esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 6 Conclusions  
In this paper, a single-feed broadband quasi-isotropic antenna with CP characteristics was proposed. The proposed antenna is compact in terms of its electrical size _ka_ of 0.94 with an extremely low profile of 0.0015A. It shows a broadband matched impedance bandwidth of 80% from 1.8 to 4.2 GHz with a maximum radiation efficiency of 90%. The maximum gain deviation of less than 10 dB is achieved from 1.9 to 3.3 GHz, or 53.8%, and it is the widest bandwidth considering the small size of the antenna. The wide spatial coverage of the CP performance was also verified experimentally. Furthermore, the performance of the proposed antenna was verified under the NLOS communication environment, showing that a higher level of received power can be obtained by utilizing quasi-isotropic patterns. While the operating frequencies with the quasi-isotropic radiation pattern nearly match the impedance bandwidth, the frequencies for a relatively large coverage of the CP greater than 40% occur only at higher frequencies. A more improved design that simultaneously meets the quasi-isotropic pattern and CP characteristic in a broad bandwidth remains an achievable goal in the near future. The parametric study results given in this work and on the other parameters, such as the number and length of the fingers, are worth revisiting in this regard.  
Conceptualization, S.M.R., M.-S.L. and I-J.Y.; Methodology, S.M.R., M.-S.L. and I-J.Y.; Software, S.M.R., M.-S.L. and I-J.Y.; Validation, S.M.R., M.-S.L. and I-J.Y.; Formal Analysis, S.M.R., M.-S.L. and I-J.Y.; Investigation, S.M.R., M.-S.L. and I-J.Y.; Resources, S.M.R., M.-S.L. and I-J.Y.; Data Curation, S.M.R., M.-S.L., S.H.C. and I-J.Y.; Writing--Original Draft Preparation, S.M.R.; Writing--Review and Editing, I.-J.Y.; Visualization, S.M.R. and S.H.C.; Supervision, I.-J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Chungnam National University.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Frequency [GHz]** & **Peak Gain [dBi]** \\ \hline Omnidirectional antenna & Length of the primary dipole & 24.5 \\ Proposed antenna & 1.8–4.2 & 1.6–4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antennas used for demonstration.
## 4 Modeling and Simulation Methods and Their Applications
### Metamodel and Metamodeling Supporting an Autonomous System
It focuses on creating a skeleton framework and looks like a metamodel, which will become the heart of the model-based approach, and the architecture can be configured and optimized to ensure that the system is enhanced [26]. Different from the traditional application of a V-model, working toward complex development and simulation processes such as swarm, we should have a digital system prototype at the beginning to connect the development of the top-level SoS and the design of the underlying multidisciplinary components of unmanned systems. We should incrementally create and deploy a coherent and consistent Digital System Model (DSM) integrating specific models as a source of digital twins of system specification, design, analysis, verification, and validation. The system architecture represents the structure, behavior, and constraints of complex systems to deliver an effective solution satisfying the needs of stakeholders. Therefore, the metamodel is the initial prototype for the system architecture, which will serve as the starting point for system development and support the system's evolution in a M&S environment. The process to develop a metamodel is also a micro-cyclic iterative development process within the whole framework. Following the idea of the Model-Based System Architecture Process (MBSAP) [13] to connect or transform an SoS mission architecture to a system architecture, the first mapping converts capabilities to Operational Viewpoint (OV); the next mapping transforms the OV into Logical/Functional Viewpoint (LV), where the refinement of system elements, services, functions, interactions, and behaviors is carried out. Then, the development of physical specifications is accomplished by mapping the LV to Physical Viewpoint (PV). Synchronously, digital system models of autonomous systems support the M&S of complex dynamic systems, particularly swarm, and allow engineers to continuously express new solutions and conduct L-V-C online testing before  
Figure 6: Example of SysML activity models for a metamodel of UAS. Figure 7: Example of SysML interface models for a metamodel of UAS. In our methodology, we firstly clarify and explain the aforementioned conceptual models in swarm ontology for the stakeholders and/or team members, which include the whole iterative context of the system definition and the engineering process of autonomous vehicles, and then apply the approach of the metamodel to capture and specify the technical information needing to be developed for the DSM in communication and computation, command and control, motion planning, perception, other knowledge representations about the problem description, and the solution specification to support decision making within MBSE.
