esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study  
### Structure Prototype  
In the case study, three RC shear walls, namely W1, W2 and W3, with story numbers of 8, 20 and 30, respectively, were studied. These three wall structures were all modified from the 8-story RC shear walls in the FEMA-440 [34]. Detailed information of design procedure of the RC shear walls can be found in [34; 35]. The elevation and the reinforcement details of the three RC shear wall structure models can be found in Appendix A. The assumed gravity load for each story was 1468 kN for the 8-story wall structure and 2348.48 kN for the 20-story and 30-story shear wall structures.  
The SAP 2000 Software nonlinear version [36] was employed to model the RC shear wall structures. The direct integration method adopting Wilson-\(\theta\) algorithm was used to conduct NLRHA of wall structures, and the value of \(\theta\) was set to be 1.4 to ensure the convergence of the integration algorithm. Grade 60 steel rebar was adopted for the W1 and W2, and Grade 75 steel rebar was adopted for W3. The yield strength of the Grade 60 and Grade 75 steel rebar is 413.7 MPa and 517.1 MPa, respectively. The Park parametric stress-strain relation was employed to model the nonlinear property of the steel rebar. The concrete property was modeled adopting the Mander concrete stress-strain curve. The unconfined concrete has a specified compressive strength of 27.6 MPa for W1 and W2 and 34.4 MPa for W3. The steel and concrete hysteresis behavior was modeled using the kinematic and concrete hysteresis models [36], respectively.  
A multi-layer shell element was used to model the shear walls. In all the wall models, only the flexural inelasticity of shear walls was modeled, and the shear deformation of walls was set to be elastic since it was assumed that walls have sufficient shear strength. Table 1 summarizes the properties of the first five modes of the wall structures, where it is found that the modal mass coefficients of all the RC shear wall structures fulfill the relations of Equations (5) and (6) simultaneously, and the first two modes dominate the structural response of all the wall structures.  
Figure 1: Flowchart of MGMS procedure of wall structures.  
In this study, 5% Rayleigh damping was assigned to the first and third modes to model the inherent damping of the structure. A few studies in the past [37; 38; 39] illustrated that this approach could result in fictitiously large damping forces, which underestimated the first-mode and higher-mode dominated responses. These studies proposed solutions to mitigate this shortcoming. Future studies should also consider alternative approaches proposed in the literature for modelling the viscous damping of the structure and investigate the effect of modelling damping on record-to-record variabilities.
## 4 Results and Discussion
### Reduction of Computational Costs
Both graphs show good agreement of the meshes with reduced resolution (M1-M3) with the base mesh (MB) for almost the entire oscillation cycle. Only small deviations are visible in the velocity values at the beginning and end of the cycle, which are the result of different instantaneous turbulent fluctuations at point P1. Additionally, the fluctuations in the results were higher for the coarser meshes, which is obvious in the velocity evolution, especially during the closing phase of the cycle (Figure 12b). These higher fluctuations are due to the numerical noise added by the coarsening process. Figure 13 further shows the time-averaged pressure distribution along the center axis of the channel and the time-averaged velocity profiles in the mid-coronal plane, 8 mm downstream of the glottis exit (P1 in Figure 1). The time averaging was performed over the three simulated cycles. Again, both the mean pressure distributions and the velocity profiles of M1-M3 resembled the results obtained  
Figure 11: Flow rates for one oscillation cycle for different mesh resolutions MB–M3. Figure 12: Instantaneous (**a**) pressure and (**b**) velocity evolutions for one cycle of oscillation at the point P1 introduced in Figure 1 for all the meshes. The pressure evolutions were smoothed by a low-pass filter (Savitzky-Golay filter [73] with a linear polynomial and a window size of 0.3 ms) to reduce numerical noise. with MB. On evaluating the pressure values within the glottal duct and the velocity profiles in more detail, M3 showed more consistency with the base mesh MB. Summarizing all these comparisons of flow variables, M3 with the lowest grid density among the three unexpectedly produced the best results in comparison with the base mesh MB. The L2 error norms of the presented parameters relative to the base mesh for M3 are smaller than those for M1 and M2 (Table 4). Furthermore, these errors are one order of magnitude smaller than the validation errors in Table 3. The average number of cells was reduced by more than 50% with the mesh resolution M3 compared with the base mesh. Additionally, the time step size was also increased by a factor of 1.36. As a result, the elapsed simulation time of one oscillation cycle decreased significantly to 19 h in comparison with 79 h for MB. The mentioned elapsed times are related to the parallel simulations using one compute node of the RRZE's Emmy cluster of Erlangen-Nurnberg University, which is introduced in the next section.
## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model
### Comparison of LIME and Grad-CAM Explanations for VGG19  
The explanations generated by Local Interpretable Model-agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) provide insights into the VGG19 model's decision-making process. This section compares the two techniques based on their ability to explain the classifications of drilled holes in melamine-faced chipboard.  
LIME produces explanations by perturbing the input data and observing the changes in the model's predictions. It generates local approximations of the model's behavior, highlighting the features most influential in a particular decision. Grad-CAM, on the other hand, uses the gradients of the target class flowing into the final convolutional layer to create a localization map highlighting the important regions in the input image.
## 3 Methodology
### Extraction Layer
#### 3.2.1 Syntactic Feature Extraction  
Generally, a dependency tree (Dep.Tree) can capture dependencies between aspect terms and their corresponding opinion words, maintaining valid in the long-distance dependency problem. Therefore, dependency trees are often used to extract syntactic information from sentences. However, not all information on the dependency tree is beneficial to our task, and introducing noise (unrelated relations of dependencies) makes it difficult for each aspect word to accurately capture the corresponding contextual sentiment information. For example, the dependency tree parsing of sentences is shown in Figure 2, and the \"conj\" relation between \"delicious\" and \"terrible\" is invalid for our task, but the aspect term \"taste\" may be associated with the opinion word \"terrible\", reducing the ability to accurately capture \"delicious\" in the opinion words.  
Figure 1: The framework of the proposed SSEMGAT model.  
Moreover, the dependency tree reveals relations between words, the relationship between clauses and between aspects that is difficult to capture. Based on this, we use constituent trees, which mainly consist of phrase segmentation and hierarchical structures that help to correctly align aspect words with their corresponding opinion words of sentiment information. Phrase segmentation can easily divide a sentence into multiple clauses and refine the syntactic position of each word in the sentence. The structured hierarchy can distinguish different relationships between aspect words to infer different aspects of sentiment information from a clause-level perspective. For example, the result of parsing the constituent tree of sentences is shown in Figure 3. The whole sentence is divided into four parts: clause \"The taste is delicious\", phrase segmentation term \"but\", clause \"the service and price are terrible\", and \".\". In hierarchical structure, according to the phrase segmentation term \"and\", we can find that the aspect words \"service\" and \"price\" have the same sentiment polarity, while according to the phrase segmentation term \"but\", it is concluded that it has the opposite sentiment polarity towards the aspect word \"taste\" and the aspect words of other clauses.  
Integrating information from different structural levels can obtain more accurate syntactic information. Therefore  
Matrix \(CA\): The constituent tree has a hierarchical structure, and in each layer, if words \(w_{i}\) and \(w_{j}\) belong to the same clause phrase,  
\[CA_{i,j}^{l}=\left\{\begin{array}{ll}1,&\text{if }w_{i},w_{j}\text{ in same phrase }of\left\{ph_{u}^{l}\right\}\\ 0,&\text{otherwise}\end{array}\right. \tag{3}\]  
Then, the \(CA\) and \(DA\) matrices are combined via position-wise addition as the extracted syntactic feature matrix \(A_{syn}\):  
\[A_{syn}=CA+DA \tag{4}\]
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 4 Application: An Experiment on a Simulated CCS with a Hole  
Here, we provide a case highlighting the algorithm's application, based on an experiment on a simulated CCS with a hole in the laboratory. The sample used in the experiment was composed of three layers of rocks, namely, granodiorite, sandstone, and gabbro were assembled together to form a simulated CCS with the dimension of 298 mm \(\times\) 300 mm \(\times\) 48 mm (Figure 6). The thickness of the granodiorite, sandstone, and gabbro layer is 80 mm, 140 mm and 80 mm, respectively. A circular cavern with diameter of 83 mm was excavated in the center of the sandstone layer (Figure 6).  
