esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### PM\({}_{2.5}\) and PM\({}_{10}\) Concentration Measurements  
During our research, more than 900 individual (duration of exposure is 30 min) values and approximately 300 daily-averaged values of PM\({}_{2.5}\) and PM\({}_{10}\) concentrations in the air were obtained. Some statistical characteristics of PM concentrations are in the Table 1.  
Time series plots of PM\({}_{10}\) and PM\({}_{2.5}\) concentrations in the atmosphere in Sevastopol are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PM\({}_{10}\)**, **mg m\({}^{-3}\)** & **PM\({}_{2.5}\)**, **mg m\({}^{-3}\)** \\ \hline Max & 0.114 & 0.057 \\ Min & 0.00 & 0.00 \\ St.Dev. & 0.013 & 0.009 \\ Average & 0.019 & 0.014 \\ Total number & 912 & 903 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical characteristics of PM daily-averaged concentrations in the atmosphere in Sevastopol.  
Figure 3: Time series plots of PM\({}_{2.5}\) (**a**) and PM\({}_{10}\) (**b**) concentrations in the atmospheric air in Sevastopol.  
As it can be seen in Figure 3, concentrations of PM in the air in Sevastopol in 2021-2022 are slightly higher, as compared to previous years. The average ratio PM\({}_{2.5}\):PM\({}_{10}\) is approximately 0.7. These data are in agreement with [16], where local PM\({}_{2.5}\):PM\({}_{10}\) ratio estimates are between 0.2 and 0.8. Nevertheless, these ratios vary from 0.05 to 0.97 in our study, depending on air masses transported over Sevastopol. The maximum of these ratios were observed from May till August 2021.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 5 Conclusions  
Aspect-based multimodal sentiment analysis is an important task in emotion analysis. In this paper, we proposed a visual enhancement capsule network (VECapsNet) based on interactive learning and built an aspect-based multimodal sentiment dataset (MTCom) by crawling reviews from the Meituan website. First, an adaptive mask memory capsule network is proposed for text feature learning. The model combines the memory networks and the capsule networks to integrate context semantic information into local word embedding and obtain capsule features related to aspect phrases by an adaptive mask attention mechanism. Second, we propose a novel aspect-guided capsule network for learning image sentiment features, which captures the local image information related to aspect phrases using a Text2 vision attention mechanism to guide the sentimental learning on image feature capsules. Last, the multi-round fusion module is constructed by enhancing the text sentimental representations using the aspect-guided image features for aspect-based sentiment classification. The results of extensive experiments on the publicly available single-text evaluation datasets, the multimodal Multi-ZOL dataset, and on our MTCom dataset show that our method proposed in the paper performs satisfactorily on the different multimodal aspect-based sentiment analysis tasks. Compared with the existing models, VECapsNet can better capture the local semantic features correlated to the aspect phrases in text and images, which is more suitable for general text-based multimodal user-generated reviews. In the future, we will further study how to more deeply integrate long-text semantics and image interactions into the capsule features and apply our method to different multimodal sentiment analysis tasks.  
Conceptualization, Methodology, Original draft preparation, Y.Z.; Data processing, Experiments and analysis, Z.Z.; Supervision, Funding acquisition, Review and editing, D.W. and S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62172086, 61872074).  
Not applicable.  
Figure 4: Average context length and image number on two multimodal datasets. (**a**,**b**) show the statistics of samples on the Multi-ZOL dataset and the MTCom dataset, respectively.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors are grateful to [PERSON] and [PERSON] for providing the datasets used in the experiments.  
The authors declare no conflict of interest.
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 2 Blockchain: Background Knowledge
### Structure of Blockchain  
* **Block:** In blockchain, verified transactions are stored in a block. Any node in a blockchain system can initiate a transaction and send a copy to other available nodes on the network. When network nodes verify that all stored transactions in the block are valid, the next step is to add a block to the blockchain. Each block contains certain information such as transaction time, number of occurred transactions, etc. Each block is linked to the hash of the previous block via a hash function ( Hash functions such as MD5 and SHA-256 transform an input message into a fixed size value called a hash, making it impossible to go back to the original text, but possible to check that a copy is exact by comparing generated hashes [17]) to ensure immutability. A unique hash code is then assigned to each block which is generated by the hash function and differentiates a current block from other blocks.
* **Merkle tree:** It is defined as a binary search tree in which tree nodes are linked to each other using hash pointers. The blockchain transactions are arranged in a Merkle tree structure. The hashes of all nodes are combined to create the Merkle tree [18], in which each two child nodes are combined together into one node called parent node as shown in Figure 1. This process is repeated from down to top to reach the root of the tree. The root of the Merkle tree verifies all the transactions in the block. An advantage of Merkle tree is that it allows us to prove both the integrity and validity of data. If an adversary attempts to change the transaction then it is needed to change all the subsequent block hashes.
