esa-sceva/satcom-chunk-collection huggingface dataset:
# Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
## 3 Validation Experiment
### Case 2: Single Fault Signal
Figure 9: Frequency spectrum and Envelope spectrum of Vibration Signals in Various Faults of CWRU Bearing Dataset: (**a,b**) Ball Faults, (**c,d**) Inner Race Faults, (**e,f**) Outer Race Faults  
signals are decomposed into six wavelet coefficients by one-level multiwavelet decomposition. Then, the relative energy ratios of the correlation coefficients in three characteristic frequency bands were calculated to select the decomposition coefficients that are sensitive to the fault frequency band. Finally, the coefficients with the highest relative energy ratio are further verified. Figures 10 and 11 show the most sensitive decomposition coefficients for the three different fault frequency bands by LMWT and EWT. Based on the decomposition coefficients, the corresponding relative energy ratios can be calculated which are shown in Figures 12 and 13. Figure 12 shows the relative energy ratios of the wavelet coefficients of the LMWT method under different bearing faults. It can be found that all wavelet coefficients can effectively decompose relevant fault features, especially outer ring faults. As shown in Figure 13, the relative energy ratios obtained by different decomposition coefficients are not stable. Moreover, the relative energy ratios are close to 0 at some coefficients. The details of relative energy ratios and average values for the two methods are shown in Table 3.  
As shown in Figure 10, the corresponding fault feature frequency peaks can be clearly observed from the decomposition coefficients obtained from LMWT. In contrast, although the characteristic frequencies of inner and outer ring faults can be observed in the decomposition coefficients obtained from EWT, the fault frequency corresponding to the rolling element cannot be observed. Specifically, distinct fault frequency characteristics are evident in the LMWT method within the envelope spectrum of the three faults, and average relative energy ratio is much higher than EWT's, respectively. However, in the case of ball faults, both methods fail to isolate the fault frequency characteristics of the ball. This suggests that for rolling element faults, the LMWT method proposed in this paper can more accurately extract and analyze its characteristic frequency, which can improve the accuracy and reliability of bearing fault diagnosis. Figure 11: Envelope spectrum obtained by the EWT method of bearing at different faults in CWRU bearing dataset. Figure 12: Relative energy ratios obtained by the proposed method of bearing at different faults in CWRU bearing dataset. Figure 13: Relative energy ratios obtained by the EWT method of bearing at different faults in CWRU bearing dataset.
## 3 Determination of a Reasonable Solution to Pass Through Fault F6  
### Proposed Two Solutions for Crossing the Fault  
Considering the conditions of the #3 up coal seam and the actual occurrence characteristics of the F6 fault, if using the forcible passage method, the range of rock breaking would be large, the propulsion speed would be slow, and it would be easy to cause serious damage to the mining machines and generate a high coal-gangue mixture. If using the detour method, the coalface would need to be partially dismantled and reinstalled, the on-site construction time would be long, the implementation process would be complex, and the fault protection coal pillar may easily experience spontaneous combustion in the mined-out area, increasing the safety production risk of the coalface. In view of the above reasons, in order to achieve safe and efficient production of the coalface, two solutions for crossing the F6 fault were proposed. Solution 1 uses PDRs with rock pillars, while solution 2 uses a single PDR without rock pillars.  
(1) Solution 1: PDRs with rock pillars  
Figure 4 shows the schematic of solution 1. In this solution, starting from the F6 fault exposed in the headgate, according to the mining level of the coalface, a group of roadways with rectangular cross-section are pre-driven into the panel parallel to the main investigation roadway, with each roadway width of 3 m and height of 3 m. Each pre-driven roadway intersects with the secondary investigation roadway, and a certain width of rock pillar is left between adjacent pre-driven roadway. The surrounding rocks of the PDR are supported by a combined system of \"anchor-net-ladder-beam-cable\".  
