esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 2 Applications of Short Text Clustering  
Many clustering methods have been used in several real-world applications. The following disciplines and fields use clustering:  
* Information retrieval (IR): Clustering methods have been used in various applications in information retrieval, including clustering big datasets. In search engines, text clustering plays a critical role in improving document retrieval performance by grouping and indexing related documents [30].
* Internet of Things (IoT): With the rapid advancement of technology, several domains have focused on IoT. Data collection in the IoT involves using a global positioning system, radio frequency identification technology, sensors and various other IoT devices. Clustering techniques are used for distributed clustering, which is essential for wireless sensor networks [31; 32].
* Biology: When clustering genes and samples in gene expression, the gene expression data characteristics become meaningful. They can be classified into clusters based on their expression patterns [33].
* Industry: Businesses collect large volumes of information about current and prospective customers. For further analysis, customers can be divided into small groups [34].
* Climate: Recognising global climate patterns necessitates detecting patterns in the oceans and atmosphere. Data clustering seeks to identify atmospheric pressure patterns that significantly impact the climate [35].
* Medicine: Cluster analysis is used to differentiate among disease subcategories. It can also detect disease patterns in the temporal or spatial distribution [36].
## 2 Backgrounds
### Data Rebalancing Methods
#### 2.1.1 Over-Sampling Methods  
* Random duplication (RDUP)--A sample of the minority class is randomly selected and then duplicated.
* Synthetic minority over sampling technique (SMOTE) [34]--A sample of the minority class is randomly selected and the weighted mean of the nearest neighbors of it is used to produce a new sample of the minority class.
* Borderline-SMOTE [35]--Two SMOTE variant methods, borderline-SMOTE1 (BSMOTE1) and borderline-SMOTE2 (BSMOTE2), were further developed. They are the same as SMOTE except that a new sample of the minority class is produced near the borderline between classes. In addition, BSMOTE1 chooses the nearest neighbor from only the minority class, whereas BSMOTE2 does so from any class.
* Support vector machine (SVM)-SMOTE (SSMOTE) [36; 37]--Similar to borderline-SMOTE methods, the new minority-class sample is produced near the borderline but the borderline is determined by the support vector machine classifiers.
## 6 Discussion  
Four practical engineering problems show that the URI based on uncertainty theory is a better indicator than PRI for quantifying epistemic uncertainties. The established URBDO model can meet structural design requirements of engineering applications with insufficient sample data. In general, when the CEP and USGA methods can solve the same  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weight** & **Optimal Results** & **RBDO (RIA)** & **URBDO (USGA)** \\ \hline \multirow{3}{*}{\(\omega_{1}=0.4\)} & \((d_{1}^{*},d_{2}^{*},d_{3}^{*},d_{4}^{*})\) & (5.51, 150.38, 271.59, 5.91) & (5.46, 159.18, 273.21, 5.91) \\  & Objective1 (\(f_{1}\)) & 2.6917 & 2.7612 \\  & Objective1 (\(f_{2}\)) & 0.1961 & 0.1924 \\ \hline \multirow{3}{*}{\(\omega_{1}=0.5\)} & \((d_{1}^{*},d_{2}^{*},d_{3}^{*},d_{4}^{*})\) & (5.60, 161.08, 252.90, 5.99) & (5.54, 170.44, 254.47, 5.99) \\  & Objective1 (\(f_{1}\)) & 2.6385 & 2.7092 \\  & Objective1 (\(f_{2}\)) & 0.2395 & 0.2349 \\ \hline \multirow{3}{*}{\(\omega_{1}=0.6\)} & \((d_{1}^{*},d_{2}^{*},d_{3}^{*},d_{4}^{*})\) & (5.68, 171.97, 236.11, 6.08) & (5.63, 182.03, 237.37, 6.08) \\  & Objective1 (\(f_{1}\)) & 2.5971 & 2.6680 \\ \cline{1-1}  & Objective1 (\(f_{2}\)) & 0.2901 & 0.2852 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Solution results under different weight coefficients for the welded beam problem.  
Figure 10: Comparison of optimal results under different weights.  
optimal design problem, the computational efficiency of the CEP method is higher than that of the USGA method. Nevertheless, the CEP model sometimes has no solution when faced with high-dimensional nonlinear or non-monotonicity challenges. Consequently, the USGA algorithm can solve high-dimensional nonlinear and non-monotonic problems, although it is time-consuming.  
