esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Attentional Visualization Experiment
It is important to note that in the first attention heat map of early blight, GoogLeNet and VGG networks identified non-disease areas, such as the hole formed by the branch in the upper left corner and the overlap of the leaf edge and background, as diseased spots. As depicted in the figure, these networks tend to show \"false spots\" in red regions of their attention heat maps. In contrast, after pre-training on the PDDA dataset, IBSA_Net demonstrated a superior ability to distinguish between \"false spots\" and actual diseased regions. The attention heat map of IBSA_Net mostly identifies diseased spots, assigning higher attention weights to these regions. The \"false spots\" related to the holes formed by the branch are colored light yellow and cyan, indicating lower network attention to these areas. Similarly, in the Target_Spot dataset, IBSA_Net effectively avoided predicting shadows or non-disease black spots as diseased regions, whereas other networks made such errors. These findings suggest that IBSA_Net can self-learn the differences between diseased regions and the environmental illusions of leaves and better distinguish between them. Conversely, in the PDDA model cluster, there are significantly more warm regions in the attention heat maps and more overlap with the disease spot region. Precisely, the warm color regions of IBSA_Net largely overlap with the disease spot regions and cover most of the spots. This suggests that the ability to distinguish complex backgrounds from leaf spot regions was learned from the PDDA dataset and that IBSA_Net had the most significant ability to generalize from the source domain to the target domain. To further quantify the level of attention each model pays to the diseased regions of tomato leaves, we calculated the percentage of warm regions versus diseased regions for each model's attention visualization when predicting the PlantDoc++ test set after training on different datasets. The attention matrix was converted to a NumPy array format, reshaped to the same size as the original image, and then superimposed on the original image to generate a visualized attention map. The number of pixels in the attention map was calculated based on the location of the tomato leaf spots or the bounding box [36]. The number of pixels in the warm area of the attention map was determined by applying a set threshold. Finally, the ratio of the number of pixels of the object to be recognized to the number of pixels in the warm area was obtained. Table 8 displays the attention level of each model for the two tomato leaf diseases, Early_blight and Target_Spot, depicted in Figure 13. For both diseases, the PDDA model group has a higher proportion of warm-colored regions covered in the attention heat map compared to the PlantVillage model group. Furthermore, the area percentage of IBSA_Net, proposed in this paper, has the highest value in all the results. Figure 13: Attentional heat map, PlantVillage represents the PlantVillage model group; PDDA represents the PDDA model group.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion  
In order to be sure that we observed a standard RO event, the first step in the analysis of the data was to obtain Doppler frequency estimates for the occulting satellite (PRN24) using the RINEX files provided by the JAVAD receiver for the radio-occultations antenna. The evolution of the Doppler presents the expected shape for a setting satellite: the Doppler increases with time, and towards the end it stagnates and starts decreasing again. Towards the end of the radio-occultation event, when the SNR of the received signal drops considerably, the Doppler estimates become noisier.  
From now on the presented results have been obtained using the IF recorded and post-processed data.
## 2 Materials and Methods
### Acoustic Emission Analysis Methods
#### 2.2.1 K-Means Cluster  
K-means is a simple and effective unsupervised clustering algorithm, which is widely used to process concrete acoustic emission signals [27]. K-means clustering selects N initial cluster center positions according to the number of clusters selected N and redistributes the cluster center positions by calculating the distance from each datapoint to different cluster centers and iterations until the intra-cluster distances were minimized and the inter-cluster distance was maximized. K-means clustering can effectively divide the normalized acoustic emission data into different clusters, and different clusters correspond to different concrete damage. The standard implementation workflow of K-means includes the following steps:  
1. Determine the preset number of K clusters;
2. Randomly select K sample points as initial centroids;
3. Assign the remaining data points to the nearest neighboring centroid's cluster based on the Euclidean distance criterion;
4. Iteratively update the coordinates of each cluster's centroid.  
Repeat steps 3-4 until the convergence criteria are met (e.g., the centroid offset is less than a threshold or the maximum number of iterations is reached).
