esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spotify API and Google Cloud  
We leverage the Spotify API, accessed through the Spotify library, to retrieve crucial audio features of Thai songs. The API facilitates fetching song data; we search for songs (potentially using Thai titles or artists) and obtain unique Spotify IDs. More importantly, it provides access to a range of audio features that are essential for emotion classification, such as tempo (BPM), energy, danceability, valence, acoustics, instrumentals, liveness, loudness, and speeches. The SpotifyOAuth class manages the necessary authentication,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Accuracy** \\ \hline Random Forest & 0.95 \\ XGBoost & 0.89 \\ Decision Tree & 0.85 \\ Logistic Regression & 0.79 \\ SVM & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy results.  
Figure 1: The accuracy and loss functions of the random forest training and test sets.  
enabling us to access public track information and audio features. We used Google Cloud Storage to store the dataset of Thai songs, including the extracted audio features, lyrics, and assigned emotion labels, as well as the trained classification models (random forest). We also used Google Compute Engine or AI Platform to provide the computational resources for efficiently training these models. We deployed the trained model on Google Cloud for real-time predictions for a production-ready system.  
Furthermore, we used Google Cloud's database solutions (Cloud SQL or Cloud Datastore) to manage the song data. At the same time, the Cloud Natural Language API was utilized for lyrics analysis, including sentiment analysis and entity recognition. We considered the Spotify API central to accessing and extracting audio features, while Google Cloud provided the infrastructure for storage, computation, deployment, and potentially, NLP tasks. We illustrate the steps in creating and deploying a random forest model for mood classification using the Spotify API and Google Cloud in Figure 2.  
1. Spotify API (Spotify Library): used to gather a dataset of Thai songs, including their audio features and lyrics;
2. Mood labeling: each song is manually labeled with one of four mood classes: love, happy, sad, and angry;
3. Feature extraction: relevant features are extracted from the audio data (e.g., tempo, energy, danceability) and lyrics (e.g., sentiment, keywords).
4. Data preprocessing: the data is cleaned and preprocessed, handling missing values and converting categorical features;
5. Model training: the labeled data is used to train a random forest model on the Google Cloud AI Platform;
6. Model evaluation: the model's performance is evaluated using accuracy, precision, and recall metrics;
7. Model deployment: the trained model is deployed on Google Cloud for real-time mood classification;
8. Integration with Spotify: the deployed model is integrated with the Spotify API to generate mood-based playlists.
# Scan Matching by Cross-Correlation and Differential Evolution
## 3 Background
### Iterative Closest Point  
The Iterative closest point algorithm is the de-facto standard point-based scan matching algorithm. It is based on the search for the pairs of the closest points in the matched laser range scans [51]. For each point in the aligned scan, it finds the nearest point in the reference scan and seeks transformation parameters that minimize the Euclidean distance between these two points [31]. The ICP consists of the following steps [51]:  
1. Preprocessing
2. Matching
3. Rejection
4. Cost function evaluation
5. Minimization of the cost function (go to step 1. when termination criteria are not met)  
In the preprocessing step, the points that are not useful for matching of the two point clouds (e.g., outliers) are filtered out to reduce the volume of the data. The pairs of the closest points in the matched scans are found in the matching phase. The rejection phase discards the point pairs with too large distance in-between.  
The cost function, \(f_{\text{cost}}\), that evaluates the level of alignment between the reference scan, \(\mathbf{A}\) and the current scan, \(\mathbf{B}\), is defined as [52]  
\[f_{\text{cost}}\left(\mathbf{A},\mathbf{B},t_{x},t_{y},\Phi\right)=\sum_{s=1} ^{s_{\text{max}}}\left\|\mathcal{T}(\vec{a}_{s},t_{x},t_{y},\Phi)-\vec{b}_{ \theta(s)}\right\|^{2}, \tag{6}\]  
where \(\mathcal{T}\) is an affine transformation defined in Equation (5), \(\phi\) represents the function that finds for each point \(\vec{a}_{s}\in\mathbf{A}\) the nearest point \(\vec{b}_{\theta(s)}\in\mathbf{B}\). The parameters of \(\mathcal{T}\) in ICP are found by an arbitrary real-parameter optimization method, for example the Newton-Raphson method [53].
## 5 Results and Verification
### Model Verification  
To verify the developed model, two verification methods were applied. First, there was a method of comparing the measured value with the predicted value, and the second was a method of calculating and analyzing the sensitivity and specificity.
