esa-sceva/satcom-chunk-collection huggingface dataset:
# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 3 Methods
### Problem Definition: Few-Shot Classification  
1. Few-shot classification's main goal is to efficiently train a model to categorize unlabeled samples using a small number of labeled samples from each category. Two key datasets, the support set S and the query set Q, which both share the same label space, are required for each independent classification job T. While the query set Q contains unlabeled data for classification predictions, the support set S is made up of labeled samples. The N-way K-shot classification problem entails N separate categories that need to be identified and K-labeled samples for each category in the support set S.
2. The few-shot learning problem has been successfully addressed using meta-learning techniques, which have received widespread validation. In order to enable the model to quickly adapt to new tasks, meta-learning models train both base learners and meta-learners. The parameters of base learners are optimized over a training subset of a task during the meta-training process, whereas the parameters of meta-learners are optimized over a testing subset of a task. We use the episodic training strategy in this study, which has been widely used in many relevant studies [8; 29; 33]. The core concept of episodic training is to sample a lot of few-shot learning tasks that are similar to test tasks from a sizable, labeled training dataset. We assume that the distribution of training tasks in episodic training is comparable to that of testing tasks.  
As a result, we can improve the performance of the model in the testing tasks by training a model that performs well on the training tasks.  
The N-way K-shot issue is specifically divided into training tasks and testing tasks within episodic training, both of which are specified as follows: \(T=S\cup Q\) where \(S=\{(x_{i},y_{i})\}_{i=1}^{N\times K}\) and \(Q=\{(x_{i},y_{i})\}_{i=N\times K+1}^{N\times K+T}\). Here, \(T\) stands for the quantity of query samples, and \(x_{i}\) and \(y_{i}\in\{C_{1},\cdots C_{N}\}=C_{T}\subset C\) stand for the \(i\)-th input data and, correspondingly, its label. The dataset used for training or testing contains a collection of categories called \(C\). In each set, the support set \(S\) is treated as the labeled training set, and the model is trained on this set to minimize its prediction loss on the query set \(Q\), even though both training tasks and testing tasks are samples from the same task distribution. Up until the model converges, this training process iterates over each set.  
This is a 2-way 2-shot model, with nodes outlined by dashed lines representing the query set and nodes without dashed lines representing the support set. The long blue arrows represent hierarchical residuals, with only one node per layer depicted in each layer. The upper-left section illustrates the construction of the initial graph, while the upper-right section depicts node feature updates, and the lower part shows edge feature updates. The lower-left corner demonstrates the prediction of query node labels.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Selected Plant Species with Antioxidant Activity
#### 4.11.5 Vitis vinifera L.  
Vitis vinifera L. is a plant that belongs to the family Vitaceae. It comes from Asia and is cultivated predominantly in Europe, mainly for its edible fruit. The most numerous groups of active substances and the most potent antioxidants of the grape vine are proanthocyanidins, condensed tannins, and leucocyanidins, with 65% of grape polyphenols being oligomeric proanthocyanidins (OPCs). These consist mainly of 2-4 catechin or epicatechin units and a small number of oligomer units--approx. 5-7. These are substances that have strong antioxidant effects, scavenge free radicals, and are characterized by anti-inflammatory, antimutagenic, and lipid peroxidase-inhibiting effects. Active compounds that have a beneficial effect on the skin are found primarily in the seeds, skin, and stalks--these are primarily resveratrol, proanthocyanidins, and flavonoids (quercetin) [221]. Resveratrol is an antioxidant that controls skin cancer and UV light-mediated skin aging. Polyphenols have skin-lightning properties which are related to their antioxidant activity able to reduce melanin biosynthesis [220].  
Ingredients derived from _Vitis vinifera_ are known for their diverse applications in cosmetic formulations. Vitis Vinifera seed extract, in particular, is reported to serve multiple functions, including acting as an anticaries agent, antidandrruff agent, antifungal agent, antimicrobial agent, antioxidant, flavoring agent, light stabilizer, oral care agent, oral health care drug, and sunscreen agent. Many other _V. vinifera_-based ingredients primarily function as skin-conditioning agents, with some also exhibiting antioxidant properties. Additionally, five key ingredients--seed extract, fruit powder, juice, juice extract, and skin extract--are recognized for their use as flavoring agents. Of these, all except the seed extract and skin powder also function as colorants [32].  
V. vinifera-derived ingredients are typically used in cosmetic formulations at relatively low concentrations. Vitis Vinifera leaf extract is incorporated at levels of up to 3% in leave-on products, such as perfumes. Vitis Vinifera fruit extract and Vitis Vinifera juice are used at concentrations of up to 2% in rinse-off skin cleansers, paste masks, and mud packs. All other _V. vinifera_-based ingredients are generally included at amounts of less than 1% in formulations [32].  
