esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Fault Diagnosis of Rolling Bearings
###### Abstract  
The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field.  
rolling bearing; diagnosis; fault detection; fault type recognition; signal processing; machine learning +
Footnote †: journal: _Algorithms_
# PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)
## 5 Conclusions and Future Direction  
This study proposed a FURIA-based model for PDF malware detection. The proposed model is benchmarked with some of the well-known ML models, which are NB, J48, HT, and QDA. The performances of all these models are evaluated using some of the standard assessment measures that include MAE, ACC, FM, MCC, precision, and recall on the dataset taken from the UNB repository. The overall outcome presents a better performance of the proposed model, with an accuracy of 99.81% and a lowest error rate of 0.0022. FURIA outperforms other models; however, there are some limitations of FURIA. FURIA generates a large number of rules, which can make it difficult to understand and  
Figure 7: Accuracy percentage difference between FURIA and other employed models.  
interpret the resulting model. This complexity can also lead to longer processing times and increased computational resources. Although fuzzy rules can be more interpretable than other machine learning models such as neural networks, they can still be difficult to interpret in complex data sets, which can limit their usefulness in some applications.  
The FURTIA-based model outperforms other well-known machine learning models for PDF malware detection; there are several potential future directions for research. There may be opportunities to refine the model further. For example, the model's parameters could be further optimized, or new features could be added to improve the model's performance. It may be beneficial to explore the potential benefits of combining the FURTIA-based model with other ML models. Such hybrid models have the potential to improve the overall performance of the model.  
Writing--original draft, S.G.; Writing--review & editing, S.M. All authors have read and agreed to the published version of the manuscript.  
This research work is supported by Alfaisal University.  
The authors declare no conflict of interest.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 5 Conclusions  
This article presents a novel density peak clustering method, DPC-MS, which is grounded in hybrid clustering. This approach amalgamates the strengths of the mean-shift algorithm and the DPC algorithm while mitigating their respective weaknesses. DPC-MS comprises three pivotal steps. Firstly, during the initial phase, iterative shifts are performed utilizing kernel functions and an adaptive bandwidth-enhanced mean-shift algorithm to identify all local density extremum points. Subsequently, in the second step, the adaptive distance from each data point to its closest local density extremum point is computed, based on the principles of the DPC algorithm, thereby assigning the point to its nearest local density extremum point. Finally, in the third step, the method employs a weighted Euclidean distance for distance weight calculation to improve the performance of the DPC-MS algorithm in clustering non-convex shapes. This method not only overcomes the challenges faced by the DPC algorithm in efficiently handling manifold data but also addresses the limitations of the mean-shift algorithm in potentially misclassifying non-local density peaks, thus averting the El Nino effect induced by misclassification. To gauge the effectiveness of the proposed method, extensive experiments were conducted on both synthetic and real datasets, with the results underscoring the efficacy and robustness of the proposed approach.  
Conceptualization, L.G. and W.Q.; methodology, Z.C.; software, X.S.; validation, W.Q.; investigation, L.G.; writing--original draft preparation, W.Q.; writing--review and editing, L.G.; visualization, X.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions.  
The authors declare no conflict of interest.
