esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 4 Algorithm Performance Analysis and Comparison
### Evaluations on Fault Diagnosis Ability Using Different Algorithms
This is because the distance between the connector and the bonding wire is very close, both are at the input side of the bare chip, while the solder ball is at the output side of the bare chip, as shown in Figure 8. Because of this relative closeness, there is a modest similarity between the characteristics of fault mode 1 and mode 2, resulting in a slight tendency for misclassification. ROC curves and AUC provide insight into the overall detection capability of the algorithms under various conditions of the given dataset. ROC curves of the three algorithms are shown in Figure 15. While all three of the algorithms perform well, it can be seen that the performance of the algorithms trained with S\({}_{11}\) is better than that of the algorithms trained with S\({}_{21}\) with steeper slopes and points closer to (0.1). When zooming into the corner of the chart to view more detail, it is illustrated that GBDT performs better than SVM and LR algorithms while they perform quite the opposite when trained on S\({}_{11}\) and S\({}_{21}\). This is in good agreement with the analysis results for the above evaluation sessions. In addition, the AUC of the three algorithms were also tabulated, as shown in Table 7. All of the AUC values are above 90%, confirming therefore that it is feasible to leverage machine learning algorithms as a fault detector to localize and diagnose categorized faults
## 5 Experiment Evaluation
### Optimal Combination of Parameters  
From Section 4.3, the key role in the proposed Tor HS identification method for the Snowflake scenario is the selection of the first n messages in the data transmission phase and the number of selected messages with accumulative payload length sampling frequency m. The selection of the appropriate number of messages n can improve the identification efficiency of the whole framework and maintain a good identification accuracy. In addition,  
Figure 5: Tor-Snowflake traffic identification effect based on fingerprint features.  
Figure 6: Importance ranking of the fingerprint features.  
for the selection of the m value, as m increases, the interval distance between sampling points becomes smaller, and the loss caused by sampling will be less. On the other hand, a large number of features will have a negative impact on the calculation time consumption, which will reduce the learning efficiency and increase the risk of overfitting. In order to obtain the best combination of parameters, we set the interval of the massage number n from 50 to 350 and take 50 as the step size. The number of sampling points m range from 20 to 200 and are taken in steps of 20. We construct the model using the Random Forest algorithm for testing. The results of the experiments are shown in Table 4.  
The higher the precision rate, the smaller the number of n and m, and the less training time is consumed, for the optimal combination of parameters we need. The balance between the recognition effect of the model and the time consumption of feature extraction is achieved. Therefore, the optimal number of messages in the data transmission phase is the first 300 messages, and 40 sampling points are extracted as additional features by the ACCUMUL method for the accumulative payload length in the data transmission phase. From the experiment results, the optimal parameter combinations for (n, m) are (300, 40).  
We believe that this method is more beneficial to reflecting the difference between the construction of Tor circuits and Tor HS circuits, because it is implemented for the process of circuit establishment by extracting the payload length of the transmitted data. There is no decryption of the data, and these features are easily extracted. To summarize the above, we believe that the method is portable and expandable, and can be applied to the future problem of difficulty in identifying HS traffic brought about by the new obfuscated PT.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Clustering Algorithms  
Clustering methods divide a collection of documents into groups or subsets. Cluster algorithms seek to generate internally coherent clusters yet distinct from one another. In other words, documents inside one cluster must be similar as feasible, whereas documents in different clusters should be as diverse as possible. The clustering method splits many text messages into many significant clusters. Clustering has become a standard strategy in information retrieval and text mining [145]. Concurrently, text clustering faces various challenges. On the one hand, a text vector is a high-dimensional vector, typically ranging in the thousands or even the ten thousand dimensions. On the other hand, the text vector generally is sparse, making it challenging to identify the cluster centre. Clustering has become an essential means of unsupervised machine learning, attracting many researchers [146; 147].  
In general, there are three types of clustering algorithms: hierarchical-based clustering, partition-based clustering and density-based clustering. We quickly discuss a few traditional techniques for each category; clustering algorithms have been extensively studied in the literature [148; 149].
