esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
###### Abstract  
Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology's importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate's location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters' contours within the grayscale image. Third, the detected the alphanumeric characters' contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.  
Keywords:real-time; image processing; ANPR; machine learning; mobile computing +
Footnote †: journal: _Algorithms_
Sensors_**2015**, _15_, 10026-10047; doi:10.3390/s150510026  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 1**  
1 School of Software and Electrical Engineering, Swinburne University of Technology,  
Melbourne 3122, Australia; E-Mail: [EMAIL_ADDRESS]  
2 School of Computer Science and Software Engineering, University of Wollongong,  
Wollongong 2522, Australia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel: +61-3-9214-4899.  
Academic Editor: [PERSON]  
_Received: 5 March 2015 / Accepted: 22 April 2015 / Published: 28 April 2015_  
**Abstract:** Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage.  
**Keywords:** multi-agent systems; wireless sensor networks; packet routing
## 2 Materials and Methods
### Sample Preparation  
The blends were prepared in the molten state in a two-step process. Before processing, PA6 pellets were dried for 12 h in a vacuum oven at a temperature of 80 \({}^{\circ}\)C, while PCL pellets were dried in a ventilated oven at a temperature of 50 \({}^{\circ}\)C for 12 h. PA6 and PCL granules were manually mixed at different relative amounts and subsequently melt-compounded through a Thermo Haake Rheomix 600 internal mixer equipped with counter-rotating rotors, operating at 60 rpm for 5 min at a temperature of 230 \({}^{\circ}\)C. The obtained blends were then compression-molded in a Carver hot-plate press at 235 \({}^{\circ}\)C for 8 min, under an applied pressure of 1.9 MPa. In this way, square sheets 10 \(\times\) 10 \(\times\) 2 mm\({}^{3}\) were prepared. Thicker sheets, having a thickness of 5 mm and the same lateral dimensions, were also prepared for the evaluation of the fracture toughness (both in quasi-static and impact conditions). The produced samples were denoted as PA6_xPCL, where \"\(\times\)\" is the weight fraction of PCL in the blends, with \(x\) = 5, 10, 20, and 30 wt%. Also, the samples of the neat matrices (PA6, PCL) were prepared for comparison.
# On-Demand Waveguide-Integrated Microlaser-on-Silicon
## 1 Introduction
However, despite these pioneering works mentioned above as well as the recent development of topological integrations, simple schemes to achieve a further efficient integration that requires a small coupling area have been increasingly demanded [35; 36]. Moreover, as more miniaturized and complicated photonic circuits are pursued, it has become of great importance to achieve the on-demand addressability at target locations. In particular, integration without causing significant mechanical damage, optical losses, and/or performance degradation has become critical but remains a challenge [21; 30; 31]. In this study, we propose a waveguide-integrated microlaser-on-silicon by utilizing a previously developed microtip-assisted transfer-printing method. In the following section, we illustrate a rational design including a microscale Si-post and an on-demand Si-waveguide-integrated microlaser (Section 2.1). Then we describe the important fabrication steps for Si-post structures in the vicinity of strip-type Si waveguides (WGs) and for the separately top-down fabricated arrays of semiconductor microdisks (Section 2.2). The precise alignment transfer of a microdisk on top of a Si-post readily enabled the realization of on-demand WG-coupled microlaser-on-Si integration. Spectroscopic measurements demonstrated the successful lasing emission, light coupling, and propagation through the WG (Section 2.3). The experimental observations were qualitatively verified through numerical simulations (Section 2.4).
