esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 2 Materials and Methods
### Second Iteration of the Literature Review  
The new systematic review process followed the same steps as the first. The purpose of the second systematic review focused on U-Learning, connective learning, and the xAPI user experience standard to concentrate on discovering the conceptualization, process, and operation of U-Learning. RQ3: _How to develop a U-Learning model that integrates connective learning and xAPI user experiences?_  
A search for six keywords in English, Spanish, and Portuguese included the results of the database searches in these languages: xAPI, User Experience API, Tin Can API, U-Learning, Ubiquitous Learning, and Connective Learning. As in the previous review, we searched six databases, and the inclusion and exclusion criteria were the same.  
We defined a general query, but no results were found with the three keywords. We then described a string with two keywords, and no results were found either, which is why we decided to perform searches by words. Word String Term 1: xAPI, TIN CAN API, User Experience API; Word String Term 2: U-Learning, Ubiquitous Learning; Word String Term 3: Connective Learning.  
The graph shows that the search by terms generated 824 papers, of which 767 were rejected because they did not meet the established inclusion criteria. We finally selected 57 articles. Figure 2 shows the papers accepted in each database in detail.  
Figure 2: Accepted and rejected papers, second systematic revision [2].  
RQ3: The works found the relationships between U-Learning and different concepts and technologies. We found some ontologies referred to as U-Learning and measurement models implemented in other institutions [9].  
The research was designed to implement the xAPI standard with an LMS and to mix pedagogical strategies with U-Learning.  
Connective learning has been proposed as one of the theoretical bases of U-Learning. Philosophical, ethical, and legal debates have been raised regarding managing data generated with U-Learning and the xAPI standard.  
The book \"The future of Ubiquitous Learning\" [10] exposes the need to update pedagogies and apply new technologies in education to implement U-Learning. We found no U-Learning models based on connective learning and the xAPI user experience standard. In total, 58% of the works that directly study on U-Learning issues suggest the need to investigate and continue developing models, methodologies, and strategies. However, various authors have exposed the same problem [8; 10; 11; 12].  
With these results, it is possible to conclude that it is feasible to develop a U-Learning model based on learning experiences and connective learning. As observed in most of the papers reviewed, many models implement ICT in education.  
It is necessary to propose conceptualizing and characterizing the concepts of U-Learning and connective learning as well as learning experience to develop the model.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 4 Our Approach  
The overall framework of the proposed model is illustrated in Figure 3, providing a comprehensive overview of its key components and processes.  
In this research, we systematically organized and categorized bug reports and source code files stored in the bug repository to facilitate effective analysis. To preprocess these data, several techniques were applied. For bug reports, tokenization and stop-word removal were utilized to refine these textual data and eliminate irrelevant elements, as suggested by [39]. For source code files, CamelCase formatting [40] was implemented to improve readability and enhance these data's suitability for further analysis.  
To identify the top-K code segments that exhibit similarity to the given bug report, the BM25 algorithm [41] was employed. This step enabled the ranking and selection of the  
Figure 3: An Overview of Our Approach.  
Figure 2: An example of textual and contextual alignment between a bug report and source code.  
most relevant code files based on their textual similarity to the bug report. Specifically, the BM25 algorithm produced a ranked list of top-K code candidates (Step b).  
The model training process involved feeding the extracted top-1 and top-K code features, along with the corresponding bug report features, into the LSTM-Attention algorithm (Step c). The LSTM-Attention mechanism was designed to capture both the sequential relationships in these data and the contextual relevance between the bug report and source code features.  
Finally, based on the trained model, the most likely buggy source code file corresponding to the input bug report was predicted (Step d). This comprehensive approach ensures an efficient and accurate mapping of bug reports to relevant source code files, significantly improving the bug localization process.
# Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation
## 4 Discussion  
### General Discussion  
One of the main objectives was to investigate the respective segmentation capabilities of an experienced medical and a non-medical investigator. There are no previous data about the difference between medical versus non-medical investigators in the case of carotid artery lumen segmentation. In our analysis, the medical examiners performed the segmentation in consensus reading, which could be a limitation of our study. ACRIN (American College of Radiology Imaging Network) indeed emphasizes that potential variability among observers requires the same level of attention as potential variability among study subjects [28]. The consensus interpretation in imaging research was classified as a limitation for study by [29], however, in our study design, not the interobserver variability, but rather the variability due to the different segmentation methods was the aim of the investigation. The intraobserver variability by remodeling evaluation was published in the original articles [21].  
