esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Comparison of Training Results with Other Positioning Network Models  
To better demonstrate the superior performance of the HST-Net model in the precision and recall of insulator surface defect detection in substations, five state-of-the-art algorithms are chosen for comparative analysis with the validation set, such as SSDlite-mobilenetv2 [38], YOLOv5, YOLOX [39], YOLOF [40], and SSD [41]. The experiments are run under identical environments, using the same equipment and the same image pre-processing procedures. The comparison results are shown in Table 4. It can be seen that the proposed method achieved optimal values on both indicators.  
The change curve of mAP from five network models with the training rounds is plotted in Figure 10. As can be clearly observed, with the same 200 Epochs of training, the mAP outcomes on the insulator defect dataset are superior for HST-Net, YOLOF, and YOLOX as compared to SSD and SSDlite-mobilenetv2. Although in the early Epochs, the proposed HST-Net required a longer convergence time, the final mAP peak was 0.02 and 0.10 higher than YOLOX and YOLOF, respectively, attaining a score of 0.84201. SSD and SSDlite-mobilenetv2 scored mAP values of 0.635 and 0.630 respectively, reflecting subpar precision. It further substantiates the effectiveness of our proposed HST-Net model in  
Figure 9: mAP of Transformer-CSP modules at different locations.  
In assessing the effectiveness of model detection, we gauge the precision of the model by its confidence score and measure its completeness through the defect classification recall (DCR). DCR refers to the recall of insulator defects. As depicted in Figure 11, the proposed HST-Net attains the highest confidence score and a 100% DCR in the test images, thus delivering the most efficient detection results. Notably, the SSDlite-mobilenetv2 model, as illustrated in Figure 11d, overlooks two instances of surface damage and one normal insulator, rendering it the least effective model with a mere 50% DCR. Furthermore, SSD, portrayed in Figure 11c, exhibits numerous instances of redundant detection, resulting in a lower confidence score. Meanwhile, YOLOX suffers from defect omissions, and YOLOF shows a lower confidence score. In conclusion, the enhanced HST-Net demonstrates the most adept detection performance in the test images.  
Based on the analysis, the HST-Net defect detection model exhibits a fast speed, boasting high detection precision. This ensures a balance between precision and speed, making it suitable for deployment in unmanned aerial vehicle (UAV) inspections of insulator surface defects in substations. The scalability of this model could facilitate an improvement in the efficiency and efficacy of defect detection in substations across various scales of operation.
# Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter
###### Abstract  
To evaluate the performance of quasi-planetary cutting tools, three shrubs were selected and studied using a combination of numerical simulation and cutting test bench experiments. Based on the constitutive model of shrub material and LS-DYNA simulation, the effects of tool speed (\(n\)), feed speed (\(v\)), and shrub diameter (\(D_{\text{A}}\)) on peak cutting force (\(F_{\text{max}}\)) and peak cutting power (\(P_{\text{max}}\)) were analysed through a single-factor simulation test. Using the shrub-cutting test bench, an orthogonal test was designed with \(n\), \(v\), and moisture content (\(w\)) as factors and \(F_{\text{max}}\) and \(P_{\text{max}}\) as indicators. A regression model was established, and a single-factor comparison test for \(\mathrm{w}\) was conducted. The results indicate that \(F_{\text{max}}\) decreases as \(n\) increases, while \(P_{\text{max}}\) initially decreases and then increases. Both \(F_{\text{max}}\) and \(P_{\text{max}}\) increase with rising \(v\) and \(D_{\text{A}}\). As \(w\) increases, \(F_{\text{max}}\) and \(P_{\text{max}}\) first decrease and then increase. When \(n\) is 1813 \(\mathrm{r/min}\), \(v\) is 30 \(\mathrm{mm/s}\), and \(w\) is 10.9%, \(F_{\text{max}}\) and \(P_{\text{max}}\) reach their optimal values of 8.42 \(\mathrm{N}\) and 282.99 \(\mathrm{W}\), respectively, with verification test errors of 2.68% and 1.56%. The findings provide methodological and data support for studying the cutting performance of new cutting tools.  