# Cationic Moieties in Polystyrene Gels Swollen with D-Limonene Improved Transdermal Delivery System
## 3 Results and Discussion
### Effect of Limonene as Chemical Enhancer and Density of the Network Structure on Permeability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Entry**} & \multirow{2}{*}{**Gel**} & \multicolumn{2}{c}{**Mol \% of**} & \multicolumn{2}{c}{**Flux, \(J_{\text{ss}}\)**} & \multirow{2}{*}{**Lag Time (h)**} & \multirow{2}{*}{**D/L\({}^{2}\) (h-1)**} & \multirow{2}{*}{**KD/L (\({}^{\textbf{*}}\)10\({}^{\textbf{-4}}\) cm/h)**} \\  & & **Caitonic Moiety** & & & & & \\ \hline
1 & Gel F & 2 & 55.2 \(\pm\) 0.80 & 0.51 \(\pm\) 0.15 & 0.32 \(\pm\) 1.14 & 67.1 \(\pm\) 0.10 \\
2 & Gel G & 2.5 & 44.2 \(\pm\) 7.22 & 1.22 \(\pm\) 0.37 & 0.14 \(\pm\) 0.45 & 53.7 \(\pm\) 0.88 \\
3 & Gel H & 3 & 34.4 \(\pm\) 1.71 & 1.67 \(\pm\) 0.48 & 0.10 \(\pm\) 0.35 & 41.8 \(\pm\) 0.21 \\
4 & Gel I & 4 & 18.1 \(\pm\) 3.13 & 3.77 \(\pm\) 0.18 & 0.04 \(\pm\) 0.93 & 22.0 \(\pm\) 0.38 \\
5 & Gel J & 5 & 11.6 \(\pm\) 2.03 & 3.27 \(\pm\) 0.08 & 0.05 \(\pm\) 2.00 & 14.1 \(\pm\) 0.25 \\
6 & Gel K & 0.5 & 30.8 \(\pm\) 3.14 & 2.22 \(\pm\) 0.42 & 0.07 \(\pm\) 0.40 & 37.4 \(\pm\) 0.38 \\
7 & Gel L & 1 & 22.1 \(\pm\) 2.42 & 3.02 \(\pm\) 0.90 & 0.06 \(\pm\) 0.18 & 26.9 \(\pm\) 0.29 \\ \hline \end{tabular}
\end{table} 
Table 3: The skin permeation parameters of ibuprofen from PS-4 VP gel (gel F–J) and PS-VBAC (gel K and L). Figure 6: In vitro cumulative release profile of ibuprofen from PS-4 VP gel by various feeding ratio of 4 VP from 2–5 mol %; Gel F (**a**), Gel G (**b**), Gel H (**c**), Gel I (**d**), Gel J (**e**) and PS-VBAC gel by various feeding ratio of VBAC 0.5 and 1 mol %; Gel K (**f**) and Gel L (**g**) (\(n\) = 3). corneum. The effect of cationic moiety was still stronger than in PS-4 VP gel. Gel K and L was shown the permeability with 37.4 \(\pm\) 0.38 and 26.9 \(\pm\) 0.29 cm/h, respectively (Table 3, entries 6 and 7), while Gel F, G and H showed 67.1 \(\pm\) 0.10, 53.7 \(\pm\) 0.88 and 41.8 \(\pm\) 0.21 cm/h (Table 3, entries 1-3). It was indicated that the PS-VBAC gels included smaller amount of cationic moiety (0.5-1 mol %-VBAC) could possess higher efficiency on their prolonged release of ibuprofen than that of PS-4 VP gels.
## 4 Models and Algorithm Design
### Algorithm Development
#### 4.4.1 Sequence of Operations for Deploying the Algorithm  
The possible implementation of the described algorithm will be outlined here in a very simple way. A simplified flowchart of the operations sequence with the presented algorithm is shown in Figure 4. The entire process runs in a continuous loop. The calculation block contains the calculation of the Equations of the algorithm (22)-(24). The yellow block indicates the starting initialization. Finding a consensus means that all online agents in the network have reached the same value of \(\lambda_{i}(k)\). If the convergence has been achieved then the production plan can be passed to the individual generators, which is represents by green block. The gray block represents changes in the network settings, desired load, or other inputs from other agents. These modifications can lead to a change in the \(\lambda_{i}(k)\) value or the local requirements of \(P_{D_{i}}\), and the entire algorithm is thus recalculated.  
At this place it is important to mention that the entire algorithm runs online and thus reacts to any changes in the input information itself. This fact will be further demonstrated  
Figure 4: Simplified flowchart of the sequence of operations with the presented algorithm.  
by examples in Section 5. The aim of this analysis is to provide better insight into the possible integration of the presented algorithm.
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC
### Typical Starting Composition of Cement and Mineral Phases  
The starting composition of clinker is generally indicated by the oxide content (in wt.%). The main relevant oxides forming an OPC clinker are CaO, SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\). These oxides form different mineral phases when forming the clinker. Since clinker primarily contains these four chemical species, it is possible to represent these phases with the Bogue formulae, thus indicating CaO with C, Al\({}_{2}\)O\({}_{3}\) with A, Fe\({}_{2}\)O\({}_{3}\) with F, SiO\({}_{2}\) with S, and water with H [26]. Furthermore, there are also several further minor components deriving from natural clays, e.g., sodium (Na), potassium (K), magnesium (Mg), and other metal ions [48; 49]. Table 1 summarizes the chemical composition of OPC, expressing the single component content in wt.%. Additionally, Table 2 describes the approximate composition of the cement clinker [50].