The two software tools, which were used in this study, were developed for different purposes, but both are unique in their special fields. Currently, VascuCap has the FDA approval for a clinical research setting. However, there is no carotid plaque analysis software for clinical use with FDA approval, although high correlation and low bias have already been published between the in vivo software image analysis and ex vivo histopathologic quantitative measures of atherosclerotic plaque tissue characteristics, as well as low reader variability and high sensitivity for changes in plaque characteristics [22; 29]. Carotid plaque geometry and tissue composition have been proven to be measured reliably from clinical CTA images using the VascuCap software. A minimum change, 4% was detectable in  
Figure 6: Results of the CFD simulations for Case 010 and Case 042. (**a**) Time-averaged WSS field near the stenosed vessel segment visualized on the vessel wall. (**b**) Flattened TaWSS maps near the stenosis for easier comparison. (**c**) circumferential TaWSS values at different altitudes (Line12) from the bifurcation point; WSS: wall shear stress TaWSS: Time-averaged WSS. CFD: Computational fluid dynamics.  
plaque volume when the same observer performed both measurements, but it increased to 10% for different observers [30]. There is no published data about lumen segmentation alone, only about the plaque volume (lumen + plaque burden) with VascuCap. Similar observations were made as in the MATCH study, in the case of manual segmentation mostly over-segmentation was found [24].
## 3 Results
### Dataset Used and the Results of the Operation Type Recognition Using Comments
It could be noted that the gains obtained using a word embedding with GloVe are significant, taking into account big differences in the vocabulary used in comments with different types of vocabulary. Finding the type of operation using the comments could be useful in looking for the potential mistakes made by accountants, e.g., salary operations often are quite strictly \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
1 & 99.2 & 0 & 0.2 & 0.2 & 0 & 0.2 & 0.2 & 0 \\
2 & 0 & 96.2 & 2.4 & 0.3 & 0.3 & 0.3 & 0.2 & 0.3 \\
3 & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 \\
4 & 0 & 1.1 & 0.2 & 97.1 & 0.3 & 0.8 & 0.1 & 0.4 \\
5 & 0 & 0 & 1.8 & 0.4 & 92.7 & 4.0 & 0 & 1.1 \\
6 & 0.4 & 0 & 1.2 & 2.1 & 0.3 & 95.4 & 0.1 & 0.5 \\
7 & 0 & 0 & 0 & 0.1 & 0 & 0.3 & 99.6 & 0 \\
8 & 0 & 0.3 & 0 & 0 & 0.2 & 0.2 & 0 & 99.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The recognition accuracy (in %) of the financial operation type using GloVe method. In this table, 1 means shareholder operations, 2—operations with salaries, 3—return operations, 4—other operations, 5—other financial operations, 6—residual operations, 7—depreciation operations, 8—sales operations. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline
1 & 98.6 & 0 & 0.2 & 0.3 & 0 & 0.4 & 0.5 & 0 \\
2 & 0 & 94.2 & 3.2 & 0.2 & 1.6 & 0.6 & 0 & 0.2 \\
3 & 0 & 0 & 99.1 & 0 & 0.2 & 0.4 & 0.3 & 0 \\
4 & 0 & 2.4 & 0.2 & 95.7 & 0.3 & 1.1 & 0.3 & 0 \\
5 & 0 & 0 & 2.1 & 0.4 & 91.7 & 4.5 & 0 & 1.3 \\
6 & 0.4 & 0 & 1.2 & 3.2 & 0.8 & 93.1 & 0.2 & 1.1 \\
7 & 0 & 0 & 0 & 0.2 & 0 & 0.2 & 99.6 & 0 \\
8 & 0 & 0.3 & 0.1 & 0 & 0.2 & 0.3 & 0 & 99.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The recognition accuracy (in %) of the financial operation type using tf-idf values. In this table, 1 means shareholder operations, 2—operations with salaries, 3—return operations, 4—other operations, 5—other financial operations, 6—residual operations, 7—depreciation operations, 8—sales operations. related to particular sums of money, and deviation from the sum could indicate a potential problem. Below we will explore how comments are related to the amount of money used in the operation. It is hypothesized that semantically similar comments most often need to be related to similar sums of money. Further, we will check this hypothesis.
