esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analyses
### Analysis of the Free Parameter  
In this subsection, the influences of different parameters to objective fusion performances are presented and analyzed. The fusion performance was evaluated by the values of three fusion quality metrics, i.e., \(Q^{ABF}\), MEF-SSIMc and DIIVINE. When analyzing the influence of the patch size n, other parameters were set to \(\varepsilon=0.1\), \(\alpha=0.05\) and \(\eta=0.15\). Then, when analyzing the influence of \(\varepsilon\), other parameters were set to n = 9, \(\alpha=0.05\) and \(\eta=0.15\). Next, \(\alpha\) and \(\eta\) were analyzed in the same way. As shown in Figure 12, with the increase of n, the value of \(Q^{AB/F}\) and MEF-SSIMc increased slowly, and the value of DIIVINE showed almost no fluctuation. Furthermore, with the decrease of \(\varepsilon\), the value of \(Q^{AB/F}\) and MEF-SSIMc changed a little, while the value of DIIVINE has improved. When changing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{6}{c}{**Method**} \\ \cline{2-7}  & **[PERSON]**[15] & **[PERSON]**[16] & **[PERSON]**[18] & **[PERSON]**[12] & **[PERSON]**[14] & **The Proposed Method** \\ \hline balloons & **0.9694** & 0.8887 & 0.9476 & 0.9239 & **0.9619** & 0.9527 \\ recorder & **0.9738** & 0.8128 & 0.9393 & 0.9441 & 0.9469 & **0.9567** \\ Belgium House & **0.9709** & 0.8340 & 0.9639 & 0.9053 & **0.9660** & 0.9513 \\ mask & **0.9584** & 0.9609 & 0.9670 & 0.9663 & 0.9862 & **0.9863** \\ StudioEV & **0.9752** & 0.9256 & 0.9544 & 0.9397 & 0.9528 & **0.9530** \\ landscape & **0.9762** & 0.9520 & 0.9421 & 0.9799 & 0.9775 & **0.9777** \\ lighthouse & **0.9802** & 0.9547 & 0.9501 & 0.9681 & 0.9547 & **0.9690** \\ Kluki & **0.9801** & 0.9560 & 0.9482 & 0.9550 & 0.9636 & **0.9638** \\ desk & **0.9497** & 0.8421 & 0.9219 & 0.8970 & 0.9245 & **0.9227** \\ farmhouse & **0.9810** & 0.9398 & 0.9647 & 0.9616 & **0.9804** & 0.9633 \\ office & **0.9845** & 0.9609 & 0.9674 & 0.9663 & **0.9854** & 0.9786 \\ Madison capitol & **0.9773** & 0.9433 & 0.9479 & 0.9175 & **0.9619** & 0.9472 \\ \hline Average & **0.9780** & 0.9142 & 0.9512 & 0.9437 & **0.9635** & 0.9602 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different methods on the 12 image sequences, using the structural similarity index (MEF-SSIMc) [38]. Top two values are shown in bold.  
To better understand the proposed method, a set of comparative experiments were carried out to analyze the effect of the recursive filter in the fusion process. The experimental results show that the recursive filter can effectively remove the noise of the initial weight map and optimize the fusion result, as shown in Figure 13.
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Statistical Validation  
The significance of the RCWTB content on the mechanical properties of SCC was verified in the same way as in the previous cases, through a one-way ANOVA with a significance level of 5%, and by calculating the correlations between the content of this residue and the values of the mechanical properties. As shown in Table 8, the effect of RCWTB was significant in all mechanical properties except Poisson's coefficient. In fact, negative correlations of high absolute value were obtained in all these cases, both linear (Pearson) and monotonic (Spearman), which confirmed the negative relevance of the amount of RCWTB in the results of the mechanical properties [75]. As expected, the correlations for Poisson's coefficient had very low absolute values.  
