esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Datasets
#### 3.1.1 CSE-CIC-IDS2018  
The Canadian Institute for Cybersecurity Intrusion Detection System (CSE-CIC-ID2018) dataset consists of approximately 2,830,540 samples [20] of traffic that was simulated during the period between February and March 2018. Each file is approximately 200 KB [21] and contains 79 features. The data are binary-classified, with each sample labeled as either \"Benign\" or \"Infiltration\". The 28 February 2018 file, for instance, includes 540,568 samples of the Benign class and 68,462 samples of the infiltration class (after removing outliers). The dataset encompasses various attack types, such as brute-force, Heartbleed, botnet, DoS, DDoS, web attacks, and network infiltration [21]. Several research papers have utilized this dataset to evaluate machine learning algorithms [22]. Given the large size of this dataset, training the RLFOUA framework would be time-consuming. Thus, we performed a preliminary test by selecting a random subset consisting of 250 infiltration and 7500 benign activities from the 28 February 2018 file for training and validating the RLFOUA framework within a reasonable timeframe to identify machine learning algorithms that exhibit superior performance when combined with RLFOUA. Finally, for main testing purposes, the entire dataset was used to train, validate, and test the RLFOUA framework. Consequently, all files for each reported month from the CSE-CIC-ID2018 dataset, totaling 6,493,978 samples, were divided into training, validation, and testing sets. The RLFOUA framework underwent training, validation, and testing on high-performance computing systems to generate a new model. Subsequently, its performance was assessed using an independent dataset that had been previously set aside for the purpose of evaluating the effectiveness of the RLFOUA framework.
# Synchronization and Application of a Novel Hyperchaotic System Based on Adaptive Observers
## 6 Conclusions  
Our study addresses current challenges in chaotic systems, specifically focusing on the introduction and analysis of a novel hyperchaotic system. Through dynamic analysis, it is concluded that there is significant hyperchaotic behavior, paving the way for novel applications in secure communication. To validate the physical feasibility of our proposed system, an equivalent electronic circuit (see Figure 9) was created. The success of our adaptive observer-based synchronization under parameter uncertainty demonstrates a robust solution to real-world scenarios. This synchronization method, analyzed through Lyapunov stability theory, showcases its effectiveness by converging the synchronization error to 0 and accurately estimating unknown parameter values. The application of adaptive observer-based hyperchaotic system synchronization to secure communication, employing the chaotic masking method (see Figure 13), adds a layer of practical significance. Our system not only ensures the secure transmission of information but also successfully decodes the masked signal at the receiving end. Our study contributes to the understanding and application of hyperchaotic systems, with notable findings in both theoretical and practical domains. However, it is crucial to acknowledge limitations. While our methodology demonstrates success in simulation, future work should involve implementing the proposed system in hardware-based applications to bridge the gap between simulation and real-world scenarios. In conclusion, our research not only highlights the dynamic properties of the proposed hyperchaotic system but also showcases a synchronization method with promising applications in secure communication. By addressing current challenges, presenting innovative solutions and acknowledging the path forward, we contribute to  
Figure 14: (**a**) Information signal; (**b**) transmitted encrypted signal; (**c**) the signal obtained after synchronization; (**d**) recovery error.  
the advancement of chaotic systems research and open avenues for future exploration and refinement.  
Conceptualization, E.O. and A.G.; methodology, E.O. and A.G.; validation, A.G.; formal analysis, E.O. and A.G.; investigation, E.O.; simulations, E.O.; writing--original draft preparation, E.O. and A.G.; writing--review and editing, E.O. and A.G.; visualization, E.O.; supervision, A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Proposed Joint Decoding (JD) Framework for Video
### Fusion Approach
#### 3.2.3 Final Frame Reconstruction  
The final reconstructed frames \(\text{F}^{\prime\prime}_{\text{NK}}\) within the GoP are generated by adding the \(\text{F}_{\text{r}}\) and \(\text{F}_{\text{P}}\) frames. It is a standard point-to-point addition that is expressed in Equation (11). By doing so, uniformity in terms of frame measurements (Y) is achieved, i.e., the measurements computed for F\({}^{\prime\prime}_{\text{NK}}\) is to some extent equal to the measurements YNK:  
\[\text{F}^{\prime\prime}_{\text{NK}}=\text{F}_{\text{r}}+\text{F}_{\text{P}} \tag{11}\]  
After the keyframes, F\({}^{\prime}_{\text{K}}\) (F\({}_{0}\) and F\({}_{\text{j}}\) from Y\({}_{0}\) and Y\({}_{\text{j}}\)) are reconstructed using BCS-TV-AL3, they are used as the reference frames for reconstructing the non-key frames F\({}^{\prime}_{\text{NK}}\) between them.  