Another issue that should be discussed is the LSF. It is clear that the LSF are assumed to be explicit in our proposed models. In many real engineering problems, however, these functions may not be that easy to obtain. Integrating surrogate models with uncertain variables, would be a rational way to solve the implicit function problems. Fortunately, because of the good adaptability of the USGA algorithm, the proposed method is able to handle this issue. Structural designers can verify this with some reliability benchmark problems elaborated in [46].  
It should be emphasized that to use the URBDO model and corresponding solving techniques, the above processes should be carried out under the framework of uncertainty theory using uncertain variables, instead of random variables, since the sampling of uncertain variables is based on the inverse uncertainty distribution. People can calibrate and validate the proposed URI and URBDO models by adding the design optimization module to the UQLab framework. Inspired by the research shown in [46], another research direction is to reduce the computational burden of the USGA algorithm by introducing an active learning strategy.
# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 1 Introduction  
There is often a great need to be able to predict future students' behavior in order to improve curriculum design and plan interventions for academic support and guidance on the curriculum offered to the students. This is where Data Mining (DM) [1] comes into play. DM techniques analyze datasets and extract information to transform it into understandable structures for later use. Machine Learning (ML), Collaborative Filtering (CF), Recommender Systems (RS) and Artificial Neural Networks (ANN) are the main computational techniques that process this information to predict students' performance, their grades or the risk of dropping out of school.  
Nowadays, there is a considerable amount of research and studies that follow along the lines of predicting students' behaviour, among other related topics of interest in the educational area. Indeed, many articles have been published in journals and presented in conferences on this topic. Therefore, the main goal of this study is to present an in depth overview of the different techniques and algorithms proposed that have been applied to this subject.
## 4 Security Analysis and Simulation Verification
### Performance Analysis and Security Comparison  
#### 4.4.1 Performance Analysis  
In this section, the computational cost of the protocol authentication key-agreement phase is used as a comparison standard, and the computational cost of the protocol is analyzed and compared with that in the literature [26; 27; 28]. Since computational cost is primarily determined by the number of executions of time-consuming operations, the computational cost of various protocols can be objectively evaluated through testing and statistical analysis of such operations on different platforms, combined with theoretical analysis.  
For convenience, Mul represents the elliptic-curve scalar multiplication operation, Add represents the elliptic-curve point-addition operation, Exp represents the power multiplication operation, Hash represents the hash operation, and Xor represents the XOR encryption and decryption operation.  
With an Intel Core i5-4460 3.20 GHz processor, 8 GB of RAM, and Windows 10 operating system platform [32], the test results show that when the p value is 192 bits, the average time required for a scalar multiplication operation is 2.1780 ms, the average time required for a point-addition operation is 0.0239 ms, the average time required for a hash operation is 0.0186 ms, and the average time required for an XOR encryption and decryption operation is 0.0091 ms. The test results are shown in Table 3.  
With an ARM Cortex-M3 100 MHz processor, 512 kb of RAM [10]. The test results show that the average time required for a symmetric encryption and decryption (including XOR encryption and decryption operation) operation is about 0.1 ms. When the \(p\) value is 192 bits, the average time required for a scalar multiplication operation is 87 ms, the average time required for a point addition operation is 0.36 ms, the average time required for a hash operation is 0.065 ms. The test results are shown in Table 3.  
Consumption of the authentication-key negotiation process of each protocol is shown in Table 4.  
According to the Table 4, the protocol described in this solution is superior to the industry representative protocol in terms of computational cost.  
\begin{table}
\begin{tabular}{c c c} \hline
**Operation** & **Average Time per Session (PC)** & **Average Time per Session (ARM Cortex-M3)** \\ \hline Mul & 2.1780 ms & 87 ms \\ Add & 0.0239 ms & 0.36 ms \\ Hash & 0.0186 ms & 0.065 ms \\ Xor & 0.0091 ms & 0.1 ms \\ \hline \end{tabular}
\end{table}
Table 3: Average time consumption of each operation.
# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Tool: OPC UA-GraphQL Wrapper  
OPC UA-GraphQL wrapper is a server application that connects to an OPC UA interface to provide it as a GraphQL interface. The wrapper was developed as a master's thesis by [PERSON] [38] and presented and evaluated in a conference publication [39]. The source code is published as open source in GitHub [40]. A GraphQL wrapper was installed to the IImatar crane on a Raspberry Pi, and an example control application was made for it, presented in Section 3.11.  
GraphQL is a query language and execution engine open-sourced by Facebook in 2015 [41]. Since then, GraphQL has become a popular query language among developers [42]. GraphQL overcomes multiple shortcomings of the popular REST API and is seen as a successor for them [43].  
