esa-sceva/satcom-chunk-collection huggingface dataset:
# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 2 Related Work  
Traditional text classification methods are mainly based on feature engineering. Feature engineering includes the bag-of-words model [18] and n-grams, etc. Later, there was research [19; 20] that converted text into graphics and performed feature engineering on graphics. However, these methods cannot automatically learn the embedding representation of nodes. Compared with traditional methods, deep learning-based methods can automatically acquire features, and they can learn the deep semantic information of the text. In terms of deep learning models, [PERSON] 2014 [3] uses a simple convolutional neural network (CNN) for text classification. [PERSON] et al. 2015 [21] proposed a gated recurrent neural network. [PERSON] et al. 2016 [17] proposed the use of hierarchical attention networks to model and classify documents. [PERSON] et al. 2016 [22] introduced the attention mechanism into LSTM. [PERSON] et al., 2020 [23] introduce the Bidirectional Encoder Representation from Transformers (BERT) [10] and the self-interaction attention mechanism. Most attention-based deep neural networks mainly focus on local continuous word sequences, which provide local context information. However, the ability to capture the global characteristics of the relevant information may not be sufficient.  
In recent years, many studies have tried to apply convolution operations to graph data. [PERSON] and [PERSON] 2017 [12] proposed Graph Convolutional Networks (GCNs), and the model has achieved good results on node classification tasks. [PERSON] et al. 2018 [24] proposed a Graph Attention Network (GAT), which uses the attention mechanism [25] to assign weights to the neighborhood information of nodes. Compared with the GCN without adding weights, it has a better promotion. Later, [PERSON] et al. 2019 [13] proposed the TextGCN model, which introduced the Graph Convolutional Network to text classification for the first time. By modeling the text into a graph, the nodes in the graph are composed of words and documents, and a good effect is finally obtained. [PERSON] et al. 2019 [26] introduced a new setting for graph embedding, which considers embedding communities instead of individual nodes.  
Now, many studies try to combine GCN with other models. [PERSON] et al. 2020 [14] input the word and document nodes trained by GCN and BERT's word vector into the BiLSTM classification model, where the input vector of GCN is a one-hot vector. [PERSON] et al. 2020 [15] integrated the vocabulary graph embedding module with BERT and achieved good results in many public datasets, where GCN uses BERT's word vector. In contrast to this, in our method, GCN uses BERT's word vectors and uses dependencies when building the text graph. One graph focuses on extracting dependencies between words, and the other graph focuses on word co-occurrences. We use a two-layer BiLSTM with attention combined with GCN and BERT; one layer is used to focus on the word sequence, the other layer focuses on the sentence sequence, and a residual operation is added after BiLSTM to learn the residual.
## 2 Proposed Methodology
### Hierarchical Graph for Disaster Event Classification
#### 2.2.2 Graph Convolutional Network for Classification  
To learn the relationship between coarse granularity events and fine granularity events, we use the residual gated graph convolutional network (RG-GCN) proposed by [PERSON] and [PERSON] [28]. It is known that the vanilla GCN can define the feature vector \(h_{i}\) as [29]:  
\[h_{i}^{l+1}=GCN\left(h_{i^{\prime}}^{l},\ \left\{h_{j}^{l}:j\to i \right\}\right)=ReLU\left(U^{l}h_{i}^{l}+\sum_{j\to i}\
u^{l}h_{j}^{l} \right), \tag{27}\]  
where \(l\) denotes the layer level, \(h_{j}\) is a set of unordered feature vectors of all neighboring nodes, and \(U,V\) are learnable parameters for the message passing on current node and neighboring nodes. After adding a gating mechanism to the edge [30]:  
