esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitations and Future Work  
Although ECGnet exhibits encouraging outcomes in the denoising of ECG signals, it is imperative to recognize several inherent limitations. The present investigation is predicated on the utilization of synthetic noise, which is produced through the amalgamation of pristine ECG signals and resampled EMG data. While this methodology facilitates a controlled and reproducible assessment, it fails to comprehensively account for the variability and intricacies inherent in real-world noise conditions, including motion artifacts and baseline wander encountered in ambulatory environments. Subsequent research endeavors will integrate authentic noisy ECG datasets, such as the MIT-BIH Noise Stress Test Database, and perform cross-dataset validation to enhance the evaluation of clinical generalization.  
Secondly, while ECGnet employs an encoder-only Transformer architecture and retains a comparatively low parameter count, the computational requirements associated with self-attention mechanisms could still present significant challenges for ultra-low-power edge devices. In this context, subsequent investigations will delve into hybrid architectures that integrate lightweight convolutional front-ends with attention-based modules. Additionally, the research will encompass compression methodologies, includingquantization, pruning, and knowledge distillation, aimed at minimizing model size and inference latency.  
In conclusion, although this research primarily addresses ECG signals, the architecture of the model is designed to be modular and applicable across various contexts. Initial studies indicate that the existing framework may be modified for the purpose of denoising additional biomedical signals, including EEG and PPG, both of which exhibit similar temporal structures and are prone to noise interference. Subsequent investigations will focus on customizing ECGnet for these modalities and assessing its efficacy on pertinent datasets to examine its potential utility in more extensive physiological monitoring scenarios.
## 2 Literature Review
### CAP Detection and Classification
Overall, the summarized metrics shown in Table 1 reveal that the model performs better on the balanced dataset than on the unbalanced dataset. This is directly caused by the number of B-phase examples (87.4% of the unbalanced dataset), which increased the difficulty of identifying the A-phase examples. A novel approach was explored by [PERSON] et al. (2021) to automate A-phase detection (A-phase vs. Non-A-phase) through local and multi-trace analysis [PERSON] et al. (2021). They found that channel F4-C4 performed better than all the other analyzed channels, achieving F1 scores from 61.38% to 63.88% via local analysis on this channel. In comparison, their multi-trace approach resulted in F1 scores ranging from 64.34% to 66.78% on the different patient subsets. A total of 41 recordings from the CAP Sleep Database were analyzed in this research. Further details of the data and the methodology are shown in Table 1.  
Finally, the GTransU-CAP model was designed by [PERSON] et al. (2022) [PERSON] et al. (2022), and this was trained on the same data subset from [PERSON] et al. (2022). This model represents an automatic labeling tool for CAPs in sleep EEG, using a gated transformer-based U-Net framework with a curriculum-learning strategy. In A-phase detection, F1 scores of 67.78% in healthy patients and 72.16% in patients with nocturnal front lobe epilepsy (NFLE) were achieved. For the A-phase subtype classification, the non-weighted average F1 scores are 59.45% and 59.55% for healthy and epileptic patients, respectively. A summary of the previous literature review is presented in Table 1, where the different methodologies and classification approaches can be identified. Additionally, a list of the metrics reported for each approach is included in the last column. The