esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.  
**Keywords:** convolutional neural networks (CNNs); end-to-end detection; transfer learning; remote sensing; single shot multi-box detector (SSD); You Look Only Once-v3 (YOLO-v3); Faster RCNN
## 2 Materials and Methods
### Statistical Analysis  
All the data are presented as the means \(\pm\) standard deviation (SD). The data were tested for normal distribution with the Shapiro-Wilks test and for homogeneity of variances with [PERSON]'s test. A two-way ANOVA for repeated measures was performed to analyze the effect of previous injury and the time of measurement along with interactions among these factors on the study variables. Additionally, a two-way ANOVA for repeated measures considering the initial 20 m was carried out to analyze the effect of previous injury on the different variables of the RSA test. As in previous studies [19], the initial 20 m of the sprints were specifically analyzed because hamstring muscles implication predominate in the acceleration phase of the sprint [19]. A two-way complementary ANOVA for repeated measures comparing the last three sprints were analyzed to observe if there were differences between the groups or the interaction on the variables of the RSA test. The last three sprints were selected because maximum fatigue occurs there, and hamstring muscles implication is influenced by fatigue [19]. When the Mauchly sphericity assumption was not met, the Greenhouse-Geisser correction was used. Bonferroni post-hoc tests were conducted where significant differences were found in any of the analyzed factors. The effect size of the pairwise comparisons was calculated by Cohen's d considering d \(<\) 0.5 as small, d \(<\) 0.8 as moderate, and d \(>\) 0.8 as large [63]. The effect size of the ANOVA for repeated measures was calculated by partial eta-squared (np2) and the small, moderate,  
Figure 2: Diagram of the placement of the photocells in the RSA.  
and large effects corresponded to values equal or greater than 0.001, 0.059, and 0.138, respectively [64]. The data were analyzed using the SPSS statistic software, version 26.0, for Windows (IBM Corporation; Armonk, NY, USA). The significance level was set at \(p<0.05\).
# Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training
###### Abstract  
Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students' learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection using TensorFlow's object detection API. When a piece of equipment is detected, the corresponding AR-based tutorial will be displayed on the screen. The mean average precision (mAP) of the developed equipment detection model is 81.4%, while the average recall of the model is 85.3%. Furthermore, to demonstrate practical application of the proposed framework, we develop a multimeter tutorial where virtual models are superimposed on real multimeters. The tutorial includes images and web links as well to help users learn more effectively. The Unity3D game engine is used as the primary development tool for this tutorial to integrate DL and AR frameworks and create immersive scenarios. The proposed framework can be a useful foundation for AR and machine-learning-based frameworks for industrial and educational training.  
artificial intelligence; augmented reality; machine learning; object detection; computer in education; lab equipment tutorial
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 4 Experimental Results  
In this section, we first present the experimental set-up and signal collection environment. Then, the performance of the proposed DCTF identification schemes is evaluated.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 3 Transmitter Design
### Analog Precoder Design: Average AoD knowledge at User Terminals  
For the second case, it is assumed that the \(u\)th user has knowledge of the average AoD, \(\phi_{q}^{tx,u},\theta_{q}^{tx,u},q=1,\ldots,N_{cl}\), of each cluster of its own channel. Based on this knowledge, user \(u\) computes the correlation matrix \(\mathbf{A}_{tx,u}\mathbf{A}_{tx,u}\), where the matrix \(\mathbf{A}_{tx,u}\) is given by [29]  
\[\tilde{\mathbf{A}}_{tx,u}=[\mathbf{a}_{tx,u}(\phi_{1}^{tx,u},\theta_{1}^{tx,u} ),\ldots,\mathbf{a}_{tx,u}(\phi_{q}^{tx,u},\theta_{q}^{tx,u}),\ldots,\mathbf{ a}_{tx,u}(\phi_{N_{cl}}^{tx,u},\theta_{N_{cl}}^{tx,u})]\in\mathbb{C}^{N_{tx} \times N_{cl}}, \tag{9}\]  
with \(\mathbf{a}_{tx,u}(\theta_{q}^{u})\) computed from (2) for the UPA case. Let \(\mathbf{A}_{tx,u}\mathbf{A}_{tx,u}=\mathbf{A}_{tx,u}\mathbf{\Sigma}_{tx,u} \mathbf{\Lambda}_{tx,u}^{H}\) be the eigenvalue decomposition of the previous correlation matrix, thus the analog precoder vector of the \(u\)th user is set as  
\[\mathbf{f}_{a,u}(n)=\frac{1}{\sqrt{N_{tx}}}\exp\{j\arg(\mathbf{\Lambda}_{tx,u }(n,1))\}, \tag{10}\]  
with \(1\leq n\leq N_{tx}\). As mentioned only partial CSI (average AoD's, \(\phi_{q}^{tx,u}\) and \(\theta_{q}^{tx,u}\)) is required at each UT, which may be obtained at the base station, coded with \(b\) bits each one, and then, sent to each user terminal by a feedback channel. For instance, for a channel with \(N_{cl}=4\) and \(b=4\), this results  
Figure 1: General block diagram of the \(u\)th user terminal.  