Vitis vinifera extracts for cosmetics are primarily obtained using glycerin or alcohol-based extraction methods, with preservatives like potassium sorbate and sodium benzoate ensuring stability. Grape fruit and leaf extracts are typically glycerin-based, while grape seed extract undergoes alcohol extraction, filtration, distillation, and spray-drying, with a 133:1 plant-to-extract ratio. Grape skin extract is produced through aqueous steeping, fermentation, and vacuum evaporation and is commonly used as a color additive [32].
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 3 System Modeling and Problem Description
### Coverage Model  
The edge-server coverage model aims to achieve efficient task processing and distribution to improve the system performance and user experience. This model considers the random generation of differently sized task datasets performed by each user, as well as the different computing abilities and coverage capacities of the edge and cloud servers. In this model, to promote synergy between the cloud and edge, the cost is minimized while ensuring quality of service. An effective strategy is to deploy edge servers in crowded places but to allow users in sparsely populated places to directly offload tasks to the cloud servers for processing.  
Assuming that the edge-server coverage radius is \(r\), a user is covered by the edge server when the distance \(d\) between the user and edge server is less than \(r\). The Haversine formula can be used to calculate \(d\). Taking the user latitude and longitude coordinates as \((lat_{1},lon_{1})\) and the edge-server latitude and longitude coordinates as \((lat_{2},lon_{2})\), we obtain  
\[d=2r\times\arcsin(\sqrt{\sin^{2}(A)+C\sin^{2}(B)}), \tag{1}\]  
where \(A=(lat_{2}-lat_{1})/2\), \(B=(lon_{2}-lon_{1})/2\), \(C=\cos(lat_{1})\cos(lat_{2})\).  
When a user is covered by an edge server, the user's tasks are offloaded to the nearest edge server for processing in order to reduce data transmission latency. When a user is not covered by an edge server, their tasks are offloaded to a cloud server for processing. It is important to note that if an edge server reaches its computing capacity limit \(\mu\) due to the arrival data volume \(\eta\), even if it covers the user, the tasks cannot be offloaded to that particular edge server. In such cases, the user has the option to offload the tasks to the nearest edge server or cloud server that has not reached its computing capacity limit and can cover the user.
## 3 The Proposed Model
### Hyperparameter Optimization: SSA  
In this work, the hyperparameters of the RetinaNet model such as number of epochs, batch size, learning rate, and momentum are adjusted by the design of SSA. The SSA is simulated from the aggregation performance of salps that procedure a chain of salps and then hunt and move. The salp chain was developed from two kinds of salps, leader and follower [21]. The leader is the salp at the head of chains. Individual salps at the back of chains are followers. During the salp technique, food source \(F\) was determined as the individual with optimum fitness amongst every individual. The food source of _tth_ order is \(F(t)\). The steps of SSA technique are provided under.  
1. Initialization of the population. In order for every individual, the places were arbitrary numbers amongst the upper as well as lower limits. They then compute fitness of every individual and sorted them. An individual with minimal fitness is the food source \(F(t)\). \(t=1\), since one iteration was ended.
2. The population place was upgraded. The leader place was upgraded as: \[x_{j}^{i}(t+1)=\begin{cases}F_{j}(t)+c_{1}\big{[}(ubj-lb_{j})c_{2}+lb_{j}\big{]} &c_{3}\geq 0.5\\ F_{j}(t)-c_{1}\big{[}(ubj-lb_{j})c_{2}+lb_{j}\big{]}&c_{3}<0.5\end{cases}\] (9) where \(i=1\), i.e., the count of leaders is 1. It ranks primary from the populations. \(j=1,2\cdots D\). \(F_{j}\), \(ub_{j}\), and \(lb_{j}\) are \(F(t)\), \(ub\), and \(lb\) from the \(j\)th dimensional correspondingly. \(c_{2}\) and \(c_{3}\) implies the arbitrary numbers from zero and one. \(c_{2}\) affects the step length of leader movement. \(c_{3}\) defines if the leader moves forward/backward to food sources. \(T\) signifies the maximal number of iterations. \(c_{1}\) refers the co-efficient of moving length. \[c_{1}=2e^{-(4t/T)^{2}}\] (10) The place of follower is: \[x_{j}^{i}(t+1)=0.5\Big{(}x_{j}^{i-1}(t)+x_{j}^{i}\left(t\right)\Big{)}\] (11) where \(i\geq 2\), and is the sequence of followers from the population. \(j=1,2\cdots D\).
3. Compute the fitness of every upgraded individual. The sort of individuals. Upgrade \(F(t)\). Improve \(t\) by 1.
4. If the iteration accuracy condition was attained or \(t=T\), the iteration terminates; or else, go to (2) to remain the iteration.
