esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### The Results of Feature Selection and Bayesian Optimization  
Figure 2 presents an in-depth analysis of the feature selection strategy and its influence on intrusion detection performance across three widely used benchmark datasets, CIC-IDS2017, CSE-CIC-IDS2018, and CIC-DDoS2019, by using the XGBoost-assisted RandomForest, which enables a synergistic approach to both feature selection and detection. Random Forest ranks the importance of features, and XGBoost performs intrusion detection on the features with the highest importance rankings.  
The comparative evaluation across datasets demonstrates that the method effectively reduces feature dimensionality while preserving--and, in some cases, enhancing--detection accuracy. Specifically, the relationship between the number of selected features and XGBoost's classification accuracy reveals that, for CIC-IDS2017, the accuracy peaks at 99.982% when 65 top-ranked features are utilized. In the case of CSE-CIC-IDS2018, the model achieves 100% accuracy with only 26 features, underscoring its ability to isolate a compact and highly discriminative subset of features. Similarly, on CIC-DDoS2019, selecting the 56 most informative features results in the highest accuracy of 95.042%. These findings illustrate the model's robustness in identifying critical attributes while eliminating redundant or irrelevant information.  
The visualization of feature ranking for datasets CIC-IDS2017, CSE-CIC-IDS2018, and CIC-DDoS2019 are shown in Figures 3-5. The results emphasize the model's focus on a concise set of high-impact features, which improves computational efficiency and further enhances intrusion detection performance. This is particularly advantageous in resource-constrained IIoT scenarios, where real-time processing and low-latency response are essential.  
The integration of Random Forest for feature ranking and XGBoost for multi-class intrusion detection enables the model to accurately prioritize the most influential features, thereby reducing noise and improving both computational efficiency and detection effectiveness. The application of EarlyStopping further ensures that the model avoids overfitting by retaining only the most relevant features during training. In addition, the incorporation of SMOTE for oversampling addresses the prevalent issue of class imbalance in cybersecurity datasets, where malicious instances are typically under-represented. This mechanism enhances the model's sensitivity to anomalies in the minority class, thereby reducing the risk of misclassifying rare yet critical attack patterns.  
After using XGBoost-assisted Random Forest for feature selection, we performed Bayesian optimization on XGBoost. Since the detection rate of CSE-CIC-IDS2018 using XGBoost reached 100%, it does not require subsequent Bayesian optimization. After five-fold cross-validation and 20 iterations of Bayesian optimization, the optimal parameter combinations for detecting CIC-IDS2017 and CIC-DDoS2019 using BO-XGBoost were obtained and are shown in Table 3.  
Figure 4: Feature importance of CSE-CIC-IDS2018.  
Figure 5: Feature importance of CIC-DDoS2019.
## 3 Methodology  
This section will approach the methodology adopted in the study to define the best locations for gateway installations to obtain maximum coverage with the least number of gateways. To monitor the electric bus and its parameters (i.e., location, level, and temperature of the vehicle's batteries), a survey of the geographic data of latitude and longitude of the route, 2.5 km along the campus, was conducted, as illustrated in Figure 5.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Operating frequency: & 915 MHz \\ Effective radiated power: & 20 dBm \\ Spreading factor: & 7 \\ Bandwidth: & 125 KHz \\ CodingRate: & 5(4/5) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device configuration parameters used.  
Figure 4: Measurement scheme.  
For this purpose, measurements were carried out with GPS in which 957 geolocations were verified, which stipulate the route to be served by the LoRa network that will be implemented.  
Therefore, a technical feasibility analysis was carried out to install gateways at strategic points on the campus, considering the infrastructure to meet the needs of electrical installation and connection to the database server via the Internet. Thus, the positions of the 22 candidate points were defined for the possible installation of the gateways, as shown in Figure 6.  
After defining the candidate points, the following data pre-processing steps were applied. The propagation losses and distance between the transmitter and receiver were calculated. Next, each step of the methodology was described, along with the adjustment and application of the UFPA propagation model to approximate the measured and predicted data. Then, a statistical analysis of the coverage threshold was conducted, and the EPSO technique was used to define the best locations for the gateways. Finally, Monte Carlo simulations were performed.  
Figure 5: The route to be covered by the LoRaWAN network is represented in blue.  
Figure 6: Candidate points for installing the gateways are represented in red.
