esa-sceva/satcom-chunk-collection huggingface dataset:
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 3 The Microsoft Kinect
### Success of Kinect Technology  
The Microsoft Kinect (formerly known as the Natal project) made its first public appearance during the Electronic Entertainment Expo in June 2009 [29]. Initially, this optional accessory was intended for the XBox 360 video game console. In the digital leisure context, it allowed direct user interaction with a video game's content without any controller, with the player simply being the controller. The Kinect can be controlled by either motion recognition or voice commands.  
More than 8 million copies were sold within 60 days of its release in November 2010. The XBox 360 holds the Guinness World Record as the fastest-growing consumer electronics device ever sold [30]. In February 2012, Microsoft unveiled the official use of this depth sensor for personal computers. After the great success of version 1, Microsoft continued its development project and released the Kinect V2 in 2014. This giant company then resuscitated the Kinect after announcing a production halt in 2015, presenting a new connected camera, the Microsoft Azure Kinect, at the 2019 MWC in Barcelona [31].  
Thanks to the innovation on different versions of Kinect, scientific researchers have shown Kinect's potential as a platform for both science and industry to create innovative new applications. Kinect has opened enormous opportunities for the scientific community to tackle societal issues such as health, education, and aging.
# Second-Harmonic Generation in Mie-Resonant GaAs Nanowires
###### Abstract  
We investigate the enhancement of second-harmonic generation in cylindrical GaAs nanowires. Although these nanostructures confine light in two dimensions, power conversion efficiencies on the order of 10\({}^{-5}\) with a pump peak intensity of \(\sim\)1 GW/cm\({}^{2}\) are possible if the pump and the second-harmonic fields are coupled to the Mie-type resonances of the nanowire. We identify a large range of nanowire radii in which a double-resonance condition, i.e., both the pump and the second-harmonic fields excite normal modes of the nanowire, induces a high-quality-factor peak of conversion efficiency. We show that second-harmonic light can be scattered with large efficiency even if the second-harmonic photon energy is larger than 1.42 eV, i.e., the electronic bandgap of GaAs, above which the material is considered opaque. Finally, we evaluate the efficiency of one-photon absorption of second-harmonic light and find that resonant GaAs nanowires absorb second-harmonic light in the near-field region almost at the same rate at which they radiate second-harmonic light in the far-field region.  
nanophotonics; nonlinear optics; second-harmonic generation; Mie resonances +
Footnote †: journal: Applied Physics Letters
## 4 Discussion
### Effect of Heat Treatment on Tuning the Stiffness and Damping Capacity of NiTi WMD
#### 4.3.3 Significance of Mesh Density and Heat Treatment Temperature  
Mesh density is one of the most significant factors that influence the vibration isolation performance of SS 304 WMDs. As revealed by prior work [18], the dynamic stiffness and loss factor of the SS 304 WMD can be improved by 9.8 times and decreased by 1.7 times, respectively, by increasing the mesh density from 2.0 to 4.0. In the present work, however, the adjustable range of SE NiTi WMD's mesh density is relatively smaller than that of the SS 304 WMD due to the poor formability of the SE NiTi wire. Extremely large or small mesh density would either radically increase the forming pressure or make it hard to form the NiTi WMD. Finally, the acceptable mesh density is limited to a narrow range from 0.25 to 0.38, which only gives rise to a 86.9% improvement and a 33.4% reduction in the NiTi WMD's dynamic stiffness and loss factor, respectively.  
A comparable adjustable range of dynamic stiffness and loss factor up to 85.5% and 28.1%, respectively, can be obtained by altering the heat treatment temperature in the present work. Nevertheless, it is expected that a larger tunable range of dynamic stiffness and loss factor can be obtained by increasing the heat treatment temperatures to 450 \({}^{\circ}\)C. Pseudo-static test results in Section 3.1 indicate that the as-fabricated NiTi WMD is readily to be stiffened by nearly 8 times after being heat-treated at 450 \({}^{\circ}\)C.  
