, we initiated the training with a learning rate of 1 \(\times\) 10\({}^{4}\) and a batch size of 1. Throughout the training process, we employed the Ranger [49] optimizer to fine-tune our network. Additionally, we incorporated the standard Dice loss [50] into our network architecture. The dimensions for both input and output data were consistently set to 128 \(\times\) 128 \(\times\) 128.
## 3 Results and Discussion
### Correlation Analysis of Core Microorganisms and Principal Metabolites
Figure 6: Spearman correlation analysis of major differential metabolites and key microorganisms. (**A**) Non-volatile metabolites. (**B**) Volatile metabolites. * _p_\(<\) 0.05 and ** _p_\(<\) 0.01. As shown in Figure 6B, _Aspergillus_ exhibited a strong negative correlation with 12 metabolites, including p-menth-8-en-3-ol, acetate (CAS:89-49-6), linalyl acetate (CAS:115-95-7), etc.) and strong positive correlations with 2 metabolites (cyclohexanecarboxylic acid, methyl ester (CAS:4630-82-4) and 5-methyl-(E)-2-hepten-4-one (CAS:102322-83-8). Five of these metabolites are esters and impart a fruity aroma. The extracellular hydrolases secreted by _Aspergillus_ may have converted these esters to other aromatic substances, thereby altering the aroma profile of summer black tea from fruit-like sweet and floral to fungal during stuck fermentation. _Aspergillus_ is often introduced in fermented foods to produce various metabolites [35]. For instance, it is the core microorganism in Pu-erh tea and catalyzes the production of linalool and its oxides during fermentation [35]. In addition, the crude enzymes secreted by _Aspergillus_ regulate the aroma of traditional green tea or other tea products [35]. [PERSON] et al. demonstrated that _Aspergillus_ is the dominant fungus in Fu brick tea, and its metabolic activity enhances the fungal aroma [35]. The genus _Bacillus_ was negatively correlated with 1-nonanol (CAS:143-08-8) and positively correlated with 5-methyl-(E)-2-hepten-4-one. _Bacillus_ _bruxellensis_ and _Acetobacter indonesiensis_ are known to oxidize 1-nonanol to nonanoic acid [45]. _Bacillus_ also produces volatile substances, including pyrazines, aldehydes, ketones, and alcohols, which are the precursors of aromatic compounds in some alkaline fermented foods, conditions, and wines [46]. _Penicillium multicolor_ synthesizes the aroma precursor substance \(\beta\)-primeverosides [35]. _Penicibacillus_ also a major microorganism in some traditionally fermented foods [44]. However, little is known regarding the impact of _Penicillium_ and _Penicibacillus_ on the production of volatile metabolites during tea fermentation. _Basidiomycota_gen_Incerta_sedis_ is an unknown microorganism that likely plays a key role in shaping the metabolite profile of fermented tea, and warrants further investigation. In summary, microbes alter the quality, taste, and aroma of summer black tea during stuck fermentation by producing extracellular enzymes.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Volatile Compounds Evaluation
#### 3.6.2 The Contribution of Volatile Compounds  
To assess the impact of drying methods on volatile profiles, PLS-DA was conducted using SIMCA 14.1 (Figure S4), with normalized relative peak areas (%) of 60 identified volatile compounds as input variables. The model demonstrated strong explanatory and predictive capabilities. The PLS-DA score plot showed that VFD and ND clustered closely, indicating similar volatile retention, while HD, VD, and MD were distinctly separated, consistent with PCA results.  
Figure 3: The heatmap of volatile compounds in AS fruits under different drying methods (\(n=3\)).  
Figure 2: Principal component analysis (PCA) score plot of volatile compounds in AS fruits under different drying methods (\(n=3\)).  
To evaluate the contribution of individual volatile compounds to group differentiation, Variable Importance in Projection (VIP) scores were calculated using PLS-DA, with VIP \(>\) 1 and \(p\) < 0.05 considered significant [46; 47]. As shown in Figure S5a, 28 compounds had VIP \(>\) 1, indicating their substantial role in distinguishing drying methods (compound details in Table S5). Further statistical analysis identified 12 key volatiles, meeting both VIP \(>\) 1 and \(p\) < 0.05 criteria, confirming their significance in sample classification (Table S6). These compounds could serve as potential biomarkers for differentiating AS fruit samples processed by various drying techniques. To assess potential overfitting, 200 rounds of cross-validation were performed, evaluating the R\({}^{2}\) and Q\({}^{2}\) values. The validation plot (Figure S5b) displayed a steep slope, confirming that the PLS-DA model was not overfitted.
