esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Model
Without loss of generality, under zero visual axis jitter error, the radial error of \(r\) the received beam follows a Rayleigh distribution, and the Probability Density Function (PDF) of pointing error \(h_{p}\) can be expressed as follows [17]:  
\[f\big{(}h_{p}\big{)}=\frac{\xi^{2}}{A_{0}^{5}}h_{p}\xi^{2-1}\ \big{(}0\leq h_{p}\leq A_{0}\big{)}, \tag{9}\]  
In Equation (9), \(\xi^{2}=w_{z_{eq}}^{2}/(4\sigma_{s}^{2})\), \(\sigma_{s}^{2}\) represents the jitter variance of the receiver. The UWOC link path loss \(h_{l2}\) can be expressed using the Elamassie continuous path loss closed form formula as follows [10]:  
\[h_{l2}=\bigg{(}\frac{D_{R}}{\theta_{1/\epsilon}d}\bigg{)}^{2}e^{-c_{0}d(\frac {D_{R}}{\theta_{1/\epsilon}d})^{T}}. \tag{10}\]  
Equation (10) is a function of the receiver aperture diameter \(D_{R}\), beam divergence angle \(\theta\), link transmission distance \(d\), and extinction coefficient \(c_{0}\). It may seem simple, but it has the ability to fit both absorption scattering effects and geometric errors through the correction factor and full width beam divergence angle. It actually makes sense, after Monte Carlo simulation, and has been used in many papers. Obviously, when these variables are fixed, they become a constant \(h_{l2}\). Furthermore, assuming that turbulent fading \(h_{f}\) is also known, there is a conditional PDF:  
\[ \begin{array}{rl}f\Big{(}h_{2}\Big{|}h_{f}\Big{)}&=f\big{(}h_{p}\Big{)}\bigg{|} _{h_{p}=\frac{h_{2}}{h_{p}^{2}h_{l2}^{2}}}\bigg{|}\frac{\partial h_{p}}{ \partial h_{2}}\bigg{|}=\frac{1}{h_{f}h_{l2}}f_{h_{p}}\Big{(}\frac{h_{2}}{h_{ f}h_{l2}}\Big{)}\\ &=\frac{\xi^{2}}{\big{(}A_{0}h_{f}h_{l2}\big{)}^{\xi^{2}}}h_{l2}^{\xi^{2}-1}\, \ (\frac{h_{2}}{A_{0}h_{l2}}\leq h_{f}\leq+\infty).\end{array}  \tag{11}\]  
By combining Equations (16) and (17), the two equations can be combined into a general formula:  
\[f_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{r\Gamma(a)\gamma_{2}}G_{1,2}^{2,0} \Bigg{[}\frac{1}{(bA_{0})^{c}}\bigg{(}\frac{\gamma_{2}}{\rho_{r}}\bigg{)}^{\frac {\xi}{\xi}}\Bigg{|} \begin{array}{c}\frac{c+\xi^{2}}{c}\\ \frac{c^{2}}{c},a\end{array} \Bigg{]}, \tag{18}\]  
in the above equation, \(r=1\) represents \(\mathrm{HD}\); \(r=2\) represents \(\mathrm{IM}/\mathrm{DD}\) detection. \(\rho_{1}=h_{\mathrm{I2}}\rho_{\mathrm{heterodyne}}=\frac{\eta_{\gamma}\
abla_{ 2}h_{\mathrm{I2}}}{\sigma_{2}^{2}}\), \(\rho_{2}=h_{\mathrm{I2}}^{2}\rho_{IM/DD}=\frac{\eta^{2}P_{2}h_{\mathrm{I2}}^{2} }{\sigma_{2}^{2}}\). Deduction of Cumulative Distribution Function of Instantaneous Received SNR for R-D Composite Fading Link  
Substituting Equation (18) into its CDF definition \(F_{\gamma_{2}}(\gamma_{2})=\int_{0}^{\gamma_{2}}f_{\gamma_{2}}(x)\ dx\), there is the following:  
\[F_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{r\Gamma(a)}\int_{0}^{\gamma_{2}}x^{ -1}G_{1,2}^{2,0}\Bigg{[}\bigg{(}\frac{1}{bA_{0}}\bigg{)}^{c}\rho_{r}^{-}\frac{ c}{r}\frac{c}{x}r\Bigg{|} \begin{array}{c}\frac{c+\xi^{2}}{c}\\ \frac{\xi^{2}}{c},a\end{array} \Bigg{]}dx.
