esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Analysis by Serve Origin
#### 4.2.2. Origin and Ball Destination  
From the 536 serving actions analyzed, the cross-tabulation of origin (right, left, and center of the court) and destination (1, 2, 3, 4, 5, 6, 7, and 8) revealed statistically significant differences (\(\chi^{2}=31.770\), \(p=0.004\), ES = 0.172). Overall, the right, left, and center origins represented 38.99%, 27.99%, and 33.02% of the total actions, respectively, whereas Zones 5 and 6 received the most serves (26.49% and 41.23%, respectively).  
However, distinct distribution patterns exist based on their origin. For the right origin (209 serves, 38.99% of the total), most serves were directed to Zones 6 (15.67% of total actions) and 5 (13.62%), with fewer serves directed to other zones. For the left origin (150 serves, 27.99%), most serves were directed to Zones 6 (11.01%) and 5 (6.34%), and there was an increase in serves in Zone 8 (3.17%) compared with the right origin. For the center origin (177 serves, 33.02%), Zones 6 (14.55%) and 5 (6.53%) were primarily targeted, but there was a slight increase in serves to Zone 7 (2.80%) and a somewhat higher proportion of serves to Zone 4 (1.12%) than the other origins.
## 2 Materials and Methods
### Qualification of Aroma Compounds  
The aroma compounds in wines were detected according to the previously published method by using a headspace solid-phase microextraction (HS-SPME) equipped with 2 cm DVB/CAR/PDMS 50/30 \(\upmu\)m SPME fiber (Supelco, Bellefonte, PA, USA), and an Agilent 7890 gas chromatography-5975 mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) [25; 26]. The wine sample of 5 mL was mixed with 1.00 \(\pm\) 0.01 g NaCl (to promote the volatilization of the compound) and 10 \(\upmu\)L 4-methyl-2-pentanol (internal standard, 1.0006 g/L) in a 20 mL autosampler vial. Then the vial was immediately capped tightly with a PTFE-silicon septum and put in the CTC CombiPAL autosampler (CTC Analytics, Zwingen, Switzerland). After the sample in the vial was heated at 40 \({}^{\circ}\)C for 30 min, the activated SPME fiber was automatically inserted into the headspace of the vial, to be continually slightly shaken for 30 min at 40 \({}^{\circ}\)C to assure the distribution of aroma compounds in the three phases (wine sample, headspace, and SPME fiber) to reach equilibrium. After that, the fiber was desorbed by being inserted into the GC injector under splitless mode at 250 \({}^{\circ}\)C for 8 min. The aroma compounds were separated on an HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA) with carrier gas of high-purity helium (He > 99.999%) with a 1 mL/min flow rate. The column oven first held an initial temperature of 50 \({}^{\circ}\)C for 1 min, then heated up at the rate of 3 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C and held for 5 min. The mass spectrum applied electron ionization (EI) at 70 eV energy to ionize the molecules of aroma compounds, the ion source and quadrupole temperatures were 250 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The mode of the mass detector was a full scan (\(m/z\) 30-350).  
The aroma compound identification was completed using the automatic mass spectral deconvolution and identification system (AMDIS) and NIST 11 database based on the retention indices calculated by C7-C24 n-alkane series (Supelco), retention time, and mass spectrum of volatile compounds. The peak areas were calculated using Chemstation software (Agilent Technologies, Santa Clara, CA, USA). Then, the peak area ratio for each volatile compound to the internal standard was brought into the corresponding standard curve to calculate the concentration. For compounds with and without available standards, the calibration curve of standards and the standards with similar chemical structure, functional group or similar carbon number for quantification, respectively, were used. A synthetic matrix containing 2 g/L glucose, 7 g/L tartaric acid, and 13% alcohol (\(v/v\)) with a pH of 3.5 was prepared, then standards were mixed into it and it was diluted into 10 successive levels.
# Crack Propagation Law of Reinforced Concrete Beams
## 2 Comparison of Finite Element Model and Experiment Results
### Comparison of the Experimental and Finite Element Modeling of Cracks  
In the process of concrete propagation, cracks often bifurcate and skew due to the lack of coordination in deformation among different materials within the concrete. The development direction of cracks is uncertain. In concrete members, the presence and propagation of cracks serve as direct indicators of damage to the member. Therefore, evaluating the degree of damage to concrete based on the progression of cracks holds practical significance.  