diversity of the used data subsets, classification approaches, and performance measurements reported by the researchers hinders a direct comparison between them. As described in Table 1, a diverse range of A-phase classification methodologies exist, and a key point stood out: only three investigations included the identification of A-phase subtypes (A1, A2, and A3) under two different classification strategies (one including only A-phase subtypes and the second including also B-phases). Thus, there is still a window of opportunity to solve the A-phase classification problem. The literature review's extended results are presented in Table A1, which reveals that classifying the naturally unbalanced CAP data is not trivial. The number of B-phases compared to A-phases hinders A-phase detection [PERSON] et al. (2021). Moreover, the nature of the A2 subtype (basically, a mixture of A1 and A3) hampers its correct classification [PERSON] et al. (2022); [PERSON] et al. (2022). Finally, since none of the previous works reviewed a symbolic or distributional approach to sleep micro-structure analysis, there is a chance to explore these approaches and implement new tools based on them.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 3 Deep-Learning Scheme Based on the MIMO-NOMA System
### Feasibility Analysis of DL in a MIMO-NOMA System  
First, it should be proved that it is realizable to use DL to detect the MIMO-NOMA signal instead of an SIC receiver. We assume that the number of transmitting and receiving antennas is \(M\) and \(N\). The number of UEs is \(K\). The MIMO-NOMA transmission signal in (1) can be expressed in matrix form as (12):  
\[S=\left(S_{1},S_{2},\cdots,S_{M}\right). \tag{12}\]  
\(S_{m}(m\in[1,M])\) is the \(m\)-th transmission antenna and it can be expressed as:  
\[S_{m}=\sum_{k=1}^{K}\sqrt{P_{k}}S_{m}^{k}. \tag{13}\]  
Figure 6: Construction of the recurrent neural network (RNN). For M-ary phase shift keying (MPSK) modulation, \(S_{m}^{k}\in X_{i}\) is the kth UE transmission signal of mth antenna, and \(X_{i}\) is the set of \(M\) transmission signals:  
\[X_{i}=A\exp\left(j\omega t+\varphi_{i}\right)\quad(i=1,2,\ldots,M). \tag{14}\]  
The MIMO-NOMA signal uses the power dimension to improve the channel capacity, so the channel matrix can be doted by a three order tensor \(H\in\mathbb{R}^{N\times M\times K}\). \(h_{nm}^{k}\) (\(k\in[1,K],m\in[1,M]\) and \(n\in[1,N]\)) is the channel gain of the \(k\)th UE from the \(m\)th transmitting antenna to the \(n\)-th receiving antenna. (15)  
Transforming the tensor \(\mathbf{H}\) into a matrix \(\mathbf{H}_{(n)}\) is called the node-\(n\) matricization [19]. Here, a mode-3 matricization \(\mathbf{H}_{(3)}\) can be expressed as:  
\[\mathbf{H}_{(3)}= \begin{bmatrix}h_{11}^{1}&\cdots&h_{1N}^{1}&\cdots&h_{11}^{K}& \cdots&h_{1N}^{K}\\ h_{21}^{1}&\cdots&h_{2N}^{1}&\cdots&h_{21}^{K}&\cdots&h_{2N}^{K}\\ \vdots&\ddots&\vdots&\vdots&\vdots&\ddots&\vdots\\ h_{M1}^{1}&\cdots&h_{MN}^{1}&\cdots&h_{M1}^{K}&\cdots&h_{MN}^{K}\end{bmatrix} . \tag{16}\]  
The mode-3 matricization of the received signal can be expressed as:  
\[\mathbf{Y}_{(3)}=\mathbf{S}\mathbf{H}_{(3)}+\mathbf{N}_{(3)}. \tag{17}\]  
The channel gain and receiver signal of the \(k\)th UE are denoted by \(\mathbf{H}^{k}\) and \(\mathbf{Y}^{k}\), respectively:  
\[\mathbf{H}^{k}= \begin{bmatrix}h_{11}^{k}&h_{12}^{k}&\cdots&h_{1N}^{k}\\ h_{21}^{1}&h_{22}^{k}&\cdots&h_{2N}^{1}\\ \vdots&\vdots&\ddots&\vdots\\ h_{M1}^{k}&h_{M2}^{k}&\cdots&h_{MN}^{k}\end{bmatrix} , \tag{18}\]  
\[\mathbf{Y}^{k}= \begin{pmatrix}y_{1}^{k}&y_{2}^{k}&\cdots&y_{M}^{k}\end{pmatrix} . \tag{19}\]  
The SIC detection needs a continuous process of decoding, reconstructing, and signal canceling. Assume that the power allocated to the UEs decreases gradually \((P_{1}>P_{2}>\cdots>P_{K})\). \(l_{q}^{k}\) is the estimation output of the \(q\)th UE signal at the \(k\)-th UE receiver. The channel state information (CSI) is perfectly known.
