esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### IoT Cloud-Based Framework for Real-Time Water Quality Monitoring  
[PERSON] et al. [18] proposed a real-time water quality system that is founded on the IoT. The system utilizes sensors of pH, temperature, turbidity, water level, and discharge rate. Data are collected by these sensors and transmitted to the microcontroller (MCU). The MCU determines whether the water is potable or not. They did not incorporate an ML model into their system, nor did they utilize cloud storage to store the data. This is the reason why users are unable to remotely determine the water's condition or parameter values.  
A real-time water quality monitoring system with an IoT-based approach was presented by [PERSON] et al. [19]. The data were collected using a variety of sensors. Subsequently, the data were compared to standard values and processed using Spark streaming analysis, Spark MLlib, deep learning neural network models, and the Belief Rule Base (BRB) system. They also implemented an automated SMS warning system for contaminated water. However, there was still no cloud database system in place to retain the data for subsequent analysis.  
A cost-effective cloud-based water monitoring system was proposed by [PERSON] et al. [20]. The data are collected using a variety of sensors and then transmitted to the cloud for prediction by ML models. Their prediction model was a decision tree model. Additionally, a web-based application and a mobile notification system were made available to inform consumers about the water quality. Additionally, Grafana was implemented to construct the water monitoring system's dashboard. However, the decision tree model accuracy was only 97%, which is very low compared to that of others [14]. Additionally, they did not specify how the cloud architecture would be maintained or the approach to data timing in their storage system.  
The water quality monitoring system introduced by [PERSON] et al. [7] is equipped with IoT sensors, a microcontroller, a cloud platform, and a decision tree classifier. The decision tree model's accuracy is a mere 84%, which is exceedingly low. They have not specified the method by which the cloud architecture would be managed in the event that it required expansion. They employ an SMS notification system; however, a real-time dashboard would be more beneficial, as it would enable users to identify the parameters demonstrating that the water quality is deteriorating.
## 2 Materials and Methods  
### Sample  
In Figure 1 two scanning electronic microscope (SEM) cross sections of the FeCrAlYr alloy/T91 steel coating-substrate interface are shown. Along the cross-cut in Figure 1a, a clear transition from the coating alloy to the bulk substrate can be distinguished at a depth of around \(19\pm 4\)\(\upmu\)m. The complementary SEM image of the focused ion beam (FIB) cut sample provides improved contrast between the phase domain orientations. This image infers a coating thickness of 30 \(\upmu\)m. The discrepancy between the two estimated thicknesses results from the poor coating-substrate interface contrast in the FIB/SEM image. As there exists a gradient in the element distribution, and therefore also in the microstructure along the depth direction, one can expect that the acoustic velocitiesof the coating layer differ from those of the bulk material, the differences being expectedly governed by the difference in density between the layers as a result of the larger aluminum content in the coating layer. The aluminum enrichment in the coating layer was confirmed by an energy-dispersive X-ray spectroscopy [32] measurement, as shown in Figure 1c, where brighter regions correspond to increased aluminum content. An optical photograph of the sample is depicted in Figure 1d. The chemical compositions of the two layers, listed in Table 1, were taken from [1].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **C** & **Si** & **Mn** & **Cr** & **Al** & **Fe** & **Y** \\ \hline
**T91 (wt. \%)** & 0.109 & 0.43 & 0.38 & 8.26 & & Rest & \\
**FeCrAlY+GESA (wt. \%)** & & & & 15.2 & 5 & Rest & \(<\)0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the most abundant elements in the T91 steel substrate and FeCrAlY coating as measured in [1].  
Figure 1: **(a)** SEM image of a polished cross-cut of an AlFeCr alloy/T91 steel sample showing the coating-substrate interface with a coating thickness of \(\sim\)19 \(\upmu\)m. (**b**) SEM picture of FIB cut cross section of the sample reveals a gradient in the phase domain sizes along the depth direction, extending to 30 \(\upmu\)m in depth. (**c**) Aluminum enrichment in the coating layer as measured by the XEDS technique. Bright regions correspond to a larger aluminum content. (**d**) Photograph of the sample, depicting the macroscopic surface roughness.
