esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Platform Gait Analysis and Fall Detection Wearable Device
## 1 Introduction
In CPGAFDWD, the development board was used as Arduino board. The sensing modules, such as gyroscope and three-axis accelerometer, were used. The communication modules, such as Bluetooth and WiFi, were used. CPGAFDWD was integrated the Arduino board with the gyroscope, three-axis accelerometer, Bluetooth, and WiFi to detect the gait analysis and fall accident. The sensed data of gait analysis and fall accident from CPGAFDWD were transmitted to APP in real time. CPGAFDWD collected the sensed data via Bluetooth immediately. Then CPGAFDWD uploaded these data to cloud database called MongoDB, via WiFi or 4G in real time. In CPGAFDWD, a web APP was designed by Node.js. Since web APP was used by browser without constraining to any platform, CPGAFDWD could be used in multi-platform of mobile phone. The users could track the status of gait analysis and receive the alarm of fall accident by CPGAFDWD. The experiments of gait analysis included the step, length, and angle of pace. In ths statistical treatment with relevant comparisons, our pace step measurement was compared with the actual pace step of laboratory members on walking. Our pace angle measurement was compared with the actual pace angle of laboratory members by using protractor. For the fall detection, it was compared with the actual fall simulated by laboratory members. The experimental results showed that the rate of correct pace step measurement, the rate of correct pace length measurement, and the rate of correct pace angle measurement all could be up to 90%. For the fall detection, the experimental results showed that the detection rate of fall accident could be up to 100%. In the above results, it proved that CPGAFDWD could be applied for gait analysis and fall detection applications. In CPGAFDWD, the experimental results showed that the user could track the status of gait analysis and receive the alarm of fall accident via the Internet in real time. Moreover, the above results were used in both of iOS and Android to prove that CPGAFDWD could be applied in cross-platform of mobile phone. Figure 2: User interface of CPGAFDWD in different platform. The remainder of this paper was arranged in the following sections. Section 2 presented the related work. Section 3 stated our proposed CPGAFDWD. Section 4 stated our experimental results. Section 5 showed the discussion. Finally, Section 6 concluded this paper.
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
###### Abstract  
For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it to split the histogram. Then, the AICHE algorithm uses the local grayscale correction algorithm to correct the grayscale to prevent the image over-enhancement and gray-level merging problems that arise with the traditional algorithm. It is experimentally verified that the AICHE algorithm can significantly improve the histogram segmentation effect and enhance the contrast and detail information while protecting the average brightness of the input image, and thus the image quality is significantly increased.  
complex working conditions; histogram equalization; Otsu algorithm; machine vision; image enhancement +
Footnote †: journal: Applied sciences
## 3 Home Area Network
### Home Communication General Architecture
#### 3.1.1 Home Gateway with M2M Interface as Enabler for Smart Home Services  
Machine-to-machine (M2M) refers to technologies that can enable both wireless and wired systems to interact with other devices of the same nature and where one device generates events and the other interprets them. Among a wide range of possible applications at the early development of M2M, smart homes have the highest market potential for the reason that it is closely related to human life [44,45].  
Along with the growth of M2M related technologies, wireless networks (WN) are in the process of being applied to smart home systems, therefore, enabling a greater ability for devices and a more reliable and richer function of the smart home system [45,46]. M2M wireless networks can help to increase the production and efficiency of machines and to improve the safety and reliability of complex systems [47].  
The main concerning issues in these M2M service networks come from the vertical fragmentation and complexity of M2M markets. This complexity is as a result of the number of connectivity means, embedded devices, and service platforms--particularly of their heterogeneity [48].
## 4 \(\Delta T\)_Vector_ TTHCA Extension  
Section 3 showed that the essential condition for the TTHCA algorithm to be unable to detect a wormhole route is for the malicious nodes to increase \(\Delta T_{TOT}\) within the strict bounds defined in Equation (4). Any successful tampered \(\Delta T_{TOT}\) will always be greater than the actual \(\Delta T_{TOT}\) though simply analysing \(\Delta T_{TOT}\) as a sum of individual \(\Delta T_{i}\) values will not necessarily identify the wormhole route because these usually exhibit high variance.  