(2) Solution 2: A single PDR without rock pillars  
Figure 5 shows the schematic of solution 2. In this method, a single roadway is pre-driven into the panel from the intersection between fault F6 and the headgate. With a width of 12 m and a height of 3 m, this roadway has an extra-large cross-section. This scheme will form a 12 m-wide rockless area. In the rockless area, a joint support method of \"anchor-net-ladder-cable\" is used, as well as a composite support method of \"individual hydraulic props and beams\".  
Figure 4: Solution of pre-driven roadways (PDRs) with rock pillars.
# A Collaborative Framework for Customized E-Learning Services by Analytic Hierarchy Processing
## 3 A General Framework for E-Learning 2.0  
The proposed general framework in Figure 5 contains the following modules:  
1. _A Data Lake of Course Material_: Various types of course materials can be collected in this module. Each course material's metadata will be extracted, and course contents summarized [6; 16; 17], hyperlinked, and integrated with other course materials (as Node A indicates).
2. _Automatic Course Portfolio Analysis_: Different courses composed of many units can be analyzed to find their relationships and similarities, and then clustered into many groups as node B specifies. This can be conducted by text mining processes in the background to help the system reduce the candidate courses when keywords are issued by a learner. By acquiring the necessary preferences input by users or instructors (node D), we can employ AHP (or Fuzzy AHP) to generate some of the alternatives containing the related learning objects for different learners (node C).
3. _Course Warehousing_: To inspire an open e-learning system and offer hooks for integration with other systems, this module recommends using a multi-dimensional course warehousing structure to index the result obtained in the previous modules (node E). Based on such a flexible structure, the course content can be organized into hierarchies and pivoted for visualization from different perspectives. The course warehouse is only a multi-dimensional index pointing to the corresponding SCORM standard for further integration with other courses (node G).
4. _Assessment_: By employing the multi-dimensional structure derived from the previous module, instructors can arrange two-way specification tables (like the example in Table 1) for a system to generate the test banks for the assessment of the corresponding courses (node F).
5. _User Interfaces_: To provide a versatile user interface for learners or instructors to communicate with the system. Flexible visualization support is indispensable, as it  
Figure 5: The proposed framework for E-Learning 2.0.  
offers users (or instructors) to query the course database through a versatile multi-dimensional indexing structure (node H).  
As indicated in [1], a PLE was not just an application software. It may consist of different apps used for learning in our daily life. Therefore, the framework in Figure 5 can be considered as a guideline to connect versatile apps to build an ecosystem for customized PLE. In the following, we discuss the mechanism (i.e., node C, the main focus of this study) for flexible course portfolio generation by orchestrating the learner and course design experts.
## 2 Materials and Methods
The better visibility of satellites and their spatial arrangements have been described in previous studies using multi-GNSS receivers [40]. The authors of reference [41] showed significant findings in the ability to resolve the ambiguity using a combination of L1 and L2 ranges. Furthermore, this work positively evaluated the use of a Inertial Navigation System (INS) signal combined with RTK using the L1 frequency. In practice, the high initial costs of multifrequency (also multi-GNSS) technology have been complemented with the widely used MEMS technologies (especially due to their favourable prices and often sufficient parameters) [42]. When a RTK receiver is unableto take measurements in the most accurate mode or, in the worst case, the GNSS signal fails completely, for example, due to the entrance into a tunnel, it is appropriate to use the so-called \"Dead Reckoning\" method [43]. This technique uses inertial position data from the AHRS systems from the IMU. With suitable algorithms allowing for the fusion of GNSS and RTK signals with data from AHRS, the INS is created, thus enabling the determination of the position with more accuracy. This system is capable not only of improving the ambiguity estimates but, also, in addition, it is equipped with spatial orientation mechanisms [21; 27; 28; 44]. The authors of reference [45] described the significantly increased success rate of solving the ambiguity under a viaduct by the joint use of two RTKs of two mobile devices. This study also explored the possibilities to determine their relative position using LiDAR.