just in a feedback of \(2\times 4\times 4=32\) bits. Notice that the channel correlation-based approaches would require the feedback and/or estimation of the full correlation matrix, which has \(N_{rx}^{2}\) entries. Then, the proposed analog precoder has a clear advantage compared to the several channel correlation SVD-based beamforming proposals which have been proposed in the literature [25], since it has both low-complexity and low CSI requirements.
## 2 Related Work
### Hybrid SOSK-Means
```
1:Input:\(Eco\_size\): population size \(\mathit{UILSS}\): upper limit for search space \(\mathit{Max\_iter}\): maximum number of iterations \(\mathit{DDim}\): problem dimension Output: Optimal Solution
2:Create an initial population of organisms \(X=\ (X_{1},X_{2},\ldots,X_{\mathit{cossize}})\)
3:Calculate the fitness of each organism
4:Keep the initial population's best solution \(\mathit{BestX}\)
5:while\(\mathit{iter}\ \leq Max\_iter}\)
6:for\(i=1\ to\ Eco\_size\)do
7:// 1 st Phase: Mutualism //
8: Select index j \((1\ \leq j\leq Eco\_size;j\
eq i)\) randomly
9:\(\mathit{BF}_{1}=\ (\ 1+round(rand(0,1)))\)
10:\(\mathit{BF}_{2}=\ (\ 1+round(rand(0,1)))\)
11:\(X_{mutual}=\left(\frac{X_{i}+X_{j}}{2}\right)\)
12:for\(k=1\ to\ PDDim\)do
13:\(X_{\mathit{inew}}=X_{i}+rand(0,1)*(\mathit{BestX}-\mathit{BF}_{1}*X_{mutual})\)
14:\(X_{\mathit{inew}}=X_{j}+rand(0,1)*(\mathit{BestX}-\mathit{BF}_{2}*X_{mutual})\)
15:endfor
16:if\((ObjFn(X_{\mathit{inew}})<ObjFn(X_{i})\)
17:\(X_{i}=X_{\mathit{inew}}\)
18:endif
19:if\((ObjFn\left(X_{\mathit{inew}}\right)<ObjFn\left(X_{j}\right)\)
20:\(X_{j}=X_{\mathit{inew}}\)
21:endif
22:// 2 nd Phase: Commensalism //
23:Select index j \((1\ \leq j\leq Eco\_size;j\
eq i)\) randomly
24:for\(k=1\ to\ PDDim\)do
25:\(X_{\mathit{inew}}=X_{i}+rand(-1,1)*\left(\mathit{BestX}-X_{j}\right)\)
26:endfor
27:\(\mathit{ObjFn}(X_{\mathit{inew}})<\ ObjFn(X_{i}\ )\)
28:\(X_{i}=X_{\mathit{inew}}\)
29:endif
30:// 3 rd Phase: Parasitism //
31:Select index j \((1\ \leq j\leq Eco\_size;j\
eq i)\) randomly
32:for\(k=1\ to\ PDDim\)do
33:\(X_{\mathit{parse}}=\ \mathit{X}_{i}\)
34:else
35:\(X_{\mathit{parse}}=\ \ rand(0,1)*(\mathit{UILSS}[K]-\mathit{LLSS})+\mathit{LLSS}\)
36:endif
37:endfor
38:if\(ObjFn\left(X_{\mathit{parse}}\right)<\ ObjFn\left(X_{j}\right)\)
39:\(X_{j}=X_{\mathit{parse}}\)
40:endif
41: Update current population's best solution \(\mathit{BestX}\)
42://K-means Clustering Section//
43:K-means' initialization using the position of the \(\mathit{BestX}\)
44:Execute K-means clustering
45:endfor
46:\(\mathit{iter}=\mathit{iter}+1\)
47:endwhile
```  
**Algorithm 1**Hybrid SOSK-means clustering pseudocode [26]
# Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning Approach
## 4 Fallback Scenario Formulation
### State and Perception  
State \(s\) denotes an ego vehicle that collects physical information to perceive surrounding vehicles and road environments. It comprises nine parameters, as expressed in Equation (1).  
