esa-sceva/satcom-chunk-collection huggingface dataset:
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 3 Methodology of the Proposed Bangla Alphabet Detection Algorithm
### Overview of YOLOv11 and ResNet50  
3.1.1. YOLOv11  
YOLO (You Only Look Once) is a single-stage object detection algorithm that reformulates object localization and classification as a unified regression problem. Unlike two  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Models** & **Datasets** & **Classes** & **Accuracy** \\ \hline \multirow{2}{*}{[PERSON] et al. [11]} & \multirow{2}{*}{CNN} & Banglalekha-isolated & \multirow{2}{*}{50} & \multirow{2}{*}{91.23\%} \\  & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [12]} & Transfer learning, & \multirow{3}{*}{3 DCNN} & Banglalekha-isolated & \multirow{3}{*}{84} & 87.45\% \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [13]} & \multirow{2}{*}{SVM} & 27,000 samples & \multirow{2}{*}{50} & 82.44\% valid, \\  & & & & 81.84\% test \\ \hline \multirow{3}{*}{[PERSON] et al. [14]} & \multirow{2}{*}{ResNet-18} & Banglalekha-isolated, & \multirow{2}{*}{84} & \multirow{2}{*}{94.52\%} \\  & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & \multirow{2}{*}{DCNN} & 76,000 images & \multirow{2}{*}{50} & \multirow{2}{*}{99\%} \\ \hline \multirow{3}{*}{[PERSON] et al. [16]} & \multirow{3}{*}{YOLOv5} & Banglalekha-isolated & \multirow{3}{*}{83} & 96\% in \\  & & & & character and \\  & & & & 91\% in word \\ \hline \multirow{3}{*}{[PERSON] et al. [17]} & ResNet-50 with transfer learning & \multirow{3}{*}{84} & \multirow{3}{*}{96.12\%} \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different Bangla alphabet or character datasets using different models.  
stage detectors, YOLO performs detection in a single forward pass, enabling high-speed, real-time inference. The YOLOv11 model used in this research represents an evolved and customized version of the original framework, adapted explicitly for fine-grained character detection.  
Model Architecture: YOLOv11 segments the input image into a \(S\times S\) grid, with each grid cell predicting bounding boxes and associated class probabilities. Each cell is responsible for detecting objects whose centers fall within its region. This design facilitates rapid and efficient detection, which is particularly important for real-time applications such as live Bangla script recognition.  
Feature Extraction and Enhancements: The architecture incorporates multi-scale feature maps to effectively detect characters of various sizes and orientations. Improvements over previous YOLO versions include enhanced anchor box prediction mechanisms and refined feature aggregation strategies. These enhancements significantly boost performance in detecting small, overlapping, and visually similar characters, which are common in the Bangla script.
## 5 Controllable Queuing Model
### Continuous-Time Markov Decision Process  
To address the issue of optimal resource reallocation, it is crucial to select the best course of action for determining the volume of resources to be reallocated based on the current state of the system. This problem can be defined as a continuous-time MDP. The MDP model is represented by a four tuple \((\mathcal{S},\mathcal{A}_{\mathbf{s}},\mathbf{Q}_{\alpha},R(\mathbf{s}))\):  
1. Set \(\mathcal{S}\) of states \(\mathbf{s}=(m,n_{1},n_{2})\in\mathcal{S}\), defined by Formula (1);
2. Set \(\mathcal{A}_{\mathbf{s}}\) of actions \(a\) available from state \(\mathbf{s}\);
3. Matrix \(\mathbf{Q}_{a}\) of transition rates under action \(a\);
4. Reward \(R(\mathbf{s})\) received while in state \(\mathbf{s}\).
## 3 Results and Discussion  
### Pixel Dimension  
Regression was conducted by utilizing the three windows (1 \(\times\) 1, 3 \(\times\) 3 or 5 \(\times\) 5) to determine which one was the best for this study. The results confirmed that the 3 \(\times\) 3 window achieved the best R\({}^{2}\) value compared with those of the two other windows. Table 2 shows the result of the application of different windows. It can be clearly seen that the lowest and highest R\({}^{2}\) values were obtained by the 1 \(\times\) 1 and 3 x 3 windows, respectively. The R\({}^{2}\) value for each window is given by 0.62, 0.79 and 0.77, respectively. This result indicated that the regression coefficient decreased when the window was larger than 3 \(\times\) 3. Furthermore, 3 \(\times\) 3 windows instead of 1 \(\times\) 1 and 5 \(\times\) 5 windows have been widely used and discussed by previous researchers [34; 53; 54; 55; 56].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Window** & **Algorithm** & **R\({}^{2}\)** \\ \hline \(1\times 1\) & \(y=659.26x-468.15\) & 0.62 \\ \(3\times 3\) & \(y=564.68x-881.98\) & 0.79 \\ \(5\times 5\) & \(y=557.53x-358.4\) & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression analysis for different windows.  