# Sequential Polar Decoding with Cost Metric Threshold
## 2 Preliminaries  
### Polar Codes  
Polar codes harness channel polarization, wherein initially identical independent channels evolve into two distinct sub-channel types: highly reliable and highly unreliable. Through iterative transformations, these sub-channels converge into two polarized extremes: noisy and nearly noise-free. For finite code lengths \(N\), polarization is not complete, resulting in sub-channels of varying reliability between the extremes. The challenge is to select a subset \(\mathcal{I}\) of \(K\) sub-channels from \(0,1,\ldots,N-1\) to encode \(K\) information bits, while the rest form the frozen sub-channels, denoted as \(\mathcal{F}\). A binary \((N,K)\) polar code is defined by the information index set \(\mathcal{I}\) and its complement \(\mathcal{F}\) with \(|\mathcal{I}|=K\), \(|\mathcal{F}|=N-K\), and \(N=2^{n}\), where \(n\in\mathbb{N}\). For some message \(\mathbf{d}=(d_{0},\ldots,d_{N-1})\) and for some codeword \(\mathbf{x}=(x_{0},\ldots,x_{N-1})\), the encoding process is the basic equation \(\mathbf{x}_{0}^{N-1}=\mathbf{d}_{0}^{N-1}\mathbf{G}_{N}\), where \(\mathbf{d},\mathbf{x}\in\{0,1\}^{N}\), and \(\mathbf{G}_{N}\) is the generator matrix of \(N\)-th order, formed as \(\mathbf{G}_{N}=\mathbf{F}^{\otimes n}\), with \(\mathbf{F}\) being Arikan's standard polarizing kernel \(\mathbf{F}\triangleq\left[\begin{smallmatrix}1\\ 0\end{smallmatrix}\right]\) and \(\bigotimes n\) is the \(n\)-th Kronecker power of the matrix \(\mathbf{F}\).
## 3 Results and Discussion
### Whack-a-Mole
#### 3.1.1 Accuracy Rate Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**MMSE**} & \multicolumn{2}{c}{**Kruskal–Wallis**} \\ \cline{3-6}  & **Game Level** & \multicolumn{1}{c}{**Normal (1)**} & \multicolumn{1}{c}{**MCI (2)**} & \multicolumn{1}{c}{**Moderate-to-Severe (3)**} & \\ \cline{3-6}  & & **Mean \(\pm\) SD a** & **Mean \(\pm\) SD a** & **Mean \(\pm\) SD a** & **\(X^{2}\)** & \(p\)-Value** \\ \hline \multirow{3}{*}{Time-limited} & Slow & 0.98 \(\pm\) 0.05 & 0.98 \(\pm\) 0.04 & 0.97 \(\pm\) 0.06 & 0.229 & 0.892 \\  & Medium & 0.99 \(\pm\) 0.03 & 0.99 \(\pm\) 0.03 & 0.88 \(\pm\) 0.10 & 17.147 & 0.000 * \\  & Fast & 0.41 \(\pm\) 0.33 & 0.11 \(\pm\) 0.16 & 0.06 \(\pm\) 0.09 & 10.187 & 0.006 * \\ \hline \multirow{3}{*}{Time} & Slow & 0.99 \(\pm\) 0.03 & 0.99 \(\pm\) 0.03 & 0.97 \(\pm\) 0.06 & 1.057 & 0.590 \\  & Medium & 1.00 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 0.95 \(\pm\) 0.10 & 4.859 & 0.088 \\ \cline{1-1}  & Fast & 0.97 \(\pm\) 0.06 & 0.86 \(\pm\) 0.19 & 0.80 \(\pm\) 0.20 & 6.247 & 0.044 * \\ \hline \hline \end{tabular}  
* SD indicates standard deviation.
* \(p<0.05\). The null hypothesis that the medians of the three populations are equal is rejected.  
\end{table} 
Table 4: Posterior comparisons of the different groups by test scores using Kruskal–Wallis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Game Level**} & \multicolumn{2}{c}{**Friedman**} \\ \cline{3-6}  & **MMSE** & **Slow(A)** & **Medium(B)** & **Fast(C)** & \multirow{2}{*}{\(X^{2}\)} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-6}  & & **Mean \(\pm\) SD a** & **Mean \(\pm\) SD a** & **Mean \(\pm\) SD a** & **\(X^{2}\)** & \(p\)-Value** \\ \hline \multirow{3}{*}{Time-limited} & Normal & 0.98 \(\pm\) 0.05 & 0.99 \(\pm\) 0.03 & 0.41 \(\pm\) 0.33 & 28.167 & 0.000 * \\  & MCI & 0.98 \(\pm\) 0.04 & 0.99 \(\pm\) 0.03 & 0.11 \(\pm\) 0.16 & 19.419 & 0.000 * \\  & Moderate-to-Severe & 0.97 \(\pm\) 0.06 & 0.88 \(\pm\) 0.10 & 0.06 \(\pm\) 0.09 & 20.600 & 0.000 * \\ \hline \multirow{3}{*}{Time-unlimited} & Normal & 0.99 \(\pm\) 0.03 & 1.00 \(\pm\) 0.00 & 0.97 \(\pm\) 0.06 & 6.500 & 0.039 * \\  & MCI & 0.99 \(\pm\) 0.00 & 1.00 \(\pm\) 0.00 & 0.86 \(\pm\) 0.19 & 7.000 & 0.030* \\ \cline{1-1}  & Moderate-to-Severe & 0.97 \(\pm\) 0.06 & 0.95 \(\pm\) 0.10 & 0.80 \(\pm\) 0.20 & 11.217 & 0.004 * \\ \hline \hline \end{tabular}  
* SD indicates standard deviation.