## 2 Problem Formulation and Preliminaries
 \tag{24}\]  
_a constant \(U\geq 0\) and a \(p.\)\(d.\) symmetric matrix \(P\in R^{n\times n}.\) A positive first-order system:_  
\[\dot{\rho}=\varphi(\rho,\ U),\ \rho_{0}=\|x_{0}\|_{P},\ Y=cp \tag{25}\]  
_where \(\rho(t)=\|x(t)\|_{P},\) such that \(\|y(t)\|\leq Y(t),\)\(\forall t\in T,\)\(\forall u:\|u\|\leq U\) and \(\forall p\in\wp\) is said to be a majorant system of the system (24)._  
**Remark 2**. : _It is worth noting that a majorant system of a system belonging to the class of the uncertain time-variant nonlinear systems (24) (which of course includes also the class of linear uncertain systems) is a time-invariant first-order system. So it can be used to easily solve numerous analysis and synthesis problems, such as the analysis of practical stability, the robust stabilization, the robust tracking (see also [21; 34; 35; 38])._  
**Lemma 1**. : _Let \(P\in R^{n\times n}\) be a symmetric p.d. matrix, \(Q(t,x,p)\in R^{n\times n}\) be a symmetric matrix, \(t\in[0,\ t_{f}]=T\subseteq R,\)\(x\in R^{n},\)\(p\in\wp,\)\(\wp\) be a compact subset of \(R^{n},\)\(w(t,x,p)\in R^{m}\) be a vector continuous with respect to \(t\in\mathcal{T},\)\(x\in R^{n}\) and \(p\in\wp,\)\(B\in R^{n\times r}\) be a matrix of rank \(r\) and \(C\in R^{m\times n}\) be a matrix of rank \(m\). Then, \(\forall\rho\geq 0\) and \(\forall x\in C_{P,\rho}\), the following inequalities hold:_  
\[\max_{t\in T,\ x\in C_{P,\ \rho}\ \rho\in\wp}x^{T}Q(t,x,p)x\leq\max_{t\in T,\ x\in C _{P,\rho},\ \rho\in\wp}\lambda_{\max}(Q(t,x,p)P^{-1})\rho^{2}\leq\max_{t\in T,\ x\in C_{P, \rho},\ \rho\in\wp}\lambda_{\max}(Q(t,x,p)P^{-1})\rho^{2} \tag{26}\]  
**Lemma 5**. : _Let \(P\in R^{2m\times 2m}\) be a symmetric p.d. matrix, \(r\in R^{m}\) a reference signal with bounded second derivative and \(u_{c}\) a compensation signal such that \(\|\tilde{r}-f-Fu_{c}\|\leq\|\tilde{r}-f\|\). Then if \(\|f(t,\ \xi,d,p)\|\leq\varphi_{\gamma_{0}}(\|\xi\|)\) where \(\xi=\left[y^{T}\ \dot{y}^{T}\right]^{T}\) and \(\varphi_{\gamma_{0}}\) is a function of class \(K_{\gamma_{0}}\) then:_  
\[\|w\|=\|\tilde{r}-f-Fu_{c}\|\leq\psi_{\gamma_{c}}(\|x\|_{P}) \tag{32}\]  
_where \(x=\left[e^{T}\ \dot{e}^{T}\right]^{T}=\left[\left(r-y\right)^{T}\ (\dot{r}-\dot{y})^{T}\right]^{T}\) and \(\psi_{\gamma_{c}}\) is a function of class \(K_{\gamma_{c}}\), with \(\gamma_{c}=\max\|\tilde{r}\|+\varphi_{\gamma_{0}}\bigg{(}\max\|\left[r^{T}\ \dot{r}^{T}\right]^{T}\|_{P}/\lambda_{\min}(P)\bigg{)}\)._  
Proof. : Note that \(\|\xi\|\leq\|\xi\|_{P}/\lambda_{\min}(P)=\|\left[r^{T}\ \dot{r}^{T}\right]^{T}-x\|_{P}/\lambda_{\min}(P)\leq \bigg{(}\|x\|_{P}+\max\|\left[r^{T}\ \dot{r}^{T}\right]^{T}\|_{P}\bigg{)}/\lambda_{\min}(P)\) Hence:  
\[\|\tilde{r}-f-Fu_{c}\|\leq\|\tilde{r}-f\|\leq\|\tilde{r}\|+\|f\|\leq\|\tilde{r }\|+\varphi_{\gamma_{0}}(\|\xi\|)\leq\|\tilde{r}\|+\varphi_{\gamma_{0}}(\Big{(} \|x\|_{P}+\max\|\left[r^{T}\ \dot{r}^{T}\right]^{T}\|_{P}\Big{)}/\lambda_{\min}(P)\Big{)}\leq\psi_{ \gamma_{c}}(\|x\|_{P}). \tag{33}\]
# Optical Boundaries for LED-Based Indoor Positioning System
## 5 Results and Discussions
### PDR vs. BER Relationship  
Here we present a validation of the PDR and BER relationship proposed in (11). This is done by comparing the theoretical performance of the system with the performance using the simulation. By varying BER between 0.0001 and 0.1 with steps of 0.0001, and substituting the values in (11), the theoretical curve shown in Figure 9 is plotted. The simulation values are derived using the values of the BER with increments of 0.05 as the probability of bits in error in an optical channel using MATLAB(r) software. Five hundred thousand packets are sent and the number of uncorrupted packets received is counted, the PDR is calculated as the ratio of the number of uncorrupted packets received to the total number of packets transmitted.  