The OPC UA-GraphQL wrapper was developed after noticing a hindrance in the crane interface: OPC UA, even though being a common standard in the industry, is not familiar to web software developers and it is fairly complicated compared to, for example, REST APIs. The OPC UA-GraphQL wrapper makes data from the OPC UA server of the crane easier to access, therefore facilitating software development for the IImatar. The OPC UA-GraphQL wrapper also comes with a built-in node viewer, so a user can use any standard web browser to click through the nodes and their current values. As a downside, not all features (e.g., publish-subscribe) of OPC UA are yet supported.  
The wrapper represents an important phenomenon in the development of integrated digital twins: adapters. It takes some of the development burdens away from application development. While OPC UA might be technically possible to implement in any project, it may not be feasible during projects with a limited time frame, for example, due to the lack of libraries for some programming languages. The wrapper is also located between two cultures: OPC UA servers are typically installed in closed intranet networks in factories, whereas GraphQL servers are typically available via the public Internet. The benefits of networked digital twins can only be achieved if the twins can send messages to each other, for which the public Internet currently seems the most prominent option. Hence, data adapters that couple operation data with the Internet are important enablers for digital twins, although security solutions still need to be developed before the public Internet can be used.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Use Case Specific Challenges  
#### 5.4.1 Communication Range  
One of the most significant perks of PC5 (and Uu)-based communication over 802.11p-based communication is the enhanced communication range. However, the increased range means an increased number of received packets to handle as well. A higher number of received packets means that processing the packets also requires more effort, and, practically, a more powerful--and more expensive--processor unit, which leads to higher equipment costs. This means that the higher range also requires a strong use-case-based justification. If we calculate with two vehicles approaching each other with a 300 km/h relative speed difference, a 1 km range results in a 12-s time for scenario mitigation in the case of unchanged speeds [133].
# Experimental Investigations on Laser Ablation of Aluminum in Sub-Picosecond Regimes
## 1 Introduction  
A scientific goal of this study is a better understanding of the physical phenomena and identification of ablation mechanisms in sub-picosecond regimes. A practical goal is to explore and enable sub-picosecond (i.e., in the range of several hundred of femtoseconds) lasers as an alternative to both femtosecond and picosecond systems in high precision micromachining processes.  
Even though there are already femtosecond industrial lasers able to work for production environments, given the full advantages of material micromachining without the necessity of post-processing tools, they are not considered the first choice for many manufacturers. Still, the complex and unstable nature of femtosecond systems undermines their reliability and efficiency in commercial use, in contrast to the picosecond laser systems [1].  
An effective micromachining process depends on the laser beam parameters, such as pulse duration, wavelength, energy, frequency or irradiation time, as well as target material properties [2]. These parameters affect the course of laser ablation, which engages different mechanisms than longer, i.e., picosecond and nanosecond, pulses [3; 4]. We found this region between picosecond and femtosecond laser pulses interesting for further investigation, since both short and long pulse interactions with matter may appear.  
Most of commercial femtosecond lasers offer the pulse length of tens of femtoseconds. In our research, we used a popular 35 fs Ti:S laser and a 650 fs prototype Yb:KYW laser. Both offered adjustable wavelength (UV/VIS/IR) and frequency. The pulse energy ranges were picked in the manner to makethe results maximally comparable for both lasers. This approach resulted in a complex parametric study, whereas previous research mainly focused on one or two selected parameters [2; 3; 4; 5].
## 6 Discussion
### Methodological Insights  
The combination of an autoencoder and a GMM provided a robust feature extraction and clustering mechanism. The autoencoder efficiently compressed high-dimensional sensor data into meaningful latent representations, preserving essential information while reducing computational complexity. The GMM then effectively categorized engines into distinct health states based on the extracted features.  
A key finding from the study is the ability of the GMM to capture the natural structure of the data without predefined labels. This probabilistic clustering method, in contrast to K-means, successfully handles overlapping clusters and varying distributions of engine states. Furthermore, the evaluation metrics, particularly reconstruction error and cluster separation results, indicate that the model performs well in differentiating between healthy, middle, and unhealthy engine states.  
Figure 6: Engine RUL prediction error for the CMAPPS engine dataset and real-life engine data.  
The application of LSTM networks for RUL prediction demonstrated promising results, particularly in capturing temporal dependencies in sensor data. By training separate LSTM models for each health state, the framework enhanced prediction accuracy. The integration of self-attention layers further improved the model's ability to focus on critical temporal patterns, enhancing its predictive capability.  
