esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model for Information Flow Management in Intelligent Transport Systems
## 4 Results
The quantified effectiveness of the proposed method, as compared to those of other methods, is presented in Table 3.  
The proposed solutions have a high potential for facilitating the further introduction of information commutation and digital technologies into the modern reality of transport systems operation, as they allow for the parallel processing of multiple \"inputs\" and \"outputs\" within intelligent transport systems and ensure adequate amounts, or a \"database\", of optimization parameters. The digital mechanisms that are currently used in the transport sector have a high potential for employing the proposed method for more effective decision making and process optimization. Possible applications include intersection traffic safety analysis and signaled crossing safety assurance [74; 75]; hardware and software packages for measuring driver reaction time in road accidents investigation [76]; and transport safety assurance with the use of intelligent driver assistance systems [77]. The proposed method will prove useful also in modeling the transport infrastructures for modern cities with growing efficiency; measuring the effectiveness of automated road accident scene sketching based on data from a mobile device camera; analysis of road safety, the reliability of the sustainability criteria for urban passenger transport, and route-optimization-based mechanisms for improving the safety of cargo transportation in urban agglomerations; forecasting the levels of energy consumption and greenhouse gas emissions from vehicles; introducing pedestrian early warning systems into intelligent transport system infrastructures; traffic accident risk analysis in conditions of urban traffic demand change; and designing a man-machine interface for self-driving vehicles with account of the time needed to take back control [78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89]. Since the proposed model is suitable for use in problems with an unlimited number of system inputs (indicators), it is possible, and advisable, to include in databases not only vehicle performance (mileage, volume of transportation, cargo turnover, etc. ), but also the performance of cargo-handling facilities (terminals). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Decision-Making Method** & **Solution Variant** & **Quantified Effectiveness** \\ \hline [PERSON] criterion & 1 & 0.200 \\ Savage criterion & 2 & 0.660 \\ [PERSON] criterion & 3 (4) & 0.366 (0.676) \\ [PERSON] criterion & 4 & 0.4975 \\ Fishburne sequences & 4 & 0.5034 \\ Proposed method & 4 & 0.8400 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Method effectiveness comparison table.
###### Abstract  
Accuracy metrics have been widely used for the evaluation of predictions in machine learning. However, the selection of an appropriate accuracy metric for the evaluation of a specific prediction has not yet been specified. In this study, seven of the most used accuracy metrics in machine learning were summarized, and both their advantages and disadvantages were studied. To achieve this, the acoustic emission data of damage locations were collected from a pile hit test. A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics. Test results showed that the training algorithm of \"TRAINGL\" exhibited the best performance for predicting damage locations in deep piles. Subsequently, the artificial neural networks were trained using three different datasets collected from three acoustic emission sensor groups, and the prediction accuracies of three models were evaluated with the seven different accuracy metrics. The test results showed that the dataset collected from the pile body-installed sensors group exhibited the highest accuracy for predicting damage locations in deep piles. Subsequently, the correlations between the seven accuracy metrics and the sensitivity of each accuracy metrics were discussed based on the analysis results. Eventually, a novel selection method for an appropriate accuracy metric to evaluate the accuracy of specific predictions was proposed. This novel method is useful to select an appropriate accuracy metric for wide predictions, especially in the engineering field.  
accuracy metrics; artificial neural network; acoustic emission; damage location; deep pile sciences Article
## 4 Wavelet Based Unsupervised Learning Algorithm
### Linear Predictors of Wavelet Coefficients and Their Errors  
Along with performing the wavelet filtering, we also augmented the data with the calculation of linear predictors and their associated errors. Linear predictors, developed by [PERSON] and [PERSON] in [PERSON] and Buccigrossi (1986), attempt to express each wavelet coefficient magnitude as a linear combination of nearby, or neighboring, coefficient magnitudes. These neighbors can be located on the same level or in nearby levels. In the work of [PERSON] and [PERSON], wavelet filters were applied to 2-dimensional images. It was shown that detail coefficients adjacent to each other on the same level and adjacent to each other on different levels had related values. The reasoning was that distinct image structures such as an edge have an effect on neighboring coefficients and subsequent levels since these coefficients are being created from the same pixels. Since blood sugar readings in close temporal proximity should have dependent values, an analogous result should hold for the detail coefficients in our time series analysis.  