In this paper, to analyse \(\Delta T_{i}\) for each intermediate node, \(\Delta T_{TOT}\) is replaced by a new \(\Delta T\)_Vector_ comprising all the measured \(\Delta T_{i}\) values. This extension means that some new features for the TTHCA route discovery process are introduced to support the embedding of the \(\Delta T\)_Vector_ as shown in the Figure 2 flowchart, with the shaded blocks highlighting these new elements.  
The RREQ and _graylist_ broadcast procedures remain as in original TTHCA [17], but instead of using a \(\Delta T_{TOT}\) parameter, the \(\Delta T\)_Vector_ is included in the RREP packet by the destination node. The time taken from receiving the RREQ until sending the RREP at the destination node is added as the first element \(\Delta T_{I}\). Each intermediate node receiving and forwarding the RREP then adds its \(\Delta T_{i}\) (\(\Delta T_{\text{RREQ}}+\Delta T_{\text{RREP}}\)) as a new element in the \(\Delta T\)_Vector_.  
Figure 1: MANET scenario where A and B are the source and destination nodes, M\({}_{1}\) and M\({}_{2}\) are malicious wormhole nodes, \(t_{i}\) is _2 x PTT_ between two successive nodes, \(\Delta T_{i}\) is the routing packet processing time, RTT is the round trip time of the route, and \(RTT_{WH}\) is the RTT of the wormhole link.  
When the RREP is received by the source node, each \(\Delta T_{i}\) element of the \(\Delta T\)_Vector_ consists of the processing times incurred by the RREQ and RREP packets. If a PM wormhole attack is launched alongside a time tampering attack, at least one of the \(\Delta T\)_Vector_ elements will be falsely increased in accordance with Equations (6), (7) and (8). A suitable outlier detection technique can then be applied to identify tampered \(\Delta T_{i}\) values (see Section 4.1) from the \(\Delta T\)_Vector_ dataset. If a suspicious \(\Delta T_{i}\) is identified, TTHCA then requests a new route by issuing a _graylist_ broadcast. If no suspicious \(\Delta T_{i}\) is found, the normal PTT/HC analysis is performed for both HM and PM wormhole detection.
###### Abstract  
Recently, deep learning-based defect inspection methods have begun to receive more attention--from both researchers and the industrial community--due to their powerful representation and learning capabilities. These methods, however, require a large number of samples and manual annotation to achieve an acceptable detection rate. In this paper, we propose an unsupervised method of detecting and locating defects on patterned texture surface images which, in the training phase, needs only a moderate number of defect-free samples. An extended deep convolutional generative adversarial network (DCGAN) is utilized to reconstruct input image patches; the resulting residual map can be used to realize the initial segmentation defects. To further improve the accuracy of defect segmentation, a submodule termed \"local difference analysis\" (LDA) is embedded into the overall module to eliminate false positives. We conduct comparative experiments on a series of datasets and the final results verify the effectiveness of the proposed method.  
defect detection; texture analysis; deep convolution generative adversarial network (DCGAN); unsupervised learning +
Footnote †: journal: Applied  
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Footnote 0: _Applied_: 26 December 2020  
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1. The proposed method uses an unsupervised learning approach in which only defect-free images are used to train the network. This is very meaningful in industrial applications because the occurrence of defective samples is random and unpredictable. ii. A novel network which adopts an adversarial learning paradigm is applied to improve reconstruction of the input image. iii. A submodule embedded in the whole network is used to implement feature difference analysis, further eliminating false positives. A regional estimation can be drawn from feature analysis based on this module.  
This rest of this paper is organized as follows. Section 2 summarizes related work and some classical inspection approaches. In Section 3, our defect detection method is described in detail. Relevant experiments and a discussion are presented in Section 4, and conclusions and limitations are noted in Section 5.
## 2 Materials and Methods
### Control Equations  
Among the RANS methods, the Renormalization Group (RNG) model is widely applied. Researchers have validated turbulence models through wind-tunnel experiments and found that the RNG model is the optimal choice for predicting pollutant dispersion [32]. Therefore, this paper selects the RNG model to achieve better simulation results. In the investigation of pollution diffusion within the flow field of complex geographical environments, with the aim of enhancing computational efficiency, reducing the computational burden, and minimizing errors, the air flow is typically assumed to be an incompressible gas. Simultaneously, it is postulated that the wind-direction recirculation zone and the pollution source maintain stable existence without undergoing temporal variations. The species transport model is opted for, wherein air is treated as an ideal gas. Pollutant emissions can be categorized into two scenarios: local dispersion and long-distance transport from external regions. The coupled algorithm is implemented for both the pressure field and the velocity field, and all discretization schemes employ the second-order upwind scheme.  