Figure 2: Data processing flowchart.
## 4 Analysis of Optimization Calculation and Results
### Comparative Analysis before and after MCS Optimization  
The comparison between the single-level multi-objective programming optimization and the bi-level multi-objective collaborative optimization, along with the objective parameters before the optimization, is shown in Figure 10. It can be seen that the investment cost before optimization is low, but it is greatly increased after optimization. Analysis shows that this is because the optimization takes into account the relevant factors of the whole life cycle of the system increasing the renewable energy input during the optimization process and inducing the initial investment cost added.  
However, compared with the planning optimization and collaborative optimization, the investment cost of collaborative optimization is slightly lower. The analysis shows that this is because collaborative optimization comprehensively considers the influence  
Figure 10: Comparison of before and after optimization of the example MCS.  
Figure 9: Comparison of decision variables under optimal decisions.  
of the operation after the completion of the system. The early construction investment is optimized more reasonably in the collaborative process. The improvement of renewable energy input and permeability of engineering are considered from different levels or angles by both optimization models. Extending to the whole life cycle of the MCS, the annual operational cost of the system is significantly reduced after optimization. The change in the system maintenance cost before and after optimization is small. Analysis of this phenomenon indicates that the system maintenance cost is an unavoidable part of the expenditure in the course of the system's operational life cycle; the optimization has little effect on it. Annual carbon emissions of purchased electricity and natural gas are greatly reduced after optimization, mainly due to the use of renewable energy. Although the purchased electricity carbon emissions from bi-level optimization are slightly higher than those from single-level optimization, the natural gas carbon emissions of bi-level optimization are much lower than those of single-level optimization. Extending to the whole life cycle, the overall carbon emissions of the bi-level optimization are still lower than those of the single-level optimization.  
Consequently, it is of great practical significance to improve the investment in renewable energy and maintain a good penetration rate of renewable energy for the realization of the goal of \"carbon neutrality\".
## 3 Analysis of Rock Damage Characteristics
### Mesoscopic Analysis of Rock Damage Characteristics
For the charge segment and stemming segment, \(\Delta\alpha\) for different specimens decreases with increasing in situ stress, indicating that a higher in situ stress results in less damage. At the bottom segment, \(h\) = 20 mm, as the in situ stress increases from 0 MPa to 3 MPa, \(\Delta\alpha\) decreases from 0.3546 to 0.2323. At the bottom segment, \(h\) = 40 mm, as the in situ stress increases from 0 MPa to 3 MPa, \(\Delta\alpha\) decreases from 0.3588 to 0.2398. Similarly, at \(h\) = 60 mm in the charge segment, \(\Delta\alpha\) decreases from 0.3736 to 0.2622. At \(h\) = 80 mm in the charge segment, \(\Delta\alpha\) decreases from 0.3881 to 0.2710. At \(h\) = 100 mm in the stemming segment, \(\Delta\alpha\) decreases from 0.3956 to 0.2806. Under in situ stress conditions of 1 MPa, 2 MPa, and 3 MPa, the \(\Delta\alpha\) values at \(h\) = 10 mm, \(h\) = 20 mm, and \(h\) = 30 mm are very close and do not decrease with increasing in situ stress. This is primarily because, under these in situ stress conditions, the rock in the range of \(h\) = 0-30 mm (located at the bottom segment) does not exhibit significant macroscopic damage caused by blasting. The differences in \(\Delta\alpha\) values are mostly attributed to the initial damage of the rock or errors in the CT scanning. Under the action of blasting loading, rock forms a crushed zone, a fractured zone, and an elastic vibration zone extending outward from the borehole. The development and connectivity of the fractured zone leads to macroscopic rock failure. Under in situ stress, the rock's ability to resist the effects of blasting stress waves and blasting gas is enhanced. Therefore, in the experiments, the overall damage level of the sandstone specimens gradually decreases with increasing in situ stress. Additionally, the experiment uses hole-bottom blasting, with a shorter stemming segment. Due to the reflection of blasting stress waves [24], in the same sandstone specimen, the damage level is the highest in the stemming segment, followed by the charge segment, and the lowest in the bottom segment. With increasing in situ stress, the damage levels in all three segments gradually decrease, and the cracks in the bottom segment disappear first, thus maintaining better integrity.