## 5 Experiments and Results
### Visual Evaluation  
A visual evaluation with some sample queries and their retrieved patches provides transparency and insight into the functioning of the CBMIR system. This allows pathologists to understand how the CBMIR framework responds to different queries. The results of feeding the proposed framework with the original and normalized images are presented in the four figures below.  
Figure 7 illustrates four random queries in their original color space and the top five retrieved images. As can be seen, for each of the queries, the retrieved images have different colors, not only to the corresponding query, but also among themselves. This highlights the need for color normalization as a pre-processing step for the searching framework.  
Figures 8 and 9 show the same random queries as Figure 7, but they were normalized by Mac and Vah, respectively. These figures clarify the numerically reported results and confirm that the effects of Vah as a CN technique can decrease the accuracy of retrieval.  
Figure 10 corresponds to the same four queries as Figure 9 and their top five retrieved patches from the data set. In this figure, images were normalized using BKSVD. As can be understood from the figure, normalizing the images using BKSVD can enhance the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**K** & **Original** & **BKSVD** & **Vah [33]** & **Mac [32]** \\ \hline
3 & 0.73 & 0.91 & 0.66 & 0.68 \\ \hline
5 & 0.83 & 0.97 & 0.79 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The achieved top K accuracy in the proposed CBIR method on color-normalized images from the CAM17 data set. K = 3 and 5.  
Figure 6: Confusion matrices show the effects of BKSVD, Vah, and Mac on the performance of CBMIR in retrieving the patches with the most similarity. “C” and “NC” stand for cancerous and non-cancerous tissue.  
performance of the search engine in finding similar histopathological patterns without the negative impact of color variation.  
Figure 8: Four random patches of breast cancer data set with their corresponding top five retrieved images. The images were normalized by [PERSON] and the red boxes show the non-similar patches based on their labels.  
Figure 7: For each random query, the five top similar patches are presented. The red lines show the miss-retrieved patches. The patches are in their original colors.  
Figure 10: Four random queries from the normalized data set by BKSVD with their five top retrieved images.  
Figure 9: Four random breast queries with their five top retrieved images. The images were normalized by [PERSON]. The miss-retrieved images are marked in red.  
In these figures, the images were compared with their queries based on the labels provided by expert pathologists in the ground truth of the data set. The returned patches with the same label as the query were considered as the correct retrieved patches. However, images with different labels to the query were highlighted by a red square in order to clarify the miss-retrieved patches based on their labels.
# Dual-Band UWB Monopole Antenna for IoT Applications +
## 3 Antenna Design  
Figure 1 shows the antenna design proposed in this paper. In this antenna, we used substrate FR4; the thickness of the substrate is 1 mm, and 4.4 is the value of the relative dielectric constant. The proposed coplanar antenna is 24 \(\times\) 25 \(\times\) 1 mm\({}^{3}\) in size. We used a 50-Ohm SMA connector for excitation. The proposed antenna's numerical simulations, including the SMA connector modeling, were carried out by using the Ansys Electronics student version. The antenna dimensions proposed in this paper are listed in Table 1. The bandwidth of the proposed antenna can be significantly increased by inserting a slit, while the proposed antenna's impedance bandwidth is close to that of the antenna without a slit. We can enhance the bandwidth of the antenna proposed in this paper by simultaneously adding a slit in the ground plane.  
Figure 1: Design of a proposed antenna.  
In this design, we made a slit l\({}_{1}\) in the ground plane, which affects the impedance bandwidth of the antenna. If we increase the length of that slit graph, the impedance bandwidth is shifted towards the upper frequencies. In the upper frequencies, a coplanar resonant mode is significantly affected by the size of slit l\({}_{1}\). By adjusting the length of the slit, we can obtain the widest bandwidth. We used a slit length l\({}_{1}\) = 5.5 mm in this proposed antenna to obtain the widest bandwidth. The ground plane's effect on antenna performance was also investigated. Return loss for the proposed antenna at various ground-plane heights (h) can be observed as height h grows, and the suggested antenna's first resonant frequency shifts toward a higher frequency. The ground plane h's size has a minor effect on the bandwidth compared to the parameters of l\({}_{1}\) and l\({}_{2}\). An h 8.0 mm was chosen to obtain the most significant 10-dB impedance.
