esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
###### Abstract  
We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from unmanned aerial vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood, utilizing calculated masks over each component of the LAB colorspace, as well as using an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying hysteresis thresholding segmentation. The proposed method is tested and compared with variations and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-score, respectively. The results and computational efficiency of the proposed method show that it is suitable for onboard data execution and decision-making during UAV flights.  
Keywords:flood detection; image segmentation; remote sensing; unmanned aerial vehicle (UAV); unsupervised segmentation +
Footnote †: journal: _Remote Sens._**2024**, _16_, 2126. [[https://doi.org/10.3390/rs16122126](https://doi.org/10.3390/rs16122126)]([https://doi.org/10.3390/rs16122126](https://doi.org/10.3390/rs16122126))
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
## 6 Conclusions  
In this paper, a novel method for mode selection and excitation in rail defect detection is proposed based on ultrasonic guided waves. The mode shape data in the CHN60 rail is obtained at the frequency of 35 kHz by using SAFE method. And the modes with good non-dispersion characteristics, which  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & **Time Difference** & **Theoretical Group** & **Simulation Defect** & \multirow{2}{*}{**Positioning Error**} \\  & **(ms)** & **Velocity Value (m/s)** & **Position (m)** & \\ \hline
7 & 0.6137 & 3215.1 & 0.9866 & 1.34\% \\
3 & 0.6478 & 3019.9 & 0.9781 & 2.19\% \\
1 & 0.6913 & 2852.7 & 0.9860 & 1.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Defect detection of each mode.  
Figure 18: Rail base defect detection. (**a**) Mode 7 rail base defect detection; (**b**) mode 3 rail base defect detection; (**c**) mode 1 rail base defect detection.  
are respectively sensitive to the defects of the rail head, rail web and rail base, are selected combining the strain energy distribution diagrams with group velocity dispersion curves. The optimal excitation direction of the expected modes is solved by the Euclidean distance, and the optimal excitation node is calculated by covariance matrix. Then, phase control and time delay technology is applied to achieve the enhancement of expected modes and suppression of interferential modes. After that, ANSYS is used to simulate the expected modes excitation and defects detection in the different rail parts with phase velocity and group velocity to validate the proposed methods. The simulation results present an acceptable error and demonstrate the effectiveness of the guided wave mode selection and excitation method, which proposes a feasible scheme for the field test and application to rail defect detection.  
This paper is a result of the full collaboration of all the authors; [PERSON] and [PERSON]. ([PERSON]) conceived and proposed the main idea of the paper; [PERSON] combined with phase control and time delay technology to improve the mode excitation method; [PERSON]. ([PERSON]) simulated the defect detection and analyzed the data; [PERSON]. ([PERSON]) and [PERSON] put forward their views on data processing; [PERSON] and [PERSON]. ([PERSON]) wrote the paper.  
The work is supported by the National Key Research and Development Program of China (2016 YFB1200401), Supported by Foundation of Key Laboratory of Vehicle Advanced Manufacturing, Measuring, and Control Technology (Beijing Jiaotong University), Ministry of Education, China.  
The authors declare no conflict of interest.
## 1 Introduction
### Anomalies in Rules  
There are two types of anomalies that will be discussed in this study: the subsumption redundancy rule and the circular rule. First, a subsumption redundancy rule exists in the present of another rule if two rules conclude the same action, but one has additional constraints in the rule condition, which may or may not be necessary (a specific kind of redundancy). Based on Figure 1, the presence of rule R2 will be considered as redundant subsumption because its antecedent items become the superset of antecedent items in R1 (i.e., age:elderly, sbp:prehypertension\(\supset\) age:elderly). According to the nature of logical testing in knowledge structure verification, one rule that contains more items in rule condition will be simply discarded without considering whether those rules imply the same knowledge. However, in real-world interpretation, if rule R1 is kept while rule R2 is discarded, then it might be too general to imply that elderly patients are more likely to have abnormality in their MRI brain result, whereas it is possible for R2, which has more items, to be more interesting than one with fewer items in rule condition. If R2 has at least a higher or equal interesting measurement than R1 or another subset of R2 (i.e., sbp:prehypertension\(\rightarrow\)mri: abnormal), then it is obvious that rule R1 is no longer sufficient to represent knowledge of R2. Consequently, eliminating R2 in the presence of R1 may lead to the loss of more interesting knowledge with more constraint information as a rule base. Therefore, taking interestingness measurement into consideration to eliminate redundant rules without losing more useful knowledge is necessary.  