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 4 Corrosion Fatigue Lifespan Assessment of Parallel Wire Cable
### Service Environment Effect on Corrosion Fatigue Lifespan
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature\({}^{\circ}\)C**} & \multicolumn{6}{c}{**Humidity Level/\%**} \\ \cline{2-7}  & **50–60** & **70** & **75** & **80** & **85** & **90** \\ \hline
10 & \(7.431\times 10^{-15}\) & \(5.8\times 10^{-14}\) & \(2.48\times 10^{-13}\) & \(3.29\times 10^{-13}\) & \(4.08\times 10^{-13}\) & \(4.25\times 10^{-13}\) \\
20 & \(1.5817\times 10^{-13}\) & \(2.01\times 10^{-13}\) & \(2.28\times 10^{-13}\) & \(3.1\times 10^{-13}\) & \(4.85\times 10^{-13}\) & \(5.39\times 10^{-13}\) \\
30 & \(1.6596\times 10^{-13}\) & \(1.83\times 10^{-13}\) & \(1.86\times 10^{-13}\) & \(2.78\times 10^{-13}\) & \(3.35\times 10^{-13}\) & \(7.32\times 10^{-13}\) \\
40 & \(1.1253\times 10^{-13}\) & \(1.77\times 10^{-13}\) & \(1.89\times 10^{-13}\) & \(5.93\times 10^{-13}\) & \(8.08\times 10^{-13}\) & \(1.43\times 10^{-12}\) \\
50 & \(5.3786\times 10^{-14}\) & \(1.69\times 10^{-13}\) & \(4.15\times 10^{-13}\) & \(9.06\times 10^{-13}\) & \(1.27\times 10^{-12}\) & \(1.8\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Wire corrosion rate values under different combinations of temperature and humidity levels (units: m/s). temperature. When the temperature rises from 10 to 50 degrees Celsius, the lifespans are as follows: 39, 29, 24, 14, and 12 years, respectively. The lifespan decreases from 39 to 12 years as the temperature increases. It can be inferred that temperature and humidity have a more severe impact on the corrosion fatigue lifespan of the steel wires compared to the load. The high-temperature and high-humidity environment accelerates the formation of pitting on the surface of the wires, leading to the initiation of cracks at an earlier stage. To reflect the lifespan of suspension cables in a high-temperature and high-humidity environment, the individual lifespans of the 109 steel wires in the cable were calculated at a temperature of 20 \({}^{\circ}\)C, humidity of 90%, and an equivalent stress amplitude of 23.65 MPa. The results are shown in Figure 21. The lifespans of the 1 st, 2 nd, 4 th, 6 th, 8 th, and 10 th wires correspond to 34, 39, 40, 41, 42, 43, 4  
43, 44, 47, and 57 years, respectively. For the entire cable with 109 wires, when the specified failure rate is reached, the service lifespan is approximately 39 years. By comparing with Figure 16, it can be observed that the detrimental effect of the corrosive environment on the corrosion fatigue lifespan is much more significant than that of the load.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 4 The Experimental Results
### Determination of Active Nodes  
We used recovery symbol constellation diagrams at the destination to determine whether the node was active or not. The constellation diagrams of 500 symbols at the sensor nodes and at the destination are shown in Figure 3. Figure 3a is the original symbol constellation, where red diamond markers denote the inactive nodes and blue square markers denote the active nodes.  
Figure 3b,c show the recovery constellations of reconstructed symbols at the destination with different signal/noise ratios at the transmitters. It can be seen that reconstructed symbols are more concentrated near the center with a higher SNR. The inner black circle is the decision boundary, and the red dots represent the symbol of inactive nodes. The red dot symbol inside the decision boundary is considered as the inactive node, while the one outside the decision boundary is considered as the active node where the decision error occurs. In order to decrease decision error such that the inactive node is considered as active one, the training sequence is used to determine the boundary. Moreover, the training sequence is used to estimate the channel matrix. The furthest distance between zero and all reconstructed training sequence symbols of inactive nodes is set as the decision boundary, as indicated by the dotted line in Figure 3b,c.  
If most recovery symbols are located inside the decision boundary, the sensor node is considered inactive. According to this rule, we found that, with lower SNR, although there are more red diamond markers appearing outside the boundary, we can still correctly decide which node is inactive. It proves the robustness of this scheme.  
Figure 2: The structure of sensor nodes and destination.