This takes account of the packet-based synchronization protocol which is implemented in hardware such that any packet which is not received correctly is discarded [85]. The illustration of the comparison is presented in the semi-logarithmic plot of Figure 9.  
The simulation is done using the popular 12-bit Sony infrared packet [88] and a novel 4-bit packet designed in Reference [85]. In both cases, the curves validate the relationship between BER, PDR, and the number of bits in a packet as presented in (11). In terms of the performance of the packets, by comparing the two curves in Figure 9, the 4-bit packets provide a higher PDR for high BER values. Therefore, it has a faster rate of determining positioning. The 12-bit curve has low PDR values at high BER which implies that packets are easily discarded under conditions which result in high BER. Examples of these conditions are low SNR at optical boundaries and tilted receivers. Therefore, indoor positioning protocols are to be designed with the lowest possible number of bits to avoid unnecessary delays due to packet loss under the conditions. Another way to avoid the delay is to define minimum PDR conditions at the receiver. This results in a receiver-defined optical boundary as discussed in Section 3.2 and the effect is quantified in Section 5.5.
## 3 Improved SSA Based on Halton Sequence and LX
### Optimal Position Perturbation Based on LX  
The LX generates a pair of offspring, \(y_{1}=\left(y_{1}^{1},y_{1}^{2},\cdots,y_{1}^{q}\right)\) and \(y_{2}=\left(y_{2}^{1},y_{2}^{2},\cdots,y_{2}^{q}\right)\), from a pair of parents, namely \(x_{1}=\left(x_{1}^{1},x_{1}^{2},\cdots,x_{1}^{q}\right)\) and \(x_{2}=\left(x_{2}^{1},x_{2}^{2},\cdots,x_{2}^{q}\right)\). The descendants produced by the LX are symmetrically positioned relative to the parents. LX random numbers are determined according to the following rules [27]:  
\[l_{s}=\left\{\begin{array}{l}p-q\log_{e}(u_{s})\;v_{s}\leq 1/2\\ p+q\log_{e}(u_{s})\;v_{s}>1/2\end{array}\right. \tag{10}\]  
where \(u_{s}\) and \(v_{s}\) are two random numbers uniformly distributed within the range [0, 1]; \(p\in R\) is the location parameter, which can be used to control the distribution of the offspring positions in the search space; and \(q>0\) is the scale parameter. A smaller \(q\) value  
Figure 1: (**a**) Halton sequence initialization; (**b**) random sequence initialization.  
results in offspring positions being closer to the parents, while a larger \(q\) value leads to offspring positions being farther from the parents. In this study, the value of q was 1, and the value of p was 0. The rules for offspring generation are as follows:  
\[\begin{array}{l}y_{1}^{g}=x_{1}^{s}+l_{s}\big{|}x_{1}^{s}-x_{2}^{s}\big{|}\\ y_{2}^{g}=x_{2}^{s}+l_{s}\big{|}x_{1}^{s}-x_{2}^{s}\big{|}\end{array} \tag{11}\]  
If the range of offspring positions exceeds the search space, i.e., \(y_{j}<lb_{j}\) or \(y_{j}>ub_{j}\), then \(y_{j}\) is set to a random number from the interval \(\big{[}lb_{j},ub_{j}\big{]}\).  
The pseudocode of HLSSA is shown in Algorithm 1.  
