esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 4 Result and Discussions
### Compare FPM with an Equivalent Model  
The suggested FPM works on a dataset with two sets of values and predicts a new value based on the relation between these pair of values, which makes it more equivalent to linear regression algorithm. As we mentioned in Section 2, the behavior of such systems is fuzzy rather than stochastic or deterministic; accordingly, in this section, we will compare FPM with a linear regression algorithm to prove this assumption. The following steps provide the comparison study for our FPM model with linear regression model:  
1. First, the final exam data are transferred to rate (100%) to fit the attendance rate.
2. Second, building the linear regression model using formula in Equations (5) and (6). Table 4 presents this linear regression model:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\begin{tabular}{c} **Attendance** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Final** \\ **Exam** \\ **100\%** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Attendances –** & \multirow{2}{*}{\begin{tabular}{c} **Final – Mean** \\ **Mean (\(\mathbf{x}-\overline{\mathbf{x}}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **(\(\mathbf{y}-\overline{\mathbf{y}}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **(\(\mathbf{x}-\overline{\mathbf{x}}\))** \\ **(\(\mathbf{y}-\overline{\mathbf{y}}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **(\(\mathbf{x}-\overline{\mathbf{x}}\))** \\ **(\(\mathbf{x}-\overline{\mathbf{y}}\))** \\ \end{tabular} } \\ \hline
1 & 0.76 & 0.375 & \(-0.1365\) & \(-0.31375\) & 0.042827 & 0.018632 \\ \hline  
\[y=b_{0}+b_{1}\;x \tag{5}\]  
\[b_{1}=\frac{\sum_{1}^{n}\left(x-\overline{x}\right)\left(y-\overline{y}\right)}{ \sum_{1}^{n}\left(x-\overline{x}\right)^{2}} \tag{6}\]
3. Third, the regression model is tested using the same test data as shown in Table 5  
4. Finally, Table 5 shows that the linear regression model predicted the final exam with 0.076 MSR, which is greater than our suggested FPM model (0.062242). This indicates that our suggested FPM model gives better results than the learner regression model. In addition, the prediction of the single values in our suggested FPM model showed better results than linear regression (Figures 8 and 9).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Attendance Rate**} & \multirow{2}{*}{**Expected Final Exam**} & \multirow{2}{*}{**Actual Final Exam in**} & \multirow{2}{*}{**Mean Squared Error**} \\  & & **100 \%** & **the Tested Data** & **Mark and Expected** \\  & & **100\%** & **Final Exam Mark** \\  & & & **Using This Model** \\ \hline  
\multicolumn{3}{c}{Mean squared error (MSR)} & 0.075834 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The result of testing the linear regression model.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 4 Simulation Results
### Network Lifetime and Stability Period  
This simulation illustrates the network lifetime of the proposed IEE-LEACH protocol compared with the other existing protocols. The number of dead nodes versus the number of rounds in the WSNs is shown in Figure 6. It is obvious that the nodes start to die after 1678 rounds, 1933 rounds, 2178 rounds, 1836 rounds, 2326 rounds, and 2416 rounds for LEACH, LEACH-M, O-LEACH, LEACH-C, EE-LEACH, and SEEN protocols, respectively. However, for the proposed IEE-LEACH, the nodes do not start to die until 2636 rounds. In addition, the number of rounds for all the nodes dead in the IEE-LEACH protocol is 3160 rounds as compared to 2245 rounds in the LEACH protocol. From the obtained results, the IEE-LEACH improves the network lifetime by approximately 9% to 57% compared with the other protocols.  
Figure 6: Comparison of life cycle.
# An Improved Hybrid Control Scheme of a Switched Reluctance Motor for Torque Ripple Reduction
## 1 Introduction
Other control approaches, i.e., sliding mode control [39] and iterative learning control [40], have also been incorporated into ADRC for performance improvements. In this paper, an improved hybrid control scheme based on model predictive torque control (MPTC) and LADRC was proposed to achieve satisfactory performance. The MPTC consisting of a modified TSF and MPC was introduced to reduce the torque ripple, whereby hysteresis control was eliminated. On this basis, a speed controller using improved LADRC was introduced to enhance the anti-disturbance ability. The remainder of this paper is organized as follows. In Section 2, the modeling of SRM and modified TSF are described and analyzed. In Section 3, we present the MPTC strategy according to MPC instead of hysteresis control in TSF. Then, in Section 4, improved LADRC is utilized to design a speed controller. Experimental results are presented in Section 5, followed by the conclusion in Section 6.
