esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Feature Selection
#### 3.3.5 Random Forest Importance (RFI)  
Random Forest (RF) is a robust ensemble learning approach that enhances both the accuracy and reliability of a model by integrating the predictions from multiple decision trees, each trained on different data subsets, to generate more robust predictions [46; 47]. It uses the bagging (bootstrap aggregation) technique, where multiple samples of the dataset are created with replacement, and each sample is used to train an individual tree. By averaging the predictions of these noisy but unbiased models, RF reduces variance and increases robustness. During the tree-growing process, RF uses the bootstrap technique on the dataset, creating random subsets of input variables to ensure that each tree is de-correlated from the others [9]. This randomness, injected through the selection of random feature subsets for each tree, helps prevent overfitting while maintaining prediction accuracy. RFI ranks features based on their contribution to reducing prediction error by measuring the contribution of each feature to the model's predictive accuracy. This method reduces overfitting compared to traditional decision trees and provides more accurate predictions by leveraging multiple diverse trees. RF chooses \(m\) variables at random from \(p\) variables. This procedure is driven by the regression predictor, which is mathematically denoted as follows:  
\[\hat{f}_{rf}^{B}(x)=\frac{1}{B}\sum_{b=1}^{B}T_{b}(x) \tag{8}\]  
In Equation (8), \(B\) represents the total number of trees, \(T_{b}(x)\) is the prediction made by the \(b\)-th tree for the input \(x\), and \(\hat{f}_{rf}^{B}(x)\), the final prediction made using the Random Forest algorithm, is calculated as the average of the predictions from all \(B\) trees. Feature importance is a measure that determines the growing tree split point using RF techniques. As a result, features with a high correlation show little or no measure of importance. RFI employs Out-of-Bag (OOB) samples generated at the base of each tree to assess and record prediction performance [47].
## 2 Materials and Methods
### Support Vector Machines (SVMs)  
A support vector machine (SVM) [32] is a supervised learning method that creates a hyperplane or set of hyperplanes in a high-dimensional space. The algorithm itself has a wide variety of applications, including classification. In this case, good separation (called a functional margin) is achieved by finding a hyperplane with the highest distance to the nearest data points of any given class. A larger margin in this case indicates that the algorithm can generalize better, improving the classification quality.  
SVMs are effective tools for solving problems with high-dimensional feature spaces and non-linear data structures. They are particularly useful when the number of dimensions exceeds the number of samples. SVMs are well-suited for complex machine learning problems, such as the ones presented in this paper, where traditional algorithms may struggle to find an accurate solution.  
The parameters used for training the SVM are as follows:  
* method: onevsone;
* kernel: radial basis function (RBF);
* C (regularization parameter): 100;
* gamma (kernel coefficient): 0.001.  
Feature Selection for SVM Using Sequential Feature Selection  
Sequential feature selection (SFS) [33] is a method used in machine learning to choose a subset of important features for building a model. SFS aims to reduce the number of input features to enhance the model's performance. This method is especially helpful when dealing with datasets that have a large number of parameters, some of which may be unnecessary or duplicative.  
Sequential feature selection for SVM operates as follows:  
1. Define a random, non-stratified partition for 10-fold cross-validation on \(n\) observations, where \(n\) is the number of observations in the dataset.
2. Initialize the selected feature set \(S\) as an empty set.
3. For each feature \(x_{i}\) not in \(S\), compute the cross-validated criterion value using the SVM function. This involves training the SVM model on the dataset including \(x_{i}\) and evaluating its performance.
4. Add the feature \(x_{i}\) with the smallest criterion value (or the largest if the criterion is accuracy or another measure where higher values are better) to \(S\).
5. For each feature \(x_{i}\) not in \(S\), define a candidate feature set \(C_{i}\) as \(S\cup\{x_{i}\}\). Compute the cross-validated criterion value using the SVM function for \(C_{i}\).
6. Among the candidate sets \(C_{i}\), select the set that reduces (or increases) the criterion value the most when compared to the criterion value for \(S\). Add the feature corresponding to the selected candidate set to \(S\).
