esa-sceva/satcom-chunk-collection huggingface dataset:
# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
###### Abstract  
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. In this paper, we address the problem of deep neural network training under sample deficiency by investigating several sample-efficient deep learning techniques. We concentrate on applying these techniques to skin burn image analysis and classification. We first build a large-scale, professionally annotated dataset of skin burn images, which enables the establishment of convolutional neural network (CNN) models for burn severity assessment with high accuracy. We then deliberately set data limitation conditions and adapt several sample-efficient techniques, such as transferable learning (TL), self-supervised learning (SSL), federated learning (FL), and generative adversarial network (GAN)-based data augmentation, to those conditions. Through comprehensive experimentation, we evaluate the sample-efficient deep learning techniques for burn severity assessment, and show, in particular, that SSL models learned on a small task-specific dataset can achieve comparable accuracy to a baseline model learned on a six-times larger dataset. We also demonstrate the applicability of FL and GANs to model training under different data limitation conditions that commonly occur in the area of healthcare and medicine where deep learning models are adopted.  
self-supervised learning; federated learning; data augmentation; medical image analysis; burn severity +
Footnote †: journal: Applied Physics
## 4 Results and Discussion  
### Phase 1: Assess the Potential and Maturity for IoT Solution Implementation  
A compilation of questionnaire answers is described in this section. Only the most relevant data are presented, analyzed, and discussed, as shown in Table 5. The results are divided into topics, as is the survey. Percentage was chosen for a better understanding and comparison.
# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 5 Results and Discussion
### Training CNN Models on Generated Images  
After training the six Conditional Generative Adversarial Networks (CGANs) and generating the synthetic images, we employ several CNN models to evaluate their quality. Specifically, we employ state-of-the-art models such as VGG-Face, ResNet-50, FaceNet, and DeepFace, along with our custom CNN model. For the custom CNN model, all input images are standardized to a resolution of \(256\times 256\) pixels, with three color channels (RGB). The CNN processes the input image starting with the first layer, which consists of 32 convolutional filters, each of size \(3\times 3\).  
A series of convolutional filters with varying sizes (32, 64, 128, and 256) are applied in sequential pairs to extract features at different scales. After each pair of convolutional filters, a max pooling layer with a \(2\times 2\) filter is used to reduce the spatial dimensions of the feature maps. Following the convolutional and max pooling layers, a fully connected layer is added to integrate the learned features for further processing.  
The network architecture includes two dense layers, each comprising 4096 nodes, to process the features more deeply. Dropout is employed as a regularization strategy to mitigate over-fitting, with a dropout rate designed to prevent the network from becoming overly reliant on any single feature. Additionally, to ensure the stability of the learning process and prevent overfitting, a momentum of 0.5 is maintained during training.
# Raman 2D Peak Line Shape in Epigraphene on SiC
###### Abstract  
We measured a 2D peak line shape of epitaxial graphene grown on SiC in high vacuum, argon and graphene prepared by hydrogen intercalation from the so called buffer layer on a silicon face of SiC. We fitted the 2D peaks by Lorentzian and Voigt line shapes. The detailed analysis revealed that the Voigt line shape describes the 2D peak line shape better. We have determined the contribution of the homogeneous and inhomogeneous broadening. The homogeneous broadening is attributed to the intrinsic lifetime. Although the inhomogeneous broadening can be attributed to the spatial variations of the charge density, strain and overgrown graphene ribbons on the sub-micrometer length scales, we found dominant contribution of the strain fluctuations. The quasi free-standing graphene grown by hydrogen intercalation is shown to have the narrowest linewidth due to both homogeneous and inhomogeneous broadening.  
Keywords:epitaxial graphene; silicon carbide; Raman spectroscopy; 2D peak line shape; G peak; charge density; strain +
Footnote †: journal: Applied Sciences
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Storage Design
#### 3.1.4 Leaching Brine Management  
Approximately 7 m\({}^{3}\) of water will be required to dissolve 1 m\({}^{3}\) of salt [24], about six times more than that corresponding to each open cavern, which represents a demand during the leaching process of at least 3 million m\({}^{3}\) of fresh water per cavern. As the Torca Salada stream and the Homino river are located near the project site, it is feasible to use their waters by requesting the corresponding water use permit from the Ebro Hydrographic Confederation.  