## 2 Background and Related Work
The query optimization problem is addressed in [20], where authors deal with the efficiently execution of JOIN queries on top of Hadoop query language, Hive, over limited Big Data storages. A novel data integration methodology to query data individually from different relational and NoSQL database systems is proposed in [21]. Such a solution does not support joins and aggregates across data sources, but it only collects data coming from different separated DBMS according to the filtering options and migrates them. The proposed method is based on a metamodel approach and it covers the structural, semantic and syntactic heterogeneities of source systems. In [22], authors present a piece of framework able to automatically map a MySQL relational database to a MongoDB NoSQL database. In particular, an algorithm that uses metadata stored in the MySQL system tables is used. Join operation are treated by means of embedded documents. In [23] authors show how dynamic SPARQL access to non-relational data can be achieved considering MongoDB. In particular, they use SparqlMap-M, which is an extension of the SPARQL-to-SQL tool that performs a partial transformation of SPARQL queries by using a relational abstraction over a document store. Furthermore, duplicated data in the document store is used to reduce the number of joins and custom optimizations are introduced. Event though, such a solution allows performing join operations, it need to create a new abstraction of the whole data model not considering the advantage of a direct access to MongoDB. Ref. [24] introduces a framework that aims at analysing semi-structured data applications using NoSQL database MongoDB. The proposed framework focuses on the key aspects needed for semi-structured data analytics in terms of data collection, data parsing and data prediction. A performance analysis for select+fetch operations needed for analytics, of MySQL and MongoDB is carried out where NoSQL database MongoDB outperforms MySQL database. Despite the the large adoption of MongoDB and the presence of many scientific and technical works focusing on different aspects of it, to our knowledge, at the time of writing, no work has directly faced the inner join problem so far.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Experimental Results (A)**} & \multicolumn{2}{c}{**FE Analytical Results (B)**} & \multicolumn{2}{c}{**Diff. (B/A, \%)**} \\ \cline{2-7}  & **Ultimate** & **Failure** & **Ultimate** & **Failure** & **Ultimate** & **Failure** \\  & **Force (kN)** & **Slip (mm)** & **Force (kN)** & **Slip (mm)** & **Force** & **Slip** \\ \hline S1 & 1653.4 & 11.87 & 1668.5 & 12.0 & 100.9 & 101.1 \\ \hline S2 & 789.6 & 6.03 & 910.4 & 6.0 & 115.3 & 99.5 \\ \hline S3-20 & 904.0 & 4.27 & 1168.6 & 5.0 & 129.3 & 117.1 \\ \hline S3-45 & 1418.0 & 9.53 & 1584.5 & 8.0 & 111.7 & 83.9 \\ \hline S4-20 & 1552.4 & 6.91 & 1620.9 & 8.0 & 104.4 & 115.8 \\ \hline S4-45 & 1730.0 & 7.76 & 1773.2 & 8.0 & 102.5 & 103.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of experimental and analysis results (maximum load and slip displacement). strength caused by the hole tolerance that mainly occurs in conventional embedded nuts. In conclusion, the Y-shaped demountable stud bolt shear connectors presented in this paper can be used as replacements for welded stud shear connectors because they exhibit a ductile behavior that exceeds the design standards and a shear resistance strength higher than that of conventional welded stud bolts. Further, they enable quick installation and removal of slabs. However, brittle fracturing of the stud bolts occurred at the nut fastening portions in the experiments conducted on the proposed model (model S4). Therefore, it is necessary to induce the failure mode to cause shear fracturing in the slab concrete embedded part. To do so, additional research on optimizing the Y-shape is needed. From a practical engineering perspective, the top-flange of girders receives tensile stress in the negative moment section of the middle support of a continuous bridge, unlike in the positive moment section of a span center. To apply the bolted demountable shear connector proposed in this study, the tensile cross-sectional area of the upper flange of the girder was reduced in the negative moment part because of the perforated hole; therefore, further review is required. Conceptualization, D.-S.J. ; Methodology and analysis, S.-H.P. and D.-S.J. ; Experiments and investigation, J.-W.H. and C.-Y.K. ; Writing-original draft preparation, D.-S.J., S.-H.P. and T.-H.K. ; Writing-review and editing, S.-H.P. and T.-H.K. ; Visualization, D.-S.J. and S.-H.P. ; Supervision, D.-S.J. ; All authors have read and agreed to the published version of the manuscript. Korea Agency for Infrastructure Technology Advancement: 2 ICTAP-C164073-01. This study was conducted with financial support (2 ICTAP-C164073-01) from the Korea Agency for Infrastructure Technology Advancement, part of the Ministry of Land, Infrastructure and Transport. We would like to express our gratitude for this support. The authors declare no conflict of interest.
# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
###### Abstract
By employing generic phoneme sets, these data gaps can be filled, consequently enhancing the performance of speech recognition and synthesis. The objective of this research is to construct a comprehensive, multi-language phoneme set by amalgamating common phonemes across languages while preserving the unique phonemes of each language. This is achieved through the utilization of pronunciation features for phoneme differentiation [22]. One advantage of a multilingual phoneme set is that the model can utilize a standardized set of symbols akin to the Latin alphabet, as well as a shared feature representation employed across different languages. This paper presents a three-fold contribution:  
1. Firstly, we provide a summary of the characteristics of pronunciation and identify the shared properties of pronunciation across multiple languages. Subsequently, we establish a universal phoneme set based on these attributes and employ it to annotate each word in different languages. This approach enables our model to leverage a combination of universal symbol inventories resembling Latin alphabets and cross-linguistically shared feature representations. 2. We build a deep neural network based on an encoder\(-\)decoder architecture as a grapheme-to-phoneme (G2P) model for four languages, namely Chinese, Tibetan, English, and Korean. 3. We exploit a near-optimal active learning method during the process of the construction of a pronunciation dictionary. This method selects valuable samples from a large unlabeled corpus and sends them to experts for labeling. The goal is to achieve the same accuracy as the training method on the large data set, while reducing the cost of manual labeling. This paper is organized as follows: Section 2 summarizes the approach for building a multilingual universal phoneme set. Section 3 describes the G2P model based on a deep neural network. Section 4 presents the near-optimal active learning method for the construction of the pronunciation dictionary, along with setting the objective function. Section 6 reports on our experiments and the analysis of the experimental results. Finally, Section 7 presents the conclusions drawn from this work.
# Analysis of the Impact of the New Two-Lane Shield Tunnel Underpass on the Existing Tunnels
## 4 Analysis of Settlement Monitoring Results
### Theoretical Calculation, Numerical Simulation, and Monitoring Data Comparison  
The vertical displacement of the arch on top of the left line of the existing tunnel is selected as an example. The theoretical settlement value is calculated using Equation (7) and compared with the numerical simulation results and on-site monitoring data. The comparison results are shown in Figure 12.  
Figure 11: Layout of monitoring sites. (**a**) Schematic layout of surface measurement points. (**b**) Schematic diagram of measuring points in the main section of the tunnel.  
Figure 10: Influence curve of grouting pressure on deformation of existing tunnels.  
After comparing the settlement values of the shield tunnel obtained from the two methods with the measured data, it is observed that the formula-based calculations yield slightly smaller values than the numerical simulation results. The maximum difference does not exceed 1 mm.  
The shape of the curves is similar, and most of the monitoring data on vault settlement falls within the curves. Since the monitoring points are in the shield penetration area, they are inevitably influenced by various construction conditions. The data fluctuations caused by the specific operational environment are unpredictable, leading to some monitoring data exceeding the calculated range. Overall, both the calculation formula and numerical simulation encompass most of the section's monitoring data, and the predicted curves align with the surface settlement pattern. This makes the methods applicable to the construction of similar working conditions or strata.
## 4 Conclusions  
In the present work, the diffusional adsorption kinetics of the cations Cs\({}^{+}\), Rb\({}^{+}\), Co\({}^{2+}\), and Sr\({}^{2+}\) were investigated in single system. By applying the Weber-Morris model, mathematical treatment of the kinetics (batch) data evidenced a multi-step process with distinctive diffusion regimes that were linked to the pore size distribution of the material. W-M kinetic rate constants were found to be dependent on the following intrinsic characteristics of the hydrated cation: ionic potential; hydrated radius; \(B\)-viscosity parameter; and molar Gibbs energy of hydration of cations. By applying adequate diffusion models, a detailed mathematical analysis of the observed W-M kinetics stages allowed us to obtain the _key_ diffusion parameters governing the successive diffusion regimes observed. The surface diffusion coefficient for each cation (\(D_{s}\)) was estimated from the mathematical expression relating it to both the pore diffusion coefficient (\(D_{p}\)) and effective diffusion coefficients (\(D_{\mathit{eff}}\)).  
These calculations revealed the strong contribution of surface diffusion to the adsorption kinetics during the course of the second and third kinetics stages of the W-M model. It was noticed that the magnitude of the surface diffusion coefficient was strongly dependent upon the capacity of hydrated cationic species to lose water molecules when penetrating brick pores.  
