esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Isolation of a Surveillance System Equipped in a Drone with Mode Decoupling
###### Abstract  
Vibration isolation with mode decoupling plays a crucial role in the design of an intelligent robotic system. Specifically, a coupled multi-degree-of-freedom (multi-DOF) model accurately predicts responses of system dynamics; hence, it is useful for vibration isolation and control with mode decoupling. This study presents a vibration isolation method with mode decoupling based on system identification, including a coupled multi-DOF model to design intelligent robotic systems. Moreover, the entire procedure is described, including the derivation of the governing equation of the coupled multi-DOF model, estimation of the frequency response function, and parameter estimation using least squares approximation. Furthermore, the suggested methods were applied for a mobile surveillance system suffering from resonances with mode coupling; it made the monitoring performance of the surveillance camera deteriorate. The resonance problem was mitigated by installing vibration isolators, but limited to eliminate the coupling effects of natural frequency deterioration performances of vibration isolation. More seriously, system identification with a simple decoupled model limits the prediction of this phenomenon. Hence, it is difficult to enhance the performance of vibration isolators. In contrast, the presented method can accurately predict the vibration phenomenon and plays a critical role in vibration isolation. Therefore, dynamic characteristics were predicted based on a vibration isolator using the coupled three-DOF model, and a final suggestion is presented here. The experiments demonstrated that the suggested configuration decreased vibration up to 98.3%, 94.0%, and 94.5% in the operational frequency range, i.e., 30-85 Hz, compared to the original surveillance system in the fore-after, side-by-side, and vertical directions, respectively. The analysis suggests that the present method and procedure effectively optimize the vibration isolation performances of a drone containing a surveillance system.  
vibration isolation; frequency response analysis; transmissibility; system identification; coupled multi-degree-of-freedom model +
Footnote †: journal: Applied Sciences  
e1 Department of Safety Engineering/Big Data, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea; [EMAIL_ADDRESS]  
2 Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongiak-gu, Seoul 06974, Korea; donggeunbuk@[EMAIL_ADDRESS]  
3 Department of Intelligent Energy and Industry, Chung-Ang University, 84 Heukseok-ro, Dongiak-gu, Seoul 06974, Korea; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel: +82-2-820-8385
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 5 Conclusions  
This paper presents a smart HEM system that consists of both hardware and software. The system utilizes various instruments for the wireless transmission of energy, temperature, and illuminance information, which the main controller uses to remotely generate and send commands to the actuating circuitries of the hardware. The commands include turn on/off, temperature control, and illuminance adjustment commands. The system software includes an effective rule-based HEM algorithm that receives real-time inputs from the various system circuitries for the control of the appliances in such a way that reduces their power consumption without affecting the lifestyle of the user. The results of the experimental implementation of the proposed system confirmed its control reliability, with reductions of 23.5 kW and $2.898 being achieved in the daily energy consumption  
Figure 26: Total (**a**) energy consumptions and (**b**) bills without and with the use of the HEM system.  
and bill, respectively. In the future work, the hardware design could be improved by adding new supporting circuitries or other functionalities, such as detecting the number of persons in the room. Furthermore, adopting fuzzy or heuristic optimization based approaches could enhance the rule based scheduling algorithm. The proposed HEM system promises to be an asset for households with high energy consumptions.  
Conceptualization, H.S. and E.A.-H.; Methodology, H.S., E.H.; Software, E.A.-H.; Validation, H.S., E.A.-H. and R.S.; Formal analysis, H.S., E.A.-H. and R.S.; Resources, H.S.; Writing--Original draft preparation, E.A.-H., H.S.; Writing--Review and editing, R.S.; Visualization, H.S., E.A.-H. and R.S.; Supervision, H.S.; Project administration, H.S.; Funding acquisition, H.S. and R.S. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the United Arab Emirates University vide fund code 31N259-UPAR (2) 2016.  
The authors declare no conflict of interest.
### Contributions of the Work  
The main objective of this work is to present a new CoAP-IoT dataset, perfectly labelled, easy to follow and manipulate, and designed for traffic anomaly detection in IoT environments, and present its validation through the application of supervised learning algorithms. The main contributions of this research are:  
* An exhaustive overview of existing IoT datasets and their validation using machine learning algorithms for anomaly detection.