## 5 Conclusions  
We designed a real-time citrus fruit detection system combining a mobile operation platform and edge computing equipment to solve the problems of inconvenient data capture in mountain orchards and the unbalanced speed and accuracy of the target detection model at the edge. By expanding the current most advanced target detection network and improving its feature extraction and reasoning ability, the citrus fruit image data collected by UAV is detected on the edge computing equipment. The test results show that when there are targets with different degrees of occlusion in the natural orchard environment, the accuracy and detection speed of the model are up, which is improved compared with the original baseline. Specifically, we carry out real-time target detection at the edge in the currently popular UAV captured image scene, which is suitable for working in different environments. The main contributions of this paper are as follows:  
1. Benefiting from the strong maneuverability and high security of UAV aerial images, and not limited to the actual scene of Mountain Orchard, we collected and labeled 1800 citrus images for target detection model training.
2. Improve the current most advanced target detection model and use CBAM attention mechanism and data enhancement to improve the generalization and accuracy of the model; At the same time, the L2 regularization method constraint model adds to delete the redundant channel with the minimum weight of 30% and fine-tune it. It is detected in the Jetson nano edge computing device. The results are achieved. While ensuring accuracy, a faster detection speed is achieved.  
In this paper, for citrus detects by combining visual algorithm, mobile flight equipment, and edge operation platform, we hope this work can provide some help for fruit farmers to estimate yield and improve the operation efficiency of mountain orchards.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Multi-Robot Formation Control Simulation Experiment
#### 4.1.4 Simulation and Results Analysis of Dynamic Formation in Barrier-Free Scenes  
In a real scene, there are obstacles in the environment where the robots perform tasks, so in this study we also verified the formation-maintaining algorithm for multiple robots in an obstacle scenario. Under the premise of ensuring the uniform distribution of the global density function, we first used a specific constraint shape to specify the desired formation and then reduced the density value corresponding to the obstacle's position to ensure that the center of mass was not divided in the obstacle area. That is, the characteristics of the CVT algorithm were used to realize the repulsive force field that drives the robot to avoid obstacles, thereby realizing a multi-robot formation in an obstacle scene.  
In MATLAB, it was also assumed that the robot was a first-order dynamic model, the formation scenario map was 30 \(\times\) 30 m\({}^{2}\), 3000 \(\times\) 3000 virtual sensors were evenly placed on the map, and the values of the sensors were fitted to the density function of the CVT algorithm. In the process of dynamic formation, we consider constructing virtual constraint boundaries. Since the density function is always uniformly distributed, the robots are distributed in a square formation in the area \(Q\), surrounded by the dynamic virtual constraint boundary.  
The density in the scene map was set to be uniformly distributed, and the area \(Q\), surrounded by the virtual constraint boundary, was evenly divided into four Voronoi cells using the Voronoi algorithm. Assuming that the movement path of area \(Q\) is known, the control robot completes the tracking of its corresponding center of mass and maintains a square formation. The formation under the obstacle-free scenario is shown in Figure 8a,b; when the number of iterations \(k=75\), the position error eventually tends to zero. To measure the effect of formation maintenance, we assume that the geometric centroid of the Voronoi algorithm is the center of the formation. The sum of the distances between the robots and the formation center is shown in Figure 8d, and when the distance tends to \(\sqrt{2}*l\), the formation reaches a stable state, where \(l\) is the side length of area \(Q\).  
Figure 7: Multi-robot linear formation simulation: (**a**) density function distribution; (**b**) linear formation; (**c**) position error; (**d**) cost function.