The HSDM model was used for describing and simulating (single) the adsorption of cations in a fixed-bed column under optimized operational conditions. A breakthrough curve simulation clearly indicated the predominance of surface diffusion in the intra-particle diffusion mechanism under fixed operational conditions, in agreement with a mathematical analysis of the (batch) adsorption kinetics data. The simulated (apparent) surface diffusion coefficients were found to be close to those calculated from diffusion-based models, which permitted us to validate the modelling procedure which was employed.  
The capillary porosity and connectivity of the capillary pores in the modified brick were found to be relevant characteristics in the ionic diffusion of cations (Cs\({}^{+}\) and Sr\({}^{2+}\)) within the porous structure, evidencing the immobilization of hydrated nuclides in the form of outer-sphere complexes.  
Since artisanal gold mining activities are growing significantly in developing countries, mercury is being released at elevated levels to the environment. This led our research group to study mercury adsorption on alkali-activated brick. This work will permit future projects to implement dynamic water treatment systems in poor regions, which would be capable of eliminating this toxic metal from immediate contaminated waters and of obtaining potable water for the local population efficiently and at a low cost. Furthermore, experiments regarding the adsorption capability of alkali-activated brick toward soluble uranium species present in waters contaminated by uranium extraction mines in the Central African Republic are also underway in the laboratory.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 7 Conclusions  
The results in this work show the importance of transfer learning to the optimization of speech-to-text systems with limited data. The results demonstrate the effectiveness of the transfer learning approach to optimize a speech-to-text system with European Portuguese as the target and attain a state-of-the-art performance of WER = 0.0503, which is a clear improvement over existing ASR systems for the Portuguese language previously mentioned in Section 2.2. It was shown that the transfer learning with the SpeechDat dataset has the greatest impact on the performance of the models tested with European Portuguese SpeechDat. Varying the proportion of MLS and SpeechDat, it was clear this proportion directly influences the transfer learning process and performance for MLS and SpeechDat targets, respectively.  
The use of data from the Brazilian Portuguese dataset has shown an overall increase in performance relative to situations of mid-range performance (WER = 0.2662) and little impact when in situations of near-maximum performance (WER = 0.0503). Thus, we derive the following conclusions:  
* Data quantity is essential for building deep neural models, either from scratch or using transfer learning.
* The larger the amount of data from a domain used in the training or transfer process, the better the performance over the test set from the same domain.
* Data quality also plays an important role in the model's performance, since the dataset with better data quality (the SpeechDat dataset, as defined in Definition 2) yields the best performance.  
Conceptualization, investigation, methodology, writing and editing--E.M., L.C., L.R., P.Q. and P.S.; Software--E.M., L.C. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research has been partially funded by Alice Labs under contract Ref. \"Optimized Portuguese Speech to Text\".
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Log-Based Attack Detection
#### 4.4.3 Text Analysis-Based Detection  
The text analysis-based detection regards logs as plain text. The methods utilize mature text processing techniques such as the n-gram to analyze logs. Compared with log feature extraction-based methods, this method understands log content at the semantic level and therefore has stronger interpretability.  
In log-based detection, extracting text features from logs and then performing classification is the usual approach. When analyzing texts, a small number of keywords have large impacts on the whole text. Thus, the keywords in the field of cyber security aid in improving the detection effect. [PERSON] et al. [64] proposed an SQL-injection detection method for the Internet of Things (IoT). They collected and labeled logs from a real environment. The logs provide the contextual information of the SQL injection attack. First, they extracted 479,000 high-frequency words from the logs and then added 862 keywords that appear in SQL queries to compose a dictionary. Then, they removed duplicate and missing records from the log and balanced the data with SMOTE. Next, they extracted features using the n-gram algorithm and selected features using Chi-square tests. Finally, they trained an SVM model to perform classification, achieving accuracy, precision, recall, and F-measure scores of 98.6%, 97.4%, 99.7% and 98.5%, respectively.  
In an actual network environment, normal samples are in the majority, and abnormal samples are rare. One-class classification, a type of unsupervised learning method, uses only normal samples for training, which solves the problem of a lack of abnormal samples. [PERSON] et al. [65] proposed a web attack detection method based on the isolate forest model. They used the data of the CSIC 2010 dataset. First, they extracted 2572-dimensional features from HTTP logs with the n-gram. Then, they utilized an autoencoder to remove irrelevant features. Finally, they trained an isolation forest model to discover abnormal webs, which reached an accuracy of 88.32%.