7. Repeat steps 5 and 6 until adding a feature does not decrease (or increase) the criterion value by more than the termination tolerance value.  
This process results in a subset of features that are deemed most relevant for the SVM model, thereby potentially improving the model's performance by reducing the overfitting and improving generalization.
## 2 Field Inventories and Remote Sensing for Biomass Estimation
In a broader perspective, both active and passive remote sensing sensors are valuable for carbon stock mapping, but active sensors, such as Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR), have enormous potential for more accurate and useful proxies for forest biomass inventories than the passive sensors [23]. The rise of advanced remote sensing instruments with high spatial and temporal resolution had advanced the monitoring of biomass and carbon flux inventories at global, regional and local scales [47]. The ability of airborne LiDAR observation to penetrate the upper canopy layer produces highly accurate AGB estimates in multi-layered tropical forests. The mapping accuracy increases further when the LiDAR data are combined with high-spatial-resolution multispectral or hyperspectral images. The prime focus of remote-sensing-based carbon stock assessment studies is largely related to the AGB which accounts for 450-650 PgC globally [48]. Likewise, carbon sequestration studies emphasized on the balance between net primary productivity (NPP), ~60 PgC per year globally [48], and losses, ~3.5 PgC per year [49], due to anthropogenic (e.g., deforestation, degradation, land cover land use (LCLU) change) and natural influences (e.g., fire) on vegetation and vegetation processes [50]. Remote sensing facilitates to quantify and understand the role of Earth's biosphere in governing the flux rates of atmospheric CO2 concentration by monitoring NPP and Net Ecosystem Production (NEP) [47]. Remote sensing provides promising products (such as NPP, Gross Primary Production (GPP), Sun-Induced Chlorophyll Fluorescence (SIF)) in process-based ecosystem models for spatio-temporal assimilation and quantification of carbon fluxes on regional and global scales [47; 51].
## 3 Results and Discussion  
### Effects on the Pyrolysis Behavior of OS  
#### 3.1.1 XRD Analysis  
The mineral compositions of different samples before and after mixing and pyrolysis were studied through X-ray diffraction (XRD) analysis. Figure 2 shows the XRD analysis results of various samples, including OS, different mixed samples, and residues at different temperatures. In general, quartz and calcite are the main minerals of OS, with 26.8\({}^{\circ}\) for quartz and 29.7\({}^{\circ}\) for calcite in the X-ray diffraction spectra [7]. According to Figure 2a, calcite decreased significantly when the OS samples were wet mixed with acidic chloride salts, while calcite did not change much when dry mixed. These suggest that the acidic chloride solution removes most of the calcite from the OS matrix. In addition, during the wet mixing process, a large amount of carbon dioxide clearly bubbled out of the solution, confirming the large-scale decomposition of calcite described above. However, some calcite remains in the wet-mixed sample according to Figure 2a. This may be due to the fact that calcite is uniformly dispersed in the matrix of OS and intermixed with kerogen and other minerals; hence, it is not easily and completely consumed by the acidic chloride salt solution.  
The various residues of different samples after pyrolysis at different temperatures were also analyzed by XRD analysis, as shown in Figure 2b. The results indicated that the calcite in OS remained almost unchanged before and after pyrolysis. However, after pyrolysis at 400 \({}^{\circ}\)C, the calcite content in the mixed samples decreased significantly, especially in DMOS + AlCl\({}_{3}\cdot\)6H\({}_{2}\)O. Moreover, the calcite in DMOS + AlCl\({}_{3}\cdot\)6H\({}_{2}\)O and WMOS + AlCl\({}_{3}\cdot\)6H\({}_{2}\)O completely disappeared after pyrolysis at 520 \({}^{\circ}\)C. These results indicate that AlCl\({}_{3}\cdot\)6H\({}_{2}\)O can effectively react with calcite in the OS matrix during pyrolysis,  
Figure 2: X-ray diffraction spectra of (**a**) OS, various mixed samples, and (**b**) residues.  
and the degree of reaction increases with increasing temperature. This may be due to the continuous decomposition of kerogen and release of hydrocarbon products during pyrolysis, where calcite embedded in the kerogen and matrix is continuously exposed and can be more completely consumed.