## 4 Results
### Detection of Eruptive Deposits (SWIR, DInSAR, SAR)  
Volcanic eruptions can generate various types of eruptive deposits, including lava flows, pyroclastic flows, mud flows, pyroclastic deposits, ash fall, etc. The best-suited parameter to detect these from  
Figure 5: Example of surface deformation detection related to magma intrusions at Piton de la Fournaise (Reunion Island). (**a**) Wrapped interferograms and (**b**) deformation maps in radar line-of-sight (LOS), obtained from interferogram unwrapping (see Figure 4). (**c**) Deformation score DEF computed from the deformation map. (**d**) Number of hot pixels detected in the S2 SWIR image (orange curve, computed by MOUNTS), and Volcanic Radiative Power (VRP) recovered from MODIS data (black markers, computed by MIROVA). The eruptive episodes are numbered from 1 to 5, and highlighted by gray areas based on the eruption timing provided by the OVPF (Observatoire Volcanologique du Piton de la Fournaise): 0.7, 34.6, 0.8, 47, and 19.9 days respectively, publically available at [[http://www.ippg.fr/fr/ovpf/activite-recente-pion-de-fournaise](http://www.ippg.fr/fr/ovpf/activite-recente-pion-de-fournaise)]([http://www.ippg.fr/fr/ovpf/activite-recente-pion-de-fournaise](http://www.ippg.fr/fr/ovpf/activite-recente-pion-de-fournaise)). Eruptive episode 5 is shown in detail in Figure 9, in which SO\({}_{2}\) data is also displayed.  
space will depend on both the volcanic product type and the surface on which it is emplaced (i.e., arid surface, vegetated surface). We here describe two extreme eruptive case scenarios: the Erta Ale eruption (Ethiopia, ongoing since January 2017), characterized by an effusive activity where lava flows are emplaced in a desert environment, and the Fuego eruption (Guatemala, June 2018), characterized by a violent explosive activity which generated pyroclastic flows (i.e., avalanche of hot blocks and ash which flowed down the surrounding valleys in a densely vegetated environment).
## 2 Literature Review
### Reassigned and Synchrosqueezed Wavelet Transform
[45] proposed a method named second order multi-synchrosqueezing transform, which improved the energy concentration of the traditional synchrosqueezing transform, thus revealing more critical information in rub-impact detection. Due to better localization effect, synchrosqueezed wavelet transform, being a special kind of reassigned wavelet transform, provides a promising base for a method that can enrich the signals locally, allowing easier quantification of eventual signal trend to be highlighted.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 4 Results  
### Classification Performance  
#### 4.1.1 Experiment 1 (Individual Features)  
Figure 2 shows the classification performance of models trained with individual features. The values reported here are the average of the five-fold cross-validation. Each bar chart within the figure illustrates the accuracy, precision, recall, and F1 score metrics achieved by specific algorithms, with feature types denoted using different colors.  
Closer inspection of the accuracy scores reveals that algorithms trained with TF-IDF, LIWC, and NRC features demonstrate comparable accuracy. Particularly, LR, RF, and SVM achieved accuracy scores between 75% and 80%, and NB achieved approximately 60% accuracy across all feature types. Notably, in the LR and RF models, training with LIWC features resulted in higher accuracy, with RF obtaining 82.80%.  
The distinctions in performance among the TF-IDF-, LIWC-, and NRC-trained models became more evident upon further analysis of the precision and recall metrics. LR, RF, and SVM trained with TF-IDF exhibited higher precision compared to the models utilizing other features. In contrast, TF-IDF-trained NB resulted in the lowest precision at 33.04%.  
Training with LIWC features yielded higher recall scores across all algorithms compared to other feature types. However, TF-IDF and NRC showed mixed results, with TF-IDF-trained RF recording the lowest recall of 17.07%, and SVM trained on NRC obtaining 21.95%. RF trained with TF-IDF achieved the highest precision (86.99%) while recording the lowest recall score. Conversely, NB trained with LIWC achieved the highest recall (83.90%) but with lower precision at 38.61%. These two cases demonstrate the precision-recall trade-off.  
This trade-off is further highlighted when analyzing the overall trends. Algorithms with higher precision (LR, RF, and SVM) tend to exhibit lower recall, suggesting a higher number of false-negative predictions. In contrast, the NB algorithm, which scored the lowest precision with all types of features, had the highest recall.  
Considering this trade-off, relying solely on precision or recall to determine the best-performing model might be challenging. Thus, assessing the F1 score, a harmonic mean of precision and recall, provides a more comprehensive overview. Comparing F1 scores across models trained on various features, it becomes more apparent that models using LIWC features generally outperform those using TF-IDF or NRC. Specifically, RF trained with TF-IDF and SVM trained with NRC demonstrated the lowest F1 scores (28.12% and 31.17%, respectively), whereas LR trained with LIWC achieved the highest F1 score at 59.17%.