## 3 Methodology
### Non-Stationary Transformer with Deep Deterministic Policy Gradient
#### 3.3.5 Optimisation and Policy Learning within DDPG Framework  
The DDPG algorithm lies at the heart of our optimisation technique. It is a model-free, off-policy actor-critic algorithm using deep function approximators that can learn policies in high-dimensional, continuous action spaces. Our implementation encapsulates an actor network designed to map states to actions, and a critic network that evaluates the action given the state.  
The initialisation of these networks is performed with random weights, which are subsequently refined through training iterations. The actor network proposes an action given the current state, while the critic network appraises the proposed action by estimating the Q-value. The policy, parameterised by \(\theta\), generates actions predicated on the current state, aiming to maximise the cumulative reward:\[J(\mu_{\theta})=\int_{S}\rho_{H}(s)r(s,\mu_{\theta}(s))\,ds=\mathbb{E}_{s\sim\rho_{ \theta}}[r(s,\mu_{\theta}(s))] \tag{10}\]  
where \(\mu_{\theta}\) is the deterministic policy, \(Q^{\mu}\) is the action-value function according to policy \(\mu\), and \(\rho^{\mu}\) is the state distribution under policy \(\mu\). Considering the dynamic realm of portfolio management, we recalibrate this function to account for the sequential product of portfolio values:  
\[J_{T}(\mu_{\theta})=J_{0}\prod_{t=1}^{T}(1+r(s_{t},\mu_{\theta}(s_{t}))) \tag{11}\]  
where \(J_{0}\) represents the initial portfolio valuation.  
To ensure stability and promote convergence, we employ target networks for the actor and critic, which are slowly updated to track the learned networks. The update for the target networks is governed by a soft update parameter, \(\tau\), ensuring that the targets change gradually, which helps with the stability of learning:  
\[\theta_{\text{target}}\leftarrow\tau\theta+(1-\tau)\theta_{\text{target}} \tag{12}\]  
The policy is refined through gradient ascent on the expected cumulative reward, while the critic's parameters are adjusted based on the temporal difference error. To maintain exploration, a noise process \(\mathcal{N}\) is added to the actor's policy action:  
\[a_{t}=\mu_{\theta}(s_{t})+\mathcal{N}_{t} \tag{13}\]  
where \(a_{t}\) is the chosen action, \(\mu_{\theta}\) is the current policy, \(s_{t}\) is the current state, and \(\mathcal{N}_{t}\) is the noise term at time \(t\), which decays during training to allow the policy to exploit the learned values as it matures.  
This methodical learning process culminates in developing a policy that maximises expected returns, aligning with our objective of enhancing portfolio management strategies.
## 2 Related Work
In the ADI task stated in [17], to discriminate between four Arabic dialects and MSA, deep learning models showed encouraging results compared to the ML techniques that were mentioned earlier for the same task [18; 19]. For example, [PERSON] [21] used an adaptation of a CNN that included four layers: input, convolution, max-pooling and a fullyconnected softmax layer. He used randomly generated embeddings as features that kept updating during training. The model achieved an F1 score of 43.29%. [PERSON] and [PERSON] [22] employed a character-level CNN with seven layers: embedding, dropout, multiple parallel convolutions, max-pooling, fully connected, and softmax layers. The CNN using character embeddings as features reached an F1 score of 48.34%, outperforming the model in [21]. For the same ADI task mentioned earlier in [23] with data provided by [24], [PERSON] [25] employed a character-level CNN combined with dialect embedding vectors and a representation extracted from linguistic features. He experimented with three CNN architectures that differed in the input layer before the convolution layer. The first CNN used a one-hot character representation for the input layer. The second used an embedding layer before the convolution layer and the third used a GRU recurrent layer before the convolution layer. The three architectures scored accuracies of 57.11%, 56.97% and 57.59%, respectively. All of the studies mentioned above mainly involved the problem of distinguishing the Arabic dialects spoken in different countries and MSA. However, there has been an increasing level of interest in studying dialects within the same country in the past two years. Differentiating the dialects of different provinces of the same country is an even more complex task, as capturing linguistic differences becomes more intricate and convoluted. In an attempt to explore the problem of ADI on a province level, [PERSON] et al. [26] recently shared a Twitter-based dataset covering a total of 100 provinces from 21 Arab countries. Using the same data as in [26], [PERSON] et al. [27] built an ensemble of five models to classify province-level dialects: complement Naive Bayes (CNB), decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). In addition, they used TF-IDF with unigram features to train the system. The SVM outperformed the other models on the training data, with an F1 score of 4.73. The final ensemble classifier reached an accuracy of 4.8 and an F score of 4.55, outperforming other transformer techniques used for the same ADI task [26; 28]. In summary, solving the problem of ADI on province-level dialects is still in the early stages, and more work needs to be performed. As we saw on ADI using country levels, models utilising character n-gram features showed higher accuracies than those using word n-gram features. Therefore, the former was chosen in this study to be investigated further for solving the problem of ADI on province-level dialects.