This paper uses the Reynolds Averaged N-S Equation (RANS equation) to model the flow state [33; 34; 35]. The two-equation model (k-\(\varepsilon\) model) in the RANS equation closes the set of equations [36; 37], so it is necessary to solve the turbulent kinetic energy equation and the turbulent dissipation rate equation as follows:  
\[\mu_{\rm i}\frac{\partial\mu_{\rm i}}{\partial\chi_{j}}=\left(\frac{\rho-\rho_ {0}}{\rho}\right)g-\frac{1}{\rho}\frac{\partial\bar{\rho}}{\partial\chi_{j}}+ \frac{\partial}{\partial\chi_{j}}\left(v\frac{\partial\bar{\mu}_{\rm i}}{ \partial\chi_{j}}-\bar{\mu}_{i}\bar{\mu}_{j}\right) \tag{3}\]  
The mass transfer equation for NO\({}_{2}\) is as follows:  
\[\frac{\partial C}{\partial\epsilon}+\overset{\rightarrow}{v}\cdot\
abla C= \
abla\cdot(D\
abla C)+S \tag{4}\]  
where C is the concentration of NO\({}_{2}\) (kg/m\({}^{3}\)), D is the diffusion coefficient of NO\({}_{2}\) in the fluid, and S is the source or sink term of NO\({}_{2}\). The first term on the left-hand side of the equation represents the rate of change of NO\({}_{2}\) concentration with respect to time, and the second term is the convective transport of NO\({}_{2}\) caused by fluid flow. The first term on the right-hand side is the diffusive transport of NO\({}_{2}\) due to the concentration gradient, and the second term represents the generation or consumption of NO\({}_{2}\) (for example, generation or removal caused by chemical reactions). The value range is from 0.2 to 1.3. The pollutant emission rate S = 10\({}^{-5}\) kg/m\({}^{3}\)/s, which is small enough to ensure that it does not interfere with the pollutant diffusion process.
## 3 Methodology
### Evaluation Metrics  
In this research, we evaluated the CNN-ResBiGRU model's effectiveness by comparing it to baseline methods using key multi-class classification measures. These measures include overall accuracy, precision, recall, and F1-score, which offer crucial insights into the reliability of activity classification both within specific categories and across various datasets.  
* In the context of the model, accuracy refers to the proportion of action occurrences that were accurately identified. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (7) The variables _TP_, _TN_, _FP_, and _FN_ represent true positives, true negatives, false positives, and false negatives, respectively.
* _Precision_ is defined as the positive value of predictions, which is the ratio of correctly identified positive identifications (class \(\times\) predicted) to the total number of positive identifications. \[Precision=\frac{TP}{TP+FP}\] (8)
* _Recall_, also known as sensitivity, quantifies the true-positive rate, the ratio of correctly predicted real positive cases (actual class) to the total number of occurrences. \[Recall=\frac{TP}{TP+FN}\] (9)
* The _F1-score_ is a statistic that combines accuracy and recall, providing a balanced measure. \[F1\text{-}score=2\times\frac{Precision\times Recall}{Precision+Recall}\] (10)  
Figure 6: Structure of ResBiGRU.  
Model performance evaluation encompasses precision, memory, and accuracy, allowing for a comprehensive assessment of overall corrections and the efficacy of identifying various types of activities while ensuring that classes with fewer instances are not overlooked. The \(F1\)-_score_ consolidates these factors into a single, comprehensible number. Examining different models using these measures offers a more valuable understanding than only focusing on accuracy.
