esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Analysis of Vitamin C, Gallic Acid, Ellagic Acid, and Daidzin Using HPLC  
Ellagic acid and vitamin C have been widely reported to be present in the Kakadu plum fruit, and these provide the major functional elements of and are responsible for the antioxidant activity [42]. The quantification of vitamin C, ellagic acid, and phenolic acid along with flavonoids was performed using HPLC. Table 2 demonstrates the amount of ellagic acid present in our fruit samples, ranging from 0.01 \(\pm\) 0.00 to 0.36 \(\pm\) 0.01 mg/g DW, whereas gallic acid varies from 0.13 \(\pm\) 0.00 to 5.10 \(\pm\) 0.03 mg/g DW. Recently, other literature has recorded the presence of a significant amount of ellagic acid and ascorbic acid in the Kakadu plum [43].  
_T. fendinandiana_, the Kakadu plum (KKD), is well known as a source of vitamin C (mainly known as ascorbic acid) [28]. According to previous studies, the Kakadu plum contains 75 times more vitamin C than oranges [43]. Vitamin C, or ascorbic acid, is a dietary antioxidant that is necessary for humans, which can easily be degraded in the presence of excessive heat, water, and air [44; 45]. Though the drying process and reflux were our choice for the extraction method in this study, the aqueous extract of KKD from the NT still exhibited the highest amount of vitamin C compared with the KKD from WA. Dry weights of 88.66 \(\pm\) 2.92 mg/g and 52.83 \(\pm\) 1.85 mg/g of vitamin C were determined from KKD-NT and KKD-WA, respectively. According to previous reports, the Kakadu plum has a higher level of ascorbic acid (vit-C) than other domestic fruits, and 900 times more ascorbic acid than blueberries [44; 45]. Moreover, it has been reported that KKD contains a much larger amount of vitamin C than common natural sources of vitamin C such as lemons and oranges [46]. Although lemons and oranges are common sources of vitamin C, our results showed a smaller amount of vitamin C in those fruits than in the Kakadu plum, which may due to the extraction of the samples at a higher temperature. It is known that vitamin C can degrade easily at high temperatures. However, despite their extraction at higher temperatures, the vitamin C content in KKD did not degrade significantly. According to previous reports, KKD contains 7000 mg/100 g DW of vitamin C, which is 100 times higher than the amount in lemons and oranges [47].  
Furthermore, in comparison to other fruits, our results showed that KKD possesses a much higher amount of vitamin C. To enhance the benefits of the Kakadu plum as a source of phytonutrients, its isoflav profiles was examined. Based on the results (Table 2), daidzin was present only in the Kakadu plum and pomergnate, while in the other samples it was not detected or was present in only a trace amount.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 4 Numerical Simulations  
The described nonlinear dynamics are integrated forward in time utilizing a fixed time step Runge-Kutta method of fourth grade to get a single flight path. [1] shows the validation of this modeling and solving approach for aerial platforms. To demonstrate the precision of the novel methodology introduced in this research, which is based on neural networks, the obtained results are compared to the obtained outcomes in [29]. The methodology in [29] features a Kalman based hybridization [43; 44] of GNSS, IMU and semiactive laser quadrant photo-detector sensors. To integrate the equations of motion and their interactions with GNC system and environment, MATLAB/Simulink R2020a on a personal computer with a processor of 2.8 Ghz and 32 GB RAM is used.  
The remainder of this section is separated in two subsections. The first one presents the noncontrolled trajectories to which the developed navigation, guidance, and control algorithms will be applied. The second one performs Monte Carlo simulations of ballistic flights, Kalman hybridization based controlled flights, and neural networks based controlled flights. Moreover, an ideal controller without induced errors in the line of sight is also developed to compare results with ideal results.
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 7 Limitations and Future Research  
This research gives useful insights into consumer acceptance of blockchain-based digital payment services in Bhutan, but it has some limitations. Since the sample consists of five districts, the sample could not fully represent the overall context of the country. The cross-sectional design does not allow insight into how attitudes change over time. Furthermore, the study does not include consumer acceptance due to technological skills and adoption costs and takes into account the perspectives of policymakers, enterprises, and business partners. The implementation of blockchain-based payment solutions in Bhutan encounters numerous legislative hurdles such as unclear regulatory guidelines, mandatory alignment with centralized financial regulations, limitations on international transactions, and missing consumer protection mechanisms. The need for regulatory approval for cryptocurrencies presents further obstacles. The successful implementation of blockchain-based payments in Bhutan depends on overcoming these limitations by conducting comprehensive research and enacting policy adjustments. Future studies would need to examine how demographic factors such as age and education shape acceptance. An integrated approach, such as the use of qualitative interviews, would provide better information. Longitudinal studies tracking trends over time and incorporating new findings, such as blockchain development and regulatory developments, would further provide insight into the dynamics affecting blockchain adoption in Bhutan.