## 3 Material and Methods
### Layers of the Architecture
#### 3.3.1 Convolution Layer  
The convolution layer in the architecture contains multiple local filters (kernel) which serve as convolution kernels to generate feature maps in the convolution layers. The convolution layer extracts feature map of the image or feature maps generated by previous layers with a kernel size of 2 \(\times\) 2. An example of convolution layer is further mentioned in Algorithm 1 where kernel size is written by Ki \(\times\) Kj (Line 4). It produces an output value for each Ri \(\times\) Rj (Line 4) filter of the input image or feature map by calculating the product of each kernel weight (Line 11) and the value at the preceding location of the matrix in the input image or a feature map as shown in Figure 6.  
The first convolution layer in the architecture uses 32 filters to form feature maps of the input of X-ray image as shown in Figure 5. The second convolution layer further uses 64 filters to form feature maps from the feature maps generated by the first convolution layer. Adding two convolution layers, one after another increases the nonlinearity and reduces the number of parameters to fight overfitting. The third convolution layer occurs after the first Max-pooling layer which uses 64 filters on the feature maps generated by the Max-pooling layer. The patterns become more complicated as the layers are executed, resulting in greater combinations of patterns to catch. To capture as many variations as possible, the filter size is increased in subsequent layers. The fourth and last convolution  
Figure 5: Complete layered architecture of proposed CNN model.  
Figure 6: Example of a convolution layer which takes images/feature maps of previous layers as input and generates its own feature maps.  
layer containing 128 filters was made after the second max-pooling layer. All convolution layers were about 2 \(\times\) 2 kernel size.
# Chaotic Evolutionary Programming for an Engineering Optimization Problem
## 1 Introduction
Since different chaotic maps lead to diverse behavior, the chaos maps are potential alternatives to pseudo random sequences. [PERSON] et al. [20] provided an in-depth analysis of different chaos maps as an alternative to conventional pseudo random numbers. The investigation of chaotic bat algorithm using generalized benchmark test functions leads to a conclusion that the use of chaos is advantageous. Similarly, chaotic practical swarm optimization [21], chaotic differential evolution [22], and crisscross differential evolution [23] have been used to solve engineering problems. [PERSON] et al. [24] investigated the performance of chaos based multi-objective evolutionary algorithms and concluded that chaotic maps improved the performance of evolutionary algorithms to solve a problem. An advancement in the field of chaotic numbers involves the application of adaptive symmetry to create chaos [25] and digital chaotic systems [26]. The use of chaos map based random numbers in EP may be advantageous to ensure that the algorithm generates diverse solutions and potentially explores the multimodal objective landscape. An algorithm with these properties can result in better exploration and a better convergence rate [27]. On the basis of the above facts and arguments, it is necessary to empirically investigate the performance of the EP by incorporating a chaotic sequence. Therefore, a hybrid algorithm, chaotic evolutionary programming and pattern search (CEPPS), has been proposed by implementing the following steps:  
1. Introduction of the chaotic sequence based population initialization process. 2. A chaotic mutation operator is proposed and employed. 3. A chaos guided tournament selection operator is considered to select better candidates. 4. The [PERSON]'s pattern search is applied to enhance the exploitation of the proposed algorithm. The standard benchmark test function and a practical problem of ELD for a multi-fuel generator problem are used to analyze the performance of CEPPS. The results are compared with the results of available algorithms from the past. The paper is organized into seven parts. Section 2 presents the formation of the multi-fuel ELD problem. Section 3 presents the mathematical foundation of evolutionary programming algorithms. Section 4 discusses the mathematical foundation of the chaotic EP algorithm. Section 5 presents the details of various generalized benchmark functions and the multi-fuel economic dispatch problem considered in the study. Section 6 presents the numerical results by CEPPS and comparisons with recently published work. Finally, Section 7 concludes the paper.