# Nonlinear Vibration Analysis of a Beam with a Breathing Crack
## 5 Analysis of a Beam with a Breathing Crack  
The resonance conditions of the cracked beam are determined by using the multiple-scale method. This method assumes that the system has a weak damping and a weak exciting force. To facilitate the analysis, a parameter \(\varepsilon\) is introduced as the designator for the damping matrix \({\bf C}\), stiffness matrix \({\bf K}_{t}\) and the force vector \({\bf F}\). Now, Equation (13) can be rewritten as  
\[{\bf M}\ddot{\bf U}+{\bf K}_{001}{\bf U}=\varepsilon\overline{\bf F}- \varepsilon\overline{\bf C}\dot{\bf U}+\varepsilon{\bf K}_{02}\bigcup_{n=1}^ {N}\overline{k}_{n}\cos(n\omega t+\varphi_{n}). \tag{14}\]  
where  
\[\overline{\bf F}=\frac{\bf F}{\varepsilon},\ \overline{\bf C}=\frac{\bf C}{ \varepsilon},\ \overline{k}_{n}=\frac{k_{n}}{\varepsilon}.\]  
To predict the steady state response of the cracked beam, a mode superposition method can be used. The linear transformation is introduced as  
\[\left\{ \begin{array}{l}{\bf U}=\psi{\bf\eta}\,\\ \dot{\bf U}=\psi\dot{\bf\eta}\,\\ \ddot{\bf U}=\psi\ddot{\bf\eta}.\end{array} \right. \tag{15}\]  
where \(\psi\) is the matrix of the modal transformation that can be obtained from the FEM of the cracked beam, and \({\bf\eta}\) is the modal coordinate vector. Substituting \({\bf U}\), \(\dot{\bf U}\), and \(\ddot{\bf U}\) given in Equation (15) into Equation (14) and premultiplying it by \(\psi^{\rm T}\) yields  
\[\ddot{\bf\eta}+{\bf K}_{0001}{\bf\eta}=\varepsilon{\bf F}_{0}-\varepsilon{\bf C }_{0}\ddot{\bf\eta}+\varepsilon{\bf K}_{002}{\bf\eta}\sum_{n=1}^{N}\overline {k}_{n}\cos(n\omega t+\varphi_{n}). \tag{16}\]  
where  
\[{\bf K}_{0001}=\psi^{\rm T}{\bf K}_{001}\psi,\ {\bf C}_{0}=\psi^{\rm T} \overline{\bf C}\psi,\ {\bf F}_{0}=\psi^{\rm T}\overline{\bf F},\ {\bf K}_{002}=\psi^{\rm T}{\bf K}_{02}\psi.\]  
The solution of Equation (16) can be expressed as  
\[{\bf\eta}={\bf\eta}_{0}(T_{0},T_{1},\ldots,T_{m})+\varepsilon{\bf\eta}_{1}(T_ {0},T_{1},\ldots,T_{m})+\cdots+\ \varepsilon^{m}{\bf\eta}_{m}(T_{0},T_{1},\ldots,T_{m}). \tag{17}\]  
where the independent time variable is defined as [43]  
\[T_{m}=\varepsilon^{m}{\bf\tau},\ m=\ 0,\ 1,\ 2,\ \ldots, \tag{18}\]  
where \(T_{0}\) is the fast timescale, and \(T_{1}\) and \(T_{2}\) are slow timescales that are much slower than \(T_{0}\); \(\tau\) is the normalized time. According to the multiple-scale method, the time derivatives with respect to \(\tau\) are given by [43]  
\[\left\{ \begin{array}{l}\frac{d}{dt}=D_{0}+\varepsilon D_{1}+\varepsilon^{2}D _{2}+\cdots,\\ \frac{d^{2}}{dt^{2}}=D_{0}^{2}+2\varepsilon D_{0}D_{1}+\varepsilon^{2}(D_{1}^ {2}+2D_{0}D_{2})+\cdots.\end{array} \right. \tag{19}\]  
where \(D_{m}=\frac{\partial}{\partial T_{m}}\), is partial derivative operator symbol. The first-order approximation of \(\eta\) can be written as  
\[\eta=\eta_{0}(T_{0},T_{1})+\varepsilon\eta_{1}(T_{0},T_{1}). \tag{20}\]  
The \(r\)-th element in the vector \(\eta\) can be expressed as  
\[\eta_{r}=\eta_{0r}+\varepsilon\eta_{1r},\,r=1,\,2,\,\ldots,\,q. \tag{21}\]  
where \(q\) is the number of DOFs.