## 2 Literature Review
### Related Work
#### 2.3.1 Information-Based Learning Techniques  
[PERSON] et al. [22] enhanced IDSs by using ML algorithms to monitor resources, services, and networks, detecting cloud attacks based on monitoring data. The model accuracy rate achieved 99.99% on the BoT-IoT dataset, which includes RF algorithms for intrusion prediction and graphic visualization. However, the study has limitations, as it only uses the Bot-IoT and NSL-KDD datasets for evaluation and does not examine various cloud computing deployment models or compare different ML algorithms on system performance. [PERSON] et al. [23] developed an IoT IDS to detect abnormal behavior in an IoT network using ML methods and feature dimensional reduction. They applied preprocessing steps using min-max to normalize the data and then applied PCA to select the most relevant features from the 49 attributes on the UNSWNB15 dataset. After trimming the dataset, XGBoost was trained on it. The study did not evaluate other IoT network typologies. [PERSON] et al. [24] presented a hybrid feature selection approach designed to decrease the complexity of the model, prediction latency, and attack prediction performance without adversely affecting the model. They used LightGBM, a lightweight gradient-boosting framework, on the CIC-IDS 2018 dataset. With the proposed feature selection, prediction latency was reduced by 45.52% to 2.25%, and construction time was reduced by 52.68% to 17.94%. With LightGBM, the model achieved 97.73% accuracy, 99.3% sensitivity, 99.3% precision, and a short prediction delay. According to the proposed model, accuracy and precision were improved by 1.5% and 3%, respectively, over the existing model.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 1 Introduction
### Review of Existing Research  
The integration of IT technologies into aircraft maintenance processes has become a cornerstone of modern aviation operations. These technologies are crucial for enhancing task management, improving record keeping, and boosting overall operational efficiency. Despite the widespread adoption of IT-based support systems in aviation, there are notable gaps in research regarding their practical application in optimizing resource utilization and enhancing reliability. This shortfall is particularly evident in the framework of Intelligent Transportation Systems (ITSs), which aim to integrate advanced technologies into transportation systems for improved efficiency and sustainability. Military aviation, with its unique operational demands, presents specific challenges that require customized approaches to maintenance planning and execution [12; 13; 14].  
Research on aviation maintenance support systems has primarily focused on improving maintenance techniques [15; 16], increasing fleet availability [17], reducing the incidence of technical failures [18], and enhancing scheduling efficiency [19; 20]. Digital systems for real-time monitoring, as highlighted in numerous studies, demonstrated significant potential for streamlining maintenance planning and execution [21; 22]. These systems excel at collecting and organizing vast amounts of operational data. However, their effectiveness in providing actionable insights for reliability improvements and efficiency gains remains underexplored. The current literature rarely addresses how these systems could reduce maintenance delays or optimize workflows based on observed task durations, leaving a critical gap in practical applications. This oversight underscores the need for research that bridges theoretical knowledge with real-world implementation.
## 5 Properties Analysis of the Functions \(\varphi_{m}^{s}\)
### Single-Resource-Block Maritime User Allocation  
Based on the previous subsection, we further optimize the maritime user allocation on a single resource block (RBUA) with the DP algorithm. Therefore, the effective user set \(A_{s}*\) is a variable satisfying the upper limit in condition \(w3*:|A_{s}*|\leq a,a\in\{0,1,\ldots,A\}\). The main idea of the RBUA scheme is to recursively calculate the elements of three arrays: \(\psi\), \(X\), and \(Y\). Let \(a\in\{0,1,\ldots,A\}\), \(n\in\{1,2,\ldots,T\}\), and \(m\in\{n,n+1,\ldots,T\}\). \(\psi[a,n,m]\) is defined as the optimal value for the following formula: \(W_{RB}[a,n,m]\). \[W_{RB}[a,n,m]:\psi[a,n,m]=max\sum_{l=n}^{T}\varphi^{s}_{l}(\widetilde{p}^{s}_ {l}). \tag{32}\]  
