esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Sparse SLMS Algorithms
### Fifth Proposed Algorithm: SLMS-L0  
It is well known that the \(\ell_{0}\)-norm penalty can exploit the sparse structure information. Hence, the L0-LMS algorithm is constructed as  
\[G_{L0}\left(\mathbf{w}(n)\right)=\frac{1}{2}e^{2}(n)+\lambda_{L0}\left|\left| \left.\mathbf{w}(n)\right|\right|\right|_{0} \tag{31}\]  
where \(\lambda_{L0}>0\) and \(\left|\left.\mathbf{w}(n)\right|\right|_{0}\) stands for optimal \(\ell_{0}\)-norm function. However, it is a NP-hard problem to solve the \(\ell_{0}\)-norm sparse minimization [22]. The NP-hard problem in Equation (31) can be solved by an approximate continuous function:  
\[\left|\left.\left|\left.\mathbf{w}(n)\right|\right|\right|_{0}\approx\sum\
olimits_ {i=0}^{N-1}\left(1-e^{-\theta\left|\left|\left.\mathbf{w}_{i}(n)\right|\right|}\right)\right. \tag{32}\]  
Then, the previous cost function (29) is changed to  
\[G_{L0}\left(\mathbf{w}(n)\right)=\frac{1}{2}e^{2}(n)+\lambda_{L0}\sum\
olimits_{i=0 }^{N-1}\left(1-e^{-\theta\left|\left.\mathbf{w}_{i}(n)\right|\right.}\right) \tag{33}\]The first-order Taylor series expansion of exponential function \(e^{-\Theta\left|w_{i}\left(n\right)\right|}\) can be expressed as  
\[e^{-\Theta\left|w_{i}\left(n\right)\right|}\approx\begin{cases}1-\theta\left|w_{i }\left(n\right)\right|,&\text{when }\left|w_{i}\left(n\right)\right|\leq 1/ \theta\\ 0,&\text{others}\end{cases} \tag{34}\]  
Then, the LMS-L0 based robust adaptive sparse channel estimation algorithm is given as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu e(n)\mathbf{x}(n)-\rho_{L0}\text{sgn}\left(\mathbf{w}(n) \right)e^{-\Theta\left|\mathbf{w}\left(n\right)\right|} \tag{35}\]  
where \(\rho_{L0}=\mu\lambda_{L0}\). In Equation (35), the exponential function still exhausts high computational resources. To further reduce it, a simple approximation function \(\zeta\left(\mathbf{w}(n)\right)\) is proposed in [20]. By introducing sign function into Equation (35), the SLMS-L0 based robust channel estimation algorithm is written as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu\text{sgn}\left(e(n)\right)\mathbf{x}(n)-\rho_{L0}\zeta \left(\mathbf{w}(n)\right) \tag{36}\]  
where \(\rho_{L0}=\mu\lambda_{L0}\) and \(\zeta\left(w_{i}(n)\right)\) are defined as  
\[\zeta\left(w_{i}(n)\right)=\begin{cases}2\theta^{2}w_{i}(n)-2\theta\text{sgn }\left(w_{i}(n)\right),&\text{when }\left|w_{i}\left(n\right)\right|\leq 1/ \theta\\ 0,&\text{others}\end{cases} \tag{37}\]  
for \(\zeta\left(\mathbf{w}(n)\right)=\left[\zeta\left(w_{0}(n)\right),\zeta\left(w_{1} (n)\right),\ldots,\zeta\left(w_{N-1}(n)\right)\right]^{T}\).