* Design and implementation of a semi-synthetic IoT environment to program request and respond COAP messages, introducing alterations of the default behaviour of the CoAP packets to produce anomalies in the traffic.
* An annotated, versatile, easy to follow, and manipulated CoAP-IoT dataset that facilitates tests with supervised learning algorithms.
* Analysis and feature extraction of IoT dataset wireless sensor networks.
* Implementation and data optimisation for binning, handling unbalanced data, feature extraction, and hyperparameter tuning in real environments.
* Application of diverse machine learning techniques and their evaluation using different traditional metrics to validate the selected dataset.
###### Abstract  
Traditional contact printing technology is primarily controlled by the shape of the mask to form the size, while for the more popular non-contact printing technologies, in recent years, adjusting the print parameters has become a direct way to control the result of the printing. High-viscosity conductive pastes are generally processed by screen printing, but this method has limited accuracy and wastes material. Direct-write printing is a more material-efficient method, but the printing of high-viscosity pastes has extrusion difficulties, which affects the printed line width. In this paper, we addressed these problems by studying the method of printing high-viscosity conductive paste with a self-made glass nozzle. Then, by parameter optimization, we achieved the minimum line width printing. The results showed that the substrate moving speed, the print height, and the feed pressure were the key factors affecting the line width and stability. The combination of the printing parameters of 0.6 MPa feed pressure, 200 mm/s substrate moving speed, and 150 um print height can achieve a line width of approximately 30 um. In addition, a mathematical model of the line width and parameters was established, and the prediction accuracy was within 5%. The results and the prediction model of the parameters provide an important reference for the printing of high-viscosity pastes, which have immense potential applications in electronics manufacturing and bioprinting.  
conductive paste; high viscosity; glass nozzle; direct-write printing; parameter optimization Article
## 3 Results
### Raman Spectroscopy  
The anatase-to-rutile transition of Cu-TiO\({}_{2}\) was further examined using Raman spectroscopy. A typical Raman spectrum shows characteristic modes at different wavenumbers, corresponding to Raman shifts. The active modes for anatase are A\({}_{\text{1g}}\), 2B\({}_{\text{1g}}\) and 3E\({}_{\text{g}}\) at 147, 197, 396, 516 and 638 cm\({}^{-1}\) respectively. For rutile, the Raman active modes are A\({}_{\text{1g}}\), B\({}_{\text{1g}}\), B\({}_{\text{2g}}\) and 3E\({}_{\text{g}}\) at 144, 238, 446 and 612 cm\({}^{-1}\)[15]. Raman spectra displayed the presence of the titania peaks and did not exhibit the occurrence of copper/copper oxides or other impurities (Figure 5). The Raman results are consistent with the data observed in XRD analysis. The anatase phase of titania is retained well by the Cu doping. Similar results were also observed for higher concentration (<0.5 mol %) of Cu-doped samples [71; 72; 73]. However, as observed in the figure below, the doped sample shows a small decrease in frequency of 2.35 cm\({}^{-1}\) (red shift as illustrated in the inset of the image). Decrease in frequency is the resultant of the Cu insertion in the TiO\({}_{2}\) lattice, which subsequently weakens the bond between the O-Ti-O bond, therefore leading to a higher lattice constant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Calcined Temperature (in \({}^{\circ}\)C)** & **Particle Size (in nm)** \\ \hline Control\(\bullet\) & 500 & 8.83 \\ \hline
0.5\% Cu-TiO\({}_{2}\) & 500 & 11.34 \\ \hline
0.5\% Cu-TiO\({}_{2}\) & 600 & 22.35 \\ \hline
0.5\% Cu-TiO\({}_{2}\) & 650 & 28.84 \\ \hline
0.5\% Cu-TiO\({}_{2}\) & 700 & 32.93 \\ \hline Control\(\bullet\) & 700 & 33.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average particle size of samples calcined at various calcination temperatures.  
Figure 4: (a) XRD profiles of pure anatase TiO\({}_{2}\), pure rutile TiO\({}_{2}\), Cu-TiO\({}_{2}\) samples (calcined at 500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 650 \({}^{\circ}\)C and 700 \({}^{\circ}\)C) and (b) XRD profiles of pure anatase TiO\({}_{2}\), pure rutile TiO\({}_{2}\) and the Cu-TiO\({}_{2}\) calcined at 650 \({}^{\circ}\)C.