Our analysis expressed each first level detail coefficient magnitude as a linear combination of four neighboring coefficient magnitudes. They were the coefficient immediately prior to it in the time series analysis, the neighbor coefficient two values past it in the time series, the _parent_ coefficient in the next level of high pass filtering, and the _grandparent_ coefficient in the level above the parent. Each first level coefficient, \(v_{i}\), can then be associated with a linear equation in four unknowns, \(w_{1},w_{2},w_{3},w_{4}\):  
\[v_{i}=w_{1}q_{1}+w_{2}q_{2}+w_{3}q_{3}+w_{4}q_{4} \tag{3}\]  
where \(q_{i}\) is a value of one of the four predicting coefficients. Putting each coefficient, \(v_{i}\), into a column vector, **v**, and the corresponding predicting coefficients into a matrix, \(Q\), gives the matrix equation  
\[Q\textbf{w}=\textbf{v} \tag{4}\]  
Because this is an overdetermined system, the most predictive **w** will minimize the quadratic error function  
\[E(\textbf{w})=(\textbf{v}-Q\textbf{w})^{2} \tag{5}\]  
This amounts to setting (The derivation of this can be found in [PERSON] and [PERSON] (1986).)  
\[\textbf{w}=\left(Q^{T}Q\right)^{-1}Q^{T}\textbf{v} \tag{6}\]  
The base two log error for the linear predictor is also computed as  
\[\text{Error}\;=\;\log_{2}\left(\textbf{v}\right)-\log_{2}\left(\left|Q \textbf{w}\right|\right) \tag{7}\]  
By taking the logarithm of a vector, we mean taking base two log of each component of the vector. Additionally, \(\left|Q\textbf{w}\right|\) denotes taking the absolute value of each component of the vector \(Q\textbf{w}\).
## 7 New Approaches to Monitoring and Optimisation of Microalgae Cultivation Systems
### Multiparameter Flow Cytometry
In recent years, growing interest in the integration of MFC with multivariate data analysis methods such as principal component analysis (PCA) and k-means clustering has enabled more advanced approaches to data interpretation and identification of metabolic patterns in microalgae cultures. The application of these methods in combination with automation and machine learning opens up new possibilities for the precise monitoring and optimisation of microalgae cultivation conditions, which can lead to higher biomass yields and improved production of industrially relevant secondary metabolites.
## 4 Discussion
### Limitations  
This study has some limitations, such as a relatively low response rate, data loss, underestimation of specific behaviors, and unequivocal categorization of activity locations. Although response rates were low (3.5% of the 14,889 randomly recruited individuals agreed to participate, and 78.7% of these individuals actually wore the devices), our final study sample is comparable to other studies [33; 49]. Data loss was due to the inability to match all GPS and accelerometer data points, insufficient wear time, and the interference of urban canyons (surrounding high buildings), trees, or building materials (e.g., in a tunnel or at home) with satellite communication. Besides, wearing the accelerometer on the hip may have led to an underestimation of PA, since upper-body movements or non-ambulatory movements (e.g., cycling) are less accurately recorded [50]. The locations were classified based on land use and building data, but classification is not unequivocal. For example, it is likely that walking the dog in larger green areas was classified as transport, and not as an activity that took place in larger green areas. Although this is not misclassification, since it is both correct, future research may consider further classifying transport into categories (e.g., transport in larger green areas). Besides, a diary kept by participants may improve accuracy of the determination of activity locations. Another limitation may be that determinants, which were not controlled for in this study, also determine the effect of neighborhood on PA. For example, social norms were significant predictors of PA in previous studies [51; 52]: individuals who often saw other people exercising or walking in their neighborhood had higher levels of leisure-time MVPA and walking than individuals who did not often see other people exercising and walking in their neighborhood [52].