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.22 Carrier Aggregation
They provided a multi-objective optimization problem and represented UE throughput and power consumption using CC and RB indicators. They tackled the throughput maximization problem using the maximum-largest weighted delay first (M-LWDF) method and prioritized users that demanded reduced delay and packet loss rate objectives. Their objective was to choose CCs and assign RBs to maximize total user throughput while minimizing user power consumption and meeting QoS criteria. They offered a multi-objective optimization issue and quantified UE throughput and power consumption using CC and RB metrics. They used the maximum-largest weighted delay first (M-LWDF) algorithm to address the throughput maximization problem, giving precedence to users that demanded reduced delay and packet loss rate objectives. The NOMA approach may also be used with CA to reportedly improve customers' data rates and transmission capacity up to 100 MHz, subject to spectrum availability and user compatibility. A user pairing based on the NOMA approach and coupled with CA was investigated to increase system performance [127]. The number of aggregated CCs was determined to allow for both the anticipated high traffic loads in certain of the CCs and the inherent channel variety of the NOMA concept. A CC was created for the macro-BS to offer coverage for the macro-cell, and another CC was created for the tiny cells to provide specialized coverage. Thus, aggregated CCs shared by macro-BS and small cells were supported by the same CA structure, boosting system throughput. According to their findings, the CA-NOMA method significantly improved the total rate and EE, thereby improving the predicted peak user throughput for 5G networks. In [PERSON] et al. [128], an mmWave analog RoF link was employed to demonstrate the transmission of carrier aggregated OFDM signals in accordance with the 5G new radio numerology. The number of aggregated CCs was determined to allow for both the anticipated high traffic loads in certain of the CCs and the intrinsic channel variety of the NOMA concept. A CC was developed for the macro-BS to provide coverage for the macro-cell, and another CC was created to offer specialized coverage using the tiny cells. Thus, macro-BS and small cells shared aggregated CCs supported by the same CA structure, boosting system throughput. The CA-NOMA method showed great improvement in terms of total rate and EE, thereby enhancing the peak user throughput projected for 5G networks, according to the findings in the paper.
## 3 Security Level  
Security and the Quality of Service (QoS) are known to operate in contradiction to each other. Furthermore, because we are dealing with WSN networks with limited capabilities in terms of CPU and energy. Therefore, finding a solution that balances achieving security and raising service quality is one of the most important contributions of this paper. Reference [26] will be used for authentication and encryption (FlexCrypt) between CH nodes and WSN nodes that are not fully trusted. In addition, if the WSN nodes have sufficient trust, the security factor will be lowered to conserve energy consumption. This is carried out through the use of a unified secret key for all WSN nodes that receive the 'trust' address and also by using less complex encryption of the data sent and received. Equation (6) shows the block sizes (\(b\)) in the encryption algorithm based on the _TL_ value.  
\[b=\begin{cases}8,&TL\big{[}x^{i}\big{]}\geq 0.7\\ 32,&TL\big{[}x^{i}\big{]}<0.7\end{cases} \tag{6}\]  
The process of building trust begins gradually between WSN nodes, which means that the process of authentication and key generation is carried out using the FlexCrypt algorithm and after the WSN node reaches the full trust, it can then receive the T table, which contains only all the trusted nodes. This table also contains the master key shared between those WSN nodes. Next, the WSN node detaches from the FlexCrypt algorithm's authentication system and starts using the table (T) information to exchange data. However, if one of those WSN nodes (Trusted) turns into the CH, it uses the two systems based on the FlexCrypt and the table (T) to handle both trusted and untrusted WSN nodes.  
Furthermore, all the information about both techniques (e.g., keys, nonce) should be present in the AP. If the CH transmits data from the child's CH to the parent's CH, the incoming data is encapsulated using the trust technique. Untrusted nodes are initially encapsulated by the associated CH and then passed between the parent CHs using the trust technique. Therefore, no CH node can be fully trusted. Figure 4 shows a summary of the previous steps in the process of determining the security level and selecting the security algorithm.
# Detection of Rock Mass Defects Using Seismic Tomography
## 4 Application  
### Study Area  
This study was conducted in a tantalum-niobium mine belonging to the Hunan Fugui-hengtong Mining Company. The mine is located in Gaolong Town, 50 km northeast of Chaling County, adjacent to the borders of Hunan and Jiangxi. The geographical coordinates are as follows: east longitude, 113 deg45'29''-113 deg47''05''; north latitude, 27 deg02'30''-27 deg03'28''. Three groups dominate the north of the mining area's tectonic line (NNE, NNE, and NNE). In this area, folds and faults are developed, the latter of which predominate, and the metallogenic conditions are superior.  
According to historical data on the mine and the results of the field investigation, a large number of underground mining tunnels and gob areas were generated in the early underground mining period in what is currently the tantalum and niobium mine (as shown in Figure 8). However, due to the limitations of the previous investigation technology and management level, some hidden gob areas remain undetected and must be identified to ensure the safety of future tantalum and niobium open-pit mining operations. It was necessary to define and identify the underground goals formed due to historical mining in the mine area. In this study, wave velocity tomography technology was used to identify the boundaries of empty and non-empty anomalous areas to delineate hidden goats in the mining area.  
Figure 7: Recognition of empty areas through wave velocity imaging. Blue lines indicate boundaries of actual empty areas. The dots, cross-hatching, and vertical hatching in subplot (**d**) indicate recognition performance for normal area, abnormal area, and total, respectively.