As shown in Figure 11, in the process of the shear failure of the test beam, due to the stress concentration at the loading point and support, the cracking phenomenon occurred at the support. Stress concentration at the loading point and support led to cracking in these areas. As the load increased, the mid-span bending moment reached the cracking moment, resulting in the appearance of the first crack in the pure bending section of the concrete beam. The test beam entered the stage of damage stabilization development, with small cracks appearing at the bottom of the shear bending section and relatively stable development. New cracks appeared on the surface of the test beam at the beginning of loading and expanded steadily as the load increased. At the 60 kN load, cracks in the pure bending section extended upwards and a bending shear inclined crack developed adjacent to this section due to the combined effect of shear force and bending moment. At the 120 kN load, the depth of the web shear inclined crack near the upper surface of the test beam continued to increase. When the load reached 140 kN, most of the concrete in the test  
Figure 10: Test stirrup strain–load curve.  
Figure 9: Load–strain comparison curves of test-deflection element.  
beam was crushed and new cracks appeared in the shear section, expanding continuously. Eventually, the diagonal cracks in the shear section connected with each other and there was a slight increase in the depth of the cracks in the pure section.  
Figure 11 illustrates the damage process of the beam. At the stage of stable damage development, the small cracks appearing at the bottom of the beam bending section do not have a large increase in width, stabilizing at about 0.3-0.4 mm; however, the number of them keeps increasing, which results in a smaller spacing of the cracks and a denser crack region. The concrete plastic damage model, while useful for quantitatively deriving the percentage of concrete damage, may not fully capture the width, spacing, and depth of macroscopic cracks. However, it can qualitatively demonstrate the development direction and propagation process of cracks through the DAMAGET distribution. It provides quantitative information on the extent of concrete damage; a complementary qualitative assessment of crack development and propagation is facilitated by the DAMAGET distribution.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.2. GOSIF Data  
A global Orbiting Carbon Observatory-2 (OCO-2) Solar-induced chlorophyll fluorescence (SIF) dataset, known as GOSIF, was measured in W\(\cdot\)m\({}^{-2}\cdot\)um\({}^{-1}\cdot\)sr\({}^{-1}\). The period from January 2002 to December 2020 was retrieved from the following website: [[http://data.globalecology.unh.edu/data/GOSIF_v2/](http://data.globalecology.unh.edu/data/GOSIF_v2/)]([http://data.globalecology.unh.edu/data/GOSIF_v2/](http://data.globalecology.unh.edu/data/GOSIF_v2/)), accessed on 1 August 2023. GOSIF was developed by integrating discrete OCO-2 SIF soundings, remote sensing data from MODIS, and meteorological reanalysis data and showed a high correlation with GPP from 91 Global Flux Network (FLUXNET) sites [33]. It offers global coverage with high spatial and temporal resolutions (0.05\({}^{\circ}\)and 8 days). GOSIF has gained widespread use in assessing terrestrial photosynthesis and studying vegetation responses to drought under the background of global climate change [34; 35]. In this study, monthly and yearly GOSIF data were used to evaluate its effectiveness in tracking vegetation changes and monitoring drought conditions.
## 3 Constitutive Model and Simulation Results with MATLAB
### Model Prediction Results  
With the incremental loading method by MATLAB, the uniaxial tension and uniaxial ratchettting behavior were simulated. With the material parameters determined above, it can be seen from Figure 8 that the model has a good fit for the uniaxial tension curve of the material.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Style** & \multicolumn{2}{c}{**Parameter**} \\ \hline Uniaxial Tension & \(r^{(1.8)}\) 80, 56, 69, 72, 80, 61, 31, 15 & \(\gamma^{(1.8)}\) 8000, 1500, 350, 180, 150, 72, 65, 12 \\ Uniaxial Ratchet & & \(m_{i}\)=7 \\ Martensite Content & \(\Delta\
u\) = 0.05 & \(h\) = 0.1 & \(\omega_{m}\) = 1 \\ Isotropic Hardening & & \(Q\) = 560 & \(b\) = 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material parameters of the constitutive model.  