Figure 20: Mechanical properties of the SCC mixes: (**a**) compressive strength; (**b**) elastic modulus; (**c**) Poisson’s coefficient; (**d**) splitting tensile strength; (**e**) flexural strength.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 2 Geometry of Stripmap SAR Observation
### Model of the Stripmap SAR Observation Area
\begin{cases}\alpha_{H}=2\mathrm{atan}\left[\frac{\cos\beta_{0}\sin\alpha_{0} \sqrt{1-(d\sin\beta_{0}+r\cos i)^{2}}\pm\sqrt{\Lambda_{H}}}{d\cos^{2}\beta_{0} -r\cos i\sin\beta_{0}+\cos\beta_{0}\cos\alpha_{0}\sqrt{1-(d\sin\beta_{0}+r\cos i )^{2}}}\right]\\ \beta_{H}=\mathrm{asin}(d\sin\beta_{0}+r\cos i)\end{cases}  \tag{6}\]  
where \(\Delta_{H}=\cos\beta_{0}^{2}\Big{[}1-(d\sin\beta_{0}+r\cos i)^{2}\Big{]}-\big{(} d\cos^{2}\beta_{0}-r\cos i\sin\beta_{0}\big{)}^{2}\), and \"\(\pm\)\" means that \"\(-\)\" is taken when \(\Delta_{Lng}\leq\pi/2\), and \"\(+\)\" is taken when \(\Delta_{Lng}>\pi/2\). Figure 3: Observation area of spaceborne SAR operating in stripmap mode. When the spaceborne SAR observes the right area of the flight path, the geodetic coordinates of the observation area are expressed as  
\[\left\{ \begin{aligned} \alpha_{L}&=2\text{atan}\left[ \frac{\cos\beta_{0}\sin\alpha_{0}\sqrt{1-\left(d\sin\beta_{0}-r\cos i\right)^{2 }}\pm\sqrt{\Lambda_{L}}}{d\cos^{2}\beta_{0}+r\cos i\sin\beta_{0}+\cos\beta_{0} \cos\alpha_{0}\sqrt{1-\left(d\sin\beta_{0}-r\cos i\right)^{2}}}\right]\\ \beta_{L}&=\text{asin}(d\sin\beta_{0}-r\cos i)\end{aligned} \right. \tag{7}\]  
where \(\Delta_{L}=\cos\beta_{0}^{2}\Big{[}1-\left(d\sin\beta_{0}-r\cos i\right)^{2} \Big{]}-\left(d\cos^{2}\beta_{0}+r\cos i\sin\beta_{0}\right)^{2}\), and \"\(\pm\)\" means that \"\(+\)\" is taken when \(\Delta_{Lug}\leqslant\pi/2\), and \"\(-\)\" is taken when \(\Delta_{Lug}>\pi/2\). Equations (6) and (7) are functions of the look angle \(\gamma\) of the antenna. A one-to-one correspondence is established between the time-varying area observed by the spaceborne SAR operating in the boresight stripmap mode and the look angle of the antenna. Therefore, the area observed by the spaceborne SAR operating in stripmap mode can be estimated by estimating the look angle of the antenna.
## 5 Conclusions  
Through field research, drivers' eye movement data were collected by eye-tracking equipment. By analyzing these data, the study explored how luminance levels affect drivers' visual characteristics (pupil area and fixation point) when driving through the tunnel interior zone. Only six drivers (a small sample size due to the integration of safety considerations, limited time, and funds), were used in this study because this process was conducted as a pilot test. The conclusions are as follows:  
1. When driving through the tunnel interior zone, the pupil area remains at a relatively stable level. The results showed that the luminance level had a significant effect on the drivers' eye movement indicators, pupil area, and fixation point position.
2. The average pupil area was negatively correlated with luminance level, which implies that the driver's dynamic visual acuity can be improved by reducing his pupil area, which in turn increases his ability to identify moving objects. Moreover, the relationship between pupil area and luminance level fitted a linear function.
3. When driving in the tunnel, the participants' fixation areas are mainly on the front road pavement, on the top wall surface, and on the dashboard. The results revealed that the road pavement is the most important region for drivers in the tunnel's interior zone.
4. Although the study presented some promising practices, the study also has limitations. The main limitation is the small sample size (i.e., 6 drivers). Future studies should increase the number of drivers to confirm the results. Also, future studies should be carried out under different weather conditions. Furthermore, the tests should distinguish between the novice subjects and the experienced subjects, male and female subjects, in addition to other parameters.  
