esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
### Contributions, Limitation, and Future Work  
In this study, we have prepared a new dataset using tweets from Nigerian bank customers. This dataset will enhance the research in topic discovery in the Nigerian banking context. Secondly, we explored the use of a language model (BERT), which requires little or no pre-processing of text (input) data to create document embeddings. Lastly, we demonstrated the use of kernel principal component analysis and k-means clustering for extracting topics. This study provided a comprehensive explanation of the methods deployed. To the best of our knowledge, this is the first study to compare TM approaches in the Nigerian banking context. Our study is useful for service industries to discover emerging topics. Specifically, the banks can adopt our approach to monitor their customers' preferences towards their product and service. For example, our findings showed topics around USSD code, general enquiry, mobile app, and ATMs were the leading themes in the bank customer discussion.  
A major limitation is that there are limited lexical resources publicly available to support the pre-processing of Pidgin terms. Most notably, there is a need to pre-train the language models with Pidgin English words/documents to provide opportunities for research in that area. In the future, there is need to explore the use of the kernel trick on spectral clustering as the model has a within dimensionality reduction component. This is because, in our experiment, the combination of KPCA and spectral clustering also yielded a competitive result. Another area to explore in the future is the use of domain knowledge to generate pre-defined labels such that BERTopic can be deployed as a semi-supervised learning algorithm to generate N coherent topics. In addition, there is a need to create a labelled set to use supervised learning algorithms for topic discovery. This will enhance the evaluation processes by comparing the model prediction against the ground truth.  
Conceptualization, B.O.; methodology, B.O.; software B.O.; validation, B.O.; formal analysis, B.O.; investigation, B.O.; resources B.O.; data curation, B.O.; writing--original draft preparation, B.O.; writing--review and editing, T.M., L.H., J.G. and T.B.; visualization, B.O.; supervision, T.M., L.H., J.G. and T.B.; project administration, T.M., L.H., J.G. and T.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset used in this work is available on Kaggle repository [[https://www.kaggle.com/datasets/batoog/bank-customer-tweets-10000](https://www.kaggle.com/datasets/batoog/bank-customer-tweets-10000)]([https://www.kaggle.com/datasets/batoog/bank-customer-tweets-10000](https://www.kaggle.com/datasets/batoog/bank-customer-tweets-10000)) and the code [[https://www.kaggle.com/code/batoog/bertopic](https://www.kaggle.com/code/batoog/bertopic)]([https://www.kaggle.com/code/batoog/bertopic](https://www.kaggle.com/code/batoog/bertopic)).  
The authors declare no conflict of interest.
## 3 Time-Varying Pole Radius of the Filter
### Quality Indices of Filtering  
For the purpose of the qualitative evaluation of the presented filtering algorithm, the following performance indices were taken into account:  
* SNR improvement [14; 15] \[SNR_{\rm i}=10\cdot\log_{10}\left(\frac{\sum_{i=1}^{K}(x(i)-s(i))^{2}}{\sum_{i=1 }^{K}(y(i)-s(i))^{2}}\right),\] (13)
* cross-correlation coefficient related to the original and filtered signal \[\rho=\frac{\sum_{i=1}^{K}(y(i)\cdot s(i))}{\sqrt{\sum_{i=1}^{K}y(i)^{2}\cdot \sum_{i=1}^{K}s(i)^{2}}},\] (14)  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling frequency \(f_{\rm s}\) & 500 Hz \\ Notch frequency \(f_{0}\) & 50 Hz \\ Starting pole radius \(r_{start}\) & 0.944 \\ Final pole radius \(r_{final}\) & 0.995 \\ Starting bandwidth \(\overline{BW}_{start}\) & 8.9127 Hz \\ Final bandwidth \(\overline{BW}_{final}\) & 0.7958 Hz \\ Variation horizon \(M\) & 200 samples \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the proposed notch filter with time-varying pole radius.  
Figure 3: The second derivative of the approximation function \(f(r)\).  
* percentage root mean square difference \[PRD=\sqrt{\frac{\sum_{i=1}^{K}(s(i)-y(i))^{2}}{\sum_{i=1}^{K}s(i)^{2}}}.\] (15)
* mean square error defined by Equation (8).  
In the above defined indices, \(K\) is the number of signal samples of the filter input signal under consideration, \(x(n)\) is the input signal to be filtered, \(y(n)\) is the filter output signal, and \(s(n)\) denotes the original input signal without disturbances.