## 2 ML Methodologies
### Semisupervised Learning
#### 2.3.2 Semisupervised Clustering
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Ref.** & **Description** \\ \hline \multirow{3}{*}{Self-training} & \multirow{3}{*}{[158]} & \multicolumn{2}{c}{In self-training, a model is initially trained on the labeled data and then used to make predictions on the unlabeled data. The confident predictions are added to the labeled set, and the process is iterated to improve the model’s performance.} \\ \hline \multirow{3}{*}{Co-training} & \multirow{3}{*}{[159]} & Co-training involves training multiple models on different subsets of features or data and then using their predictions to label the unlabeled data. The models iteratively update each other by adding the confident predictions, enhancing classification accuracy. \\ \hline \multirow{3}{*}{Multiview learning} & \multirow{3}{*}{[160]} & Multiview learning utilizes multiple views or perspectives of the data to improve classification performance. Each view provides different information and combining them leads to a more comprehensive understanding of the underlying patterns and relationships. \\ \hline \multirow{3}{*}{Generative models} & \multirow{3}{*}{[161]} & Generative models, such as Gaussian mixture models (GMM) or variational autoencoders (VAE), learn the underlying data distribution and generate synthetic samples. These models can be used to generate additional labeled data for training the classification model. \\ \hline \multirow{3}{*}{Graph-based methods methods} & \multirow{3}{*}{[162]} & Graph-based methods construct a graph representation of the data, where nodes represent instances and edges capture relationships. Techniques like label propagation or graph-based regularization propagate labels through the graph to classify unlabeled instances. \\ \hline \multirow{3}{*}{Transductive support vector machines (TSVMs)} & \multirow{3}{*}{[163]} & TSVM treats the labeled and unlabeled data as separate sets and aims to find a decision boundary that separates the labeled instances while considering the unlabeled instances as potential support vectors. It leverages the information in both labeled and unlabeled data for classification. \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Semisupervised classification algorithms for water resources management.
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
###### Abstract  
This paper proposes a new voltage control methodology using the particle swarm optimization (PSO) technique for smart grid loadability enhancement. The goal of this paper is to achieve reliable and efficient voltage profile/stability regulation in power grids. This methodology is based on the decouple power flow equations and the worst-case design technique. Specifically, the secondary voltage control (SVC) problem is formulated as an L-infinity norm minimization problem which considers overall load voltage deviations in electrical power systems as an objective model, and the PSO technique is employed to determine a robust control action which aims to improve voltage profile and to enlarge transmission grid loadability by optimal coordinated control of VAR sources. The methodology was successfully tested on several IEEE benchmark systems.  
evolutionary computation; maximum loadability; particle swarm optimization (PSO); power system control; smart grid; static voltage stability +
Footnote †: journal: Applied Sciences
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 4 Using Our D2S Anonymous Scheme in the MQTT 5.0 Context  
In this section, we describe how we can apply our proposed D2S anonymous scheme in the MQTT 5.0 context.  
[PERSON] et al. [41] has shown how to implement their two-phase challenge-response protocol for enhancing the MQTT authentication, using the MQTT v3.1 [43] CONNECT API. The new MQTT standards v5.0 [5] has extended the MQTT v3.1 API to incorporate a new feature called the enhanced authentication. The enhanced authentication facilitates designers implementing their own security protocols, using the new API called AUTH and the extended fields in the existent CONNECT and CONNACK APIs. The enhanced authentication using these APIs facilitates a query-response style message exchange between a MQTT broker and its clients. The client specifies the authentication method field in the CONNECT packet, this field is included through the following AUTH packets exchanged, and it is also specified in the final CONNACK packet.  
To implement the proposed protocols, one can define our proposed D2S anonymous scheme as an authentication method (a code), and then specifies the code in the CONNECT/AUTH/CONNACK packets. The exchanged messages in our protocols are transmitted as the authentication data in the exchanged AUTH packets.  
Figure 7: The message sequences of the D2S authentication.
###### Abstract  
Analysis of salivary volatile organic compounds (VOCs) may offer a novel noninvasive modality for disease detection. This study aims to optimise saliva headspace VOC analysis and assess longitudinal variation of salivary VOCs. Whole saliva from healthy participants was acquired in order to assess four methodological parameters: saliva collection, volume, dilution, and acidification. Saliva VOCs were analysed using untargeted proton transfer reaction time-of-flight mass spectrometry. Using the optimised method, five saliva samples collected over 3 weeks assessed the longitudinal VOC variability and reproducibility with targeted selected ion flow tube-mass spectrometry analysis. The method of saliva collection influenced VOC detection and was a source of contamination. An amount of 500 \(\mu\)L of whole saliva by passive drool yielded optimal VOCs. Longitudinal variation was negligible with target short chain fatty acids and aldehydes. However, certain compounds showed variability suggesting the influence of potential exogenous factors. Overall, there was an acceptable range of inter- and intraindividual VOC variability. Standardisation with morning sampling after a 6 h fast is recommended demonstrating minimal intersubject variability. Future studies should seek to establish salivary VOC levels in healthy and diseased populations.  
volatile organic compounds; saliva; headspace; variation; direct mass spectrometry; selected ion flow tube-mass spectrometry; proton transfer reaction-mass spectrometry +
Footnote †: journal: Medical  
and removed cellular debris to analyse the supernatant, which may eliminate important salivary components for analysis.  