## Appendix B
\begin{array}{ll}&D_{0}\mathbf{x}^{(2)}-\mathbf{A_{CS}}\mathbf{x}^{(2)}-\mathbf{B_ {CS}}\mathbf{x}_{d}^{(2)}=\mathbf{Q}_{1}^{(2)}\left(A_{1},A_{2}\right)e^{i\omega_{c }\tau_{0}}\\ &=\Big{[}-\big{(}\mathbf{I}+\tau_{n0}e^{-i\omega_{c}\tau_{n0}}\mathbf{B_{CS}}\big{)}D_ {2}A_{1}\mathbf{X}_{1}+\big{(}\mathbf{A_{SS}}+\mathbf{B_{SS}}e^{-i\omega_{c}\tau_{n0}}\big{)} \mathbf{X}_{1}A_{1}+\mathbf{Q}_{1}^{F_{2}}A_{1}^{2}A_{2}\Big{]}e^{i\omega_{c}\tau_{n0}} \leavevmode\
obreak\ \leavevmode\
obreak\.\end{array}  \tag{105}\]  
Introducing a possible unstable solution vector of the form  
\[\mathbf{x}^{(2)}=\mathbf{P}_{1}^{(2)}(A_{1},A_{2})e^{i\omega_{c}\tau_{0}} \tag{106}\]  
into Equation (105) and making the solution vector indeterminate, one obtains the solvability condition:  
\[\left| \begin{array}{ccc}v^{-1}Q_{A}^{-1}+i\omega_{c}&v^{-2}\big{(}1-\psi_{CS }k_{r}\big{(}1-e^{-i\omega_{c}\tau_{n0}}\big{)}\big{)}&0&Q_{11}^{(2)}\\ -1&i\omega_{c}&0&Q_{12}^{(2)}\\ 0&\psi_{CS}\big{(}1-e^{-i\omega_{c}\tau_{n0}}\big{)}&Q_{T}^{-1}+i\omega_{c}&Q_{13 }^{(2)}\\ 0&0&-1&Q_{14}^{(2)}\end{array} \right|=\mathbf{0}. \tag{107}\]Rewriting Equation (A37) gives the modulation equation in complex form:  
\[\dot{A}_{1}=-\frac{C^{(\delta)}}{C^{(D)}}\psi_{CS}A_{1}-\frac{C^{(C)}}{C^{(D)}}A _{1}^{2}A_{2},\] (A38)  
where  
\[ \begin{array}{ll}C^{(n)}&=Q_{13}^{(n)}\Big{[}v^{-2}+\psi_{CS}k_{r}v^{-2} \big{(}1-e^{-i\omega_{c}\tau_{m0}\omega_{c}}\big{)}+i\omega_{c}Q_{A}^{-1}v^{- 1}-\omega_{c}^{2}\Big{]}\\ &+Q_{14}^{(n)}\Big{[}\Big{(}Q_{T}^{-1}+i\omega_{c}\Big{)}\Big{(}v^{-2}+\psi_{ CS}k_{r}v^{-2}\big{(}1-e^{-i\omega_{c}\tau_{m0}\omega_{c}}\big{)}+i\omega_{c}Q_{A} ^{-1}v^{-1}-\omega_{c}^{2}\Big{)}\Big{]}\ \.\\ &-\psi_{CS}\big{(}1-e^{-i\omega_{c}\tau_{m0}\omega_{c}}\big{)}\Big{[}Q_{11}^{( n)}+Q_{12}^{(n)}\Big{(}i\omega_{c}+Q_{A}^{-1}v^{-1}\Big{)}\Big{]},(n=D,C, \delta.)\end{array} \] (A39)  
By comparing Equation (A39) with Equation (54), can be obtained as follows:  
\[c_{r1}=\text{Re}\Bigg{[}-\frac{C^{(\delta)}}{C^{(D)}}\Bigg{]},c_{11}=\text{Im} \Bigg{[}-\frac{C^{(\delta)}}{C^{(D)}}\Bigg{]}\] (A40)  
\[c_{r2}=\text{Re}\Bigg{[}-\frac{C^{(C)}}{C^{(D)}}\Bigg{]},c_{11}=\text{Im} \Bigg{[}-\frac{C^{(C)}}{C^{(D)}}\Bigg{]}.\] (A41)  
_Appendix B.5. \(P_{3}^{(2)}\), \(\overline{P_{3}^{(2)}}\)_  
\[\mathbf{P}_{3}^{(2)} =\Big{(}3i\omega_{c}\mathbf{I}-\mathbf{A_{CS}}-e^{-3\tau_{m0}\omega_{c}} \mathbf{B_{CS}}\Big{)}^{-1}\mathbf{Q}_{3}^{F_{2}}\] (A42) \[\overline{\mathbf{P}_{3}^{(2)}} =\Big{(}-3i\omega_{c}\mathbf{I}-\mathbf{A_{CS}}-e^{3\tau_{m0}\omega_{c}} \mathbf{B_{CS}}\Big{)}^{-1}\overline{\mathbf{Q}_{3}^{F_{2}}}\] (A43)
###### Abstract  
This paper is concerned with the design of a context-based fuzzy C-means (CFCM)-based multi-granular fuzzy model (MGFM) with hierarchical tree structures. For this purpose, we propose three types of hierarchical tree structures (incremental, aggregated, and cascaded types) in the design of MGFM. In general, the conventional fuzzy inference system (FIS) has problems, such as time consumption and an exponential increase in the number of if-then rules when processing large-scale multivariate data. Meanwhile, the existing granular fuzzy model (GFM) reduces the number of rules that increase exponentially. However, the GFM not only has overlapping rules as the cluster centers become closer but also has problems that are difficult to interpret due to many input variables. To solve these problems, the CFCM-based MGFM can be designed as a smaller tree of interconnected GFMs. Here, the inputs of the high-level GFMs are taken from the output to the low-level GFMs. The hierarchical tree structure is more computationally efficient and easier to understand than a single GFM. Furthermore, since the output of the CFCM-based MGFM is a triangular fuzzy number, it is evaluated based on a performance measurement method suitable for the GFM. The prediction performance is analyzed from the automobile fuel consumption and Boston housing database to present the validity of the proposed approach. The experimental results demonstrate that the proposed CFCM-based MGFM based on the hierarchical tree structure creates a small number of meaningful rules and solves prediction-related problems by making them explainable.  