## 2 Materials and Methods
### Methods  
The workflow of the present includes satellite/gridded data acquisition, data preprocessing, image classification, geographic information system (GIS) operations, and result analysis). The flowchart is shown in Figure 2. The present study is mainly divided into two parts:  
1. The first part of the study focuses on assessing vegetation cover loss due to the expansion of mining areas during the study periods (e.g., 1990-2000, 2000-2010, 2010-2020, and 1990-2020) using multi-temporal Landsat series satellite datasets;
2. The second part of the study focuses on accounting for vegetation GPP, NPP, and VT losses due to mines-induced vegetation cover loss by using MODIS and PML_V2 satellite-based products.  
The details of a few key steps of the present study are discussed in further sub-sections.
# Soft Contrastive Cross-Modal Retrieval
## 3 Methodology
### Problem Definition  
**Definition 1**.: _Cross-Modal Retrieval. Given two modalities: an image modality \(I\) and a text modality \(T\). \(I=\{i_{1},i_{2},...,i_{n}\}\) and \(T=\{t_{1},t_{2},...,t_{m}\}\) represent the set of images and text, where \(n\) and \(m\) are the number of images and texts, respectively. A cross-modal retrieval task can be defined as learning a similarity measure \(S\), which can quantify the similarity between an image and a text:_  
\[S:I\times T\rightarrow\mathbb{R} \tag{1}\]  
_Taking image-to-text retrieval as an example, given a query image \(i_{q}\), the goal is to find the most matching text description \(t^{*}\) in the text set \(T\):_  
\[t^{*}=\underset{t\in T}{\text{argmax}}\ S(i_{q},t) \tag{2}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathcal{D}\) & a multimedia dataset \\ \(\mathbf{I}_{i}\) & the \(i\)-th image sample \\ \(\mathbf{T}_{i}\) & the \(i\)-th text sample \\ \(\mathbf{L}_{i}\) & the semantic label vector of the \(i\)-th image–text pair \\ \(Q\) & a cross-modal query \\ \(\mathcal{R}\) & the set of results \\ \(N\) & the number of samples in a batch \\ \(S(\cdot,\cdot)\) & the multimodal semantic similarity function \\ \(f_{\mathbf{I}}(\cdot)\) & the mapping function of the visual network \\ \(g_{\mathbf{T}}(\cdot)\) & the mapping function of the textual network \\ \(\theta_{\mathbf{I}}\) & the parameters of the visual network \\ \(\theta_{\mathbf{T}}\) & the parameters of the textual network \\ \(\tau\) & hyperparameter, temperature \\ \(\varepsilon\) & hyperparameter, smooth parameter \\ \(\hat{p}(\cdot)\) & the prediction value of the label classifier \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary of the notations frequently used in this paper.  
**Definition 2**.: _Multi-Modal Similarity Function Given a multi-modal dataset \(\mathcal{D}\), \((\mathbf{I},\mathbf{T})\) is a image-text pair, where \(\mathbf{I}\in\mathcal{D}\) and \(\mathbf{T}\in\mathcal{D}\). And the multi-modal similarity between \(\mathbf{I}\) and \(\mathbf{T}\) is calculated as follows, respectively:_  
\[S(\mathbf{I},\mathbf{T})=\frac{\sum_{i}(f_{I}(I)_{i}\cdot g_{T}(T)_{i})}{\sqrt{\sum_{i} (f_{I}(I)_{i})^{2}}\cdot\sqrt{\sum_{i}(g_{T}(T)_{i})^{2}}} \tag{3}\]  
Here, \(f_{I}\) and \(g_{T}\) are the mapping functions of the visual network and textual network. For writing convenience, we write \(S(f_{\mathbf{I}}(\mathbf{I}),g_{T}(\mathbf{T}))\) as \(S(\mathbf{I},\mathbf{T})\) in the following part.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 4 Diagnosability Design Based on Structural Analysis
### Inherent Diagnosability Design
#### 4.1.3 Diagnosable Structural and Functional Optimization Design Based on Structural Analysis  
To improve the diagnosability and meet the qualitative and quantitative diagnosability needs of the system, it is necessary to optimize the structure and function, increase the redundancy relationship, improve the information content of the faults, and then increase the structural detectability and structural isolability.  