The volume of brine obtained will be greater than the volume of fresh water, since it comes out loaded with salt. Adding the volume of water that we introduce and the volume of each leached cavern, we obtain a brine volume of approximately 3.5 million m\({}^{3}\) (Equation (2)).  
\[\mathrm{V}_{\mathrm{brine}}=\mathrm{V}_{\mathrm{water}}+\mathrm{V}_{\mathrm{ rock}} \tag{2}\]  
In order to assess the destination of the brine as a commercial by-product, the quantity obtained must be calculated (Equation (3)).  
\[\mathrm{m}_{\mathrm{brine}}=\rho_{\mathrm{water}}\times\mathrm{V}_{\mathrm{ water}}+\rho_{\mathrm{rock}}\times\mathrm{V}_{\mathrm{rock}} \tag{3}\]  
The densities of fresh water, salt rock, and produced brine are 1, 2.16, and 1.17 g/cm\({}^{3}\) respectively, resulting in a brine mass of 4.2 million tons.  
The brine is to be discharged into a specially constructed surface raft for subsequent sale to the chemical industry.
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 5 Conclusions  
The Legendre-Galerkin matrix (LGM) method was developed in this study to solve systems of differential equations of motion. As shown, the advantage of this spectral method is that it converts the governing differential equation of a given problem to a system of algebraic equations, based on a set of orthogonal Legendre polynomials. The final solution leads to a good estimate of the solution for a system of differential equations. In the present research study, the selected differential equations were typical of single degree (SDOF) and multi-degree-of-freedom (MDOF) structural systems.  
In order to prove the accuracy of the proposed method in the response calculation of SDOF and MDOF structures, a number of numerical examples for damped and undamped structural systems under free or forced vibrations were developed and discussed. When available, exact solutions were taken into account for the comparative analysis of LGM predictions, otherwise, the results of the numerical linear Newmark-\(\beta\) method were used to verify the estimates from the developed LGM method. The overall comparative data showed that the LGM method is of high accuracy in estimating the response of SDOF and MDOF systems and can be thus effectively employed in the solution of fundamental motion equations of structures.  
Figure 17: Free vibration analysis of a SDOF system. Comparison of \(u_{5}(t)\) as a function of time, as obtained using the LGM method or the linear Newmark-\(\beta\) method.  
**Author Contributions:** Conceptualization, M.M., M.R.B., B.J., C.B. and M.A.N.; methodology, M.M., M.R.B.; software, M.M., M.R.B. and C.B.; validation, M.M., M.R.B. and M.A.N.; formal analysis, C.B., M.A.N., M.A.H. and S.M.D.; investigation, C.B., M.A.H., M.A.N. and S.M.D.; writing--original draft preparation, M.M. and B.J.; writing--review and editing, C.B., M.A.N., B.J., S.M.D. and M.A.H.; visualization, M.M., M.R.B., B.J. and C.B.; supervision, C.B., M.A.N., S.M.D. and M.A.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Supporting data will be made available upon request.  
The authors declare no conflict of interest.
###### Abstract  
The application of hole cleaning devices can effectively solve the hole cleaning problem during the drilling of horizontal wells. Improving the hole cleaning performance is directly related to subsequent drilling time and cost. In this paper, the solid-liquid two-phase hydrodynamic characteristics under the action of the V-shaped hole cleaning device for drilling horizontal wells were studied experimentally and numerically. The decay law of spiral flow induced by the V-shaped hole cleaning device was obtained numerically using the commercial CFD code \"Fluent\" by changing the blade rotational speed, blade helix angle, and consistency coefficient of drilling fluid. The helix angle will cause the flow direction of local fluid to deflect. The swirl intensity of spiral flow increases significantly with an increase in rotational speed. The hole cleaning performance was better when the helix angle was from about 10 to 20 degrees. Increasing the fluid consistency coefficient will rapidly make the spiral flow decay, resulting in a shorter effective action distance and a worse hole cleaning effect. The simulation results were validated against the experimental results of the flow loop. Both experimental and simulation results showed that the hole cleaning performance was improved by using the V-shaped hole cleaning device. The very good agreement achieved between the results has presented an opportunity to study the hole cleaning effect of a hole cleaning device in unsteady state conditions. The results will provide theoretical guidance for efficient hole cleaning using V-shaped hole cleaning devices for horizontal well drilling.  
