esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 5 Conclusions  
In this article, a novel NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in the worst-case scenario when direct communication between the primary transmitter-receiver pair is not possible due to deep shadowing. In this scenario, we mathematically formulated a sum-rate maximization problem under transmit power constraints to enable the primary communication via a NOMA-based secondary relay selected from a potential set of secondary users. The optimization problem is solved using the proposed enhanced-artificial-bee-colony (EABC)-based technique and compared using state-of-the art computational intelligence (CI) tools, i.e., classic ABC, GA and PSO, by varying different system parameters. Simulation results confirm the superiority of the EABC-based scheme over other CI-based techniques in terms of convergence time and transmit power saving. The proposed scheme is suitable even when the primary transmit-receive pair is separated by a long distance.  
This research work was funded by Institutional Fund Projects under grant no. (IFPIP: 484-717-1443). The author gratefully acknowledges technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.  
The author declares no conflict of interest.
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 1 Introduction
[18] suggested the MobilenetV1-YOLOv4 network, which integrates Mobilenetv1 with the channel attention mechanism in YOLOv4, thereby achieving a lightweight detection model; however, this approach results in suboptimal detection accuracy. [PERSON] et al. [19] employed Ghostnet to reconfigure the extraction network in YOLOv4 and incorporated the channel attention mechanism into the path aggregation network, aiming to accelerate the detection speed of insulator defects. Nonetheless, the detection accuracy of this method reaches only 0.77%. Despite the notable results achieved in insulator and defect detection by the aforementioned studies, the primary focus has been on insulator self-explosion defects, with other types of defects receiving less attention. Furthermore, the majority of the improved models prioritize enhancing the model's speed at the cost of accuracy. This trade-off complicates the algorithms' ability to meet the inspection requirements in practical applications. In response to these challenges, we propose in this paper an insulator multi-defect detection network, AC-YOLO, grounded on an adaptive multi-attention fusion framework based on the YOLOv5 algorithm. This study's main contributions can be summarized as follows:  
(1) An adaptive weight distribution multi-head self-attention module (AWDMSM) was designed to replace certain CSP modules in the backbone feature extraction network and the path aggregation network. This helps eradicate disturbances emanating from complex backgrounds. The module establishes a balance between local and global information, effectively resolving the challenge of distinguishing between insulators and defects amidst complex backgrounds. (2) We developed an adaptive memory fusion detection head (AMFDH), superseding the original YOLO detection head. This new design fully decouples the regression and classification tasks, markedly enhancing the model's convergence speed and small target detection accuracy. (3) CBAM (convolutional block attention module) was integrated into the C3 (cross-stage partial network) module to underscore the critical shallow features and suppress irrelevant information. This allows for the accurate localization of small target defects in the network and augments the overall feature representation capability of the network. (4) The SIoU (structural similarity intersection over union) regression loss function was employed to concentrate on high-quality anchor boxes, thereby accelerating the network's convergence speed. The improvements highlighted above strike a balance between weighing accuracy and speed. Finally, validation using a dataset containing normal insulators, breakage insulators, and flashover insulator defects demonstrates the effectiveness of the improved algorithm.
## 5 Nonlinear Modeling and Dynamic Stiffness Comparison of the Newly Designed HEM and MRF Mount
### Parameter Selection and Verification of the MATLAB Solution Procedure  
According to [PERSON] and [PERSON] [26], the selected damping parameters \(\zeta_{\rm d1}=0.5\), \(\zeta_{\rm d2}=1.0\), and \(\theta=270^{\circ}\). To verify the accuracy of the nonlinear solution procedure for HEMs, the dynamic stiffness characteristics in the frequency domain are calculated with the set of lumped parameters in Table 1 from the literature [26]. Here, the ode4 algorithm is used for the MATLAB program with time step 0.001 s.  
The comparison of the numerical results and experimental results ([PERSON] and Lu [26]) of the dynamic stiffness spectrum is shown in Figure 10. Although there are some differences,the trend of the dynamic stiffness spectrum is consistent with that of the result in the literature [26], and the subsequent analysis used the MATLAB program.  
The parameters in Table 2 are used to calculate the nonlinear response of the newly designed HEM. Both rotation angles \(\theta\) and \(L_{\mathrm{i}}\) of the newly designed HEM are tunable.  