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Finding Suitable Backbone  
The first study aimed to determine the most effective backbone for the given task. The objective of our study was to maximize the outcome by limiting the parameter count. Different kinds of backbones were tested to meet our requirements. Table 2 represents this experiment. The baseline yolov5s model yields a mean average precision of 65.91% while utilizing 7.01 million parameters and a GFlops of 16. With the Swin transformer, the result drops to 3.44% in terms of mAP, attaining 62.47%, and a 4.07% decrease in the F1 score, achieving 61.14%. Moreover, the GFlops increased hugely, reaching a value of 102.00. The parameter count displayed a rise of 1.59 million. Using the RepVGG backbone resulted in a mean average precision (mAP) of 61.32% and an F1 score of 60.51%. Mobilnet v3 and Mobileone backbone were examined, and their parameter count and GFlops are relatively less than other backbone architectures. The MobileNet V3 model achieved a mean average precision (mAP) of 56.72%. The model's parameter count and GFlops were recorded at 5.21 million and 10, respectively. Mobileone, a recently invented novel architecture by APPLE, attained 58.81% mAP and a 58.67% F1 score. The parameter count for this is higher than Mobilenet v3, though both have the same GFlops of 10. Finally, the default backbone CSPDarknet was selected by us as our backbone, representing the best performance while containing an acceptable amount of parameters.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Backbone** & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **F1 Score** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **[EMAIL_ADDRESS]** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **No. of** \\ **Params. (M)** \\ \end{tabular} &
\begin{tabular}{c} **GFlops** \\ **(\%)** \\ \end{tabular} \\ \hline CSPDarknet & 66.70 & 63.80 & **65.21** & **65.91** & 7.01 & 16.00 \\ Swin Transformer & 64.11 & 58.45 & 61.14 & 62.47 & 8.60 & 102.00 \\ RepVGG & 63.12 & 58.24 & 60.51 & 61.32 & 7.30 & 17.14 \\ Mobilenet v3 & 59.43 & 54.16 & 56.36 & 56.72 & **5.21** & **10.00** \\ MobileOne & 61.27 & 56.36 & 58.67 & 58.80 & 8.15 & 10.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of the different backbones with YOLOv5s architecture.
## 4 Performance Analysis
### Analysis of Measured Magnetic Target Signals
#### 4.2.2 Drawing the Time-Frequency Contour  
The contours of short-time Fourier transform, continuous wavelet transform, and synchrosqueezed wavelet transform were drawn respectively, and the results are shown in Figure 10:  
From the abstracted contour, it can be observed that the energy distribution of the short-time Fourier transform in all frequency bands was strong as a result of serious energy leakage, and the contour lines gathered at the low-frequency band of the near geomagnetic field with high energy. The continuous wavelet transform was superior to Short-time Fourier Transform for better performance, and the former was more advantageous in the high-frequency domain; however, it was inferior in terms of energy leakage control, and the contour lines covered almost all bands. The synchrosqueezed wavelet transform was superior to both as no energy leakage was observed in the 50 Hz band, which significantly improved temporal frequency aggregation and developed a stronger anti-noise ability.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 2 Current In-Car Network Architectures and Technologies
### Legacy In-Car Networking Technologies
The Timing Master starts a frame transmission from its transmit port to the receive port of the next node in the ring, which in turn synchronously transmits the frame on its transmit port to the next node's receive port, and so on. Synchronous frames are continually transmitted from the Timing Master to the next node in the ring, which in turn puts its source data into the frame and transmits the frame to the next node in the ring. Table 2 summarizes the main features of current in-car networks. Point-to-Point TechnologiesToday, cameras or displays with high-definition (HD) or sophisticated Light Detection and Ranging (LiDAR) sensors require data rates that go beyond 1 Gbps. Serializer/Deserializer (SerDes), also called low-voltage differential signaling (LVDS), pixelink, or high speed video link, are the general names for the point-to-point technologies that support the high-speed transmission of binary data to transmit pixel-precise information \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **LIN** & **CAN** & **CAN FD** & **FlexRay** & **MOST** \\ \hline Max. & & & & & 25, 50, \\ Bandwidth & 20 Kbps & up to 1 Mbps & up to 5 Mbps & 20 Mbps & 150 Mbps \\ \hline Cost & very low & low & low & low & high \\ \hline Cabling & 1-wire & UTP & UTP & UTP & \begin{tabular}{c} POF (25, 50) \\ UTP (150) \\ \end{tabular} \\ \hline Topology & bus & bus & bus & bus, star, \\  & & & & hybrid & ring, star \\ \hline Safety- & & & & & \\ critical & no & yes & yes & no \\ applications & & & & & \\ \hline Main & body and & several & several & \
\begin{tabular}{c} safety- \\ critical, \\ x-by-wire \\ \end{tabular} &
