esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Current Strategies and Methods for BIM Implementation in the Academic Field
## 4 Discussion
### Challenges, Advantages, and Disadvantages of BIM Implementation in AEC Curricula  
Research demonstrates the effectiveness of using BIM as a teaching tool for design and construction [43, 44, 50, 54]. One of the studies states that students have a high initial interest, which makes the learning experience very effective [23]. For this reason, integrating BIM within the curriculum can create motivated students. It also improves their skills in developing projects and helps them to understand building materials more easily; the visualization helps them to better understand how a building is constructed [44, 52, 54, 55].  
Another frequently perceived positive effect of BIM is improved collaboration and communication [48]. Collaborative work is key to experiencing the benefits and challenges of BIM training [38, 46, 50]. Coordination meetings produce an even greater impact on students than the instructor's performance. Students reinforce the importance of collaborative work in interdisciplinary BIM processes, where it is observed that the better the ability to work as a team is, the better the students' designs and personal experiences will become [45]. There is a direct relationship between the usefulness of BIM for developing course practices and its usefulness for acquiring teamwork skills [31, 45, 46].  
Some of the limitations found in the studies are problems with interoperability between BIM tools and the fact that teachers focus on the use of BIM analysis and creation tools and on collaborative work but not necessarily on information management [47]. As some authors note, the 3D tool could be a barrier to BIM implementation, so it is recommended that teachers emphasize that the 3D tools in themselves are not BIM, thereby turning students' attention to the methodology [46]. Innovative and critical thinking is much more important than mastering software [39].  
The obstacles to incorporating BIM into education processes depend on the academic environment, BIM concepts, and BIM tools [55, 63]. Most articles highlight these obstacles as common drawbacks across all universities. The academic environment affects the lack of time or credits to incorporate the large amount of technology, motivation, and resources. BIM concepts are affected by a lack of collaboration and teamwork, traditional teaching, and individualized instruction, and by a lack of coordination between institutions. Finally, the teaching of BIM tools is affected by a lack of interest, resulting mainly from a lack of knowledge among teaching staff.
## 4 Classification Effect on the Dataset  
In this paper, we use the XGBoost classifier (i.e., XGBoost classifier) to classify EEG data and evaluate our method's performance. XGBoost (eXtreme Gradient Boosting) is an enhanced machine learning model suitable for regression, classification, and ranking problems. This model is based on the gradient boosting algorithm, which improves the overall predictive performance by iteratively building and combining multiple weak classifiers [26]. Specifically, we first normalize the feature data so that the mean is \(0\) and the standard deviation is \(1\). Then, we set the parameters of the XGBoost classifier. After that, we use stratified cross-validation (StratifiedKFold) to divide the data into ten folds, with nine folds used for training and one fold used for validation. The performance of the XGBoost classifier on the given EEG dataset is evaluated through \(10\)-fold cross-validation. We conduct experiments using our framework on both binary classification and five-class classification tasks. For each individual method, the final selected network features are as follows: For VG, HVG, LPVG, and MFDM, the following seven network measures were selected: average degree, maximum degree, average clustering coefficient, network density, network diameter, global efficiency, and average path length. For QG, the average jump length was selected.  
Our method involves concatenating the network measures obtained from each of the aforementioned algorithms into a long vector. We then use ANOVA to assess the relationship between each feature and the labels, selecting the features that have a significant impact on the classification task. The \(p\)-value for each feature indicates whether there is a significant difference in the mean across different categories. If the \(p\)-value is greater than \(0.05\), it suggests that the feature does not have a significant impact on the classification task, and thus, the feature is discarded. The \(p\)-value of the computed features are shown in Table 1.  
After calculation, we find that the \(p\)-value of the network diameter feature under the VG and LPVG methods are \(0.54\) and \(0.81\), respectively, so we filter out the network diameter feature for both the VG and LPVG methods. Additionally, we observe that removing any feature would lead to a decrease in classification accuracy, negatively impacting the classification task. Therefore, we select these \(27\) features as the feature vector, as they can more comprehensively capture the intrinsic characteristics of the signal.  