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 2 Related Work
Vehicular FL [12; 30] works well for structured mobility but is not as effective for general IoT applications with unpredictable movement. Reinforcement learning-based approaches [13; 31; 32] optimize selection over time but come with high computational overhead, making them impractical for resource-limited devices. MACS integrates predictive mobility modeling, real-time resource-aware selection, and adaptive mechanisms to enhance client participation in FL. Unlike FedCS and Latency-Aware FL, MACS proactively selects clients based on anticipated mobility, reducing instability in training. It also achieves lower computational complexity than reinforcement learning-based methods, which require significant processing power. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Mobility Handling** & **Client Selection** & **Computational Efficiency** & **Limitations** \\ \hline FedCS [11; 27; 28] & No & \begin{tabular}{c} Communication- \\ based \\ \end{tabular} & Moderate & \begin{tabular}{c} Assumes clients \\ remain available, \\ leading to instability \\ in high-mobility \\ scenarios. \\ \end{tabular} \\ \hline Latency-Aware FL [9; 29] & No & Low-latency clients & High & \
\begin{tabular}{c} Lacks predictive \\ mobility modeling, \\ causing frequent \\ disconnections. \\ \end{tabular} \\ \hline Vehicular FL [12; 30] & Yes (structured) & Location-based & Moderate &
\begin{tabular}{c} Designed for \\ structured mobility \\ but ineffective for \\ general IoT \\ applications. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Mobility-Aware FL approaches. This work introduces the Mobility-Aware Client Selection (MACS) strategy to improve Federated Learning (FL) in dynamic environments. Unlike other methods, MACS uses a clever mobility prediction model. Macs looks at past data and movement patterns to predict where clients will go next. MACS strikes a balance between selecting mobile clients that contribute diverse data and stable clients that ensure consistent participation. It includes a resource-aware evaluation mechanism that assesses each client's computational power and network conditions, ensuring that only capable clients are chosen for training. This prevents delays caused by resource-limited devices. Additionally, MACS features a dynamic resource allocation mechanism that adjusts in real-time to changing network conditions, maintaining training efficiency even as device capabilities fluctuate. These features enable MACS to handle dynamic environments more effectively than previous methods.
## 3 Multi Objective Optimization of SFD Parameters
### Analysis Results of Optimization Parameters of Rotor System with the Misalignment Fault
For elastic support stiffness \(K_{\mathrm{a}}\), when its value increases, the amplitude of fundamental and 3\(\times\) fundamental frequency increases gradually, while the amplitude of 2\(\times\) fundamental frequency is shown as the opposite trend. For the viscosity of the lubricating oil \(\mu_{\mathrm{s}}\), when its value increases, the amplitude of fundamental frequency and 3\(\times\) fundamental frequency decrease gradually, while the trend of the amplitude of 2\(\times\) fundamental frequency showed decreases at first, and increases at the end. The SFD optimization parameter of rotor system is carried out by using NSGA-II, and the parameter configuration of NSGA-II algorithm is same as the parameter configuration with misalignment faults. The optimal solution of SFD is obtained as \(C_{\mathrm{S}}=8\times 10^{-5}\); \(K_{\mathrm{a}}=6\times 10^{5}\); \(\mu_{\mathrm{s}}=5\times 10^{-2}\). According to the design parameters, the numerical simulation of the SFD-rotor system is carried out under the state of the misalignment-rubbing coupling fault, and the dynamic response spectrum of the system is obtained, compared with spectrum in Figures 14a and 17, it can be seen that after being optimized, the amplitude of fundamental frequency of system dynamics response decreases from 1.116 to 1.033, the amplitude decreases by about 7.4%; the amplitude of 2\(\times\) fundamental frequency decreases from 0.099 to 0.048, the amplitude reduces by about 51.5%, the amplitude of 3\(\times\) fundamental frequency decreases from 0.214 to 0.178, and the amplitude reduces by about 16.8%. Figure 15: Comparison between calculated results and approximate model with the misalignment-rubbing coupling fault. (**a**) fundamental frequency; (**b**) 2\(\times\) fundamental frequency; (**c**) 3\(\times\) fundamental frequency.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Features
#### 4.2.5 Event Based  
To detect abnormal and normal events presented in videos, event-based approaches are useful. There are many examples such as terrorism, mobile hijacking, robbery scenes, the recognition and monitoring of sudden changes in the environment, etc., in which the observation of anomalous/suspicious features is performed using detection models. To produce the video digest, the frames with abnormal scenes are joined using a summarization algorithm [37]. Hereafter, we present such event-based approaches.  