## 4 Framework Implementation and Validation Strategy
### Communication Structure Implementation  
The three-layer communication structure described in the methodology manifests in the visualization strategy through:  
1. Determination layer implementation * Rule-based responses are implemented through consistent visualization formats across different metrics. * Contextual adaptation is achieved through interactive filtering and drill-down capabilities. * Mode selection is demonstrated through multiple visualization types for different aspects of the data.
2. Efficiency layer implementation * Information density is optimized through the use of multi-dimensional visualizations. * Quality preservation is maintained through careful selection of visualization techniques. * Token optimization is achieved through intuitive visual hierarchies.
3. Interaction layer implementation * User feedback loops are enabled through interactive features. * Adaptive responses are implemented through dynamic filtering options. * Mode transparency is maintained through clear visual hierarchies and consistent design patterns.
## 2 Background and Related Work  
The FoF includes highly connected systems in an environment characterized by data processing in real time across them and the associated supply chains. There is a high degree of networking of the manufacturing landscape and the integration of existing technologies and tools such as embedded systems, sensors and other industrial hardware within an ecosystem and with connection to outside systems. The generic term Industry 4.0, which was coined in Germany at the beginning of the 2010s [10], summarizes all these developments as a concept for the networking of the industrial landscape. Technologies, digital methods and processes in the context of a networked FoF, such as additive manufacturing, autonomous machines, collaborative robots, machine learning, augmented reality and big data analytics, offer a wealth of new possibilities for development, cost savings and monitoring as well as new business areas. However, these key elements of flexible automation, connectivity and intelligence may come with serious vulnerabilities if cybersecurity is not adequately addressed. Therefore, it is necessary to include cybersecurity aspects from the beginning of innovation processes and developments in the industry environment. With regard to the FoF, while conventional anomalies will persist (such as technical failures), new ones will come in addition, i.e., cyber-attacks on operational technology (OT) connected to networks or Automated Guided Vehicles (AGVs), as in the use-case described below. This means additional attack vectors for threat actors (TA) as well as new incentives for TAs. While financial gains are one of the key motivations for cyber-attacks, networked FoF environments offer also novel opportunities for industry espionage.
## 4 Results
In this section, we show how the different performance metrics, based on the grouping (M0-M6) are distributed on the applied areas reported in Figure 13. Regarding the data presented in Figure 14 shows that requirement-based studies are the lowest in numbers in M0 group (studies reported zero metric), only one study contributes to this list, while design-based publications are the highest with a total of 17 studies in the same group, yet, both requirement and design studies maintained consistency with the rest of the groups (M1 to M6), except design-based studies which had not appeared in M5 group. On the other hand, studies under management areas are the second-lowest according to M0, M2, and M3 sets, but also appeared in M4 and M5. Although the testing areas are the highest in numbers and verification studies are the lowest, yet, testing areas become the second highest in M0, and ranked in second-lowest in M2 and M3, while there are studies that employed four metrics and six metrics in M4 and M5, respectively, but had not appeared in M6 group. Generally, the graph shows a decline in the studies that employed more than two metrics. \begin{table}
\begin{tabular}{l l} \hline \hline
**Metrics** & **References** \\ \hline Management & [7,49,56,71,78,89,102,122,128,129,135,141] \\ Requirements & [6,69,88,90,96,101,104–107,116,121,126,130,131,133,134,137,143,146,147] \\ Design & [3,47,48,51–55,58,59,61–63,65,67,68,77,79,87,95,98,99,103,109–115,123,132,136,145] \\ Testing & [46,50,57,60,64,66,70,72–76,80–86,91–94,97,100,108,117,118,120,124,125,127,138–140,142,144] \\ Verification & [119] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: List of references of the applied areas. Figure 13: Studies by applied areas in SE.