Conceptualization, L.Q. and X.-H.S.; methodology, L.Q. and Q.-L.C.; software, Q.-L.C.; formal analysis, L.Q. and Y.-N.W.; writing--original draft preparation, L.Q.; writing--review and editing, A.S.L.; supervision, X.-H.S.; funding acquisition, X.-H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ZHEJIANG PROVINCIAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number LQ21E080005; by NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number 61701069; by MAJOR SCIENCE AND TECHNOLOGY SPECIAL PROJECT IN JIANGBEI DISTRICT, NINGBO CITY, grant number 201901A03.  
The study was approved by the Institutional Review Board of School of Information Science and Engineering, Ningbo University.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data can be found on [[https://www.researchgate.net/publication/35](https://www.researchgate.net/publication/35)]([https://www.researchgate.net/publication/35](https://www.researchgate.net/publication/35)) 1935804_Original_eye_movement_data.  
The authors declare no conflict of interest.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 3 The Proposed Method
### Network Design
#### 3.2.2 Attention Mechanism  
Most previous networks have treated channel and pixel features equally during image dehazing, resulting in unsatisfactory results after dehazing. Meanwhile, for images with uneven haze distribution, such networks cannot achieve good results. In order to better handle different parts of information, we designed an novel attention mechanism module, as shown in Figure 3. Compared to networks that treat channel and pixel features equally, the attention mechanism module of IFE-Net assigns different weights to different regions based on the importance of features. The more information the features contain, the greater their weight values. The application of the attention mechanism in IFE-Net focuses more on learning important information with high weights. The channel attention mechanism, pixel attention mechanism, and texture attention of the attention mechanism module can be expressed separately as follows:  
\[F_{1}(y)=\text{ReLU}[\text{Conv}_{7}[\text{ReLU}(\text{Conv}_{6}(y)]], \tag{6}\]  
\[F_{2}(y)=\text{ReLU}[\text{Conv}_{9}[\text{ReLU}(\text{Conv}_{8}(F_{1}(y))]], \tag{7}\]  
\[F_{3}(y)=\text{pool}(F_{2}(y)), \tag{8}\]  
Figure 2: Multiscale feature extraction block.  
where \(y\) is the output of the multiscale feature extraction block, which serves as the input of the attention mechanism module; \(\text{Conv}_{6}(y)\) and \(\text{Conv}_{7}(y)\) denote the 1 \(\times\) 1 convolution layer; \(\text{Conv}_{8}(y)\) and \(\text{Conv}_{9}(y)\) denote the 3 \(\times\) 3 convolution layer; pool represents the 5 \(\times\) 5 channel maxpooling operation; \(F_{1}(y)\) denotes the output of the channel attention mechanism; \(F_{2}(y)\) denotes the output of the pixel attention mechanism; and \(F_{3}(y)\) denotes the output of texture attention. The attention mechanism block effectively assigns different weights to the features of different regions, enabling the entire network architecture to better retain effective information while suppressing the impact of unimportant information.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## Appendix A  
**Algorithm A1.** Soft Actor-Critic with Multi-Head Attention (SAC-Attention)  
**Purpose:** Optimize wellbore trajectory in complex geological environments with obstacle avoidance  
**Inputs**  
Environment E: State space S (well position + geological features), action space A (continuous deflection angle increments)  
Hyperparameters: Learning rate lr = 3e-4, discount factor gamma = 0.99, batch size B = 256, replay buffer capacity D_max = 1e5, soft update rate tau = 5e-3  
Attention settings: Number of heads h = 8, hidden dimension d = 256, attention layers L = 2  
**Initialization**  
Actor network pi(theta): Contains multi-head self-attention blocks + Gaussian policy head (outputs mean mu and log standard deviation log_sigma)  
Twin critic networks Q(phi1), Q(phi2): Each with multi-head self-attention blocks + Q-value head  
Target critic networks Q\({}^{\prime}\)(phi1), Q\({}^{\prime}\)(phi2): Initialize by copying parameters from Q(phi1), Q(phi2) (phi1\({}^{\prime}\) = phi1, phi2\({}^{\prime}\) = phi2)  
Replay buffer D: Empty  
Optimizers: Adam for pi(theta), Q(phi1), Q(phi2) with learning rate lr  
