esa-sceva/satcom-chunk-collection huggingface dataset:
# Bird Detection on Power Transmission Lines Based on Improved YOLOv7
## 4 Experimentation and Analysis
### Experimental Dataset  
The COCO (Common Objects in Context) dataset is a widely recognized benchmark in computer vision, serving as a comprehensive collection of images for object detection,segmentation, and image captioning tasks. Comprising over 200,000 images with diverse scenes, COCO features a rich array of object categories in complex real-world settings, making it an essential resource for developing and evaluating advanced algorithms in understanding image content, particularly in the context of intricate scenes and objects of varying scales. The annotations encompass accurate bounding boxes, segmentation masks, and descriptive captions, thereby equipping researchers with top-quality data to propel advancements and innovation in the realm of computer vision.  
In this article, the experiments were conducted using a self-created dataset, which was compiled by selecting images with bird-related labels from the COCO dataset and combining them with pictures captured using cameras and other mobile devices near high-voltage power lines.  
The augmentation techniques employed on the training dataset, which includes 3317 images, are crucial for enhancing the model's capacity to generalize and reducing the potential for overfitting. The augmentation methods encompass a spectrum of transformations, each geared towards imbuing the dataset with greater diversity. These transformations include:  
Mixup: Mixup is a data augmentation technique that expands a dataset by creating new samples through linear combinations of samples from multiple different datasets. The core principle of mixup involves linearly combining two different images based on a certain ratio to generate new samples, with the labels of the new samples also obtained through linear combinations.  
Multi-Angle Rotation: As explained earlier, multi-angle rotation involves applying rotations to images at various angles (e.g., 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)) to augment the training dataset. This helps the model become more robust against different object orientations.  
Image Brightness Adjustment: This technique involves changing the brightness of an image by scaling the pixel values. By adjusting the brightness, the model learns to recognize objects in different lighting conditions, making it more adaptable to varying environments.  
The augmented training set ultimately comprises 6393 images, encompassing both 3317 original images and an additional 3076 images that have undergone enhancement. A visual representation detailing the distribution of this augmented training set is depicted in Figure 4.  
Figure 4: Distribution of the augmented training set.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Vehicle Onboard Sensing  
Vehicle onboard sensing is complimentary to infrastructure-based sensing. It happens on the road user side. The sensors move with road users and thereby are more flexible and cover larger areas. Additionally, vehicle onboard sensors are the eyes of an intelligent vehicle, making the vehicle see and understand the surroundings. These properties pose opportunities for urban sensing and autonomous driving technologies, but at the same time create challenges for innovation. A major technical challenge is the irregular movement of sensors. Traditional ITS sensing on infrastructure-mounted sensors deal with stationary backgrounds and relatively stable environment settings. For instance, radar sensors for speed measurement know where the traffic is supposed to be. Camera sensors have a fixed video background so that traditional background modeling algorithms can be applied. Therefore, in order to benefit from vehicle onboard sensing, it is necessary to address the challenges.
## 4 Proposed Method
### Multi-Scale Transformer Module
#### 4.3.2 Pairwise Subframe-Level Transformer  
While the subframe-level transformer captures local features, adjacent subframes share contextual information crucial for detecting embedding rates. The pairwise subframe-level transformer models these dependencies, integrating subframe features into higher-level representations. By merging subframes early, it reduces feature space and computational complexity while preserving essential information for frame-level modeling.  
