esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 4 Experiments and Discussion
### Datasets
#### 4.1.2 The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)  
The RAVDESS is a collection of audio recordings that capture various emotions through both speech and song, and it is freely accessible for research purposes. It spans a broad spectrum of emotions, ranging from neutral to disgust, and is available in both English and French. In this dataset, actors have voiced the same 13 sentences, each designed to showcase a range of emotions. This collection has been thoroughly vetted for accuracy in identifying emotional cues, making it a reliable resource.  
The diversity of emotions and performers makes RAVDESS ideal for building versatile emotion-detection systems. Notably, the dataset includes not only spoken words but also songs, thereby broadening its utility for various audio research projects. The recordings were made by 24 professionals, evenly divided between men and women. The speech component of the dataset comprises 1440 files, each formatted as a WAV with specific technical parameters. Each emotion is equally represented, with the exception of the neutral emotion, as illustrated in Figure 4.  
In essence, RAVDESS is a goldmine for researchers focusing on emotion recognition, particularly those utilizing machine learning. It offers a well-balanced emotional representation in its speech segment.
## 3 Methodology
### Analysis Methods and Procedures
#### 3.1.2 Consideration of Inherent Torsion-Dynamic Analysis Method Based on ASCE 7-16  
Dynamic analysis is conducted on the three-dimensional structure and involves modifying the dynamic properties of the structural system, in order for the dynamic amplification of the accidental torsion to be considered directly. One way to modify the structural system for dynamic analysis is by reassigning the lumped mass for each floor to other points offset from the original identified center of mass, and by modifying the mass moment of inertia of structural system. Alternatively, the center of mass can be physically relocated on each floor by modifying the horizontal mass distribution, instead of the typical presumed uniform distribution. This approach is computationally expensive. This is because all possible configurations must be analyzed, primarily two additional analyses for each principal axis of the structure. However, this approach automatically produces the dynamic effects of direct loading and accidental torsion. Practical disadvantage is cumbersome calculations.  
When dynamic analysis is conducted, amplification of the accidental torsion using the prescribed amplification factor \(Ax\), in accordance with ASCE 7-16 Section 12.8.4.3 is not applicable. This is because repositioning the center of mass incorporates the expected coupling between the torsional and lateral modal responses and captures amplification of accidental torsion directly.  
The computer program _ETABS_ produced by _CSI_ America, is used in this study to analyze the structure and include accidental torsion in a _MRSA_ for static analysis, and then to conduct dynamic analysis where the center of mass is shifted physically.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 3 Experimental Validation
### Testing of Pulse Splitter and Combiner  
A test using the pulse splitter and pulse combiner was carried out to measure the matching of the pulse splitter and pulse combiner. The test was conducted using the pulse splitter connected to the combiner via three identical 50 \(\Omega\) coaxial cables. The output waveform of the combiner can be seen in Figure 19. The results of this test show perfect matching as the output pulse is not distorted, and the signal does not contain ripples caused by reflections.
# Handling Skewed Data: A Comparison of Two Popular Methods
## 3 Results
### Application to Real-Life Data
#### 3.2.1 The Lipid Data  
We have real data from 95 subjects at a California hospital and are interested in whether triglyceride levels differ between patients considering their gender. The data were observed to be skewed and fitted to gamma distribution according to AICc values. The Q-Q plot and the frequency histogram of the untransformed and log-transformed lipid data are presented in Figure 5. Using the \(t\)-test after log transformation, we got a \(t\)-value of \(t_{\text{58.47}}=~{}2.56\) with a \(p\)-value \(=~{}0.0065\), indicating a significant difference between the two gender means, with the male triglyceride average larger than the average for females. An application of the GLM to the original data with gamma distribution and log link function yielded a \(p\)-value \(=0.006\). For this data, the results from the \(t\)-test of log-transformed data and the GLM both indicated evidence of a difference in triglyceride means between males and females.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Example**} & \multirow{2}{*}{**Sample Size**} & \multirow{2}{*}{
\begin{tabular}{c} **Parameters** \\ **(Shape, Scale)** \\ \end{tabular} } & \multicolumn{2}{c}{**Two-Group \(t\)-Test between**} \\ \cline{3-5}  & & & **SD(s)** and **SD(s)** \\ \hline \multicolumn{5}{c}{**Trans. Mean**} & \multicolumn{2}{c}{**Actual Mean**} \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Standard deviation estimates for the original and log-transformed gamma-distributed data.  