The proposed scheme produces the non-key frame F\({}^{\prime\prime}_{1}\) from Y\({}_{1}\), F\({}_{0}\), and F\({}_{\text{j}}\); similarly F\({}^{\prime\prime}_{2}\) is generated from Y\({}_{2}\), F\({}_{0}\) and F\({}_{\text{j}}\). The method endures for all the remaining non-key frames. The reconstructed frames' quality is expected to drop as the distance of the key frames from the non-key frames increases. Thus, the quality of the reconstruction may decrease with the increase in the GoP size (J). A complete GoP (J = 8) reconstruction for News video sequence is shown in Figure 4. The highlighted red dotted circles clearly show that as the difference among the key frames and non-key frame increases the reconstruction quality decreases.
## 4 Results and Analysis
### Dataset
#### 4.1.3 Semantic Segmentation Image Data Set  
For the semantic segmentation task, a total of 1325 original experimental images were acquired during the tangerine peel drying tests, from which 1680 real tangerine peel drying images were obtained after screening and enhancement. The primary objective was to achieve accurate segmentation of the tangerine peel. The image labels were categorized into tangerine peel and background and were subsequently divided into training and test sets at a 9:1 ratio. The training set comprised 1512 images, while the test set contained 168 images, ensuring a balanced distribution for effective model training and evaluation. The high quality and precision of the image annotations provide a reliable basis for subsequent experiments.
## 4 Secure FSO Link Experimental Demonstration
### Experiment Setup  
Figure 10 depicts the lab-based experiment setup of the secure FSO link. A Red Pitaya SDR transceiver acts as the interface between the optical hardware and GNU Radio software in a loop-back connection. The host computer of GNU Radio communicates with the Red Pitaya's onboard FPGA via an Ethernet cable. Once the Red Pitaya processes the data through its digital-to-analogue converter (DAC), it is sent through the output RF port to the optical hardware. The optical hardware consists of a THORLABs LDC 202C laser driver, an Arima laser ADL 65074, 10 mm focal length optics and a THORLABs PDA10A2 detector. The output of the Red Pitaya is connected to the modulation port of the laser driver. The driver modulates the power of the laser to match the output of the Red Pitaya. The laser driver also limits the laser current to 10 mA to prevent any damage. The laser emits visible red light at 650 nm, which is first collimated by an optic and then focused onto the receiver by a second optic of the same specification. By focusing the light correctly, a voltage of 60 mV can be achieved at the optical receiver. The receiver will output a voltage, which can be connected straight into the input port of the Red Pitaya to be processed by its onboard digital down-converter (DDC) and sent, via Ethernet cable, to the GNU Radio interface.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Link length & 500,000 m \\ Wavelength & 1550 nm \\ Rx diameter & 0.05 m \\ Tx diameter & 0.005 m \\ Tx Half-angle diverge & 0.133 mrad \\ Visibility range & 1000 km \\ Pointing jitter & 0.2 mrad \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite to ground-station link parameters for low earth orbit satellite mission that uses miniature optical communication transceiver (MOCT) developed at the University of Florida [16].  
Figure 10: Secure FSO lab-based experiment setup.  