To elucidate the contribution of each module within our proposed framework, an ablation experiment was conducted. This experiment involved removing the self-attention mechanism from the LSTM-based RUL prediction module. The ablation study results clearly indicate that the use of the self-attention layer in the LSTM network markedly improves RUL prediction accuracy. Comparative metrics are provided, in Table 2, which quantitatively confirm that the full model outperforms any version with one or more components removed by 62-77%, underscoring its role in identifying critical temporal patterns. For example, without attention, late predictions (Score = 4.859 for Healthy CMAPSS) spiked due to equal weighting of all sensor timesteps. Attention heads selectively amplified features like exhaust temperature (Sensor 19) and fuel flow (Sensor 39), which correlate strongly with degradation. We observe that the latent feature representations become less well-separated when attention is removed (e.g., the clusters in latent space overlap more), indicating that the autoencoder relies on attention to capture important temporal correlations across the input sequence. This suggests that self-attention helps the network focus on critical parts of the sensor signal history, which improves the regression. Thus, our ablation confirms that self-attention is a key component for capturing complex patterns in the engine data and improving model performance. The lack of results means that there were no Unhealthy engines in the test group.
# EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration
## 1 Introduction
That is, the center region where the small vehicles are located (i.e., shown as a red parallelogram in Warping, a sky-blue rectangle in Upsampling #1, a green rectangle in Upsampling #2) is set as the RoI for the second detector. When drawing boxes for detecting small vehicles for the first 1000 images from the Argoverse dataset, many small vehicles were focused on the center (the red, sky-blue, or green rectangular region can be set to indicate the RoI for small vehicle detection). There are exceptions (outside of the RoIs), but most driving is assumed to occur in a straight line instead of curves (continuous curves are exceptionally rare). If the second detector focuses on the RoI, the accuracy of the small vehicle detector can be improved. This study's contributions are as follows:  
* Since the location of the camera in autonomous vehicles is fixed, we assume no height difference is present for the perspective view with either the training or test data. Therefore, we propose acquiring AccumulateImage, which distributes the location of small vehicles from the training dataset, and configuring the RoI with this distribution. \begin{table}
\begin{tabular}{c c c c c} \hline
**YOLOv7-Tiny** & **AP\({}_{0.5}\)** & **AP\({}_{0.5}\)-Small** & **AP\({}_{0.5}\)-Medium** & **AP\({}_{0.5}\)-Large** \\ \hline
1920 \(\times\) 1216 & 0.81 & 0.67 & 0.76 & 0.86 \\
960 \(\times\) 608 & 0.79 & 0.63 & 0.75 & 0.86 \\
480 \(\times\) 320 & 0.68 & 0.51 & 0.66 & 0.83 \\ \hline \end{tabular}
\end{table} 
Table 1: Accuracy results for Argoverse validation data with YOLOv7-Tiny [27]. The accuracy is decreased by 0.16, 0.10, and 0.03 for small, medium, and large vehicles, respectively, with 480 \(\times\) 320 image resolution, compared to 1920 \(\times\) 1216. * We acquired the transformed images by either using perspective transformation on the configured RoI or performing upsampling and then identifying the locations of the small vehicles in the box regions. Thus, we propose a method to convert the detection boxes to the coordinates of the RoI region using an inverse perspective transformation. * Furthermore, we propose an ensemble-based vehicle detection method that considers the requirements of real-time processing. The comparison of the detection results of the two deep learning-based models used in the ensemble method with the baseline reveals that the execution speed of vehicle detection can be improved without degrading the accuracy. To the best of our knowledge, this is the first report of improving the execution speed without degrading the detection accuracy with an ensemble-based vehicle detection method. The remainder of this study is structured as follows. Section 2 describes the research related to the proposed study. Section 3 describes the proposed method of setting an RoI where small vehicles can be located, detecting small vehicles in the transformed images, and merging the results of the two detectors. Section 4 discusses the experimental results of this study. Finally, in Section 5, the conclusions and scope for future research are discussed.
## 2 Design and Analysis
### Stub-Notch Configuration Analysis
The distribution of the stub-notch configuration is presented in Figure 3. At a lower passband frequency (\(3.736\) GHz), the current bends at the inset's upper side corners and gathers along the edges of the patch and then follows a straight path to the side stubs. So, little current flows around the center stub rather than the side stubs. As current flows in a more curved path at higher frequencies, the current changes its direction at the higher passband frequency (\(6.07\) GHz), after passing the inset picks and the edges followed by a curved path towards the center stub. Figure 2: Parametric analysis of the stub-notch configuration. (**a**) Effect of the notch width (w\({}_{7}\)) on the passband frequencies; (**b**) Effect of the stub-notch configuration on the passband frequencies.