Secondly, rules that are extracted from ARM mostly represent relationship between attributes, according to their co-occurrence in the database. Those rules are usually interpreted individually, without considering any relationship between the rules in the set of rules. In contrast, in PRSs, it is possible to deduce new final conclusions by creating a chain and linking two or more rules through a predicate logic approach or rule of inferences. However, it also possible to find another anomaly (i.e., circular condition) during the reasoning process. Circular rules tend to create a closed chain, which restrains the inference process in deducing final conclusions because of its loop. Referring to Figure 1, there is a rule chain R2 \(\rightarrow\) R3 \(\rightarrow\) R4 that contains a circularity which makes the reasoning process enter an endless loop by keeping the insertion of the same facts to the rule base of the production system without making any progress towards the solution or goal of the problem. Therefore, it will be difficult to derive a new final conclusion. Hence, further investigation for circularity reduction is also important in building rule bases to avoid contradictory logic or meanings and endless looping.
## 4 Specific Novelties & Future Prospects  
The novelties of the presented NILM framework are the derivation of current shapelets from envelopes of original current signatures and development of a multi-class classification model based on these time-series subsequences. It can be highlighted that finding out the best match for a shapelet based classification approach is a strenuous task, which can cause computational tribulations in practical implications. However, in this work, simple, computationally cost efficient and robust shapelet finding and matching criteria are established by presenting analytical premises and experimental evidences. It is proposed that as any device starts at any moment, there happens to be a sharp rise in the current envelope, which initiates searching and matching work flows for shapelets. And from this time instant of sharp rise, when and how to locate the right segments to extract shapelets are explained in this article.  
The future prospects of this research work are two folds. There is a potential scope to extend this research application for more sophisticated NILM databases, in where more device measurements collected from multiple residential and industrial units can be taken into account for shapelets extraction. In addition, there is a prospect to determine comparative premises of the proposed method with deep neural networks, especially architectures based on convolutional neural network (CNN) and recurrent neural network (RNN) models.
# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 1 Introduction
As an alternative, the technological advancements in wireless communications and the Internet of Things (IoT) allows various low cost and low power, small sensors to be used, that can be employed for surveillance of large areas such as forests. Wireless sensor networks (WSNs) are a technology that is using standards such as WiFi, Bluetooth, ZigBee [1], or mobile broadband (3/4/5G) [10] and can be utilized widely for forest surveillance and management [8,11]. In this article, we introduce an acoustic surveillance-based methodology for detecting logging in a forest. The presented methodology is modular and since it relies on audio evidence, it can be adapted to different forest characteristics and can be operated equally well during day and night. The remainder of this article is structured as follows: In Section 2, related work and systems in the literature are presented; in Section 3 a description of the framework for acoustic surveillance of forests for detection of illegal logging is described; in Section 4, the experimental study is described; in Section 5, the results of the study on audio-based logging identification are presented; and finally, in Section 6, the presented work is concluded.
