esa-sceva/satcom-chunk-collection huggingface dataset:
# System Design and Experimental Study of a Four-Roll Bending Machine
## 4 Control System Design
### Human-Machine Interface Design
#### 4.4.4 Database Module  
The database module of the CNC four-roll bending machine HMI serves as the central data hub, responsible for storing, managing, and retrieving critical parameters. It is deeply integrated with SQL Server Management Studio, providing standardized and intelligent data support for the bending processes.  
Multi-dimensional Parameter Storage Functions: Roller Parameters: Records geometric parameters (diameter, roller clearance) and installation coordinates for the four rollers (main roller, side roller, support roller). Material Parameter Library: Stores mechanical property data (material code, name, elastic modulus, Poisson's ratio, density, yield stress) for commonly used materials including carbon steel, stainless steel, and aluminum alloys, compliant with GB/T 228.1-2021 standards. Profile Parameter Library: Contains cross-sectional types (e.g., round tubes, square tubes, angle steel) and geometric dimensions (length, width, thickness) of profiles. Built-in SQL Query Engine: Supports rapid parameter retrieval based on material codes, profile sections, and other criteria.  
The system adopts a relational database model with the following core table structures. A relational database model was employed to store multi-dimensional parametric data, with the specific content detailed in Tables 6-8.
###### Abstract  
In this paper, the plasmonic resonant absorption of gold nanorods (GNRs) and GNR solutions was studied both numerically and experimentally. The heat generation in clustered GNR solutions with various concentrations was measured by exposing them to Near Infrared (NIR) light in experiment. Correspondingly, calculations based on the discrete-dipole approximation (DDA) revealed the same relationship between the maximum absorption efficiency and the nanorod orientation for the incident radiation. Additionally, both the plasmonic wavelength and the maximum absorption efficiency of a single nanorod were found to increase linearly with increasing aspect ratio (for a fixed nanorod volume). The wavelength of the surface plasmonic resonance (SPR) was found to change when the gold nanorods were closely spaced. Specifically, both a shift and a broadening of the resonance peak were attained when the distance between the nanorods was set to about 50 nm or less. The absorbance spectra of suspended nanorods at various volume fractions also showed that the plasmonic wavelength of the nanorods solution was at 780 \(\pm\) 10 nm, which was in good agreement with the computational predictions for coupled side-by-side nanorods. When heated by NIR light, the rate of increase for both the temperature of solution and the absorbed light diminished when the volume fraction of suspended nanorods reached a value of 1.24 \(\times\) 10\({}^{-6}\). This matches with expectations for a partially clustered suspension of nanorods in water. Overall, this study reveals that particle clustering should be considered to accurately gauge the heat generation of the GNR hyperthermia treatments.  
hyperthermal; gold nanoparticles; near infrared radiation; surface plasmonic resonance; discrete dipole approximation 1
## 4 Discussion
### Performance of the OLCI K\({}_{d}\)(490) Algorithm
#### 4.1.3 Applicability of the Algorithm to Other Lakes  
To examine its wider applicability, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm was applied to Lake Chaohu, which is the fifth largest freshwater lake in China and is a typical eutrophic lake [63; 64]. A total of 23 synchronous satellite and ground data points for Lake Chaohu were obtained by following the synchronous satellite and ground data matching criteria. The estimation accuracy of the dual band ratio algorithm was found to be low when it was used directly with the Lake Chaohu data (Figure 11a; R\({}^{2}\) = 0.60, RMSE = 1.38 m\({}^{-1}\) and MAPE = 21.85%), and the intersection points between the estimated and measured values of \(K_{\mathrm{d}}\)(490) are generally below the 1:1 line. This is because the coefficient of the algorithm was obtained by empirical regression based on the Lake Taihu data. Thus, the parameters of the algorithm for Lake Taihu were calibrated against the Lake Chaohu data. As a result, the estimation accuracy of the algorithm was improved significantly (Figure 11b; R\({}^{2}\) = 0.80, RMSE = 0.54 m\({}^{-1}\) and MAPE = 7.55%), and the intersection points between the estimated and measured values of \(K_{\mathrm{d}}\)(490) were generally distributed near the 1:1 line; in addition, a reasonable spatial distribution of \(K_{\mathrm{d}}\)(490) can be observed by comparing the RGB images (Figure 11c,d). Therefore, the dual band ratio algorithm developed in this study exhibits high applicability for turbid inland waters; however, its coefficient must be re-parameterized based on the optical properties of the study waters.