Although the competitive advantage of SE NiTi WMD disappears when the input acceleration is higher than 1.5 g, it is technically feasible to make it applicable for higher input accelerations by heat treatment at higher temperatures. Moreover, heat treatment is a much more practical approach than increasing the wire mass of NiTi WMD as far as the expensive price of NiTi wire is concerned.
## 1 Introduction
Firstly, a quantitative comparison was conducted to assess the prediction accuracy of two representative wind speed estimation algorithms widely used in previous studies, the drive train model-based method and the Kalman filter-based method, both applied to the same wind turbine; this comparison enabled our determination of the preferable wind speed estimation algorithm. Secondly, the performance comparison of these wind speed estimation algorithms not only evaluated their prediction accuracy, but also assessed their applications to control algorithms intended for actual wind turbines. The evaluation aimed to determine which method offers superior control performance through the application of control algorithms utilizing estimated wind speed as an input parameter; this included both classical and modern control algorithms, such as feed-forward control, available power estimation algorithms, and LQRF algorithms [8; 13; 14; 15; 16; 19; 20]. Thirdly, the Bladed program, a validated aeroelastic simulation tool typically used for wind turbine certification, was utilized instead of a simple Matlab-based wind turbine model to compare the control algorithms, which were implemented as external controllers in dynamic link library (DLL) format, and performance analysis was conducted under 3D turbulent wind conditions, simulating environments closely resembling those encountered by actual wind turbines.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 2 Related Works and Backgrounds
### Incorporating Sensor-Based Monitoring Systems into Existing Buildings  
The term \"smart building\" describes a suite of technologies used to make the design, construction and operation of buildings more efficient and is applicable to both existing and newly-built properties [30]. The smart building in the future will adjust in every situation to provide personalized service to every individual inside the building. To do this, the building must have the ability to sense and to interpret situations automatically. Buildings without smart technology cannot transmit environmental information in real time because they do not incorporate network-connected sensors. However, reconstructing a building to make it \"smart\" would be an inefficient waste of both time and financial resources.  
For energy savings in commercial buildings, [PERSON], [PERSON] et al. [31] have presented a design for converting a conventional building into a smart building. They have developed several mechanisms for activating the HVAC system, IT equipment and lighting depending on occupancy and usage. For monitoring, they attach sensors to the building and install building management systems. They estimate that the cost of converting a building to make it \"smart\" is offset by the energy savings it produces. [PERSON], [PERSON] et al. [1] also propose using middleware to reduce energy in existing buildings. They use software to monitor and control the lighting and HVAC services. They also design the context framework using an ontology-based knowledge repository and a rule-based context recognition system. Whereas existing smart building systems focus primarily on energy-reduction, our system provides a cost-effective, smart emergency alert and response network that provides situationally-specific and personalized service for users.
# BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions
## 1 Introduction  
Rolling bearings play a vital role in rotating machinery fields, which are widely equipped in various machinery facilities such as induction motors, wind turbines, power generators, robots, and vehicles [1, 2]. Bearings operate with high reliability. Once faults occur, this leads to the whole mechanical system's breakdown. It is known that bearing failure is the most common cause of mechanical facility failure [3, 4]. Hence, it is essential to detect and diagnose any existing or potential defects in rolling bearings. Fault diagnosis involves sensors and signal processing techniques, which is aimed at fault prediction and maintenance scheduling [5]. The procedure for bearing defect diagnosis can be concluded as follows: signal acquisition, signal processing, feature extraction, and fault (pattern) recognition [6].  
Vibration analysis is one of most common techniques. In spite of numerous studies contributing to rolling bearing fault diagnosis, it still remains limited in practical application:  
1. Signal processing is vital to feature extraction. Although numerous research has been developed in fault diagnosis, there is still a lack of analysis of the feature extraction effect with classifier accuracy. Inferior quality feature data will reduce the accuracy and efficiency of the classifier [7].
