esa-sceva/satcom-chunk-collection huggingface dataset:
# Sudden Event Prediction Based on Event Knowledge Graph
## 3 Preliminaries
### Event Knowledge Graph Model  
In the event knowledge graph, the event occurs at  
during which the event occurs or lasts. There are elemental relations (including hasAction, hasParticipant, hasObject, hasLocation, hasTime) between events and event elements.  
The event knowledge graph is an event-based knowledge base built for different application domains, containing an event ontology and a large number of event instances. Its definition can be described as follows.  
\[EKG::=\langle EOs,EIs\rangle \tag{2}\]  
where _EOs_ and _EIs_ denote the event ontology and the event instance library, respectively. The event ontology includes: (1) event class concepts, (2) event class relations, (3) assertion and inference rules for the event classes. The definition of the event ontology can be described as follows.  
\[EOs::=\langle ECs,Event\_Relations,Rules\rangle \tag{3}\]  
_ECs_ represent the set of event classes. \(Event\_Relations\) represent the set of event class relations, including a taxonomy relation (_is_a_) and logical relations (including causal, isComposedOf, follow, and concurrence). \(Rules\) represent a collection of inference rules for implementing various kinds of reasoning for event classes. The event ontology contains an event hierarchy model and a set of event scenario models. The event scenario model is formed by associating the event classes through logical relations. Furthermore, it reflects the pattern about the occurrence of an initial event and a set of subsequent events triggered by it. The event hierarchy model consists of event classes and a taxonomy relation.  
The event instance library contains a large number of event instances, defined as follows.  
\[EIs::=\langle Events,Event\_Relations\rangle \tag{4}\]  
where _Events_ represents a collection of event instances and \(Event\_Relations\) represents the relations between event instances. An event instance is a concretization of the event class concept and represents a specific event. Event instances can commonly be obtained from text or from a database. The same logical relations exist between event instances as between event classes, expressing the internal logic and evolutionary patterns between events. The structure of the event knowledge graph model is shown in Figure 1.  
Event knowledge graphs are represented and stored through extended RDF(s) to achieve a normalized description and storage of event knowledge, i.e., event knowledge graphs are represented and stored through RDF triples. The main triple schema of relations is defined in Table 1.  
Figure 1: Event knowledge graph model structure.
## 4 Proposed Architecture and Implementation
### Considerations for Optimizing Performance  
In our research project, we implemented several performance optimization measures to ensure that the blockchain-based communication system works efficiently, especially in high-traffic environments with numerous vehicles and roadside units (RSUs). These optimizations focused on edge processing and scalability to keep the system running smoothly without overloading the blockchain or causing significant delays.  
* Edge processing-to minimize the communication load on the blockchain, we deployed RSUs (roadside units) as edge computing nodes [28; 29]. RSUs are strategically placed at the roadside and are responsible for local data processing tasks such as traffic monitoring, hazard detection, and vehicle coordination. By processing data locally, RSUs significantly reduce the need for constant interaction with the cloud or blockchain network.
* RSUs are able to make real-time decisions at the border. For example, if a hazard is detected, the RSU analyzes the data locally and transmits the warning directly to nearby vehicles. Only important data, such as final decisions or important transaction details, are sent to the blockchain for logging, reducing the overall communication load of the system.
* This implementation reduces latency by keeping processing close to the data source and minimizing the need to forward non-critical information to the cloud or blockchain. It also conserves bandwidth and improves the overall responsiveness of the system to real-time traffic conditions.  
* Scalability-to ensure that the system can handle a large number of vehicles, we designed the blockchain with a hierarchical architecture. The system includes lightweight nodes (vehicles) and more powerful nodes (RSUs) that manage the ledger and perform more resource-intensive tasks.
* Lightweight nodes (vehicles): Vehicles act as lightweight nodes in the network. They primarily generate and verify smaller transactions (e.g., position updates or hazard warnings) and rely on the RSUs for more complex blockchain interactions.
* Powerful nodes (RSUs): RSUs serve as more robust nodes capable of managing parts of the blockchain ledger and participating in the consensus mechanism. By distributing the more computationally intensive tasks to the RSUs, the system remains scalable even if the number of vehicles increases.
## 4 Results and Discussion  
### Preprocessing of Stator Current  
The current signal preprocessing included two parts: extracting the fault frequency band and suppressing the fundamental frequency interference.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 2 Materials and Methods
### Methods
#### 2.2.3 Morphological Operations  
Morphological operations are mathematical procedures for extracting picture elements such as regions and boundaries. The retrieved zones produced using simple threshold or clustering algorithms are defective in the great majority of cases. As a result, morphological techniques can be employed as post-processing techniques to reduce noise or refine the structure of a complicated item [10]. In addition, one of the most common applications of morphological processes is image segmentation based on object form. Morphological methods link a picture to a limited number of points called structuring components. The structural element can be any shape or size, with the output image being the same size as the input [39].  