###### Abstract  
The study of site amplification effects is crucial to assess earthquake hazards that can produce great damage in urban structures. In this context, the gravity and the ambient noise horizontal-to-vertical spectral ratio (H/V) are two of the most used geophysical methods to study the properties of the subsoil, which are essential to estimate seismic amplification. Even though these methods have been used complementarily, a correlation between them has not been thoroughly studied. Understanding this correlation and how it depends on geology could be important to use one method as an estimator of the other and to make a distinction between the seismic and gravimetric basement. In this research, a comparison between the residual gravity anomaly and the H/V predominant period is performed using a long dataset from different projects on sedimentary basins in a group of the most important cities in Chile. To simplify the geological information, a seismic classification is used for soils, which considers the Vs30 and the predominant period of vibration (T0). The results of this comparison show a direct correlation between both parameters, the higher the negative residual gravity anomaly the higher the H/V predominant period. This correlation improves when only soft soils are considered, increasing the R2 value in more than a 50% in all the individual cities with respect to the overall correlation. When all the cities are considered, the R2 value for soft soils increases up to 0.87. These results suggest that the ideal geological background for this correlation is when a soft soil layer overlies a homogeneous bedrock. Heterogeneities in the bedrock and in the soil column add dispersion to the correlation. Additionally, the comparison between the depth to basement inferred by both methods show differences of less than 15% in soft sites; in denser sites, the difference increases up to 30% and the definition of a clear H/V peak is more difficult. In general, the gravimetric basement is deeper than the seismic one. However, gravimetric depths to basement can be under/over-estimated in zones with a heterogeneous soil column.  
gravity residual; near-surface geophysics; H/V predominant period +
Footnote †: journal: Appl. Sci.  
13 September 2021  
Accepted: 1 October 2021  
Subject head: 12 October 2021
## 4 Interference Characterization  
The inter-user interference of wearable electronics is caused by a number of factors, such as distance between networks, human effects, and environmental effects. In the paper, we model the effects of human effects by observing the locations of BSN users and interaction between them in the waiting area of a hospital. Moreover, we model the channel on a human body by introducing an on-body communication channel model and an inter-body communication channel model, where the channel characteristics are set through experimental results. There are several metrics to depict the performance of a BSN, such as bit error rate, packet error rate, and throughput.  
The following terms are utilized to describe our model:  
* _Signal to interference and noise ratio (SINR)_ is the ratio of the desired signal power received from transmitter to the total power from interferers and receiver noise.
* _Packet error rate_ is the number of incorrectly received data packets divided by the total number of received packets.
* _Spatial throughput_ measures the number of BSNs that transmit simultaneously and successfully within a unit area. It measures the overall performance of BSNs.
* _Spatial average rate_ is a measure of the quantity of users or services that can be simultaneously supported by a limited radio frequency bandwidth in a defined geographic area.  
The notations and symbols involved in this paper are presented in Table 1.  
\begin{table}
\begin{tabular}{|c|c|} \hline Notation & Description \\ \hline \(\alpha_{s}\), \(\alpha_{I}\) & On-body and inter-body path loss exponent \\ \hline \(\Omega_{s}\) & The interference power received at the intended receiver \\ \hline \(\Omega_{s}\) & The noise power received at the intended receiver \\ \hline \(r\) & The distance between sensor nodes in a BSN \\ \hline \(\lambda\) & The BSN density \\ \hline \(\beta\) & The SINR threshold \\ \hline \(P_{\mathit{R}EE}\) & The average bit error rate \\ \hline \(P_{\mathit{r}EE}\) & The average packet error rate \\ \hline \(P_{\mathit{SINR}}\) & The average SINR \\ \hline \(t_{\text{min}}\), \(t_{\text{max}}\) & The lower and upper integral limits of time factor \\ \hline \(\gamma_{\text{min}}\), \(\gamma_{\text{max}}\) & The lower and upper integral limits of shadow fading factor \\ \hline \(B\) & The bandwidth \\ \hline \(P_{o}\) & The outage probability \\ \hline \(f_{\mathit{SINR}}\) & The PDF of SINR \\ \hline \end{tabular}
\end{table}
Table 1: The notations of the selected terms.