This stage captures dependencies between adjacent subframes while reducing the feature space. The process begins with pairwise grouping, a key step in the pairwise subframe-level (PSL) transformer that facilitates subframe interaction modeling. Specifically, each frame, consisting of four subframes, is divided into two adjacent subframe pairs to enhance contextual representation. The process is as follows:  
\[\mathbf{C_{psl}}[t,1] = Contact(\mathbf{C^{3}_{sl,t,1}},\mathbf{C^{3}_{sl,t,2}}),\] \[\mathbf{C_{psl}}[t,2] = Contact(\mathbf{C^{3}_{sl,t,3}},\mathbf{C^{3}_{sl,t,4}}), \tag{18}\]  
Therefore, we can obtain the following result:  
\[\mathbf{C_{psl}}=[\mathbf{C_{psl,1,1}},\mathbf{C_{psl,1,2}},\dots,\mathbf{C_{psl,m,2}}]. \tag{19}\]  
Then, to normalize the pairwise representation for subsequent transformer operations, a linear transformation is applied to each sub-pair:  
\[\mathbf{C^{proj}_{psl}}=\mathbf{C_{psl}}\mathbf{W_{psl}}+\mathbf{b_{psl}}. \tag{20}\]  
This reduces the dimensionality of each pair from \(2d\) to \(d\).  
The second step is multi-head attention (MHA) within pairs, which captures relationships between subframes within each pair.  
\[\mathbf{C_{mha-psl}}=\mathbf{\text{Concat}}(H_{psl,1},H_{psl,2},\dots,H_{psl,h})\mathbf{W^ {O}_{psl}}. \tag{21}\]  
The third step is the residual connections and normalization operations as follows:  
\[\mathbf{C^{1}_{psl}}=LayerNorm(\mathbf{C^{proj}_{psl}}+\mathbf{C_{mha-psl}}). \tag{22}\]  
The fourth step is to use the FFN further to process the pairwise representations by the following equation:  
\[\mathbf{C^{2}_{psl}}=LayerNorm(\mathbf{C^{1}_{psl}}+\mathbf{FFN}(\mathbf{C^{1}_{psl}})). \tag{23}\]  
The output \(\mathbf{C^{2}_{psl}}\) aggregates dependencies across adjacent subframes.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 3 ANN-Based Design Methodology for Low-Power Interconnect
### Artificial Neural Network (ANN)  
An Artificial Neural Network (ANN) inspired by the neural network in a person's brain attempts to simplify and mimic our brain behavior. The ANN can be trained in a supervised or unsupervised manner. In this design methodology, a supervised learning ANN model trained by given data predicts the interconnect properties. In this kind of learning, the ANN model can deliver high accuracy prediction by providing enough training data and employing appropriate data processing techniques. The activation function stimulates the biological neuron in ANN. These neurons form a hierarchy network referred to as multiple layers in which the output of some neurons is input to others. Each neuron called a node takes multiple weighted inputs (\(w\)), bias (\(b\)) applied the activation function (\(f\)) to the summation of these inputs, and in doing so generates an output (\(H\)). The general form of the activation function is described in Equation (2). The neuron and the ANN model are shown in Figures 3 and 4, respectively.  
\[H(w,b)=f(x\times w+b) \tag{2}\]  
In the supervised learning ANN, the idea is to reduce the error between the input and the desired output. The training procedure to minimize this error has a significant impact on the ANN performance. This process is strongly influenced by the training algorithm such as Gradient Descent or the backpropagation method. While the Gradient Descent uses the gradient of the error function to make an informed step-change in weight (w) to achieve minimum error, the backpropagation allows the ANN to determine the impact of one specific weight on errors by calculating the partial derivatives. This training process is an iterative method that involves multiple steps.  
Figure 4: Artificial Neural Network.  
Figure 3: Node in an Artificial Neural Network.
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 4 Performance Analyses of the Cognitive IA Schemes
### Feasibility Condition Analysis
More specifically, we can empower a feasible IA-based network with cognition by adding \(N_{R,p}-1\) antennas at each transmitter to eliminate the cognitive interference, and one more antenna at each receiver to align the cross-tier interference.