## 1 Introduction
It was noticed that the flexural response of an under-reinforced RC beam was controlled by the yielding of steel, and a slight increase in ductility was noticed because of the reduced interlocking of recycled aggregates. The reduction in beam stiffness was observed with the increase in the recycling of aggregates because of the degradation in the concrete elastic modulus and tensile strength. [PERSON] et al. [53] analyzed the flexure performance of RC beams of 200 mm \(\times\) 300 mm \(\times\) 1500 mm size constructed using natural and recycled aggregates. A 10% reduction in the ultimate flexural strength was reported for RAC beams as compared to those prepared using NAC. Similarly, the flexural behavior of reinforced RAC beams was investigated by [PERSON] et al. [54]. They prepared RC beams by replacing 0%, 50%, and 100% coarse NAs with coarse RCAs and were subjected to four-point bending tests. Their study concluded that with the same reinforcement ratios, a slight difference in the failure load was observed for reinforced RAC beams. Further, the amount of coarse recycled aggregate in the concrete had a large influence on the size of the failure surface and the severity of concrete degradation at the flexural failure of the beam. They found that the size of the failure surface and severity of concrete disintegration increased as the recycled aggregate content increased. Although some past studies showed a positive impact of fine RCAs and RBAs on the properties of sustainable mortar and concrete [55; 56; 57], a literature review revealed that there is no research that specifically highlights the effect of fine RCAs or fine RBAs on the flexural response of RC structural members. Therefore, an experimental investigation is required to assess the influence of fine RCAs or fine RBAs on the properties of reinforced RAC. In this context, the present study aimed to investigate the impact of fine and coarse recycled concrete and brick aggregates on the bond and flexural performance of reinforcedconcrete. Four-point flexure tests were performed to study the flexural response, while the bond performance was assessed by carrying out direct pull-out testing. A comparison of bond strength predicted by various equations proposed in the literature and experimentally obtained values in this study has also been presented in this contribution. The bond and flexural performance of RAC made using RCAs or RBAs is compared with the performance exhibited by NAC. A total of thirteen concrete mixes, including one control mix, were prepared and used to cast specimens of pull-out tests and RC beams. All concrete mixes were also tested to determine their strength in compression.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## Materials and Methods
### Characterization of SMA/SiO\({}_{2}\)  
Fourier transform infrared spectra (FT-IR) of SiO\({}_{2}\), KH570/SiO\({}_{2}\) and SMA/SiO\({}_{2}\) were obtained by a FT-IR spectrometer (Nicolet 6700, Thermo Scientific, Waltham, MA, USA.). The nuclear magnetic resonance (\({}^{1}\)H-NMR) spectrum of SMA/SiO\({}_{2}\) was determined by an NMR spectrometer (Bruker AV500, BRUKER OPTICS, Karlsruhe, Germany), and the sample was dissolved in CDCl\({}_{3}\). The particle size distribution (PSD) of SMA/SiO\({}_{2}\) in aqueous solution was measured with a particle size analyzer (NanoBrook Omni, Brookhaven Instruments Corporation, Holtsville, NY, USA.). The morphology of SMA/SiO\({}_{2}\) in aqueous solution was recorded by a transmission electron microscope (TEM, JEM-2100 UHR, JEOL Ltd., Akishima, Tokyo, Japan). The thermogravimetric analysis (TGA) of SMA/SiO\({}_{2}\) was carried out on a TGA/DTA thermogravimetric analyzer (NETZSCH 209F3, Netzsch Group, Selb, Germany). During the experiment, the heating rate was set at 10 \({}^{\circ}\)C/min under a N\({}_{2}\) atmosphere, while the heating range was set at 25-600 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Content (wt\%)** & **Component of Clay Mineral** & **Content (wt\%)** \\ \hline Quartz & 20 & Illite & 12 \\ Calcite & 28 & Chlorite & 3 \\ Plagioclase & 29 & Kaolinite & 3 \\ Iron dolomite & 2 & Illite/smectite mixed layer & 82 \\ Potassium feldspar & 6 & & \\ Clay mineral & 15 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineral content of the shale samples.  