* **Digital signature:** It ensures the data validity by using a cryptographic algorithm. The digital signature scheme is comprised of three components. The first component is a key generation algorithm, which generates a key pair known as a private key and public key. The private key is kept secret and is used to sign a message, whereas the public key is available to the public and is used to verify the message whether the message has been signed with the corresponding private key. Second component is a signing algorithm, which uses the given private key to creates a signature on the input message. The third component is a verification algorithm, that takes three parameters as input, such as signature, a message, public key and validates the message signature by using the public key. The advantage of using a digital signature is to prevent non-repudiation so participants on the blockchain network cannot deny their own activities.  
Figure 1: Merkle tree structure on blockchain.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### OFDM-OQAM Transceiver
#### 4.1.1.1 Transmitter Description
\begin{array}{l}\bar{P}[0]=1,\\ \bar{P}[c]^{2}+\bar{P}[K-c]^{2}=1,\ \ \ \ \ \text{for}\ \ c=1,2,\ldots,\left\lfloor\frac{K}{2} \right\rfloor,\\ \bar{P}[c]=0\ \ \ \ \ \text{for}\ \ c=K,K+1,\ldots,\frac{L_{p}-1}{2},\end{array} \right. \tag{8}\]  
where \(c\)= \(1,2,...,\left\lfloor K/2\right\rfloor\) and \(\bar{P}[0]\) for \(k=K,K+1,...,\frac{(L_{p}-1)}{2}\). For this system, the values of \(\bar{P}[c]\) are initialised and designed as shown in Table 5 for different values of overlapping factors. Figure 17 illustrates the simulated prototype impulse response \((p[n])\) at different overlapping factors \((K)\) using Equation (7). In the figure, it can be observed that, at \(K\) = 1, the FBMC scheme adopts the rectangular pulse shaping of OFDM systems.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 4 Analysis of the Feature Compatibility Problem  
When using cluster federation, software companies often combine a central on-premise cluster with additional clusters from one or more hosted Kubernetes vendors such as EKS [32], AKS [33] and GKE [34]. The clusters provided by these vendors are hosted product types, which free the software companies from a tedious installation and configuration process that is typically the case with installer or distribution K8s products. Moreover, hosted K8s products are the preferred choice for running applications in production environments, as these hosted K8s cluster products are security hardened, come with service-level agreements (e.g for availability) and extensive certification and provide support for governance and policy compliance.  
The open-source distribution of K8s is highly customizable through various configuration manifests that are composed of configuration files, plug-in components and libraries. However, these manifests are not part of the standardized RESTful APIs of K8s. Therefore, vendors can decide to hide these manifests from the customer and lock them in a particular configuration setting. A specific problem with vendors of the hosted product type is that these vendors hide the all components of the K8s control plane; as such, a cluster administrator cannot modify the configuration manifest of the control plane components such as the API server. Finally, as the RESTful APIs are organized in different API groups, not all of these API groups must be supported by a K8s vendor in order to be certified by the cloud-native computing foundation.  
This brings great challenges to achieving the aforementioned consistency requirements in cluster federations as explained in Section 1. In our previous work [35], we have found that 30 out of 162 documented features of the open-source distribution of K8s v1.13 are not consistently activated or de-activated in the aforementioned leading vendors of the hosted product type, and these feature settings cannot be modified by the cluster administrator due to hidden configuration manifests of the Kubernetes control plane.  
The following subsections describe three types of configuration manifests that are causing the difficulties: (i) APIServer configuration, (ii) KubeletConfiguration and (iii) configuration of network plugins.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 2 Related Work
### Attention Mechanism  
In the field of computer vision, the application of attention mechanisms began with the introduction of spatial and channel attention. [PERSON] et al. [28] proposed the Squeeze-and-Excitation (SE) network, which introduces channel attention by learning the importance of different channels to enhance the quality of feature representations. The Transformer architecture, originally developed by [PERSON] et al. [29] for natural language processing tasks, is centered around the self-attention mechanism. [PERSON] et al. [30] applied a pure Transformer architecture to image classification tasks by segmenting images into fixed-size patches and treating them as sequences, achieving remarkable results. In addition, attention-related work such as [31; 32; 33; 34; 35] proposed improvements in self-attention.  
The self-attention mechanism has shown tremendous potential in the field of remote sensing image classification. [PERSON] et al. [36] proposed a spatial-spectral Transformer for hyperspectral image classification, successfully combining spatial and spectral information to significantly improve classification accuracy. Their study shows that the self-attention mechanism effectively captures complex spatial-spectral dependencies within hyperspectral data. [PERSON] et al. [37] introduced the SpectralFormer, a Transformer architecture specifically designed for HSI classification. By rethinking the characteristics of hyperspectral data, SpectralFormer successfully applies the Transformer to this domain, showcasing the advantages of self-attention mechanisms in handling high-dimensional spectral data. [PERSON] et al. [38] introduced a hierarchical Transformer that is aware of group dynamics, further improving classification performance.  