# A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation
## 4 Methods for Generation of Tunable OAM  
The three kinds of tunable OAM modes can be considered as not only the superposition of classical OAM modes physically, but also the combination of x and y polarizations with spatially-variant amplitude and phase mathematically. Thus, the modes can be realized by some free-spatially optical elements that change the polarization of each point on the cross section of mode. Equations (17)-(19) show purely mathematical expressions in the form of Jones vectors, where the first and second elements of the vectors represent components of the field along the horizontal (x) and vertical (y) axes. The A(\(\theta\)), B(\(\theta\)), \(\varphi_{\rm x}(\theta)\) and \(\varphi_{\rm y}(\theta)\) represent the spatially-variant amplitude and phase factors, respectively.  
\[\text{Orbital PS}:\left[\begin{array}{c}\text{E}_{\rm x}(a_{1}\text{e}^{-i \theta}+b_{1}\text{e}^{i\varphi_{1}}\text{e}^{+i\theta})\\ \text{E}_{\rm y}(a_{1}\text{e}^{-i\theta}+b_{1}\text{e}^{i\varphi_{1}}\text{e} ^{+i\theta})\end{array}\right]=\left[\begin{array}{c}\text{E}_{\rm x}( \text{A}(\theta)\text{e}^{i\varphi_{\rm x}(\theta)})\\ \text{E}_{\rm y}(\text{B}(\theta)\text{e}^{i\varphi_{\rm y}(\theta)})\end{array} \right] \tag{17}\]  
Figure 4: Intensity profiles, polarization states and phase distributions of superimposed modes when \(\ell=1\) and \(n=2\). The north pole N\({}_{l}^{\rm R}\) and south pole S\({}_{n}^{\rm L}\) represent orthogonal circularly-polarized modes with topological charges of \(l\) and \(n\); The points H\({}_{l,n}\) and V\({}_{l,n}\) represent the two points on the equator; The A\({}_{l,n}\) and B\({}_{l,n}\) points represent the two modes with 0.5 and 1.5 average OAM values, respectively.  
Figure 5: Sketch of the progressive relationship from the orbital Poincaré sphere (PS) to higher-order PS, then to hybrid-order PS.  
\[\text{Higher}-\text{order}\;\text{PS}:\left[\begin{array}{c}(a_{2} \text{e}^{-i\theta}+b_{2}\text{e}^{i\varphi_{2}}\text{e}^{+i\theta})\\ i(a_{2}\text{e}^{-i\theta}-b_{2}\text{e}^{i\varphi_{2}}\text{e}^{+i\theta}) \end{array}\right]=\left[\begin{array}{c}\text{A}(\theta)\text{e}^{i\varphi _{\text{x}}(\theta)}\\ \text{B}(\theta)\text{e}^{i\varphi_{\text{y}}(\theta)}\end{array}\right] \tag{18}\] \[\text{Hybrid}-\text{order}\;\text{PS}:\;\text{e}^{i\frac{(l+n)}{2} \theta}\left[\begin{array}{c}(a_{3}\text{e}^{i\frac{(l-n)}{2}}\theta+b_{3} \text{e}^{i\varphi_{3}}\text{e}^{-i\frac{(l-n)}{2}}\theta)\\ i(a_{3}\text{e}^{i\frac{(l-n)}{2}}\theta-b_{3}\text{e}^{i\varphi_{3}}\text{e}^{- i\frac{(l-n)}{2}}\theta)\end{array}\right]=\text{e}^{i\frac{(l+n)}{2}\theta} \left[\begin{array}{c}\text{A}(\theta)\text{e}^{i\varphi_{\text{x}}(\theta) }\\ \text{B}(\theta)\text{e}^{i\varphi_{\text{y}}(\theta)}\end{array}\right] \tag{19}\]
## 2 Wettability Measurement Methods
### Methods Based on Contact Angle Measurement
#### 2.2.3 Contact Angle Measurement Using SEM  
The idea of bombarding the surface of samples with beams of high-energy electrons appeared in the 1930s. At this stage of technology development, scanning electron microscopy (SEM) is used to analyze the crystal structure, chemical composition, and surface topography of samples under various conditions [21]. The use of SEM in combination with X-ray mapping for different rock types helps to understand the wetting heterogeneity at the micro level [22; 23]. Figure 4 shows the surface images of carbonate rock before (Figure 6a) and after (Figure 6b) soaking it under palmatine acid. Correspondingly, the measured contact angles before and after soaking are shown in Figure 6c and 6d, respectively. With SEM, it is possible to obtain high-quality images of the sample surface, which requires a corresponding computational power. This implies the costliness of this method and the lack of information about the wettability within the rock matrix.