Figure 11 shows the GRC file for the secure FSO link using the SDR and optical hardware. As explained in Section 3, the implementation of the system in GNU Radio consists of four parts: the initial setup, the transmitter, the receiver, and the channel, which is replaced with hardware. The figure shows that the FSK packet transmitter, and receiver are connected to the Red Pitaya sink and source, respectively, using the OOT blocks [31]. The Red Pitaya SDR acts as the interface between the optical hardware and GNU Radio software.  
Figure 11: The GRC file for the secure FSO link using the optical hardware.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 3 Challenge: Data Quality
### Data Augmentation
#### 3.1.2 Signal-Processing-Based Methods  
Traditional signal processing methods for data augmentation are usually heuristic. For images, e.g., used in visual inspection [64], commonly used methods are rotating, flipping, cropping, color channel manipulation, and noise injection. For time series, e.g., used in anomaly detection [65], data augmentation can be conducted in the time domain, frequency domain, and a hybrid with local averaging, perturbation, shuffling, etc.
## 3 Results and Discussion
### Effect of the Tested Molecules on Caco-2 Cells Viability  
Preliminary cytotoxicity tests on the selected molecules were performed to define the concentrations to be used in the absorption studies. Therefore, three different concentrations of compounds (0.02, 0.1, and 0.2 mg/mL) were evaluated by an MTS-based assay. No cytotoxic effect was observed for any of the tested molecules at any concentration, as evident by cell viability % registered in comparison to the control after 6 and 24 h of incubation. The intermediate concentration (0.1 mg/mL) (Figure 3) was thus selected for the absorption test, as it was considered safe on Caco-2 cells to avoid any cell viability impairment on the absorption data.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 4 The LoRaWAN Protocol
### LoRaWAN Message Format  
LoRaWAN uses the physical frame format described in Section 3.3. The header and CRC are mandatory for uplink messages, which makes it impossible to use a spreading factor of six in LoRaWAN. Downlink messages have the header, but not the CRC. The code rate that should be used is not specified and neither is when the end-devices should use the low data rate optimization.  
The message format is detailed in Figure 8. _DevAddr_ is the short address of the device. _FPort_ is a multiplexing port field. The value zero means that the payload contains only MAC commands. When this is the case, the _FOptsLen_ field must be zero. _FCnt_ is a frame counter. _MIC_ is a cryptographic message integrity code, computed over the fields _MHDR_, _FHDR_, _FPort_ and the encrypted _FRMPayload_. _MType_ is the message type, indicating among other things whether it is an uplink or a downlink message and whether or not it is a confirmed message. Acknowledgments are requested for confirmed messages. _Major_ is the LoRaWAN version; currently, only a value of zero is valid. _ADR_ and _ADRAckReq_ control the data rate adaptation mechanism by the network server. _ACK_ acknowledges the last received frame. _FPending_ indicates that the network server has additional data to send and that the end-device should send another frame as soon as possible so that it opens receive windows. _FOptsLen_ is the length of the _FOpts_ field in bytes. _FOpts_ is used to piggyback MAC commands on a data message. _CID_ is the MAC command identifier, and _Args_ are the optional arguments of the command. _FRMPayload_ is the payload, which is encrypted using AES with a key length of 128 bits. The minimal size of the MAC header is 13 bytes; its maximal size is 28 bytes. Knowing this, it is possible to compute the maximum channel capacity available for application data payloads with given modulation parameters thanks to Equations (1) and (3). As packets are sent from a device to the network server and vice versa, there is no destination address on uplink packets, and there is no source address on downlink packets.
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 5 Design and Implementation of the Codec  
### Performance Analysis of Codec Algorithm  
In the subsequent trials, we combined several coding algorithms with the MSA algorithm to design the (1536, 1024) LDPC codec uniformly quantized on (6, 2), involving eight iterations and a 2/3 code rate in the IEEE802.16e standard. In any case, we considered BPSK modulation and AWGN channel transmission. The corresponding codec performance simulation results are illustrated in Figure 4.  
At a bit error rate (BER) of \(10^{-5}\), the codec's BER obtained by the high-efficiency and low-complexity coding algorithm is about 0.25 dB lower than the recursive-iterative coding algorithm and about 1.25 dB lower than the standard coding algorithm. The simulation results presented in Figure 3 conclude that the LDPC codec implemented by the high-efficiency and low-complexity coding algorithm is char  
Figure 4: Performance simulation diagram of different coding algorithms.  