Figure 5: Artificial excitation experiment with copper–aluminum–steel composite structure. The gray cylinders are the piezoelectric ceramic sensors, and the red dots are the hypocenters located by AE signals. The white lines are the ray paths from the hypocenter to each sensor after straight-line path correction.  
A roughly horizontal non-penetrating crack in the middle of the sandstone layer was produced accidently during the preparation of the sample prior to the experiment. Pressure was applied to the sample in a vertical direction (Figure 6). We progressively raised the pressure until the sample fractured. The goal of the experiment was to simulate the failure process of the layered rock cavern by detecting the fractures.  
Cubic cell with side length of 1 mm was used to mesh the sample for computation. The adjacency radius was set as 7 times the cell size. Figure 7 shows the locations of the AE sources in four stages during the experiment. The results show that the AE events initiated along the pre-existing crack in the sandstone layer (subplot of 25 s in Figure 7). Then, the tensile cracks, which emerged at the top and bottom of the hole, gradually expanded through the granodiorite and gabbro layers, indicating that the confining hard rocks at the top and bottom cannot obviously hinder the propagation of the tensile cracks (subplots of 600 s, 840 s, and 1200 s in Figure 7).  
The AE location of multilayer samples has been a difficult task for a long time in the laboratory due to low accuracy. Our experimental results show that the travel time and ray path of such samples can be effectively calculated, and subsequently the accurate AE location can be obtained.  
Figure 6: (**a**) Schematic diagram showing the simulated CCS with a hole. The pink, yellow and dark gray block indicates granodiorite, sandstone and gabbro, respectively. The light grey cylinders denote the AE sensors. (**b**) The sample after experiment. The black dotted line is the pre-existing and non-penetrating crack, while the red dotted line is the new crack.  
Figure 7: Distribution of AE hypocenters in four stages during the experiment. The red dots are the AE hypocenters. The numbers at the top represent the loading time.
# Review of Recent Automated Pothole-Detection Methods
## 2 Comparison of Automated Pothole-Detection Methods  
A vision-based method uses images or videos as input data and determines the presence of potholes on the road surface by applying image-processing and deep-learning technology. The vision-based method is more cost-effective than the 3D reconstruction-based method and is also suitable for determining the number and approximate shape of potholes. However, the vision-based method has a limit in measuring the volume and depth of potholes because it utilizes two-dimensional information. It is also affected by lighting and shadow conditions [2; 3; 4; 5; 6].  
A vibration-based method judges the existence of potholes and predicts the depth of potholes based on the data acquired from the acceleration sensor in the vehicle. The vibration-based method is most cost-effective among the three methods. It requires small storage in the data-acquisition process and real-time data processing can be applied. The vibration-based method has a limit in providing the exact shape of potholes because it  
Figure 1: The general process of pothole detection.  
analyzes only the vibration information of the acceleration sensors. It is also affected by the sensor and vehicle applied in the data-acquisition process [6; 7; 8].  
A 3D reconstruction-based method predicts the shape of potholes and measures the volume of them based on stereo-vision technology. The 3D reconstruction-based method predicts the shape of potholes and measures the volume of them most accurately among the three methods. It has a disadvantage in that it is expensive to detect potholes compared to other methods and hard to recognize when the potholes filled with water or dirt [6; 8].  
The characteristics of automated pothole-detection methods are presented in Figure 2. The strengths and weaknesses of each automated pothole-detection method are also summarized in Table 1.