## 4 Analysis and Discussion
### Analysis of PUF-Based Authentication and Its Communication Protocol  
We explain all the terms in Table 7 to presenting Table 8. A detailed analysis of PUF-based authentication and its communication protocol follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Explanation** & **Symbol** & **Explanation** \\ \hline CoAP & Constrained Application Protocol & AMQP & Advanced Message Queuing Protocol \\ \hline MQTT & Message Queuing Telemetry Transport & HTTP & Hypertext Transfer Protocol \\ \hline DDS & Data Distribution Service & UDP-based & User Datagram Protocol \\ \hline XMPP & Extensible Messaging and Presence Protocol & Modbus-TCP & Modbus Communication Protocol \\ \hline \hline \end{tabular}
\end{table}
Table 7: An explanation of terms of PUF-Based Authentication and Its Communication Protocol.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Current** & & & **Evaluation Methods** & \\ \cline{3-6}  & **IoT** & **Objectives** & **Results** & **Analyzer Tools** & **Metrics** & **Major Disadvantages** \\  & & & & **(Hardware + Software)** & & \\ \hline \multirow{8}{*}{[97]} & \multirow{8}{*}{CoAP, MQTT, AMQP} & Design and build an application layer & \multirow{8}{*}{CoAP performs discover how these protocols work. Experiments were performed on a realistic testbed with wired, Wi-Fi, and 2/3/4G networks.} & \multirow{8}{*}{CoAP performs reliably during connection and is less network. Detection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Current**} & \multicolumn{5}{c}{**Evaluation Methods**} \\ \cline{5-8}  & & & & **Analyer** & & & \\  & **IoT** & **Protocols** & **Objectives** & **Results** & **Tools** & **Metrics** & **Major Disadvantages** \\ \hline \multirow{9}{*}{[98]} & & & & In system control, & & & \\  & & & & instant messaging, & & & \\  & & & & and VoIP for & & & \\  &  
Eleven studies discussing PUF-based authentication and communication protocols were reviewed and analyzed. The detailed analysis is shown in Table 5. Table 5 describes the IoT protocols that are being developed or adopted in the literature. AMQP and MQTT are the most widely adopted protocols that are being used in the literature and are being deployed and tested for wired as well as wireless technologies. Wireshark and Raspberry Pi are the two popular choices in terms of hardware testbeds to observe the performance of the proposed protocols. Based on further analysis, it can be found that the above-mentioned researchers did not use or suggest a method for assessing the challenges facing security. This is a drawback, as a real assessment of security is necessary. Most researchers, such as [39; 40], have used MQTT because it is lightweight and the mechanism is simple in a network. [PERSON] et al. designed a new protocol [87] and a new schema, which was different in metrics and key performance indicators (KPIs) [42].
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 2 Topography Description and Field Measurement Setup
### Setup of the Lidar  
The setup of the lidar system is divided into site selection, measuring scheme, installation, debugging, and testing. The details are as follows:  
1. Site selection: After a detailed field investigation, the lidar is installed beside the highway. The site is open and unobstructed, the horizontal distance from the midspan of the bridge is about 1700 m, and the vertical height difference is about 200 m, which is convenient for transportation and field electricity consumption, as shown in Figures 2 and 3.
2. Measuring scheme: In order to measure the distribution characteristics of wind parameters in the spanning direction of the bridge, measuring points are arranged at 5 key positions: the west bridge tower, 1/4 span, midspan, 3/4 span, and east  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Numbers** & **Indicators** & **Description** \\ \hline
1 & Radial Detection Range & 45–6000 m \\
2 & Radial Range Resolution & 15 m/30 m/User Defined \\
3 & Laser Wavelength & 1550 nm, Invisible and Safe to Human Eyes \\
4 & Data Refresh Frequency & 1 Hz–10 Hz \\
5 & Range of Radial Measured Wind Speed & 0–75 m/s \\
6 & Range of Wind Direction & 0–360\({}^{\circ}\) \\
7 & Accuracy of Wind Speed & \(\leq\)0.1 m/s \\
8 & Accuracy of Wind Direction & \(\prec\)3\({}^{\circ}\) \\
9 & Scan Modes & RHI / PPI/ DBS/ VAD/ Scripting \\
10 & Range of Servo Scanning & Horizontal Direction: 0–360\({}^{\circ}\), Vertical Scanning: \\
11 & Weight & \(\prec\)90 kg \\
12 & Size Length/Width/Height & 638 mm /626 mm /907 mm \\
13 & Power Supply & 220 V /50 Hz \\
14 & Communication Mode & Ethernet/3G/4G/Modbus \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance indicators of the lidar.  
Figure 5: Diagram of Wind3D 6000 lidar wind measuring system. (**a**) Contour line at bridge site, (**b**) Wind3D 6000 Lidar system.  
bridge tower, respectively. Five virtual wind measuring towers are set up, namely from VWT-1 to VWT-5, as shown in Figure 2.
3. Installation: the lidar system is equipped with a power supply, monitoring, and a 4G network to facilitate data transmission and site monitoring, as shown in Figure 5.
4. Debugging: after field debugging, with little interference from the surrounding environment, the signal-to-noise ratio (SNR) is greater than 10, the lidar system runs normally, and the wind parameter measurement is normal and reliable.
5. Testing: according to the gale wind season from 11 November 2020 to 3 May 2021, from late autumn to early summer, the operating state of the lidar system was real-time monitored, and a continuous 173 days of data was measured and recorded.