2. A large number of features burden computational cost and execution speed. Moreover, the redundant features can decrease the diagnosis accuracy by misleading the classifier model in the training stage. Hence, the redundant features need to be recognized and eliminated.  
To overcome these problems, this paper proposes a novel algorithm for fault diagnosis, which is proposed under real working conditions. The main contributions of this work are summarized as follows:  
1. Benefiting from adaptive and robust properties, VMD is more capable of vibration signal decomposition and obtains more fault characteristics. The improvement in feature quantity increases the diversity of data selection and improves the quality of selected data.
2. The SVM model combined with PCA feature selection is utilized, which improves generalization and detection accuracy for the classifier. Additionally, computational complexity is decreased in terms of feature selection. Furthermore, the SVM model benefits from a few adjustable parameters as well as better generalization capability.
3. The effectiveness of the proposed algorithm is verified on numerous real-life bearing fault signals, either for the detected sensitivity or specificity.  
The reminder of this article is structured as follows: Section 2 gives a brief literature review of related work. Section 3 describes the proposed fault classification technique. Section 4 exhibits the experimental results and discussion. Finally, Section 5 concludes this article.
###### Abstract  
Currently, radio frequency (RF) waveforms are widely used in wireless communication systems and are widely used in many fields to improve human quality of life. In Internet of Things (IoT) systems and satellite systems, the installation and deployment of wireless communication systems have become easier and offer many advantages compared to wired communication. However, high RF frequencies can have detrimental effects on the human body. Therefore, the visible light bandwidth is being researched and used as a replacement for RF in certain wireless communication systems. Several strategies have been explored: free-space optics, light fidelity, visible light communication, and optical camera communication. By leveraging time-domain on-off keying, this article presents a multiple-input-multiple-output (MIMO) modulation technique using a light-emitting diode (LED) array designed for IoT applications. The proposed scheme is versatile and suitable for both roller shutter and global shutter cameras commonly found on the market, including CCTV cameras commonly found in factories and buildings. By using deep learning for threshold prediction, the proposed scheme could achieve better performance compared to the traditional scheme. Despite the compact size of the LED array, the precise control of the exposure time, camera focal length, and channel encoding enabled the successful implementation of this scheme and supported four links at various positions within a communication distance of 22 m, taking into account the mobility effect (3 m/s).  
two-dimensional on-off keying; optical camera communication; optical Internet of Things +
Footnote †: journal: Applied  
0
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Results and Discussion
For example, when the output SNR of the receiver is 10 dB, for \((N=2,M=5)\) and \((N=2,M=3)\), the ABER of the multi-hop parallel hybrid systems with \(\rho=1\) are \(2.033\times 10^{-4}\) and \(1.864\times 10^{-4}\), respectively. Therefore, for strong turbulence and weak turbulence, increasing hop number (\(M\)) will significantly improve the ABER of the system, and increasing paths (\(N\)) will significantly reduce the ABER of the system; however, the multi-hop parallel hybrid system is little affected by the pointing errors in strong turbulence, while the system is significantly affected by the pointing errors in weak turbulence. For moderate turbulence, increasing the number of paths (\(N\)) will significantly reduce the ABER of the system; however, it is little affected by the pointing errors at this time. Furthermore, the ABER of the system will also increase when the number of hops (\(M\)) is increased. When the fading parameter \(m^{RF}=2\) of the RF link and the FSO link is in strong turbulence, Figure 6 shows the relationship between the outage probability and the normalized SNR (\(U\) is the ratio of the specific SNR threshold to the average SNR) of the multi-hop parallel hybrid FSO/RF systems with different structures, the hybrid FSO/RF direct links and the FSO-only direct links under different pointing errors. Figure 4: Relationship between the ABER and the SNR of the multi-hop parallel hybrid system under different relay-assisted structures and turbulence intensity. From Figure 6, we can see that, in the strong turbulence, as the pointing error parameter \(\rho\) increases, the outage probability of the multi-hop parallel hybrid FSO/RF systems with different structures, the hybrid FSO/RF direct links, and the FSO-only direct links will decrease. No matter how the pointing errors changes, the outage probability of the FSO-only direct link is the largest, followed by the hybrid FSO/RF parallel transmission link, and the outage probability of the multi-hop parallel hybrid FSO/RF system \((N=4,M=3)\) is the smallest. In the multi-hop parallel hybrid FSO/RF system, the outage performance of the system decreases with the increase of hops, and the outage performance of the system can be improved with the increase of the number of paths. Compared with the hybrid FSO/RF direct link and the FSO-only direct link, the multi-hop parallel hybrid FSO/RF system has the best outage performance. The analytical results are well matched with the MC simulations presented in all figures. Figure 5: The relationship between ABER and SNR of the multi-hop parallel hybrid FSO/RF system and the hybrid FSO/RF direct link under different turbulence intensities, different relay-assisted structures, and different pointing errors. (**a**) weak turbulence. (**b**) Moderate turbulence. (**c**) Strong turbulence.