shrub cutting; quasi-planetary cutter; orthogonal experiment; simulation analysis; cutting test bench +
Footnote †: journal: Applied sciences  
0000-0002-3270-3270][PERSON]
# Dynamic Response of Copper Plates Subjected to Underwater Impulsive Loading
###### Abstract  
Understanding the mechanical response and failure behaviors of thin plates under impact loading is helpful for the design and improvement of thin plate structures in practical applications. The response of a copper plate subjected to underwater impulsive loading has been studied in fluid-structure interaction (FSI) experiments. Three typical copper plates, (a) without a pre-notch, (b) with a cross-shaped pre-notch (+), and (c) with a ring-shaped pre-notch (\(\Circle\)) were selected. A high-speed photography system recorded the full-field shape and displacement profiles of the specimens in real time. The 3D transient deformation fields' measurements were obtained using a 3D digital image correlation (DIC) technique. Strain results from DIC and the strain gauges technique were in good agreement. A dimensionless deflection was used to analyze the effect of plate thickness and loading intensity on the deformation of the copper plates. The typical failure modes of different copper plates were identified. The test plates exhibited large ductile deformation (mode I) for copper plates without a pre-notch, and large ductile deformation with local necking (mode Ic), splitting (mode II), splitting and tearing (mode IIc), and fragment (mode III) for the copper plate with a pre-notch.  
copper plate; underwater impulsive loading; non-liner dynamic deformation; 3D digital image correlation +
Footnote †: journal: Applied Sciences
## 3 Proposed Optimal Strategy  
### Control Mode of Wind Turbine  
#### 3.1.1 Active Power Control  
Maximum power point tracking (MPPT) control is usually employed to maximize the energy conversion efficiency of a wind turbine by regulating the rotational speed of a variable-speed wind turbine. Depending on the wind aerodynamic conditions, an optimal operating point exists that may allow the extraction of maximum power from the turbine. This point is called the maximum power point (MPP). The power captured with the wind turbine can be substantially maximized by adjusting the coefficient \(C_{p}\), which represents the aerodynamic efficiency of the wind turbine and is dependent on the TSR \(\lambda\). The wind turbine can generate maximum power as expressed in Equation (1). Then, the maximum mechanical output power of the turbine is given by  
\[P_{max}=0.5\rho\pi R^{2}S_{w}^{3}C_{p,opt}\left(\beta,\lambda_{opt}\right) \tag{13}\]  
The optimal TSR of the mechanical output power can be represented as follows:  
\[\lambda_{opt}=\frac{\omega_{m,opt}R}{S_{w}^{3}} \tag{14}\]  
For the PMSG wind turbine, the maximum value of the power coefficient (\(C_{p,opt}\) = 0.4412) is obtained as an optimal value of TSR (\(\lambda_{opt}\) = 6.9), calculated using Equation (14). The coordination of TSR to its optimal value with the power coefficient reaching \(C_{p,opt}\) guarantees maximum power extraction. Equation (15) provides the formulation for maximum power determined using the MPPT control:  
\[P_{max}=\omega_{m,opt}^{3}\times K_{opt} \tag{15}\]  
with  
\[K_{opt}=0.5\rho\pi\Bigg{(}\frac{C_{p,opt}}{\lambda_{opt}^{3}}\Bigg{)}R^{5} \tag{16}\]  
This MPPT control allows the wind turbine to extract as much power as possible for each wind speed. However, when the power system begins to impose requirements on the wind power for objectives, such as frequency regulation, other operational modes are required. For wind turbine generators to improve in frequency support capability, they must secure sufficient reserves. In this study, the PMSG wind turbines are operated at the de-loaded power point (over speeding) instead of at the MPP. The de-loaded control mode can also be implemented using a combination of control techniques for rotational speed and pitch angle. Figure 2 shows the MPPT and de-loaded power extraction curves for a PMSG wind turbine at various wind speeds. In setting the maximum de-loaded values for a PMSG wind turbine, both the current rating of the converters and maximum allowable limit of the rotational speed need to be considered. In the 5% de-loaded control for a PMSG wind turbine, the power output is \(P_{del}\) = 0.95\(P_{max}\). To provide a reliable operation range of PMSG, this study focuses on the maximum rotational speed of 1.2 p.u., with a reduction in certain fatigue loads as an additional objective. Figure 3 shows the de-loaded controller. To utilize the proposed de-loaded control mode, the power margin \(P_{margin}\) of a wind turbine is represented as follows:  
