esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Manipulation of Micro Actuator
### Acoustically Manipulated Micro-Actuator/Robot  
In addition to magnetic and optical control, acoustic-based actuation has its own unique advantages and is considered a promising method in that it can be applied in various areas due to its non-invasive/non-contact nature. However, the development of micro-actuator/robot motion control using acoustics remains in its infancy, and a small number of studies have been performed. A study on acoustically actuating a polydimethylsiloxane (PDMS) polymeric micro-rotor fabricated using conventional lithography was conducted [105]. The acoustic streaming flow was generated by acoustically oscillating the sharp-edge structures of a pre-defined polymeric micro-rotor, and the rotation of the micro-rotor was achieved. This showed the idea that an acoustic micro-actuator/robot offers great potential for diverse fields such as microfluidic applications and targeted material delivery.  
Attempts have been made to apply this principle to actuators processed through TPP. [PERSON] et al. were able to generate non-contact acoustic propulsion from armed micro-bubbles that could last for several hours under forced oscillations and had a range of 10-20 \(\upmu\)m [91] (Figure 4). An armed micro-bubble (AMB)-based micro structure mounted on a solid pedestal countering bubble buoyancy was fabricated using direct laser writing. Then, the shell-shaped structure was placed into a PDMS cell filled with NaCl water solution containing 2 \(\upmu\)m spherical particles to observe the stream line. The acoustic resonance of the AMBs was determined by capillary force, not by gas volume, and the micro-swimmer showed good motility and moving speed. AMBs are expected to be promising for self-moving microscale objects via acoustic force as basic elements for controlled active assembly that are ultimately for mixing and transport applications.
## 4 Results and Discussion
### FCS-MPC: Variable-Switching-Frequency Operation  
#### 4.1.1 Real-Time Results with Step Change in the Amplitude of the Capacitor Voltage Reference  
For this case, a step change in the amplitude of the capacitor voltage reference to be tracked by the predictive control algorithm was applied. The step change ranged  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Symbol** & **Value** \\ \hline DC voltage & \(V_{dc}\) & 580 V \\ \hline Filter inductance & \(L_{f}\) & 2.2 mH \\ \hline Filter capacitance & \(C_{f}\) & 20 \(\mu\)F \\ \hline Resistance in linear load & \(R_{L}\) & 15 \(\Omega\) \\ \hline Resistance in non-linear load & \(R_{NL}\) & 10 \(\Omega\) \\ \hline Capacitance in non-linear load & \(C_{NL}\) & 10 \(\mu\)F \\ \hline \end{tabular}
\end{table}
Table 2: System parameters.  
Figure 6: View of the real-time setup: **(1)**: OPAL-RT OP5600, **(2)**: host computer.  
from 60 [V] RMS to 80 [V] RMS. The waveforms for capacitor voltages using linear and non-linear loads are shown in Figures 7a and 7b, respectively.  
Thus, it can be seen that the capacitor voltage waveforms obtained for both loads correctly follow the voltage reference imposed on the control algorithm.  
In addition, the waveforms obtained for the load current (\(i_{a}\)) for the cases of linear and non-linear loads are shown in Figures 7c and 7d, respectively.  
As for the waveforms obtained for the capacitor voltages, when faced with a step change in the amplitude of the capacitor voltage reference, the system delivers more current to the isolated load.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 2 Theoretical Method for Determining Prestress Loss of BFRP Tendons
### Prestress Loss Due to Anchorage Deformation and Prestress Retraction  
After the BFRP tendons are stretched, relative displacement is produced due to the deformation of the anchor and the base plate as well as the retraction of the BFRP tendons, and as a result, the prestress loss caused by anchorage deformation and prestress retraction \(\sigma_{11}\) occurs around the anchorage area and can be calculated with Equation (1).  
\[\sigma_{11}=\frac{a}{l}E_{\text{fp}} \tag{1}\]  
where \(a\) is the length of the anchorage deformation and prestress retraction of BFRP tendons, \(l\) is the distance between the tension end and the anchor end, and \(E_{\text{fp}}\) is the elastic modulus of BFRP tendons.