Figure 7: Parameter determination of the constitutive model: **(a)** isotropic hardening, **(b)** martensite transformation, **(c)** determination of parameter \(\omega_{m}\).  
From Figure 9, the predicted stress-strain response of this constitutive model is not greatly different from the experimental value, and the stress-strain response of different loading conditions can be reasonably predicted.  
It can be seen from Figure 10 that the ratcheting strain predictions are quite effective for the load case with a mean stress of 290 MPa and amplitude stress of 240 MPa and for the load case with a mean stress of 290 MPa and amplitude stress of 215 MPa. The predictions are a little higher for the other two load cases, which are further from the load case used for the model parameter determination.  
Figure 8: Simulation of uniaxial tension.  
However, this does overcome the disadvantages of early lower prediction and later over prediction by other models. Although there is little difference between the Ohno-Wang II model and the proposed model in predicting uniaxial ratcheting strain, the Ohno-Wang II model always overpredicts ratcheting strain in late cycles, and is not as effective as the proposed model. This is because the body centered tetragonal martensite is much harder than the face centered cubic austenite, which can improve the hardness of materials. It is necessary to consider the martensitic transformation.  
As the experiments were conducted at cryogenic temperatures, to observe any change of martensite content at any time was not convenient. Therefore, by measuring the content of the deformation-induced martensite after the experiments and comparing it with the simulated values, the deviation between the predicted value of martensite content and the experimental value can be observed in Figure 11, within the acceptable range. Therefore, we considered that the model could reasonably predict the content of the deformation induced martensite at cryogenic temperatures.
## 4 Results and Discussion
### Trigram Features  
Overall performance of the individual algorithms only outperformed on the aggregate Naive Bayes did not provide the most accuracy but did give us a 68.25% accuracy. Most classifications we obtained in both Naive Bayes and SVM were almost the same, with 74.39% and 74.23% accuracy. SVM also allows us to have trigrams enabled to formulate Random Forest and obtained the lowest accuracy of 59.20%. The three-word usage resulted in lower scores across the models, which means that this style can add some extra noise or difficulty that makes it harder for the models to identify and classify offensive content embedded within Urdu text. The reasoning behind this decision is probably that there are offensive words that do not need to be combined into three words to be identified.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 2 Spatial Perception Correntropy Matrix  
Spectral and spatial correlation plays an important role in HSI classification. It is a common strategy to extract spatial correlation through the local structure of HSI. However, the spectral correlation, which can offer important discriminative information, is not fully exploited in the field of HSI classification [37]. In this paper, we propose a new spectral-spatial feature extraction method based on spatial perception correntropy matrix to jointly exploit the spatial and spectral correlation. The focus of the proposed method is how to determine the spatial correlation in the process of the correntropy matrix construction. The correntropy matrix of a pixel is expected to be constructed by the pixels belonging to the same class. In this way, the correntropy matrix can discriminate different classes. Figure 1 describes the process of SPCM method in detail.
## 3 Budgeting Emittance at Multiple Accelerating Gradients at the Cathode
Yet another source can be related to the non-uniformity of the photocathode quantum-efficiency map due to an aging effect. Figure 5 shows the slice emittance along the bunches at the three peak accelerating gradients under consideration. A central slice emittance growth around the current peaks with respect to the 56 MV/m case is about 6% at 50 MV/m and 30% at 40 MV/m. The peak currents are about 9.0 A, 8.5 A and 7.8 A by the end of the injector for 56 MV/m, 50 MV/m and 40 MV/m, respectively. Furthermore, an FEL gain parameter is defined accordingto [12] to evaluate potential SASE performance of individual slices along the bunches, in terms of bunch quality parameters, that is, G =\(\sqrt[]{I}/\epsilon^{5/6}\), where I stands for the current and \(\epsilon\) represents the slice emittance. Figure 6 illustrates the defined normalized gain parameter along the three optimized bunches. A higher gain parameter indicates higher peak current over smaller slice emittance of the bunch segments. As compared to Figure 5, the peaks in the gain parameter are shifted away from the current peaks towards the slices with lower emittance implying a dominating role of the emittance of the produced bunches. The gain parameter of the central slices reduces with decreasing the cathode gradient. However, in this case, the uniformity of the gain parameter turns out to be better in the case of lower gradients. Figure 4: Optimized transverse projected emittance at varied cathode peak accelerating gradients using a nominal Gaussian laser pulse length of 3 ps (rms) at the European XFEL. The solenoid strength, the cathode laser spot size as well as the gun operation phase are optimized for minimizing the projected emittance at the injector exit. The inset numbers represent peak accelerating gradients at the cathode. The purple markers denote the optimum solenoid strength on the right axis. The used gun phases w.r.t. maximal mean momentum gain phase are \(-1.25\) degrees, \(-1.75\) degrees and \(-3.25\) degrees for the 56.3 MV/m, 50 MV/m and 40 MV/m cases, respectively. Figure 5: Slice emittance along the optimized bunches for three peak accelerating gradients at the cathode by the end of the injector. The current profiles are shown on the right axis. Blue: 56.3 MV/m; Red: 50 MV/m; Yellow: 40 MV/m. Solid: emittance; Dashed: current profiles.