# The Architecture of the Access Protocols of the Global Infocommunication Resources +
## 1 Introduction
When developing the model, the use of Time Division Multiplexing (TDM) technology for organizing multiple access to the transmission channel was envisaged. The mathematical model is a Queuing model in discrete time and includes two phases, each of which has the form \(M^{d}/G^{d}/1\). In this article in contrast with [1]:  
* First, the emphasis is on developing the architecture of protocols for interaction between terminal devices and the base station. The proposed architecture is an extension of the previously known and developed Institute of Electrical and Electronics Engineers architecture described in the 802 group of standards [6]. * Secondly, the problem of studying the impact of traffic restriction procedures at the first stage of access on the probability-time characteristics of the data delivery process from the terminal devices of the base station is solved. The peculiarity of the developed architecture, protocols, scenario and mathematical model of information interaction of the network component in the last mile is that they are focused on providing access services to global infocommunication resources of various terminal devices: both smartphones (people) and smart things, as provided in the 5 th generation networks and Network 2030 [7].
## 2 Materials and Methods
### DNA Sequencing  
The PCR products were further purified to remove unincorporated nucleotides and excess primers via the ExoSAP method using Exonuclease 1 (Exol) and Shrimp Alkaline Phosphatase (SAP). A reaction mixture containing 5 \(\mu\)L of PCR products, 0.5 \(\mu\)L of Exonuclease 1 and 1 \(\mu\)L of SAP was placed in a PCR tube. The mixture was further incubated in a thermal cycle at 37 \({}^{\circ}\)C and 85 \({}^{\circ}\)C for 15 min each. The reaction was used for Sanger sequencing. DNA sequencing was performed with 1 \(\mu\)L of Big Dye Terminator DNA v3.1 Ready Reaction mix (Applied Biosystems, Warrington, UK), 5 \(\mu\)L of genomic DNA (0.6-4 ng/\(\mu\)L), 1 \(\mu\)L of primer, 1.5 \(\mu\)L of 5X BigDye Sequencing buffer and 1.5 \(\mu\)L of molecular-grade water. Cycle conditions included denaturation at 96 \({}^{\circ}\)C for 10 s, annealing at 50 \({}^{\circ}\)C for 5 s, and extension at 60 \({}^{\circ}\)C for 4 min over the course of 25 cycles. The sequencing products were purified by adding 2.5 \(\mu\)L of 125 mM EDTA and 30 \(\mu\)L of 99.9% ethanol, which were vortexed briefly. The reaction mixture was incubated at room temperature for 1 h, protected from light, and then washed using 70% ice-cold ethanol. The ABI 3500 XL Genetic Analyser Data Collection System by Applied Biosystems, UK generated the chromatogram. The chromatograms were converted into text format using chromas software version 2.0, and sequences were opened using Bioedit Alignment Editor and analysed using the Basic Local Alignment Search Tool (BLASTn) Bioinformatics tool [24], accessible from NCBI ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)) (accessed on 13 June 2023)). The sequences of MexB in the most common and essential clinical Gram-negative pathogens were retrieved from the NCBI nucleotide database ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)) (accessed on 13 June 2023)). The sequences were from strains of _S. rhizophila_, _A. chroococcum_, _A. viinelandii_, _Pseudomonas aeruginosa_, _Cupriavidus sp._, _Cupriavidus oxalicus_, _Ralstonia picketti_ and _K. necator_. The percentage similarity to other identified organisms was also recorded. The fasta sequence was aligned using clustal Omega version 1.2.1 on [[https://www.ebi.ac.uk/Tools/msa/clustalo/](https://www.ebi.ac.uk/Tools/msa/clustalo/)]([https://www.ebi.ac.uk/Tools/msa/clustalo/](https://www.ebi.ac.uk/Tools/msa/clustalo/)) (accessed on 13 June 2023) to investigate mutations in the nucleotide sequences [25; 26]. To confirm the variations in complementary amino acids, the nucleotide sequences were converted into amino acids using the Expasy translate programme ([[https://web.expasy.org/translate/](https://web.expasy.org/translate/)]([https://web.expasy.org/translate/](https://web.expasy.org/translate/)) (accessed on 13 June 2023)).