In order to improve the object structure, morphological techniques such as thinning, thickening, and object marking can be used. Morphological image processing techniques are based on some operations called dilation, erosion, opening, and closing. Dilation is a basic morphological action that causes a foreground object to increase in size by expanding the outer layer of the object, adding pixels to the contour of the object. The erosion process, on the other hand, reduces the size of the object item. Our method involves performing morphological operations on binary pictures obtained after applying a multilevel threshold, with white pixels representing the tumor and black pixels representing the background. After multiple tests, the morphological operation chosen was three iterations of \"erosion\" followed by three iterations of \"dilation\" to preserve the original size of the tumor in the brain.
## 3 Results
### Field Stressors Such as Copper, Zinc and High-Temperature also Induce Persisters Cells in X. citri  
To investigate if _X. citri_ persister cells are induced by stressors they face in the field, we tested copper, which is widely used for _X. citri_ control [2], zinc, which was recently proposed to control _X. citri_[45], and high temperature [46]. When we attempted to induce persistence using 10 \(\times\) MIC tetracycline after 10 \(\times\) MIC copper (1600 \(\upmu\)g/mL) or 5 \(\times\) MIC copper pretreatment, no cells survived due to the high copper concentrations. Persistence was only achieved for _X. citri_ when 1 \(\times\) MIC (160 \(\upmu\)g/mL) copper pretreatment was used before 10 \(\times\) MIC tetracycline. Hence, we tested ciprofloxacin (50 \(\upmu\)g/mL, 3 h) treatment after a 1 \(\times\) MIC of copper pretreatment at different incubation times (Figure 2A) to verify the development of persister cells. Among all copper pretreated cultures, we observed more than 10 times more survivors after 3 h ciprofloxacin killing treatment when compared to the control (no copper pretreatment). This ratio was stably maintained regardless of how long the copper pretreatment (15 to 60 min) (Figure 2A). In addition, we also found that a 60 min pretreatment with zinc at 1 \(\times\) MIC (322 \(\upmu\)g/mL) induces _X. citri_ persistence under the same treatment conditions (Figure 2A). Since our goal was only to determine if zinc could induce persister cell formation like copper, we selected only one time-point to address this question. We observed that zinc induced persistence, increasing cell survival 10 times after ciprofloxacin treatment (Figure 2A). Therefore, we found that, besides their unequivocal antibacterial activity [45; 47], metals can induce _X. citri_ persistence.  
Citrus is planted in tropical and subtropical regions where temperatures near 37 \({}^{\circ}\)C are common, and _X. citri_ must withstand such environments. Thus, we hypothesized that the induction of persistence by high temperature would be one mechanism _X. citri_ might use to remain viable under high-temperature field conditions. To test this hypothesis, _X. citri_ cells were cultured at 30 \({}^{\circ}\)C to turbidity at 600 nm \(\sim\)0.8-1.0 and were exposed to 37 \({}^{\circ}\)C from 1 to 6 h before ciprofloxacin treatment. After 3 h of ciprofloxacin treatment, we observed that cells pre-exposed to the higher temperature were more tolerant to the antibiotic since we obtained around 4 to 7 times more survivors than the control, depending on how long the heat-treatment was applied (Figure 2B). We conclude that besides chemical stress (tetracycline, copper, zinc), physical stress (high temperature) can also induce the formation of persister cells in _X. citri_.
## 2 Methods
### Network Model  
The proposed framework is a Seq2 Seq model with an attention mechanism, consisting of two main components: a stack of a bidirectional LSTM encoder and an LSTM decoder based on an attention network. Figure 2 shows the overall network architecture of the designed model.  
Let \(X^{(s)}=[x^{(s)}_{1},\cdots x^{(s)}_{N_{t}}]\) and \(Y^{(t)}=[y^{(t)}_{1},\cdots y^{(t)}_{N_{t}}]\) represent sequences of cepstral features of the ES and laryngeal speech of non-aligned parallel utterances, where \(N_{s}\) and \(N_{t}\) denote the length (frame number) of the source and target sequences, respectively. Note that these sequences do not necessarily have the same length (i.e., generally, \(N_{s}\
eq N_{t}\)).  
We consider a Seq2 Seq framework that models the mapping relationship between \(X^{(s)}\) and \(X^{(t)}\). As with most sequence-to-sequence models that were originally proposed by [PERSON] et al. for machine translation applications [31], our Seq2 Seq model has an encoder-decoder structure equipped with an attention mechanism.  