n pacs: 23
## 4 Discussion
Currently, some studies have confirmed the role of NO signaling in promoting fungal metabolite accumulation in response to elicitors [36; 37], but the investigation of the related mechanism using multi-omics combined analysis has not been reported. In this study, to investigate the factors affecting triterpene accumulation, we measured NO content. On the fourth day of addition, the NO content was significantly higher than that of the WET group. Transcriptome assays and indices simultaneously revealed that NO was involved in defense mechanisms and triterpene accumulation in _S. Sanghuang_, with significant differences between WET and ET. This finding also suggests that the elicitor may have induced NO signaling and consequently promoted intracellular triterpene accumulation. The focus of this research was on the application of fungal elicitors in the liquid fermentation of _S. Sanghuang_ and their effect on secondary metabolism, studied through multi-omics analysis. The results indicated that fungal elicitors promote the accumulation of terpenoids, ketones, coumarins, and lignans, particularly ketones. However, the possible mechanisms behind these findings require further investigation. As medicinal mushrooms, the use of fungal elicitors in fungal liquid fermentation is critical for the study of triterpenes, the detection of fungal metabolites with medicinal properties, the preservation of existing natural resources, and the exploration of new products. The large-scale production of active ingredients, such as _S. Sanghuang_ triterpenes, is both of great interest to human health and has practical applications. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/jof9060604/s1](https://www.mdpi.com/article/10.3390/jof9060604/s1)]([https://www.mdpi.com/article/10.3390/jof9060604/s1](https://www.mdpi.com/article/10.3390/jof9060604/s1)). Conceptualization, X.T. ; methodology, [PERSON] ([PERSON]), T.W. and L.Z. ([PERSON]); software, [PERSON] ([PERSON]), T.W. and G.W. ; resources, X.T. ; data curation, [PERSON] ([PERSON]), T.W., Y.F., [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] ([PERSON]), [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); supervision, X.T. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (grant numbers 31772376 and 31301827) and the Special Foundation for Taishan Scholars of Shandong Province (tsqn202211188). Not applicable. All data presented in the study are included within the article and its supplementary files or have been deposited in NCBI with accession PRJNA779190. **Acknowledgments:** We thank the National Natural Science Foundation and the Special Foundation for Taishan Scholars of Shandong Province for their financial support. **Conflicts of Interest:** The authors have no conflict of interest to declare.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 3 Motivation and Contribution  
In the context of massive IoT, many devices are connected to the Internet and interact with one another to share and gather information. This requires a large amount of bandwidth and a high data rate. An increase in data-hungry applications drains the battery-powered IoT nodes at a higher rate, leading to the battery drainage and energy exhaustion challenges. Hence, the current research needs a sustainable energy solution for future wireless networks. This paper proposes a weight-based user-scheduling algorithm and a genetic-algorithm-based power optimization model in a multi-tier heterogeneous network. The SIC-based interference cancellation technique was used at the receiving end to mitigate the interference challenges.  
The key contributions are as follows:  
* A modified genetic-algorithm-based optimization model is proposed to allocate optimal power with which a particular access point transmits to a specific user to increase network efficiency.
* A weighted user-scheduling algorithm is proposed, which takes both the distances and received signal strength into account to select a user for downlink transmission.