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
###### Abstract  
For the problem that the fault states of parallel shaft gearboxes are difficult to identify, a diagnostic method is proposed to optimize variational modal decomposition (VMD) and t-distributed stochastic neighbor embedding (t-SNE) using an improved dung beetle optimization algorithm I have checked and revised all. (IDBO). IDBO is obtained by amplifying dung beetle optimization (DBO) using strategies such as chaos mapping, Levy flight policy, and dynamic adaptive weighting. IDBO is employed to optimize VMD, extracting decomposed eigenvalues restructured into high-dimensional feature vectors. Subsequently, we employ the t-SNE algorithm for dimensionality reduction to eliminate redundancy, obtaining two-dimensional vectors. Finally, these vectors are input into a support vector machine (SVM) for fault diagnosis. We apply IDBO, grey wolf optimization (GWO), DBO, and the sparrow search algorithm (SSA) to both benchmark functions and VMD, conducting a performance comparison. The results demonstrate that IDBO exhibits superior convergence speed and global search capability, effectively suppressing modal aliasing issues in VMD, thereby enhancing the algorithm's robustness. Through experimental fault diagnosis on a gear transmission system, we compare our proposed method with EMD + t-SNE and traditional VMD + t-SNE feature extraction approaches. The experimental results indicate that the fault diagnosis accuracy reaches 100% after processing the fault signals with IDBO-VMD + t-SNE. This method proves to be an effective fault diagnosis approach specifically tailored for parallel-axis gearboxes, providing a reliable means to enhance diagnostic accuracy.  
fault diagnosis; feature extraction; IDBO; t-SNE; VMD +
Footnote †: journal: Applied sciences
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 4 Experiment Verification
### Performance Comparison of Prediction Models
Combined with the evaluation indexes of the prediction results of each model in Table 3, it can be seen that the MAE, RMSE, and MAPE prediction indexes of the amplitude corresponding to 1 times the inner-ring fault frequency predicted by TCDAN are reduced to 0.0471, 0.0583, and 9.69%, respectively. The MAE, RMSE, and MAPE prediction indexes of the amplitude corresponding to 2 times the inner-ring fault frequency are reduced to 0.0497, 0.0609, and 6.27%, respectively. Compared with TCN, GRU, BiLSTM, and LSTM models, TCDAN still has the best prediction performance, which verifies the good generalization ability of the TCDAN model.
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 4 Results and Discussion  
In this section, prediction results for both \(PDR\) and \(EC\) are described. The prediction error for all values in test data is shown in the form of scatter plots. \(RMSE\) for each individual feature and the best of their combinations of all lengths is presented. In addition, to better characterize the error, histograms of percentage error are plotted.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 3 Proposed Method
### The Probability of Sense Distribution  
When context information is insufficient, cosine similarity fails to determine the correct word sense. Another idea also derived from intuition is that the correct word sense usually has a high probability of sense distribution. We adopted the same idea proposed in [PERSON] et al. [6]. The probability of sense distribution was learned from SEMCOR [14], a large sense-tagged corpus annotated by WordNet, and it was defined as shown in Equation (8). \(C(w_{i}s_{i,j})\) counts the number of times \(w_{i}\) was tagged with sense \(s_{i,j}\), \(C(w_{i})\) counts the occurrences of \(w_{i}\) in the entire corpus, and \(C(s_{i})\) counts the number of senses corresponding to \(w_{i}\). Some senses do not occur in the corpus. The Laplace smoothing technique [28] has been used to avoid zero probabilities.  