\[w1^{*}:\widetilde{p}^{s}_{1}\leq P^{s}_{max}; \tag{33}\]  
\[w3^{*}:|A_{s}*|\leq a; \tag{34}\]\[w4^{*}:\widetilde{p}_{m}^{s}\geq\widetilde{p}_{m+1}^{s}; \tag{35}\] \[w5^{*}:\widetilde{p}_{T+1}^{s}=0;\] (36) \[w6^{*}:\widetilde{p}_{n}^{s}=\widetilde{p}_{n+1}^{s}=\cdots= \widetilde{p}_{m}^{s}. \tag{37}\]  
The array \(Y\) is responsible for recording the previous elements contributing to the current optimal value \(\psi[a,\,n,\,m]\). In Equation (32), it is observed that the target function depends only on \(\widetilde{p}_{m}^{s}\), \(\widetilde{p}_{n+1}^{s},\ldots,\widetilde{p}_{T}^{s}\). If \(a=A,n=m=1\) is true, array \(\psi[A,\,1,\,1]\) is the best value for the problem \(W_{RB}^{s}\). When \(W_{RB}[a,\,n,\,m]\) achieves the maximum value, we define \(\widetilde{p}_{n}^{s}*,\widetilde{p}_{n+1}^{s}*,\ldots,\widetilde{p}_{T}^{s}*\) as the optimal solution corresponding to \(\psi[a,\,n,\,m]\). Let array \(X[a,\,n,\,m]\) record the corresponding solution of the optimal objective function. Then, we combine Condition \(w6^{*}\) to derive \(X[a,\,n,\,m]=\widetilde{p}_{n}^{s}*=\widetilde{p}_{n+1}^{s}*=\cdots= \widetilde{p}_{m-1}^{s}*=\widetilde{p}_{m}^{s}*\). Based on the relation in Equation (38), the implementation of the marine user allocation is a recursive process used to calculate the elements of \(\psi\). \[\psi[a,\,v,\,m]=\left\{ \begin{array}{c}\varphi_{1},\,\,\varphi_{1}>\varphi_{2 },\widetilde{p}^{*}>X[a-1,\,m+1,m+1]\\ \varphi_{2},\,Otherwise\end{array} \right., \tag{38}\]  
\[\varphi_{1}=\varphi_{n,m}^{s}(\widetilde{p})+\psi[a-1,\,m+1,m+1], \tag{39}\]  
\[\varphi_{2}=\psi[a,\,n,\,m+1]. \tag{40}\]  
In Equation (38), \(\widetilde{p}*=argmax\varphi(n,\,m,\,P^{s})\) and \(\varphi_{1}\) records the corresponding optimal value \(\psi[a,\,n,\,m]\) for a valid user \(m\). On the contrary, \(\varphi_{2}\) represents the optimum value for an invalid user on resource block \(s\). During the iterative procedure shown in Algorithm 3, the array \(Y\) tracks and records the previous elements contributing to the current function \(\psi[a,\,n,\,m]\). This can be traced back from index \((A,\,1,\,1)\) to an empty set. This method assists us in retrieving the optimal solution for the entire program process more conveniently in lines 29-36. From lines 6-12, we initialize the index as \(\emptyset\) to represent the end of recursion.
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 3 Main Results
### Symbol Error Probability for Nakagami-\(m\) Fading Channels
Specifically, we propose approximating the statistics of the RV \(R\) with those of a gamma distribution with scale parameter \(a\) and shape parameter \(b\), using a moment matching method. The following result holds. Proposition 3: _A closed-form approximation for the \(P_{s}\) of LoRa systems under Nakagami-m fading can be obtained as_  
\[P_{s}^{\mathrm{Nak}}\approx 1-\frac{\Gamma(a,b\tilde{x}_{N})}{\Gamma(a)}. \tag{20}\]  
_where \(\tilde{x}_{N}\) is given by (17),_  
\[a=\tilde{\mu}_{1}^{2}/(\tilde{\mu}_{2}-\tilde{\mu}_{1}^{2}),\,b=\tilde{\mu}_{1 }/(\tilde{\mu}_{2}-\tilde{\mu}_{1}^{2}), \tag{21a}\] \[\tilde{\mu}_{1}=2(1+N\gamma),\] (21b) \[\tilde{\mu}_{2}=8(1+2N\gamma)+4\gamma^{2}(1+m)N^{2}/m. \tag{21c}\]  
Proof. : Observe that \(R^{2}\) follows a squared gamma-shadowed Rice distribution and thus, using [32] (Equation (5)), its \(n\)-moment is readily obtained as  
\[\tilde{\mu}_{n} =2^{n}\bigg{(}\frac{m}{N\gamma+m}\bigg{)}^{m}\Gamma(n+1)\] \[\times{}_{2}F_{1}\bigg{(}m,n+1;1;\frac{N\gamma}{N\gamma+m}\bigg{)}. \tag{22}\]  
Using [33] (Equation (7.3.1/129)), \(\tilde{\mu}_{1}\) and \(\tilde{\mu}_{2}\) can be further simplified as (21). Finally, \(P_{s}\) can be deduced as the CDF of a gamma distribution with parameters \(a\) and \(b\) that can be obtained in closed form using a moment matching method [34] as (20) and (21), thus completing the proof. Using Proposition 2, a closed-form approximation for the \(P_{s}\) under Rayleigh fading will be obtained. Specifically, the following result holds. Corollary: _Under Rayleigh fading, a closed-form approximation for \(P_{s}\) can be deduced as_  
\[P_{s}^{\mathrm{Ray}}\approx 1-\exp\biggl{[}-\frac{\tilde{x}_{N}}{2(1+N\gamma)} \biggr{]} \tag{23}\]  
Proof. : The proof can be readily obtained by setting \(m=1\) to (15).