## 3 Study Area and Data
### Explanatory Variables
#### 3.1.3 Network Structure  
We consider the degree centrality, betweenness centrality, and the distance from metro stations to the city center as the main factors in the category of network structure. In graph theory and network analysis, degree centrality is one of the easiest centrality measures to calculate, which is simply a count of how many edges a node has. Betweenness centrality of a node is measured with the number of shortest paths (between any pair of nodes in the graphs) that passes through the target node [31]. Therefore, they are related to specific transfer stations, normal intermediate stations, and terminal stations, as well as the role of a station in allowing traffic flows to pass from one part of the metro network to the other. The city center of Shenzhen is Shenzhen Municipal People's Government in Futian District. To accurately calculate the distance of each station to the city center, we take the influence of the radius of the earth into consideration, and the distance from station \(i\) to the city center \(Dist_{i}\) is as follows (1):  
\[Dist_{i}=\mathrm{R}*\arccos(\cos(Lat_{0})*\cos(Lat_{i})*\cos(Lon_{0}-Lon_{i})+ \sin(Lat_{i})*\sin(Lat_{0}))*\frac{\pi}{180} \tag{1}\]  
where R is the radius of the earth, \((Lat_{0},Lon_{0})\) and \((Lat_{i},Lon_{i})\) are respectively the latitude and longitude of the city center and station \(i\). The required geographical information was collected from Google Maps (Source: [[https://maps.google.com](https://maps.google.com)]([https://maps.google.com](https://maps.google.com))).
## 5 Conclusions  
The dispersion power fading in RoF communication is successfully reduced using an asymmetric carrier divider with an irregular RF phase on the DD-MZ modulator. The minimum DF is obtained when the P\({}_{\text{IN}}\) is separated as \(75\%\) for P\({}_{\text{CA}}\) and \(25\%\) for P\({}_{\text{DD-MZM}}\) with an irregular RF signal of \(\theta=48^{\circ}\) and bias point value of \(\gamma=3/4\). As the result, with the same power as OSSB, this proposed structure produces DF at \(m=0.1\) and \(m=1\) value are \(0.008\) and \(0.03\), or it can decrease the DF of \(96.7\%\) at \(m=1\). The mathematical model and simulation model have very good agreement, which validates the proposed method. The proposed scheme is more suitable for external modulation. It can be applied for applications such as supporting 5G communications or supporting IEEE 802.11 wireless network. It should be noted that the limitation at the implementation, such as the use of single-mode fiber optics, power splitter, power combiner, and polarization controller to make the polarization is constant. There are several suggestions for future research. First, the validation can be improved in future studies through measurements. Moreover, information signal analysis and bit error rate (BER) calculations can be potentially included. Lastly, iterations on power divider can be made more detailed and general.  
Conceptualization, G.W. and F.U.; methodology, G.W.; software, F.U.; validation, G.W., P.S.P. and T.F.; formal analysis, F.U.; investigation, T.F.; resources, F.U.; data curation, T.F.; writing--original draft preparation, F.U.; writing--review and editing, G.W.; visualization, T.F.; supervision, P.S.P.; project administration, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by QQ Project Grant (Universitas Indonesia), grant number NKB-0299/UN2.R3.1/HKP.05.00/2019.  
The authors declare no conflict of interest.  
Figure 14: C/N penalty for the RoF link with ODSB and OSSB modulations, scheme C4 irregular \(\theta\) and scheme C2 irregular \(\theta\) at \(m=1\).
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 4 Results and Analysis
### The BERs Comparison with Conventional RNN Decoder  
To illustrate the necessity of RNN-decoder part in seq2 seq decoder, we compare the performance of the seq2 seq decoder with that of the conventional RNN decoder. That is, the decoder part of the seq2 seq decoder was removed, we retained the encoder part with the same configure as before and we replaced the last layer of encoder with a fully connected layer of size \(K\). We name this NN decoder the RNN decoder. Likewise, both NN-based decoders are evaluated under frame-by-frame transmission and multi-frame transmission cases.  