**Main Loop**  
For each episode from 1 to max_episodes:  
Reset environment: s0 = E.reset() (initial well state)  
For each step t = 0 to max_steps:  
a. Sample action via reparameterization:  
a_t = tanh(mu(s_t) + exp(log_sigma(s_t)) * epsilon), where epsilon \(\sim\) N(0,1)  
b. Execute a_t in E, get next state s_[t+1], reward r_t (combines target proximity, trajectory smoothness, obstacle avoidance), and termination signal \"done\"  
c. Store transition (s_t, a_t, r_t, s_[t+1], done) in D  
d. If size of D >= B:  
i. Sample batch of transitions ((s, a, r, s\({}^{\prime}\), done)) from D  
ii. Update critics:  
Sample next action a\({}^{\prime}\) \(\sim\) pi(theta), compute log probability log_pi(a\({}^{\prime}\)! s\({}^{\prime}\))  
Calculate target Q-value:  
y = r + gamma*(1 - done) * [ min(Q\({}^{\prime}\)(phi1)(s\({}^{\prime}\),a\({}^{\prime}\)), Q\({}^{\prime}\)(phi2)(s\({}^{\prime}\),a\({}^{\prime}\))) - alpha *  
log_pi(a\({}^{\prime}\)! s\({}^{\prime}\)) ]  
Minimize critic loss:  
L_Q = E[ Q(phi1)(s,a) - y)^2 + (Q(phi2)(s,a) - y)^2]  
Update phi1, phi2 via gradient descent  
iii. Update actor:  
Sample action a \(\sim\) pi(theta)  
Maximize actor loss:  
L_pi = E[ Q(phi1)(s,a) - alpha * log_pi(a! s) ]  
Update theta via gradient ascent  
iv. Soft update target critics:  
phi1\({}^{\prime}\) = tau*phi1 + (1 - tau)_phi1\({}^{\prime}\)_  
_phi2\({}^{\prime}\) = tauphi2 + (1 - tau)*phi2\({}^{\prime}\)_  
e. If \"done\" is True, terminate episode and break  
f. Set s_t = s_[t+1]
# Robust Speed Control of a Multi-Mass System: Analytical Tuning and Sensitivity Analysis
## 2 System Modeling
### Time Delay Model  
An inverter must adjust the electric machines' voltage supplies to manipulate the electromagnetic moments since the loading machines cannot be controlled directly. The moment control of the loading machines is realized using vector control methods and a designated field-oriented control [23]. The moment control response can be modeled as a transfer function from the reference moment to the actual motor moment [10; 31]. According to [23; 31], the time delay response in electrical components \(G_{t}\)(s) is modeled as in Equation (4)  
\[G_{t}(s)=e^{-s\ T_{d}}\ \frac{\alpha_{t}}{s+\alpha_{t}} \tag{4}\]  
where \(T_{d}\) is the time delay in the moment control loop and \(\alpha_{t}\) is the bandwidth of the electrical part of the VEL test bench (i.e., the inverter with the loading machine). If \(T_{d}\) in the moment control loop of the inverter exceeds a certain level, it can lead to instability of the angular speed control loop, particularly if the high resonance frequencies need to be considered [10; 23]. Therefore, the hardware setup of the electrical components should be optimized for small time delays and sudden changes at the moment.  
Another source of time delay is caused by the angular speed measurement of the electric loading machines, as indicated in [10], typically achieved by an incremental encoder. The working principle of the incremental encoder is based on calculating the difference in the measured rotational angle within a specific sampling time interval. Thus, significant quantization noise and time delays in the measurements are produced from the sampling scheme [32]. Hence, the measurement time delay is modeled in [10] as a transfer function \(M\)(s) with a given measurement time delay (\(T_{m}\)). Hence, \(M\)(s) model (5) yields  
\[M(s)=e^{-T_{m}\ s}. \tag{5}\]In [10], the total delay of the sum of \(T_{d}\) and \(T_{m}\) was examined for a two-mass system. As a result, the total delay transfer function is designated in Equation (6) as (\(G_{\Delta}\))  
\[G_{\Delta}(s)=G_{t}(s)M(s)=e^{-s\ (T_{d}+T_{m})}\ \frac{\alpha_{t}}{s+\alpha_{t}}. \tag{6}\]
# Kernel Density Estimation on the Siegel Space with an Application to Radar Processing +
## 3 Non Parametric Density Estimation on the Siegel Space  
Let \(\Omega\) be a space, endowed with a \(\sigma\)-algebra and a probability measure \(p\). Let \(X\) be a random variable \(\Omega\to\mathcal{D}_{n}\). The Riemannian measure of \(\mathcal{D}_{n}\) is called \(vol\) and the measure on \(\mathcal{D}_{n}\) induced by \(X\) is noted \(\mu_{X}\). We assume that \(\mu_{X}\) has a density, noted \(f\), with respect to \(vol\), and that the support of \(X\) is a compact set noted \(Supp\). Let \((x_{1},...,x_{k})\in\mathcal{D}_{n}^{k}\) be a set of draws of \(X\).  