* The suggested algorithms were evaluated for their effectiveness and compared with other techniques, including those based on particle swarm optimization, equal power, and genetic algorithms.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### Bi-LSTM for Track Outliers Detection  
Recurrent neural network (RNN) is a recursive neural network whose input are sequences data. RNN has the characteristics of memory and parameter sharing, so it can learn the nonlinear features of sequences with high efficiency. Although RNN can deal with nonlinear time sequences effectively, it has the problem of gradient explosion and disappearance for time sequences with a too long delay. The Long Short Term Memory Network (LSTM) is an evolutionary network of traditional recurrent neural networks, which can improve the gradient explosion and disappearance problem. However, the predicted output of LSTM is determined by the input of previous multiple moments, and may lead to the loss of useful information when extracting data features. In many cases, the prediction is affected by the input of previous and subsequent multiple moments.  
Therefore, this paper adopts the Bi-LSTM network with a forward and backward structure for track outliers detection [31].
# Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs
## 2 Related Work
### Few-Shot Learning  
Few-shot learning methods can be roughly divided into two categories: (1) metric-based methods [24; 25; 26; 27; 28] and (2) meta-learning-based methods [29; 30; 31; 32]. The metric-based learning method mainly includes two modules: the embedding module and the measurement module. The samples are embedded in the vector space through the embedding module, and then the similarity score is given according to the measurement module. By calculating the distance between the to-be-classified sample and the known classified sample, find the adjacent categories to determine the classification result of the to-be-classified sample. A siamese neural network learns metrics from data and then uses the learned metrics to compare and match samples of unknown classes. The two siamese neural networks share a set of parameters and weights [24]. Matching Networks is a new type of neural network structure that combines Metric Learning and Memory Augment Neural Networks. This network uses the attention mechanism and memory mechanism to accelerate learning label prediction for unlabeled samples with few samples [25]. Based on the prototype network of mixed attention, the prototype network is used to design instance-level and feature-level attention mechanisms to highlight key instances and features, which significantly improves performance and speeds up convergence [26] DN4 (deep nearest neighbor neural network) and Different from other metric-based learning methods, DN4 finds the category closest to the input image by comparing the local descriptor between the image and the category, that is, replaces the simple image feature vector with the local description [27]. DPGN is the first GNN method to use distribution propagation, and it is a relatively novel method that uses both instance-level and distribution-level relations [28]. The latter performs fast model parameter updates given the gradients of several training instances. LSTM-based meta-learning [29] learns the exact optimal algorithm through the step size of each dimension of the stochastic gradient. The main idea of MAML [30] and MT-net [31] is to train the model through two-stage training few-shot gradient update and then be able to quickly reason about new tasks.  
In fact, previous few-shot learning methods are not suitable for knowledge graph inference models because some features are lost when the few-shot learning model is pooled. Then, the logical structure of the knowledge graph can improve the interpretability of the model. At the same time, this paper believes that more entity information can be obtained by mixing invisible feature vectors and explicit feature vectors. This paper uses high-order domain aggregation vectors to enhance entity-dominant features.
## 4 Results and Discussions
### Comparison of Tests and Simulation Results
#### 4.1.2 Temperature Change with Depth  
Figure 6 shows the temperature variation in the soil at different times along the depth, located at a distance of 0.5 D. In the figure, M represents measurement and S represents simulation. The numerical results of the soil temperature at a horizontal distance of 0.5 D were consistent with the trend observed in the test results. It is worth noting that the numerical results of the FR pile better fit the test results than those of the FRPC pile. This is because the FRPC pile was modeled as a homogeneous material, and the phase change steel balls in the pile were not modeled separately. However, the results still demonstrated the thermal response characteristics of the phase change pile. The numerical results of the FRPC and FR piles reasonably described the test results. The findings of [PERSON] et al. [41] corroborated the observed patterns in the numerical results.
## 5 Upper Bounds on LDPC Codes Decoding Thresholds
### Upper Bound on LDPC Codes Decoding Thresholds Holding for \(i_{1}\geq 2\)  
As far as the above mentioned first upper bound on LDPC codes thresholds (called \(s^{*}_{\text{bound}}\) in [10]) is concerned, in [20] the following lemma was proved.  