The encoder network processes the input sequence of esophageal speech into a high-dimensional feature space in order to facilitate the decoding process. Then, it compresses and summarizes them into a fixed-length context vector, also called \"hidden representation \(h=[h_{1},\ldots,h_{t},\ldots,h_{N_{h}}]\)\", which represents all the information concerning the source features:  
\[h=Encoder(X^{(s)};\theta_{encoder}), \tag{3}\]  
where \(\theta_{encoder}=[W_{encoder},U_{encoder},b_{encoder}]\) are the parameters of the encoder model.  
At each time step \(i\), the decoder takes the output of the encoder, i.e., the hidden representations \(h_{t}\), and the previously generated features \(y_{i-1}\) considered as inputs, and applies its attention mechanism network and then progressively generates the current enhanced output \(y_{i}\).  
\[y_{i}=Decoder(h_{t},y_{i-1};\theta_{decoder}), \tag{4}\]  
Note that, in the typical Seq2 Seq framework, the last hidden layer is used as the context vector once the entire sequence is fully encoded. However, in our case, the length of the ES feature sequence is generally longer than that of the normal speech, so the ES feature vectors of length \(N_{x}\) used to obtain the target feature vectors of length \(N_{y}\) are dynamically changed at different time steps.  
To model these various nonlinear mapping relationships more accurately, we adopt an attention mechanism to obtain a self-adaptive context vector to adaptively estimate the decoder output.
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
## 5 Conclusions  
In this study, a novel ENR-TS algorithm was proposed to reduce the computational complexity of TS for large MIMO systems. The proposed ENR-QR-TS scheme rejects the most unreliable neighbors in terms of their ML metric sequentially in each layer, which reduces the burden of complexity in the best neighbor search of TS. To further reduce the complexity of the ENR-QR-TS algorithm, an ordering scheme was also proposed. Specifically, the expected metrics of layers are sorted in descending order, so more unpromising neighbors are likely to be rejected earlier. The simulation results show that the proposed ENR-QR-TS algorithms achieved nearly the same BER performance as the conventional TS while substantially reducing complexity. ENR-QR-TS with layer ordering when \(k=2\) achieves a complexity reduction approximately 82%, with respect to conventional TS.  
Data curation, U.U.; Investigation, U.U.; Methodology, U.U., J.-S.P., G.-J.J., J.-D.L.; Supervision, U.U.; Writing--original draft, U.U.; Writing--review and editing, U.U., J.-S.P., G.-J.J. and J.-D.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00158, Development of high power RF Front-End for 5G base band array antenna). The authors declare no conflict of interest.
## 3 Optimal Decision-Level Fusion Criterion (O-DFC) and Its Simplified Form
### The Simplified form of O-DFC: the Majority Voting Decision-Level Fusion Criterion (MV-DFC)  
To render the O-DFC tractable, we make a further assumption that for all sub-optimal detectors, their \(P_{\text{em}}\) are all equal to \(P_{\text{em}}\), regardless of the differences between these detectors. That is, \(a_{in}\) are all equal to \(a_{in}\) in Equation (19). Besides, it is clear that the BER satisfies \(P_{\text{em}}<0.5\) (otherwise, the communication is meaningless), so that \(1-P_{\text{em}}>P_{\text{em}}\) and \(a_{m}>0\).  
Then we can derive the majority voting DFC, called MV-DFC, as follows:  
\[u_{m}=f(u_{1m},u_{2m},\cdots,u_{m})=\begin{cases}+1,&\text{if }\sum_{i=1}^{n}u_{in}>0\\ -1,&\text{otherwise}\end{cases} \tag{22}\]  
For this case, the proposition obtained in [49] can be simplified to  
\[P_{ecm\_MFC}=\texttt{min}\left\{P_{e0m},P_{e0m}\right\} \tag{23}\]  
where \(P_{e0m}=3P_{em}{}^{2}-2P_{em}{}^{3}\). So in order to improve the performance from \(P_{em}\), the following condition should be satisfied:  
\[P_{e0m}<P_{m} \tag{24}\]  
That is,  
\[3P_{m}-2P_{em}^{2}<1 \tag{25}\]here we can get \(P_{em}<0.5\) or \(P_{em}>1\) from Equation (25). Nevertheless, consider \(P_{em}\) is the BER conditioned on 0\(<P_{em}<1\), the condition \(0<P_{em}<0.5\) should be satisfied to make an improvement. Obviously, it is quite easy to satisfy this condition in the normal communication systems.