The Dirac measure at a point \(a\in\mathcal{D}_{n}\) is denoted \(\delta_{a}\). Let \(\mu_{k}=\frac{1}{k}\sum_{i=1}^{k}\delta_{x_{i}}\) denotes the empirical measure of the set of draws. This section presents four non-parametric techniques of estimation of the density \(f\) from the set of draws \((x_{1},...,x_{k})\). The estimated density at \(x\) in \(\mathcal{D}_{n}\) is noted \(\hat{f}_{k}(x)=\hat{f}(x,x_{1},...,x_{k})\). The relevance of a density estimation technique depends on several aspects. When the space allows it, the estimation technique should equally consider each direction and location. This leads to an isotropy and a homogeneity condition. In the kernel method, for instance, a kernel density function \(K_{x_{i}}\) is placed at each observation \(x_{i}\). Firstly, in order to treat directions equally, the function \(K_{x_{i}}\) should be invariant under the isotropy group of \(x_{i}\); Secondly, for another observation \(x_{j}\), functions \(K_{x_{j}}\) and \(K_{x_{j}}\) should be similar up to the isometries that send \(x_{i}\) on \(x_{j}\). These considerations strongly depend on the geometry of the space: if the space is not homogeneous and the isotropy group is empty, these indifference principles have no meaning. Since the Siegel space is symmetric, it is homogeneous and has a non empty isotropy group. Thus, the density estimation technique should be chosen accordingly.  
The convergence of the different estimation techniques is widely studied. Results were first obtained in the Euclidean case, and are gradually extended to the probability densities on manifold (see [1; 2; 15; 25]).  
The last relevant aspect is computational. Each estimation technique has its own computational framework that presents pros and cons given the different applications. For instance, the estimation by orthogonal series needs an initial pre-processing, but provides a fast evaluation of the estimated density in compact manifolds.
## 5 Results
### Experiment Setup and Evaluation Q-Gram Filter Efficiency
This is how we tested the effectiveness of the Q-gram filter as a lower bound on the FRMS method. A demonstration of the results of the numerical study for this group of matching methods is provided in Table 4. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Similarity** & **F1-Score** & **Similarity** & **F1-Score** \\ \hline FRMS & 85.09\% & naive 2-gram (\(|p|=2\))+FRMS & 34.75\% \\
3-gram (\(|p|=2\))+FRMS & 85.01\% & naive 2-gram (\(|p|=1\))+FRMS & 34.33\% \\
2-gram (\(|p|=2\))+FRMS & 84.88\% & naive 2-gram (\(|p|=0\))+FRMS & 31.69\% \\
3-gram (\(|p|=1\))+FRMS & 84.84\% & naive 3-gram (\(|p|=2\))+FRMS & 29.07\% \\
2-gram (\(|p|=1\))+FRMS & 84.74\% & naive 3-gram (\(|p|=1\))+FRMS & 29.07\% \\
3-gram (\(|p|=0\))+FRMS & 84.71\% & naive 3-gram (\(|p|=1\))+FRMS & 29.07\% \\
2-gram (\(|p|=0\))+FRMS & 84.38\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of the combination of Q-gram filters with FRMS evaluated by F1-score. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Similarity** & **F1-Score** & **Similarity** & **F1-Score** \\ \hline FRMS & 85.09\% & [PERSON]–[PERSON] & 75.71\% \\
3-gram filter+FRMS & 85.01\% & [PERSON]–[PERSON] & 75.54\% \\
2-gram filter+FRMS & 84.88\% & [PERSON] & 75.29\% \\ Fuzzy Jaccard ([PERSON] & & & \\ \(\delta=0.8\)) & 84.17\% & Overlap 3-gram & 73.21\% \\ [PERSON]–[PERSON] & 81.45\% & [PERSON] 2-gram & 71.05\% \\ L2 Monge–[PERSON] & 80.80\% & [PERSON] 2-gram & 71.05\% \\ ([PERSON]) & 76.86\% & [PERSON] 3-gram & 70.86\% \\ [PERSON] & 76.83\% & [PERSON] 3-gram & 70.86\% \\ [PERSON]–[PERSON] & 76.25\% & Overlap 2-gram & 66.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of selected similarity functions ranked in descending order of F1-score.