The dynamic stiffness spectrums for different lengths of inertia track based on a nonlinear model and a linear model are shown in Figure 11. According to Equations (30)-(36), the lumped parameters \(L_{\mathrm{i}}\) and \(A_{\mathrm{i}}\) change with the rotation angle \(\theta\) of the moving plate screw of the newly designed HEM, but it is ensured that only \(b_{\mathrm{ie}}(R_{\mathrm{i}})\) is adjustable, and the other equivalent parameters in the TMD system are unaltered. Comparing Equation (46) and Equation (5), the nonlinear model considers more nonlinear damping of the inertia track than the linear model. Therefore, the damping of the inertia track in the nonlinear model is larger than that in the linear model for the same length of inertia track. From Figure 11, it can be seen that the dynamic stiffness spectrum from the nonlinear model follows the same tendency as that from the linear model for larger damping (smaller inertia track length). The fixed point \(P_{1}\) of the linear model and the fixed point \(P_{3}\) of the nonlinear model are close to each other.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 2 Theoretical Background  
### Material Test  
Formability refers to the ability or degree to which a material can undergo plastic deformation and is used to assess the material's suitability for forming. To evaluate formability, tests are conducted to replicate the deformation modes that the material undergoes in actual processes, among which the tensile test is the most widely used method. The circular grid pattern proposed in this paper is applied for forming-limit tests [27; 28; 29]. As shown in Figure 1, the nominal and true strains of the deformed circles in the major (\(I\)) and minor (\(2\)) directions can be calculated using Equation (1). This paper proposes applying the circular grid pattern to tensile test specimens, considering only the major direction strain to calculate the stress-strain curve in tensile tests.  
\[\text{Engineering strain}:\;e_{1}=\frac{a-d_{0}}{d_{0}},\;e_{2}=\frac{b-d_{0} }{d_{0}}\]  
\[\text{True strain}:\;\varepsilon_{1}=ln\frac{a}{d_{0}}=\ln(1+e_{1}),\; \varepsilon_{2}=ln\frac{b}{d_{0}}=\ln(1+e_{2}) \tag{1}\]
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements
### Capacitive Sensors  
Capacitive sensors are typically composed of two parallel conductive plates separated by a space. These sensors measure changes in an electrical property called capacitance. Capacitance is the response of the conductive plates when a potential difference is applied to them. This difference creates an electric field between the two plates. Each plate has positive or negative charge and, when the polarity is changed, the charge of each plate also changes. Capacitive sensors change the polarity continuously, which generates an alternating electric current. The amount of electric current is determined by the capacitance. The capacitance varies according to the area and proximity of the plates and the material used to separate them [23].  
As an example, a capacitive sensor using biocompatible ionic hydrogel films as conductive plates has been developed [24]. The flexibility of the sensor allows for measuring stretch and pressure on the human skin. Capacitive sensors are widely used, since they offer high sensitivity and low power consumption. The use of these sensors in wearable devices has some problems that have to be addressed  
in an easy and low-cost way. Chemical wearable sensors are really useful for detecting diseases or chemicals that can harm the environment. However, further research is needed to enhance wearable devices based on this material to achieve reliable and low-cost applications.
## 4 Discussion
### Impact of Observation Windows  
Expanding the observation windows beyond the traditional KDIGO criteria increased the incidence of AKI detected and, as with the traditional KDIGO criteria, these cases were still meaningfully associated with increased rates of in-hospital death and prolonged LOS. Extending SCr criterion 1a from the currently accepted 48-h window up to 168 h detected additional cases of AKI that were associated with increased risk for in-hospital death and prolonged LOS (Figures S4 and S6), providing evidence to expand the observation window for criterion 1a beyond 48 h. Compared to SCr criterion 1a, detection of AKI using SCr criterion 1b had larger associations with death and prolonged hospital stay following AKI, which is consistent with our previous findings [9]. When all of the study data are considered together, this study suggests that expanding the time interval for the diagnosis of in-hospital AKI KDIGO criterion 1a from 48 h to 168 h (with a matching 168-h observation period for criterion 1b) should be considered.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 5 Conclusions
All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Key R&D Program of China (2022 ZD0119505); Fengyun Satellite Application Advance Program of China Meteorological Administration, No. FY-APP-2021.0304; and the China Meteorological Administration Innovation and Development Project (CXFZ2022J031), Science and Technology Plan Project of Sichuan Province, No. 2023 YFS0366. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available from the corresponding author. **Acknowledgments:** We would like to thank the National Climate Center for supporting the data acquisition of this experiment. **Conflicts of Interest:** The author declares no conflict of interest.