## 4 Performance Evaluation
### Impact of Compression Ratio  
Figure 8 demonstrates the impact of the proposed multiuser access control algorithm on the user access probability and the delay violation probability of the accessed users as the video compression ratio changes. When the video compression efficiency decreases (e.g., \(C_{\text{r}}\) increases from 1/600 to 1/450), the total number of accessed users decreases. By comparing the performance gap of user access probability for \(C_{\text{r}}\) equal to 1/750 and 1/600, as well as 1/600 and 1/450, we observe that when communication, computation, and storage are jointly designed, there may exist a threshold for video compression efficiency. If the compression efficiency falls below this threshold, the system performance deteriorates significantly. This highlights the importance of multi-dimensional joint design for multiuser 360\({}^{\circ}\) VR video service systems. The delay analysis model proposed in Section 3.1 offers a way to numerically explore the involved relationship and thus fine-tune the system performance.  
By comparing Figures 5-8, we find that increasing the communication and computing resources of the system is always the most direct and effective way to enhance the number of users served by the 360\({}^{\circ}\) VR system. In the tested scenarios, when the MEC server's computing capacity increases from 0.8 Tbps to 3.2 Tbps, the user access probability increases on average by 92.53%. When the BS's bandwidth increases from 50 MHz to 150 MHz, the user access probability increases on average by 85.49%. However, the proposed multiuser access control algorithm effectively maintains the delay violation probability of the accessed users below the preset threshold under all simulation parameter settings. To further fine-tune system performance, we believe that the design of an efficient multiuser resource allocation algorithm is necessary. On the other hand, for a real system, we may need to  
Figure 7: The user access probability and the delay violation probability of the accessed users under different transmission bandwidths from the MEC server-connected BS to the users.  
admit all those requesting users via appropriately setting network communication and computing resources. For example, in the application of VR-based education, as considered in [2], one may need to admit all students belonging to the same course, because are expected to watch the teaching video interactively and simultaneously. The analysis and design methods proposed in this paper provide an approach to achieving this goal.
# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 2 Related Work
### Morphological Analyzer Developed Using Neural Networks  
[PERSON] et al. proposed TransMorpher [31], a two-level analyzer for the Turkish language that consists of a rule-based phonological normalization module and a sequenceto-sequence character translation module developed using the Transformer architecture. The model was extended to other languages, but the results did not match those of state-of-the-art models. [PERSON] et al. proposed a deep learning approach for Malayalam morphological analysis [18] at the character level. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) were used to learn the rules for identifying the morphemes automatically and segmenting them from the original word. Further, each of the morphemes was analyzed to identify the grammatical structure of the word. They made use of Romanized scripts and generated inflections based on paradigms prior to analysis, where the development of the paradigms was the challenging task. [PERSON] et al. proposed _Transformer-based Context Aware Morphological Analyzer_ for Telugu [32]. They experimented with available Telugu Transformer models and existing multi-lingual Transformer models, out of which Bert-Te outperformed other multilingual models.  
Most of the neural methods employed for the above languages were based on a prior dataset that used rules/paradigms and trained the neural architectures to generate the morphological analysis. Similar approaches can be extended to low and extremely low-resource languages.  
Among the state-of-the-art models on morphological analyzers in Kannada, a lack of standardization in transliteration has been observed. Rule-based methods have not been evaluated on benchmark datasets, and open-source platforms were not used. Despite several efforts in the morphological analysis of Kannada, the evaluations tend to be ad hoc without adherence to standard metrics or validation against benchmark datasets. The following points serve as motivation to carry out the task of creating a morphological analyzer in the Kannada language:  
1. It is observed from the literature that there is a lack of a morphological analysis dataset based on a standard dictionary.