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 2 Application Scenario of Power Distribution Station Area  
With the development of IoT in distribution, the distribution station area involves a variety of application scenarios, including data analysis of the distribution station area, intelligent operation and maintenance of distribution equipment and fault repair, energy use analysis and energy saving and carbon reduction, coordinated control of source-grid-load-storage driven by dual carbon, power quality governance, etc. [14; 15] Each scenario will contain multiple types of applications. Take the source-grid-load-storage coordination and control scenario as an example, it requires demand-side response, integrated energy services, new energy consumption, grid peak-valley regulation, etc.  
The tasks in the distribution station area are characterized by diversity, dynamism, and differentiation. Diversity is reflected in the variety of tasks and the number of scenarios involved; dynamism is reflected in the random arrival of tasks on the one hand and the large change in task information at each arrival on the other hand. Differentiation is reflected in the fact that different tasks differ greatly in terms of data volume, time delay, and importance, as well as in processing methods and resource requirements.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 4 Charging Type
### Conductive Charging  
EV charging infrastructure with EVSE components should be developed and maintained based on the different charging standards suitable for several households of the community in a particular country. Conductive charging uses a charging cable through which any EV can charge their vehicle by connecting to a connector depending on the required charging standards. There should be a physical connection between the electricity grid and the battery of the EVs for charging. EVSE plays an important role in charging the EVs by interfacing the powered grid with the electrified battery of the EVs. Therefore, it is a simple method for charging with a low cost due to the utilization of the electric cable and charging connector required for wired charging. Conductive charging is further categorized into AC slow charging and DC fast charging. We can classify AC charging into different charging levels and modes based on the fast, moderate, or slow power supply to the EVs. Charging modes can be categorized as Mode 1 and Mode 2 for AC slow charging, Mode 3 for AC moderate charging, and Mode 4 for DC fast charging. Similarly, charging levels can be divided into Level 1, Level 2, and Level 3 [53].  
Different charging levels and charging modes are defined based on the need of the varying power supplies and the equipment (for example, protection cable) required for the installation of CSs in different communities at the different locations in the city.