Figure 3: Schematic representation on the silane functionalization of SiO\({}_{2}\).
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Regression
#### 3.6.5 Generalized Least Squares Regression  
The initial phase of the analysis involved generating multiple datasets for each device within each gain range. Each dataset includes the noise figure values for every unique combination of input power, target gain, tilt, and channel index. The current goal is to develop a mathematical model that characterizes each type of EDFA under examination. The polynomial model can be mathematically expressed as follows:  
\[NF(P,\,G,\,T,\,f)=\sum_{a=0}^{4}\,\sum_{b=0}^{3}\,\sum_{c=0}^{3}\,\sum_{d=0}^{8 }\,m_{abcd}\,P^{a}\,G^{b}\,T^{c}\,f^{d} \tag{18}\]  
where \(m_{abcd}\) represents the coefficient of each monomial. The goal is to determine the polynomial coefficients using the Generalized Least Squares (GLS) method, an algorithm for estimating the parameters of a linear regression model. To perform polynomial regression, a new dataset is required. This dataset will also include a column for the noise figure and an additional column for each monomial. The GLS method requires a training set to construct our model and a test set to evaluate its performance. For each device within each  
Figure 11: Noise figure vs. channel frequency, at various input parameter configurations.  
Figure 12: An example of the significant variance in noise figure versus frequency under a worst-case scenario for a commercial EDFA.  
gain range, a model is developed by combining multiple datasets from different devices of the same brand and model to improve accuracy.  
Two types of analysis will be performed. Initially, the models will be created using a 70% training and a 30% test split to evaluate their performance. Subsequently, the models will be trained on the entire datasets, producing the final models to be used in conjunction with other models on different datasets.
## 3 Materials and Methods
### Scenario of the Incidence of SAR and Temperature Variation on Human Tissues in Indoor Environments
The components of the device included a SAR sensor, capable of detecting the absorption rate of electromagnetic field sources up to 26 GHz; a processor to calculate the SAR based on the field intensity and tissue density; an infrared temperature sensor, to measure surface temperature variations; and a Wi-Fi module to transfer the data to a cloud platform where the data were processed with a fully connected feedforward network. The power supply consisted of a rechargeable battery with optimised energy management to ensure portability. The designed device included an acquisition board (Figure 5), interconnected with a Semtech SX9306 SAR sensor and an Omron IR temperature sensor. Semtech SX9306 was selected for its precision in detecting weak signals, enabling proximity and gesture-based monitoring. Its compact size and small package facilitate a seamless integration into the electronic system. The Omron infrared sensor ensures high measurement accuracy, even for minimal temperature variations, and operates effectively in diverse environmental conditions. Its fast response time makes it ideal for monitoring electromagnetic field interference in both stationary and moving subjects. The SAR and temperature sensors use I\({}^{2}\)C interfaces for easy integration with the Raspberry Pi microcontroller. Designed for a stable performance, the Omron sensor minimizes electromagnetic and optical interference, ensuring reliable readings. The circuit board processes sensor signals by transmitting them to an amplifier that strengthens weak signals and reduces ambient noise. The analogue-to-digital converter (ADC) digitizes the signals and sends them to the Raspberry Pi, where a low-pass filter eliminates high-frequency noise, ensuring clean data acquisition. The Raspberry Pi analyses and displays real-time datum while transmitting it to a cloud platform. A feedforward neural network (FFNN) predicts the effects of SAR interference and temperature variation on human tissues, providing valuable insights for monitoring electromagnetic exposure in indoor environments.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 4 Discussion
### Protocol  
We assessed the absorption coefficients of the epidermis and hair by combining (i) in vivo measurement of the temperature increase on the surface of a shaved patch of skin with (ii) simulation of the transport of photons and heat diffusion in the tissue. The protocol was verified by measurements and simulations using a phantom with known optical and thermal properties.
## 4 Performance Metrics for Path Loss Models
### Mean Square Error (MSE)  
The mean square error (MSE) in path loss predictions serves as a statistical measure, quantifying the average squared disparity between predicted and observed path loss values. It stands as a prevalent performance metric employed to evaluate the precision and suitability of path loss models within the domain of wireless communication systems [52].  