[PERSON] proposed two methods to ensure fault isolability: the first one is to add sensors, and the second is to merge the fault modes. The effectiveness of the proposed methods was also analyzed based on the structural model of the DAMADICS valve [59]. [PERSON] built a fault characterization and isolation matrix based on the engine system's structural model to analyze the fault diagnosis performance. The fault diagnosis performance of the gasoline engine system was enhanced by designing additional residuals without adding physical sensors [60].
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 2 Materials and Methods
### Data and Preprocessing
#### 2.3.2 Normalized LAI Growth Function and Daily LAI Calculation  
\(LAI(t)\) represents the growth function of the LAI with time, which is closely related to the stand type, stand age and stand density. \(LAI(t)\) of temperate forest is a single peak curve. The normalized LAI growth function \(LAI_{norm}(t)\) is also a single peak, indicating the growth degree or defoliation degree of leaves. The calculation formula is as follows:  
\[LAI_{norm}(t)=(LAI(t)-LAI_{min})/(LAI_{max}-LAI_{min}) \tag{4}\]  
Here, \(t\) indicates the number of days in the day sequence. \(LAI(t)\) represents the LAI value at time \(t\), i.e., the LAI growth function. \(LAI_{norm}(t)\) represents the normalized LAI growth function, which changes with stand age and stand type. \(LAI_{min}\) is the minimum value of LAI in a year. \(LAI_{max}\) represents the maximum value of LAI in a year, which occurs after complete leaf spread and changes little in a period of time, during which all LAI measurements can be used as \(LAI_{max}\).  
The object of this study is a cold temperate coniferous forest, and the \(LAI_{min}\) is approximately 0; then, Equation (4) is simplified to Equation (5), and \(LAI_{norm}(t)\) is expressed as the proportional fraction of \(LAI(t)\) and \(LAI_{max}\).  
\[LAI_{norm}(t)=LAI(t)/LAI_{max} \tag{5}\]  
\[LAI(t)=LAI_{norm}(t)\times LAI_{max} \tag{6}\]  
Due to the difference in stand density, the \(LAI(t)\) of every location is different. The \(LAI(t)\) obtained in a large area cannot represent the LAI of each internal sub area; that is, the LAI value extracted from the large pixel of the MODIS LAI data cannot represent the LAI value of its internal sub area. However, the \(LAI_{norm}(t)\) of the identical stand type or similar age are basically consistent, and the \(LAI_{norm}(t)\) obtained on a larger MODIS raster area with a single stand type or similar age can almost represent the normalized growth of the LAI of each small image within the large image. If the \(LAI_{norm}(t)\) and its \(LAI_{max}\) are obtained for the small image element, the LAI growth curve can be obtained according to Equation (6). The single image element of forest stand types and the normalized LAI growth function can be extracted from MODIS LAI products as the corresponding \(LAI_{norm}(t)\) of the small image element. The spatial resolution of the Sentinel-2 image is 10 m, which is a more suitable data source for extracting the \(LAI_{max}\) on the scale of small and medium areas. \(LAI_{max}\) distribution maps can be obtained according to the traditional LAI remote sensing inversion model and the Sentinel-2 image obtained at the complete vegetation spreading leaf period, and the 10 m resolution LAI distribution maps can reflect the spatial heterogeneity of the LAI in small and medium-sized regions.
# Pilot-Aided Frame Synchronization in Optical OFDM Systems
###### Abstract  
Efficient frame synchronization is essential for data recovery in communication systems. In this study, a single pilot sequence is used to achieve both frame synchronization and peak-to-average power ratio (PAPR) reduction. The two systems considered are direct-current biased optical orthogonal frequency division multiplexing (DCO-OFDM) and asymmetrically clipped O-OFDM (ACO-OFDM). The pilot symbol is allocated to odd indexed subcarriers only. Thus, the synchronization algorithm leverages the mirror symmetric property of the pilot symbol within a frame to detect the start of the pilot signal at the receiver. This scheme has low complexity and gives precise frame synchronization at signal-to-noise ratios as low as 4 dB in an indoor visible light communication (VLC) channel.  