# Analysis of Phonetic Segments of Oesophageal Speech in People Following Total Laryngectomy
## 2 Material and Methods
### Time Domain Descriptors  
1. _ZCR_ (zero-crossing rate) is a measurement used to determine the ratio of zero crossings (the crossing of the OX axis). This is determined as the percentage of audio samples in a given fragment that change sign. The _ZCR_ is defined by the following equation [17]: \[Z(i)=\frac{1}{2W_{L}}\sum_{n=1}^{W_{L}}\left|sgn[x_{i}(n)]-sgn[x_{i}(n-1)]\right|\] (1) where _sgn(*)_ is the function, i.e., \[sgn[x_{i}(n)]=\left\{\begin{array}{ll}1,x_{i}(n)\geq 0,\\ -1,x_{i}<0.\end{array}\right.\] (2) In the research, the value of the _ZCR_ descriptor was calculated in each phonetic segments of the speech of the healthy and laryngectomised people.
2. Short-time energy (_STE_) is an audio descriptor from the MPEG-7 standard, also used in speech classification [6; 18; 19]. It describes the envelope of the signal. _STE_ is the sum of squares computed in the time domain over the length of the test frame of the signal. The _STE_ is expressed by the formula: \[STE=\sum_{n=1}^{N}x^{2}(n)\] (3) where: \(x(n)\)--is the value of \(n\)-th sample, \(n\)--Index of the sample, \(N\)--signal length (total number of samples in the processing window, corresponding to the one phonetic segments).
3. The signal mean value (_SMV_) descriptor expresses the average value of the input speech signal. Its value is estimated in the tested frame of the audio signal. It is calculated by summing the values of all samples and dividing by \(N\). The _SMV_ is given by: \[SMV=\frac{1}{N}\sum_{n=1}^{N}x(n)\] (4) Root mean square is the _RMS_ value of a (periodic) signal, also known as nominal or continuous. This feature is widely used in speech parameterization. It is expressed by the formula [6; 20]: \[RMS=\sqrt{\frac{1}{N}\sum_{n=1}^{N}x^{2}(n)}\] (5) where: \(N\)--signal length (total number of samples in the processing window, corresponding to the one phonetic segments), \(x\)--is the value of \(n\)th sample.
5. Local minimum and maximum: The local maximum is the point at which the function changes from ascending to descending. Also, the local minimum is the point at which the function changes from descending to ascending. In the research, each phonetic segment in the time domain was divided into 20 ms long windows. In each of these windows, the local minimum and maximum values were found.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 1: Collect System Information
#### 3.1.4 Step 1.4: Size Potential DER Configurations for Specific Critical Loads  
Next a preliminary high-level DER sizing study is performed for each critical load with the assumption that each critical load will be placed on its own nanogrid. This includes sizing both energy generation and storage (e.g., how large of a diesel generator? How large of a PV array? How much diesel storage? How large of a battery storage system? etc.) to meet the maximum outage duration a military base is mandated to be able to sustain (e.g., 7 days, 14 days, etc.) which often indicates how long a mission must continue at a specific military base before a different base elsewhere that is not impacted by the initiating event or realized threat can start performing the mission.  
We suggest picking a time period with very low solar irradiance and very high load if such a period exists for a specific site location and a specific load profile. For instance, in northern hemisphere locations, the December-January period often combines very low solar irradiance due to short, cloudy days and higher load profiles due to systems needing more energy to function in cold, dark conditions. However, it is possible that summer conditions in very hot climates may have high enough load profiles due to cooling needs (i.e., chillers and air conditioners to keep electronics within operating temperatures, etc.) that even with more solar irradiance to generate more PV energy, this time period is the most extreme load versus generation scenario.