* \(p<0.05\). The null hypothesis that the medians of the three populations are equal is rejected.  
\end{table} 
Table 5: Posterior comparisons of different speeds based on test scores using a Friedman Test. different (\(p<0.05\)). This result indicates that, for the subjects in the moderate-to-severe group, each speed is significantly different from the other two speeds.
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 4 Construction of the Hierarchical BRB Performance Assessment Model for UAVs
### Pivotal Performance Metrics Design  
Prior to comprehensively assessing the performance for each follower, some pivotal performance metrics are required to be designed as the inputs to the basic layer of the hierarchical BRB performance assessment model for UAVs. First, based on the kinematics of the followers (5), two time-varying kinematic pivotal performance metrics are designed as follows:  
\[\begin{split}\mathcal{M}_{11}^{i}(t)&=\frac{1}{| \mathcal{N}_{i}|}\sum\limits_{j\in\mathcal{N}_{i}}\|(q_{j}(t)-q_{i}(t))-( \Delta_{j}-\Delta_{i})\|,\\ \mathcal{M}_{12}^{i}(t)&=\frac{1}{|\mathcal{N}_{i}| }\sum\limits_{j\in\mathcal{N}_{i}}\|p_{j}(t)-p_{i}(t)\|,i=1,\ldots,N,\end{split} \tag{9}\]  
where \(|\mathcal{N}_{i}|\) denotes the neighbor amount of the \(i\)th follower and \(\|\cdot\|\) stands for the Euclidean norm. The two metrics indicate the average error between the \(i\)th follower and its neighbors in terms of the position and the linear velocity, respectively, while both reflect the distributed state consensus degree of the \(i\)th follower. It should be noted that the closer the two metrics are to zero, the better the performance of the follower can be. Then, oriented to non-kinematics, two pivotal performance metrics are determined for each follower as well. One of them is the residual energy, which can be abstracted into the following monotonically decreasing continuous function \(\Psi(\cdot)\)  
\[\mathcal{M}_{21}^{i}(t)=\Psi_{i}(q_{i}(t)),t\in[0,+\infty),i=1,\ldots,N. \tag{10}\]Here, one supposes that the more energy that remains, the better performance of the follower. Furthermore, the equipment value is taken as another non-kinematic pivotal performance metric satisfying  
\[\mathcal{M}_{22}^{i}\in\Phi,i=1,\ldots,N, \tag{11}\]  
where \(\Phi\) is a non-empty finite set composed of elements representing the equipment value of the followers. Note that the equipment value is fixed for each follower and the assumption that the higher equipment value means the better performance is made.  
Figure 2: The architecture of the hierarchical BRB performance assessment model for UAVs.
# Three-Dimensional Surface Reconstruction from Point Clouds Using [PERSON]'s Elastica Regularization
###### Abstract  
Euler's elastica energy regularizer, initially employed in mathematical and physical systems, has recently garnered much attention in image processing and computer vision tasks. Due to the non-convexity, non-smoothness, and high order of its derivative, however, the term has yet to be effectively applied in 3D reconstruction. To this day, the industry is still searching for 3D reconstruction systems that are robust, accurate, efficient, and easy to use. While implicit surface reconstruction methods generally demonstrate superior robustness and flexibility, the traditional methods rely on initialization and can easily become trapped in local minima. Some low-order variational models are able to overcome these issues, but they still struggle with the reconstruction of object details. [PERSON]'s elastica term, on the other hand, has been found to share the advantages of both the TV regularization term and the curvature regularization term. In this paper, we aim to address the problems of missing details and complex computation in implicit 3D reconstruction by efficiently using [PERSON]'s elastica term. The main contributions of this article can be outlined in three aspects. Firstly, [PERSON]'s elastica is introduced as a regularization term in 3D point cloud reconstruction. Secondly, a new dual algorithm is devised for the proposed model, significantly improving solution efficiency compared to the commonly used TV model. Lastly, numerical experiments conducted in 2D and 3D demonstrate the remarkable performance of [PERSON]'s elastica in enhancing features of curved surfaces during point cloud reconstruction. The reconstructed point cloud surface adheres more closely to the initial point cloud surface when compared to the classical TV model. However, it is worth noting that [PERSON]'s elastica exhibits a lesser capability in handling local extrema compared to the TV model.  