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
###### Abstract  
Wireless sensor networks (WSNs) have been utilized as communication infrastructure for smart grid applications. The primary requirement of WSNs for smart grid applications is to transmit delay-critical data from smart grid assets ether at the maximum rate or by reducing collision rates. Additionally, WSNs should utilize the limited resources of the network to provide the required long-term QoS. The achievement of these objectives requires a remarkable design of WSN protocols to satisfy the requirements of smart grid applications. In this study, a multi-channel cluster tree protocol is proposed to prevent collisions and increase network performance. In the proposed scheme, the cluster head serves to broadcast a beacon frame containing information on the allocated channels and time slots. This enables the new node to determine its channel and timeslot. A performance analysis reveals that the proposed scheme can achieve a low end-to-end delay and low collision rates compared with the well-known IEEE 802.15.4 MAC protocols widely used in the literature to provide QoS to smart-grid applications.  
s 202323950 10.3390/s23135950 1
## 4 Performance Evaluation
### Simulation Results  
In this section, the obtained results are based on the scenario described in Table 1; we focus on two metrics: delivery ratio and average delivery delay.  
* Delivery Ratio: This metric is the most important for evaluating the opportunistic transmission scheme in integrated UAV-ICV networks. A higher delivery ratio indicates greater accuracy in information delivery by the scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Scenario} & Simulation Time & 24 h \\  & Simulation Range & 50 km \(\times\) 50 km \\  & Simulation City & Xi’an \\ \hline \multirow{6}{*}{Node} & Transmission Mode & Wi-Fi \\  & Transmission Range of ICVs & 200 m \\  & Transmission Range of UAVs & 1500 m \\  & Movement Speed & [30, 70] km/h \\  & UAV/ICV Cache Size & 50/150 MB \\ \hline \multirow{3}{*}{Message} & Message Size & [1, 2] MB \\  & Message TTL & 5 h \\  & Generation Frequency & [1, 20] min \\ \hline \multirow{6}{*}{Communication} & Transmission Mechanism & OFDMA \\  & Channel Bandwidth & 180 KHz \\ \cline{1-1}  & Transmission Power & 30 dBm \\ \cline{1-1}  & Path Loss Model (ICV-UAV) & \(32.44+20\lg[d(\text{km})]+20\lg[f_{c}(\text{MHz})]\) \\ \cline{1-1}  & Path Loss Model (ICV-ICV) & LoS in WINNER + BI [30] \\ \cline{1-1}  & Noise Power & \(-174\ dBm/Hz\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation scenario settings.  
Average Delivery Delay: This metric can assess the transmission ability of the opportunistic transmission scheme in integrated UAV-ICV networks. Smaller delays indicate higher ability and efficiency of the scheme.
# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 2 Materials and Methods
### Convolutional Denoising Model  
For LOFAR spectrogram \(Y(T,F)\), it can be represented using the LOFAR spectrogram of the underwater target signal \(X(T,F)\) and the additive ocean noise \(N(T,F)\).  
\[Y(T,F)=X(T,F)+N(T,F) \tag{11}\]  
For the convolutional denoising model \(M(\cdot)\), the objective function \(L(\theta;Y,X)\) is to minimize the error between the LOFAR spectrogram of the underwater target signal and the LOFAR spectrogram predicted by the model.  
\[L(\theta;Y,X)=\min_{\theta}\left\|M(Y(T,F))-X(T,F)\right\|^{2} \tag{12}\]  
Here, \(\theta\) represents the parameters of the convolutional denoising model. For the image-to-image end-to-end problem, a deep convolutional network with a U-Net [19] like structure can be used. First, the encoder learns the feature representations from the spectrogram of raw acoustic signal. Then, the decoder reconstructs the feature representations into the In denoising model, the input dimension is \([B,1,T,F]\), where \(B\) is the batch size, \(T\) is the duration of the LOFAR spectrogram, and \(F\) is the frequency bandwidth. During the encoding phase, the input data undergoes a series of \(3\times 3\) convolutions, ReLU (rectified linear unit) activation functions, and Batch Normalization. The downsampling module in the encoding phase employs \(2\times 2\) stride convolutions instead of \(1\times 1\) stride convolutions because \(2\times 2\) stride convolutions have the capability to combine information from adjacent regions, reduce the size of the feature map, and extract more representative features. The first half of the model is the data encoding phase, where it is expected that the model learns a sparse representation of the data. In the second half of the model, known as the decoding phase, the decoder uses a combination of transposed convolutions, ReLU activation functions, and Batch Normalization, with the aim of separating the sparse representation into pure target signal and ocean noise. In the final layer of the model, a \(1\times 1\) convolution and softmax function are used to generate a binary mask for the spectrogram. The dimension if final output is \([B,2,T,F]\), where 2 represents two spectrograms of the same size: the first spectrogram represents the foreground LOFAR spectrogram containing only the target line spectrum, while the second spectrogram represents the background LOFAR spectrogram containing only ocean environmental noise.