# Synchrotron X-ray Radiation (SXR) in Medical Imaging: Current Status and Future Prospects
## 4 Future Direction
### Deep Learning (DL) for SXR Imaging  
Most of the investigations using SXR are mainly carried out for in vitro and animal models or on the excised human organs at the research level. Though SXR promises to provide images with higher soft-tissue contrast and spatial resolution compared to the conventional X-ray, the main limitations to be used in routine laboratory and clinical uses are the establishment cost both in terms of land area, instrumentation, and maintenance.  
In radiotherapy, the complementary information provided by two different imaging modalities (e.g., electron density by provided CT, higher soft-tissue contrast by MRI, and higher sensitivity and specificity by PET) has been exploited to carry out precise treatment planning [53; 54; 55; 56]. In this approach, images from two different modalities are first registered in a deformable manner followed by generation of target volume and dose map.  
With the current advancement of Deep Learning, it is now possible to generate synthetic images of one modality provided that the images of other modalities of the patient are available (e.g., from CT to MRI or MRI to CT) with higher precision and accuracy with peak signal-to-noise ratio of 29.38 \(\pm\) 1.75 and structural similarity matrix index of 0.98 \(\pm\) 0.01 [57]. It has also been reported that it is feasible to generate accurate synthetic images for MRI with higher strength magnets (e.g., 7T) if the lower strength magnet MRI (e.g., 3T) images are available [58].  
The most common DL architecture is U-net. The network first downsamples the original images of one modality (encoder), generate the synthetic images of another modality at down sample level, and then upsample it back to the original image space (decoder). Once the network is trained on a large dataset to calculate the optimized parameter, it can be used to any data set of the same modality on which it has been trained to provide the image of the other modality. Different U-net architectures have been used to synthesize images from CT to MRI [59] and MRI to CT [57]. Example of a U-net architecture to generate CT images from MRI image is shown in Figure 9.  
Example synthetic CT images from a T2-weighted MRI in comparison to the original CT images generated using residual learning-based u-shaped deep neural network (RUN) are shown in Figure 10 [60].  
In the future, such advancement in synthetic image generation between different modalities can be utilized to generate SXR images, provided that the X-ray-based images of the object are available using deep learning. This approach will make it feasible to have high-quality SXR images at routine laboratory or clinical practice.  
Figure 9: MRI image to synthetic CT using U-net. It consists of a contracting path or downsample (**left side**) and an expansive path or upsample (**right side**) with 3 \(\times\) 3 unpadded convolutions followed by a rectified linear unit (ReLU) and a 2 \(\times\) 2 max pooling operation with stride of 2 for downsampling. Reprinted with permission from [57].
## 4 Discussion
### Analysis of Trade-Off According to Patch Coverage Change
#### 4.2.2 Patch-Level Robustness Under Reduced Context  
Meanwhile, as also discussed in Section 3.3, upscaling causes each patch to cover a smaller real-world area, thereby reducing the available contextual information. In this section, we further analyze how each method responds when patch-level context is diminished. Upscaling enlarges the input parcel by a certain factor but extracts patches of the same size, so effectively, the patch coverage shrinks. This leads to a general decline in model performance, though each method exhibits different levels of robustness to this loss of information.  
According to the patch-level metrics presented in Table 5 (Section 3.3), switching from 1\(\times\) to 2\(\times\) caused the baseline (FPE + NCB) to drop by 0.2015 in accuracy, by 0.1835 in macro F1 score, and by 0.2151 in Kappa. In contrast, RPE + CBS led to a declines of only 0.0068 (accuracy), 0.0111 (macro F1), and 0.0082 (Kappa), indicating the smallest performance drop and maintaining high predictive stability, even under reduced context conditions. This RPE + CBS combination exhibited the least performance degradation at the patch level, thanks to its complementary approach of increased data diversity and balanced class sampling. This confirms that this strategy remains robust in scenarios where patch-level context is limited.
# Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
## 5 Case Studies
### Case Study 2: AI in Maritime Environmental Monitoring  
5.2.1. Project Overview: Port of Rotterdam  
The Port of Rotterdam, one of the largest and busiest ports in Europe, has implemented advanced AI technologies to monitor and manage its environmental impact. Facing increasing regulatory pressures and community concerns, the port sought to enhance its air and water quality monitoring systems [232; 251; 252].  