## 3 Results and Discussion
### Environmental Impact Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S. No** & **Ref** & **Location, Country** & **CSP Technology** & **Title of Literature** & **Simulation** & **TES,** & **LCOE** \\ \hline \multirow{2}{*}{14} & \multirow{2}{*}{[73]} & \multirow{2}{*}{Banglades} & \multirow{2}{*}{PTC} & Performance optimization of & \multirow{2}{*}{SAM} & \multirow{2}{*}{-} & \multirow{2}{*}{9.86 USD cents/kWh} \\  & & & & & power plants-a case study in & & \\  & & & & & Bangladesh & & \\ \hline \multirow{2}{*}{15} & \multirow{2}{*}{[74]} & \multirow{2}{*}{Lamaca, Cyprus} & \multirow{2}{*}{PTC} & Solar thermoreltic power generation in Cyprus Selection of the best system} & \multirow{2}{*}{SAM} & \multirow{2}{*}{4} & \multirow{2}{*}{18.14-19.26 USD cents/kWh} \\  & & & & & & & \\ \hline \multirow{2}{*}{16} & \multirow{2}{*}{[75]} & \multirow{2}{*}{Sevilla, Spain} & \multirow{2}{*}{STTP} & Impacts of solar multiple on the & \multirow{2}{*}{SAM} & \multirow{2}{*}{3} & \multirow{2}{*}{21.77 USD cents/kWh} \\  & & & & performance of direct steam & & & \\  & & & generation solar power tower plant & & & & \\  & & & with integrated thermal storage & & & & \\ \hline \multirow{2}{*}{17} & \multirow{2}{*}{[76]} & \multirow{2}{*}{Neom, Saudi Arabia} & \multirow{2}{*}{STTP} & Concentrated Solar Power Solar & \multirow{2}{*}{SAM} & \multirow{2}{*}{16} & \multirow{2}{*}{7.84 USD cents/kWh} \\  & & & Tower with Oversized Solar Field & & & & \\  & & & and Median Staff Thermal Energy & & & & \\  & & & Storage Working at an Annual & & & \\  & & & Average Capacity Factor of 95\% in & & & & \\  & & & NEOM City & & & \\ \hline \multirow{2}{*}{18} & \multirow{2}{*}{Bhopal, Jhansi, Jhansi, Jhansi, SJT} & \multirow{2}{*}{STTP} & \multirow{2}{*}{Current Study} & \multirow{2}{*}{SAM} & \multirow{2}{*}{12} & Bhopal, 13.22 USD cents/kWh, Jhansi, 13.62 USD cents/kWh, Jhansi, 16.40 USD cents/kWh, SJT, SJT, SJT, SJT, and Erode, 14.21 USD cents/kWh} \\  & & & & & & & \\  &
# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization
### Model Architectures
#### 3.2.3 Ggfcu  
The computational flow of GFGRU units is defined by the following equations, simulating SBL's iterative optimization through gating mechanisms:  
Update Gate and Reset Gate:  
\[z_{t}^{l}=\sigma\left(W_{x}^{l}h_{t}^{l-1}+U_{x}^{l}h_{t-1}^{l}\right) \tag{6}\]  
\[r_{t}^{l}=\sigma\left(W_{r}^{l}h_{t}^{l-1}+U_{r}^{l}h_{t-1}^{l}\right) \tag{7}\]  
\(z_{t}^{l},r_{t}^{l}\in[0,1]\): Control the retention ratio of historical states and the intensity of information resetting, respectively.  