## 2 Concepts and Applications of TMPR
### Motion Characteristic of the C-Arm  
In previous studies, the origin of the 3D coordinate system of the C-arm is located at the focal spot. Therefore, the rotation of the C-arm is equivalent to the translation and rotation of the focal spot. As shown in Figure 2a, the initial 3D coordinate system of the C-arm with a rotation angle of 0, where the isocenter (\(P_{iso}\)) is the origin and the Z-axis is parallel to the principal line (\(L_{0}\)), is defined as \(C_{r\!\!\!r}\). We assume that a point \(P_{init}\) exists in  
Figure 1: (**a**) Diagram of the source, detector, SID, SOD, image size, and object size; and (**b**) coordinate system of the C-arm and X-ray image.  
the C-arm workspace. When the C-arm rotates by \(\delta\) about \(P_{iso}\), as shown in Figure 2b, the rotated 3D coordinate system of the C-arm is defined by \(C_{rot}\), of which the origin is also located at the \(P_{iso}\) and the position of \(P_{init}\) can be calculated with respect to \(C_{rot}\). On the other hand, if the \(C_{rot}\) is fixed and \(P_{init}\) rotates by -\(\delta\) about the \(P_{iso}\), as shown in Figure 2c, we can define this rotated point as \(P_{rot}\). The position of \(P_{rot}\) can be calculated with respect to \(C_{ref}\). The position of \(P_{init}\) and \(P_{rot}\) in \(C_{rot}\) and \(C_{ref}\) are same, as shown in Figure 2b,c. Therefore, the images captured from each view should be identical. Consequently, the translation and rotation of the focal spots can be described solely by the rotation of an object about the \(P_{iso}\) in the fixed coordinate system by defining the \(P_{iso}\) as an origin of the 3D coordinate system of the C-arm. At this point, we do not consider the translation of the imaging equipment.
# Effect of Whole Body Vibration Conditions on Lower Limb Muscles during Sling Exercise
###### Abstract  
Exercising in an unstable environment, such as with the use of a sling, improves neuromuscular adaptation by providing effective training stimuli. Moreover, whole body vibration has been used to restore the physical function of patients with nervous system and musculoskeletal disorders. However, there have been few studies on the most appropriate exercise method of combining unstable surface and WBV. The purpose of this study was to investigate the effect of a sling and sonic vibration stimulation on muscle activation while performing a Bulgarian split squat (BSS). Twenty male subjects (age 25.95 \(\pm\) 2.42 years old, height: 172.59 \(\pm\) 5.56 cm, weight: 77.74 \(\pm\) 10.82 kg) participated in the study. The participants randomly performed five types of BS using a sling and sonic vibration (BSS-0 Hz, BSS-4 Hz, BSS-8 Hz, BSS-12 Hz, BSS-20 Hz, and BSS-30 Hz). Each condition was repeated five times. A 60-s break was given for each condition to minimize muscle fatigue. We measured the muscular activities in the gluteus medius, biceps femoris, rectus femoris, vastus medialis, and vastus lateralis. It was observed that muscle activation increased during exercise conditions that provided a sling and sonic vibration in all muscles. In particular, the highest activation appeared in the condition wherein a vibration of 30 Hz was provided. Our results show that the sling exercise combined with a sonic vibration of 30 Hz during the BSS has positive effects on lower limb muscle activity.  
sling exercise; sonic vibration; lower limb; muscle activity 2022  
H.-j.-y., M.; [PERSON], T.-K. Effect of Whole Body Vibration Conditions on Lower Limb Muscles during Sling Exercise. _Appl. Sci._**2022**, 12, 1299. [[https://doi.org/10.3390/app12031299](https://doi.org/10.3390/app12031299)]([https://doi.org/10.3390/app12031299](https://doi.org/10.3390/app12031299))  
Academic Editor: [PERSON]
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
## 4 Design and Implementation
### DoS and Pollution Attacks Prevention  
Our solution main phase is the prevention against \(DoS\) attacks and pollution attacks, ensuring data availability and integrity despite the existence of misbehavior in the network. It prevents a coalition of malicious \(CH\)s executing a \(DoS\) attack by dropping all the packets they receive or achieving a pollution attack by modifying the packets before their transmission to the \(BS\). It guarantees the reconstruction of original data messages at the \(BS\). For this purpose, our protocol uses the \(NC\)-techniques as described in Section 3.1. It produces \(p\) parts from a particular data message of size \(t\), where \(t<p\), so that \(t\) of them suffice for reconstructing the original one at the final destination, the \(BS\). This phase is composed of: (i) encoding and parts creation at the source node and (ii) decoding and parts reconstruction at \(BS\).