## 2 Proposed Seismic Damage Prediction Model for a Building Structure
### Ground Motion Dataset
#### 2.2.1 Training Set (Validation Set)  
The GM data from the PEER database is selected as the training set, while 5% is used as the validation set, and samples are preprocessed through the following three steps. (a) Amplitude adjustment. As shown in Figure 4, the distribution of samples in the GM dataset (before adjustment) is highly non-uniform, with a low percentage of strong earthquakes and nearly 92.72% of \"None\" and \"slight\" samples. Therefore, the same strategy as in the codes [44; 50] is used to \"scale\" the natural ground motion data using scale factors of 1.0, 2.0, 3.0, and 4.0 multiplied by the acceleration records. Richer data is obtained after adjustment, where the percentage of extensive damage samples increased by a factor of 4.36. Of course, other methods (e.g., random simulation [55], etc.) are also suitable; (b) Frequency adjustment. Samples are down-sampled to 100 Hz using a fixed interval screening method so that all samples are at the same frequency. As well, according to the Nesbitt theorem [56], the maximum analysis frequency is set at 50 Hz in AFEM, which is enough for most earthquake engineering applications [57]; (c) Duration adjustment. As shown in Figure 3a, we adjust the duration of the ground motion data to a uniform size (the minimum is 30.73 s) by good clipping or zero padding [50]. In addition, sample statistics show that energy in the intercepted data accounts for an average of 90.51% of the original data energy. So, samples retain most of the energy for the ground motion signal.  
Figure 4: The ratio of each damage state before and after amplitude adjustment.  
Figure 3: Duration adjustment and hand-crafted feature: (**a**) Before and after duration adjustment. (e.g., 1989 Loma Prieta earthquake, site Saratoga-Aloha Ave.). (**b**) Hand-crafted features: Log Mel-spectrum (top) and MFCCs (bottom).  
Finally, 32,304 samples are obtained after preprocessing, which contain 30,688 training samples and 1616 validation samples.
## 2 Materials and Methods
### Analysis  
#### 2.2.1 COP Parameters  
The COP data were first detrended (i.e., removing the COP time series offset displacement (Equations (1) and (2)). Using the detrended data, COP parameters were calculated (Equations (3)-(11)). The COP metrics computed from this zero-mean time series were the root mean square (RMS) and peak-to-peak displacement (MAXD) (i.e., RMS and MAXD for both the anteroposterior (AP) and mediolateral (ML) directions), total excursion (TE) and 95% area ellipse (AE), shown in Equations (3)-(11), respectively [18]. A custom MATLAB program was written to compute these parameters (Mathworks, Inc., Version 2023a, Natick, MA, USA). Further, parameters were collected and calculated for both pre- and post-assessment trials. In each equation, N is the number of data points over 20 second trials.  
AP COP time series without offset  
\[\mathrm{AP}=\mathrm{AP_{o}}-\frac{1}{N}{\sum}|AP[n]| \tag{1}\]  
ML COP time series without offset  
\[\mathrm{ML}=\mathrm{ML_{o}}-\frac{1}{N}{\sum}|ML[n]| \tag{2}\]  
where \(\mathrm{AP_{o}}\) or \(\mathrm{ML_{o}}\) are the AP or ML COP path relative to the origin of the force platform, respectively, and N is the number of data points.  
AP Root Mean Square (RMS\({}_{\mathrm{AP}}\))  
\[RMS_{AP}=\left[\frac{1}{N}{\sum}\mathrm{AP}[n]^{2}\right]^{1/2} \tag{3}\]  
ML Room Mean Square (RMS\({}_{\mathrm{ML}}\))  
\[RMS_{ML}=\left[\frac{1}{N}{\sum}\mathrm{ML}[n]^{2}\right]^{1/2} \tag{4}\]  
AP Peak-to-Peak Displacement (MAXD\({}_{\mathrm{AP}}\))  
\[\mathrm{MAXD_{AP}}=\mathrm{max(AP)}-\mathrm{min(AP)} \tag{5}\]  
ML Peak-to-Peak Displacement (MAXD\({}_{\mathrm{ML}}\))  
\[\mathrm{MAXD_{ML}}=\mathrm{max(ML)}-\mathrm{min(ML)} \tag{6}\]  
Figure 3: (**a**) Force plate used to acquire COP data and (**b**) example of ML COP time series.  