Global Reset Gate:  
\[g^{i\to j}=\sigma\Big{(}W_{g}^{i\to j}h_{t}^{l-1}+U_{g}^{i\to j}H_{t-1}\Big{)} \tag{8}\]  
\(\mathrm{H}_{t-1}=\left[\mathbf{h}_{t-1}^{1},\mathbf{h}_{t-1}^{2}\right]\): The concatenation of historical states from all stacked layers.  
Candidate State and Final State Update:  
\[\widehat{\mathbf{h}}_{t}^{l}=tanh\left(\begin{matrix}\mathbf{W}^{l-1}\mathbf{h}_{t}^{l-1}+ \mathbf{r}_{t}^{l}\odot\\ \sum\limits_{i=1}^{N_{l}}g^{i\to j}\mathbf{U}^{i\to j}\mathbf{h}_{t-1}^{i}\end{matrix}\right) \tag{9}\]  
\[\mathbf{h}_{t}^{l}=\left(1-\mathbf{z}_{t}^{l}\right)\mathbf{h}_{t-1}^{l}+\mathbf{z}_{t}^{l} \widehat{\mathbf{h}}_{t}^{l} \tag{10}\]  
Element-wise multiplication achieves selective information fusion. The hidden states are concatenated through horizontal unrolling steps:  
\(\mathbf{q}^{all}=\left[\mathbf{q}_{1},\dots,\mathbf{q}_{T}\right]\), generating the final ambiguity surface:  
\[P_{\text{GFGRU}}(\theta_{m})=softmax\Big{(}\mathbf{W}_{pred}\mathbf{q}^{all}+\mathbf{b}_{ pred}\Big{)} \tag{11}\]  
GFGRU comprises multiple stacked GRU layers [14], with identical input features at each timestep to simulate SBL's iterative optimization. By regulating hidden state flow via gating mechanisms and global reset gates for inter-layer interactions, GFGRU effectively suppresses sidelobes and enhances localization accuracy (see Figure 7). As demonstrated in the literature, GFGRU takes frequency-domain signals obtained from vertical array acoustic pressure measurements via short-time Fourier transform (STFT) as input. The localization problem is formulated as a sparse estimation task, where input signals are preprocessed by computing normalized covariance matrices or applying singular value decomposition (SVD). The real and imaginary upper-triangular parts of the spatial covariance matrix (SCM) are flattened into vectors as input features, which are then concatenated across frequencies. The resulting ambiguity surface outputs probability values for candidate locations through a Softmax layer, forming a probability distribution. In summary, GFGRU integrates gating mechanisms with feedback connections to mimic sparse estimation algorithms' iterative optimization, effectively addressing deep-sea multi-source localization challenges.  
Figure 6: The CNN architecture for the experiments. A convolutional layer is indicated by “conv”, and full-connectivity layer by “full” [61].
## 4 CFD Simulation of Mixing Processes
### CFD Modeling of Laminar and Turbulent Mixing
These outputs inform decisions on impeller type, rotational speed, fill level, and baffle configuration to achieve the desired mixing performance while minimizing energy consumption [57]. In practice, CFD modeling of laminar and turbulent mixing has been successfully applied across a range of food engineering applications, including the design and scale-up of vessels for sauces, creams, and batters; emulsification in high-shear devices; foam generation during whipping; and continuous powder-liquid mixing in inline homogenizers (Table 1). CFD has been used to optimize the mixing of high-viscosity doughs containing gluten and fiber, where conventional empirical approaches fail to capture the complex viscoelastic behavior. In industrial-scale planetary mixers, CFD simulations allow prediction of gluten network development and uniformity of hydration [58]. Optimization strategies typically involve iterative evaluation of geometry and process parameters. Increasingly, CFD results are integrated with statistical methods such as DoE (Design of Experiments) or machine learning models to identify key process drivers and improve robustness. Such hybrid approaches support faster development cycles, improved product consistency, and greater operational efficiency in both pilot- and industrial-scale mixing systems. This is exemplified in Figure 1, which shows CFD-predicted velocity fields and flow patterns in a stirred tank equipped with different baffle configurations, illustrating the impact of geometry on mixing intensity and vortex formation. Figure 1: Velocity contour and flow field in a stirred tank equipped with sine baffles: (**a**) standard configuration; (**b**) modified impeller-baffle setup. Source: [PERSON] et al. (2022) [59] [Reproduced under CC BY 4.0 license from _Appl. Sci._ MDPI].