## 4 Discussion
### Research Conclusions  
For parks with water bodies, the recommended LEP area ratios are 6.5:2:1.5, and for those without water, the ratios are 8.5:1.5. When the Softscape proportion surpasses 65% and Hardscape remains below 15%, parks with water can achieve satisfactory NDVI and CN values. Similarly, in waterless parks, maintaining a Softscape proportion above 83% and Hardscape under 17% is likely to result in elevated NDVI and CN values. The correlation analysis underscores that NDVI and CN are positively linked, indicating higher NDVI leads to greater CN. Furthermore, a higher NDVI corresponds to a lower Hardscape proportion and a higher Softscape proportion. Elevated CN is associated with reduced Hardscape proportion, while higher Softscape proportion corresponds to higher CN. Conversely, a higher Hardscape proportion is connected to a lower Softscape proportion.  
In analyzing the carbon neutrality capabilities of six parks in Taichung City, this study developed a set of evaluation methods for LEP, NDVI, RI, and CN. This method highlights the importance of the interactions among these factors within urban parks and provides actionable recommendations for enhancing park carbon neutrality. However, several key considerations must be addressed to apply and replicate this research framework more broadly.  
Firstly, the results of this study indicate a significant correlation between LEP, NDVI, RI, and CN, particularly in parks with higher green space proportions and lower Hardscape ratios. However, these findings are based on a limited sample size and a specific regional context. Therefore, when applying this method to other cities or regions, it is essential to account for differences in local climate conditions, vegetation types, and park management practices.  
Secondly, water bodies may play a potentially significant role in park resilience and carbon neutrality. Although this study only included a few parks with water bodies, existing literature suggests that water bodies might make substantial contributions to carbon sequestration. When replicating this research framework, it is recommended to further explore the role of water bodies in various types of parks and consider the integration of facilities such as rainwater management systems. This will contribute to a more comprehensive understanding of the impact of water bodies on park carbon neutrality.  
Lastly, this study did not thoroughly investigate the specific effects of different plant species on carbon neutrality efficiency. Future research could involve a detailed assessment of the biomass and carbon sequestration capacity of various tree and shrub species. As research progresses and expands, this study aims to develop more universally applicable methods to provide valuable insights for park planners and other professionals.
## 3 Result and Discussion
### Power Capacity Measurement
#### 3.5.1 Current Measurement  
The 1 NA219 power sensor was used to measure current consumption. It was coupled with the Arduino Mega to acquire the current and show the measurement result in a serial monitor using Arduino IDE. The Arduino Mega powered the 1 NA219 power sensor. In contrast, the sensor node was powered by an external power supply using a micro USB cable to avoid interference between the Arduino Mega and the sensor node during the current measurement. The measurement configuration is shown in Figure 12.  