# Improved Detection and Response via Optimized Alerts: Usability Study
## 4 Discussion
### Original Contribution to the Body of Knowledge  
This study begins to close a gap in the body of knowledge and represents opportunities for additional research. Prior studies have focused exclusively on specific tenets discussed herein, but not in aggregate or totality. Studies focused on visual analytics versus text-oriented interfaces, while robust, did not factor for scale or usability, nor efficiencies gained from ML/DS. Other research focused on security operations at scale to address data overload and complexity but did not address solutions for an improved analyst experience and usability with visualization. More studies addressed detailed ML/DS opportunities leading to increased efficiency and detection, but again with no focus on alert output  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**TAM Factor** & **Adjustment** & **df** & **F** & **Sig.** & **Partial Eta Squared** &
\begin{tabular}{c} **Observed** \\ **Power** \\ \end{tabular} \\ \hline PU & Bonferroni & 1 & 7.643 & 0.007 & 0.088 & 0.584 \\ \hline PEU & Bonferroni & 1 & 0.842 & 0.362 & 0.011 & 0.055 \\ \hline AU & Bonferroni & 1 & 4.566 & 0.036 & 0.055 & 0.343 \\ \hline IU & Bonferroni & 1 & 4.378 & 0.040 & 0.053 & 0.328 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Tests of between-subjects effects per individual TAM components.  
and usability. This research intentionally joined these tenets to improve security analysts' experience with optimized alert output derived from ML/DS to address challenges of scale, detection fidelity, and usability. This contribution to the body of knowledge enables industry and academia to further refine security detection methods and products to reduce risk and better protect organizations. Specific contributions follow, and are discussed further in Section 4.4:  
* Enables industry, service, and application providers to develop, deploy, and utilize tools and capabilities that include visualizations for alert output.
* Indicates that the interface for security analysts working daily with such tools and capabilities offers a favorable user experience that is rich in visual features.
* Clarifies that issues specific to this study's problem statement can be rectified with visual alert output derived from machine learning and data science intended to reduce the burden on security analysts.
## 6 Conclusions and Future Research
Therefore, the experiment was applied in an appropriate maritime zone in terms of traffic density, satisfying the requirements imposed by the Automatic Vessel Identification System on Medium Frequency (AVISOMEF) on which the proposed Maritime Communication Network on Medium Frequency is based. To conclude, based on the results obtained in the experiment described above, we can state that it is feasible to consider a Maritime Communication Network on Medium Frequency for maritime routes that meet minimum requirements of traffic density, taking advantage of the unexpended resources of the Global Maritime Distress and Safety System (GMDSS). Future research should implement the programmes designed in this study to study other maritime routes that are capable of supporting the system and the interconnection between them, making it feasible to consider a Maritime Communications Network in Medium Wave that is capable of bringing together a good part of the global maritime traffic. It is also within our objective to communicate to the International Maritime Organization (IMO), when the possibility arises, the presented potentialities in this work that have not yet been tapped by GMDSS. **Author Contributions:** Conceptualization, A.-IL-D. and M.A.G. ; methodology, A.-IL-D. and M.A.G. ; software, M.A.G. ; validation, A.-IL-D., M.A.G., A.O.P., E.D.-R.-N., and D.R.-R.; formal analysis, A.-IL-D. and E.D.-R.-N.; investigation, A.-IL-D. and M.A.G. ; resources, A.-IL-D. and M.A.G. ; data curation, A.-IL-D., M.A.G., A.O.P., E.D.-R.-N., and D.R.-R.; writing--original draft preparation, A.-IL-D. and M.A.G. ; writing--review and editing, A.-IL-D., M.A.G., A.O.P., E.D.-R.-N., and D.R.-R.; visualization, A.-IL-D., M.A.G., A.O.P., E.D.-R.-N., and D.R.-R.; supervision, A.-IL-D., M.A.G., A.O.P., E.D.-R.-N., and D.R.-R.; project administration, A.-IL-D. and M.A.G. ; funding acquisition, A.-IL-D. and M.A.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