optical OFDM; pilot symbol; frame synchronization; symbol timing estimation; correlation-based method; optical wireless communication +
Footnote †: journal: Applied Signal Processing
# On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach
## 5 A Case Study
### ANFIS in SDP Simulation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Description/Value** \\ \hline Fuzzy structure/FIS training data & Sugeno/genfis1 \\ Generation of FIS object & grid partition on the data \\ MF type (Input/Output) & Trim/linear \\ Number of variables (inputs/outputs) & 3/1 \\ Number of terms (Input1/Input2/Input3) & 3/9 \\ Optimization method & backpropagation \\ Maximum number of training epochs & 200 \\ Initial step size & 0.001 \\ AndMethod/OrMethod/ImpMethod/ & prod/probor/prod/sum/wtaver \\ AggMethod/DefuzzMethod & \\ Data for training/data for testing/data for & 70/15/15 \\ checking & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANFIS hyper-parameters. Figure 4: The proposed ANFIS network for the impact of HF on deviation from the predicted SDP task execution time. According to the above process, following optimization, the value of the HF in the SDP can vary and may fall into a different predetermined linguistic term interval. As an illustration, the possibility of deviation is moderate prior to optimization and increases after optimization. The resulting surface of the optimized HF in the SDP is presented in Figure 5.  
In this study, FIS used three input parameters with three fuzzy sets for each input to create a complete rule set of 27 rules for building the ANFIS model. It is important to note that increasing the partition size of the input space exponentially increases the number of influencing rules, greatly improving the speed of system learning and application. Some examples of the fuzzy rules are as follows:  
* IF (hf1 is None) AND (hf2 is None) AND (h3 is None) THEN (PD (Note: Possible deviation) is Very High);
* IF (hf1 is None) AND (hf2 is Good) AND (h3 is Moderate) THEN (PD* is High);
* IF (hf1 is Moderate) AND (hf2 is Excellent) AND (h3 is Good) THEN (PD* is Low);
* IF (hf1 is None) AND (hf2 is Low) AND (h3 is Good) THEN (PD* is High);
* IF (hf1 is None) AND (hf2 is Good) AND (h3 is Good) THEN (PD* is Moderate). The influence of HF in SDP prediction after optimization employing ANFIS is presented in Figure 6.  
Figure 5: The resulting surfaces of the optimized HF in SDP prediction based on different input variables: (a) motivation; (b) experience; and (c) availability.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 2 Materials and Methods
### Mathematical Model
#### 2.1.4 Chemical Kinetic Model  
In this study, kerosene, C\({}_{12}\)H\({}_{23}\), was employed as the fuel. Thus, the chemical kinetic mechanism of kerosene/air determines the chemical reaction rate. A single-step total package reaction cannot fully describe the reaction process, and an extremely complex reaction mechanism significantly increases the computational cost. Therefore, in this study, a six-species, four-step reaction mechanism was adopted [27]. Considering that the kerosene burning in the supersonic combustor does not easily achieve self-sustaining combustion--that is, it is easy to flameout--the ignition delay of kerosene was appropriately shortened in this paper. Specifically, the reaction rate parameters were selected as n = 1 in Table 1 of the reference. Considering that high temperature will lead to a dissociation effect, reversible reactions are adopted in all four steps, as follows:  
\[\text{C}_{12}\text{H}_{23}+6\text{O}_{2}<=>12\text{CO}+11.5\text{H}_{2}\] (R1)  
\[\text{C}_{12}\text{H}_{23}+12\text{H}_{2}\text{O}<=>12\text{CO}+23.5\text{H}_{2}\] (R2)  
\[\text{H}_{2}+0.5\text{O}_{2}<=>\text{H}_{2}\text{O}\] (R3)  
\[\text{CO}+\text{H}_{2}\text{O}<=>\text{CO}_{2}+\text{H}_{2}\] (R4)  
The adiabatic flame temperature and ignition delay were used as indicators to verify the chemical reaction. The comparative data were obtained from Figure 1 in reference [28], and the verification results were shown in Table 1. The two working conditions in Table 1 were derived from the initial temperature of the subsonic combustor under the two working conditions of the DCR, as shown in Table 2. It shown that the relative error of flame temperature under condition 1 and condition 2 is 2.1% and 1.7%, respectively. However, the ignition delay is nearly two orders of magnitude smaller than that in the comparative literature, indicating that the chemical reaction rate in this paper was higher than that in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Flame Temperature**} & \multicolumn{2}{c}{**Ignition Delay**} \\ \cline{3-6}  & **Initial Temperature, K** & **2185 Species, K** & **Six-Species, K** & **2185 Species, s** & **Six-Species, s** \\ \hline Condition 1 & 1600 & 2780 & 2721 & \(3\times 10^{5}\) & \(2.6\times 10^{7}\) \\ Condition 2 & 2200 & 2948 & 2898 & \(3.5\times 10^{6}\) & \(4.6\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of adiabatic flame temperature and ignition delay with literature.  