\[s=(x_{EG},y_{OE},\ \phi_{OE},x_{EA},y_{EA},\phi_{EA},x_{EB},y_{EB},\phi_{EB}). \tag{1}\]  
The details of the coordination and spatial variables are presented in Figure 2. These nine parameters represent three pieces of information: goal-related, localization, and relationship states. A goal-related state is the information utilized to achieve a learning goal. The ego vehicle attempts to reach the goal line such that the goal-related state is the longitudinal relative distance \(x_{EG}\) between the current \(x\) position of the ego vehicle \(x_{OE}\) and the goal line \(x_{OG}\). The localization state represents the information through which an ego vehicle perceives its location in the environment. The chosen localization state parameters were the lateral absolute position \(y_{OE}\) and rotational absolute yaw angle \(\phi_{OE}\), as illustrated in Figure 2a. The longitudinal absolute position \(x_{OE}\) was ignored because it was already included in the goal-related state \(x_{EG}\) which is the distance between the ego vehicle and the goal position. The relationship state is the information between the ego vehicle and the other vehicles. This state is scalable depending on the number of surrounding vehicles, and each relationship state has three degrees of freedom for each vehicle: one longitudinal and lateral distance each and one rotation angle \((x,y,\phi)\), as illustrated in Figure 2b. The ego vehicle had six relationship state parameters: \(x_{EA}\), \(y_{EA}\), and \(\phi_{EA}\) for Vehicle A and \(x_{EB}\), \(y_{EB}\), and \(\phi_{EB}\) for Vehicle B.  
State \(s\), which includes spatial variables, serves as a training input for the deep reinforcement learning algorithm, allowing the autonomous system to make informed decisions based on comprehensive environmental awareness. By processing this input, deep reinforcement learning can train AVs to execute complex tasks, such as navigation and obstacle avoidance, and learn optimal strategies through repeated interactions with various simulated scenarios.  
More parameters must be considered as states, such as velocity and acceleration, that is \(\dot{x}_{OE}\) and \(\ddot{x}_{OE}\), respectively. This information was correlated with distance information. In other words, a part of each piece of information is already contained in its parameters. Hence, they need not be included in the state; they are duplicate or trivial.