multi-granular fuzzy model; hierarchical tree structure; context-based fuzzy c-means clustering; information granule; granular fuzzy model +
Footnote †: journal: Medical Science
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 3 Surface Inspection Process
### Defect Classification and Localization
#### 3.3.1 Classification  
The feature map was extracted by the convolutional neural network, and the dimension of the feature map was \(13\times 13\times 1024\). Each position of \(13\times 13\) represented a specific area in the original  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** & **Kernel Size/Stride** & **Output Size** \\ \hline Convolutional & \(3\times 3\times 32\) & \(416\times 416\) \\ Max Pooling & \(2\times 2\) & \(208\times 208\) \\ Convolutional Residual & \(\left[\begin{array}{c}3\times 3\times 64\\ 3\times 3\times 64\end{array}\right]\times 2\) & \(208\times 208\) \\ Max Pooling & \(2\times 2\) & \(104\times 104\) \\ Convolutional Residual & \(\left[\begin{array}{c}3\times 3\times 128\\ 3\times 3\times 128\end{array}\right]\times 2\) & \(104\times 104\) \\ Convolutional & \(3\times 3\times 3/2\), \(256\) & \(52\times 52\) \\ Convolutional Residual & \(\left[\begin{array}{c}1\times 1\times 128\\ 3\times 3\times 256\end{array}\right]\times 2\) & \(52\times 52\) \\ Convolutional & \(3\times 3/2\), \(512\) & \(26\times 26\) \\ Convolutional Residual & \(\left[\begin{array}{c}1\times 1\times 256\\ 3\times 3\times 512\end{array}\right]\times 3\) & \(26\times 26\) \\ Convolutional & \(3\times 3/2\), \(1024\) & \(13\times 13\) \\ Convolutional Residual & \(\left[\begin{array}{c}1\times 1\times 512\\ 3\times 3\times 1024\end{array}\right]\times 2\) & \(13\times 13\) \\ Avgpool Softmax & Global, 4 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network structure.  
image. We followed the Single Shot MultiBox Detector (SSD) [29] and Faster R-CNN [30] to associate 6 anchor boxes at each location of the feature map. Each anchor box was responsible for predicting whether there was a defect at the position or not. If there was a defect, it would then predict the defect category and calculate the probability of the defect belonging to a certain defect category. In this paper, there were four categories of defects. The loss function of the classification task was defined as follows:  
\[\begin{array}{l}L_{cls}(x,c)=-\frac{1}{N}\underset{i\in Pos}{\sum}x_{ij}^{p} \log(\delta_{i}^{p})-\frac{1}{N}\underset{i\in Neg}{\sum}\log(\delta_{i}^{0}) \\ \delta_{i}^{p}=\frac{\exp(\delta_{i}^{p})}{\sum_{p}\exp(\delta_{i}^{c})}\end{array} \tag{3}\]  
where \(N\) is the total number of anchor boxes, \(i\) refers to the anchor box index, \(j\) refers to the ground-truth box index, \(p\) refers to the category index, and 0 represents the background. \(x_{ij}^{p}=1\) when category \(p\) of \(i\)-th anchor box and category \(p\) of \(j\)-th ground-truth box match, otherwise \(x_{ij}^{p}=0\). \(c_{i}^{p}\) indicates the predicted probability of the category \(p\) corresponding to the \(i\)-th anchor box.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 3 Proposed Method
### RAG Mechanism
#### 3.2.5 System Interaction Workflow  
Figure 8 illustrates the interaction workflow between the user, the Retrieval-Augmented Generation (RAG) system, the vector database, and the large language model (LLM). This workflow highlights the sequence of operations that enable the system to process user queries and deliver accurate responses.  