## 5 Derivation of Semi-Empirical Models
### Limit Stress Derivation  
The nonlinear regression procedures employed for deriving the above semi-empirical equations for the CLS were extended to obtain two nonlinear expressions for the limit stress, for YPT pipes and RHT pipes. The values of the limit stress were obtained based on the values of the peak load on the axial load vs. axial deformation plots for pipes subjected to uniform axial compression. The following regression equations are required for determining the appropriate fractions of the limit stress that feed into the semi-empirical equations for evaluating the CLS of pipes subjected to combined axial compression + bending:  
For YPT pipes,  
\[\begin{array}{ll}\sigma_{lim,YP}=&6.16262(586)*[(8.49601-7.19159.\pi_{2})*( \pi_{1}{}^{-0.307568})]\\ &*[(-8.36548)+8.36562*(Exp(\pi_{2}){}^{5.59778*10^{-6}})]\\ &*[(1.52236.\pi_{1})+350.286*(\pi_{3}{}^{-0.038848})]\end{array} \tag{7}\]  
For RHT pipes,  
\[\begin{array}{ll}\sigma_{lim,RH}=&5.45103(586)*[(7.32071-6.48533.\pi_{2})*( \pi_{1}{}^{-0.107001})]\\ &*[(8.45167)+6.90582*(Exp(\pi_{2}){}^{0.843233})]\\ &*[(5.23197)+7.78213*(\pi_{4}{}^{-0.451323})]\\ &*[(-5.67028)+5.67057*(\pi_{5}{}^{-2.72452*10^{-6}})]\end{array} \tag{8}\]
## 2 Materials and Methods
### Methods  
#### 2.3.1 Change Trend of Land NPP  
The Theil-Sen trend analysis [55] and Mann-Kendall statistical test [56] were used to conduct pixel-by-pixel change trend analysis and significance tests for timeseries land NPP in the Han River Basin. The advantage of the Theil-Sen trend analysis is that it does not require the sample to obey a specific distribution and is not subject to outliers. The Mann-Kendall test is a non-parametric test that is used to test data that are not normally distributed. The Theil-Sen slope (\(\rho\)) is calculated as follows:  
\[\rho=median\left(\frac{x_{j}-x_{i}}{j-i}\right)\,(1<i<j<n) \tag{1}\]  
where \(\rho\) is the slope of land NPP; _median_ is the median function; \(i\) and \(j\) are timeseries; \(x_{i}\) and \(x_{j}\) are land NPP values in the \(i\)-th and \(j\)-th years, respectively. When \(\rho>0\), it indicates an upward trend in land NPP; when \(\rho<0\), it indicates a downward trend in land NPP; when \(\rho=0\), it indicates an insignificant trend in land NPP. According to the Mann-Kendall test, five significance levels are assigned to characterize the change trend of land NPP: highly significant increase (\(\rho>0\), \(p<0.01\)), significant increase (\(\rho>0\), \(0.01<p<0.05\)), no significant change (\(p>0.05\)), significant decrease (\(\rho<0\), \(0.01<p<0.05\)), and highly significant decrease (\(\rho<0\), \(p<0.01\)).
## 4 Proposed Method
### Fault Prediction Method  
#### 4.3.1 Attention Mechanism Principle  
The attention mechanism is a mechanism for weighting information at different locations in the sequence data [45]. The introduction of the attention mechanism in the neural network model allows the model to make different attention adjustments to the input at different moments when processing the sequence data so that the key information in the sequence can be processed more flexibly. The structure of the attention mechanism is shown in Figure 11.  
The formula expression of the attention mechanism can vary according to the specific variant. The following is a general formula expression of the attention mechanism:  
Given the input sequence \(H=(h_{1},h_{2},\ldots,h_{t})\), the attention mechanism calculates the attention weight\(\backslash\)alpha and the context vector c as follows:  
1. Calculate attention weight:  
\[\alpha_{i}=\frac{exp(e_{i})}{\sum_{i=1}^{T}exp\big{(}e_{j}\big{)}}\]  
where \(e_{i}\) is the result of the score function score, which measures the correlation of position i with other positions in the sequence.  