## 6 Results and Discussion
### Comparative Methods  
The proposed SPWO based routing was evaluated with existing techniques, such as Sunflower based grey wolf optimization (SFG) [45], Multi-Adaptive Routing Protocol (MARP) [46; 47], and Crow Whale-ETR [38; 39]. The proposed classification method, named the SPWO-based Deep Maxout network, was analyzed with conventional schemes, such as deep learning [19], CNN [24], and Hybrid DNN [25].
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 4 Hyper-Parameters Optimization
### Best Route Preservation  
Preserving the top-performing chromosome across generations significantly improved the performance of the genetic algorithm, particularly in more complex scenarios involving 300 charging stations. This strategy ensured the retention of high-quality solutions, enabling the algorithm to effectively explore the solution space while safeguarding strong candidates during crossover and mutation. However, the impact of this approach varied with the problem's complexity. As depicted in Figure 10, with only 150 charging stations, the benefits were less pronounced and occasionally resulted in reduced performance. These findings indicate that the effectiveness of preserving the best route is influenced by factors such as population size and the complexity of the problem.  
Figure 8: Crossover comparison.  
Figure 10: Route preservation.  
Figure 9: Incorporating extreme chromosomes.  
Passing the Best Route (Elitism) During Dynamic Assessment  
In the dynamic recalculation of the best route for updated segments, the top-performing route and corresponding charging station data from the current segment are propagated to the next (Elitism). This practice of elitism ensures that the solution quality is maintained as the algorithm adapts to changes in the problem space. By carrying forward high-quality routes, the algorithm can quickly converge on optimal or near-optimal solutions even as new challenges are introduced. Consequently, elitism contributes to a more robust and resilient approach, enhancing the algorithm's ability to handle dynamic and evolving scenarios efficiently.  
In Figure 11, it can be seen that this strategy yields modest improvements across most population sizes when there are 150 charging stations. Nevertheless, in more complex scenarios with 300 charging stations, the performance enhancements are more substantial and consistent. Based on these results, it appears that propagating the best route is strongly influenced by problem complexity, with larger and more complex problems benefiting most from this approach.
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 2 Methods  
### Overall Process  
The GIHS [14; 15] fusion algorithm is simple and efficient, with no limitation on the number of bands, and the NSST [16] has an excellent multi-scale decomposition capability and low computational complexity. Therefore, we utilize the GIHS as a CS algorithm and the NSST as an MRA algorithm in the novel coupling model shown in Figure 1c. Figure 2 specifically shows the algorithm flow, and Figure 3 is an expansion upon the subfigure within the solid red line of Figure 2 and shows the specific steps of the fusion of the component and PAN images. Both this paper's method and GIHS essentially add detailed gain to the up-sampled MS image, while the difference lies in the source of the detailed gain. GIHS uses a difference map from PAN and multispectral intensity component I, as detailed gain. Additionally, this paper's method first extracted principal component PC1 from PAN and MS using the PCA transform. Then, it used NSST to extract the new spatial structure component (New_SSC) from PC1 and PAN. Finally, the difference map of New_SSC and intensity component I were used as the detailed gain. This improvement meant that the New_SSC contained more detailed information from the MS and PAN images while retaining some spectral information, thus improving both the spatial and spectral accuracy of the final fused image. For the NSST in the framework, we propose a low-frequency coefficient fusion rule, based on a gradient-domain SVD and bootstrap filter (see Section 2.2 for details), and use the local spatial frequency reflecting the pixel neighborhood variation to fuse the high-frequency coefficients (see Section 2.3 for details). The specific steps are as follows:  
1. Up-sample the MS image using three convolutional interpolations to obtain \(MS^{*}\), making it the same size as the PAN image.