## 4 Results and Discussion
### Dynamic Performances
#### 4.2.2 Udds  
As mentioned in Section 4.2.1, the behavior of each performance indicator shows a similar trend even under the UDDS condition. However, comparing the average values of effectiveness and fin efficiency according to the driving conditions in Figures 10 and 11 shows 0.924/0.765 and 0.887/0.768, respectively. The effectiveness of HWFET is slightly higher than that of UDDS; however, fin efficiency shows the opposite result.  
If it is possible to secure the driving cycle according to the type of eco-friendly vehicle, it is expected to be able to apply the optimal heat exchanger design through dynamic performance analysis. This would be useful, for example, to optimize the heat transfer rate, effectiveness, fin efficiency, and the total weight of a vehicle that maintains a low or high driving load state for a long time; moreover, it will be possible to perform an optimal design considering multiple operating points for each vehicle. At this time, it is thought that research is needed to properly set the items and their respective weights to be reflected in the objective function.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 3 Numerical Analysis
### Channel Model  
The hybrid FSO-PON system is designed with the GG model. The GG channel distribution is described as [34; 35]:  
\[\mathrm{f_{s_{a}}(s_{a})}=\frac{2(xy)^{(\frac{x+y}{2})}}{\Gamma(x)\Gamma(y)}s_{ a}(^{\frac{x+y}{2})-1}\mathrm{J_{x-y}}\Big{[}2(xys_{a})^{\frac{1}{2}}\Big{]} \tag{1}\]  
where \(s_{a}\) is atmospheric turbulence, \(\Gamma(.)\) represents Gamma function, \(\mathrm{J_{x-y}}\) is the modified Bessel function having order (x \(-\) y). Additionally, x and y represent the numbers of large as well as small-scale effective eddies, respectively, which are given for a spherical wave as [34; 35; 36]:  
\[\mathrm{x}=\frac{1}{\left[\exp\left\{\frac{0.49a_{0}^{2}}{\left(1+0.18d^{2}+0.56 a_{0}^{2}\right)^{\frac{2}{6}}}\right\}-1\right]} \tag{2}\]  
and  
\[\mathrm{y}=\frac{1}{\left[\exp\left\{\frac{0.51a_{0}^{2}\left(1+0.18d^{2}+0.56 a_{0}^{2}\right)^{\frac{2}{6}}}{\left(1+0.90d^{2}+0.62d^{2}a_{0}^{\frac{2}{6}} \right)^{\frac{2}{6}}}\right\}-1\right]} \tag{3}\]  
where \(a_{0}^{2}\) and d represent the optical wave number and diameter of the spherical wave, respectively. The exp indicates exponential function.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## 2 System Model and Protocol Descriptions
### Hybrid Cooperative Cognitive Radio Networks Based on OMA and NOMA
State S2: The PU transmits a new packet while ST transmits its own packet. This transmission is operated under interference for the PU and SU. The next state is S1 or S4 depending on the ACK or NACK feedback from PR. State S3: In the overlay NOMA cooperative mode, ST transmits the superimposed signal \(\sqrt{\alpha_{P}P_{S}}x_{P}+\sqrt{\alpha_{S}P_{S}}x_{S}\) to PR and SR, simultaneously. The next state moves to S1 irrespective of whether the PR feedback is ACK or NACK. State S4: PT retransmits that the packet failed previously while SU keeps silent. The next state moves to S1 irrespective of whether the PR feedback is ACK or NACK. The four-states Markov model for the proposed protocol is shown in Figure 3, where the state transition probabilities can be defined as follows:  
\(P_{1}\): Packet success probability at the channel \(\text{PT}\rightarrow\text{PR}\). \(P_{2}\): Packet success probability at the channel \(\text{PT}\rightarrow\text{PR}\) under interference from the channel \(\text{ST}\rightarrow\text{SR}\). \(P_{3}\): Packet failure probability at the channel \(\text{PT}\rightarrow\text{PR}\) and packet failure probability at the channel \(\text{PT}\rightarrow\text{ST}\). \(P_{4}\): Packet failure probability at the channel \(\text{PT}\rightarrow\text{PR}\) and packet success probability at the channel \(\text{PT}\rightarrow\text{ST}\). \(P_{5}\): Packet success probability at the channel \(\text{ST}\rightarrow\text{PR}\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **PT** & **ST** \\ \hline S1 & PU’s packet & Silent \\ S2 & PU’s packet & SU’s packet \\ S3 & Silent & PU’s packet and SU’s packet \\ S4 & PU’s packet & Silent \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hybrid cooperative cognitive radio networks based on OMA/NOMA of transmission state. Figure 3: Markov chain mode. The Markov chain of states transition probabilities and the steady state probabilities have following relationships:  