## 4 Performance Modeling of the IASF-IoT Framework
### Performance Parameters and Equations  
The model defines several key parameters to represent the operational characteristics of typical IoT devices. These parameters are based on common specifications found in the literature for low-power IoT sensors [65; 66; 67]:  
* \(P_{c}\): Processing power of the IoT device (MHz);
* \(M\): Available memory (MB);
* \(E_{b}\): Battery capacity (mAh);
* \(B\): Network bandwidth (Mbps);
* \(S_{o}\): Size of security overhead (KB);
* \(F\): Frequency of security operations (per hour);
* \(V\): Voltage (V);* \(T\): Total runtime (hours);
* \(L_{base}\): Baseline network latency (seconds).  
Using these parameters, the model employs a set of equations to calculate the projected impact of the IASF-IoT framework on device performance.  
Processing Impact: The fraction of processing power used for security operations is calculated as  
\[PI=\frac{S_{o}\times F}{P_{c}\times 3600}, \tag{10}\]  
Memory Usage: The fraction of memory used by security features is determined by  
\[MU=\frac{S_{o}}{M}, \tag{11}\]  
Energy Consumption: The energy consumed per hour due to security operations is calculated as  
\[EC=\frac{PI\times E_{b}\times V}{T}, \tag{12}\]  
Network Latency: The additional latency introduced by security measures is computed using  
\[L=\frac{S_{o}\times 8}{B\times 10^{6}}+L_{base}, \tag{13}\]
# A Method of Estimating an Object's Parameters Based on Simplication with Momentum for a Manipulator
## 4 Parameter Estimation Simulation
### Simulation Result and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Motion Range**} \\ \cline{2-10}
**Estimation** & \multicolumn{3}{c}{**0.1**} & \multicolumn{3}{c}{**0.05**} & \multicolumn{3}{c}{**0.025**} \\ \cline{2-10}
**errors** & \multicolumn{3}{c}{**Noise Level**} & \multicolumn{3}{c}{**Noise Level**} & \multicolumn{3}{c}{**Noise Level**} \\ \cline{2-10}
**(\%)** & **Low** & **Mode** & **High** & **Low** & **Mode** & **High** & **Low** & **Mode** & **High** \\ \hline \(e_{m}\) & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \(e_{r_{G/R,y,x}}\) & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.3 \\ \hline \(e_{r_{G/R,y,y}}\) & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\ \hline \(e_{r_{G/R,y,z}}\) & 0.3 & 0.1 & 2.6 & 0.1 & 0.5 & 3.8 & 0.7 & 1.8 & 9.8 \\ \hline \(e_{l_{xx}}\) & 0.2 & 0.9 & 5.6 & 0.6 & 0.8 & 7.8 & 1.2 & 2.0 & 7.3 \\ \hline \(e_{l_{xy}}\) & 0.2 & 3.0 & 4.5 & 2.8 & 1.2 & 23.5 & 8.2 & 5.6 & 75.5 \\ \hline \(e_{l_{zz}}\) & 2.3 & 1.9 & 6.4 & 2.6 & 2.0 & 6.1 & 2.0 & 6.4 & 18.8 \\ \hline \(e_{l_{yy}}\) & 0.0 & 0.2 & 4.0 & 0.7 & 0.3 & 8.2 & 0.8 & 1.6 & 22.5 \\ \hline \(e_{l_{yz}}\) & 0.3 & 1.4 & 15.9 & 2.1 & 9.4 & 26.9 & 1.1 & 0.9 & 41.0 \\ \hline \(e_{l_{zz}}\) & 0.1 & 0.6 & 2.5 & 0.4 & 1.0 & 0.1 & 1.3 & 3.0 & 4.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation error for Object 2. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Motion Range**} \\ \cline{2-10}
**Estimation** & \multicolumn{3}{c}{**0.1**} & \multicolumn{3}{c}{**0.05**} & \multicolumn{3}{c}{**0.025**} \\ \cline{2-10}
**Errors** & \multicolumn{3}{c}{**Noise Level**} & \multicolumn{3}{c}{**Noise Level**} & \multicolumn{3}{c}{**Noise Level**} \\ \cline{2-10}
**(\%)** & **Low** & **Mode** & **High** & **Low** & **Mode** & **High** & **Low** & **Mode** & **High** \\ \hline \(e_{m}\) & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\ \hline \(e_{r_{G/R,y,x}}\) & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\ \hline \(e_{r_{G/R,y,y}}\) & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline \(e_{r_{G/R,y,z}}\) & 1.0 & 1.0 & 0.4 & 1.0 & 1.0 & 1.8 & 0.9 & 0.0 & 7.6 \\ \hline \(e_{l_{xx}}\) & 3.0 & 3.3 & 0.6 & 3.3 & 3.2 & 9.8 & 3.6 & 1.1 & 35.8 \\ \hline \(e_{l_{xy}}\) & 0.7 & 4.9 & 8.3 & 0.1 & 3.4 & 22.1 & 4.4 & 8.8 & 18.8 \\ \hline \(e_{l_{zz}}\) & 0.0 & 0.3 & 1.0 & 0.9 & 0.4 & 9.6 & 2.4 & 0.8 & 20.9 \\ \hline \(e_{l_{yy}}\) & 4.5 & 4.6 & 1.6 & 4.3 & 5.1 & 10.4 & 4.5 & 0.0 & 33.5 \\ \hline \(e_{l_{yz}}\) & 3.2 & 2.5 & 1.9 & 2.9 & 0.4 & 0.2 & 0.1 & 2.8 & 9.2 \\ \hline \(e_{l_{zz}}\) & 0.6 & 0.2 & 1.1 & 0.6 & 1.8 & 3.7 & 1.4 & 0.4 & 7.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Estimation error for Object 3.