Figure 3: Implementation flow of MSA algorithm.  
performance and low complexity. As a reminder, this paper designs and implements the high-efficiency and low-complexity coding algorithm used by the LDPC coder, whereas the decoder employs the MSA algorithm.
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 4 Improved Ant Colony Algorithm
### Pheromone Processing
#### 4.2.2 Pheromone Update Method  
After each iteration of the algorithm, only the pheromone on the best path in the current iteration is updated. The global best path is not selected here, because if the global best path is selected, the algorithm may be unable to find a better solution than the global best solution in several rounds of iterations, resulting in excessive pheromone on the global best path. This makes it difficult to discover new solutions, reducing the global search ability of the algorithm. The iterative best path is often different in each round of iteration, so the pheromone will increase on each local best path, avoiding the relatively high pheromone on a certain path.  
The pheromone update rule is shown in Equation (15):  
\[\tau_{ij}(t+1)=(1-\rho)\tau_{ij}(t)+\Delta\tau_{ij} \tag{15}\]  
where \(\rho\) is the pheromone volatilization factor.  
The calculation of the pheromone increment refers to the most commonly used ant cycle model, as shown in Equation (16):  
\[\Delta\tau_{ij}=\begin{cases}Z/C^{iter},&(i,j)\in\text{iterative optimal path}\\ 0,&\text{otherwise}\end{cases} \tag{16}\]  
where \(Z\) denotes the total amount of pheromone carried by the ants and \(C^{iter}\) denotes the path length of the best solution in this iteration.  
Because the ant colony algorithm easily enters a stagnant state in the middle and late stages and falls into a local optimum, to enhance the ability of the algorithm to jump out of the local optimum, this paper proposes a pheromone backtracking mechanism and an adaptive pheromone volatile factor.  
The basic idea of the pheromone backtracking mechanism is to change the pheromone value on the path to the value before the algorithm falls into a local optimum, thereby enhancing the algorithm's ability to jump out of the local optimum. In the iterative process, if the best solution obtained by consecutive \(A\)-th iterations does not change, it is considered that the algorithm has fallen into a local optimum, and the pheromone on the path is set to the value before the \(A\) iteration. To prevent ants from falling into the same local optimum again after pheromone backtracking, an adaptive pheromone volatile factor is proposed, as shown in Equation (17):  
\[\rho=\begin{cases}\rho_{s}+c\Delta\rho,&\rho\leq\rho_{\max}\\ \rho_{\max},&\rho>\rho_{\max}\end{cases} \tag{17}\]where \(\rho_{s}\) is the initial pheromone volatilization factor, \(\Delta\rho\) is the increase in the volatilization factor, and \(c\) is the number of pheromone backtracking events. By increasing the value of \(\rho\), more pheromone on the suboptimal path can be volatilized, thus increasing the possibility of ants choosing other paths, thereby improving the algorithm's ability to jump out of the local optimum.