The process begins with the user submitting a query through the interface. The query is then sent to the RAG system, which acts as the central processing unit [39]. Within the RAG system, the query is processed to extract its semantic meaning and match it with relevant information stored in the vector database. The vector database, which holds embedding representations of document chunks, is queried to identify the most relevant documents based on the similarity between the query vector and stored embeddings.  
If relevant documents are found, they are retrieved and combined with the original user query to create a unified context. However, if no relevant documents are identified, the system falls back to utilizing the LLM's internal knowledge, ensuring that the user query is addressed even in the absence of external data. This context, either enriched with retrieved documents or relying solely on internal knowledge, is then passed to the pre-trained LLM, which uses the provided information to generate a coherent and contextually relevant answer [40].  
The LLM-generated answer is returned to the RAG system, where it is prepared for delivery. The final response is sent back to the user interface, completing the interaction loop. This structured workflow, including fallback mechanisms, ensures robust handling of user queries and highlights the seamless integration of retrieval and generation components. Figure 8 clearly demonstrates the collaboration between the various components, emphasizing the seamless flow of information that enables the system to meet user needs in Swahili Natural Language Processing applications [41].  
Figure 8: System interaction workflow. This figure illustrates the sequence of operations, from query submission to response generation, with fallback mechanisms in case relevant documents are not found in the vector database.
## 4 Materials and Methods
### RNA Extraction, cDNA Synthesis, and RT-qPCR Analysis  
Total RNA was extracted from different samples following the manufacturer's standard protocol of the OminiPlant RNA Kit containing DNase I (CWBIO, Beijing, China). The quality and concentration of RNA samples were assessed by Agarose gel and NanoDrop 2000 (ThermoFisher Scientific, Waltham, MA, USA). Afterward, 500 ng of total RNA was mixed with Oligo (dT)15 primer and random hexamer primer and reversed by M-MLV Reverse Transcriptase (Promega, Madison, WI, USA) following the instruction. Gene expression levels were evaluated with specific primers listed in Table S3 by RT-qPCR following our earlier studies [7]. The relative transcripts were normalized by _GmACTIN_ (Glyma.19G147900.1) and \(\beta\)-_tubulin_ (Glyma.03G124400.1) and calculated by 2\({}^{\text{-}\Delta\text{ACT}}\)[45]. All the data were obtained from at least three biological replicates. The data were further subjected to TBtools and normalized by log\({}_{2}\)(FPKM + 1) for heatmap construction [45].
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 3 Materials and Methods: Subtitling Solutions and Stimuli
### Presentation Modes  
By presentation modes, we refer to how the subtitles are integrated and presented with the audiovisual content, even when exploring the immersive environment.  
The two most widely implemented presentation modes for VR360 video (e.g., [9; 13; 18; 19]) have been adapted and adopted for their adequate integration with 3D VR content:  
* _Mode A--Always-visible subtitles_: The approach for 3D VR is identical to that for VR360 content. Subtitles are attached to the virtual camera, thus being always visible at the bottom center of the FoV (although the positioning could be dynamically personalized, as in [5; 9]), regardless of where the user is looking (see Figure 1, top left image).
* _Mode B--Fixed-positioned subtitles_: Unlike in VR360 video, in which the users can just explore the 360\({}^{\circ}\) environment (i.e., 3 DoF), 3D VR content brings a depth dimension, allowing free navigation within the environment (i.e., 6 DoF). Therefore, in addition to the latitude and longitude coordinates typically used in VR360 subtitling [9], the depth dimension acquires increased relevance when implementing _Fixed-positioned subtitles_ in 3D VR content and, for example, a 3D Cartesian coordinate system (\(x\), \(y\), \(z\)) needs to be adopted as the reference. However, when the speakers' locations do not significantly vary, the implementation of this presentation mode for 3D VR content is similar to that for VR360 content (see Figure 1, top right image). This is the case of the scenarios considered in this paper, as detailed later.  