## 7 Risk Analysis
### Camera
#### 7.7.1 Tampering  
1. **Component**  
_Security risk_: Tampering with the camera itself in an ACN system compromises the accuracy of the crash data, potentially leading to inaccurate emergency response, delayed aid, and challenges in insurance claims processing. This endangers lives, undermines accountability, and hampers the system's overall effectiveness.  
_Security measures_: Physical tamper-evident enclosures: Implementing tamper-evident enclosures for cameras can deter unauthorized access. These enclosures are designed to show clear signs of tampering if someone tries to open or manipulate the camera, alerting administrators to potential breaches.  
2. **Ports** _Security risk_: Tampering with the port connecting the ACN camera compromises the integrity of the data transmission, potentially leading to delayed or disrupted crash notifications. This can result in delayed emergency responses and hinder accurate accident reporting. _Security measures_: Intrusion detection algorithms that monitor the connection status and data flow through the ports can be implemented. Any abnormal patterns or unauthorized access attempts trigger alerts, allowing administrators to take appropriate action.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 2 Related Works and Motivation
### Motivations for a Secondary Study on PLA in Wireless Network Security  
Physical layer authentication plays a considerable role in wireless security communications. Before performing this review, we came across different papers applying PLS and PLA to wireless networks for security purposes. As shown in Table 1, we looked at several factors, including year of publication, publisher, main topics, inclusion of ML, environment, and focused issues. Obviously, no SLRs had collected only works relating to PLA. Although the studies mentioned dealt with PLS, few focused on PLA, and none focused on deep learning techniques. Nevertheless, we encountered reviews [28; 30] directly related to PLA and machine learning.  
Consequently, the main contribution of this study is filling a gap in the required detail and analysis of potential physical layer authentication systems based on ML and DL techniques for various security purposes. In addition, we aim to present brief and valuable content for newcomers to catch up in these areas. To the best of our knowledge, no previous surveys have classified and analysed PLA studies that used deep learning technologies to improve security service performance. The study discusses the challenges and description of the most remarkable issues and future trends of PLA in wireless networks and communication.  
Recently, physical layer authentication methods have attracted substantial research interest. Unfortunately, none of the retrieved secondary studies in Table 1 addressed our research questions detailed in Section 4.1. Therefore, insights into identifying open issues and guidelines for future research are provided. This work examines research studies published since 2015 on PLA systems intended to secure wireless networks. Through the paper selection process, 248 papers were chosen and narrowed down to 31 primary studies. This survey will directly help academics and professionals understand PLA developments to secure emerging wireless technologies.
## 3 Biosynthetic Routes to Nitrogen-Containing and Phenolic Metabolites in Cannabis
### Biosynthesis of Spermidine and Spermidine Alkaloids
used is not the same in the two species, this could explain the difference in the position of said double bond; or the condensation takes place with a saturated fatty acid, which is subsequently modified to obtain the desired molecule. However, in extracts of hemp infloresences, a study reports having identified myristic acid using UHPLC, which is a saturated fatty acid with 14 carbons, representing, depending on the cultivar, between 0.3% and 1.6% of the composition of the fatty acids of the flowers [53]. Additionally, the structure of cannabisister presents two hydroxyl groups on C8 and C9 of its fatty acid moiety. Although it is possible that a hydratase could convert a double bond to an -OH group, this reaction alone would not be sufficient to produce cannabisister, requiring an additional hydration. Thus, myristic acid is a realistic candidate for cannabisister synthesis, which may require cytochrome P450 enzymes to obtain the two hydroxyl groups and quite possibly a dehydrogenase to produce the double bond at C5-C6. The other potential candidate, its close cousin, myristoleic acid, also contains 14 carbons, but is distinguished by its double bond at C9-C10 (Figure 8). Furthermore, it is more likely that myristic acid is the precursor of these alkaloids between the two presented candidates, given the ubiquity in eukaryotes of the enzyme \(N\)-myristoyltransferase (NMT, E.C. 2.3.1.97), catalyzing the condensation of a myristic acid linked to coenzyme A, a product of the reaction of an acyl-CoA ligase (E.C. 6.2.1.3), to the nitrogen atom on the N-terminal glycine of a protein, forming an amide function [54]. This enzymatic reaction is consistent with the hypothesized first step in the biosynthesis of cannabisister, with the only difference being the conjugation of an amine from spermidine, rather than a terminal glycine of a peptide (Figure 9). Additionally, the double bond of myristoleic acid would restrict freedom of movement, possibly hindering its positioning in the active site, unlike myristic acid. Anhydrocannabisativine should normally follow the same biosynthetic pathway as its analogue. It remains uncertain whether the dehydration of cannabisister can occur spontaneously or whether it requires the activity of an enzyme. On the other hand, it is known that it is possible to obtain anhydrocannabisativine by sufficiently heating cannabisister (180-185 \({}^{\circ}\)C, 2 min) [40]. Furthermore, cannabisister seems to be the predominant spermidine alkaloid in _Cannabis_ roots, with reported levels of 2.5 mg/kg, compared to anhydrocannabisativine, with levels of 0.3 mg/kg [40; 55]. Figure 8: Structure of fatty acids potentially involved in the biosynthesis of spermidine alkaloids of _Cannabis_.