\begin{tabular}{c} multimedia, \\ infotainment \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Current in-car networking technologies. on the lowest layers of the ISO/OSI layering model [23]. The primary use of a SerDes is to provide data transmission over a single line or a differential pair in order to minimize the number of I/O pins and interconnects. Although it is often used synonymously with SerDes, LVDS is the physical principle of SerDes interfaces. In fact, LDVS is a standard that specifies electrical characteristics of a differential, serial signaling standard that operates at low power and can run at very high speeds using inexpensive twisted-pair copper cables. LVDS is a physical layer specification only; many data communication standards and applications use it and add a data link layer as defined in the OSI model on top of it.
## Vehicle Detection
### Experimental Methods and Results: Faster RCNN construction  
#### 3.3.1 Comparison of the Detection Performance of Different Models  
This paper mainly tests the vehicles in the road and produces 2500 vehicles image data sets collected in the real road environment. The camera resolution of the acquisition equipment is 1280 * 720. Due to the limitation of data workload, the data set mainly includes sedans and sport utility vehicles. Then we use an image labelling tool to mark the image dataset with the ROI with each image containing 1 to 2 marked target vehicles, as shown in Figure 3. Figure 3 shows the marked vehicles, and the target position of the vehicle mark. The small data set can establish the RCNN training process faster. We randomly select 1500 sheets as the training set and 1000 sheets as the test set.  
The article's vehicle inspection data set has fewer data problems, so the data is supplemented and improved. The article combines a homemade dataset with a subset of the standard dataset. In many public driving data sets, the BDD100K dataset from Berkeley contains a variety of weather data, which contains approximately 100,000 images of 1280 \(\times\) 720 containing vehicle targets. The article selects 6000 images as the test set and 4000 images as the test set. Therefore, there are 7500 images in the training set and 5000 images in the test set.  
Figure 3: Production of vehicle data sets.  
Figure 2: Boundary regression of the target.  
To verify the detection capability of the Faster RCNN model, three representative vehicle detection methods based on CNN are selected in this paper. The methods are the CNN, the RCNN and the Fast RCNN. The different method approaches are presented in Table 1.  
In this paper, training sets are used to train various models, and test sets are used to test the effect, as shown in Table 2. Among them, the accuracy rate calculation method is: correct detection\(/(\text{correct detection}\,+\,\text{false detection})\). The recall rate calculation method is: correct detection\(/(\text{correct detection}\,+\,\text{missed detection})\).  
The experimental results show that the principle and mechanism of a method will lead to different experimental results. The sliding window method of CNN obtains the sliding window features in different image positions, but this method is less efficient than CNN. RCNN uses a selective search algorithm to extract a candidate boundary and it combines a Support Vector Machine (SVM) to extract regions. RCNN has a better extraction effect. Compared with RCNN, Fast RCNN also uses a selective search algorithm to extract a candidate boundary, but it employs the softmax classifier, which leads to a better classification result because it introduces an interclass competition mechanism. Compared with Fast RCNN, Faster RCNN uses RPN to extract the candidate boundary and omits a selective search algorithm while achieving end-to-end training. Faster RCNN uses the convolution feature and RPN sharing method based on Fast RCNN, and its detection time averages about 94 ms.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Mathematical Logic  
Model checking aims at verifying whether a finite-state model of a system meets a given specification. In order to solve such a problem algorithmically, both the model of the system and its specification need to be expressed in some precise mathematical language. The fields of mathematical logic and formal language theory provide us the tools we need to reason about the system states in a symbolical and rigorous manner. Mathematical logic is the systematic study of reasoning. By reasoning, we intend the abstract capability to provide _arguments_ supporting the fact that a given statement, called the _conclusion_, follows from some other statements, called the _premises_. The steps through which conclusions are reached from premises are termed _inferences_. Mathematical logic relies on _formal languages_ in order to provide a symbolic representation of abstract concepts, like statements about the model of the system we are interested to reason about, that can then be manipulated through formal, mathematical processes. We will rely on common concepts stemming from logic, and we will specifically introduce and define just the ones that are more relevant to this work. We refer the interested reader to [21; 22] for a comprehensive exposition of those subjects.