## 3 Materials and Methods
### Coexistence of 5G Networks and IoT Applications
The table includes several 5G services, such as 5G NR, 5G-V2X, 5G mMTC, 5G-UHD, 5G-IoT, 5G-eMBB, 5G-URLLC, 5G-mIoT, 5G-gNB, and 5G-Slicing, and describes their potential applications and system description. The 5G services offer different benefits and are designed for specific applications, such as ultra-reliable and low-latency communications for industrial automation, vehicle-to-everything communications for traffic management and safety, massive machine-type communications for smart cities and agriculture, ultra-high-definition video communications and virtual reality, Internet of Things communications for smart homes and wearables, enhanced mobile broadband for high-speed data transfer and streaming, ultra-reliable and low-latency communications for mission-critical applications, massive IoT communications for smart cities and agriculture, gigabit-class communications for high-speed data transfer and streaming, and network slicing for multiple services and applications. The table provides a useful overview of the coexistence of 5G and IoT services and can be helpful in understanding the potential benefits and challenges of deploying these technologies together.
## 5 Experimental Setup
### Testbed Setup  
A circuit diagram for controlling LED as Li-Fi Transmitter is shown in Figure 6(a). For building the VLC transmitter testbed an Arduino Micro programmable board is used. Three off-the-shelf multi-color LEDs are connected to the board. To avoid direct exposure to these bright lights and to have a well-defined shape transmitter a simple diffuser is used. Thus with a diffuser, the radius of the transmitter is 3 cm. Figure 7 shows the implemented VLC Transmitter under different conditions such as (a) circuit diagram, (b) without a diffuser, (c) with a diffuser, and (d) operational. Each modulation technique is tested for a variable distance between transmitter and receiver and at different frequencies. Figure 8 shows the experimental setup for the performance evaluation of the proposed technique.  
The SNR was measured by keeping the brightness, contrast and exposure parameters constant under different ambient light values i.e., 50 lux and 120 lux. Equation (14) gives sum of pixel intensities of the image where, \(P_{ij}\) is pixel intensity of grayscaled image at position \(i\), \(j\) and \(Z_{i}\) is an image frame. As shown in Equation (15) \(A_{i}\) is image frame containing active transmitter while \(B_{i}\) is image frame containing inactive transmitter. The average pixel intensity observed with transmitter 'on' was considered as Signal Power while the average pixel intensity of frame with transmitter 'off' was considered as Noise Power to estimate SNR.  
\[Z_{i}=\sum_{j=1}^{y}\sum_{i=1}^{x}P_{ij} \tag{14}\]  
\[SNR_{dB}=10\times\log\frac{\sqrt{\frac{1}{n}\sum_{i=0}^{n-1}|A_{i}|^{2}}}{ \sqrt{\frac{1}{n}\sum_{i=0}^{n-1}|B_{i}|^{2}}} \tag{15}\]
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 1 Introduction
The method takes into account the time varying of the target backscatter coefficient within the corresponding synthetic aperture time and builds a fast simulation architecture, including the EM wave transmission simulation and multiple backscatter field computation. Based on the spatial segmentation theory and using the echo matrix cell method to design an efficient CUDA kernel function, each echo matrix cell is regarded as a subfield of the total backscatter field, and the two-dimensional envelope window corresponding to each cell is used as a constraint to remove the incoherent lattices in each latticed block, which can effectively reduce the workload of each thread to perform the task. Based on the CUDA platform, the target backscatter field can be quickly obtained by traversing the full echo matrix cell in parallel, and the simulated echo is processed by the RD imaging algorithm to obtain the target SAR simulated image. The main contributions of this paper are as follows:  
1. A novel EM wave transmission simulation method based on RD imaging geometry relation is designed. The method utilizes the scanning mode of SAR spatial traversal and the BVH algorithm to assist in the generation of the latticed radiation area and EM wave transmission simulations, considering the time-varying characteristics of the latticed radiation area and shadow area. 2. A novel fast SAR image simulation method based on spatial segmentation is proposed. The method integrates the idea of spatial segmentation into the echo matrix cell method based on the CUDA platform, including the time-varying characteristics of the target backscatter coefficient and multiple scattering. It is applicable to distributed composite scenes. This paper is organized as follows. Section 2 introduces the BVH-assisted computation of the target multiple backscatter field, mainly involving the content of the kernel function to be designed. Section 3 introduces the workflow of fast SAR image simulation based on spatial segmentation, mainly including the CUDA thread organization and the detailed workflow of echo generation and imaging. Section 4 carries out some simulation tests on some typical target models, and the simulation results are compared and analyzed quantitatively and qualitatively to verify the effectiveness of the proposed method. Section 5 presents the conclusions.