# Theoretical Methane Emission Estimation from Volatile Fatty Acids in Bovine Rumen Fluid
## 4 Conclusions  
In this study, the reliability of the CER method for measuring VFA concentrations in rumen was evaluated and compared with the conventional method. For C3+ VFAs, substantial differences in sensitivity and accuracy were observed between both methods, especially for butyric acid. When used to measure a standard mixture solution prepared with the known concentrations, the results show that both methods proved relatively reliable for acetic acid and propionic acid. However, the CER method appeared to be superior for measuring concentrations of the other less abundant VFAs, especially butyric acid. For this VFA, only the CER method allowed relatively accurate measurement, while the sensitivity of the conventional method for butyric acid was less than 20%. The results of this study provide the first empirical evidence that application of the CER method may result in more accurate measurements of C3+ VFAs compared with the conventional method. Therefore, a new approach using the CER method can be recommended to analyze not only the most important substances (acetic acid and propionic acid) but also others, including butyric acid.  
S.-R.L. conceived, designed, and drafted the research and interpreted the data; Y.C., H.K.J., and E.K. interpreted the data and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Measurement and Risk assessment Program for management of Microplastics Project, funded by Korea Ministry of Environment (MOE) [RE202101439], \"Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01429702)\" Rural Development Administration, Republic of Korea, and Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry [318014], Ministry for Food, Agriculture, Forestry and Fisheries, Republic of Korea.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 4: Ratio of the measured concentration to the known one for each component of the standard VFA solution, comparing the catalytic esterification reaction method to the conventional method.
## 1 Introduction
However, to our knowledge, there have been few attempts to employ the DL architecture for manufacturing quality prediction. By considering the superiority performance of ML in reducing the dimensions of multi-parameter inputs, and DNN for learning sophisticated features of manufacturing processes, a manifold learning-based deep regression network (MDRN) is proposed for the multi-parameter manufacturing quality prediction conducted in this paper. The contribution of this work is integrated with DNN for reinforcing feature representation via ML for reducing redundant information, thus reducing the modeling complexity and improving the prediction performance. The proposed MDRN model was realized in the following three stages: (i) the ML technique was firstly used to learn the intrinsic coupling features of the relevant manufacturing multi-parameter inputs through dimension reduction, (ii) the DL method was then applied to dig the features of the pattern with the manufacturing quality and the low-dimensional information achieved by ML, and (iii) the ANN for regression was finally employed to model the deep representations generated by the DL and achieve the manufacturing quality predictions. Stage (ii) and stage (iii) were integrated into the deep regression network (DRN). To investigate the prediction capacity of the proposed model, a machinery parts manufacturing system with multi-parameter inputs was utilized for modeling and validation. Comparisons with other peer models were also performed. The rest of the paper is organized as follows. Section 2 presents the manifold learning, deep regression network, and proposed model, respectively. Section 3 describes the application of the addressed model. Section 4 gives the results with relevant discussion. Section 5 concludes this work.
# Design and Development of an Omnidirectional Three-Wheeled Industrial Mobile Robot Platform
## 2 Related Work
### Omnidirectional Wheel Designs  
The design and arrangement of the omnidirectional wheels are critical to ensure the unique mobility of these robots [6]. Among the most common designs are the following:  
* Mechanism wheels: These widely used wheels feature rollers arranged at a fixed angle (usually 45\({}^{\circ}\)) with respect to the main wheel axis as depicted in Figure 1. They offer good maneuverability and allow smooth movements, although their design can generate unwanted slippage and requires greater complexity in their manufacturing [18].