```
1. set t = 0
2. Initialize the population using Equation (9).
3. compute the fitness value for each sparrow individual using the fitness function, and then arrange them in order, noting the best position \(X_{best}\) and best fitness value \(f_{g}\).
4. While (t < \(iter_{\text{max}}\))  determine the proportion of founders \(PD_{1}\), the ratio of scroungers \(PD_{2}\) and the proportion of sparrows with danger perception \(PD_{3}\)  For i = 1: \(PD_{1}\)  Update the founders positions based on Formula (3). End for  For i = 1: \(PD_{2}\)  Update the scroungers positions based on Formula (4). End for  For i = 1: \(PD_{3}\)  Update the sparrows with danger perception positions based on Formula (5). End for  Calculate the fitness value for each sparrow and select the best one. Disturb the optimal position according to Formula (11) and compute its fitness value. Compare the fitness values before and after the disturbance and choose the better one.  t = t + 1  End while 5. Return \(X_{best}\),\(f_{g}\)
```  
**Algorithm 1** Pseudocode of HLSSA
## 3 Results
### Mapping Key Research Streams Through the Most Cited Contributions
The line graph below (Figure 4) illustrates the emergence and evolution of five major research themes: waste forecasting, recycled materials, environmental monitoring, digitalization and circular economy, and AI and industrial systems. The data reveal three distinct phases: an initial focus on forecasting and isolated applications of AI (2016-2018), followed by an expansion into recycled materials and optimization (2019-2021), and finally, a surge in environmental and industrial integration with Digital Twins and deep learning (2021-2023). This trend indicates a growing systemic perspective, where AI and DT are not only used for prediction or optimization but are increasingly embedded within environmental governance, industrial valorization processes, and sustainability frameworks. In support of this visualization, Table 7 summarizes the temporal distribution of the most-cited articles, organized by theme. This format emphasizes the continuity and expansion of specific research areas and provides direct references to key contributions over time. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Theme** & **Timeline** & **References** \\ \hline Digitalization and Circular Economy & 2016 & [43] \\ Waste Forecasting & 2016–2019 & [45; 46; 47; 50] \\ AI and Industrial Systems & 2020–2021 & [51] \\ Recycled Materials & 2020–2023 & [39; 41; 43; 50; 55] \\ Environmental Monitoring & 2021–2022 & [48; 49; 52; 53] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Chronological distribution of top-cited articles by thematic area. Our elaboration. Figure 4: Evolution of research themes (2016–2023). Our elaboration. This integrated temporal and thematic analysis contributes to a more nuanced understanding of how research on AI and Digital Twins for sustainable waste management has matured and diversified.
# Fast 3D Content Update for Wide-Angle Holographic Near-Eye Display
## 4 Update of the Overlap View Region
The concept behind this approach is presented in Figure 3a, where minimal errors are obtained for frequencies of radius corresponding to the location of frequency shift of radius \(f_{s}\), while the same errors will be generated for the corresponding frequency circle of \(f_{p2}=f_{diff}(1/2\Delta\ 1/6\Delta)\) and the center point \(f_{p1}\). The \(f_{s}\) is calculated as a mean value of frequency shifts \(f_{diff}\) of \(f_{p2}\) and \(f_{p1}\). Thus, the optimal frequency shift is given as  
\[f_{s}=-\frac{\alpha}{2\lambda}\left(1+\sqrt{1-\frac{10\lambda^{2}}{36\Delta^{ 2}}}\right). \tag{8}\]  
The graphical interpretation for selecting \(f_{s}\) within the frequency space is illustrated as the red line in Figure 3a. For the chosen criteria, \(82\%\) of the FoV will have an approximation error that is smaller than the center point of the object. Accordingly, \(82\%\) of the image space has a smaller reconstruction error than obtained for the center point. The values of frequency shift \(f_{s}\) depend on the tilting angle \(\alpha\), which grows with the overlap parameter. Figure 3b illustrates this effect and presents the minimal and maximal errors. Notably, the largest error is obtained at the corner of the FoV. To show the error magnitude there, a red dashed line has been added to Figure 3b which corresponds to the point object for angular coordinates of \((0.95 FoV_{x},0.95 FoV_{y})\). Its frequency is illustrated in Figure 3a with a star mark. The distribution presented in Figure 3a is calculated for the overlap factor \(F=0.5\) which, as can be seen in Figure 3b, is the least optimal case. As illustrated in Figure 2, calculations of the update are carried out for the corresponding part of the hologram. For a smaller value of the \(F\) factor, the size of the considered hologram region is smaller, and thus \(\Delta f_{xu}\) increases. Accordingly, the corresponding shift error is smaller. When determining \(f_{s}\), the calculations of the overlap part can be written as  