## 5 Security Analysis
### Advantages of the Proposed Scheme  
The proposed scheme has significant advantages over other blind or partially blind signature schemes:  
* Flexibility of the parameter \(T\): Other schemes, such as those based on standard blind signatures, offer fixed anonymity, while the tempered scheme allows for adjusting the level of confidentiality.  
* Mathematical strength: By integrating blinding and using difficult cryptographic problems (discrete logarithm, factorization), the scheme offers a high level of security against brute-force and adaptive attacks.
* Performance: Blinding and verification operations are more efficient than in traditional schemes, due to the simplification of cryptographic functions in the presence of the parameter \(T\). An important aspect of the scheme is its integration into distributed systems, such as blockchain. In this context,
* Blockchain immutability: Valid signatures are permanently stored, preventing subsequent data modification.
* Public key distribution: Blockchain nodes validate signatures using \(K_{pub}\), without requiring access to the private key \(K_{priv}\), thus ensuring the security of the distribution.
* Sybil attack resistance: The parameter \(T\) can be used to control the level of anonymity of IoT devices in the network, preventing attacks based on multiple false identities. This analysis illustrates how the proposed scheme adjusts its resistance to cryptographic attacks, including adaptive, brute-force, man-in-the-middle, and side-channel attacks, depending on the parameterization. The flexibility of the T parameter allows the level of security and anonymity to be adapted to the requirements of each beneficiary.
## 4 Discussion
In the present study, the specific effects of food order on glycemic response were not directly evaluated, as numerous studies have already demonstrated that consuming vegetables first and carbohydrates last plays a significant role in mitigating glycemic and insulin excursions [9; 11; 12; 22]. However, it is important to emphasize that food order--consuming vegetables first and carbohydrates last--is a crucial factor. Even when eating in a full-course meal manner, consuming carbohydrates first elevates postprandial glucose and insulin responses, thereby diminishing the beneficial effects of eating over a long period of time [11; 12; 22]. Several limitations of the present study should be acknowledged. First, this study was designed as an acute interventional trial to examine the effects of consuming a meal in a full-course meal manner, replicating the prolonged mealtimes characteristic of social gatherings, such as parties or banquets. However, the study protocol did not include the consumption of alcohol, which is typically present during parties or banquets, nor did it incorporate social interactions, such as conversations with others. As a result, the findings may not fully capture the metabolic responses associated with these additional factors, limiting the generalizability of the results. Second, the study population consisted of small number of young, healthy Japanese women; therefore, it is uncertain whether these results are applicable to individuals with T2 DM, different genders, elderly people, or other racial groups. Third, GLP-1 and other incretin hormones may play a role in the regulation of blood glucose and insulin levels, as well as in delaying gastric emptying, in individuals both with and without T2 DM, when meals are consumed one dish at a time, with intervals between courses. However, GLP-1, GIP, and gastric emptying rate were not investigated in this study; the role of incretin hormones when eating in a full-course meal manner remains unclear. Therefore, future investigations, particularly large-scale clinical studies, are worth conducting to confirm the overall mechanisms and effectiveness of eating in a full-course meal manner regarding glycemic and hormone responses in individuals with and without T2 DM.
## 4 Conclusions  
This research proposed a novel approach of data transformation called neuro-fuzzy transformation with the minimum entropy principle. The proposed model was used to create new features for predicting classes of both types of PM. The raw data from eight fixed-site data monitoring stations were received from the PCD, Thailand, to create prediction models. Several experiments were conducted. The results showed that the new informative features of six hours prior were appropriate for the generation of historical data. In addition, the applied fuzzy membership function with the minimum entropy principle can improve model performance. It is evident from all experimental results that the proposed NFT-MEP model for data transformation outperformed in predicting both PM\({}_{10}\) and PM\({}_{2.5}\) classes for all selected data monitoring stations.  
[PERSON] contributed to data acquisition, data analysis, model creation, and writing--original draft preparation; [PERSON] contributed to validation and writing--review and editing, supervision. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data of this work are available from the Pollution Control Department, Ministry of Natural Resources and Environment, Thailand, upon request.  
This study was supported in part by The Graduate School, Chiang Mai University.  