2. Calculate the context vector:  
\[c=\sum_{i=1}^{T}\alpha_{i}\text{h}_{i}\]  
\begin{table}
\begin{tabular}{l l l l l} \hline
**The Fault Types** & **On the Limit** & **Under the Limit** & **Ideal Value** & **Unit** \\ \hline Feed speed fault & \(20\times 10^{-3}\) & \(5\times 10^{-3}\) & \(18.8\times 10^{-3}\) & in/r \\ Actual speed fault & \(15\) & \(12\) & \(13.5\) & in/r \\ Spindle magnification fault & \(0.95\) & \(0.83\) & \(0.89\) & \% \\ Spindle speed fault & \(8\times 10^{3}\) & \(7\times 10^{3}\) & \(7.5\times 10^{3}\) & r/min \\ Feed rate fault & \(3.4\) & \(2.7\) & \(3.2\) & mm/r \\ Actual feed fault & \(6.5\) & \(2\) & \(4.7\) & mm/r \\ Spindle load fault & \(8\) & \(5\) & \(5.3\) & \% \\ \hline \end{tabular}
\end{table}
Table 4: Equipment fault types and corresponding indicators.  
Figure 11: Principle of attention mechanism.  
In the self-attention mechanism, the common scoring function score has the following forms:  
Product attention: \(e_{i}=h_{i}\cdot h_{i}\);  
Additive attention: \(e_{i}=tanh(W_{x}h_{i}+b)\);  
Where \(W_{x}\) is a learnable weight matrix and b is a learnable bias vector.  
Scaled dot product attention: \(e_{i}=\frac{h_{i}\cdot h_{i}}{\sqrt{d}}\);  
Where d is the dimension of the input sequence.  
The above formula is the general form of the attention mechanism, and the specific application scenario and model will determine the appropriate scoring function and calculation method.  
The introduction of attention mechanisms in neural networks can improve the long-term dependency problem of LSTM and GRU neural networks, improve model performance, handle variable-length input sequences, and provide interpretability and explainability. This makes the model more flexible, accurate, and interpretable in sequence tasks.
# Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts
## 3 Methodology  
This section is structured to examine key components vital to our research. Section 3.1 lays the groundwork by introducing vehicle longitudinal dynamics models with bidirectional information flow. Subsequent Sections 3.2 and 3.3 introduce phase shift effects and employ a framework rooted in this concept to analyze the interconnected behavior of vehicles within a platoon under bidirectional communication. Building upon this groundwork, the study further evaluates the rear-end collision risk and conducts a stability analysis in Sections 3.4 and 3.5, respectively. And culminating findings are presented in Section 3.6.
# Detection and Evaluation of Machine Learning Bias
## 5 Methodology  
Our empirical evaluation intends to find out if the dependency of the hypothesis on the protected attribute. If the predictor predicts significantly different labels with different density if the alternation function is applied, then this is an indicator that the hypothesis is dependent on that PBA. In contrast, if the hypothesis is independent of the PBA (i.e., the two predicted labels densities are similar), the hypothesis is not biased. Figure 1 illustrates the proposed framework to quantify and detect bias. We consider the density of the predicted class label of the original dataset **D** to be the original distribution in the divergence evaluation denoted as p. While the density prediction of the alternated dataset is the other density denoted as q. \(D_{KL}\) quantifies how much \(p\) is deviated from \(q\). In other words, it evaluates the divergence between the density of predicted class label \(p_{\Phi}\) and the density of the predicted class label \(q_{\mathbf{y}}\) after applying the alternation function with respect to the PBA's values.  
To evaluate bias in the gender attribute, we should do the following:  
1. Train the model \(f(\cdot)\) on the dataset \(\mathbf{D}\).
2. Predict the class label for each data point in \(f(\mathbf{D})\rightarrow\mathbf{\hat{y}}\).
3. Apply the alternation function on the gender attribute \(\varphi(\mathbf{D})\rightarrow\mathbf{D}_{\varphi}\).
4. Train the model \(f(\cdot)\) on the alternative dataset \(\mathbf{D}_{\varphi}\).
5. Predict the alternative predicted label \(f(\mathbf{D}_{\varphi})\rightarrow\mathbf{\hat{y}}_{\varphi}\).
6. Using Equation (3), evaluate KL Divergence between the distributions of \(\mathbf{y}\) and \(\mathbf{\hat{y}}_{\varphi}\) for each gender distinctively across all folds. That is, \(D_{KL}(p_{\mathbf{y}_{f}}||q_{\mathbf{\hat{y}}_{\varphi f}})\) and \(D_{KL}(p_{\mathbf{y}_{m}}||q_{\mathbf{\hat{y}}_{\varphi m}})\).
7. The difference between the \(D_{KL}\) with respect to the gender represents the bias. The larger \(D_{KL}\) is, the larger the bias will be.  