## 3 Results
### Essential Oil Characterization  
A total of 26 components, accounting for >95% of the essential oils, were identified. Table 2 reports the percentages of the main compounds (\(\geq\)1%) found in the two essential oils. This highlights that \"Erectus\" is characterized by a high level (30.40 \(\pm\) 1.71%) of bornyl acetate followed by alpha-pinene (13.17 \(\pm\) 0.85%). \"Prostratus\" is characterized by alpha-pinene (35.30 \(\pm\) 1.50%) followed by bornyl acetate (13.31 \(\pm\) 0.62%). Thus, alpha-pinene and bornyl acetate are the compounds with the highest differences between the two varieties. They are followed by camphor (8.28 \(\pm\) 0.42% and 8.10 \(\pm\) 0.35%), and campene (8.07 \(\pm\) 0.30% and 10.02 \(\pm\) 0.70%) for \"Prostratus\" and \"Erectus\", respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound.** & **Rt (min)** & \multicolumn{2}{c}{**Variety (\%)**} \\ \hline  & & **“Prostratus”** & **“Erectus”** \\ \hline alpha-pinene & 8.6 & 35.30 \(\pm\) 1.50 a & 13.17 \(\pm\) 0.85 b \\ campene & 9.3 & 8.07 \(\pm\) 0.30 b & 10.02 \(\pm\) 0.70 a \\ cis-pinocamphone & 12.2 & 1.74 \(\pm\) 0.31 b & 2.51 \(\pm\) 0.22 a \\ (+)-4-Carene & 12.5 & 1.78 \(\pm\) 0.11 a & 0.70 \(\pm\) 0.05 b \\ limonene & 13.3 & 1.77 \(\pm\) 0.17 a & 0.56 \(\pm\) 0.14 b \\ eucalypt & 13.4 & 2.57 \(\pm\) 1.50 a & 1.29 \(\pm\) 0.36 a \\ alpha-terpinene & 15.2 & 1.09 \(\pm\) 0.19 a & 0.04 \(\pm\) 0.05 b \\ alpha-phellandrene & 15.9 & 1.98 \(\pm\) 0.40 a & 0.42 \(\pm\) 0.07 b \\ p-cymene & 18.1 & 3.48 \(\pm\) 1.82 a & 2.18 \(\pm\) 0.90 a \\ camphor & 21.9 & 8.28 \(\pm\) 0.42 a & 8.10 \(\pm\) 0.35 b \\ linalool & 22.2 & 1.49 \(\pm\) 0.38 a & 0.11 \(\pm\) 0.15 b \\ humulene & 25.8 & 1.42 \(\pm\) 0.21 a & 1.29 \(\pm\) 0.15 b \\
4-ol-terpinen & 26.1 & 2.31 \(\pm\) 0.27 b & 4.40 \(\pm\) 1.10 a \\ bornyl acetate & 26.1 & 13.31 \(\pm\) 0.62 b & 30.40 \(\pm\) 1.71 a \\ endo-borneol & 26.2 & 2.65 \(\pm\) 0.71 b & 8.78 \(\pm\) 0.52 a \\ alpha-terpineol & 28.6 & 7.41 \(\pm\) 1.26 a & 7.26 \(\pm\) 0.61 a \\ eugenol & 33.1 & 1.52 \(\pm\) 0.66 b & 3.99 \(\pm\) 0.86 a \\ \hline compound identified (\%) & & 96.17 & 95.22 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition of essential oils (\(\geq\)1%) for the two roosary varieties (mean \(\pm\) standard deviation) (n = 3). Letters (a, b) indicate statistically significant differences using the Tukey HSD post hoc test (\(p<\) 0.05).