## 1 Introduction
Indeed, the crack can grow through the tight aggregates, soft mastic or interface of aggregate/mastic parts. For more realistic analysis of the cracked asphalt mixtures, it is better to consider the asphalt composite material as a heterogeneous mixture, and hence some research papers have focused on the failure behavior of asphalt mixtures from this viewpoint. For example, [PERSON] et al. [36] used discrete element method (DEM) and image processing technique (IPT) for 2-D modeling of a cracked heterogeneous asphalt mixture. [PERSON] et al. [37] investigated the growth and propagation of cracks using the semi-circular bend (SCB) samples by means of IPT and numerical modeling. They estimated the crack propagation path using the cohesive elements. [PERSON] et al. [38] used 2-D finite element (FE) modeling and IPT to predict the fracture behavior of asphalt mixtures. Their goal was to predict the fracture path of the asphalt mixture under indirect tension. [PERSON] and [PERSON] [39] numerically predicted the path of crack growth for the SENB and split tensile (ST) samples. [PERSON] et al. [40] used the aggregate generation and packing algorithm (AGPA) and 2-D FE models to estimate the trajectory of fracture in the SCB samples. They found that heterogeneous modeling of asphalt mixtures leads to a more realistic prediction for the path of crack propagation and the mode I and mode II stress intensity factors are sensitive to the crack tip location. Using the FE modeling and AGPA method [41; 42; 43] [PERSON] and coworkers also simulated the fracture behavior of asymmetric semi-circular bend (ASCB) sample by assuming the heterogeneity for the asphalt mixtures. [PERSON] and [PERSON] [44] employed the AGPA and FE models to estimate the trajectory of crack growth in the SCB samples. They concluded that the distribution of aggregates, gradation, and angularity of the aggregates have considerable effects on the path of fracture. [PERSON] et al. [37, 45] showed that the path of crack growth is sensitive to the aggregate distribution and cohesive strength of the aggregate/mastic interface. However, in most of the previous works the effects of composite mixture and testing conditions have only investigated on the fracture toughness and load bearing capacity values. Indeed, very-limited studies are available for considering the influence of the mixture heterogeneity on the fracture trajectory and fracture surface of the asphalt composite mixtures at different testing and loading conditions. Therefore, in this research, using the results of experimental tests conducted on a typical asphalt mixture with different test geometries, the fracture trajectory is investigated for different loading modes and testing conditions. It is shown that the distribution of aggregates and testing temperature can significantly affect the path of mode I and the mixed mode fracture of the asphalt concrete.
## 3 Algorithmic Errors
### AI-Enhanced Navigation Systems  
AI algorithms are pivotal in optimizing shipping routes, significantly boosting both safety and efficiency in maritime operations. These algorithms process extensive datasets, including real-time weather patterns, ocean currents, vessel traffic, and historical navigation routes, to determine the most efficient and safest paths for ships. By continuously updating and recalculating routes based on the latest data, AI ensures that vessels can avoid hazardous conditions such as severe weather, high-traffic areas, and navigational obstacles. Additionally, AI-driven route optimization minimizes fuel consumption by selecting the most efficient pathways, thus reducing operational costs and mitigating environmental impact. This capability not only enhances the profitability of maritime operations but also aligns with global efforts to reduce greenhouse gas emissions and promote sustainable shipping practices [58; 59; 60].