\[O_{p_{2}}(x_{2},y_{2})=\int\widetilde{O}_{ps_{1}}\big{(}f_{x_{2}},f_{y_{2}} \big{)}\exp\big{\{}2\pi i_{p}f_{z}\big{\}}\exp\big{\{}2\pi i\big{(}xf_{x_{2}} +yf_{y_{2}}\big{)}\big{\}}df_{x_{2}}df_{y_{2}}, \tag{9}\]  
Figure 3: (**a**) Difference map of spatial frequencies; (**b**) The error of introduced frequency shift as a function of overlap parameter. where  
\[\widetilde{O}_{ps_{2}}\big{(}f_{x_{2}},f_{y_{2}}\big{)}=\int O_{p_{1}}(x_{1},y_{1 })\exp\{-2\pi ix_{1}f_{s}\}\exp\{2\pi i\big{(}xf_{x_{2}}+yf_{y_{2}}\big{)}\}df_{x _{2}}df_{y_{2}}. \tag{10}\]  
The proposed method of recalculating optical fields for different hologram positions introduces an error of frequency shift with acceptable magnitude. The proposed approximating method reduces the processing time of reused information by several times compared to the AS-based method. This significantly speeds up the recalculation of the overlap area.
# Estimation of Thermal Radiation in Bed Mattresses
## 4 Calculation of Radiation from Flames
### General  
Thermal radiation is emitted by flames, and the released heat is transferred to arbitrary object surfaces. This thermal radiation is calculated by the atmospheric absorption rate, emissive power of a flame, and configuration factor. The general expression of thermal radiation is expressed as Equation (2). However, although the atmospheric absorption of thermal radiation is assumed to be homogeneous for \(\tau\) (transmissivity), it is occasionally necessary to calculate the atmospheric absorption rate considering the absorption rate of water vapor and carbon dioxide. This study did not consider the effect of atmospheric absorption under the assumption that it was homogeneous.  
\[\dot{q}^{\prime\prime}=\tau\mathrm{E}F_{12}=\tau\varepsilon\sigma T^{4}F_{12} =\tau(1-e^{-kl})\sigma T^{4}F_{12}, \tag{2}\]  
where \(\dot{q}^{\prime\prime}\) is the radiation heat flux to a target (kW/m\({}^{2}\)), \(\tau\) is atmospheric transmissivity (\(-\)), E is the emissive power of a flame (kW/m\({}^{2}\)), \(F_{12}\) is the configuration factor of a flame to a target (\(-\)), \(\varepsilon\) is emissivity, \(\sigma\) is the Stefan-Boltzmann constant given as \(5.67\times 10^{-11}\) (kW/m\({}^{2}\cdot\)K\({}^{4}\)), \(T\) is the temperature (K), \(k\) is the radiation absorption coefficient of a flame considering the diameter as the characteristic length (m\({}^{-1}\)), and 1 is the equivalent diameter of a flame (m) [19]. For calculating thermal radiation, various models have been developed to determine the configuration factor based on the flame heat source shape. The formula for calculating this configuration factor is slightly different for each scholar. In this study, we calculated the incident thermal radiation from the flame on the measuring surface of the heat flux meter, assuming the flame shapes formed on the upper part of the mattress (Figure 7a,b) through two methods, as described in the SFPE Handbook [5]. In addition, assuming the flame shape shown in Figure 7c according to the method of ISO 24678-7, two calculation methods in which the direction of the heat flux meter was horizontal and vertical, as well as the Mudan-Croce formula and the Shokri formula, were used to compare the results with experimental values. Finally, a relevant analysis was performed.  
Figure 6: Heat flux measured by the heat flux meters installed at the same height as the top surface of mattress with the measuring surface turn-up at the position of **(a)** 0.2 m and **(b)** 1.0 m away from the side of mattress.
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Three Integration Paths
#### 5.1.2 The 5G Adapter Module  
An adapter module shall be designed to support both 5G and traditional field bus protocols. By connecting field bus devices through physical means, this module is responsible for converting the physical signals of devices into 5G signals and sending them to base stations. In addition, it can convert the 5G signals received from base stations back to physical signals for the devices.