# Nonparametric Probability Density Function Estimation Using the Pade Approximation
## 5 Quantitative and Comparative Assessment
### Example 4: The Exponential Distribution
Nevertheless, it is crucial to realize that this can give a false impression of enhanced performance, as the distortion does not accurately represent the true nature of the Exponential distribution. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & \(\downarrow\)**WD** & \(\downarrow\)**BD** & \(\uparrow\)**CC** & \(\downarrow\)**KLD** & \(\downarrow\)**L1** & \(\downarrow\)**L2** \\ \hline KDE & 15.6920 & 5.3070 & 0.9526 & 0.0003 & 0.2042 & 0.0197 \\ \hline Adaptive KDE & 15.0852 & 5.3081 & 0.9716 & 0.0009 & 0.1705 & 0.0158 \\ \hline GMM & 9.3181 & 5.3093 & 0.9447 & 0.0003 & 0.2478 & 0.0209 \\ \hline QHBS & 10.2243 & 5.3060 & 0.9769 & 0.0004 & 0.1755 & 0.0135 \\ \hline One-Point Padé & 22.2235 & 5.3160 & 0.8965 & **0.0002** & 0.3207 & 0.0281 \\ \hline Proposed Method & **4.5099** & **5.3035** & **0.9819** & 0.0013 & **0.0891** & **0.0120** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance metrics for different methods for the Exponential distribution using 300 samples. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & \(\downarrow\)**WD** & \(\downarrow\)**BD** & \(\uparrow\)**CC** & \(\downarrow\)**KLD** & \(\downarrow\)**L1** & \(\downarrow\)**L2** \\ \hline KDE & 12.0407 & 5.3112 & 0.9802 & 0.0004 & 0.2441 & 0.0128 \\ \hline Adaptive KDE & 12.6357 & 5.3144 & 0.9461 & 0.0016 & 0.1867 & 0.0206 \\ \hline GMM & 10.0232 & 5.3256 & 0.9134 & 0.0008 & 0.3727 & 0.0285 \\ \hline QHBS & 9.8939 & 5.3125 & 0.9213 & 0.0003 & 0.2599 & 0.0295 \\ \hline One-Point Padé & **8.9574** & 5.3130 & 0.9510 & 0.0015 & 0.2252 & 0.0226 \\ \hline Proposed Method & 16.1897 & **5.3065** & **0.9829** & **0.0003** & **0.1399** & **0.0119** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance metrics for different methods for the Exponential distribution using 100 samples. Figure 9: Visual assessment of different density estimation methods. The true exponential PDF (black), KDE (blue), Adaptive KDE (red), GMM approximation (green), QHBS (pink), One-point approximation (orange), and our method (cyan). The results for different sample sizes are shown from left to right: 300 samples (**a**), 100 samples (**b**), and 50 samples (**c**).
## 2 The Principle of the Drift-Insensitive Self-Comparison Method and the Experimental Setup
### Description of the Experimental Setup of \({}^{87}Sr\) Optical Lattice Optical Clock  
We experimentally verify the validity of the DISC method based on the one-dimensional \({}^{87}Sr\) optical lattice clock, the details of which is described in reference [26; 28]. After two-stage laser cooling, about 8 \(\times\) 10\({}^{4}\) atoms are loaded into a horizontal one-dimensional optical lattice. Using the Pound-Drever-Hall (PDH) technique, the lattice laser wavelength is stabilized to an ultra-low expansion (ULE) cavity at 813.42 nm where the atomic polarizability of the clock ground state and the excited state is same, the so called \"magic wavelength\" [29]. Following that, the atoms are spin-polarized to the Zeeman sublevels of \(\big{|}\,^{1}S_{0},m_{\rm F}=+9/2\,\big{>}\) with a spin-polarized purity of more than 99%. The clock laser is locked to an ULE cavity with a finesse of 400,000 by PDH stabilization at 698.44 nm corresponding to the \(\rm{5s^{2}}\,^{1}S_{0}\)\(\rightarrow\)\(\rm{5s5p}\,^{3}P_{0}\) transition. The line-width of the clock laser is about 1 Hz obtained by beating with another similar clock laser system. The polarization of the clock laser and the lattice laser is linear and the direction is along the one of the magnetic field quantization axis which is parallel to the gravity. According to the resolved sideband spectroscopy, the longitudinal and radial temperatures of atoms trapped in the lattice are 2.9 and 2.7 uK, respectively. Additionally, the misalignment angle between the clock laser beam and the lattice light is about 6 mrad extracted from the Rabi oscillation of the carrier transition [30; 31].