Figure 5: The normal Q–Q plots (up) for the Triglycerides variable before and after transformation and the frequency histogram of the Triglycerides variable on the left (down) is fitted with a gamma distribution (green).
# Influence of Soil Characteristics on Wood Biodeterioration by Brown Rot Fungi
## 3 Results
### Influence of Wood Decay on the Stiffness of Wood  
Changes in wood stiffness were directly correlated to weight losses caused by brown rot fungi (Figure 4). The variation in stiffness after fungal incubation was higher for non-impregnated than for impregnated wood specimens and significant differences between fungal species were found. For instance, non-impregnated wood specimens incubated with _A. serialis_ were completely destroyed showing a reduction in stiffness of 86.02%, whereas in wood specimens impregnated with 0.6% CC merely a low reduction in stiffness of 7.17% was recorded. Reduction in stiffness after fungal treatment was higher in the longitudinal samples than in radial wood specimens; however, significant differences were not recorded. No significant differences in weight losses caused by brown rot fungi were found between longitudinal and radial samples. The highest weight loss was recorded for _A. serialis_ (57.41%) on non-impregnated wood specimens (radial samples) while _S. himantioides_ caused the highest weight losses (6.64%) on Cu-impregnated wood specimens (longitudinal samples).  
Figure 4: Effect of brown rot fungi on weight losses and stiffness of CC-treated wood after 12 weeks incubation. (**a**) Longitudinal samples. (**b**) Radial samples. Lowercase letters denote significant differences between the wood preservative concentrations for the same brown rot fungi. Uppercase letters denote significant differences between brown rot fungi for the same wood preservative concentrations. Data represented as mean \(\pm\) SD of three replicates (\(p<0.05\)).  
Figure 3: Effect of brown rot fungi on wood pole segments before (**1a–5a**) and after 24 months incubation (**1b–5b**). 1; Control. 2; copper-chromium (CC) treated poles. 3; (Bis-(N-cyclohexyldazeniumdioxy) -copper) (Cu-HDO) treated poles. 4; copper-chromium-boron (CCB) treated poles. 5; Alkaline Copper Quaternary (ACQ) treated poles.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 4 Computational Experiments
### Computational Results
#### 4.2.5 Final Algorithm  
Based on the previous experiments, we augment the arc-based formulation (TDTSP-A) with two-cycle-free path-based pricing, the combination of lifted subtour and \(D_{k}^{+}\) inequalities, and all of the primal heuristics. In addition, we use the propagation method introduced in Section 3.5.1 to fix binary variables whenever possible.  
Based on the combined improvements, we are able to solve 46 of the 50 small instances to optimality. The remaining gap of the four unsolved instances averages 4.5%.  
While we are unable to solve any of the large instances to optimality, we reduce the original remaining gap from 55% to 19% (see Tables 1 and 5). In order to compare our algorithm with a state-of-the-art solver, we used Gurobi as a black-box solver on the same instances. As a black-box solver, Gurobi uses neither a column-generation approach, nor the specialized heuristics and valid inequalities we introduced above, consequently averaging at a significantly larger gap of 44% on the large instances.