The formula for calculating MSE is presented in (22), as follows [51; 52; 53]:  
\[MSE=\frac{1}{N_{test}}\sum_{i=1}^{N_{test}}{\left(PL_{i}^{msd}-PL_{i}^{pred} \right)^{2}}, \tag{22}\]  
where \(PL_{i}^{msd}\), \(PL_{i}^{pred}\), \(N_{test}\), and \(i\) are the measured path loss value, predicted path loss values, total number of samples to be tested, and the index of the measured sample, respectively.  
The MSE is calculated by taking the squared differences between the predicted and observed values for each data point, summing those squared differences, and then averaging them by dividing by the total number of data points. A lower MSE indicates a path loss model with better predictive accuracy. In other words, a lower MSE suggests that the predicted path loss values are closer to the actual observed values, indicating a better fit of the model to the data.  
However, it is essential to note that MSE gives more weight to large errors due to the squaring operation. As such, the root mean square error (RMSE), which is the square root of the MSE, is often used to provide a measure of the average magnitude of prediction errors, and it is presented in (23) as follows [52; 54]:  
\[RMSE=\sqrt{\frac{1}{N_{test}}\sum_{i=1}^{N_{test}}{\left(PL_{i}^{msd}-PL_{i}^{ pred}\right)^{2}}}. \tag{23}\]
## 4 Simulation Results  
In this section, the simulation results of the trajectory tracking for the biomimetic spherical robot on the surface of the water are provided, which highlights the feasibility of the proposed MPC-based trajectory tracking control method. All of the simulations were conducted while using MATLAB 2015a on a Windows 7 equipped with an Inter core i7 CPU at 3.6 GHz and 8 GB of RAM. During the simulations, the updating of the robot state is based on the identification dynamic model in Section 2.3 and Equation (9). Six parameters need to be set in the MPC controller, including the prediction horizon \(N_{p}\), the control horizon \(N_{c}\), the weight matrix \(Q\) and \(R\), the sampling time \(T\), and the weight coefficient \(\rho\). The sampling time \(T\) is set as 0.05 \(s\), which is consistent with the sampling time of the sensors installed on the robot. The prediction horizon \(N_{p}\) can affect the tracking effect of the proposed algorithm. The larger \(N_{p}\), the better the stability of the controller, but the slower the response. The control horizon, \(N_{c}\), is almost half of the \(N_{p}\). According to the experience and the simulation results, \(N_{p}\) is set to 25, and \(N_{c}\) is set to 15. The weight matrix \(R\) prevents the control input from fluctuating too much. Matrix \(Q\) determines the weight of the error between the real and desired trajectories in the cost function. The larger \(Q\) is, the faster the response of the controller. In general, \(Q\) is set to the identity matrix and \(R\) is set to a matrix that is smaller than the identity. According to the simulation effect, the weighting matrices \(R\) are adjusted to \(5\times I\), where \(I\) is an identity matrix and \(Q\) is adjusted to an identity matrix. The weight coefficient \(\rho\) is set to avoid the case of no solution in the quadratic optimization, which has little effect on the simulation result. Parameter \(\rho\) is set to 10 in the simulation. In addition,the limit of each thruster is 3 (\(N\)), and torque limit based on each thruster is 0.086 \((N*m)\). The constraint ranges of the force and torque increment are [\(-\)0.08, 0.08] \((N)\) and [\(-\)0.02, 0.02] \((N*m)\), respectively.
## 4 Routing Strategies and Hop Distance Distributions  
In this section, we introduce two routing strategies and derive the probability distributions of their hop distances. As the microwave system analysis in [13], to implement the routing schemes, it is assumed that all nodes know their own locations and the source knows the direction towards the destination. Suppose the number of the blockage-free nodes in \(\mathcal{A}\) is \(K\). \(K=0\) corresponds to the outage. On the other hand, if \(K\geq 1\), we consider two routing schemes, where the next-hop node is the furthest and nearest LoS nodes in its coverage \(\mathcal{A}\) respectively, as shown in Figure 2. We only consider these two simple but representative routing schemes because this paper is focused on the impact of the blockage effects in mmWave multi-hop M2M communications in terms of hop distance to gain system-level design insights. In practice, the routing protocol should be chosen based on various system requirements such as latency, throughput, reliability, and energy-efficiency. In addition, in case of the M2M networks with selfish nodes, we need consider game theory-based protocol design as in [49; 50], which offers incentives to stimulate forwarding. However, it is beyond the scope of this paper.  