## 2 Materials and Experimental Process
### Experimental Process  
#### 2.2.1 Specim Preparation  
The moisture content and xanthan gum concentration (xanthan gum to soil ratio in mass; \(M_{XG}/M_{S}\)) of the specimens used for direct and interface shear tests are listed in Table 3. All the soil and soil-structure samples were first prepared using dry sand and xanthan gum powder (Sigma-Aldrich(r), CAS number 11138-66-2) with an initial water content (in mass ratio) of 20%. The ratios of xanthan gum mass to that of sand were 0% (i.e., untreated), 0.5%, 1.0%, and 2.0% (corresponding to 2.5%, 5.0%, and 10% ratios to the mass of water). Purified xanthan gum powder was dissolved in distilled water at room temperature (21 \({}^{\circ}\)C), and the mixture was magnetically stirred to obtain a uniform xanthan gum solution (i.e., hydrogel). Once done, the solution was mixed with dry sand, and cylindrical bulk soil and cylindrical soil-structure specimens with dimensions of 60 mm diameter and 25 mm height were prepared using a specimen ring for direct shear tests (Figure 3a). The initial dry density of specimens was controlled to fit within 1450 \(\pm\) 10 kg/m\({}^{3}\) where the relative density (\(D_{r}\)) of soil showed a range of 36-44% (medium state), via tapping to minimize the effect of different particle packing density on soil strengthening [38; 39]. The specimens were prepared for three representative moisture states (i.e., initial, dried, and resubmerged). The initial state indicates when the specimens were tested immediately after preparation (i.e., approximately having 20% of water content in mass ratio), while dry condition samples were tested after oven-drying the specimens at 40 \({}^{\circ}\)C for 21 days where specimen water content reached less than 3%. The resubmerged samples were prepared by fully immersing the dried samples in deionized water (i.e., water level reached the top of the sample) at room temperature for 24 h to achieve a condition close to saturation as much as possible [40].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variables** & **Direct Shear Test** & **Interface Shear Test** \\ \hline Shearing condition & Soil–soil & Soil–solid structure \\ \hline Moisture state & Initial state & Initial state \\  & Dried state & Dried state \\  & Resubmerged state & Resubmerged state \\ \hline Xanthan gum concentration & 0, 0.5, 1, 2 & 0, 0.5, 1, 2 \\ \([M_{XG}/M_{S},^{\circ}]\) & 0, 0.5, 1, 2 & 0, 0.5, 1, 2 \\ \hline Vertical confinement pressure [kPa] & 50, 100, 200, 400 & 50, 100, 200, 400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.  
Figure 3: Structural materials: (a) schematic diagram; (b) typical surface profiles.