Total Excursion (TE)  
\[TE=\sum\
olimits_{n=1}^{N-1}\left[(AP[n+1]-AP[n])^{2}+\left(ML[n+1]-ML[n]^{2} \right)\right]^{1/2} \tag{7}\]  
95% Area Ellipse (AE)  
\[\text{AE}=2\pi F_{05[2,n-2]}\left[g_{AP}^{2}s_{ML}^{2}-s_{APML}^{2}\right]^{1/2} \tag{8}\]  
where,  
\[s_{AP}^{2}=\left[\frac{1}{N}{\sum}AP[n]^{2}\right] \tag{9}\]  
\[s_{ML}^{2}=\left[\frac{1}{N}{\sum}ML[n]^{2}\right] \tag{10}\]  
\[s_{APML}=\frac{1}{N}{\sum}AP[n]~{}ML[n] \tag{11}\]
# Industrial-Grade Edge Computing Device for Smart Furniture Products
## 4 Solution/Candidate Product
### Security Aspects
#### 4.1.4 Communication Channel(s)  
A communication channel must always be protected either by using a Virtual Private Network (VPN) connection or some other similar method of secure connection. Some networks like the Narrow-Band IoT cellular network can provide safe communication channels that are not interconnected with the \"Internet\" (utilizing private the Access Point Name (APN) WWAN method) and can assure separate data connection to a desired datacenter or a private Internet Protocol (IP) address. Assuring safe and secure communication is paramount in creating a robust and safe system.  
Some communication devices/modules are certified for a secure connection to certain platform services like Microsoft Azure Sphere [33] and similar services. Such devices embed needed protocols and communication methods to facilitate the easier establishment of secure connections on the WAN side of the system.  
On the internal side, there are local connections present, mostly with sensors or user devices. These connections are facilitated using Wireless Local Area Network (WLAN) [34], Bluetooth (BT) [35], Zigbee [36], or similar protocols. All of these communication methods have their vulnerabilities that must be addressed when creating a local system around the edge device.
# Multi-Granularity User Anomalous Behavior Detection
## 5 Experiments
### Anomaly Detection on Different Behavior Data Ratios
#### 5.3.1 Results on Behavior Level (CAD)
On the CERT R4.2 dataset used in this study, the optimal balance between training volume and detection effectiveness is achieved with a training data ratio of approximately 60% to 80%.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 3 System Model
### Hardware Model
#### 3.1.3 TPC-Enabled Buck-Boost Converter  
The variation of supercapacitor voltage \(V_{SC}\) would change from 0 V to the nominal voltage \(V_{Nom}\) (usually 5.5 V), so a Buck-Boost converter is essential to regulate the output voltage \(V_{Out}\) to \(V_{DD}\) (usually 2.4 V for low-power electronics), it also optimizes the power efficiency of wireless sensor node.  
Figure 2: Overview of DC-DC Boost converter with Maximal Power Transferring Tracking (MPTT).  
Furthermore, different transmission power of the wireless unit requires different supply power, such as 20 mA for +7 dBm, 29 mA for +13 dBm, 87 mA for +17 dBm, et al. (LoRa SX1278, Semtech Corporation, Camarillo, CA, USA). Thus, the operation process of DC-DC regulator should be precisely managed to improve the conversion efficiency and reduce the dissipation. A TPC-Enabled DC-DC converter circuit is shown in Figure 3. In addition, the NMOS2 controller is also controlled by the wireless node.