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 3 Proposed Protocol
### System Setup Phase  
The service provider \(S_{j}\) independently chooses a number \(x\in Z_{p}^{*}\), which is a one-way hash function \(H(\cdot)\). Then, \(S_{j}\) calculates \(X=x\cdot P\) (\(P\) is a generator of the abelian group \(G\) in the elliptic curve), publicizes the parameter \(H(\cdot)\), \(X\), and reserves a long, private, secret key \(x\).
# Application of the _Aliium_ Test in Toxicity Studies of Lead and Copper: A Cytological Perspective
## 3 Results
### Correlations Between Cytological Indices Under Copper and Lead Treatments  
Correlation analyses of cytological dynamics under the influence of different treatments have revealed varying correlation coefficients, depending on the treatment applied.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Source** & **Df** & **Sum Sq** & **Mean Sq** & **F Value** & **Pr(-F)** \\ \hline Metal\_Type & 1 & 31.08 & 31.082 & 18.33 & \(3.32\times 10^{-5}\)*** \\ \hline Residuals & 148 & 250.94 & 1.698 & & \\ \hline Significance level: ***\(p<0.001\). & & & & \\ \end{tabular}
\end{table}
Table 6: ANOVA test for comparison of the impact of lead and copper on the abnormalities index.  
Figure 6: Boxplot showing the difference between the impact of \(Pb^{2+}\) and \(Cu^{2+}\) on the abnormalities index.  
According to the results, \(Cu^{2+}\) has a more acute impact on cellular division, highlighted by more extreme correlations compared to those for \(Pb^{2+}\).  
Regarding the correlation coefficient between the mitotic index (MI) and the abnormality index (AI), the results indicate a negative correlation. This underlines that as the mitotic index decreases, the number of abnormal cells increases. Our results show a significantly stronger negative correlation for \(Cu^{2+}\) (\(-0.69\)) compared to \(Pb^{2+}\) (\(-0.29\)), further highlighting the higher genotoxic effect of \(Cu^{2+}\) (Figure 7).  
Figure 7: Mitotic index and abnormality index dynamics under \(Pb^{2+}\) vs. \(Cu^{2+}\) treatments.  
Exposure time also shows a negative correlation during heavy metal treatments, with a correlation of \(-0.92\) in the case of \(Cu^{2+}\) and \(-0.86\) for \(Pb^{2+}\). This indicates that the mitotic index decreases significantly for both metals as exposure time increases (Figure 7).  
The number of abnormalities is also strongly linked to the number of dividing cells, showing a negative correlation for both metals studied. The greater accumulation effect in favor of \(Pb^{2+}\) can be observed in the correlation of \(-0.67\) between the AI and cells in interphase. This suggests that as the treatment concentration increases, \(Pb^{2+}\) accumulates in the cells, halting and preventing cellular division. On the other hand, the same is not true for the correlation between the AI and the number of cells in interphase for copper, where the correlation is only \(-0.19\). However, \(Cu^{2+}\) shows a stronger correlation with the mitotic index (\(-0.69\)). This indicates that copper's effect on halting cellular division is much more acute compared to lead. Lead, on the other hand, exhibits a cumulative effect, disrupting mitotic activity as it accumulates in the cells (Figure 7).