2. Combine intensity component I according to Equation (1), perform PCA transformation on the combination of \(MS^{*}\) and PAN images, extract the first principal component PC1, and histogram match the PAN images and PC1 with \(I\) as the standard to obtain \(PAN^{*}\) and \(PC^{*}1\).
3. Perform NSST transform decomposition of \(PC^{*}1\) and \(PAN^{*}\) to obtain the low-frequency components (\(L^{PC},L^{PAN}\)) and the high-frequency component (\(H^{PC}_{j,k},H^{PAN}_{j,k}\)), respectively.
4. Fuse the low-frequency coefficients to obtain the new low-frequency components (\(L^{F}\)), and fuse the high-frequency coefficients to obtain the new high-frequency component (\(H^{F}_{j},k\)).
5. The new low-frequency and high-frequency components are then NSST inverse transformed to obtain the primary fusion image (\(F1\)), and the \(GAIN\) will be obtained(\(F1-I\)). Finally, use the f \(Fi=Mi+\omega iGAIN\) formula to obtain the fusion image (\(F\)), where the detail modulation coefficient (\(\omega\)) is set to \(1\).
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 2 Digital Agriculture
### Application--Smart Irrigation System  
Water is, of course, essential for life, especially so in the desert. Global warming, growth of the population, and inefficient use or scarcity of water demand smart irrigation systems. Various kinds of sensors (temperature, moisture, ultrasonic, etc.) can be used to monitor the water level, soil moisture, plant/crop condition, and weather to optimise the use of precious water. These sensors are deployed remotely, battery-powered, and have low computational power. An actuator is deployed based on the sensory data. Aerial systems are also used to monitor soil and moisture content using cameras (thermal or RGB) deployed on drones or low-Earth-orbit satellites. This creates a wide attack surface that is difficult to defend against and is vulnerable to exploitation. The threats to smart irrigation and sensors can range from physical compromise to falsifying the data. As mentioned in Table 1, the traditional threats are equally applicable to different layers of a smart irrigation system.  
Figure 1: An overview of digital agriculture and its various applications.
## 4 VoLTE Scenario and Simulation Report
### Simulation Analysis
#### 4.4.2 Frame Loss Ratio Analysis  
The FLR calculates the percentage of lost frames in a communication network. It is used as an indicator to evaluate the network's QoS. As lost frames in real-time communication can lead to a worse user experience, FLR is a crucial indicator in VoIP networks. A voice application should have less than 1% end-to-end FLR and 150 ms mouth-to-ear delay [25]. Table 8 presents the single eNB frame loss mean and standard deviation values, while Figure 6 illustrates the vector analysis observed during the 60 s call. In the single eNB simulation, four out of ten UEs (UE[1], UE[2], UE[4], UE[9]) experience significant frame loss, while the other six UEs do not experience significant frame losses.  
Table 9 presents the dual eNB frame loss mean and standard deviation values, while Figure 7 showcases the dual eNB frame loss results experienced in the simulation.  
In the dual eNB scenario, all UEs only experience a few frame losses. There is a significant improvement in the dual eNB scenario, which is also confirmed by the statistical analysis. The F-test showed that the F value (20.39) > F Critical one-tail (3.44); thus, we reject the null hypothesis, proving that there are significant differences in the variances of the \"frame loss\" means across the two UE simulation groups. Moreover, the \(T\)-test showed that the t Stat (2.05) < t Critical two-tail (2.30); thus, we do not reject the null hypothesis, showing that the \"frame loss\" means of the two UE simulation groups are significantly different from each other. The above statistical differences are illustrated in the vector analysis of Figures 6 and 7.
## 3 Data Processing and Analysis for Smartphone Real-Time Kinematic (RTK) Positioning
### Data Processing for Smartphone RTK Positioning
#### 3.1.2 Partial Ambiguity Resolution
We modify the abovementioned algorithm on the RTKLIB open-source GNSS software (v2.4.3) [28], and the smartphone GNSS observations in integer time periods are used for RTK positioning. The main positioning specifications are listed in Table 2, and the other parameters use the default values in RTKLIB.