**Remark 2**.: _A 27-dimensional feature vector filtered via an ANOVA reduces redundancy while retaining discriminative power. The average jump length, \(\Delta_{\text{QG}}\), from \(QG\) contributes most significantly to class separation._
## 1 Introduction
However, FTIR micro-spectroscopy is able to measure not only the fingerprint region but also the so-called high wavenumber region between 2500 and 3700 cm\({}^{-1}\), corresponding to absorption bands related to lipid, protein, and nucleic acid vibrational modes. Such a spectral range has not been frequently investigated because it is characterized by few broad and/or overlapping bands; therefore, it was considered that it did not contain useful information for diagnostic purposes. Several years ago, [PERSON] et al. [16] investigated the possibility to discriminate cells of different types within breast tissue by means of FTIR measurements in the 3100-3700 cm\({}^{-1}\) spectral range. Recently, [PERSON] et al. found that glass coverslip is able to discriminate different types of cell lines deposited over them according to FTIR spectra in the 2700-3100 cm\({}^{-1}\) wavenumber region [17]. In this paper, we explore the possibility to discriminate cells of different type according to their infrared absorption spectrum in the 2750-3000 cm\({}^{-1}\) spectral range, which is dominated by spectral features related to lipid and protein vibrational modes. Our investigation was carried out for two types of cell lines (normal and cancerous ones) grown on four different substrates: conventional glass slide, glass coverslip, CaF\({}_{2}\) window, and low-e glass slide. We found that the two types of cells can be discriminated for all the four types of substrate, both by means of PCA technique (according to PC1 component) and by ratiometric analysis, according to the relative intensity of peaks at 2956 and 2924 cm\({}^{-1}\). In particular, CaF\({}_{2}\) and low-e substrates are able to provide large separation capability according to PCA technique, whereas low-e and glass slides yield a large discrimination according to ratiometric analysis. The cheapness and availability of these substrates (mainly conventional glass slides) support the proposal of the FTIR technique in medical diagnostics.
## 4 Performance Evaluation
### Affective Feedback Using Projected Avatars
#### 4.5.1 Questionnaire Overview
Following a similar approach, a different study explored user's perception when interacting with an artificial pet with two types of embodiment (virtual and physical robot), that transferred from one embodiment to the other, leaving only one of them active at a time [65; 66]. These studies pointed out the importance of making the user perceive that they were interacting with the same entity that migrated from one embodiment to the other. In our approach, both types of embodiment, the robot and the avatar, were active simultaneously instead of one at a time. For this reason, we included one last question at the end of the questionnaire to try to understand the perception of the robot embodiment:  
From the following statements, choose the one that most closely reflects your perception of the robot body interface:  
* I perceive the robot body interface as two entities: an avatar and a robot,
* I perceive the robot body interface as one entity: the robot and its avatar.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
[24] introduced a new method that creates binary images from unfolded point clouds for segment-wise point cloud segmentation. Based on the extracted designed tunnel axis, the method applies image processing techniques to segment tunnel segments and constructs an as-built tunnel model in a BIM platform to complete tunnel deformation detection with high accuracy. [PERSON] et al. [25] proposed a novel and efficient deep learning model for disease detection using mobile laser scanning (MLS) point cloud intensity images. The model integrates a new residual network module (Res2 Net) with a cascaded structure to form a unified framework for extracting multi-scale and discriminative features of tunnel diseases, significantly improving detection accuracy. However, existing tunnel point cloud processing methods still have several limitations. First, many approaches rely on image segmentation and tunnel point cloud template matching for tunnel segmentation. However, this process is constrained by point cloud quality, lighting conditions, and image resolution, making it difficult to ensure segmentation accuracy and robustness. Therefore, in cases where image data are unavailable, it is particularly important to explore tunnel lining segmentation methods based solely on point cloud data. Second, traditional cross-section extraction methods based on the designed tunnel axis have certain limitations in tunnel positioning and detection, making them less adaptable to the structural characteristics of deformed tunnels. Therefore, introducing more precise and robust cross-section extraction techniques to improve the accuracy and applicability of tunnel structure analysis is a key research direction. To address the aforementioned challenges, this study proposes two novel algorithms--GuSAC and TMatch--along with a ring-based cross-section extraction technique, to enhance the automated segmentation of tunnel point clouds using only point cloud coordinate data. First, the GuSAC algorithm, which integrates RANSAC, normal vector fitting, and sliding window fitting, effectively segments the designed tunnel axis, eliminates noise and non-tunnel structural Point Cloud in long tunnel sections, and reduces the reliance of traditional methods on manual intervention, thereby improving the overall efficiency of automated tunnel denoising. Second, the TMatch algorithm, combining clustering analysis and the Gaussian Mixture Model (GMM), enables the modular segmentation of tunnel point clouds solely based on point cloud coordinate data. This method accurately distinguishes different tunnel ring segments, providing precise point cloud data for further deformation analysis and structural assessment. Finally, the proposed ring plane fitting technology, based on plane fitting methods and utilizing the SVD algorithm, overcomes the dependency on spacing intervals in axis-based extraction methods, allowing for more flexible cross-section extraction of single-ring tunnels.