\[h_{i}^{l+1}=G\text{-}GCN\left(h_{i^{\prime}}^{l},\ \left\{h_{j}^{l}:j\to i \right\}\right)=ReLU\left(U^{l}h_{i}^{l}+\sum_{j\to i}\varphi_{ij} \bigotimes V^{l}h_{j}^{l}\right), \tag{28}\]  
\[\varphi_{ij}=\sigma\Big{(}\overline{U}h_{i}^{l}+\overline{V}h_{j}^{l}\Big{)}, \tag{29}\]  
where \(\varphi_{ij}\) denotes the edge gates it brings two sets of weight parameters \(\overline{U}\) and \(\overline{V}\) to learn on edges. \(\sigma\) is the sigmoid activation function and \(\otimes\) is the point-wise multiplication operator. Adding the residual mechanism, the RG-GCN is simply denoted as [25]:  
\[h_{i}^{l+1}=RG-GCN\left(h_{i^{\prime}}^{l},\ \left\{h_{j}^{l}:j\to i \right\}\right)=G-GCN\left(h_{i^{\prime}}^{l},\ \left\{h_{j}^{l}:j\to i \right\}\right)+h_{i}^{l}, \tag{30}\]  
With the depth of graph convolution, each event node can aggregate the features of neighboring event nodes to achieve the update to obtain new own features and the weighting of edges in RG-GCN can model the correlation between different disaster events. For all nodes \(l_{i}^{l+1}\) in \(h_{i}^{l+1}\), as described in Equations (27)-(30), the node features at the \(l+1^{th}\) layer will take into account both the features of its own node \(l_{i}^{l}\), the neighboring node \(l_{j}^{l}\), and of the edge \(e_{ij}^{l}\) between \(l_{i}^{l}\) and \(l_{j}^{l}\) at the \(l^{th}\) layer. \(e_{ij}^{l}\) is the edge feature weighted by the edge gates \(\vartheta_{ij}\) and its unweighted initial value can be obtained in the adjacency matrix \(A_{ij}\) described in Equation (23).  
We used a three-layer RG-GCN for learning nodes and edges in the hierarchical graph. We classify the learned event node features for classification. In the multimodal graph, we fused event nodes of the same granularity, as follows:  
\[\hat{y}_{i}=\sigma\Big{(}W_{fc}\cdot\Big{\{}I_{ij}^{l}\Big{\}}\Big{)}\;i\in[ Coarse,Fine],\;j\in[Visual,Audio],l=3 \tag{31}\]
## 6 Discussion
Despite reaching nominal failure for the SCR and exhibiting unsatisfactory performance in the peel separation tests, the geomembrane continues to fulfill its primary function as a hydraulic barrier. With a minimal probability of reaching nominal failure in terms of tensile strength at 0.4%, this translates to a 99.6% probability of successfully preventing the percolation of contaminants from the tailings reservoir. This not only ensures the integrity of the groundwater resources but also enhances the safety of drinking water sources downstream. Moreover, by containing pollutants within the reservoir, the geomembrane plays a pivotal role in mitigating their impact on local flora and fauna. The adequate performance of the geomembrane, specifically within the context of mining tailings reservoirs, aligns with the Sustainable Development Goals (SDGs). Although the SDGs are interdependent, the success of the geosynthetic barrier directly supports goals such as Goal 6 (Clean Water and Sanitation) and Goal 15 (Life on Land), as well as Goal 3 (Good Health and Well-being), Goal 9 (Industry, Innovation, and Infrastructure), Goal 11 (Sustainable Cities and Communities), Goal 12 (Responsible Consumption and Production), and Goal 13 (Climate Action), while also contributing to other SDGs in a transversal manner [15]. Thus, this application of geomembranes underscores their critical contribution to sustainable mining practices and global environmental conservation efforts. Furthermore, by employing non-parametric statistical methods, we provide a viable approach to assessing the probability of success of geomembranes as hydraulic barriers, despite the numerous variabilities among the application scenarios of HDPE geomembranes in mining tailings reservoirs. The findings underscore the importance of considering the unique environmental and operational conditions of each installation when evaluating geomembrane performance. Also, they contribute to a broader understanding of geomembrane durability and reliability, offering valuable guidance for future applications in similar contexts.