# The Effect of Varying Out Beverage Ratios on the Characteristics of Fermented Dairy-Oat Beverages
## 1 Introduction
The development of co-fermented oat-dairy beverages aligns with these trends, offering a novel product that combines the benefits of both plant and dairy ingredients. Consumers are increasingly interested in foods that promote digestive health, cardiovascular well-being, and metabolic balance, making co-fermented oat-dairy beverages a promising addition to the functional food market. Fermentation with lactic acid bacteria can further improve the nutritional value of oat-based beverages by increasing the concentration of beneficial organic acids and reducing sugar content, thus enhancing the overall health benefits of the beverage. Changes in the chemical composition of oat-based beverages, resulting from the addition of oat beverage, affect the fermentation process and the quality of the final product. The balance between oat and dairy components influences factors such as pH, microbial activity, and texture, which are critical to the sensory attributes and stability of the beverage. While the potential benefits of varying oat beverage ratios in fermented dairy-oat beverages are significant, challenges such as maintaining bacterial viability and optimizing sensory attributes must be addressed. Advanced food processing techniques and formulation strategies can help overcome these challenges, ensuring a high-quality final product that meets consumer expectations. Further research could explore the balance between oat and dairy components to maximize health benefits while ensuring consumer acceptance. Investigating the effects of different fermentation conditions, bacterial strains, and oat-dairy ratios could lead to optimized formulations that enhance both nutrition and flavor. Additionally, consumer preference studies could provide insights into the ideal characteristics of co-fermented oat-dairy beverages, guiding product development to align with market demands. In conclusion, the co-fermentation of oats and dairy presents an exciting opportunity to develop innovative functional beverages that cater to modern consumer needs. By combining the nutritional and sensory advantages of both ingredients, these beverages have the potential to become a valuable addition to the plant-based and probiotic beverage market, supporting health and well-being while offering a delicious and accessible alternative to traditional dairy products. The aim of this study was to determine the effect of different proportions of oat beverage in cow's milk on the physicochemical, textural, sensory, and microbiological properties of fermented dairy-oat beverages, as well as to assess the stability of these properties during cold storage. The obtained results can be used by food manufacturers to develop new, innovative oat-based products with optimized nutritional, sensory, and stability properties, meeting the needs of various consumer groups.
## 1 Introduction
However, the weight determination methods are generally too subjective or too dependent on objective data, and therefore they cannot adequately reflect the potential risk factors of the project itself, nor fully consider the correlations among risk factors [15]. For example, conventional weight determination only adopts a single method (either subjective or objective weighting), which results in uneven weight distribution. When evaluating the bridge risks, experts' subjective decisions should be considered, in addition to the objective reality of the bridge. Therefore, the single weighting method is not sufficient. Moreover, distortion, jump, low resolution, strong subjectivity, and other phenomena influence the membership relationship among risk indicators when simply applying a fuzzy comprehensive evaluation. Considering the limitations of using a single evaluation method, it is necessary to combine grey system theory and fuzzy comprehensive evaluation methods to build a grey fuzzy comprehensive evaluation model of bridge risks. This enables the comprehensive consideration of the influence of various factors, which leads to a more accurate evaluation. Therefore, this paper proposes a new weighting method comprising analytic hierarchy processes and the entropy weight method (AHP-EWM) to determine the combined weights of bridge risk indicators. Then, fuzzy comprehensive evaluation and grey theory are used to calculate the degree of membership matrix of bridge risk indicators. Finally, the combined weights are multiplied by the degree of membership matrix to obtain the risk level of a bridge during its period of operation.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 4 Machine-Learning Algorithm
### Data Preprocessing  
The input features consist of specific characteristics of IaaS services considered of importance for determining the price of a specific service. The selection is based on previous studies by the authors [38; 39] when linear regression models are used. The features reflect the basic characteristics, functional and not functional, of reserved and on-demand instances and are presented in in Table 4. They all have significant bearing on the price of the instances. To reduce numerical errors due to variable scale, normalization techniques were applied to the numerical variables. Normalization scales the data to a specific range, such as between 0 and 1. These techniques eliminate any biases caused by varying scales or units of measurement, allowing for fair comparisons and accurate analysis. We also introduced a new variable to replace the effective date, reflecting the quarter in which the offer was made. This approach was adopted to reduce the fine-grained nature of the data in terms of the dates of the offers; we are rather interested in quarterly rather than daily predictions. Table 5 summarizes the location of the offers contained in the dataset. The majority of offers are concentrated in the Asia-Pacific, United States, and Europe regions, and offers in these regions span the entire dataset from 2016 to 2022, unlike other regions. We have, therefore, decided to assume offers only from these three regions in the training and remove the location area as a feature. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline LeaseContractLength & Length of the lease contract for reserved instances \\ PurchaseOption & Payment option (no upfront, partial upfront, all upfront) \\ Location & Region where the instances are hosted \\ vCPU & Number of virtual CPUs \\ Memory & Memory capacity \\ OperatingSystem & Operating system (Windows or Linux) \\ NetworkPerformance & Network performance capability \\ StorageSize & Storage capacity \\ DiskType & Type of storage disk \\ YearQuarter & Combined feature of the year and quarter \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Considered Features for the Machine-Learning Algorithm.