## 3 Results
### Relationships under Different Sky Conditions
Comparing the pair of manual sensors (CS) at Larnaca and the CSD3 sensor at Mennologia, during the period 2009-2012, it was found that the frequencies of negative differences were about 60% under 'partly cloudy' sky conditions, while under 'clear' and 'cloudy' sky conditions, the positive differences are 66 and 54%, respectively. In the case of the comparison of CS and the pyrheliometric method (Pr) at Athalassa during the period 2012-2015, it was found that the frequencies of negative differences are approximately about 70% under 'partly cloudy' sky conditions. The percentages of negative differences under 'cloudy' and 'clear' sky conditions are about 47% and 56%, respectively. As can be seen, the largest frequencies of negative differences between the electronic and manual devices were recorded under 'partly cloudy' sky conditions, and this can be attributed to the fact of the influence of the clouds which cover the solar disk in quick succession. On this occasion, the CS recorder will continue the burning of a trace on the card and overestimates the sunshine duration. This occurs mostly when the sun elevation is high [24].
# A New Approach to Image-Based Estimation of Food Volume
## 3 System Evaluation
All of the required tools, including dishes and scale, were prepared in advance and, right after cooking, pasta was taken out of the pot and put in different dishes virtually at the same time. A scale was used to measure the amount of pasta in each dish. Table 2 shows the results obtained on the non-solid food test set. The average error rate is 6.7%. The images included in this test set provide stronger features, which produce more robust results, since their rougher surface causes strong variations of light intensity. However, a sparser scattering of the pasta pieces over the dish can lead to a less linear correlation between the volumes estimated for different amounts of the same kind of food. Within this group, the best results were obtained with rice due to the larger number of features it provides and to the smaller gaps between rice seeds with respect to larger-sized pasta. On the contrary, the Chifferi pasta, with its large and curvy pieces, obtained the worst results because it provides fewer visual features and has larger gaps between the pieces. Figure 7 and Table 3 show the results of volume estimation for every test case. As shown, repeating the tests on the same amount of pasta on different dishes of different shapes, we obtained reasonably consistent results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Row** & **Bread** & **Error (\%)** \\ \hline
1 & & 7.2\% \\
2 & & 4.9\% \\
3 & & 9.5\% \\
4 & & 1.7\% \\
5 & & 19.1\% \\
6 & & 7.2\% \\ \hline \hline  & average & 8.27\% \\ \hline \hline  & average processing time & 21.5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error rate in the reconstructed models’ volume estimations for solid shapes. Although the non-solid food types provided more robust results thanks to their higher number of features, they needed a larger computation effort in the modeling process, which increased the total processing time. The average processing time for the first test set, for the whole process from segmentation to volume estimation, was around 21 s, while the average time for the second test set was 24.01 s. Globally, the average processing time was 23.52 s. Table 4 reports the average execution time for each processing step.
## 1 Introduction
In light of the above, we propose the following Research Questions (RQs):  
RQ 1: Is it possible to process the RSBD data in NRT to keep their freshness? RQ 2: Can we optimize the execution time with cloud and parallel computing tools? RQ 3: Can we integrate the pre-processed data in Hadoop for extra processing? We firmly believe we can handle RS data's complexity due to our RSBD analytics expertise in batch, streaming, cloud, and parallel computing. Hence, this paper explains the ingestion layer, which is regarded as an essential part of the proposed BD architecture. The developed SAT-Hadoop-Processor enables us to pre-process heterogeneous satellite data and extracts only useful and potential datasets with high exactness and low volume related to the EO application, such as AP mapping, natural hazard supervision, climate change monitoring, etc. Secondly, we optimized the total execution time of processing by 90%, equivalent to 20 times the speed-up. Thirdly, we integrated satellite data in the Hadoop framework, particularly the Hadoop Distributed File System (HDFS), HBase, and Hive, for scalable processing with MR and Spark. Finally, the proposed software is adaptable to many RS data sources and formats. As a result, it could be implemented in a ground station for processing. Furthermore, the developed framework is flexible with several EO applications and customized depending on the requirement. The remainder of this manuscript is ordered as follows: Section 2 provides a background on RSBD processing technologies and cites some related works. Section 3 describes the proposed SAT-Hadoop-Processor software architecture. Section 4 shows the experimental analysis results. Section 5 goes into the details of the comparison. Section 6 elaborates on discussing the anticipated RQs, and Section 7 highlights the conclusions and some perspectives.