In addition, previous works, such as [9; 25] and the _Avatar movie_ (Section 2), have revealed the potential of adopting more dynamic presentation modes for subtitles, by rendering them in a close position to the associated speaker. By leveraging the insights and lessons learned from these state-of-the-art implementations and studies, a third novel presentation mode is proposed in this paper:  
* _Mode C--Comic-style subtitles_: This consists of adding bubbles associated with the speakers' faces, like in comics, and presenting the subtitles in the planes making up these bubbles (see Figure 1, bottom left image). In this mode, the size of the bubble is slightly adjusted for an adequate fit of the active subtitles' text. The size is not intended to vary significantly, because the characters per subtitle frame are typically limited to two lines and 40 characters per line [13; 26], for any presentation mode. Similarly, because the subtitles are rendered in 2D planes, a dynamic algorithm was adopted to always render these planes orthogonally to the users' viewing perspective to maximize readability. The bubbles are only made visible when associated subtitles need to be presented.
## 5 Experiments and Results
\begin{table}
\begin{tabular}{c c c c}  & **Daily Initial** & **Daily Optimized** & **Daily Optimized** \\  & **Consumption (€)** & **Consumption** & **Consumption (Statistical) (€)** \\ \hline
**User 1** & 17.4936 & **8.2495** & 10.0484 \\
**User 2** & 8.1198 & **0.4782** & 0.8866 \\
**User 3** & 12.9331 & **1.7787** & 3.4489 \\ \hline \end{tabular}
\end{table} 
Table 1: Energy cost comparison. \begin{table}
\begin{tabular}{c c c c}  & **Daily Savings Consumption** & **Daily Savings Consumption** \\  & **\% (Meteorological)** & **\% (Statistical)** \\ \hline
**User 1** & 52.8428 & 42.556 \\
**User 2** & 94.1107 & 89.081 \\
**User 3** & 86.2469 & 73.3328 \\
**Mean Saving Percentage** & 77.73347 & 68.3233 \\ \hline \end{tabular}
\end{table} 
Table 2: Saving percentage. \begin{table}
\begin{tabular}{c c c c}  & **Max Daily Peak Not** & **Max Daily Peak Optimized** & **Difference [\%]** \\  & **Optimized (W/h)** & **(W/h) (Meteorological)** & **Difference [\%]** \\ \hline
**User 1** & 7.40 & 6.40 & 13.51 \\
**User 2** & 3.8 & 3.8 & 0 \\
**User 3** & 4.75 & 3.9 & 17.895 \\
**User 4** & 7.40 & 6.40 & 13.51 \\ \hline \end{tabular}
\end{table} 
Table 3: Peak comparison between optimized and not optimized scheduling (meteorological method). In Table 4, different behaviors are shown. Indeed, the maximum daily peak optimized for \"User 2\" is higher than the maximum daily peak not optimized. Consequently, the \"Difference [%]\" is negative. Probably, this is due to the power production prediction, which is statistically calculated and does not consider meteorological changes.
# Shear Strength of Sand: Integrated Analysis of Initial Porosity and Stress Effects
## 1 Introduction
Based on the literature review, it can be stated that the phenomenon of sand shear strength is very complex. Even after extensive studies and analyses, the most recent research continues to uncover valuable new findings. The reason for often divergent interpretations in soil strength is the overly general definition of the internal friction angle and the lack of information on whether it is the peak internal friction angle or the critical one. These values, especially in highly compacted soils, differ significantly from each other. The second issue is the application of a simplified linear relationship of shear strength depending on normal stresses. Despite numerous studies on the shear strength of cohesionless soils, the application of one of the key strength parameters--the internal friction angle--still raises certain concerns. One of the fundamental questions is whether this parameter can be considered constant. While in practical engineering applications, a cautious estimation of the internal friction angle using a linear strength envelope may be acceptable, scientific research and advanced numerical analyses require that this parameter account for all factors influencing its value. One such factor is the stress state, which is also examined in this study. This approach is particularly important for the optimisation and design of geotechnical systems, where the accurate characterisation of shear strength parameters is critical for ensuring safety, reliability, and performance [27; 28]. The main objective of the present study is to propose an integrated approach to the shear strength of cohesionless soils by investigating the relationships between the initial porosity index, the critical porosity index, and the stress range. All dependencies are expressed through nonlinear mathematical relationships, representing an extension of existing models. This article presents research findings that elucidate the disparities in the peak and critical angle of internal friction. Furthermore, it demonstrates the manner in which these values are contingent on stress range and initial soil porosity index.