\[\left(\pi_{\text{S}_{1}},\pi_{\text{S}_{2}},\pi_{\text{S}_{3}},\pi_{\text{S}_{4}} \right) \begin{pmatrix}0&P_{1}&P_{4}&P_{3}\\ P_{2}&0&0&1-P_{2}\\ 1&0&0&0\\ 1&0&0&0\\ 1&0&0&0\\ \end{pmatrix} =\left(\pi_{\text{S}_{1}},\pi_{\text{S}_{2}},\pi_{\text{S}_{3}}, \pi_{\text{S}_{4}}\right), \tag{9}\]  
\[\pi_{\text{S}_{1}}+\pi_{\text{S}_{2}}+\pi_{\text{S}_{3}}+\pi_{\text{S}_{4}}=1, \tag{10}\]  
where \(\pi_{i}\) is the steady state probabilities of state \(i\) with \(i\in I=\{\text{S}_{1},\text{S}_{2},\text{S}_{3},\text{S}_{4}\}\). The steady-state probabilities of PUs can be obtained by solving the Equations (9) and (10):  
\[ \begin{cases}\pi_{\text{S}_{1}}=\frac{1}{1+2P_{1}+P_{4}+P_{3}-P_{1}P_{2}},\\ \pi_{\text{S}_{2}}=\frac{P_{1}}{1+2P_{1}+P_{4}+P_{3}-P_{1}P_{2}},\\ \pi_{\text{S}_{3}}=\frac{P_{4}}{1+2P_{1}+P_{4}+P_{3}-P_{1}P_{2}},\\ \pi_{\text{S}_{4}}=\frac{P_{3}+P_{1}-P_{1}P_{2}}{1+2P_{1}+P_{4}+P_{3}-P_{1}P_{2 }}.\end{cases}  \tag{11}\]
## 1 Introduction
In recent studies, they have demonstrated that micro-nano morphology and surface area are the key factors affecting the rate capability and long-term cyclic stability of the double metal hydroxide electrode material [22; 23]. Thus, several studies are needed to explore the new micro-nanomorphology with large surface area for the double metal hydroxide to resolve the abovementioned issue. In this trend, the current study has tried to develop and enhance the performance of the cobalt supercapacitor electrode materials through designing nanofiber structures for layered metal double hydroxides with new species such as divalent and tetravalent tin. At the same time, it can provide data for the positive effect of the conversion of 2D nanomaterials to 1D nanofibers. In the present work, a series of different nanostructures of the Sn/Co HDS and Sn/Co LDH materials, such as plate-nanofibers and nanofibrous morphologies have been synthesized by altering the percentage and the oxidation state of the precursor (Sn). The controlling of the morphology has been studied in detail through intercalating dual anions of both cyanate and nitrate anions in the confined space among the Sn/Co nanolayers. Then, the different morphologies leading to a different electrochemical property have been investigated. This study can provide a reference for future studies about morphological control and provide strategies to enhance the performance of supercapacitor electrode materials. Additionally, nanofibers may be useful for designing and fabricating nanodevices.
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 4 Experimental Results and Analysis
### Extraction of Vegetation from the Data Set of the University of Pavia  
For the Pavia University data set, 0.5% of each type of ground feature is selected as the training sample. Because the vegetation information in the data set is relatively rich and there is no water body information, only the vegetation part is tested. The 5-fold cross-validation shows that the value of the regularization parameter is 10. At this time, the feature matrix becomes very sparse, and the interactive features of the vegetation part can be seen in Table 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Recall of NDWI** & **Recall of BAND9** \\ \hline
0 & 0.942 & 0.939 \\
255 & 0.209 & 0.251 \\ Overall Accuracy & 0.893 & 0.893 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Quantitative comparison results of water extraction from the Washington DC Mall data set.  