## 2 Materials and Methods
### Surface Electromyography (sEMG) Instrumentation and Measurement  
The neuromuscular activity of the vastus lateralis (VL) muscle was evaluated following the recommendations of the Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles (SENIAM) regarding skin preparation and fixation of the electrodes in the specified positions related to the VL muscle [48], and the reference electrode was positioned on the spinous process of C7. The sEMG signal was collected in microvols (\(\mu\)V) in a computer and was analyzed using the root mean square (RMS) (EMG832 WF, EMGSystem(r), Sao Paulo, Brazil). In the sEMG assessment, the individuals were asked to sit on a chair with the back straight, feet approximately shoulder-width apart and placed on the floor, and the arms crossed over the chest. Then, they were instructed to perform five repetitions of sitting and standing [49].  
Figure 3: Position of the goniometer on the lower limb for measurement of knee range of motion (ROM).  
The RMS amplitude signal was recorded before and after the first session (acute effect of the first session), and before and after the last session (acute effect of the last session). The %RMS in each condition was calculated dividing the RMS after the session by the RMS before the session (acute effect of the first and of the last session) multiplied by 100. The %RMS to the cumulative effect was calculated in each condition by dividing the value of the RMS before the last session by the RMS before the first session multiplied by 100.
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 6 Experiment and Evaluation
### Dataset and Date Processing  
In our experiment, we used two 1D barcode datasets to measure the performance. The first dataset is the ArteLab Rotated Barcode dataset (referred to as Arte-Lab hereafter) with ground-truth  
Figure 3: Line segment selection procedure: Given the rectangle region from the region estimation network, if the midpoint of the line segment is in this region, the line should be chosen as a barcode line segment. After removing the line segments with a short length and deviated angle, the selected line segments will generate a convex hull.  
provided by [PERSON] et al. [5]. It contains 365 EAN (European Article Number) barcodes captured with different cellphones. The second dataset is the WWU Muenster Barcode Database (referred to as Muenster hereafter) provided by [PERSON] et al. [21]. It consists of 1055 EAN and UPC-A (Universal Product Code) barcodes captured with a Nokia N95 phone. The ground-truth is provided for 595 images [3]. In this experiment, the image sizes of both datasets are 640 \(\times\) 480.  
We prepared two kinds of data to train the object detection network and region estimation network. We obtained information about the bounding box from the mask image, which can be used to train the object detection network. The object detection network (YOLO) was modified to only find one class, 1D barcode.  
Further data processing was necessary for the region estimation network. We cut the bounding box region individually and estimated the rotation angle of the 1D barcode with the approach in Section 4. Then, we rotated it and generated a new image. We gathered the 1D barcode rectangle information from the mask image, which also was rotated in the same way as the source image. To train a robust model which can predict the barcode rectangle region in different scales, one-third of the images were not cut from bounding box region, but other processes continued operating. Figure 4 shows an example. After the above processing, we fed the data to train the region estimation network.  
We trained two networks with 80% random data in two datasets as the training set; the remaining data was used to test the training result. As a result of the constraints of GPU memory and the neural network model size, the batch size we set to 64, and the momentum to 0.9. The initial learning rate was set to 0.001, decreasing by 0.5 ratios in every 4000 steps or when the training loss value tended to be stable.
## 4 Review of Microgrid Control and Design  
### Microgrid Control Methods  
The challenge of classifying and identifying control techniques in distributed energy resources (DERs) based on microgrids remains difficult due to the fact that each microgrid has unique characteristics [13]. Microgrid control strategies aim to maintain power balance, regulate voltage and frequency, facilitate seamless islanding transitions, and optimize economic dispatch. These strategies fall into three categories, which are represented in Figure 20a-c, respectively, for centralized, decentralized, and hierarchical approaches.  