\[P_{margin}=P_{max}-P_{del} \tag{17}\]
## 3 The Inversion Strategy of FPAR
### The Strategy of Inversion
#### 3.2.2 Vegetation Division  
The PROSAIL model involves many parameters that require sensitivity analysis to set a reasonable range. The study finds that the reflectance spectrum of sparse vegetation is mainly affected by soil reflectivity, while the reflectance spectrum of dense vegetation is mainly affected by the biochemical composition of vegetation. Therefore, we classified the study region into two classes for the inversion of FPAR based on the value of the normalized difference vegetation index (NDVI) (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FY** & **Band Range** & **MODIS** & **Band Range** & **Correlation (R)** \\ \hline Blue (b1) & 0.420–0.505 & Blue (b3) & 0.459–0.479 & 0.6650 \\ Green (b2) & 0.506–0.609 & Green (b4) & 0.545–0.565 & 0.6767 \\ Red (b3) & 0.610–0.819 & Red (b1) & 0.620–0.670 & 0.7562 \\ Near Infrared (b4) & 0.820–0.920 & Near Infrared (b2) & 0.841–0.876 & 0.5946 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Band range and correlation statistics of FY and MODIS surface reflectance data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**NDVI** & **Vegetation Types** \\ \hline NDVI \(\leq\) 0 & No vegetation area \\ \(0<\) NDVI \(\leq\) 0.4 & Sparse vegetation area \\ \(0.4<\) NDVI \(\leq\) 1 & Dense vegetation area \\ \hline \hline \end{tabular}
\end{table}
Table 5: Decision rules used for classifying the area. Normalized difference vegetation index (NDVI).  
For the partitioning indicator NDVI of the vegetation partition, the effective value was [-1,1]. When NDVI \(\leq\) 0, there was no vegetation in the area; then, we had a sparse and lush subdivision in the interval of NDVI in [0,1]. The threshold set in this paper was 0.4, based on our prior knowledge of the relevant references and experimental statistics we have conducted. Hence, the application of the partition operation for different land types depended on the actual prevailing situation.
## 3 Materials and Methods
### Interference and Network Optimization Difficulties
#### 3.9.3 Optimization Challenges in 5G Networks
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Description** \\ \hline Bandwidth & More bandwidth to supply applications with high speed. \\ \hline AI & Artificial intelligence can analyze all the data generated by the IoT devices to take accurate decisions. \\ \hline Real-time monitoring and management & The capability to monitor and manage electronic devices remotely. \\ \hline Swarm intelligence & Each IoT device can be a node, and working with other nodes, can work as a swarm intelligence. \\ \hline QoS & 5G can provide a better QoS in IoT connections. \\ \hline Low Latency & 5G reduces the latency, which means that the IoT devices can take immediate decisions. \\ \hline Cloud computing & 5G can provide better connections to the cloud, which means each IoT device can use all the resources of the cloud. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Expectation for 5G in IoT. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Interference** & **Description** & **5G Impact** \\ \hline Adjacent Channel Interference & This occurs when the frequency channel of our device is overlapped. & If we do not have good bandpass filters, there will be interference from nearby frequencies. In 5G devices, this can have a significant impact. \\ \hline Intra-cell and inter-cell interference occurs when two users in neighboring cells attempt to use the same frequency simultaneously. Intra-cell interference occurs when there is a simultaneous short-range transmission by two BSs, with distortion from the user and the other equipment in the same cell. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Technologies for the elderly It is pertinent to consider how this type of interference can affect or impact 5G networks today, since it must be kept in mind with the explosive development of technology. In turn, this can affect different 5G architectures, since the main objectives of these networks are to improve things such as latency, throughput, transmission speeds, etc. Table 9 provides an overview of different types of interference that can occur in 5G networks and their potential impacts on network performance. Adjacent Channel Interference occurs when the frequency channel of our device is overlapped with nearby frequencies, leading to interference. Intra-cell and inter-cell interference occurs when multiple users
## 3 Theoretical Analysis
### Mean Acquisition Time  
Since the analysis models for mean acquisition time of zero-padding, XFAST, DF-XFAST and DC-XFAST are similar and all based on \(\mathrm{P_{d}}\) and \(\mathrm{P_{fa}}\), a common formula could be given. Only single dwell time is considered and FFT operation is assumed to be used in code phase search for acquisition that means the process time for each correlation is equal to chip rate. The mean acquisition time normalized to system clock can be written as follows:  
\[\overline{\mathrm{T_{a}}}=\frac{1}{\mathrm{P_{d}}}+\frac{2-\mathrm{P_{d}}}{2 \mathrm{P_{d}}}\bigg{(}\left\lfloor\frac{\mathrm{LM}}{\mathrm{qN}}\right\rfloor -1\bigg{)}(1+\mathrm{qkP_{fa}}) \tag{20}\]  
\(\mathrm{q}\) denotes the number of blocks that can be handled in each process. For XFAST, DF-XFAST and DC-XFAST, \(\mathrm{q}\) usually takes number of local code segment moved by one step, usually takes local code segment folding times. For the zero-padding method, \(\mathrm{q}=1\). L denotes a long PN-code period, N the block length, M the samples on each chip and k the penalty time index. The penalty time index for folding methods like DF-XFAST, XFAST and DC-XFAST are \(\mathrm{q}\) times the zero-padding method one due to detection ambiguity.
# Insights into Membrane Damage by \(\alpha\)-Helical and \(\beta\)-Sheet Peptides
## 1 Introduction
Sequence patterns that perfectly segregate polar-nonpolar residues in \(\alpha\)-helices are (XXYYXXY)\({}_{2}\) and (XXYY)\({}_{2}\)(XYYY)\({}_{2}\). Finally, the hydropathy of the C-terminus has been shown to play an important role in accounting for the rate of water permeation. In particular, amidation of the C-terminus was reported to increase water permeation in several peptides [42; 43; 44; 45] and it is the natural form of many natural AMPs [46]. This effect of C-terminal amidation is not completely understood [42] and could emerge from the stabilization of \(\alpha\)-helical conformations due to an additional hydrogen bond or/and from the neutral charge of the amidated C-terminus compared to a peptide with a carboxylic acid at the C-terminus. In this study, we examine the interactions of four peptides with an anionic lipid membrane to elucidate how water permeation is influenced by the type, length, and amphiphilicity of a peptide's secondary structure, as well as the hydropathy of its C-terminus. Peptides M01 [Ac-(FFKFE)\({}_{2}\)-NH\({}_{2}\)] and M02 [Ac-FFKKFFEE-NH\({}_{2}\)] are isomers with (XY\({}_{1}\)XY\({}_{2}\))\({}_{2}\) and (XXYY)\({}_{2}\) sequence motifs that favor \(\beta\)-sheet and \(\alpha\)-helical conformations, respectively. In contrast to M02 peptides, isomers M03 [Ac-(FFKKFFEE)\({}_{2}\)-NH\({}_{2}\)] and M04[Ac-FFKKFFEEFKKFFEEF-NH\({}_{2}\)] form extended \(\alpha\)-helices capable of spanning both bilayer leaflets. Peptides M03 and M04 differ in their C-terminal composition, which is made of only charged residues for M03 but contains a nonpolar residue for M04. Moreover, M04 exhibits a more pronounced segregation of polar and nonpolar residues at the membrane interface, following the (XXYY)\({}_{2}\)(XYYX)\({}_{2}\) template, while M03 retains the original (XXYY)\({}_{4}\) pattern. This study was carried out by first using long unbiased all-atom molecular dynamics simulations to predict the propensity of each peptide to induce water permeation and to uncover the underlying molecular mechanisms of this phenomenon. These predictions were subsequently validated by experimental dye leakage assays.