# Agrochemical Contamination and Ageing Effects on Greenhouse Plastic Film for Recycling
## 4 Results  
The results of the mechanical tests are reported in Table 2.  
In Figures 3 and 4 the results of the mechanical tests are reported in terms of percent variation over time compared to the value N0 of virgin PE-LD plastic film (i.e., the initial value has been considered = 100%). Specifically, these figures picture the variations in maximum strength and elongation at break due to ageing in contact with the respective  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PE-LD Film** &
\begin{tabular}{c} **Maximum Strength** \\ **(N mm\({}^{-2}\))** \\ \end{tabular} & **Percentage Elongation at Break (\%)** \\ \hline N0 & 26.00 & 716.06 \\ \hline N2 & 24.63 & 687.44 \\ \hline A2 & 20.75 & 601.16 \\ \hline F2 & 16.30 & 402.33 \\ \hline N4 & 20.18 & 580.87 \\ \hline A4 & 13.80 & 402.33 \\ \hline F4 & 10.01 & 51.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the mechanical tests.  
Figure 2: Mechanical analysis of greenhouse plastic films: specimen dimensions (**left**), tensile test (**right**).  
contaminants. From these results, it is evident that the ageing of PE-LD plastic films in contact with agrochemicals greatly reduces their mechanical characteristics. After 4 weeks of artificial ageing, i.e., around six months in real conditions of ageing in Southern Italy, the material in contact with a pesticide/fungicide has reduced its elongation at break below 50% of the value as new, so it has to be removed.  
The results in terms of Carbonyl Index (CI) confirm the tendency of the material to degrade rapidly when in contact with anti-aphid and fungicide contaminants. More specifically, as reported in Table 3--where the percent increase in the Carbonyl Index over the different time period (i.e., after two weeks and after four weeks) are shown--the increase in CI of the uncontaminated sample is lower after 4 weeks of artificial ageing, clearly suggesting degradation effects taking place (mostly chain scissoring). On the other hand, the samples sprayed with agrochemicals experience a remarkable increase in CI values after 4 weeks of ageing. These samples are most likely undergoing degradation of the polymeric chains, whereby new carbonyl groups are being created, justifying an increase in the value [29]. As in the case of mechanical performances, the sample contaminated  
Figure 4: Elongation at break percent variation compared to virgin film (i.e., initial value N0 = 100%) on aged PE-LD plastic film with no contamination (N), one spraying with anti-aphid insecticide (A) and one spraying with fungicide (F).  
Figure 3: Maximum strength percent variation compared to virgin film (i.e., initial value N0 = 100%) on aged PE-LD plastic film with no contamination (N), one spraying with anti-aphid insecticide (A) and one spraying with fungicide (F).  