## 2 Related Work
Both A-MSDU and A-MPDU are defined by IEEE 802.11n [25] standard for achieving higher throughput at the MAC layer. Although the new frames are smaller than legacy 802.11, they did not improve the network throughput, especially when added with the small payload. Moreover, network performance is highly affected while transmitting the A-MSDUs due to a lack of control and retransmission. Implementing IEEE 802.11n does not aggregate MPDU for voice traffic due to its specific end-to-end delay requirements. [PERSON] and [PERSON] [26] identified that the network's performance is highly degraded in saturated traffic conditions when multiple nodes access the medium for transmitting voice traffic. The authors proposed a QoS-aware adaptive A-MPDU aggregation scheme (QAA-MPDU). The QAA-MPDU scheme optimises throughput performance by aggregating MPDU for voice traffic and reducing protocol overhead. [PERSON] et al. [27] developed an adaptive A-MPDU MAC scheme. [PERSON] and [PERSON] [28] developed a frame concatenation with block acknowledgement for providing a QoS guarantee for time-sensitive applications. However, the proposed scheme is only suitable for client-server applications. To provide fairness and enhance the performance of EDCA, [PERSON] and [PERSON] [29] proposed an Adaptive TXOP Allocation (ATA) scheme. In the ATA scheme, stations adjust the TXOP interval based on traffic load and delay bound required by the application. The TXOP interval is increased in two steps: at first, STA increases its TXOP to satisfy the QoS guarantee required by its packet queue and then when the traffic load is low. For reliable communication, the receiving station acknowledges every received packet. However, this mechanism decreased the overall network performance due to an increasing number of acknowledgement packets. As the solution, researchers proposed various approaches utilising and optimising block acknowledgement ([PERSON] et al. [30]). The optimized block acknowledgement (O-BlockAck) developed by [PERSON] et al. [30] reduces the delay and increases the number of users within the network. The o-BlockAck scheme uses a single service and a mixture of services used on the node. The empirical results have shown that fragment size 12 is more appropriate for a mixture of services, and supported users may be increased from 30 to 35 within a network. Another mechanism for improving the EDCA scheme is called holding time aggregation (HTA), developed by [PERSON] et al. [31]. In HTA, each STA calculates the time a packet takes for its journey and the time an application may tolerate delay. However, the proposed HTA scheme is only suitable for UDP applications. A review of the literature on frame aggregation and block acknowledgement schemes in providing better QoS and reducing transmission overhead is summarised in Table 1.
## 3 Experiment
The corresponding slot for measurements can be significantly extended in a more compact and flexible manner by constructing the BFL using a PM photonic crystal fiber (PCF) [35], which exhibits a notable temperature-insensitivity, in place of the conventional PMF, and an electrically, instead of manually, actuated PC with endless control of polarization [36]. Finally, the BFL was followed by a booster erbium doped fiber amplified (EDFA) to compensate for insertion losses of \(\approx\)10 dB and guarantee the same level of received power at the diagnostic equipments so as to ensure a fair comparison between measurements with and without the BFL.
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 4 The Interacting Dimensions of this Complex Socio-Technical Ecosystem
### Statistical and Discrete Mathematics
#### 4.5.2 Learning and Graph Theory for Deep Learning  
Usually, the point where graph theory meets deep learning is in graph neural networks (GNNs), where NNs are the architecture being referred to when deep learning (DL) is mentioned [66]. The NN architecture is built upon the concept of perceptrons, which are inspired by the interaction of neurons in the human brain. In the graph, there are a couple of different types of traversals, i.e., directed and undirected. If the graph is directed, the traversal follows a single direction, otherwise it is undirected. Building on the concept of traversals, let an undirected graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) be representative of a set ofvertices \(\mathcal{V}=\{1,2,\cdots,V\}\), which represent the set of gNBs to which users can connect and the edge set \(\mathcal{E}=\{1,2,\cdots,E\}\) represent the possible connection links between gNBs and users. The state of a given user at a given time slot can be defined using the characteristic parameters concerning its association with the gNB to which it is connected. In addition, user-gNB association, which is a stochastic process, has been studied extensively in the literature using matching theory with parallels to game theory in [67], as an optimization problem in [68], and modeled as a stochastic game in [69].  