## 3 Methodology
### Transfer Learning  
TL is a method that enhances the learning process for a new task by leveraging knowledge from a related, previously learned task. This is similar to how humans learn, where prior knowledge accelerates the acquisition of new skills. For example, when learning a new language, individuals use their understanding of the alphabet and grammar rules, which eliminates the need to start from scratch.  
In our study, we employ TL to connect the domains of image recognition (computer vision) and speaker recognition with the downstream domain of SER. By repurposing pre-trained models originally developed for image and speaker recognition, we aim to adapt the rich features these models have learned to the task of SER. The fundamental difference between traditional machine learning and TL is shown in Figure 2.  
TL offers several key advantages in SER research. Primarily, it reduces the need for extensive labelled emotional speech datasets, which are often difficult to obtain. By utilizing pre-trained models, the training time and computational resources are significantly reduced, as models are not trained from scratch. Moreover, TL allows for the transfer of domain-specific knowledge, enabling the models to adapt learned features specifically to the SER task. This is achieved by fine-tuning pre-trained models on SER datasets, allowing them to learn the task-specific features.
## 7 Results and Discussion
### Comparative Analysis through Evaluation Metrics  
In our comprehensive evaluation, depicted in Figure 9(a), Random Forest (RF) achieved an impressive accuracy of 96.15%, surpassing the accuracy of NB (81.77%), DT (94.55%), KNN (89.43%), and LR (87.44%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Measures** & **NB** & **DT** & **KNN** & **LR** & **RF** \\ \hline
**Accuracy** & 81.77 & 4.55 & 89.43 & 87.44 & **96.10** \\
**Precision** & 87.75 & 97.94 & 91.80 & 86.98 & **98.91** \\
**Recall** & 73.82 & 90.06 & 86.57 & 88.02 & **93.34** \\
**F1-Score** & 80.19 & 94.29 & 89.11 & 87.50 & **96.01** \\
**Specificity** & 73.81 & 90.04 & 86.56 & 88.01 & **92.91** \\
**ROC\_AUC** & 81.77 & 94.55 & 89.43 & 87.44 & **96.15** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance measures (in percentage) for sentiment classification.  
Figure 10: _Cont._  
Furthermore, RF emerged as the most precise classifier with a precision of 98.91% compared to NB (87.55%), DT (97.94%), KNN (91.8%), and LR (86.98%), as highlighted in Figure 10b. Additionally, RF exhibited a remarkable recall rate of 93.34%, exceeding NB (73.82%), DT (90.06%), KNN (86.57%), and LR (88.02%), as illustrated in Figure 10c. In terms of the F1-score, RF excelled with a rate of 96.01%, eclipsing NB (80.19%), DT (94.29%), KNN (89.11%), and LR (87.5%), as shown in Figure 10d. Moreover, RF displayed a specificity rate of 92.91% in Figure 10e, outperforming NB (73.81), DT (90.04%), KNN (85.56%), and LR (88.01%). Finally, RF demonstrated a superior predictive performance achieving an ROC_AUC rate of 96.15%, as illustrated in Figure 10f. This performance clearly outpaced NB (81.77%), DT (94.55%), K-NN (89.43%), and LR (87.44%). These findings emphasize RF's exceptional performance and reliability across a range of evaluation metrics.  