the comparative literature. The hydrogen/oxygen heater used in the comparison literature will generate H\({}_{2}\)O into the air, which will increase the rate of chemical reactions. Therefore, it is likely that the actual ignition delay difference between the comparison literature is not that large. In conclusion, the chemical reaction mechanism can be used for the numerical simulation of DCR and scramjet.
## 4 The Proposed Algorithm
### Decomposition into Sub-Problems  
Robust optimization is essentially a multi-objective problem that increases product performance at the expense of its robustness. Therefore, robust optimization generally does not have a unique solution, but a set of solutions called the Pareto optimal set [2]. It is a family of solutions that is optimal in the sense that no improvement can be achieved in any objective without degradation in others for a multi-objective problem. The Pareto-optimal solutions can be obtained by solving appropriately formulated single objective optimization problems on a one-at-a-time basis. At present, a number of multi-objective genetic algorithms have been suggested. The primary reason for this is their ability to find multiple Pareto-optimal solutions in parallel. From the viewpoint of mathematical optimization, genetic algorithms are a kind of suitable method for solving a general multi-objective optimization. However, the efficiency of a genetic algorithm is usually much lower than the gradient-based optimization algorithms, which has become the main technical bottleneck limiting its practical application [31; 32]. Although a priori information is not required when using genetic algorithms, most designers have some engineering experience in practice. Therefore, for the specific problem shown in Equation(8), the robustness objective of \(Bel(f(\mathbf{d},\mathbf{X},\mathbf{P})\leq v)\) is often handled as a reliability constraint [2; 33]. In this paper, the EBRO problem is transformed into a series of sub-problems under the given target belief measures:  
\[\left.\begin{array}{l}\min v\\ \text{s.t.}\ Bel(f(\mathbf{d},\mathbf{X},\mathbf{P})\leq v)\geq Bel_{j}^{T}\\ \mathbf{d}^{l}\leq\mathbf{d}^{u},\ \overline{\mathbf{X}}^{l}\leq\mathbf{X}\leq\overline{\mathbf{X}}^{ l}\end{array}\right\}j=1,2,\ldots,n_{T} \tag{9}\]where \(Bel_{j}^{T}\) represents the \(j\)-th target belief measure; and \(Bel(f\leq v)\geq Bel_{j}^{T}\) is the reliability constraint derived from the robust objective. In many cases, the designer may focus on the performance values under some given conditions based on the experience or quality standard. This condition is usually a certain probability of \(f\leq v\), namely \(Bel_{j}^{T}\).
# Biogas Production Prediction Based on Feature Selection and Ensemble Learning
## 2 Materials and Methods
### Biogas Prediction Based on Heterogeneous Ensemble Learning
#### 2.2.2 K-Fold Cross-Validation  
Generally, in model evaluation, the data is divided into a training set, a validation set, and a test set, and then the data set is put into the model for training, and the data from the test set is input into the model after training, and the results obtained will be used to judge the merits of the model. If the trained model gets a small error in the training set and a large error in the test set, it indicates that the model is overfitting. In stacking ensemble learning, the data used to train the primary and meta-learner may lead to a higher risk of overfitting if they are duplicated. The reason for overfitting is that the tuning process of model hyperparameters relies on the performance of the validation set on the model as feedback, and each tuning will leak more information into the model, and multiple cycles of this process will cause the model to be overfitted on the test set. In order to effectively avoid the impact of information leakage on the model and reduce the risk of overfitting the model, K-fold cross-validation is used. The training set is randomly divided into K subsets of the same size, and when training the model, each subset is selected in turn as the test set, while the other K-1 subsets are used as the training set. The number of training tests for each learner is K, and the result of the last K tests is used as the final return value.  
In K-fold cross-validation, the value of \(K\) is generally \(2\leq K\leq 10\), and the specific value is shown in Equation (2).  
\[\left\{\begin{array}{l}K\approx\ln N\\ N/K>3d\end{array}\right. \tag{2}\]  
where \(N\) is the total number of samples and \(d\) is the number of features.