## 4 Life-Cycle Assessment (LCA)
### Maintenance  
For the operation and maintenance process, the primary environmental impact is induced by vehicle fuel consumed by emergency vehicles and maintenance trucks and the electricity supply for normal operations. Table 10 lists the average fuel and electricity usage per year. The fuel for the emergence vehicles is estimated to be 2381 L/yr. The electricity consumed by the monitoring system is assumed to be 910,345 kWh/yr. Moreover, the fuel for maintenance trucks (i.e., the platform truck and vehicles for mechanical and structural maintenance) is around 1984 L/yr. CO\({}_{2}\) emissions and energy consumption are also calculated based on their corresponding CO\({}_{2}\) emission and energy consumption coefficients, which are also exhibited in Table 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Activity** & **CO\({}_{2}\) Emissions (Ton/Yr)** & **Energy Consumption (\(\times\)10\({}^{6}\) MJ)** \\ \hline Electricity & \
\begin{tabular}{c} Monitoring system, \\ 910,345 kW-h/yr \\ \end{tabular} & 14.6 & 10.6 \\ \hline Truck fuel &
\begin{tabular}{c} Platform truck and \\ vehicle for mechanical \\ and structural \\ maintenance; 1984 L/yr \\ \end{tabular} & 0.12 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Basic annual maintenance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Dimensions** & \begin{tabular}{c} **CO\({}_{2}\) Emissions Coefficients** \\ **(gCO\({}_{2}\)/kg)** \\ \end{tabular} & \
\begin{tabular}{c} **Energy Consumption** \\ **(MJ/kg)** \\ \end{tabular} \\ \hline Ballast &
\begin{tabular}{c} Thickness of 0.3 m; \\ Width of 3.95 m \\ \end{tabular} & 0.037 & 0.04 \\ \hline Sleepers & 280 kg per sleeper & 885.1 & 3.71 \\ \hline Rails & 60.4 kg/m & 1690 & 16.4 \\ \hline Fastening system & 2.4 kg per sleeper & 1690 & 16.4 \\ \hline Elastomeric pads & 1 kg per sleeper & 580.9 & 79.29 \\ \hline Fuel & 8 L/hr & 72.09 & 56.26 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Material characteristics for rail construction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Total Mass (Ton)** & **CO\({}_{2}\) Emissions (Ton)** &
\begin{tabular}{c} **Energy Consumption** \\ **(\(\times\)10\({}^{6}\) MJ)** \\ \end{tabular} \\ \hline Ballast & 6664.4 & 0.25 & 0.27 \\ Sleepers & 1312.3 & 1161.5 & 4.9 \\ Railway & 169.8 & 287.0 & 2.8 \\ Fastening system & 11.2 & 18.9 & 0.18 \\ Elastomeric pads & 4.7 & 2.7 & 0.37 \\ Fuel & 15.2 & 1.1 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 9: CO\({}_{2}\) emissions and energy consumption of material production for rail construction.
## 4 Simulator Development
### Fuel Cell Model  
The fuel cell stack block [22; 23] in the Simscape library was used to model the fuel cell in the HPS. Two different versions are available for this block: a simplified model and a detailed model. The simplified model describes a fuel cell stack as a controlled voltage source in series with constant resistance (see Figure 4). In this model, the fuel cell voltage is expressed as:  
\[V_{fc}=E_{oc}-V_{act}-V_{r} \tag{6}\]  
with activation overpotential of:  
\[V_{act}=NAln\left(\frac{i_{fc}}{i_{0}}\right)\cdot\frac{1}{sT_{d}/3+1}, \tag{7}\]  
and ohmic and mass transport overpotential of:  
\[V_{r}=r_{ohm}\cdot i_{fc}, \tag{8}\]  
where \(E_{oc}\) is the open-circuit voltage (V), \(N\) is the number of cells, \(A\) is the Tafel slope (V), \(i_{fc}\) is the fuel cell current (A), \(i_{0}\) is the exchange current (A), \(T_{d}\) is the settling time (s) defined by three time constants, and \(r_{ohm}\) is the combined ohmic and mass transport resistance (ohm).  