This study reports on direct headspace VOC analysis with real-time quantification and elimination of preprocessing steps. To mitigate any bias, the collection and analysis of salivary VOCs need to be standardised. Furthermore, before the wider adoption of salivary volatolomics, it is important to establish the variation in VOC profiles both within and between healthy subjects over a defined period.  
This study aims to (i) establish an optimised method to standardise collection for salivary VOC analysis and (ii) assess the intra- and interindividual variability of VOC levels within whole saliva as a potential method for clinical diagnostics.
## 5 Conclusions  
In this preliminary study, we aimed at characterizing in greater detail noise properties in CT imaging with ASiR-V and ASiR by means of spatial noise maps obtained from multiple repeated acquisitions of a homogeneous phantom and of a phantom with different contrast objects. Our results confirm the potential of ASiR-V and ASiR in reducing noise (up to 60% or more) as compared with conventional FBP. In this regard, while ASiR-V outperforms ASiR in general, this does not necessarily hold true for the CT imaging of structures with high contrast. Both ASiR-V- and ASiR-reconstructed CT images show a higher spatial nonuniformity of noise than FBP-reconstructed images. This effect increases with increasing blending level of reconstruction and reflects a less effective noise reduction potential of ASiR-V and ASiR at the boundary between different structures.  
In summary, an exhaustive characterization of noise properties in CT imaging with IR algorithms is needed to actually assess the dose reduction potential of this technique and its possible limitations for specific clinical applications. This study suggests that, when using IR algorithms, the conventional approach of estimating the noise level by computing the SD of CT numbers within a given ROI is inadequate and precludes showing any non-negligible spatial nonuniformity of noise, which may vary with the blending level of reconstruction. Overall, our findings indicate that a voxel-based characterization of noise properties is recommended when dealing with a novel IR algorithm or when comparing the performances of different IR algorithms.  
Conceptualization, P.B. and M.G.; methodology, P.B., D.M. and M.G.; software, D.M.; validation, P.B., D.M., and M.G.; formal analysis, D.M.; investigation, P.B., D.M., and M.G.; resources, A.C.T. and M.G.; data curation, P.B. and D.M.; writing--original draft preparation, P.B., D.M., and M.G.; writing--review and editing, P.B., D.M., C.M., S.C., S.D., A.C.T. and M.G.; visualization, P.B., D.M., C.M., S.C., S.D., A.C.T. and M.G.; supervision, M.G.; project administration, M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on reasonable request from the corresponding author.  
The authors would like to thank [PERSON] for a stimulating and constructive discussion.  
The authors declare no conflict of interest.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Construction of Seq2 Seq-Based Restoration Model
#### 4.3.1 Seq2 Seq Model  
In this study, the Seq2 Seq model is used as the basis for constructing a trajectory restoration model. Seq2 Seq is a generalized encoder-decoder structure. It consists of three parts: an encoder, a decoder, and an intermediate state vector that connects the two parts [28]. This underlying model architecture utilizes the RNN cell structure as the core component of the encoder and decoder. The encoder is responsible for extracting features from a variable-length data sequence and encoding this information into a fixed-size state vector. The decoder receives this state vector and generates a variable-length output sequence by learning from it. The model is shown in Figure 6.  
In this paper, the AIS data trajectory points are selected by the model features to determine the input features and step size, and then the divided dataset is input into the model. The encoder accepts an input sequence, \(X=\{x_{1},x_{2},\cdots,x_{l}\}\), and generates a hidden state vector from its information. This vector is a high-level semantic representation of the input sequence, representing the core features of the input sequence. The calculation process is represented as follows:  
\[C=E(\{x_{1},x_{2},\cdots,x_{l}\},\theta_{E}) \tag{11}\]  
where \(C\) is the hidden state vector, \(E(\ )\) is the function of the encoder, and \(\theta_{E}\) is the network parameter to be learned by the encoder. The decoder then generates the output sequence Y of the predicted data step-by-step based on this potential representation \(C\), and the calculation process represents the following equation.  
\[Y=D(\{y_{1},y_{2},\cdots,y_{n}\},\theta_{D},C) \tag{12}\]  
where \(D(\ )\) is the function of the decoder, and \(\theta_{D}\) is the network parameter to be learned by decode.