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 2 Two-Dimensional (2D) Materials in Organic Light Emitting Diodes (OLEDs)
### Composite Layers Based on 2D Materials
#### 2.2.3 Composite HIL Based on 2D Materials
As shown in Figure 4e, the work function of the MoS\({}_{2}\):MoO\({}_{3}\) film is 4.9 eV, which is at the same level with that of PEDOT:PSS (5.0 eV) and higher than that of MoS\({}_{2}\) (4.3 eV). The surface roughness of MoS\({}_{2}\):MoO\({}_{3}\) thin film is comparable with PEDOT:PSS film and better than MoO\({}_{3}\) nanoparticle film. The improved performance can be attributed to the good surface morphology of the MoS\({}_{2}\):MoO\({}_{3}\) composite HIL and the excellent conductivity of the MoS\({}_{2}\), which facilitate hole transport in the device and reduce the sheet resistance. Moreover, the stability of OLED devices employing MoS\({}_{2}\):MoO\({}_{3}\) HILs is better than that of the device with PEDOT:PSS HIL. [PERSON] reported Alq\({}_{3}\)-based OLEDs with HILs of MoS\({}_{2}\), ultraviolet/ozone-treated MoS\({}_{2}\) (MoS\({}_{2}\)-UVO) and MoS\({}_{2}\)-doped PEDOT:PSS (MoS\({}_{2}\):PEDOT:PSS), as shown in Figure 4f [41]. Alq\({}_{3}\) OLEDs using MoS\({}_{2}\) HILs with different concentration (0.1, 0.3, 0.5 mg/mL) were introduced. The maximum EQE of Alq\({}_{3}\) device with 0.3 mg/mL MoS\({}_{2}\) is 7.0 cd/A, showing a 52% enhancement compared with the control device without HIL. Higher CE of 8.1 cd/A was observed in Alq\({}_{3}\) OLED with MoS\({}_{2}\):PEDOT:PSS (2:1) composite HIL. The hole-injection capacity of three HILs was tested to meet the relation: MoS\({}_{2}\) \(<\)MoS\({}_{2}\)-UVO \(<\) MoS\({}_{2}\):PEDOT:PSS. The improved performance can be ascribed to the increased conductivity and hole injection ability of MoS\({}_{2}\):PEDOT:PSS composites.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Monitoring of the Sea Status  
Radar remote sensing techniques are being used in the measurement of the sea status. Radar satellite configurations employing ships, islands, and land-based stations are able to measure the ocean wave-height spectrum by employing a range of frequencies [102; 103; 104; 105; 106; 107]. A promising and innovative approach was proposed by [PERSON] et al., who developed a theoretical model using the scattered Global Positioning System (GPS) signal as a function of geometrical and environmental parameters based on a bistatic radar equation. This study suggests that different time delays and different Doppler shifts of the scattered GPS signal may form small spatial cells relatively to the sea surface. This may allow an accurate evaluation of sea wind velocity [108], and, e.g., practical methods employing GPS followed [109].
# Randomized Feature and Bootstrapped Naive Bayes Classification
###### Abstract
Similarly, other approaches [6] relaxed the independence assumption by explicitly incorporating feature correlations into NB, enhancing its adaptability. Additionally, sparse feature selection techniques [9] have been explored to improve NB performance within resource-constrained scenarios. A comprehensive review by [PERSON] and [PERSON] [24] further underscored the critical role of feature selection methodologies in optimizing model accuracy and interpretability. Advanced Bayesian variants have also emerged to address NB's independence assumption limitations, including tree-augmented naive Bayes (TAN), which explicitly models feature dependencies using Bayesian networks [25], and weighted naive Bayes (WNB), which assigns discriminative weights to features based on information gain or chi-squared metrics [26; 27]. Although these methods improve classification performance, they incur increased computational complexity due to explicit dependency modeling or feature weight optimization. Motivated by these methodological gaps, this study introduces randomized feature naive Bayes (RF-NB) and randomized feature bootstrapped naive Bayes (RFB-NB). These novel classifiers employ randomized feature selection and bootstrap aggregation, enhancing robustness and predictive stability without explicitly modeling feature dependencies or assigning fixed feature weights. Thus, our proposed methods offer computational efficiency and robust predictive performance, particularly advantageous in noisy, heterogeneous, or high-dimensional datasets. This research specifically aims to investigate whether randomized feature selection and bootstrap aggregation can significantly enhance NB classifier performance and to rigorously assess how these proposed methods compare to established classifiers across diverse real-world datasets. The primary contributions of this study include introducing two ensemble-based naive Bayes classifiers--RF-NB and RFB-NB--that effectively mitigate the limitations imposed by the independence assumption without explicitly modeling feature dependencies and rigorously justifying their enhanced robustness and predictive stability through a theoretical explanation and focused empirical validation beyond traditional accuracy and F1-score evaluations. The manuscript is structured as follows: Section 2 reviews the relevant literature on enhancements to naive Bayes classifiers, feature selection methods, and bootstrap aggregation techniques. Section 3 details the proposed RF-NB and RFB-NB methodologies, covering theoretical formulation, algorithmic implementation, and computational considerations. Section 4 describes the datasets used for performance evaluation, emphasizing their characteristics and practical relevance. Section 5 systematically presents the empirical results, comparing RF-NB and RFB-NB performance against established benchmark classifiers. Finally, Section 6 concludes by summarizing the key findings of this research.