Figure 6a,b shows the BER performances of two NN-based decoders under frame-by-frame transmission and multi-frame transmission. We can observe that in the frame-by-frame case, both decoders can approach the MAP decoding performance level and outperform the table look-up method. However, in the multi-frame transmission case, the performance of RNN decoder decreased as the number of transmitted frames increased (when the number of transmitted frames was 1-4, the performances of the two decoders were essentially the same, and therefore are not plotted in the figure). This indicates that the seq2 seq decoder has a greater ability to transmit multiple frames compared to the RNN decoder, which enhances the throughput of the system, at the cost of having more training parameters than the RNN decoder.  
Figure 5: The BER performance of seq2 seq decoder over 4B6B RLL codes. (**a**) Frame by frame transmission case; (**b**) multi-frame transmission case.  
Figure 6: The BER performance comparison of seq2 seq decoder and RNN decoder. (**a**) Frame by frame transmission case; (**b**) multi-frame transmission case.
# Acoustic Equivalent Lasing and Coherent Perfect Absorption Based on a Conjugate Metamaterial Sphere
## 2 Scattering Model of an ACM Sphere
According to Equations (4) and (5), the scattering coefficient for the (\(q\)-1)-th region of order \(l\) can be obtained as  
\[s_{q-1}^{l}=\frac{\eta_{q-1}D_{q}^{l}h_{l}^{(2)}\left(k_{q-1}R_{q}\right)-\eta _{q}C_{q}^{l}h_{l}^{(2)^{\prime}}\left(k_{q-1}R_{q}\right)}{\eta_{q}C_{q}^{l} h_{l}^{(1)^{\prime}}\left(k_{q-1}R_{q}\right)-\eta_{q-1}D_{q}^{l}h_{l}^{(1)} \left(k_{q-1}R_{q}\right)}. \tag{6}\]  
Then, Equation (6) is a recursive formula that could apply to all \(q\in[1,\ Q-1]\). Since the spherical Hankel functions diverge at the origin, the acoustic pressure field in the \(Q\)-th region should be expressed as  
\[p_{Q}(r,\theta)=\sum_{l=0}^{\infty}a_{Q}^{l}j_{l}(k_{Q}r)P_{l}(\cos\theta), \tag{7}\]  
where \(a_{Q}^{l}\) is the complex amplitude of the penetrated channel spherical wave of order \(l\), and \(j_{l}\) is the spherical Bessel function of the first kind with order \(l\). By applying the continuous pressure and radial velocity boundary conditions at \(r=R_{Q}\), the scattering coefficient in the (\(Q-1\))-th region, i.e., \(s_{Q-1}^{l}\), can be expressed as  
\[s_{Q-1}^{l}=\frac{\eta_{Q-1}j_{l}^{\prime}(k_{Q}R_{Q})h_{l}^{(2)}(k_{Q-1}R_{Q} )-\eta_{Q}j_{l}(k_{Q}R_{Q})h_{l}^{(2)^{\prime}}(k_{Q-1}R_{Q})}{\eta_{Q}j_{l}(k _{Q}R_{Q})h_{l}^{(1)^{\prime}}(k_{Q-1}R_{Q})-\eta_{Q-1}j_{l}^{\prime}(k_{Q}R_{ Q})h_{l}^{(1)}(k_{Q-1}R_{Q})}. \tag{8}\]  
Here, \(j_{l}^{\prime}\) is the first-order derivative of \(j_{l}\). By iterating Equations (6) and (8), all scattering coefficients \(s_{g}^{l}\) (\(q=0,1,\ldots,\ Q-1\)) can be obtained. Similar to the process for solving the scattering coefficients, the transmission coefficient in the \(q\)-th region, i.e., \(a_{q}^{l}/a_{q-1}^{l}\) (\(q=1\), \(2\), \(\ldots\), \(Q\)), can be obtained accordingly by applying continuous boundary conditions. Then, the total acoustic field in the whole domain can be completely reconstructed from Equations (3) and (7) for a given incident pattern. Due to the orthogonality of the spherical channel waves, there is no interchannel scattering. According to Equation (2), therefore, the scattering matrix of the proposed ACM sphere system is diagonal with on-diagonal elements \(s_{0}^{l}\), and the scattering coefficient \(s_{0}^{l}\) is also an eigenvalue of the scattering matrix for the proposed system. In the following, we focus on the extraordinary scattering properties of the ACM sphere by analyzing the scattering coefficient \(s_{0}^{l}\).