\[P\Big{(}s_{i,j}|w_{i}\Big{)}=\frac{C\Big{(}w_{i}\cdot s_{i,j}\Big{)}+1}{C(w_{ i})+C(s_{i})} \tag{8}\]The score was combined between the cosine similarity and the probability of sense distribution when the score was not higher than the similarity threshold. The possible answer senses can be obtained from high scores. The similarity threshold is a system parameter which is further investigated in the next section.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 4 Taxonomy of Vulnerability Detection Approaches and Findings Analysis
### AI-Based Approach
#### 4.4.2 Findings Analysis
& & & \\ \hline \multirow{5}{*}{[120]} & \multicolumn{5}{c}{Isos to detect more dynamic measurements altering their} \\  & & & GBM and Lasso Regression can encounter compatibility & & & & \\  & & & issues with high payers; & & & \\  & & & Training steps require extensive time to & & & \\
2024 [13] & Regression & & IoT/ZephyOS & handle large datasets, & & \\  & &  
This study explores the utility of AI algorithms to detect vulnerabilities in different ecosystems. The principle models integrated in vulnerability detection methods integrate RNN, BLSTM, NER, LSTM, fuzzy matching, NLP, GBM, among others. As seen previously, it is possible to combine multiple AI models to enhance the capacity to discover and assess vulnerabilities and weaknesses across diverse systems. The complexity lies in the dependence on dataset quality to improve model scores and reduce false negative and positive rates. Regarding the IoT environment, and based on findings extracted from Systematic Literature Review (SLR) [137], the Intrusion Detection Systems (IDSs) based on AI methods are particularly effective in detecting anomalies and intrusions. In this context, several ML, DL models, and hybrid methods are used to face the growth of cyberattacks, including but not limited to Neural Networks (NN), Convolutional Deep Learning (CDL), Extreme Gradient Boosting (XGBoost), RNN, and Fuzzy Pattern Tree (FPT). In summary, the previously described approaches--similarity-based, graph-based, FM-based, and AI-based--all combine multiple methods and techniques for vulnerability identification processes. Each approach has its own challenges and issues, which will be discussed in the following section.
# Incorporating New Technologies in EEIO Models
## Appendix B Perfect Substitution Assumption
### Prices  
Prices are required to transform from units to million USD and vice versa in several parts of the methodology. The requirements are:  
* Price of similar commodity \(p_{s_{1}}\) in ($/unit).
* Price of the bio-commodity, in ($/unit), when being produced by each of the \(k\) new bio-industries \(p_{m+1,tech-1},p_{m+1,tech-2},\ldots,p_{m+1,tech-k}\).  
It is expected that these prices are different. In particular, is expected that the bio-commodity has a higher price. This conflicts with an implicit assumption being made with the perfect substitution. Under a perfect substitution, like the one being made in this study, both the price and the functionality should be exactly the same between the similar-commodity and the bio-commodity. Since the current economy does not incorporate externalities, if the price for the bio-commodity is higher and the functionality is the same,there is no economic incentive to replace the similar-commodity consumption with bio-commodity consumption.  
On the contrary, if the bio-commodity price is lower and the functionality is the same, people will in fact have incentives to replace similar-commodity with the bio-commodity. However, the final demand for commodities in the economy may change due to an income effect. For example, people could increase their consumption of other goods because now they expend less on fuel.  
On the other hand, if the difference in prices between the similar-commodity and the bio-commodity are ignored and all prices are considered the same, the conflict with the perfect substitution is not present anymore. However, a conflict with how the bio-commodity is being made arises. The price of the bio-commodity results from its production method, the inputs used and the quantities in which those are being used. If a different price is imposed, this price may not correspond to an economically feasible production process for the bio-commodity, thereby, resulting in economic loss for the industry producing the bio-commodity.  
If this happens, it does not make sense for that industry to be on the market. Furthermore, since the price is used to transform from units to million USD, using a different price from the one used to calculate input purchases and value added will result in inconsistencies in the quantities being produced.  
Given this, when using this methodology, we face a decision about consistency. If equal prices are used, inconsistency with new bio-industries production processes is present. If different prices are used, inconsistency with the perfect substitution assumption is present. Here, the methodology is designed in a way that the researcher can decide which price structure to use.