The proposed methodology to evaluate bias in machine learning models is a wrapper approach. Thus, it is a model-specific approach and is more expensive in terms of computational complexity. However, it is known to be more accurate with respect to the model. On the other hand, the alternation is performed on the PBAs with respect to the number of values in the attribute \(|\mathbf{a}_{i}|\). The number of the sets that would be generated for the PBAs' attributes is equal to:  
\[1+\sum_{j=1}^{|\mathbf{a}|}(|\mathbf{a}_{i}|-j),\]  
which seems large; yet, usually the PBAs' number of categories \(|\mathbf{a}_{i}|\) is small like _gender_ or _race_.
# A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation
## 3 Results
### Cluster Analysis
\\ Education & \(12.1\pm 5.23\) & \(6.60\pm 3.23\) & \(5.5\pm 2.67\) & \(8.59\pm 4.99\) & \(<\)0.001  & \(1>2.3\)4  \\ Total & \(1>3.4\)5 \\ MMSE & \(27.3\pm 3.93\) & \(27.7\pm 1.95\) & \(12.6\pm 2.92\) & \(20.1\pm 3.57\) & \(<\)0.001  & \(2>1.3\)4  \\  & \(4.3\pm 0.74\) & \(4.9\pm 0.55\) & \(1.25\pm 1.04\) & \(4.18\pm 0.97\) & \(1>3\)  \\  & \((0.026\pm 0.565)\) & \((0.800\pm 0.550)\) & \((-2.800\pm 0.842)\) & \((-0.099\pm 0.888)\) & \(<\)0.001  & \(2<1.3\)4  \\  & \(4.71\pm 0.69\) & \(4.83\pm 0.46\) & \(2\pm 0.76\) & \(3.74\pm 1.24\) & \(1>3\)  \\  & \((0.067\pm 0.472)\) & \((0.856\pm 0.487)\) & \((-1.85\pm 0.43)\) & \((-0.454\pm 1.02)\) & \(<\)0.001  & \(2>1.3\)4  \\  & \(2.93\pm 0.39\) & \(3\pm 0\) & \(2.75\pm 0.46\) & \(2.97\pm 0.17\) & \(0.016\)  & \(2>1.5\)  \\  & \((-0.056\pm 0.372)\) & \((0.108\pm 0.040)\) & \((-0.139\pm 0.472)\) & \((0.049\pm 0.162)\) & \(0.016\)  & \(2>1\)  \\  & \(4.21\pm 1.33\) & \(4.60\pm 0.72\) & \(0.50\pm 0.76\) & \(1.21\pm 0.99\) & \(1>3\)  \\  & \((0.345\pm 0.636)\) & \((1.89\pm 0.619)\) & \((-2.06\pm 1.11)\) & \((-1.88\pm 0.767)\) & \(<\)0.001  & \(2>1.3\)4  \\  & \(2.59\pm 0.69\) & \(2.50\pm 0.73\) & \(1.13\pm 0.83\) & \(1.09\pm 0.93\) & \(1>3\)  \\  & \((0.3\pm 0.637)\) & \((0.021\pm 0.18)\) & \((-0.775\pm 0.993)\) & \((-0.937\pm 0.889)\) & \(<\)0.001  & \(2>1.3\)4  \\  & \(2\pm 0\) & \(1.97\pm 0.183\) & \(1.75\pm 0.71\) & \(1.97\pm 0.17\) & \(1.85\)  & N.S. 5  & N.A. \\  & \((0.007\pm 0.050)\) & \((0.056\pm 0.302)\) & \((-0.173\pm 0.675)\) & \((0.008\pm 0.16)\) & N.S. 5  & N.A. \\ SR & \(0.89\pm 0.31\) & \(0.9\pm 0.30\) & \(0.50\pm 0.53\) & \(0.88\pm 0.33\) & \(<\)0.38  \\  & \((0.005\pm 0.300)\) & \((0.072\pm 0.532)\) & \((-0.337\pm 0.535)\) & \((0.019\pm 0.331)\) & \(0.025\)  & \(3<1.2\),\(A\)6  \\  & \(2.94\pm 0.29\) & \(2.90\pm 0.55\) & \(2.50\pm 0.76\) & \(2.88\pm 0.41\) & & \\ P & \((-0.007\pm 0.268)\) & \((0.201\pm 0.484)\) & \((-0.312\pm 0.748)\) & \((0.024\pm 0.416)\) & N.S.5  & N.A. \\ W & \(0.74\pm 0.41\) & \(0.67\pm 0.479\) & \(0.12\pm 0.35\) & \(0.32\pm 0.47\) & \(1>3\)  \\  & \((0.061\pm 0.316)\)Only two MMSE subscales were not different among clusters, which is object naming (N) and the praxis (P). Cluster #3 had the lowest MMSE total score and the worst scores in all the MMSE subscales--except for the two above mentioned equal among clusters (Figure 6 for the radar plots of the four clusters). In particular, Cluster #3 had lower scores than Cluster #1 and Cluster #2 in TO, OS, AC, Rec, SR, W, CE and D and lower scores than Cluster #4 in TO, OS, SR and CE. Clusters #1 and #2 showed preserved cognitive functions in most MMSE subscales, although Cluster #2 had significant slightly higher scores than Cluster #1 in TO, OS, Reg, AC and CE.