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 4 Numerical Experiments
### Example 2: Simulated AR(1) Processes with a Single Change-Point  
In this example, we would like to understand how a change in either the mean or the autocorrelation coefficient affects the performances of the algorithms. To study these individual effects, we design two scenarios. In the first scenario, we assume that autocorrelation \(\rho\) remains the same in a time series, while the mean jumps at each segment; the second scenario shows the opposite situation: the mean is fixed and the autocorrelation coefficient varies across segments.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 4 The GALO Algorithm
### The Problem Description
The constraint condition C2 indicates that the transmission loss from \(g_{i}\) to \(s_{i}^{b}\) should be lower than the loss threshold \(TL_{th2}\). The formula in the constraint C3 indicates the movement trajectory of the glider should be considered when the link is repaired, and the starting and ending positions are certain points on the movement trajectory. In other words, different trajectories result in different feasible regions of the objective function.
## 2 2-DOF Vehicle Dynamics Model and Ideal Reference Model
\tag{3}\]  
where \(\alpha_{f}\) and \(\alpha_{r}\) are the lateral deflection angles of the front and rear wheels, respectively; \(\mathrm{k_{1}}\) and \(\mathrm{k_{2}}\) are the lateral deflection stiffness of the front and rear axes. Equation (3) can be rewritten into a differential motion equation after derivation, as shown in Equation (4). \[\left\{ \begin{array}{l}m(\ddot{v_{y}}+\omega v_{x})=(k_{1}+k_{2})\beta+ \frac{1}{v_{x}}(ak_{1}-bk_{2})\omega-k_{1}\delta_{f}\\ I_{z}\dot{\omega}=(ak_{1}+bk_{2})\beta+\frac{1}{v_{x}}(a^{2}k_{1}-b^{2}k_{2}) \omega-ak_{1}\delta_{f}\end{array} \right. \tag{4}\]  
Figure 1: 2-DOF vehicle model. where \(\beta\) is the lateral deflection angle of the center of mass. The ideal reference model is used to describe the lateral motion of the vehicle at a steady state when the front wheel angle is minimal and can be neglected. In this case, the yaw rate is the ideal yaw rate, and, at the same time, the ideal center of mass lateral angle can be calculated. Since the front wheel angle is ignored, the vehicle is performing a uniform circular motion, therefore, \(\dot{v_{y}}\) and \(\dot{w}\) are zero, and joint Equation (4) can be obtained as follows:  
\[\left\{ \begin{array}{l}m\omega v_{x}=\beta(k_{2}+k_{1})-k_{1}\delta_{f}+ \omega/v_{x}(ak_{1}-bk_{2})\\ 0=\beta(ak_{1}+bk_{2})-ak_{1}\delta_{f}+\omega/v_{x}(a^{2}k_{1}-b^{2}k_{2}) \end{array} \right. \tag{5}\]  
The stable yaw rate, with respect to \(v_{x}\) and \(\delta_{t}\), can be determined as follows:  
\[\omega_{d}=\frac{\delta_{f}v_{x}}{L+\frac{mv_{x}{}^{2}(ak_{1}-bk_{2})}{Lk_{1}k _{2}}} \tag{6}\]  
Due to the influence of the road adhesion coefficient \(\mu\), the vehicle should meet \(a_{y}\leq\mu g\) while driving, and the motion state of the vehicle should meet the following requirements:  
\[a_{y}=\omega v_{x}+\dot{v}_{x}^{\bullet}\tan\beta+\frac{v_{x}\beta}{\sqrt{1+ \tan\beta}} \tag{7}\]  
When the vehicle is in steady state, the value of \(\beta\) is negligible, therefore, \(a_{y}\)\(=\omega_{r}v_{x}\) is desirable. Since many influencing factors exist in practice, a 15% stability margin can be processed. Taking the ideal centroid sideslip angle to be zero, the desired ideal yaw rate and centroid sideslip angle can be obtained as follows:  
\[\left\{ \begin{array}{l}\omega_{d}=\min\Bigl{\{}\left|\omega_{d}\right|, \left|\frac{0.85\mu g}{v_{x}}\right|\Bigr{\}}\Bigr{\}}\text{sgn}(\delta_{f}) \end{array} \right. \tag{8}\]  
where L is the front and rear wheelbase; \(\mu\) is the road adhesion coefficient; \(\omega_{r}\) is the actual yaw rate; \(\omega_{d}\) and \(\beta_{d}\) are the ideal yaw rate and the centroid, respectively; and g is the acceleration of gravity.