## 2 SAR Data Access Efficiency Analysis
### DDR3 Data Access Characteristics Analysis
The conventional SAR data storage method is used to store the data continuously in DDR3, as shown in Figure 5. When the size of the SAR data matrix is large, it is necessary to use multiple rows of DDR3 to store one row of the SAR data matrix. Thus, the range data are accessed sequentially, and the azimuth data are accessed in frequent cross-rows. Each row has 1024 cells in a typical DDR3. Set the burst length is set to eight, and each row needs to complete 1024 / 8 = 128 burst transmissions in range access mode; at this time, n = 128. By converting the relevant parameters (\(t_{RCD}\), \(t_{CWL}\), \(t_{WR}\), \(t_{RP}\) and \(t_{RTP}\)) of the MT41K512M16 HA-125 chip (Micron Technology, Boise, ID, USA) into a multiple of \(t_{CK}\) into the equation, the reading and writing efficiency of the range access mode can be calculated as follows [38]:  
\[\eta_{WRITE\_range}=\frac{128\times 4t_{ck}}{t_{RCD}+t_{CWL}+128\times 4t_{ck}+t_{ WR}+t_{RP}}=92.25\% \tag{3}\]  
\[\eta_{READ\_range}=\frac{128\times 4t_{ck}}{t_{RCD}+t_{RTP}+128\times 4t_{ck}+t_{ RP}}=94.64\% \tag{4}\]  
In the azimuth access mode, each row only needs to complete one burst data transmission, then proceed to the next row, so n = 1. The reading and writing efficiency of the azimuth access mode can be calculated as follows:  
\[\eta_{WRITE\_azimuth}=\frac{1\times 4t_{ck}}{t_{RCD}+t_{CWL}+1\times 4t_{ck}+t_{ WR}+t_{RP}}=8.51\% \tag{5}\]  
\[\eta_{READ\_azimuth}=\frac{1\times 4t_{ck}}{t_{RCD}+t_{RTP}+1\times 4t_{ck}+t_{ RP}}=12.12\% \tag{6}\]In the conventional SAR data storage method, the reading and writing efficiency is very high for continuous address access in the range direction, but the reading and writing efficiency will be greatly reduced for frequent cross-row access in the azimuth direction, as shown in Table 1. The azimuth data bandwidth is only about 10% of the peak bandwidth, and the imbalance of the two-dimensional data bandwidths has become a bottleneck restricting the SAR real-time imaging system. In order to improve this situation, it is necessary to avoid or reduce cross-row access as much as possible to balance the data bandwidth of the two-dimensions.
# Lightning Surge Analysis for Overhead Lines Considering Corona Effect
## 1 Introduction  
Analysis of the propagation behavior of lightning surges on overhead lines is essential for lightning overvoltage protection and insulation coordination of electric power apparatus. It is well known that lightning surges undergo distortion and attenuation when they propagate along overhead lines. Surge corona plays a significant part in the distortion and attenuation phenomena. The design of lightning overvoltage protection depends strongly on accurate knowledge of the amplitude and wavefront steepness of lightning surges [1; 2; 3; 4; 5; 6]. In the calculation of lightning overvoltages, surge corona is taken into account by its charge-voltage characteristic, namely the \(q\)-\(u\) curve. Measurements of the \(q\)-\(u\) curves have been taken in the corona cages and on the actual overhead lines [7; 8; 9; 10]. Nevertheless, the measured data were obtained mainly under double exponential surges. The distortion and attenuation were always calculated according to the \(q\)-\(u\) curves under double exponential surges, no matter what the actual surge waveshapes are [3; 11; 12; 13]. As a matter of fact, the great majority of lightning surges intruding into substations are damped oscillation surges due to the refraction and reflection of surge waves [14; 15]. There is a practical need for taking into account the corona effect on the distortion and attenuation of damped oscillation surges. Some work suited to this need was reported in literature [16; 17; 18; 19]; however, the systematic research results were still not reported in the previous work. In view of this situation, an attempt is made in this paper to comprehensively investigate the corona effect on the distortion and attenuation of damped oscillation surges. An experimental measurement is made in a corona cage. The basic difference is found between the \(q\)-\(u\) curves under double exponential and damped oscillation surges. The trajectory feature of the minor loops is also observed under positive and negative damped oscillation surges. Owing to the fact that the traditional approach is only suitable to modeling of the monotonic \(q\)-\(u\) curves under double exponential surges [20], an extended approach ispresented for modeling of the hysteresis-like \(q\)-\(u\) curves under damped oscillation surges. With the extended approach implemented into the transient analysis, an efficient method is proposed to calculate the lightning transients on overhead lines. The proposed method can effectively predict the distortion and attenuation of damped oscillation surges on overhead lines with corona. The calculated results are compared with the field test results to check the validity of the proposed method. Then, we further discuss the calculated results in the presence and absence of the minor loops to inquire into the influence of the minor loops on the distortion and attenuation of damped oscillation surges under positive and negative polarities.