A useful feature of this block is that we do not need to input elusive model parameter values. Instead, we simply input the current-voltage curve of the fuel cell to be used. The model parameter values are then automatically derived from the shape of the curve. This shape can be defined by the voltages at 0 A and 1 A, and the nominal and maximum operating points. A typical current-voltage curve of a fuel cell consists of three regions, and these four points help break the curve apart. These three regions represents the activation, ohmic, and mass-transport losses. We attempted to input the four points of the selected fuel cell, but in its data sheet, clear distinctions were not made between the nominal, peak, and rated powers. They are all likely to be 1 kW. Therefore, the maximum operating point  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{3}{*}{Fuel cell} & Type & PEM & \\  & Configuration & 48S & \\  & Voltage (V) & 45.5–28.8 & \\  & Power (W) & 1000 & Nominal \\ \hline \multirow{3}{*}{Fuel storage} & Type & Composite & Full-wrapped carbon fiber \\  & Configuration & 4P & \\  & Mass (kg) & 0.71 & Compress at 310 bar \\ \hline \multirow{3}{*}{Battery} & Type & NMC & \\  & Configuration & 758P & \\  & Voltage (V) & 29.4–17.5 & \\  & Capacity (Ah) & 20 & Nominal \\ \hline \multirow{3}{*}{DC/DC converter} & Type & Non-isolated & \\  & Voltage (V) & 19–72 to 24 & Input to Output \\ \cline{1-1}  & Power (W) & 960 & Rated \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected component specifications.  
was set to 34 V at 30 A and the nominal operating point was assumed to be 35.5 V at 20 A. Table 4 lists the fuel cell model parameters.
## 3 Problem Optimization
### Optimization Algorithm  
With the approximation method, we develop the SGPA, as presented in Algorithm 1.  
In Algorithm 1, we first set the initialization values for various parameters. Then, on line 1, we set a feasible value for the altitude of the UAV, the length of the finite block, and the transmit power, i.e., \(N\), \(H^{0}\), \(P_{u_{1}}^{0}\), \(P_{u_{2}}^{0}\)). To ensure the feasibility, the height \(H^{0}\) is set to its minimum value, i.e., \(H_{\text{min}}\). According to the finite block length \(n^{*}=N\), the transmit power of U2 is set to a small positive value \(P_{u_{2}}^{\text{s0}}\), which can guarantee covertness. On the contrary, the transmit power of U1 is set to be as high as possible, which can be expressed as \(P_{\text{max}}-P_{u_{2}}^{\text{s0}}\)). On line 3, The approximation \(\lambda_{i}\) is updated according to Equations (44) and (45). Then, the approximate solution is derived by the GP problem, if the optimization result of the throughput satisfies the threshold \(\alpha\). If the accuracy is not satisfied, jump to step 3 to continue the cycle. Finally, the solution to the problem is obtained, and the optimized parameters are outputted.  
```
0: Set the initial values for \(n\), \(\varepsilon\), \(P_{\text{max}}\), \(H_{\text{min}}\), \(H_{\text{max}}\), and \(\beta\).
0:\(H=H^{r+1},P_{u_{2}}^{*}=P_{u_{2}}^{r+1}P_{u_{1}}=P_{u_{1}}^{r+1},\eta=\eta^{r+1}\) as the optimal solution.
1: Find a feasible solution by setting \(N\),\(H^{0}\), \(P_{u_{1}}^{0}\),\(P_{u_{2}}^{\text{s0}}\).
2: Set the number of iterations r = 0.
3: Update \(\lambda_{i}\), \((i=0,\ldots,3)\).
4: Obtain the approximate solution of the GPB \(H^{r+1}\), \(P_{u_{1}}^{r+1}\), \(P_{u_{2}}^{r+1}\), \(\eta^{r+1}\).
5:if\(\left\|\eta^{r+1}-\eta^{r}\right\|\leq\alpha\)then break.
6:else

\(r=r+1\).
7:endif
8: return to step 2.