## 4 Design
### Overview
#### 4.1.2 Configuration of the End Nodes  
At the end nodes, D-ST runs a software component that assigns a VID and a priority to a frame before transmitting it to the Ethernet port. When a frame \(f_{i,j}\) is generated, the software component calculates the frame absolute deadline \(d_{i,j}\), given by the sum of the arrival time of the frame \(a_{i,j}\) and the relative deadline of the flow \(D_{i}\). The VID is calculated as  
\[V_{i,j}=N-\left\lfloor\frac{(d_{i,j}-\tau_{b})\bmod T_{\text{C}}}{u}\right\rfloor +VID_{0} \tag{3}\]  
where \(\tau_{b}\) represents the time required to transmit \(1\) bit and \(T_{\text{C}}\) is the duration of the stream gate cycle, calculated as \(T_{\text{C}}=N\times u\).  
Each sender node is allowed to transmit a frame only when the following conditions are met:  
\[\begin{cases}d_{i,j}-t>u\\ d_{i,j}-t\leq N*u\end{cases} \tag{4}\]  
where \(t\) is the current time. Moreover, the software component calculates the frame priority to be added in the PCP field of the Ethernet frame. To avoid any additional frame processing delay, the PCP is calculated right before the frame transmission to the Ethernet port, using the following function:  
\[\text{PCP}_{i,j}(t)=Q-1-\left\lfloor\frac{(d_{i,j}-\tau_{b}-t)\times Q}{T_{ \text{C}}}\right\rfloor \tag{5}\]  
This way, the frame transmission will be scheduled with a priority that is a function of the absolute deadline.
## 4 Conclusions  
One novelty among the enhanced oil recovery methods is water injection with optimum salinity. As proven by many laboratory studies, the brine concentration affects the CBR system and leads to extra recovery. However, the technique is very case-dependent. The viscosity of the oil sample is a critical parameter that should be considered as another independent criterion for LSWI/EWI screening tables. The role of crude oil viscosity has been investigated in this work. The following conclusions were observed in this study:  
* Contact angle measurements and chromatography analysis showed wettability changes in the CBR system via the mineral dissolution and activity of potential determining ions.
* The incremental recovery factor, on average, is less than 5% for high viscous oil at the tertiary stage. The average recovery for secondary recovery is 36%. The resultant analytical graph shows that the more viscous the oil (higher than 150 cp), the less the recovery factor. The successful implementation of LSWI is limited by viscosity dependence. Since the viscosity is a temperature-dependent parameter, LSWI is ineffective if the reservoir temperature is not high.
* If LSWI and engineered water injection are planned to conduct in heavy oil reservoirs, then using hybrid LSWI will boost the chances of extracting more oil. Hybrid LSWI includes a combination of LSW with a hot fluid injection. This technique will affect wettability alteration, enhance mobility ratio, and reduce the oil viscosity simultaneously. The study is based on core flooding experiments, and for field application, more investigations are required.  
Conceptualization, A.G. and L.K.; methodology, A.G. and L.K.; validation, A.G. and L.K. writing--review and editing, A.G. supervision, P.P. and M.R.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Nazarbayev University (grant number 11022021 FD2910). The authors would like to thank Nazarbayev University for supporting this research through the NU Faculty Development Competitive Research Grants Program (Grant number: 11022021 FD2910). The authors declare no conflict of interest.