## 4 Validation of the Proposed Method
### Case 1: Experiment Results on the CWRU Bearing Dataset and Performance Analysis
#### 4.2.3 Bearing Fault Diagnosis Experiment
Comparisons Among Different Fault Diagnosis Methods  
In this subsection, comparative studies between the WPT-INPSO-SVM and the proposed 1 DCNN-INPSO-SVM were performed to verify the effectiveness and superiorities of the proposed algorithm. In the comparative experiments, there were three points that need to be emphasized. Firstly, the 8-dimensional vectors extracted by the three-layer wavelet packet were used as the inputs of the INPSO-SVM classifier for fault diagnosis, and the parameters of the proposed INPSO algorithm were consistent in the contrast experiments. Secondly, for the fault diagnosis comparison test, two algorithms processed the same ten group of data (A-J) which were randomly selected from the original signal. Thirdly, the average values of the two models were taken as the final experimental results after 10 trials for each group of data, where particles iterated 100 times to find the optimal value. Diagnosis results are listed in Table 6. There were some identical recognition results, such as the 80% accuracy for the first set of data. Because the raw data used in the ten experiments were the same, and WPT used the same three-layer wavelet packet to extract 8-dimensional feature vectors, which meant that the extracted features by WPT were the same in the ten times of experiments. In addition, the INPSO algorithm might find the same optimal parameter values for SVM. Therefore, the raw data, the extracted features and the models of classifiers were all the same in this case, and the experimental accuracies for the first set of data were all 80%. For datasets B, C, D, E, F, G and I, the INPSO algorithm found different  
Figure 8: Diagnosis accuracy of PSO-SVM algorithm. Figure 9: Diagnosis accuracy of INPSO-SVM algorithm. optimal parameters for SVM, which meant the classifier models were different, we could see that the average testing accuracies of the 1 DCNN-INPSO-SVM method were higher than those of the WPT-INPSO-SVM in all 10 groups of comparative experiments, which indicated that 1 DCNN could extract richer invariant features compared with WPT.
## 3 Results and Discussion  
### Crystal Structure  
The X-ray diffraction pattern of the garnet sample YbGd\({}_{2}\)Al\({}_{1.98}\)Cr\({}_{0.02}\)Ga\({}_{3}\)O\({}_{12}\) (Yb-GAGG: Cr\({}^{3+}\)) and standard cubic-Gd\({}_{3}\)Al\({}_{2}\)Ga\({}_{3}\)O\({}_{12}\) garnet (GAGG, ICSD No. 37571) [25] are shown in Figure 1. Sample is identified as a single phase of the garnet structure, and the XRD peaks are shifted to a slightly higher degree due to one-third of the Gd\({}^{3+}\) ions being substituted by smaller Yb\({}^{3+}\) ions.  
To clarify the structure of the Cr\({}^{3+}\) doped YbGd\({}_{2}\)Al\({}_{2}\)Ga\({}_{3}\)O\({}_{12}\) sample, the Rietveld refinement on the XRD pattern of the sample within the General Structure Analysis System (GSAS) was performed (as shown in Figure 2). The refined agreement factors are as follows: \(\mathrm{Rp}=14.32\%\), \(\mathrm{Rwp}=19.52\%\). The space group of GAGG is Ia-3d and lattice parameter a = 12.257 A, with the unit cell volume V = 1841.64 A [25]. The refined result shows that, for the Cr\({}^{3+}\) doped YbGd\({}_{2}\)Al\({}_{2}\)Ga\({}_{3}\)O\({}_{12}\) sample, a = 12.110 A, V = 1776.06 A, with an unchanged Ia-3d space group. The result indicated that the unit cell volume of the sample is smaller than the GAGG host because of the smaller ionic radius of Yb\({}^{3+}\) than that of Gd\({}^{3+}\).  
Figure 1: X-ray diffraction pattern of YbGAGG: Cr\({}^{3+}\) and standard pattern of GAGG.
## 3 Procedure, Data and Methodology
### Construction of Lexical Situation Type System  
#### 3.1.1 Verb Extraction  
Constructing a situation type classification system is a bottom-up process. The number of verbs at the foundational level must be sufficiently large and their coverage broad enough to ensure the representativeness of the resulting classification system. Therefore, the primary task is to establish a large-scale lexicon of verbs. The sources of verbs in it consist of two parts.  
First, we draw from the _Verb Usage Dictionary_ by [PERSON] [40]. The reasons for using this dictionary are as follows: First, the dictionary is dedicated to the detailed division of verb senses and the explanation of verb meanings, providing a reference for determining primary senses during manual annotation. Second, the dictionary contains extensive information describing verb functions, such as the co-occurrence capabilities of verbs with  
Figure 1: Flowchart of verb situation type prediction experiment.  
aspectual markers like \"LE\", \"ZHE\", \"GUO\" and temporal quantifiers, which can be used to verify whether the annotation results of the co-occurrence of verbs and aspectual markers are accurate. Third, [PERSON] [12] annotated the verbs in [PERSON]'s dictionary, allowing for a dual verification of our annotation results to ensure their reliability.  
Second, we extracted high-frequency verbs from the CCL corpus. The rationale for this choice is twofold: first, verbs in the corpus directly reflect actual language usage. Using raw corpus data ensures that the data are more authentic and representative. In contrast, verbs in dictionaries undergo manual standardization and may not accurately capture the true face of language. This approach thus complements the verbs from [PERSON]'s dictionary. Second, the corpus contains a larger number of verbs, providing broader coverage of co-occurrence features between verbs and aspectual markers. This can effectively avoid the data sparsity problem in statistical validation.