Some researchers have favored centralized control strategies, where a single entity makes control decisions for the entire system. Centralized control employs a central controller, typically a supervisory control and data acquisition (SCADA) system, to manage all microgrid components. This approach offers precise control and coordination but is vulnerable to communication disruptions and single points of failure [64; 65]. Others have explored decentralized approaches, where different entities make control decisions independently. Decentralized control distributes control responsibilities among individual DERs, enabling autonomous responses to local conditions and peer-to-peer communication. This approach enhances resilience and scalability but may pose challenges in maintaining overall system stability [66; 67; 68; 69]. Some other researchers have proposed another approach that combines centralized and decentralized elements to optimize the operation of the microgrid and address the complex challenges associated with DGs. Hierarchical control combines the benefits of centralized and decentralized approaches by employing a multi-layered structure. A central controller oversees the overall microgrid operation, while local  
Figure 20: Microgrid control structures. (**a**) Centralized. (**b**) Decentralized. (**c**) Hierarchical [63].  
controllers manage individual DERs or groups of DERs. This approach provides flexibility and scalability while maintaining system-wide coordination [70; 71; 72; 73].  
Each one of the above control structures have some advantages and drawbacks. For instance, centralized control offers optimal coordination but may be vulnerable to failures in a central entity and can result in communication delays, while decentralized approaches enhance resilience and reduce dependence on a single entity but may lack overall coordination and efficiency. Hierarchical control combines the advantages of both, but its implementation can be complex, requiring effective coordination between levels. Table 3 provides an overview of key differences between different control strategies [74].
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 7 Kaolin in Petrochemical Catalysis: From Fundamentals to Innovations
### Kaolin-Driven Catalysis in Petrochemical Processes
#### 7.4.6. Isomerization
The study demonstrated that the Pt/ZSM-5 + HNT/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst outperformed others in converting m-xylene and ethylbenzene, achieving conversion rates of 40% and 81%, respectively, at 440 \({}^{\circ}\)C. The hierarchical micro-mesoporous structure of the support enhanced the isomerization performance, reducing the yield of undesired hydrogen products. In 2022, [PERSON] et al. [257] focused on the isomerization of n-hexane using a NiP-H\(\beta\)/modified kaolin catalyst. This catalyst was prepared by ball milling NiP nanoparticles with H\(\beta\)/modified kaolin composites, resulting in a material with abundant weak-medium acid sites and mesopores. The modified kaolin provided additional active sites for the formation of di-branched isomerized products, achieving a high conversion rate of 78.1% and selectivity toward mono-branched (75.4%) and di-branched (21.4%) i-C\({}_{6}\) products. This work underscored the potential of non-noble metal-based catalysts for efficient and environmentally friendly gasoline production. Most recently, [PERSON] et al. (2023) [258] synthesized micro-mesoporous ZSM-5 zeolite using natural halloysite aluminosilicate nanotubes as templates. The resulting Pt-containing catalysts were tested for the isomerization of C-8 aromatic fractions. The catalyst prepared with organic templates (ZSM-5(t):HNT) showed superior performance due to its higher surface area and acidity compared to its template-free counterpart. The hierarchical structure facilitated the transformation of xylenes through both intra- and intermolecular isomerization routes, minimizing side reactions and enhancing selectivity. These studies collectively highlight the advancements in using kaolin-based catalysts for isomerization processes in the petrochemical industry. The ability to tailor the acidity, porosity, and structural properties of kaolin-derived catalysts has led to significant improvements in catalytic performance, making them valuable for producing high-octane fuels and other petrochemical products.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 5 Experimental Results and Discussion
### Error Analysis  
To analyze the error of the image mosaic caused by homography matrix scaling, Azimuth1_subswath2 and Azimuth2_subswath2 were stitched at different scaling coefficients. First, an experiment with a scaling coefficient of one was performed. We recorded all the pixel coordinates of the Azimuth2_subswath2's mosaic result and regarded them as the correct coordinates. Then, experiments with scaling coefficients of 1, 0.5, 0.1 and 0.05 were  
Figure 11: GF-3 ScanSAR images: (**a**) Azimuth1_subswath1; (**b**) Azimuth1_subswath2; (**c**) Azimuth1_subswath3; (**d**) Azimuth2_subswath1; (**e**) Azimuth2_subswath2; (**f**) Azimuth2_subswath3; (**g**) mosaic result of the six GF-3 spaceborne ScanSAR images.  
conducted, and all the pixel coordinates of the corresponding Azimuth2_subswath2 mosaic results were recorded as the experimental coordinates.  