Figure 2: Hop distances of two routing strategies: furthest neighbor (FN) and nearest neighbor (NN).
# Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory
## 5 Conclusions  
In this paper, we have proposed to use the relative LLR calculation to estimate the actual LLR. Furthermore, in three-dimensional coordinates, we have transformed the bit detection problem into a clustering problem, which allows us to apply an artificial neural network in the memory channel. To solve the clustering problem, we proposed an artificial neural network-assisted error correction scheme, which has been shown by experiments to be effective in correcting the error bit when the conventional method without the neural network fails to decode. Simulation results have shown that the FER performance of our ANNAEC is significantly better than that of ANN-LDPC. For example, the ANN-LDPC can make the flash memory endure up to \(3\times 10^{5}\) h, and the proposed method provides the performance gain of approximately \(67\%\) of data retention, which makes the retention time of flash endure up to \(5\times 10^{5}\) h. Furthermore, our proposed approach can be extended to TLC or QLC flash memories.  
Conceptualization, R.H., H.H. and G.H.; methodology, R.H. and G.H.; software, R.H.; validation, R.H. and H.H.; formal analysis, R.H., H.H. and G.H.; investigation, R.H., H.H., G.H. and C.X; writing--original draft preparation, R.H., G.H. and C.X.; writing--review and editing, H.H. and G.H.; visualization, C.X.; supervision, G.H.; project administration, R.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant 61871136.  
The study did not report any data.  
The authors declare no conflict of interest.  
Figure 9: (**a**) Comparison of the raw bit error rate (RBER) performance of NAND flash memory with and without ANNAEC scheme versus data retention time at \(s=1\). (**b**) Comparison of the frame error rate (FER) performance of low-density parity-check (LDPC) coded NAND flash memory with and without the ANNAEC scheme versus data retention time at \(s=1\).
# XGBoost-Based Detection of DDoS Attacks in Named Data Networking
## 4 Experiment Analysis
### Performance Analysis of I-CIFPA  
The I-CIFPA is simulated on the ndnSIM platform, and the built-in output program of the platform, namely the trace helper, is utilized to track the simulation results. The requested statistics are collected and aggregated by this tool, where data on CacheHits are generated by ndn::CsTracer, and TimeOutInterests and OutInterests are generated by ndn::L3 RateTracer. The experimental results are shown in Figures 5-7.  
Figure 5 shows that in the I-CIFPA scenario, the number of CacheHits is higher than that under normal and I-CIFA conditions. Additionally, CacheHits significantly increases during the attack intervals, indicating that I-CIFPA has higher stealth. Although the ideal CS capacity for each routing node is set to 1000, under limited CS capacity, I-CIFPA exhausts CS resources by caching too much unpopular content, leading to link congestion. This reduces the caching and forwarding efficiency of legitimate data packets, severely impacting the performance of the NDN network.  
As shown in Figure 6, the number of TimeOutInterests under I-CIFPA is greater than those of I-CIFA, and it increases significantly during each interval attack. As a result, TimeOutInterests cannot be forwarded to publishers, so subscribers cannot receive replies from the legitimate publisher, causing a denial of service. This observation indicates that the robustness and stealth capabilities of I-CIFPA surpass those of CIFA.  
Figure 7 demonstrates that during the attack interval, the number of OutInterests generated by I-CIFPA exceeds that produced by CIFA. This indicates that in the I-CIFPA scenario, the intensive transmission of interest packets leads to higher cache occupancy rates and excessive request hotspots, necessitating substantial remote resource requests. Compared with CIFA, the I-CIFPA attack integrates characteristics of CPA by simultaneously generating massive interest packets while occupying cache resources.  
In summary, the numbers of CacheHits, TimeOutInterests and OutInterests take the form of periodic pulses under I-CIFPAs, severely damaging the NDN network and making the detection of and defense against I-CIFPAs more difficult.