[PERSON] and [PERSON] [17] described a method for creating video skims using an algorithm to dynamically build a skimming tree. They presented an implementation to obtain features that are different through online analysis. [PERSON] and [PERSON] [26] presented a study in which a single sequence of events is generated from multiple sequences and the production of a personalized video summary is performed using fuzzy TSC rules. [PERSON] et al. [39] described a study in which metadata are acquired based on the detection of events and objects in video. Taking metadata into account divides the video into segments so that each segment covers a self-contained period of a basketball game. [PERSON] et al. [36]proposed two methods for event detection. The first is detection by combining a support vector machine (CNNs-SVM) with a convolutional neural network, and the second is detection using an optimized summary network (SumNet). [PERSON] et al. [61] proposed a method to produce a personalized video summarization without supervision based on interesting events. The unsupervised Multigraph Fusion (MGF) method was proposed by [PERSON] et al. [71] to find events that are automatically relevant to the query. The LTC-SUM method was proposed by [PERSON] et al. [31] based on the design of a 2D CNN model to detect individual events through thumbnails. This model solves privacy and computation problems on end-user devices that are resource constrained. At the same time, due to this model, the efficiency of storage and communication is improved, as the computational complexity is reduced to a significant extent. PASSEV was developed by [PERSON] et al. [38] to create personalized real-time video summaries by detecting events using data from the Web. [PERSON] et al. [50] proposed a PLSA-based model for video summarization based on user preferences and events. [PERSON] and [PERSON] proposed a video segmentation method based on clock events [18]. More specifically, it is based on the rule that exists in basketball that each team has 24 s to attack by making a shot. Many events, such as fouls, shots, interceptions, and others, are immediately monitored with the restart, end, and start of the clock. Many studies [13; 14; 16; 19; 25; 46; 53; 54; 58; 60; 62; 66; 67] have proposed that personalized video summarization is also based on events.
## 3 Methodologies
### Model Description  
#### 3.2.1 Artificial Neural Network (ANN)  
ANNs are widely used to solve various tasks, including regression, and have powerful ability in learning nonlinear relationships within data, resisting noise interference, and adapting to diverse data [55; 56]. A common ANN structure consists of an input layer, one or two hidden layers, and an output layer, as shown in Figure 4. For regression tasks, to minimize errors between the actual and predicted values, the weights need to be adjusted using backpropagation and optimization algorithms such as stochastic gradient descent (SGD), Adam, and limited-memory BFGS (LBFGS) [57; 58; 59]. Furthermore, regularization techniques are crucial to prevent overfitting; L1 and L2 regularization are commonly used methods. These add a penalty to the loss function based on the absolute values or squared values of the weights, respectively. Thus, the overall objective function (\(Obj(\theta)\)) for an ANN can be formulated as:  
\[\begin{split} Obj(\theta)&=L(\theta)+\lambda\sum \limits_{j=1}^{m}|\theta_{j}|\ (\text{L1 regularization})\\ Obj(\theta)&=L(\theta)+\lambda\sum\limits_{j=1}^{m }\theta_{j}^{2}\ (\text{L2 regularization})\end{split} \tag{1}\]  
where \(L(\theta)\) represents a loss function, \(\lambda\) is a regularization parameter, \(|\theta_{j}|\) represents the absolute value of the \(j\)-th parameter for L1 regularization, and \(\theta_{j}^{2}\) represents the squared value of the \(j\)-th parameter for L2 regularization. \(m\) is the number of parameters used in the model. By minimizing this objective function, the model parameters are optimized to achieve a balance between fitting the training data well and maintaining generalizability. In this study, the ANN model's performance is controlled by several hyperparameters that need to be optimized, including the number of hidden layers (n_layers), the number of neurons in each hidden layer (n_units), the activation function, the solver for the optimization algorithm (a_solver: SGD, Adam, and LBFGS), and the regularization strength (alpha).  