2. The existing rule-based methods do not tend to generate analysis for words outside the dictionary, so the morphological analysis of for new words is not generated at all.
3. The current dataset creation strategies struggle to produce valid input datasets for deep learning models and hence demand additional efforts.
## 3 Results and Discussion
### Effect of Blockage Ratio on Heat-Transfer Behaviors  
Figure 6 depicts the behavior of convective heat transfer in the value of Nusselt number (_Nu_) plotted against Reynolds number (_Re_) at the value of \(R_{b}\) = 0.28, 0.35, and 0.42 compared with LS and plain tube. Figure 6 shows that \(Nu\) increases as \(Re\) increases and confirms the results of our previous studies [16; 23; 24]. An increase in mean velocity, which increases the \(Re\), leads to intensification of the  
Figure 4: Validation of convective heat-transfer behavior of the plain tube.  
Figure 5: Validation of friction factor behavior of the plain tube.  
turbulence, thereby improving temperature gradients. This phenomenon significantly improved the heat-transfer rate [25]. The Nusselt numbers of the tube with DWT and LS insertions were higher than those of the plain tube. These insert devices placed in the path of the flow reduced the hydraulic diameter of the flow passage. Partitioning of the flow due to blockage raises the viscous level while diminishing the free-flow area and improves flow velocity [26]. Thus, better thermal contact and fluid mixing of the surface and the fluid at the core lead to a higher heat-transfer coefficient. Based on the data trend, DWTs show a higher Nu. The main mechanism that enhances heat transfer of DWTs is related to the presence of vortices. The wings of DWTs help create longitudinal vortices that lead to better mixing at a faster rate between the fluid at the core and the surface region and result in higher temperature gradients near the tube wall [27]. The alternating direction of DWTs also generated counter-rotating vortices, which induced additional convective thermal transport [5]. At the same time, these vortices produced a streamwise velocity profile, which led to decay of the flow. Together these factors contributed to the system's high heat-transfer coefficient. As is shown in Figure 6, a higher \(R_{b}\) produces a higher \(Nu\). The higher \(R_{b}\) generated strong flow circulation and separation for higher turbulence intensity leading to higher heat-transfer performance [28]. The value of \(Nu\) in the inner tube with the addition of DWTs with \(R_{b}\) = 0.28, 0.35, 0.42 and LS are increased in the ranges of 214.4-268.4%, 271.3-318.2%, 341.1-364.3%, and 10.8-17.84% compared with the \(Nu\) of plain tube, respectively.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 3 Proposed FVT-FCM Algorithm
### Hierarchical Fuzzy Membership Model  
Given a seed point set \(\mathbf{C}=\{\mathbf{C}_{j}\), \(j=1,\) \(\ldots\), \(m\}\), \(\mathbf{C}_{j}=(a_{j}\), \(b_{j})\) is the two-dimensional coordinate of seed point \(j\). The image domain is divided into \(m\) fuzzy polygons according to the fuzzy membership of pixels to polygons. The fuzzy membership is expressed as \(\mathbf{\mu}=\{\mu_{ij},i=1,\ldots,n,j=1,\ldots,m\}\). \(\mu_{ij}\) is also called pixel level fuzzy membership, and it needs to be satisfied in \(\sum\limits_{j=1}^{m}\mu_{ij}=1,i=1,\ldots,n\). To distinguish the label of fuzzy polygons they belong to, the polygon level fuzzy membership about fuzzy Voronoi polygon \(j\) and cluster \(l\) are also established. It is expressed as \(\mathbf{W}=\{w_{jl},j=1,\) \(\ldots,m\), \(l=1,\)\(\ldots,k\}\), \(\sum\limits_{l=1}^{k}w_{jl}=1,j=1,\ldots,m\). Then, these two fuzzy membership degrees construct a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to fuzzy Voronoi polygons and fuzzy polygons fuzzily belong to clusters. The model visualization is shown in Figure 1. It shows that all pixels are related to a fuzzy polygon. Thus, this partitioning technique is also called fully fuzzy Voronoi Tessellation, where \(m\) and \(k\) are constants given empirically according to the complexity of the image.  
Figure 1: Hierarchical fuzzy membership model.