Figure 10: **(a)** Comparative analysis based on accuracy. **(b)** Comparative analysis based on precision. **(c)** Comparative analysis based on recall. **(d)** Comparative analysis based on F1-score. **(e)** Comparative analysis based on specificity. **(f)** Comparative analysis based on ROC_AUC.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 4 Limitations and Extension Approaches for the IFC Schema
### Approaches for IFC Schema Extension
The bSDD and OTLs are linked to each entity through either the previously described 'classification' approach or the forthcoming 'Linked Data Approach'. (3) Linked Data Approach:  
This method interlinks multiple datasets, each modeled in a different schema, with the entities embedded within the IFC schema. This procedure requires using semantic web technologies, notably the Web Ontology Language (OWL). In this context, bSI furnishes an ifcOWL schema, a direct manifestation of an IFC in the form of a OWL, thereby enabling such linkages. Additionally, bSI emphasized the following point and recommended attempting information extension prior to entity extension:  
\"Once the best match in existing IFC class hierarchy has been found for a new concept, it should be considered if the existing entity type can be used as such, either (1) with associated external classification (and property definitions) for further specialization, or (2) by extending its definition and adding to its available predefined types and corresponding property sets. Only when neither of these approaches is satisfactory should a new subtype of an existing entity type be considered (or adding attributes to a leaf node type) [37]\".
## 4 Discussion
This study shows that machine learning algorithms can effectively analyze complex processes like wind energy production. In particular, the accuracy rates of RF and GB algorithms and their capacity to correctly model the relationships between independent variables prove that these algorithms stand out in energy estimation. However, it was determined that LR, a linear model, was limited in reflecting the effect of independent variables on the dependent variable and was insufficient for complex relationships in energy production. In conclusion, this study comprehensively analyzed the importance of environmental and technical variables for wind energy production with statistical and machine learning methods. The superior performance of nonlinear models such as RF and GB reveals that the right algorithm selection is critical in such complex systems. This study also showed that using the ANOVA test and Pareto charts to measure the effects of independent variables is an effective approach to evaluating the accuracy of model outputs. Although this study has significant findings, it has some limitations. First, the dataset only includes wind energy production data from a specific geographical region. Generalizing the findings to the other areas and different climatic conditions is difficult. In particular, the effects of varying wind turbine types or terrain structures should have been considered, which may increase the sensitivity of the model results to regional differences. Second, the hyperparameter optimization of the machine learning algorithms used in this study was performed to a limited extent. A more comprehensive hyperparameter search, including other advanced algorithms (e.g., deep learning-based approaches), may improve the model performance and allow better modeling of the relationships between the independent and dependent variables. In addition, the effect of the time variable can be examined in more detail using methods such as time series analysis. These limitations represent essential aspects that can shed light on future studies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **R\({}^{2}\)*** & **R\({}^{2}\)*** & **Priority Variable*** \\ \hline RF & 0.988 & 88.83\% & Ws \(>\) Rs \(>\) Wf \(>\) t \(>\) Wt \(>\) Nd \(>\) Wd \\ GB & 0.985 & 89.30\% & Ws \(>\) Rs \(>\) Wf \(>\) Wt \(>\) Nd \(>\) Wd \\ kNN & 0.971 & 88.81\% & Ws \(>\) Rs \(>\) Wf \(>\) t \(>\) Nd \(>\) Wt \(>\) Wd \\ Tree & 0.980 & 88.36\% & Ws \(>\) Rs \(>\) Wf \(>\) t \(>\) Wt \(>\) Nd \(>\) Wd \\ LR & 0.808 & 88.81\% & Ws \(>\) Rs \(>\) Nd \(>\) Wf \(>\) Wt \(>\) Wd \\ \hline \hline \end{tabular} \({}^{*}\) Based on machine learning algorithms. *** Based on ANOVA. *** Prioritizing the strength of the effect of independent variables on dependent variables.  
\end{table} 
Table 10: Analysis of reliabilities and variable prioritization.