Figure 8: Extraction results of vegetation and partial enlarged image: (**a**) Ground truth information; (**b**) NDVI extraction result; (**c**) BAND41 extraction result.  
Compared with BAND18, BAND19 is more prominent. The coefficient of BAND19 is larger, indicating that the feature is more important, which can be expressed as:  
\[\text{BAND19}=\frac{\text{band11}-\text{band94}}{\text{band11}+\text{band94}}= \frac{B-NIR}{B+NIR} \tag{13}\]  
Among them, the wavelength of band11 is 480 nm belonging to the B band, and the wavelength of band94 is 895 nm, which belongs to the NIR band. The vegetation is extracted by BAND19, and the binarization results are shown in Figure 9:  
BAND19 is significantly better than NDVI in the Pavia University data set for vegetation extraction. To make the results more convincing, quantitative results are also given, as shown in Table 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Recall of NDVI** & **Recall of BAND19** \\ \hline
0 & 0.677 & 0.909 \\
255 & 0.844 & 0.882 \\ Overall Accuracy & 0.768 & 0.895 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Quantitative comparison results of vegetation extraction from the Pavia university data set.  
Figure 9: Extraction results of vegetation and partial enlarged image: (**a**) Ground truth information; (**b**) NDVI extraction result; (**c**) BAND19 extraction result.  
For the extraction of vegetation, BAND19 is significantly better than NDVI, which proves the effectiveness of BAND19.
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 3 Methodology
### Variables  
The selection of predictor variables is pivotal in achieving accurate collision severity forecasting. The following predictors are contained in the prepared dataset: Weather Condition, Roadway Alignment, Roadway Description, Collision Type, Alcohol Consumption, Belted, Bike, Traffic Signal, Districts, Area Type, Distracted, Drugs, Drowsy, Crash Date, Motor, Pedestrians, Speed, and Animal. Visualizing the correlation matrix emerged as an invaluable tool, unveiling a visual relation of interconnectedness and allowing us to perceive which variables danced in harmony and stood out in stark contrast [33]. These visualizations provided a tangible glimpse into the complex relationships that underlie collision severity, and the most influential or important factors are mentioned in Table 1.
## 5 Simulation and Analysis  
Simulations are carried out to evaluate the performance of the proposed algorithm. The following scenario is considered for data generation. As shown in Figure 3, cell radius R is assumed to be 300 m, four BSs are located at BS1 \((0,\ 0)\), BS2 \(\left(\sqrt{3}R,\ 0\right)\), BS3 \(\left(\sqrt{3}R/2,\ 3R/2\right)\) and BS4 \(\left(\sqrt{3}R/2,-3R/2\right)\), respectively. The MS is located at \((R/2,\ R/2)\). Four scatterers are randomly distributed around the MS, which satisfies the ring-of-scattreer (ROS) model or disk-of-scatterer (DOS) model. Each of the data in the below figures is calculated from 1000 independent trials, the measurement error of distance of TOA and AOA satisfies zero-mean Gaussian distribution, the unit of distance is meter (m), and the unit of AOA is degree (\({}^{\circ}\)). The identification performance is evaluated using the identification rate (\(P_{IE}\)), defined by \(P_{IE}=N_{C}/N_{T}\), with \(N_{T}\) being the number of all scattering signal combinations and \(N_{C}\) being the number of scattering signal combinations that are identified correctly.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies  
ITS communication technologies allow all systems to work in an integrated manner. The Road Side Unit (RSU) systems located on the roadside can not transfer the information they receive from the vehicle to the center. In particular, vehicle navigation systems receive vehicle location information on maps and transmit it to the center thanks to GPS. ITS communications are made from vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-anywhere (V2X). Also, quantum technology is poised to play a significant role in developing next-generation 6G wireless communications. Quantum technology can help achieve ultra-secure communication, enhanced data rates, massive parallel computation, and enhanced localization and navigation capabilities in next-generation 6G wireless networks. Using such systems as safe communication in ITS applications is possible to make systems safer [20]. ITS communication systems are shown each serves equipment's between others in Figure 5.  
Figure 4: ITS main groups [16].