Despite the significant achievements of attention mechanisms and Transformer architectures on large datasets, they face unique challenges in few-shot learning scenarios. [PERSON] et al. [21] noted that few-shot learning requires models to quickly adapt to new tasks with very few labeled samples. However, Transformer-based models generally possess numerous parameters and necessitate substantial datasets for effective training, which contradicts the essence of few-shot learning. Moreover, in few-shot conditions, having a large number of model parameters tends to result in overfitting problems [39]. Therefore, designing models appropriate for few-shot learning while maintaining the advantages of attention mechanisms is an important research direction.
## 3 Results and Analysis
### Analysis of Results  
The overall test results are as follows. In terms of adhesive strength, the maximum reclaimed butyl rubber content to ensure the performance of the plain specimen was 56% (#6) based on the previous research, as shown in Figure 13. However, it was confirmed that a reclaimed butyl rubber content of 56% (#6) poses difficulties in ensuring proper performance due to performance degradation when exposed to chemically deteriorated environments, and the reclaimed butyl rubber content that can meet all five chemical aging conditions applied in this study was found to be 44% (#5).  
The peel strength also showed a similar tendency to that of the adhesive strength. As shown in Figure 14, a maximum butyl rubber content to ensure the performance of the plain specimen was 67% (#7). However, it was confirmed that a reclaimed butyl rubber content of 67% (#7) poses difficulties in ensuring proper performance due to performance degradation when exposed to the actual chemical deterioration environment, and the reclaimed butyl rubber content that can meet all five chemical aging conditions applied in this study was found to be 44% (#5). This result suggests that, in the actual mix design using reclaimed butyl rubber, the value of performance degradation due to deterioration factors should be considered in order to ensure proper performance.  
Figure 12: Adhesion performance test result of the NaCl-treated sample.  
Figure 13: Adhesion performance test result (Crc board surface).  
Figure 15 shows a comparison between the scope of reclaimed butyl rubber content to ensure adhesion performance suggested in the previous research and that of proper content considering the deterioration factors derived from the present research. As shown in the figure, the application of a reclaimed butyl rubber replacement ratio of up to 67% to the total rubber content makes it possible to ensure adhesion performance when deterioration factors are not taken into consideration. However, when exposed to chemical deterioration factors, the performance degrades as the reclaimed butyl rubber content increases. This suggests that proper performance can be ensured only when the reclaimed butyl rubber content is limited to 44%.
# PFAS: The Journey from Wonder Chemicals to Environmental Nightmares and the Search for Solutions
### Current Classification of PFAS  
[PERSON] et al. [10] defined PFAS as an aliphatic substance containing a \(-\)C\({}_{n}\)F\({}_{2n+1}\) (where \(n\) is at least 1) moiety within its structure while the OECD/UNEP Global PFC Group proposed to include the chemical with at least one aliphatic perfluoro carbon moiety (\(-\)C\({}_{n}\)F\({}_{2n}\)\(-\)) as PFAS [11]. However, the exact definition is still under discussion. PFAS molecules can be also categorized as _short-chain_ (with \(\leq\)7 carbon atoms in the chain) and _long-chain_ with \(\geq\)8 carbon atoms in the chain) compounds (See the different color classifications in Table 1). Long-chain molecules have a higher bioaccumulation ability when compared with short-chain molecules. The unique chemical structure gives PFAS molecules a strong dielectric property which makes them excellent electrical insulators and able to prevent the electric current through materials.  
PFAS substances are also grouped into two broad categories as polymeric and non-polymeric molecules. PFOA and PFOS are two well-known _non-polymeric_ PFAS substances and these non-polymeric substances can be further divided as perfluoroalkyl and polyfluoroalkyl substances. Perfluoroalkyl substances have totally fluorinated carbon chain where polyfluoroalkyl substances have at least one (but not all) fluorinated carbon atom in the chain.  
_Polymeric_ PFAS substances are sub grouped as fluoropolymers (ex: Teflon/PTFE), side-chain fluorinated polymers, and perfluoropolyethers [2; 10; 11]. Physical characteristics like molar mass, molar volume, surface area, and melting point of PFAS are increasing with the number of carbon atoms in the chain. PFAS is also well-known for its non-stick, low-friction properties, and water-repellent properties. But due to having a very strong C-F bond PFAS molecules are also chemically inert and hence they remain stable and unreactive in the environment for a long time or forever and referred asForever Chemicals\". Thus, they became biologically and environmentally persistent and show bioaccumulation potential. PFAS molecules are water-soluble and dissolve in water and infiltrate groundwater using natural solubility pathways. This can lead to ecological impacts and drinking water supplies [12; 13; 14; 15].
# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 3 Method
### Labeling the Driving Behavior  
The proposed method makes use of supervised learning algorithms which need training data accompanied by true classification labels. One of the major issues in classifying driving behavior is to have labels that identify the behavior as safe or unsafe in an objective way. For this purpose we refer to the work presented by [PERSON] et al. [6], where by combining vehicle speed, and longitudinal and lateral accelerations, they classify any given driving moment as safe or unsafe according to the dynamic equilibrium of the vehicle. In particular, taking the value of the acceleration vector (\(\bar{a}\)) lying on the plane of the vehicle motion and its speed (\(V\)), the authors split out the plane (\(V,\bar{a}\)) into two areas representing safe and unsafe driving domains according to the Equation (1).  
\[|\bar{a}_{max}|=g\cdot\left[0.198\cdot\left(\frac{V}{100}\right)^{2}-0.592\cdot \left(\frac{V}{100}\right)+0.569\right] \tag{1}\]  
Equation (1) represents the maximum value of the tolerated acceleration when varying the vehicle's speed. As a matter of fact, it defines a quadratic relationship between acceleration and speed which  
Figure 2: Motion sensor orientations with respect to the vehicle. Vector B represents the direction of Earth’s magnetic field.  
shows that the maximum acceleration decreases when speed increases. Acceleration values that exceed \(|\bar{a}_{max}|\) are unsafe points because in these conditions the car tires are unable to sustain the forces generated and there is a loss of grip with possible car skidding.  
Using the vehicle speed obtained by the OBD-II signals and \(a_{lat}\) and \(a_{lon}\) from the smartphone accelerometer, each time window of collected tracks has been marked with a binary label generated by means of Equation (1). Labeled records have then been used to train the classifiers and to evaluate the resulting performances. Notice that \(a_{lat}\) and \(a_{lon}\) have not been included into the classification features to avoid the possibility that the classifier could learn the relationship between speed and acceleration, which would lead to the definition of the classification labels, and distorting the results.
## 2 Results
### Wine Characterization
Styrene (#14), methyl octanoate (#30), \(\beta\)-damascenone (#77), decanoic acid (#97) and Unknown 51.9854 (RI 2072, ions 85.0290, 69.0696, 41.0386, 71.0488, 43.0179, C15H26O3) were distinctive in the 2018 vintage; meanwhile, 19 compounds were distinctive for 2017. Additionally, in the 2018 vintage, ethyl 3-methylbutyrate (#7), 1-octanol (#42), ethyl phenylacetate (#73) and 2,4-di-tert-butylphenol (#99) decreased 3- to 5-fold (FC \(>\) 3.0, \(p<0.001\)). Figure 4: Venn diagram of entities present in La Changa 2017 (dark blue), La Changa 2018 (orange), Los Dolores 2017 (red) and Los Dolores 2018 (light blue). Interestingly, 2,4-di-tert-butylphenol (#99) was 5-fold higher in the 2017 vintage than in 2018. To our knowledge, it has not been reported in Cabernet Sauvignon wines; however, was detected with the same abundance in red and white wines from Portugal [37]. Furthermore, Marselan wines (Cabernet Sauvignon \(\times\) Grenache varieties) inoculated with _S. cerevisiae_ presented higher concentrations of 2,4-di-tert-butylphenol than in spontaneously fermented wines [38]. Persimmon-inoculated wines showed similar behavior [39]. This compound has antifungal and antioxidant characteristics [40] but no aromatic properties have been reported yet. Moreover, the compound was first detected at the end of alcoholic fermentation in the 2017 vintage and increased after malolectic fermentation (data not shown). Although produced by non-Saccharomyces yeasts [41] and lactic-acid bacteria [40], 2,4-di-tert-butylphenol could be a potential marker in vintage differentiation as microbial terroir cannot be discarded. The Venn diagram (Figure 4) showed that 54 compounds were present in all wines and enabled the selection of unique compounds for each one. Los Dolores 2017 wine presented 12 distinctive compounds (4-methyl benzaldehyde, trans-linalo oxide (furanoid), ethyl trans-2-butanoate, furfuryl ethyl ether, 4-ethylguaiacol, 4-ethylphenol, methyl salicylate, 2-undecanol and Unknowns 13.2265; 29.3449; 48.8990 and 52.3099), and Los Dolores 2018 wines presented only six (p-xylene, diphenyl ether, isobutyl hexanoate, ethyl ether hexanoic acid and Unknown 16.5912). La Chang 2017 wines presented three unique compounds (ethyl hydrogen succinate, acetoin and Unknown 54.4274), and the 2018 vintage presented two (1-butanol and 4,1,1-dimethyl-trans-cyclohexanol). However, compounds