## 4 Results and Discussion
### Svm Training  
In our previous research [32], we worked on individual pixels and considered each pixel as a separate sample. However, it has been found that despite the use of a household mixer, oil or Sudan mixed oil did not reach every particle of red chili; thus, all pixels were not adulterated. Although the adulteration of oil or Sudan mixed oil displayed reduced the efficiency from 99% to 56%, it did not reach 0%, as shown in Figure 11a-c. Therefore, the mean spectra of all prepared samples were calculated and used for classification.  
As it is evident from Figure 13, the wavelengths of interest fall between 500 nm and 700 nm. Thus, other wavelengths are not considered for this study. A total of 75 wavelengths are selected out of 224. For further reduction in data, PCA was applied on the selected wavelengths of all samples and the number of PCs was selected based on variance explained, i.e., 95%. It has been found that the first four PCs suffice the threshold of variance for our data as shown in Figure 14.  
Figure 12: Red chili Switzky–Golay derivative spectrum with different oil quantity adulteration.  
Figure 13: Comparison of red chili: pure, oil-adulterated, Sudan dye-adulterated.  
For training of SVM classifier, a one-vs-one method is used for three classes of interest: Pure Red chili (labeled as \"1\"), Oil-Adulterated Red Chili (labeled as \"2\"), and Sudan Oil-Adulterated Red Chili (labeled as \"3\"). The data set is divided randomly into two subsets: training (80%) and testing (20%). First, a linear SVM was trained on the training data which was available to classify all three classes with an accuracy of 97%. Second, different kernels were utilized for classification purposes. The value of \(\gamma\) and regularization parameter (C) for RBF kernel was estimated using a grid search method and 5 fold cross-validation was used to avoid overfitting. The optimal value of gamma and C is found to be 3.4 and 1, respectively. The accuracy of different kernels is shown in Figure 15.  
The classifiers we trained classify pure red chili as oil adulterated, except for cubic SVM which misclassifies oil adulterated as pure. The inspection of misclassified samples revealed that these belong  
Figure 14: Principal components of pure red chili.  
Figure 15: Confusion matrix of Linear, Quadratic, Cubic, and Guassian kernel functions for the SVM classifier. (**a**) Linear kernel function; (**b**) SVM with 2 nd Degree Polynomial Kernel; (**c**) SVM with 3 rd Degree Polynomial Kernel; (**d**) SVM with rbf Kernel(\(gamma=3.4,C=1\)).  
to the samples in which oil adulteration quantity is very small (1 mL or 3.3% w.r.t weight/Volume). It has been further noticed that if we increase the variance explained by PCA to 99% (covered by first 6 PCs) or use all 75 featured wavelengths, then SVM with all kernels including linear, quadratic, cubic, and Gaussian classifies with an accuracy of 100%.
## 1 Introduction
The methodology incorporates recent advances in dimensionality reduction techniques and cluster analysis, offering improved accuracy and reliability compared to conventional methods. This paper presents the establishment of the prediction database, details the warning level classification methodology, and demonstrates the development and validation of the prediction model through extensive field application and comparative analysis.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 5 Experimental Results  
### Experiments under Different Loss Functions  
We compared the performance of several loss functions. We took equal error rate (EER) and minimum detection cost function (minDCF) as the experimental performance evaluation index.  
The experimental results are shown in Table 2.  
From Table 2 and Figure 3, we can see that the CLLR loss obtains the optimal performance in the EER, probably because the CLLR loss is more in line with the TI-SV task. Compared to the softmax loss and the triplet loss, the center loss shows its superiority, which potentially allows speaker embeddings to be clustered into more compact representations. However, AM-Softmax loss outperforms center loss on EER and achieves the best performance on minDCF, which demonstrates the effectiveness of the angular margin penalty. However, from the results of AAM-Softmax loss, the angular marginal penalty cannot be too large. It shows that SV systems trained with one of the final verification metrics produce good scores to achieve promising results. In short, it can be seen by comparing the performance of different losses on the SV task. Better performance can be obtained by using a loss function that is more consistent with the final verification metrics or training more discriminative speaker embeddings.