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 3 Analysis of Numerical Simulation Results  
The conditions for Gaussian laser atmospheric transmission are as follows: wavelength \(\lambda\) is 532 nm, initial beam diameter is 80 mm, emission aperture is 200 mm, radius of curvature \(R\) is 1100 m, emitted optical power is 1 W, zenith angle is 90\({}^{\circ}\), horizontal transmission distance \(L\) is 1100 m, and \(V_{h}\) = 10 km, According to Formulas (8) and (9), the extinction coefficient \(\sigma_{\lambda=532\mathrm{nm}}=3.49\times 10^{-5}\mathrm{m}^{-1}\). The simulation is performed 200 times with m = 22 phase screens, each having a grid size of 512 \(\times\) 512 with a spacing of 1 mm. This study primarily investigates how single aberrations affect the intensity scintillation characteristics of Gaussian lasers during atmospheric transmission. Focusing on four typical aberrations (defocus (Z\({}_{4}\)), coma in the y-direction (Z\({}_{7}\)), spherical aberration (Z\({}_{11}\)), and secondary astigmatism in the y-direction (Z\({}_{25}\))), we analyze their impact on intensity scintillation, beam wandering, beam spread, and logarithmic intensity fluctuation probability density distribution, respectively.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & & & & & **Total** \\  & **KMO** & \(\chi^{2}\) & **df** & \(p\) & **Retained** & **Explained** \\  & & & & & **Factors** & **Variance** \\  & & & & & & **(\%)** \\ \hline Knowledge transfer behavior & 0.900 & 777.797 & 36 & \textless{}0.001 & 2 & 55.932 \\ Organizational innovation & 0.830 & 488.023 & 6 & \textless{}0.001 & 1 & 78.972 \\ Competitive advantage & 0.765 & 281.107 & 10 & \textless{}0.001 & 1 & 56.110 \\ Organizational strategy & 0.813 & 376.054 & 21 & \textless{}0.001 & 1 & 48.681 \\ Organizational performance & 0.749 & 304.598 & 6 & \textless{}0.001 & 1 & 67.604 \\ Job performance & 0.715 & 182.678 & 6 & \textless{}0.001 & 2 & 58.512 \\ \hline \end{tabular} Source: Own elaboration.  
\end{table} 
Table 3: Kaiser–Meyer–Olkin, Bartlett’s Test, and Explained Variance for Each Construct. Discriminant validity was also supported, as HTMT values were below 0.90, and their confidence intervals did not include 1. All outer loadings exceeded 0.70 and were statistically significant (\(p<0.001\)), indicating that the indicators reliably measured their respective latent constructs. Statistically significant differences in _knowledge transfer behaviors_ were identified based on the existence of formal protocols within organizations. Companies with established protocols reported significantly higher scores (Mdn = 4.66; Range = 2.22) than those without protocols (Mdn = 4.11; Range = 4.00), as shown by the Mann-Whitney U test (U = 2062.5; \(p<0.001\); Hedges' g = 0.672). This suggests that defined knowledge management structures are associated with higher levels of knowledge transfer behavior. Similarly, significant differences were also observed in relation to protocols for generational handover. Organizations that had implemented such protocols demonstrated higher levels of _knowledge transfer behaviors_ (Mdn = 4.77; Range = 2.11) than those that had not (Mdn = 4.22; Range = 4.00), with a statistically significant result (U = 1880.0; \(p<0.001\); Hedges' g = 0.575). These findings indicate that organizations with formalized transition mechanisms tend to report more effective knowledge transfer. [PERSON]'s correlation analyses revealed that organizational innovation (\(\rho\) = 0.570, \(p<0.001\), n = 166) and job performance (\(\rho\) = 0.529, \(p<0.001\), n = 166) had the strongest positive association with knowledge transfer behavior (Table 4). These results suggest that higher levels of innovation and job performance are associated with increased knowledge transfer behavior. Although competitive advantage (\(\rho\) = 0.382, \(p<0.001\), n = 166), organizational strategy (\(\rho\) = 0.395, \(p<0.001\), n = 166), and organizational performance (\(\rho\) = 0.361, \(p<0.001\), n = 166) also showed significant correlations, their effects were weaker, showing a moderate correlation, which was consistent with the PLS-SEM results.