The developed sensor node was set to work in two modes: active and low-power. The initialization, sensor reading, transmitting, and logging processes run in active mode with certain time intervals. The low-power mode runs in the interval between two CO\({}_{2}\) measurement cycles. Figure 13 shows the current consumption of sensor-node processes. The initialization process consumes a current of 42.1 mA. While in the CO\({}_{2}\) sensing and transmitting process, the sensor node consumes a current of 120.07 mA and 121.4 mA, respectively, the sensor node in the sleep stage consumes a current of 33.2 mA when the low-power mode is activated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Node (ppm)** & **Validator (ppm)** & **Deviation (ppm)** &
\begin{tabular}{c} **Absolute Percentage** \\ **Deviation (\%)** \\ \end{tabular} \\ \hline
1514 & 1515 & 1 & 0.07 \\
1600 & 1609 & 9 & 0.56 \\
1650 & 1651 & 1 & 0.06 \\
1705 & 1710 & 5 & 0.29 \\
1743 & 1749 & 6 & 0.34 \\
1812 & 1800 & \(-\)12 & 0.67 \\
1853 & 1855 & 2 & 0.11 \\
1921 & 1899 & \(-\)22 & 1.16 \\
1964 & 1950 & \(-\)14 & 0.72 \\
2024 & 2003 & \(-\)21 & 1.05 \\ \hline \multicolumn{3}{c}{Average Deviation} & \(-\)4.5} \\ \multicolumn{3}{c}{Average Percentage Deviation} & \multicolumn{1}{c}{0.50} \\ \hline \hline \end{tabular}
\end{table}
Table 5: CO\({}_{2}\) measurement results after calibration.
# New Insights into Fuzzy Genetic Algorithms for Optimization Problems
## 2 Fuzzy Genetic Algorithms: Dealing with Individuals and Fitness
### Preliminaries  
#### 2.1.1 Fuzzy Sets  
A fuzzy set generalizes the classical set, allowing not only for the classical set's 0-1 membership degrees but also a continuum of values in the range [0, 1]. A fuzzy set \(A\) is uniquely defined over a universe of discourse \(U\) by a membership function (MF) \(\mu\), \(\mu_{A}:U\rightarrow[0,1]\). A fuzzy set is normal when its highest membership degree equals 1.  
The most common MF is the triangular function. Another popular option is the trapezoidal MF:  
\[\mu(x)=\begin{cases}0&\text{if }x\leq a\\ \frac{x-a}{b-a}&\text{if }a\leq x\leq b\\ 1&\text{if }b\leq x\leq c\\ \frac{d-x}{d-c}&\text{if }c\leq x\leq d\\ 0&\text{if }d\leq x\end{cases} \tag{1}\]  
which models the linguistic expression \"approximately b to c\" or \"close to [b, c]\". Dealing with a central interval (i.e., [b, c]), instead of a point as for the triangular MF, this type of MF allows to take into account a higher level of uncertainty.  
It is worth recalling the pi-shaped MF:  
\[\mu(x)=\begin{cases}0,&\text{if }x\leq a\\ 2(\frac{x-a}{b-a})^{2},&\text{if }a\leq x\leq\frac{a+b}{2}\\ 1-2(\frac{x-b}{b-a})^{2},&\text{if }\frac{a+b}{2}<x\leq b\\ 1,&\text{if }b<x\leq c\\ 1-2(\frac{x-a}{d-c})^{2},&\text{if }c<x\leq\frac{c+d}{2}\\ 2(\frac{x-d}{d-c})^{2},&\text{if }\frac{c+d}{2}<x\leq d\\ 0,&\text{if }x\geq d\end{cases} \tag{2}\]  
which represents a smoother alternative to the trapezoidal MF.  
The connectives from the classical logic AND, OR are generalized in fuzzy logic via t-norms and t-conorms, respectively. A typical t-norm is the minimum operator, and a typical t-conorm is the maximum operator.  
A fuzzy partition can be seen as a series of \(m\) partially overlapping normal fuzzy sets with \(m\geq 2\). Such fuzzy sets represent the terms of a linguistic variable. For instance, the linguistic variable temperature may have the terms {cold, cool, warm}, each one expressed by a fuzzy set, i.e., a proper membership value. As in our natural language, the terms represent the attributes of the linguistic variable. Any linguistic variable can assume a certain value, i.e., membership degree, which has relevance in fuzzy IF-THEN rules. Figure 1 recalls the generic \(N\) terms of a linguistic variable (here denoted as term_1,...,term_N), determined by syntactic rules to form eventually fuzzy rules. Examples of fuzzy rules will be provided in the next sections.