* Universal/Omniwheels: Universal or omnidirectional wheels are fundamental elements to enable holonomic motion in mobile robots. This type of wheel consists in passive rollers distributed around the perimeter of a main wheel, with their axes perpendicular to the wheel's axis of rotation, which facilitates movement in any direction in the plane, as shown in Figure 2. Despite their versatility and low cost compared to other options such as mecanum wheels, omniwheels present limitations, such as vibrations [20] induced by discontinuity points between the rollers and the ground, as well as lower load capacity. One strategy to mitigate these vibrations is the use of dual configurations, where two wheels are assembled with offset rollers, increasing the points of contact with the floor and improving the balancing of the robot.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Platform** & **Configuration** & **Dimensions (mm)** & **Payload (kg)** & **Weight (kg)** & **Payload Fraction** \\ \hline Ridgeback (Clearpath Robotics) [12] & 4 wheels & \(960\times 793\times 311\) & \(100\) & \(135\) & \(0.74\) \\ \hline A012 Omni (Fdata Robot) [13] & 4 wheels & \(620\times 520\times 340\) & 60 & 44 & \(1.36\) \\ \hline CTP300-7 (RoboCT) [14] & 4 wheels & \(815\times 613\times 330\) & 300 & 57 & \(5.26\) \\ \hline Genius 2 (BeeWaTec) [15] & 4 wheels & \(495\times 700\) & 250 & 18.4 & \(13.6\) \\ \hline T3 (Aethon) [16] & 4 wheels & N/A & 340 & 136 & \(2.5\) \\ \hline
3 WD 48 mm (Nexus Robot) [17] & 3 wheels & \(231\times 203\times 99\) & \(1.5\) & \(1.5\) & \(1.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commercial omnidirectional mobile platforms for material handling.  
Figure 1: Mecanum wheel, with rollers arranged at 45\({}^{\circ}\)[19].
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 2 Materials and Methods
### IQA Metrics  
Although there are two categories of objective image quality evaluation metrics, namely full-reference and non-reference metrics, widely used in evaluating image interpolation quality, in this work, only full reference (FR)-IQA metrics were chosen.
# Efficient Multifocal Structured Illumination Microscopy Utilizing a Spatial Light Modulator
## 2 Methods  
### Experimental Setup  
The schematic of multifocal structured illumination microscopy system configurations is depicted in Figure 1. A single mode laser with the wavelength \(\lambda=532\) nm is used as the light source. The laser beam is expanded to illuminate an SLM (LETO-VIS-009, HOLOYE Photonisc AG, Berlin, Germany). The SLM is a reflective type of pure phase spatial light modulator. It comprises \(1920\times 1080\) pixels with a pixel size \(6.4\times 6.4\) um\({}^{2}\) and a maximal frame-refresh rate of 60 Hz. The angle of the reflective mirror (RM) is adjusted properly to ensure that the incident beam illuminates on the SLM at an incident angle \(<\)6\({}^{\circ}\), as suggested by the manufacturer to obtain desired modulation performances. The polarization of the incident beam is optimized by a half wave plate (HWP, WPZ2220-532-M-25.4, Union Optic, Pylaia, Greece). A linear polarizer (LP, SHP-1025, Union Optic, Wuhan, China) is followed to maintain the polarization of the incident beam horizontal to achieve phase-only modulation by the SLM. By programming the SLM with the phase profiles of the checkerboard phase plates, two sets of diffraction laser beams were generated. The laser beams except for \(\pm 1\) diffraction orders were blocked by a custom-made spatial filter (SF) and finally focused by a lens (L3, MAG0125, Union Optic, Wuhan, China) and microscope objective (MO2, RMS10X, Olympus, Japan) onto the focal plane.  
Fluorescence images were captured with an upright fluorescent microscopy configuration including a dichroic mirror (DM, Semrock Optics, New York, USA), a long-pass filter (LP, Edmund Optics, Barrington, New Jersey, USA) and a CMOS camera (BFS-U3-6354M-C, FLIR Systems, Oregon, USA). For the purpose of comparison, the conventional WF images were recorded by illuminating the sample only with the uniform illumination. Both the camera and SLM were synchronized and controlled by the program implemented in Labview, while all imaging processing was performed with programs written with MATLAB (MathWorks, Massachusetts, USA).