The root-mean-square error (RMSE) of the distances between the correct coordinates and the experimental coordinates was calculated as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\times\sum_{i=1}^{N}\Bigl{(}\left(x_{i}-x^{\prime} _{i}\right)^{2}+\left(y_{i}-y^{\prime}_{i}\right)^{2}\Bigr{)}} \tag{22}\]  
where \(N\) is the number of pixels, \(\left(x_{i},y_{i}\right)\) are the correct coordinates and \(\left(x^{\prime}_{i},y^{\prime}_{i}\right)\) are the experimental coordinates. Figure 12 shows the correct matching rate \(E_{m}\) and the RMSE at the different scaling coefficients.  
It can be seen from Figure 12 that \(E_{m}\) decreased as the scaling coefficient decreased. Especially when the scaling coefficient was 0.05, it decreased by more than twenty percentage points compared to the case without downsampling. Therefore, overlapping area downsampling has a negative impact on the correct matching rate so that the fault tolerance rate of the homography matrix calculation will be lower. However, the RMSE changed in an opposite trend with the scaling coefficient. When the scaling coefficients were 0.1 and 0.05, the RMSE was greater than three. Apparently, overlapping area downsampling increases the RMSE of the distances between the correct coordinates and the experimental coordinates, which causes a worse accuracy of the image mosaic. Moreover, one important aspect is that the RMSE was not zero when the scaling coefficient was 1. This phenomenon demonstrates that there is still an error in the calculation of the homography matrix itself even with the same scaling coefficient because the matched keypoints used to calculate the homography matrix were randomly selected. The homography matrixes computed by different matched keypoints will produce tiny errors.
# Material Removal Model for Lapping Process Based on Spiral Groove Density
## 4 Modified Material Removal Model  
### Solid-Solid Contact Theory  
In 1966, [PERSON] and [PERSON] published the elastic contact theory, which states that the contact between two planes is affected by the topography of surface asperities, such as shape and height, and the mechanical properties and that the number of contact points and the contact area depend on the load due to elastic deformation [29]. The elastic contact theory was then applied to the polishing process and was introduced as the theoretical background of the CMP process by [PERSON] et al. [21] in 1993, who demonstrated through a model that the direct contact between a pad and wafer was achieved under a load during the polishing process. In subsequent studies by [PERSON] [30] and [PERSON] [31], which expanded [PERSON]'s model, pad asperities were assumed to form a normal distribution, and a model for the contact between a wafer and pad was suggested as shown in Figure 12. A material removal model was proposed that considered the characteristics of abrasives, such as size, dispersion, and elastic modulus, as well as other parameters related to polishing pads, pressure, and process conditions.  
Figure 11: (a) Film thickness and frictional force as a function of the inverse number of groove density; (b) friction coefficient plotted as a function of fluid viscosity and shear velocity divided by load (Stribeck curve) with corresponding lubrication film thickness [27].  
Figure 12: Asperities on a rough surface in contact with a rigid flat surface [30].  
The pads used in the CMP process have a high surface roughness with surface asperities that are larger in size than the abrasives. Therefore, during polishing, the pad asperities are in direct contact with the wafer, while the abrasives present between the asperities and wafer remove the wafer material. However, the platen used in the lapping process has a relatively lower surface roughness and a higher elastic modulus compared to the pads used in the CMP process. In addition, because the abrasives used in the lapping process are several micrometers in size--much larger than the nanometer-sized abrasives used in the CMP process--the wafer is in direct contact with abrasives, rather than with pad asperities between the platen and wafer, in the lapping process [32]. Therefore, abrasives present on the lapping platen can be considered similar to pad asperities in the CMP process.  
The solid-solid contact theory, a contact model for pad asperities and wafers used in the CMP process, was applied to the lapping process to calculate the probability of contact between abrasives and wafers as a function of oil film thickness, which was then applied to the indentation theory to develop a new material removal model.