The attractiveness of these approaches is the long-term biased received power of the respective BS. However, 6G will comprise dense and hyper-dense deployments of small cells and a frustratingly high number of mobile devices that want to connect to the internet via these gNBs. Therefore, a deeper understanding of the user-gNB association mechanisms as well as the more complex schemes that provide the best QoS is a requisite. In this regard, future user-gNB associations need not aim only at improving system utility, sum rate, and fairness but also how quickly and efficiently those are attained in hyper-dense deployments. Therefore, the efficiency in addressing this as a primary objective should be performed with the idea that due to the IoT, the scale of the network may increase in terms of its heterogeneity [69]. In addition, the amount of information that the network infrastructure is expected to process at each particular instant increases. Both the gNB and the user devices need to be equipped with learning capabilities so that they can be able to learn from their previous experiences. Learning from previous experiences and comparing them with current information is used to achieve better resource usage performance in the infinite horizon. In order to decide how many resources to be allocated to each cluster, a policy of maximizing the expected gain per unit resource and an unbounded knapsack problem can be formulated and a greedy policy utilized.
# Accurate Determination of Geographical Origin of Tea Based on Terahertz Spectroscopy
## 2 Experiment
### Data Analysis  
In theory, the energy level of molecules such as DNA, proteins, and amino acids, for vibration and rotation lies within the THz band. Thus, every type of green tea is characterized by a unique THz absorption spectrum. This is what allows THz-TDS detection to be employed to detect biological components [18; 19]. We calculated the absorption of our green tea samples from THz time-domain data [20].  
The following is the equation for calculating the absorption coefficient:  
\[\alpha=\frac{2}{d}\ln\frac{4n(\omega)}{\rho(\omega)[n(\omega)+1]^{2}},\ \rho(\omega)=\frac{4[n^{2}(\omega)+\kappa^{2}(\omega)]^{\
icefrac{{1}}{{2}}}}{ \left[n(\omega)+1\right]^{2}+\kappa^{2}(\omega)}e^{-\kappa(\omega)d\omega/c}, \ \alpha(\omega)=\frac{2\omega\kappa(\omega)}{c} \tag{1}\]  
where \(d\) is the thickness of sample; \(n\) is the refractive index of sample; \(\kappa\) is the extinction coefficient.  
Absorption spectra were obtained and moved upward along the vertical axis, as shown in Figure 2, to make the tea samples' absorption characteristics as clear as possible. To observe the dielectric properties of the tea samples, the time domain signals (Figure 3a), absorption (Figure 3b), and refractive index (Figure 3c) of Maofeng tea were obtained. The THz absorption spectra of the four types of tea are similar and do not exhibit evident absorption peaks; i.e., the types of tea cannot be easily identified based on their absorption peaks. The four graphs in Figure 2 are only examples of the datasets of each tea sample and, in general, the datasets are much harder to distinguish. The datasets for different teas are not presented in one figure, because they would have been crossed, which would have made classification using the (second order) polynomial fit or exponential fit very challenging. To determine the THz absorption spectrum of the four types of tea, we employed pattern-recognition methods [21].  
Figure 1: Schematic diagram of the THz-TDS spectrometer.  
Figure 3: _Cont._  
Figure 2: THz absorption spectroscopy of different green tea.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 9 Multidimensional Evaluation
### Average Delivery Probability Throulout the Network
#### 9.1.1 Black-Hole Attack  
Looking first at the data points with zero black holes, it can be seen that the MaxProp protocol offered the highest chance of a message being successfully delivered. The graph shows the average taken across all five different attack times. This is not a surprise as the MaxProp Protocol was a more complex algorithm that was developed specifically to improve upon more basic protocols such as the Binary Spray and Wait algorithm and the Epidemic protocol. The surprise was that the PRoPHET protocol was only marginally more likely to deliver the message than the Epidemic protocol.  