Figure 3: Feature selection based on the Boruta algorithm.  
Note that the common activation functions include the rectified linear unit (ReLU), logistic, and hyperbolic tangent (Tanh) functions.
## 5 Sensor Signal Feature Extraction
### Signals Feature Extraction in the Time Domain  
Statistical features in the time domain were extracted from the segmented thrust force and torque signals: arithmetic mean (F\({}_{\text{mean}}\), T\({}_{\text{mean}}\)), variance (F\({}_{\text{var}}\), T\({}_{\text{var}}\)), skewness (F\({}_{\text{skew}}\), T\({}_{\text{skew}}\)), kurtosis (F\({}_{\text{kurt}}\), T\({}_{\text{kurt}}\)), signal energy (F\({}_{\text{ene}}\), T\({}_{\text{ene}}\)). Some of these features displayed a good correlation with hole number and thus, expectedly, with tool wear level. In Figure 13, as an example, statistical features extracted from the thrust force signals are plotted for all operating conditions vs hole number up to the last hole n\({}^{\circ}\)60. From the figure, it can be noticed that some of the features increase with increasing number of holes while other features decrease with growing hole number. In general, by carrying out a graphical analysis of feature trends, higher or lower degrees of correlation with hole number is verified as a function of the specific statistical feature.
# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 2 Methodology
### Selection Process  
To narrow down the result list to only the most relevant documents, a selection strategy was defined. This strategy consisted in the application of two document filtering tasks: screening and election.  
The screening process had the purpose of filtering out papers that were not related with the topic at hand. This was performed by scanning the title, abstract, and keywords of the search results. Out of the initial 1382 papers, 1315 were eliminated during the screening process because of the following reasons:  
* The document is not related to the educational field (1118 papers).
* The document is related to education, but does not present the use of a predictive algorithm or EWS (111 papers).
* The document presents the use of a predictive algorithm or EWS in education, but in a context other than higher education (66 papers, most of them centered around massive open online courses (MOOCs)).  
The remaining 67 articles were fully and independently analyzed during the election process, looking for these particular pieces of information:  
* Date on which the document was published.
* Problem that the predictor or EWS seeks to solve.
* The prediction goal of the algorithm or application (such as student grades or dropout likelihood).
* Types of data used as input.
* Technical aspects about the predictive algorithm or algorithms that the application uses.
* User collective that received the output information (most commonly either students or teachers).
* How the output information was presented to the end user.
* Specific higher education context where the tool was applied.
* Number of students that were involved in the study.
* Reliability of predictions made by the predictor or EWS.
* Evaluation of the application's impact over the students.
* Any other unique aspect that differentiates the particular predictor or EWS from the rest.  
In the election process, documents that failed to provide important information about how the EWS was built or applied in practice were ruled out. This resulted in the elimination of 41 papers for the following reasons:  
* The document was missing important information about the predictor or EWS's implementation, and inner workings (24 papers).
* The results of applying the predictor or EWS in real higher education scenarios were insufficient, nonexistent, or poorly detailed (17 papers).  
Once the election process was completed, the final number of papers that were included in this review for qualitative synthesis was 26.  
Figure 1 displays a graphical summary of the document selection process, showing the outcome of every stage.