```  
**Algorithm 1** Iterative optimization algorithm.  
By comparing the size analysis of \(\tilde{\theta}\) and \(\theta\), we can observe that the geometric mean approximation sequence converged to the point, at which the Karush-Kuhn-Tucker (KKT) condition is satisfied.  
According to [33], we list these conditions as follows. First, \(\theta\leq\tilde{\theta}\). This is a tight constraint given in Equation (37) to ensure that any solution to the GP problem is a feasible solution to the SP problem. Since the geometric mean method is used to approximate the arithmetic mean, this condition can be guaranteed.  
Second, \(\theta\big{(}H^{r},P_{u_{2}}^{r},P_{u_{1}}^{r}\big{)}\leq\tilde{\theta}\big{(} H^{r},P_{u_{2}}^{r},P_{u_{1}}^{r}\big{)}\), with which we can ensure that the solution to the GP problem can reduce value for the objective function in each iteration. From Equations (44) and (45), the geometric mean is equal to the arithmetic mean, and thus the condition can be satisfied.  
Third, \(\
abla\theta\big{(}H^{r},P_{u_{2}}^{r},P_{u_{1}}^{r}\big{)}\leq\
abla\tilde{ \theta}\big{(}H^{r},P_{u_{2}}^{r},P_{u_{1}}^{r}\big{)}\), with which we can ensure that the KKT condition for the SP problem can be satisfied by the convergence of a series of GP approximations.
## 3 Results
### Validity Examination  
To test the convergent and discriminant validity of the LEMOCOT, the results of the LEMOCOT were correlated with performance scores of the FSTS test and the BBS, LEFS, EDSS, and MMSE scales from all participants (\(n\) = 60) with MS, as well as their age and sex.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**(A)**} & **M1** & **M2** & \multirow{2}{*}{**Grand Mean**} & **ICC\({}_{2,1}\)** & \multirow{2}{*}{**SEM**} & **MDC\({}_{95\%}\)** \\  & **Mean (SD)** & **Mean (SD)** & & & **(95\% CI)** & \\ \hline Dominant LL & 21.67 (16.15) & 24.83 (16.61) & 23.25 & 0.97 & 3.01 & 8.32 \\  & & & & (0.88–0.99) & 3.47 & 9.58 \\ Non-Dominant LL & 20.00 (16.00) & 23.91 (16.86) & 21.95 & 0.96 & 3.13 & 8.65 \\ Mean Both LL & 20.83 (16.04) & 24.37 (16.63) & 22.60 & 0.96 & 3.13 & 8.65 \\ \hline \multicolumn{2}{c}{(**B**)} & \multirow{2}{*}{27.60 (12.96)} & \multirow{2}{*}{27.11} & 0.94 & \multirow{2}{*}{3.20} & \multirow{2}{*}{8.84} \\ Dominant LL & 26.42 (12.85) & & & (0.89–0.96) & & \\ Non-Dominant LL & 24.82 (14.35) & & 26.20 (13.82) & 25.51 & 0.95 & 3.11 & 8.62 \\  & & & & (0.91–0.97) & & \\ Mean Both LL & 25.62 (12.95) & & 26.90 (12.62) & 26.26 & 0.96 & 2.56 & 7.09 \\  & & & & (0.92–0.97) & & \\ \hline \multicolumn{2}{c}{(**C**)} & \multirow{2}{*}{23.36 (13.48)} & \multirow{2}{*}{26.54 (14.70)} & \multirow{2}{*}{24.95} & 0.97 & \multirow{2}{*}{2.13} & \multirow{2}{*}{5.88} \\ Dominant LL & 24.81 (15.02) & & & 0.96 & & \\ Mean Both LL & 24.09 (14.03) & & 26.40 (14.76) & 25.25 & 0.99 & \\ \hline \multicolumn{2}{c}{LL: lower limb, ICC: intraclass correlation coefficient, CI: confidence interval, M: measurement, SEM: standard error of the measurement, MDC: minimum detectable change.} & \multirow{2}{*}{3.84–8.84 targets.} \\ \end{tabular}
\end{table}
Table 2: LEMOCOT test (**A**) intra-rater within-day reliability (\(n\) = 12), (**B**) inter-rater within-day reliability (\(n\) = 45), and (**C**) inter-rater between-days reliability (\(n\) = 22) of patients with mild to moderate MS.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 4 Segmentation of Dynamic Behavior-Homogeneous Vehicular Cluster  
The limitations of wireless signals and physical spatiotemporal boundaries mean that detection algorithms covering the entire spatiotemporal spectrum could lead to high computational demands and reduced efficiency. To overcome this, the proposed research suggests segmenting VANETs into clusters with high behavioral homogeneity and strong interconnectivity. By focusing node-centric detection on vehicle clusters with similar behaviors, this method optimizes the detection process within the dynamic environment of VANETs, addressing the challenges of timeliness and information redundancy.
# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis
### Comparative Analysis of Tailings Pond Risk Prediction Experiments
The DA-LSTM model was constructed to predict each displacement component separately and the predicted results were superimposed and reconstructed to obtain the predicted value of the displacement, which made the prediction effect of tailings pond dam failure risk significantly improved. Compared with the EEMD-LSTM model, the _MAE_ of the EEMD-DA-LSTM model is reduced by 0.056 mm, the _MAPE_ is reduced by 1.75%, and the _RMSE_ is reduced by 21.9%, which indicates that combining dual attention on the basis of the EEMD-LSTM model can improve the ability of the LSTM network model to memorise the information of the long time series and the memory of the EEMD-LSTM combined model for the extreme points and inflection points. The combined models have better ability to predict extreme points and inflection points. Based on the two sets of sample data, the EEMD-DA-LSTM model was experimentally compared with the single LSTM model, the combined DA-LSTM model, and the combined EEMD-LSTM model, etc., to analyse the results of model prediction and the calculation of error indicators. The experiments proved that compared with other models, the prediction effect of this model for valley-type tailings ponds and mountainside-type tailings ponds is significantly improved and the prediction accuracy is significantly improved, which is suitable for the prediction of tailings pond dam failure risk. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Evaluation Indicators**} \\ \cline{2-4}  & _MAE_/**mm** & _MAPE_/\% & _RMSE_/**mm** \\ \hline EEMD-DA-LSTM & 0.162 & 5.594 & 0.213 \\ LSTM & 0.455 & 12.384 & 0.561 \\ DA-LSTM & 0.197 & 5.883 & 0.265 \\ EEMD-LSTM & 0.218 & 7.351 & 0.273 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction error results for each model for dataset WKB-2. Figure 10: Predicted results of dam failure risk for tailings pond WKB-2.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Model Parameter Details  
The parameters of the proposed model are estimated as follows: In the blur kernel estimation stage, the 7 \(\times\) 7 filter \(w_{\bar{y}}\) is applied over 10 layers, with each layer containing C = 16 \(\times\) 16 filters, resulting in a total of 125,440 parameters (7 \(\times\) 7 \(\times\) 16 \(\times\) 16 \(\times\) 10). In the image restoration stage, which consists of 8 steps with each image sized at 256 \(\times\) 256, the Gabor filter contributes 5000 parameters (25 \(\times\) 25 \(\times\) 8), and the Gaussian radial basis contributes 24,192 parameters (63 \(\times\) 25 \(\times\) 8). In the attention mechanism, the convolution kernel size is 7 \(\times\) 7 with 25 channels. The spatial attention mechanism results in 20,784 parameters (7 \(\times\) 7 \(\times\) 25 \(\times\) 25 + 25), and the channel attention mechanism has the same number of parameters. Parameters for other dimensions are negligible. The total number of parameters in the model is approximately 196,200, or about 0.19 M.
## 4 Experimental Analysis and Results  
This section presents a comprehensive evaluation of the proposed Arabic Named Entity Recognition (NER) model. We assess its performance on multiple datasets, compare it with state-of-the-art methods, and analyze its effectiveness through ablation studies, error analysis, and visualizations. The results validate the model's ability to address challenges specific to Arabic, such as its morphological complexity, nested entities, and overlapping spans.