## 6 Conclusions  
This study proposed implementing a secured FSO link for non-terrestrial communications against security attacks from flying objects. The system was tested in simulation and experimentally using the GNU Radio platform and software-defined radio hardware. The results proved that implementing cryptographic encryption techniques using the algorithm (Xsalsa20) within NaCl library into FSO systems is effective at stopping eavesdropping attacks and preserving data security. The results also showed that at a distance of 250 mm, the secure system achieved a packet delivery rate of 92% and a transmission rate of 10 Mbit/s. This distance achieves a minimum received electrical amplitude of 27.5 mV required at the receiver SDR to process the data. Combining encryption with FSO helps the adoption of secure non-terrestrial optical communication systems. Xsalsa20 provides sufficient data security against flying eavesdropper that has limited computing power to break the key. More work needs to be done to implement advanced encryption techniques that will increase the versatility of this communication system.  
Conceptualization, D.H. and F.M.A.; methodology, D.H. and F.M.A.; software, D.H.; formal analysis, D.H.; investigation, D.H.; resources, D.H. and F.M.A.; data curation, D.H.; writing original draft preparation, D.H. and F.M.A.; writing review and editing, F.B., Z.A., T.S., O.S., O.K.; visualization, D.H.; supervision, F.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was approved by the Ethics Review and Approval Procedure of Coventry University (Project ID P144609, approved on 30 November 2022).  
The data presented in this study are available on request from the corresponding author ([EMAIL_ADDRESS]). The data are not publicly available due to intellectual property rights.
# A Review of Optical Neural Networks
## 3 Development Processes and Categories
### Fiber-Based Optical Neural Networks
#### 3.3.3 Time Stretch
In addition to being popular candidates for neuromorphic systems, optical neural networks exhibit several advantages. Neuromorphic processors are massively distributed hardware. Therefore, they heavily rely on parallel interconnections such as those which exist between components. Optical computing for neural network computation has the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Categories** & **Advantages** & **Disadvantages** & \
\begin{tabular}{c} **Chip** \\ **Capabilities** \\ \end{tabular} \\ \hline \multirow{4}{*}{**Silicon-based optical** **neural networks**} &
\begin{tabular}{c} Mach–Zehnder modulator \\ scheme \\ \end{tabular} & Matrix multiplication has high & & \\  & & speed and low power & \\  & Micro-loop modulator scheme & consumption & O/E conversion & Inference \\ \cline{2-5}  & \multicolumn{2}{c}{3D integration scheme} & & \\ \hline
**D\({}^{2}\)NN** & Optical diffraction & Handles large amounts of data & Not conducive to reuse & Inference \\ \hline \multirow{4}{*}{**Fiber-based optical** neural networks**} & \begin{tabular}{c} Microfiber \\ \end{tabular} & \
\begin{tabular}{c} Wavelength division \\ multiplexing, biological-like \\ neurons \\ \end{tabular} & Large size & \\  & & & \\ \cline{1-1}  & \multirow{2}{*}{MMF} &
\begin{tabular}{c} It has both linear and \\ nonlinear functions \\ \end{tabular} & Not easy to control & No chip \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the different approaches to optical neural networks. advantages of low latency, low power consumption, and high bandwidth, while the use of optical interconnections can offset the disadvantages of requiring broadband in electronic devices in exchange for interconnectivity. In addition, photonic synaptic devices modulated via photonic signals can simulate retinal neurons in the real eye, and physical device computing is conceptually close to the visual system and brain computing to simulate complex neural functions through optical modulation. Furthermore, the use of optical fiber can solve the problem of on-chip optical delays. In the future, co-packaging with electronic memory can be used to solve the existing challenges.