[PERSON]'s elastica; 3D reconstruction; variational method; dual method +
Footnote †: journal: Applied Sciences
## 5 Discussion
The absence of these prerequisites will directly impede the implementation and operation of the rural intelligent emergency management system. Secondly, the intelligent emergency management mode involves multidisciplinary technological innovation research in areas such as artificial intelligence, big data, the Internet of Things, etc., which are unevenly distributed across rural regions in China. On one hand, advanced technology may lack applicable scenarios in these areas; on the other hand, it may fail to meet the actual needs of rural communities. This imbalance in technological development poses difficulties for promoting the rural intelligent emergency management mode effectively. Thirdly, there is an absence of technical and managerial standards in its application that can lead to confusion during the implementation process of the rural intelligent emergency management system and hinder achieving the desired outcomes. Lastly, government regulation also presents challenges to adopting and supporting this mode due to various reasons resulting in uncertainty that can impact both its speed of promotion and successful implementation. In the future, the construction and implementation of rural intelligent emergency management mode need to be addressed from multiple perspectives. Firstly, it is crucial to prioritize disaster infrastructure and information construction, including establishing a robust disaster warning system and enhancing monitoring and forecasting capabilities in the meteorology, geology, and hydrology fields. Additionally, strengthening the information infrastructure development in rural areas is essential. Secondly, promoting the comprehensive integration of theory with cutting-edge technologies such as artificial intelligence, big data, and the Internet of Things (IoT) should be emphasized for improving efficiency and accuracy in emergency management. Simultaneously, scientific research must be reinforced to establish a scientific and standardized management system tailored to rural characteristics. Furthermore, during the construction process of a rural intelligent emergency management mode, attention should also be given to verification procedures along with standardization efforts supported by appropriate policies. Prior to implementation, thorough verification tests and pilot studies are necessary to ensure feasibility while emphasizing the development of standardized work processes and operating procedures for enhanced efficiency. Moreover, governments should increase investment in this mode while encouraging social resources' participation through pluralistic investment mechanisms.
## 4 DOPA for Solving the Optimal Delay Problem
### DLOO Framework for Predicting the Transmission Power  
The transmission power of each UN is crucial for solving the MD1 problem. In this section, we aim to develop a DL framework that can quickly generate the transmission power for each UN at the beginning of each timeframe. The proposed DLOO framework incrementally updates the weights based on accumulated experience.  
The DLOO framework mainly includes three aspects. First, the deep belief network (DBN) is trained offline to ensure that the neural network can perform satisfactorily using past historical data. The DBN training algorithm used in this study is provided in Algorithm 3.  
Then, the trained DBN is used online to predict the transmission power of each UN. As shown in Algorithm 4, for a new UN, a new corresponding channel state is first generated. The input channel parameters are sent to the MEC server, which then runs the DLOO algorithm. The trained DBN takes the channel state information as the input and gives the output, which is the transmission power of the UNs. Based on these predicted transmission powers, the required results can be quickly obtained by solving the MD1 model. Once calculated, this dataset may be stored in the computer memory as a set of sample data to update the DBN module. Finally, as the dataset size increases, the DBN module is updated with the new dataset.
```
1:Build training set \(\{(g_{ij},\ P_{i})\}\) by solving the MD1 model;
2:Train the DBN in an unsupervised way;
3:Add the output layer on the top layer, and use the BP method to adjust the top layer weights;
4:Use the BP to fine-tune the weights of all the layers.
```
**Algorithm 3** Training of DBN  
The experiments showed that when the newly added sample data made up 1/5 th of the total dataset, the network's prediction accuracy decreased, indicating that the DBN module in the DLOO needed updating. During the update phase, a batch of training samples was extracted from the memory, and the BP algorithm was used to directly adjust the weight of the DBN module. The DBN module then updated its parameters. The updated DBN could accurately predict each UN's transmission power, according to the new weight. Subsequent observations of new channel states prompted repeated iterations and continuous updates of the DBN module.
```
1:Enter the channel state information vector into the DBN module to obtain each UN's transmission power.
2:Based on the given transmission power, transform the MD1 into a linear programming problem.
3:Solve the linear programming problem. The channel state information and the transmission power \((g_{ij},\ P_{i})\) make up the new data.
4:Add the data to the memory. If the dataset size is increased by 1/5, update the DBN module using Algorithm 3.
```  
**Algorithm 4** DLOO framework for power prediction