On introducing attempted disruption, MaxProp and Spray and Wait reacted to an increasing amount of black hole nodes in a similar way. This could be seen by the similar gradient of the linear trend lines. The gradient of the MaxProp trend lines was, however, slightly less negative and this was seen in future sections as well, which showed that MaxProp was more resilient to black-hole attacks. This also meant that it was a valid assumption that the MaxProp algorithm would never perform worse than the Binary Spray and Wait regardless of any black hole nodes added, as long as the current data were representative of those data points that could be estimated using extrapolation techniques. This went to show that when the main priority was a message reaching its desired destination, regardless of any black hole nodes, MaxProp was the algorithm to choose over Binary Spray and Wait.  
PRoPHET and Epidemic reacted differently to the introduction of black holes. Epidemic demonstrated a significant reduction in delivery probability as black holes were introduced and PRoPHET showed an increasing proportion of messages delivered. This could not continue indefinitely (as at 100% black holes no messages would be delivered) but it did show that the protocol was more resilient to black holes. This however, did not explain why the probability was increasing. It was most likely due to the reduced traffic in the network. Due to the fact that PRoPHET records all contacts, it is highly likely that  
Figure 7: The average delivery probability across multiple experiment settings.  
in areas densely populated with nodes the protocol was less effective due to the constant updating of delivery predictabilities.
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 6 Performance Evaluation
### Simulation Scenarios Evaluation
Figure 9 depicts termination time (\(\mathcal{T}\)) under RPF as a function of the connectivity radius (\(r_{c}\)) for three different sets of \(\alpha,\beta,\gamma\) and for a case of a non-energy harvesting environment. It is observed that termination time under energy harvesting environments is increased compared to the non-energy harvesting one. Note that in non-energy harvesting environments the proposed mechanism reduces to PF since there are no extra delays due to the nodes' non-operational states. Finally, it is shown that the RPF mechanism outperforms PF regarding coverage under the proposed four chain Markov model, without additional message costs, since it takes into account the  
Figure 8: Coverage (\(C(t)\)) under Probabilistic Flooding and Robust Probabilistic Flooding for \(\alpha=0.3\), \(\beta=0.2\), \(\gamma=0.6\) and \(r_{c}=0.10,0.20,0.30\) as a function of time (\(t\)). Figure 7: Coverage at termination time \(\mathcal{T}\) under Probabilistic Flooding for three different sets of \(\alpha\), \(\beta\) and \(\gamma\) for \(r_{c}=0.10\), \(0.20\) and \(0.30\). nodes' operational states. However, under the proposed mechanism, every node has to maintain the set of nodes that need to be covered. This introduces an extra memory overhead that is not expected to be discouraging for applying RPF in wireless sensor network environments since it is bounded by the number of neighbors.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 2 Materials and Methods
### Methodology
#### 2.2.1 Preprocessing Stage  
Some retinal images have lower quality because the images are taken under different lighting conditions. The common characteristics of images include: (1) exposure to external light ruins some regions close to the rim; (2) the edges of the hemorrhage are blurry, and the contrast is low. Therefore, we preprocess retinal fundus images with two approaches: (a) contrast enhancement and image brightness and (b) image sharpening. These approaches will be discussed subsequently.
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art  
Many modern vehicles, equipped with ADAS, rely solely on onboard sensors, from which perception sensors are the only mechanisms that link road infrastructure and vehicle behaviour. Those systems show reliability drawbacks under inclement weather and light conditions, regardless of how technically advanced they are. This is usually acceptable if the human is entirely responsible for the driving task and the system is only used to support them (up to SAE L2). As mobility progresses towards higher automation, however, high reliability becomes imminent. One of the promising solutions to achieve high reliability is through system redundancy. At the vehicle level, redundancy is achievable with sensor fusion but, in the broader context of the traffic level, road network digitalisation is required. In this section, we will discuss perception sensors and sensor fusion as representative vehicle-level technologies, and digital maps and vehicular communication for traffic-level redundancy.