## 4 Experimental Testing and Result Analysis
### Optimization of the YOLOv8-Pose Loss Function
Looking at the actual recognition effect, in practical detection, the comprehensive recognition effect is best when the weight is 22. If the weight is too small, the recognition accuracy of both cable numbers and terminal numbers is low. If the weight is too large, overfitting will occur, reducing the recognition rate of terminal numbers. When the weight is moderate, the recognition accuracy is the highest. The recognition results after parameter adjustment are shown in Table 3. Compared with the original model, there is a certain improvement in precision, recall rate, and mean Average Precision (mAP) o1n the test set. Figure 10: Normalized metrics sum over experiments. Figure 9: Experiments result for the recognition of cable-to-terminal connection. (**a**) Precision (**b**) Recall (**c**) mAP\({}_{50}\) (**d**) mAP\({}_{50-95}\). We conducted the experiments with the AlphaPose and OpenPose models over the same dataset, achieving recognition accuracies of 91.7% and 89.4%, respectively. All pose estimation models have a relatively high recognition accuracy, indicating that using human pose estimation models for the identification of the connection status between cables and terminals is effective. The dataset used in the experiments of this paper is relatively small, and to prevent overfitting, a smaller model, YOLO8n-pose, was adopted. To further improve recognition accuracy, more complex models from the YOLOv8 series could be used, but this would also require a larger dataset for training.
## 4 Simulations and Results
### Characterization of the Application Scenario  
The authors of [15] provide a classification of vehicular applications and their communication requirements. These categories are: safety, vehicular traffic efficiency and infotainment applications.  
All of the efficiency-oriented applications (e.g., air pollution, noise level monitoring, _etc._) require a continuous monitoring phase of the streets and city conditions. Our application scenario assumes traffic  
Figure 6: State of the channel and interference management of a wireless channel for a node in a network simulator [10].  
data generated by an efficiency application during the phase of collecting data only. The characteristics of the traffic are:  
1. Vehicles obtain data from their sensors; they process such data and generate constant-length packets.
2. The packets are sent to the closest access point (AP). This is unicast and unidirectional traffic, since the information is useful only to the authority.
3. This kind of applications does not have important delay constraints as the safety related ones, so it is suitable to transport its traffic by delay-tolerant protocols.
# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
One such application is image classification, specifically object recognition, which allows cobots to interact with different objects. This capability has been utilized for route control and pick-and-place routines [29; 30]. Additionally, computer vision in cobots has been employed for safety purposes, including gesture recognition and human position tracking [31; 32], where vision systems integrated with neural networks are applied to track the body of the user. In a specific study by [PERSON] et al. [33], a system consisting of multiple neural networks was developed for multi-modal human-robot collaboration, incorporating audio, gesture, and body position inputs. However, there was no direct interaction between the different DL systems utilized. Through our examination of the current research on human-robot collaboration, we identified a notable absence of comprehensive systems able to process inputs from different sources that facilitate human-like interactions. Moreover, the limited instances of employing multiple Deep Learning models in human-robot applications were evident in our review [12; 13]. This study seeks to address these gaps by developing a human-robot interaction system capable of processing inputs from a variety of sources. The primary aim of this study is to address the following research questions: (1) How can different Deep Learning models be integrated into a human-robot system for voice-command recognition, part classification, and hand-tracking in assisting assembly tasks? (2) What is the success rate of collaborative routines between humans and cobots facilitated by Deep Learning integration for assembly activities involving part handover and storage? These questions guide our exploration into the feasibility and effectiveness of incorporating multiple Deep Learning models for enabling human-like collaborative activities in assembly tasks. In this work, we propose the integration and application of cobots and DL to assist users in an assembly activity involving the handover and storage of parts. These two activities are supported by three Convolutional Neural Networks (CNNs). One CNN is focused on recognizing the 11 voice commands required to guide the robot in the desired activities; another CNN uses images to classify the parts received from the user. Finally, a pre-trained hand-tracking CNN facilitates the approach of the robot to the hand of the user for part handover and retrieval. Figure 1 illustrates the interconnected systems involved in each task. The rest of the paper is organized as follows. Section 2 provides information on the materials and methods used in each of the composing systems. Section 3 has a detailed description of the processes and methods applied for the stated problem. Section 4 describes the proposed experiments. Section 5 showcases the results, while Section 6 discusses and compares the results with the existing literature. Finally, Section 7 provides the conclusions drawn from the findings.