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
## 2 Mathematical Model
### Aerodynamic Effects  
The most significant perturbation acting on a satellite equipped with a drag sail is generated by aerodynamic forces. The effects of these forces are evaluated by assuming that the drag sail can be modeled as a perfectly flat plate and that the spacecraft velocity on its osculating orbit is significantly larger than the thermal velocity of the atmospheric particles. Accordingly, the hyperthermal free-molecular flow model, discussed in Ref. [60] and applied to similar scenarios in Refs. [59; 61; 62], can be enforced. The assumption of hyperthermal flow implies that the spacecraft velocity relative to the atmospheric particles coincides with the orbital velocity. Consequently, the perturbative accelerations of drag (\(\mathbf{a}_{P_{D}}\)) and lift (\(\mathbf{a}_{P_{L}}\)) are given by  
\[\mathbf{a}_{P_{D}} =-\frac{1}{2}C_{D}\,\rho\frac{A}{m}\|\mathbf{v}\|^{2}\hat{\mathbf{v}} \tag{7}\] \[\mathbf{a}_{P_{L}} =-\frac{1}{2}C_{L}\,\rho\frac{A}{m}\|\mathbf{v}\|^{2}\hat{\mathbf{L}} \tag{8}\]  
where \(\rho\) is the local atmospheric density, \(\mathbf{v}\) is the spacecraft orbital velocity vector, whose components in \(\mathcal{F}_{\mathrm{RTN}}\) are  
\[[\mathbf{v}]_{\mathrm{RTN}}=\sqrt{\frac{\mu_{\oplus}}{p}}\begin{bmatrix}f\sin L-g \cos L\\ 1+f\cos L+g\sin L\\ 0\end{bmatrix} \tag{9}\]  
and \(\hat{\mathbf{v}}\triangleq\mathbf{v}/\|\mathbf{v}\|\) is the velocity unit vector. The unit vector of the lift \(\hat{\mathbf{L}}\), in analogy with Refs. [59; 62], is  
\[\hat{\mathbf{L}}=\mathrm{sign}(\  
where \(\sigma_{N}\) and \(\sigma_{T}\) denote the normal and tangential momentum accommodation coefficients, respectively, and \(V_{R}\) is the ratio of the average thermal velocity of atmospheric particles to the spacecraft orbital velocity. Typical values taken from the literature are used in the simulations, that is, \(\sigma_{N}=\sigma_{T}=0.8\) and \(V_{R}=0.05\)[59; 62]. Finally, in Equations (7) and (8), \(m\) is the total spacecraft mass and \(A\) is the total drag sail area. Note that the attitude of the sail, expressed by the unit normal vector \(\hat{\mathbf{n}}\), influences the perturbations induced by aerodynamic forces through the drag and lift coefficients given by Equations (11) and (12).  
Based on the definitions of the unit lift vector (10), it is clear that the aerodynamic accelerations given by Equations (7) and (8) turn out to be identical if \(\hat{\mathbf{n}}\) is substituted for \(-\hat{\mathbf{n}}\) (i.e., if the unit normal vector is reversed). Consequently, when analyzing aerodynamic perturbations, it is possible to choose two equivalent unit normal vectors, while in Section 2.3 this ambiguity will be resolved when considering solar radiation pressure effects. Finally, an iterative procedure aimed at reducing the required evaluations of the atmospheric density value \(\rho\) will be discussed in Section 3.
###### Abstract
In addition, unlike other research, we do not make direct comparisons to determine the advantage of any mode. Instead, we consider the complexity of each case and the mobile characteristics of the platform in order to analyze the applicability of different modes in mobile scenarios. * Furthermore, the optimal number of relays is determined through simulation analysis, while the numerical analysis of the impact of UAV relay deployment altitude on system performance is conducted under different relay selection modes. * Finally, a valuable design guideline is provided that is useful for aeronautical communication system designers. The rest of this paper is organized as follows: In Section 2, the system model, the FSO/RF signal model, and the relay selection modes are described. Section 3 constructs the FSO/RF channel model. In Section 4, the exact closed-form expressions for outage probability and average BER are derived. The numerical results and design guidelines are presented in Section 5. Finally, we conclude the paper in Section 6.
## 4 Experimental Results
### Histogram Analysis  
For measuring the robustness against common statistical attacks, the histogram analysis [25] between the cover and stego images of the proposed method are presented. The histograms are generated from the embedding process using Aerial, Airplane, Boat and Couple images as presented in Figure 6. Generally, by embedding the secret bits in the cover image, the frequency of pixels values are changed and can become visible in the histogram. Figure 6c,g,k,o shows the frequency histogram of the cover images. Meanwhile, Figure 6d,h,l,p shows the frequency histogram of the stego images. As observed from histograms, the disparities between the constructed histograms are comparatively less for all tested images. The results indicate that distortions produced from embedding process are unnoticeable to human visual perception. The analysis results show that there is no significant difference found in histograms of the cover and stego images. The embedded information is not easily detected by the difference histogram analysis. Thus, histogram-based steganalysis attacks are not possible for the proposed method. The security of hidden secret is well protected while keeping the advantage of low visual distortion introduced by the proposed method.  
Figure 6: _Cont._