The authors declare that there is no conflict of interest regarding the publication of this paper.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Types of PM**} & \multirow{3}{*}{**Types of Output Data**} & \multicolumn{8}{c}{**Periods of Output Data**} \\ \cline{3-10}  & & \multicolumn{3}{c}{**One Hour in Advance**} & \multicolumn{3}{c}{**Two Hours in Advance**} & \multicolumn{3}{c}{**Three Hours in Advance**} \\ \cline{3-10}  & & **Accuracy** & **F-Score** & **MCC** & **Accuracy** & **F-Score** & **MCC** & **Accuracy** & **F-Score** & **MCC** \\ \hline \multirow{3}{*}{PM\({}_{10}\)} & Three-class & 85.18\% & 0.7320 & 0.6361 & 80.14\% & 0.6533 & 0.5712 & 76.25\% & 0.6001 & 0.5445 \\  & Five-class & 74.49\% & 0.6433 & 0.6035 & 67.48\% & 0.5465 & 0.4811 & 63.15\% & 0.4904 & 0.4233 \\ \hline \multirow{3}{*}{PM\({}_{2.5}\)} & Three-class & 83.37\% & 0.7838 & 0.6883 & 77.48\% & 0.7129 & 0.6249 & 73.10\% & 0.6645 & 0.5749 \\  & Five-class & 74.96\% & 0.7257 & 0.6573 & 65.44\% & 0.6233 & 0.5756 & 60.55\% & 0.5602 & 0.4984 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Implementation results to predict both types of PM with additional periods.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 1 Introduction
However, the negative sequence current algorithm cannot distinguish when an external and internal fault occur simultaneously. The extended park vector algorithm (EPVA) method can detect and distinguish turn-to-turn winding insulation failures from magnetizing inrush current transients. The EPVA also overcomes the limitation of the negative sequence current algorithm. The EPVA is not affected by the external fault and can detect the TTF accurately. Fuzzy logic was developed to detect TTFs in a transformer. Here, fuzzy logic is employed to monitor the transformer's condition and improve the protection system's performance. Using fuzzy logic overcomes the traditional PT differential scheme's limitations by detecting a low-level TTF. The fuzzy-based technique is simple, robust, and able to detect incipient faults at an early stage [4], and shows a fast and accurate trip operation [18]. The fuzzy algorithm also improves the accuracy of the diagnosis and the efficiency of transformer maintenance [19]. A combination technique using fuzzy logic and Clark's transform was used to improve the differential protection performance [20]. A TTF may occur even if no current is flowing on one side of the transformer during energization. With no current flowing in the secondary windings of the transformer, negative-sequence current-based algorithms become insensitive. This paper discusses a voting algorithm that is a combination of several protection algorithms and used to make the right decision for fault detection. The algorithms used are the negative-sequence directional algorithm, EPVA, differential negative-sequence algorithm, and the fuzzy Mamdani conventional protective algorithm. According to the simulation, each algorithm detects whether the fault belongs to an internal or external fault. The results from each algorithm were collected, and we calculated how many possibilities of internal fault and external fault happened. Section 2 of this paper introduces the turn-to-turn equivalent circuit and transformer configuration. Section 3 discusses the purpose and method to detect and distinguish PT faults. Section 4 shows the results and discussion of the TTF simulation in a PT for a 1% turn fault, followed by an external fault. Section 5 concludes the research.