_Lemma:_ Given \(\lambda(x)\) of minimum degree \(i_{1}\geq 2\), and defining \(z(s,t)\) as \(z(s,t):=\frac{s+(i_{1}-1)t}{2}\) and \(A_{j}\) as \(A_{j}:=\frac{1}{(j-1)^{2}\lambda_{i_{1}}^{2}}\), being \(\text{W}(\cdot)\) the Lambert-W function, the following asymptotic approximation holds:  
\[f(s,t)=2\sum_{j=2}^{d_{r}}\rho_{j}\text{W}(A_{j}z(s,t)\,\text{e}^{z(s,t)})+O(t ^{-1}). \tag{18}\]  
Recalling that \(\frac{\text{d}\text{W}(x)}{\text{d}x}=\frac{1}{x+\text{e}^{\text{W}(x)}}\):  
\[f_{t}(s,t)=2\sum_{j=2}^{d_{r}}\rho_{j}\frac{z_{t}(s,t)\text{e}^{z(s,t)}(1+z(s, t))}{z(s,t)\text{e}^{z(s,t)}+\text{e}^{\text{W}(A_{j}z(s,t))-\text{log}A_{j}}}. \tag{19}\]  
Applying Equation (14) to Equations (18) and (19) we get:  
\[\left\{\begin{array}{l}2\sum_{j=2}^{d_{r}}\rho_{j}\text{W}(A_{j}z(s,t)\text {e}^{z(s,t)})=t\\ 2\sum_{j=2}^{d_{r}}\rho_{j}\frac{z_{t}(s,t)\text{e}^{z(s,t)}(1+z(s,t))}{z(s,t) \text{e}^{z(s,t)}+\text{e}^{\text{W}(A_{j}z(s,t))z(s,t)-\text{log}A_{j}}}=1 \end{array}\right.. \tag{20}\]  
and Equation (17) can be rewritten as:  
\[\left\{\begin{array}{l}2\sum_{j=2}^{d_{r}}\rho_{j}\text{W}(A_{j}z(s,t)\text {e}^{z(s,t)})-t=0\\ 2\sum_{j=2}^{d_{r}}\rho_{j}\frac{z_{t}(s,t)\text{e}^{z(s,t)}(1+z(s,t))}{z(s,t) \text{e}^{z(s,t)}+\text{e}^{\text{W}(A_{j}z(s,t))z(s,t)-\text{log}A_{j}}}-1=0 \end{array}\right.. \tag{21}\]  
Its solution (\(s^{*}_{\text{bound}}\), \(t^{*}_{\text{bound}}\)) determines the bound \(\sigma^{*}_{\text{bound}}=\sqrt{\frac{2}{s^{*}_{\text{bound}}}}\), which is valid \(\forall i_{1}\), unlike the other two (\(s^{*}_{\text{approx}}\) and \(s^{*}_{\text{Jensen}}\)) reported in [10], which hold both for \(i_{1}=2\) only.
## 2 Relevant Methodology
### Convolutional Neural Networks (CNNs)  
Convolutional Neural Networks (CNNs) [26; 27] are increasingly utilized in diverse fields such as image recognition, language interpretation, and speech recognition. Compared to traditional Multi-Layer Perceptron (MLP) [28], CNNs reduce the number of trainable parameters by using a specific number of convolutional kernels, effectively diminishing the complexity of the model. This mechanism of weight sharing not only mitigates the risk of overfitting but also enhances the generalization capability of the model. Incorporating pooling operations within the CNN architecture further reduces the number of neurons, bolstering the robustness of the model against variations in the input space. Additionally, the scalability of CNNs enables the construction of deep network structures. Such deep models possess a greater expressive capacity to handle more complex classification challenges.  
[PERSON] et al. [29] proposed an adaptive 1D-CNN autoencoder that adeptly extracts rich and robust features from raw vibration signals, a critical aspect of fault diagnosis. This method effectively minimizes the shift between different data domains when combined with CORAL. It allows the model to accurately and promptly detect cross-domain faults without needing target domain label samples. Extensive experimental testing has validated the efficacy and practicality of this method. In summary, structural optimizations in CNNs make these models more efficient and robust. Meanwhile, the novel autoencoder approaches demonstrate formidable performance in specific applications, such as fault diagnosis.