## 4 Conclusions  
We measured the quadratic Zeeman shift coefficient of a \({}^{87}\)Sr optical lattice clock at -23.0(4) MHz/T\({}^{2}\) and evaluated the Zeeman shift of the system. The residual first-order and quadratic Zeeman shift was \(4(5)\times 10^{-18}\) and the quadratic Zeeman shift was \(9.20(7)\times 10^{-17}\) as obtained by performing a weight average of all measurements of the quadratic Zeeman shift coefficient at \(-(23.47\pm 0.12)\) MHz/T\({}^{2}\). To further reduce the Zeeman shift coefficient, more experimental data is required. The background magnetic field compensation system should be designed to repress the magnetic field drift and so reduce the first-order Zeeman shift. In order to decrease the frequency uncertainty of the quadratic Zeeman shift, a small bias magnetic field is useful. If it is reduced to 0.2 G, the uncertainty caused by the quadratic Zeeman shift coefficient could be as small as \(1\times 10^{-19}\).  
Software, X.L., M.Y.; Formal Analysis, T.L., Y.W.; Data Curation, T.L., M.Y.; Writing--Original Draft Preparation, X.L., H.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant Nos. 61775220), the National Key R&D Program of China (Grant No. 2016 YFF0200201), the Key Research Project of Frontier Science of the Chinese Academy of Sciences (Grant No. QYZDB-SSW-JSC004), the Strategic Priority Research  
Figure 4: The measurement results of the quadratic Zeeman shift coefficient and the lock-in data. (**a**) The quadratic Zeeman shift under different \(B_{\rm eff}\) and parabola fitting of the experimental data. (**b**) A typical Allan deviation of the quadratic Zeeman shift. The error bar corresponds to 95% confidence interval; the red line represents the linear fitting by fixing the slope at \(-0.5\). (**c**) The frequency gap between spin-polarized peaks of \(m_{\rm F}=-9/2\) and \(m_{\rm F}=+9/2\) during the routine operation of the \({}^{87}\)Sr optical lattice clock.  
Program of the Chinese Academy of Sciences (Grant No. XDB21030100), and the Youth innovation promotion Association the Chinese Academy of Sciences (Grant No. 2019400).  
We would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for their helpful discussions.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### A-Ecms  
The main purpose of the supervisory controller is to perform instantaneous optimization informed by the predictions obtained from the rollout algorithm. Inside of the supervisory controller is A-ECMS, where a novel adaptation method is accomplished by an ANN referred to as the EF predictor. A-ECMS is expressed as:  
\[\min_{\tau_{i}^{*}}\dot{n}_{f,j}(x_{i},\tau_{i}^{*})+\frac{s}{Q_{thr}}P_{batt}(x_ {i},\tau_{i}^{*})\cdot p(SOC) \tag{13}\]  
where \(\tau_{i}^{*}\) is the optimal torque split at each instant, \(s\) is the EF which serves as a conversion factor from electrical power to equivalent fuel consumption, \(Q_{thr}\) is the fuel lower heating value, \(P_{batt}\) is battery power, and \(p(SOC)\) is a multiplicative penalty function that ensures SOC boundaries are not violated [10]. A-ECMS was chosen for its simplicity, ease of online implementation, and ability to adapt to different driving scenarios. Extracting features from the speed and SOC trajectories allows the EF predictor to estimate the \(s\), which results in a near optimal torque split over \(N_{h}\). Since the rollout algorithm recalculates the state predictions many times throughout the route, \(s\) can be consistently updated to ensure that it is as close as possible to the optimal value without needing a priori route information.
## 3 Theoretical Background  
### Tensor Basics  
Tensors, also referred to multi-dimensional arrays, are higher-order extension of vectors and matrices. For example, a vector can be regarded as a 1-order tensor, and a matrix can be regarded as a 2-order tensor. The N-order tensor is expressed as \(\chi\in R^{l_{1}\times l_{2}\times\ldots\times l_{N}}\). Here are some related concepts [32; 33; 34].  
**Definition 1**.: _N-order tensor \(\chi\in R^{l_{1}  
**Definition 5**.: _If N-order tensors \(\chi\in R^{I_{1}\times I_{2}\times\ldots\times I_{N}}\) can be expressed as a form of N vector exterior product \(\chi=x_{1}\otimes x_{2}\otimes\ldots\otimes x_{N}\), \(x_{k}\in R^{k_{1}}(k=1,2,\ldots,N)\), then the tensor \(\chi\) is a rank-1 tensor._