## 3 Proposed Method
### The Optimized Kalman Filter Based on IFOA(IFOA-KF)
The mathematical description of the Kalman filter is presented as Equation (1) to Equation (6). The flowchart of the proposed method, as shown in Figure 5, is presented and the pseudo-code is demonstrated as follows:  
Figure 5: The flowchart of the IFOA-KF. ```
Procedure: IFOA-KF Parameters: \(PS\), \(iter_{\max}\), \(\Delta w\), \(LB_{j}\), \(UB_{j}\) Outputs: \(Q_{R}\)
```  
```
% Initialization Set population size \(PS\), maximum generation \(iter_{\max}\), and perturbation intensity \(\Delta w\) and \(LB_{j}\), \(UB_{j}\) %Set random initial position of fruit fly swarm For i = 1, 2,..., PS \([X(i,:),Y(i,:)]=LB_{j}+(UB_{j}-LB_{j})\times rand(1,2)\); End \(\Delta_{Q,R}\leftarrow\min_{i=1,2,...,PS}\left(MSE\left(X(i,:),Y(i,:)\right) \right)\) %Set individual with best smell concentration \([X\_axis,Y\_axis]=\Delta_{Q,R}\) For\(j=1\): \(iter_{\max}\) % if prediction is enabled IfbPredicate == TRUE thenbPredicate = FALSE \([X\_axis,Y\_axis]=[X\_axis,Y\_axis]+\Delta w\times rand(1,2)\); End For\(i=1\), 2,..., PS %Random direction and distance \([X(i,:),Y(i,:)]=[X\_axis,Y\_axis]+(UB_{j}-LB_{j})\times rand(1,2)\); If\([X(i,:),Y(i,:)]>UB_{j}\)then\([X(i,:),Y(i,:)]=UB_{j}\) If\([X(i,:),Y(i,:)]<LB_{j}\)then\([X(i,:),Y(i,:)]=LB_{j}\) %obtain smell concentration \(S(i,:)=1/Dist\left((X,Y)\right)\) %MSE of Kalman filter estimation result \(Smell(i)=MSE(Kalman(S(i,:)))\) End \(Best_{Q,R}=\min_{i=1,2,...,PS}\left(Smell\right)\) IfBest\({}_{Q,R}\leq\Delta_{Q,R}\)then\(\Delta_{Q,R}=Best_{Q,R}\) IfBest\({}_{Q,R}>\Delta_{Q,R}\)thenbPredicate = TRUE %Kalman filter estimation using best covariance \(results=Kalman(Best_{Q,R})\)
```  
**END**  
Where, \(Dist(\cdot)\) is the operation to obtain the distance from the current fruit fly location to the original coordinate; \(MSE(\cdot)\) is the mean squared error of the estimation results; \(Kalman(\cdot)\) is the Kalman estimation operation presented as Equation (1) to Equation (6); \(LB_{j}\) and \(UB_{j}\) are the lower and upper boundaries of location range, respectively.
## 1 Introduction
Some national EUPOS GNSS networks employ off-the-shelf APPS solutions by geodesy industry leaders, such as Trimble, Geo++, or Leica. Others developed their own EUPOS Geodetic algorithms. As the EUPOS Geodetic is intended to guarantee decimetre and sub-centimetre accuracy, it needs to be continuously validated, upgraded, and modified. Note further that, in addition to EUPOS Geodetic services in national EUPOS GNSS networks, there are many other international services with APPS for static GPS observations, such as AUSPOS Online GPS Processing Service ([[https://gnss.ga.gov.au/auspos](https://gnss.ga.gov.au/auspos)]([https://gnss.ga.gov.au/auspos](https://gnss.ga.gov.au/auspos)) (accessed on 30 July 2022)) by Geoscience Australia, the Scripps Coordinate Update Tool (SCOUT) by Scripps Orbit and Permanent Array Center (SOPAC), and the Online Positioning User Service (OPUS) by the National Geodetic Survey (NGS) ([[https://www.ngs.noaa.gov/OPUS](https://www.ngs.noaa.gov/OPUS)]([https://www.ngs.noaa.gov/OPUS](https://www.ngs.noaa.gov/OPUS)) (accessed on 30 July 2022)). Most of these services require at least 1 h static observations, but the session should take at least two hours for centimetre accuracy [11]. APPS services have been validated by such researchers such as [PERSON] [12], [PERSON] and [PERSON] [13], and [PERSON] and [PERSON] [14]. The present paper validates the EUPOS Geodetic service of a selected national EUPOS GNSS network. We selected the Polish Active Geodetic Network EUPOS (ASG-EUPOS) as an example of a national EUPOS GNSS network due to its significant spatial coverage in Europe. The objective of the study is to apply a change of the method for adjusting post-processing results. Actually, the APPS of the ASG-EUPOS determines point coordinates based on the method for adjusting direct and equally accurate observations with the least-squares method. The proposed modification moves towards a weighting of the unequally accurate baseline vector components. Additionally, validation of the accuracy and precision of the results of the currently used, as well as of the modified version of APPS, is carried out in the event of limiting the number of baseline vectors used in positioning. Our analyses demonstrate whether a reduction in the number of baseline vectors and modified adjustment procedure yield more accurate positioning compared to the current methods (in post-processing of 1 h dual-frequency GPS observations). It is particularly important in the case of a failure of stations near the measurement area. The research presented here is an expansion of the studies by [PERSON] and [PERSON] [15] and [PERSON] and [PERSON] [16].