###### Abstract  
A suitable range of paste and mortar margins (\(\alpha\) and \(\beta\)) to enhance compressive strength in Rich-Mix cemented sand gravel and rock (CSGR) material for application in CSGRD construction is critical. SL 678-2014 recommends margins > 1, which are specifically designed to fill the voids within the fine and coarse aggregates with paste and mortar, respectively, while allowing some excess for workability. However, the optimum ranges of values after 1 are inadequately determined, often leading to high efforts and time-consuming trial mixes that are not economical. This study evaluates two datasets to identify the optimal ranges of \(\alpha\) and \(\beta\) margins for compressive strength development in Rich-Mix CSGR, aiming to achieve the compressive strength class C\({}_{180}\)20, intended for use as cushion, protective, and seepage control layers in CSGRD. Using Pearson correlations, t-statistics, and \(p\)-values, the first dataset (7, 28, 90, and 180 days) showed weak correlations between paste margins and compressive strengths (coefficients 0.172 to 0.418, \(p\)-values > 0.05) and negligible relationships for mortar margins (coefficients \(-\)0.269 to 0.204, \(p\)-values > 0.05), affirming the contribution of other factors in the compressive strength development in CSGR. The second dataset (14, 28, 90, and 180 days) revealed significant positive correlations between paste margins and strengths at 14, 90, and 180 days (coefficients up to 0.850, \(p\)-values < 0.05). Mortar margins, however, negatively impacted strength (coefficients \(-\)0.544 to \(-\)0.628, \(p\)-values < 0.05), revealing the need to control the sand ratio. The optimal range of values was \(1.05\leq\alpha\leq 1.09\) and \(1.15\leq\beta\leq 1.25\), with a water-binder ratio of 0.7\(-\)1.3, vibrating-compacted value (VC) of 2-8 s, and sand ratio of 18-35%. These findings highlight the significance of precise paste and mortar margin ranges in the compressive strength development of Rich-Mix CSGR.  
ccepted sand; gravel and rock dam; Rich-Mix CSGR; paste margin (\(\alpha\)); mortar margin (\(\beta\)); compressive strength development; C\({}_{180}\)20; Pearson correlation coefficient
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 2 Materials and Methods
### Numerical Simulation Methods
#### 2.1.2 Solver Settings  
Fluent was used to construct the numerical model of aerosol particle deposition and to define the boundary conditions for both the fluid and discrete phases. Adapted numerical algorithms were employed to ensure the accuracy and stability of the calculations. The specific boundary conditions and solution algorithms used in this study are as follows:  
1. Viscous model: To compute non-uniform, high-curvature rotating flow fields, the realizable k-\(\varepsilon\) model is employed as the turbulence model. For near-wall treatment, the enhanced wall model is chosen to ensure compatibility with both boundary layer conditions, balancing computational efficiency and accuracy. Additionally, the curvature correction option is enabled to achieve a more accurate simulation of velocity and shear stress distribution.
2. Fluid boundary conditions: The inlet is defined as a velocity inlet with a flow velocity of 0.4 m/s, a turbulence intensity of 4%, and a hydrodynamic diameter of 4 mm. The outlet is set as a pressure outlet with a gauge pressure of 100 Pa. All wall surfaces are specified as static, no-slip boundaries with a roughness height of 0 m and a roughness constant of 0.5.
3. DPM boundary conditions: The particles are monodisperse, spherical, and inert, with a density of 1050 kg/m\({}^{3}\), and their initial velocity at the inlet is synchronized with the airflow (0.4 m/s). The interaction rules between particles and walls are defined as follows: the wafer surface adopts the Trap condition, the inlet and outlet are set as Escape, and all other walls are set as Reflect. The forces acting on the particles include gravity, Saffman force, trailing force, and rotational shear. The discrete random walk (DRW) model is employed to simulate diffusion and vortex effects.
4. Numerical solution strategy: The transient pressure-based solver is employed, utilizing the \"SIMPLE\" algorithm for pressure-velocity coupling with a convergence residual of \(10^{-4}\). The \"PRESTOI\" (Pressure Staggering Option) and \"Standard\" schemes are both interpolation options in ANSYS 2022 Fluent used to calculate pressure values on control volume faces. For pressure term discretization, the \"PRESTOI\" scheme is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Aerosol Flow Rate = 0.3 L/min**} \\ \cline{2-6}  & \(D_{\text{p}}\)/**nm** & \(D_{\text{noozle}}\)/**mm** & \(H\)/**mm** & \(V\)/**cm\({}^{3}\)** & \(\text{N/rad}\cdot\text{s}^{-1}\)** \\ \hline \(D_{\text{noozle}}\) & 300 & 1, 2, 4, 6, 8 & 15 & 3650 & 0 \\ \(H\) & 300 & 4 & 5, 15, 50 & 3650 & 0 \\ \  
applied in rotational cases, while the \"Standard\" scheme is used for non-rotational cases. The momentum, turbulence, and convection terms are discretized using the \"Second-Order Upwind scheme\".