with fungicide is subject to the most extensive degree of degradation through carbonyl bonds formation.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 5 Discussion
SAR signals contain inherent speckle noise and complex texture patterns, which can make it harder for DL approaches to properly converge. The variations in performance could be the models struggling to learn the underlying patterns in the SAR data while also handling noise. Thus, it is reasonable to say that with less epochs (10), the models might have learned some general patterns but not yet captured the complexity or fine details of the SAR images. As training progresses, the models may start to overfit to noisy features or irrelevantpatterns (leading to a decreased performance at 50 epochs). However, with more epochs (100, 200), the techniques might learn to discard these noisy or redundant features, resulting in a performance recovery. As previously mentioned, we expected a better performance from a technique with 5-shot tasks than with 1-shot ones. But it is important to realise whether this difference is statistically significant. If the difference is not statistically significant, practitioners may choose a less costly scenario with only one image per class in the support set. However, the answer to RQ_3 showed strong statistical evidence that the 5-shot option is significantly better than the 1-shot solution. This highlights the importance of having more images per class in the support set. There is no evidence that having images in the test set that are more similar to some images in the training set improves the performance of the approaches. Nor did we find that having less similar images worsens the situation. Perhaps a different choice of similarity metric, instead of the SSIM, could yield different results, but this metric was selected due to its widespread use. Randomly \"selecting\" classes for the test set proved to be the best option.
## 3 Research on Image Acquisition System of Lining Cracks
### The Method of Fast Acquisition Parameter Calculation
#### 3.2.1 Lens Zoom  
The zoom ratio adjustment is realized by adjusting the focal length of the lens. By selecting an appropriate focal length, the picture frame captured by the camera can be zoomed to the required range, and the whole section of the tunnel can be photographed. From the focal length calculation formula in Equation (2), after determining the working distance between the lens and the object, the actual size of the shooting range, and the size of the camera sensor, the focal length can be calculated according to the triangle similarity principle. The size of the camera sensor is 4.84 mm \(\times\) 6.47 mm, and the actual shooting range has been determined as 1.392 m \(\times\) 1.040 m in Section 3.1. Therefore, the calculation formula of the focal length is shown in Equation (5) or (6):  
\[f=\frac{s_{w}\times d_{i}}{l_{w}}=\frac{6.47d_{i}}{1392}=0.0047d_{i} \tag{5}\]\[f=\frac{s_{h}\times d_{i}}{l_{\rm h}}=\frac{4.84d_{i}}{1040}=0.0047d_{i} \tag{6}\]  
where \(d_{i}\) is the working distance of target position \(i\), \(s_{w}\) and \(s_{h}\) are the size of the camera sensor in different orientations, and \(l_{w}\) and \(l_{h}\) are the length of view in different directions.  
Using Equation (5) or (6), the camera zoom can be calculated after the camera working distance \(d_{i}\) is determined.
## 5 Results and Discussions
### Best Solutions and Impact of LVFIU  
Finally, Table 7 summarizes the best solutions for each case. In the columns #Vessel and Speed factor, the underscored values mean that they are decision factors and have been prescribed as the best solutions by the optimization model. Several subcolumns under \"Costs\" provide detailed information for the economic performance metrics. The total cost is the sum of the rental and operating (voyage cost and port due) costs. Column Saving  
Figure 5: Impact of the number of vessels and voyage speeds under FAFU: (**a**) transported volume; (**b**) cost breakdown at the optimal travel speeds.  
gives the cost savings from the difference between the total costs of case C0 and the case in each row. Column Saving (%) is calculated by the cost-saving of each case divided by the total cost of case C0. The undercored values in #Vessel and Speed Factor mean that they are prescribed by the optimization model.  
In case C4, we applied the LVFU policy using the current number of vessels and speed. The LVFU policy shows potential for a cost saving of 1.2% (125,200 per year) compared to case C0. All such cost saving is from the savings in the operating costs. In case C5, which also utilizes two large and two small vessels, the optimization model prescribed the same speed factors as case C1 (0.7 for the FS factor and 0.7 for the BS factor). Case C5 could show a slight cost saving of 0.2% compared to case C1. The cost savings by applying the LVFU policy are 1.2% from case C0 to C4 at the current voyage speed and 0.2% from case C1 to C5 at the lowest voyage speed. We infer that lowering the voyage speed may provide less opportunity to select a large vessel from the idle vessels in the waiting area by making most vessels travel slowly somewhere in the system.  
However, the optimal number of vessels in case C6 is the same as that in case C2. Moreover, case C7 has the same optimal number of vessels and speed factors as case C3. The LVFU policy has not affected any results of cases C6 and C7 because the optimal solutions for cases C6 and C7 use three large vessels only, making no difference between FAFU and LVFU.  
Overall, cases C3 and C7 generated the same best results among all cases tested, saving 26.8% (2,692,410 USD/year) of total costs compared with case C0. The number of vessels and speed factors significantly affected the total costs. The LVFU policy has a limited effect when the number of large and small vessels is mixed and the voyage speed is not slow. However, the LVFU policy is still a recommendable operating option because it can be adopted easily for practical operations.