## 2 Numerical Setup
[43] for comprehensive analyses of the effectiveness of RANS and IDDES approaches in elucidating turbulence flow structures and assessing the veracity of flow predictions for high-Reynolds-number ground vehicle flows under various operating conditions. The IDDES methodology evolved from the detached eddy simulation (DES) approach proposed by [PERSON] and his coworkers [44]. One known limitation of the detached eddy simulation (DES) methodology involves predicting artificial flow separation, termed grid induced separation (GIS). This occurs when the cell size within the boundary layer falls below a critical threshold. To mitigate GIS, the RANS model can be shielded from the DES formulation near the wall using a parameter that depends on eddy viscosity and wall distance [45]. This approach, known as delayed detached eddy simulation (DDES), introduces a delay in the transition from RANS to LES. The improved delayed detached eddy simulation (IDDES) model, employed in this study, represents an advancement over DDES. It incorporates various enhancements to improve its versatility for high-Reynolds-number flows. These include mechanisms to prevent excessive dissipation due to premature switching from RANS to LES and the capability to operate in wall-modeled LES (WMLES) mode for more efficient resolution of wall-bounded flows (see [PERSON] et al. [46], [PERSON] et al. [47]). For a comprehensive understanding of IDDES, interested readers are encouraged to refer to the original publications cited above. Simulations were performed using an implicit, unsteady, and segregated incompressible solver on an unstructured grid with a time step of \(10^{-4}\) s using second-order temporal discretization. A second-order upwind scheme was applied to discretize the momentum and turbulence equations, while an algebraic multigrid (AMG) method with a V-cycle was employed for a faster iterative solution of the momentum equations. A two-layer, all-\(y^{+}\) wall treatment was applied to the simulation to ensure the boundary layer improved the predictive accuracy even in cases where \(y^{+}\) was not sufficiently small due to the complexity of geometry. The total physical simulation time was approximately 6.2 s. To ensure the washout of initial effects, the data corresponding to \(t=2.2\) s were not considered and the last 4 s of data were used for subsequent analyses. This provided sufficient time for the average flow field to become temporally converged and to capture low-frequency coherent structures. However, all averages are represented using an ensemble average with a sliding window of 2 s. This sets the lowest resolved frequency (\(f\)) at 0.5 Hz, which corresponds to a non-dimensional frequency \(St\equiv f*L/U_{\infty}\) of \(3.6\times 10^{-3}\); here, \(L\) and \(U_{\infty}\) represent the characterized length and velocity scales, respectively. Note that the time-step \(\Delta t=10^{-4}\) corresponds to a Nyquist frequency of 5 KHz.
## 2 Conceptual Framework
### Sensitivity and Exposure Indexes  
The sensitivity index describes responses to climatic stimuli or climatic extremes [29]. It also represents the impact of climate forcing on the yields of the crops concerned [29]. In other words, the sensitivity index measures the quantitative decrease in agricultural  
Figure 1: The conceptual framework of vulnerability.  
yields because of climate change, climatic variability, and severe events [31; 34; 35; 36]. Thus, the sensitivity index examines how actual yields respond to precipitation during the growing season. In theory, the sensitivity index (crop vulnerability index) is directly related to the exposure index (drought vulnerability index)--mostly via evapotranspiration (Figure 1). According to the vulnerability sourcebook [30], \"sensitivity is determined by those factors that directly affect the consequences of a hazard. Sensitivity may include physical attributes of a system (e.g., building material of houses, type of soil on agriculture fields), social, economic, and cultural attributes (e.g., age structure, income structure). Thus, the understanding of sensitivity largely remains unchanged from the AR4 concept.\"  
The exposure index depicts the presence of people, livelihoods, species or ecosystem functions, services, resources, infrastructure, economic, social, and/or cultural assets in places and settings that could be adversely impacted by climate change [29]. The exposure index is therefore related to the climate stress level of a given analysis unit or system [29]. Furthermore, the vulnerability sourcebook [30] affirms the definition of exposure as, \"the presence of people, livelihoods, species or ecosystems, environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings that could be adversely affected\". The term \"exposure\" in AR5 differs from the way it is used in the AR4. Exposure is related to specific exposed elements (or elements at risk), e.g., people, infrastructure, ecosystems. Exposure can be expressed by absolute numbers, densities, or proportions, etc., of the elements at risk (e.g., population density in an area affected by drought). A change in exposure over time (e.g., change in the number of people living in drought-prone areas) can increase or decrease risk [30]. In this study, the exposure index describes the level and character of the stimulus as represented in the quantity, severity, and length of droughs, or quantitative changes in precipitation or temperature caused by climate change [31; 34; 35; 36; 20; 37]. The exposure index concentrates on how precipitation reacts to changes in climate. As with sensitivity, vulnerability has a positive correlation with exposure [38] (Figure 1).