## 5 Performance and Simulations of Instantaneous Frequency Rate Tracking Approach
### Tracking Threshold Evaluation
#### 5.4.2 Parachute Deployment  
For the parachute deployment mission phase, the Doppler rate and second-order Doppler rate are steady at the beginning. A sharp increase in each of the Doppler derivatives arise when the spacecraft is deploying the parachute, followed by a rapid declining in the Doppler dynamics. Thus, a good dynamic response of the algorithm is demanding to follow the rapid variation in the signal dynamics. According to Figure 15, the integration time of 0.25 s is recommended for the maximization of coherent gain, while the dynamic response is quite limited for long integration time. Comprehensive simulations show that frequency estimation without any outlier could be obtained with 0.1 s of coherent integration, when the CNR is 29 dBHz. The corresponding estimation result is shown in Figure 21.  
The simulation results of 100 trials for parachute deployment with 29 dB/Hz and 26 dB/Hz CNR are separately shown in Figures 22 and 23.  
Figure 21: One estimation trial of an IFR tracking approach for parachute deployment at CNR = 29 dB-Hz: (**a**) Doppler frequency; (**b**) Doppler frequency errors.  
Figure 22: Simulation results of an IFR tracking approach for parachute deployment at CNR = 29 dB-Hz: (**a**) Doppler rate; (**b**) Doppler frequency.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 2 System Model  
### Notation  
Matrices (vectors) are denoted with upper (lower) case bold-face letters (e.g., **A** or **a**); the operator \((\cdot)^{H}\) denotes the Hermitian; the operator \(\left\|\cdot\right\|\) denotes the vector norm; the operator \(\otimes\) denotes the Kronecker product; the operator \(\circ\) denotes the Hadamard product, the set of real, complex and binary numbers are respectively denoted by \(\mathbb{R}\), \(\mathbb{C}\) and \(\mathbb{B}\); the vector \(\mathbf{1}_{k}\) denotes all-one column vector with size \(k\) and the vector \(\mathbf{0}_{k}\) denotes all-zero column vector with size \(k\); the operator \(p\left(\mathcal{A}\right)\) denotes the probability of the event \(\mathcal{A}\). the operator \(\mathbb{E}\left(\cdot\right)\) denotes statistical expectation; \(\mathcal{CN}(\mu,\sigma^{2})\) denotes the circularly symmetric complex normal distribution with mean \(\mu\) and variance \(\sigma^{2}\); the function \(\mathbb{I}\{x\}\) denotes the indicator function of the event \(x\).
This study differs from others in terms of both the calculation of seismicity parameters for the entire region and the structural analysis for different story numbers. Additionally, we reveal and draw attention to the effects of structures with similar characteristics in residential areas that are in danger of large and destructive earthquakes. We also aimed at determining the effect of the same local soil conditions and earthquake ground motion level for different geographical locations in the Marmara Region (NW Turkey), which is under the highest earthquake risk. The region is an essential strategic transition corridor between the Asian and European continents. The population of the region is approximately 30% of Turkey's population. Here, we determined the effects of the updated seismic design code and seismic hazard maps on both earthquake and structural parameters on a regional basis. Moreover, the differences between previous and current regulations of the country were investigated. To this end, we considered eleven provinces in the region to reveal earthquake-ground-structure interaction. Using the earthquake ground motion levels for each province for earthquakes with 2%, 10%, 50%, and 68% probability of being exceeded within a 50-year period, Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were calculated. Short period mapping, spectral acceleration coefficient, local ground effect coefficients, design spectral acceleration coefficients, and horizontal and vertical elastic spectrum curves were obtained individually for each province. The earthquake ground motion level (DD-2) is 10% probability of exceedance (repetition period 475 years) in 50 years, and the ground type ZC was taken into consideration. These two parameter values were used for all provinces. The geographic location was selected as the only variable. In this context, the spectrum curves required for the structural analysis were obtained. Static adaptive pushover analyses were performed using these curves in the selected reinforced-concrete (RC) buildings. These analyses were chosen considering the local soil conditions. Displacement values, base shear force, stiffness, and performance criteria values were obtained for each province.