## 3 Adaptive Data Fusion Method Based on ADCNN for Fault Diagnosis
### Data Preprocessing  
Since the data range of each sensor is very different, when the multi-sensor data are fused it is easy to cause the gradient descent for finding the best solution. It takes many iterations to converge and improve the accuracy of the model. Therefore, data standardization must be normalized. The normalization method is min-max scaling, which is defined as:  
\[x_{normalization}=\frac{x-min(x)}{max(x)-min(x)} \tag{13}\]Figure 1: ADCNN architecture diagram.  
In order to improve the generalization ability of the model and avoid overfitting, ADCNN needs a large number of samples for training and testing. The sliding window sampling of the original time series data of each sensor achieves the effect of data enhancement [7]. Suppose that the time window length of the sample is n milliseconds, and the time step is 50% of the time window length, as shown in Figure 2.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 6 Numerical Results
These distance errors result from a sudden change in the leader vehicle, from significant deceleration (approximately 2.5 m/s\({}^{2}\)) to small acceleration (up to 0.3 m/s\({}^{2}\)). The vehicles closer to the tail of the platoon adjust their accelerations already upon the reception of the leader's message, with the parameters related to the preceding vehicle being received significantly later (even with a couple of seconds of time difference), which results in a decrease in the distance. This is due to the use of different communication intervals selected with the heuristic approach that may lead to a temporary loss of string stability. However, an important observation is that even such a temporary loss of stability does not result in the violation of the distance error threshold, indicating that the platoon is able to maintain a safe operation all of the time. Another observation that can be made in Figure 9 is that the adaptive algorithm introduces more significant negative distance error values, which represent a situation of an increased distance between the vehicles. Such behavior can be also considered as a negative effect, as the platooning gains diminish with the increase in inter-vehicle distances, where, with the air drag increasing, the reduction in fuel consumption (that can reach between 7% and 15%, depending on the inter-vehicle distance [3]) will be smaller. Moreover, the reliability of V2V communications is lower at higher ranges. Depending on the wireless communication protocol used, a significant drop in the reception rate of the leader packets can be observed [9; 31], which imposes limitations on the maximum platoon length (number of vehicles). Typically, an unacceptable reliability with DSRC can be observed already at a distance of 100 m in the case of heavy traffic, with C-V2X performing slightly better [9]. On the other hand, the main reason for the increase in inter-vehicle distances is that in (8), we put the constraint only on the minimum distance between the vehicles, with the aim of  
Figure 8: Example of inter-vehicle distance error evolution in a simulation run for fixed periodic transmission with \(\Delta t_{Tx}=500\) ms; numbers in the legend represent vehicles’ positions in the platoon, with 0 indicating the leader, and 5 denoting the last vehicle (the tail of the platoon). maintaining safety. Modifying the constraint to also account for the negative error values would mitigate this effect; however, at the cost of increased resources use.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 6 Conclusions  
A communication-aware cooperative target tracking algorithm was proposed, which is based on information consensus under multi-UAVs communication noise.  
The communication between UAVs was modeled as a signal-to-interference-plus-noise ratio (SINR) model. When the receiver SNR of \(i\)-th UAV from \(j\)-th UAV was greater than a threshold, we assumed \(i\)-th UAV was \(j\)-th UAV's neighbor. An UAV could communicate with its neighbors. Each UAV had a local filter to get an estimate and could receive its neighbors' observation. Each UAV used the extended information filter to fusion its local observation and its' neighbors' observation to get a better estimate.  
Each UAV calculated its own target tracking control inputs using receding horizon optimization method based on the consistency target state estimation.  
The simulation results showed that the proposed communication model could make UAVs improve the accuracy of cooperative target tracking.  
[PERSON] designed the proposed algorithm; [PERSON] built the simulation model and performed the simulation; [PERSON] wrote the first draft of the manuscript.  
The authors declare no conflict of interest.