5.2.2. AI Implementation: Comprehensive Environmental Monitoring System  
The Port of Rotterdam depkazi da jos nije accessable I da kepuje loyed an AI-powered air quality monitoring system that integrated data from a network of sensors placed throughout the port area. These sensors continuously measured levels of pollutants such as NOx, NOx, CO2, and particulate matter. AI algorithms analyzed these data in real time, identifying pollution sources and patterns [253; 254].  
The system provided actionable insights, allowing port authorities to take immediate corrective actions such as adjusting traffic flows or implementing emission reduction measures. Additionally, the AI system could forecast air quality based on weather conditions and port activities, enabling proactive management to prevent exceedances of air quality standards [246; 255].  
To address concerns about water pollution, the port implemented an AI-based water quality monitoring system. Sensors were installed in various locations to measure parameters like pH, dissolved oxygen, turbidity, and the presence of contaminants such as heavy metals and hydrocarbons. AI algorithms processed these data to detect anomalies and potential pollution events [2; 78; 256].  
The system enabled the early detection of spills or leaks, allowing for rapid response and mitigation. Furthermore, AI models predicted the dispersion of pollutants, helping port authorities to understand and manage the environmental impact of port operations effectively [257; 258; 259; 260].
## 4 Experimental Results
### Attention Module Comparison Experiments  
To validate the effectiveness of the proposed DHAM, other classical attention mechanisms (BAM, CBAM, ECA) and recently proposed attentions (LSKA, CCA) were compared in the same positions within the network and based on the selection of the VGG16 encoder. The evaluation criteria included the mIoU, mPA, and number of model parameters, as shown in Table 4. Large selective kernel attention (LSKA) [28] can adjust the receptive fields but does not perform well for small soldering defects. Context anchor attention (CCA) [29], on the other hand, enhances the central features by utilizing the interdependencies between pixels, leading to improved segmentation accuracy. BAM [30], CBAM [31], and DHAM are all hybrid attention mechanisms based on channel and spatial attention. ECA [32], however, focuses solely on channel information while ignoring spatial information, resulting in no improvement in the segmentation performance. From the experiments, it is evident that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function** & **mIoU/\%** & **mPA/\%** \\ \hline Dice & 60.28 & 63.06 \\ Focal & 61.53 & 66.80 \\ CE & 62.26 & 66.45 \\ CE + Dice & 81.56 & 87.14 \\ Focal + Dice & 81.74 & 87.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of different loss functions when utilizing our proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Encoder** & **mIoU/\%** & **mPA/\%** & **Parameters/M** \\ \hline MobileNetV3 & 73.33 & 80.76 & 8.96 \\ ResNet50 & 75.80 & 82.52 & 43.93 \\ VGG16 & 80.10 & 86.10 & 24.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different encoders.  
the hybrid attention, which simultaneously focuses on defect areas from both channel and spatial perspectives, enhances the PCB soldering defect segmentation performance. Using BAM, the mIoU reaches 80.25% and the mPA reaches 86.24%, while CBAM achieves an mIoU of 80.45% and mPA of 86.69%. DHAM, by strengthening the adaptability to context information at different scales, achieves the greatest improvement in the model segmentation performance, with an mIoU of 80.50% and mPA of 86.67%, and, as a lightweight module, it adds almost no additional parameters.
# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 3 Results and Discussion
1** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \multicolumn{10}{c}{BCN 1’s WASN data} \\ \hline HC 1 & 9.10 & 13.89 & 17.09 & 23.23 & 26.30 & 29.52 & 37.63 & 40.19 & 41.00 & 45.05 \\ KM 1 & 7.78 & 12.56 & 24.89 & 22.37 & 28.27 & 29.44 & 38.80 & 47.25 & 54.12 & 53.34 \\ DIANA & 26.85 & 28.33 & 37.19 & 40.71 & 45.27 & 48.09 & 51.31 & 53.45 & 55.73 & 58.23 \\ PAM & 15.20 & 20.51 & 25.78 & 33.39 & 38.66 & 44.63 & 46.66 & 47.84 & 55.92 & 62.45 \\ CLARA & 15.20 & 19.74 & 22.84 & 33.39 & 36.49 & 42.51 & 46.01 & 50.15 & 58.31 & 58.44 \\ SOM & 7.78 & 12.56 & 24.89 & 32.98 & 40.81 & 53.87 & 56.66 & 54.86 & 70.92 & 83.47 \\ GAUSS & 21.38 & 39.29 & 33.10 & 53.58 & 43.54 & 40.99 & 42.92 & NA 2 & 98.71 & 68.33 \\ SOTA & 29.91 & 34.73 & 36.59 & 44.04 & 47.04 & 48.21 & 62.99 & 72.67 & 74.18 & 82.76 \\ \hline \multicolumn{10}{c}{MAD 1’s WASN data} \\ \hline HC 1 & 11.98 & 19.48 & 23.13 & 25.89 & 32.93 & 35.93 & 39.33 & 41.66 & 44.27 & 46.08 \\ KM 1 & **10.08** & 21.10 & 25.50 & 28.10 & 33.08 & 38.07 & 39.48 & 41.81 & 44.43 & 50.40 \\ DINA  
mendation is 3 clusters. However, a hypothesis could be that Madrid only has three of the four behaviors identified in the full dataset. As a summary, regarding the federated dataset, see Table 3 for details, the Connectivity metric is minimized with the Hierarchical Agglomeration algorithm for \(k=3\) clusters. It is important to note that, when the amount of elements to group is small, an increase in the number of clusters will increase Connectivity, thus this metric tends to select low values for the number of clusters. Then, the Dunn index selects K-means and Hierarchical Agglomeration clustering with \(k=12\) clusters as can be seen in Table 4. Finally, the Hierarchical Agglomeration algorithm for \(k=4\) has been selected by the Silhouate Width metric, see Table 5, showing that the Hierarchical Agglomeration method has a good equilibrium between the three clustering characteristics presented in Section 2.3. After this first discussion, more details for \(k=3\) and \(k=4\) clusters cases are explained below. Applying the Hierarchical Agglomeration algorithm using \(k=3\), the data are divided into different groups, as it is graphed in a Dendogram in Figure 2. This Figure 2 shows the three main patterns that the algorithm has identified. To study the behavior of these three clusters, four box-plots graphs are shown in Figure 3, corresponding to the parameters used for the training phase, \(L_{\mathrm{d2019}}\), \(L_{\mathrm{e2019}}\), \(L_{\mathrm{n2019}}\) and \(sd_{\mathrm{2019}}(L_{\mathrm{den1d}})\). It can be observed in Figure 3 that, the first cluster is related to the nodes with high sound pressure levels during the day and evening period, medium sound pressure levels during the night, and the lowest standard deviation of the three clusters, to sum up, there are 42 nodes with
## 1 Introduction  
### Background  
Compliant mechanisms realize their function through the elastic deformation of their structure [1]. Although compliant mechanisms have a limited range of elastic deformation, they are used in various important applications due to their simplicity, passive holding, low actuation energy, small footprint, large stroke with small restoring forces, and negative stiffness zone [2].  
The compliant bistable mechanism is a mechanical structure that maintains two different stable states without requiring an external energy input. There is a deflection range between the two different stable states, which is also the motion displacement of the compliant bistable mechanism. This mechanism is promising for applications in systems that require two operating states, such as switches [3], grippers [4; 5], energy harvesters [6; 7], soft actuators [8], and metamaterials [9]. Various types of compliant bistable mechanisms are available, such as the compliant four-link bistable mechanism [10; 11], compliant tensoral-compressural bistable mechanism [12], Young bistable mechanism [13], and bistable mechanism using curved-beam plates [14]. However, they are generally designed to transform between the two steady states in a plane. Therefore, out-of-plane deflection is rarely considered in the design. When off-axis or eccentric loads are present, the out-of-plane motion of the shuttle results in the bistable behavior of the mechanism [15]. For example, when utilized for shock sensors and threshold gyroscopes [16; 17], the bistable mechanisms must maintain consistent bistable behavior while in motion to maintain sensing accuracy.