## 3 Multi-Sensor Architecture
### Components and Equipment  
3.1.1. General Components  
A microcontroller, a single-board computer, and open-source IoT automation system OpenHAB (open Home Automation Bus) are the base of the multi-sensor platform (Table 2). Each component has its importance and every one of them contributes to the overall functionality of the air quality monitoring system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**General** & **Description** & **Features** \\ \hline \multirow{7}{*}{Microcontroller} & \multirow{7}{*}{Open-source firmware and development kit that is based on the ESP8266 Wi-Fi module.} & ESP8266 Chip integrates a microcontroller with Wi-Fi capability. \\  & & \multirow{7}{*}{Lua Scripting Language makes it easy to program and interact with the device.} \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \end{tabular}
\end{table}
Table 2: General components of the multi-sensory platform.  
The NodeMCU communicates wirelessly over Wi-Fi using the MQTT protocol to transmit the collected data both to a Raspberry Pi logic block with OpenHAB installed, which sends the data for storage to an edge device--a Raspberry Pi configured as an SQL server, and to the OpenHAB cloud environment. This dual approach ensures reliable data processing, availability of data, and backup and disaster recovery capabilities. Moreover, this solution presents an opportunity to implement machine learning solutions in the near future that are under development.  
From the security point, users can access the system from only one point, which enables early detection and containment of malicious attacks. Access is via HTTPS protocol for encryption of web communications carried over the Internet.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### X-ray Fluorescence (XRF)  
XRF is a semi-quantitative analytical technique based on wavelength-dispersive spectroscopic principle similar to an electron microprobe [127; 128]. The sample material emits secondary or fluorescent X-rays after being excited by a primary X-ray source. Each of the elements present in the sample produces a set of characteristic fluorescent X-rays (i.e., a fingerprint) that is unique for that specific element, which is why XRF spectroscopy is an excellent technology for elemental and chemical analysis. This technique requires a previous sample preparation like XRD.  
Figure 8: X-ray Diffraction (XRD) analysis of the deposit formed on the membrane surface. Broken line—without antiscalant, continuous line—with 10 ppm antiscalant. Feed solution prepared by dissolving NaHCO\({}_{3}\) and CaCl\({}_{2}\) (mode ratio 2:1) 3.1 mmol HCO\({}_{3}\)\({}^{-}\)/dm\({}^{3}\). Reprinted with permission from [46], Copyright Elsevier, 2012.  
The utility of this technique in membrane fouling characterization is to determine the elemental composition of both new and fouled membranes and subsequently figure out the chemical nature of the deposit(s). This technique is distinguished by its highest accuracy and precision as well as by its simple and fast sample preparation for the elemental analysis. More details of sample preparation and procedure concerning this characterization technique can be found in [127; 128]. It must be mentioned that this technique is not used yet for fouling analysis in MD, OD and OMD. However, it was used in the assessment of long-term fouling of RO membranes [127].
# Recognition of Vehicle License Plates Based on Image Processing
## 2 License Plate Recognition System
### Distortion Correction
#### 2.4.4 Perspective Transformation  
The shape projected on the image appears differently depending on the angle of the object and camera planes. To map these two planes, the projection transformation is used. If the three points are located on a straight line, the three points are placed on the straight line after the projection transformation.  
The projection transformation is a linear transformation expressed as Equation (5).  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\\ 1\end{array}\right]=H\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{ccc}h_{1}&h_{2}&h_{3}\\ h_{4}&h_{5}&h_{6}\\ h_{7}&h_{8}&h_{9}\end{array}\right]\left[\begin{array}{c}x\\ y\\ 1\end{array}\right] \tag{5}\]  
Figure 8: (**a**) Input image; (**b**) Angle test image.  
Figure 7: (**a**) Input image; (**b**) RANSAC line fitting image.  
where \(H\) is a \(3\times 3\) matrix, \((x,\ y)\) are coordinates of the image before the transformation and \((x^{\prime},\ y^{\prime})\) are coordinates of the image after conversion. The elements of the matrix \(H\) can be calculated using the direct linear transformation (DLT) algorithm [16; 17]. To determine the elements of the matrix \(H\) using the DLT algorithm, at least five pairs of the corresponding points are required. In this study, the feature points extracted from Figure 9 are used. Thus, the coordinates of the four rectangular vertices were used as feature points on the corresponding projection plane. \(H\) was corrected using Equation (6) for distortion correction.  
\[x^{\prime}=\frac{h_{1}x+h_{2}y+h_{3}}{h_{7}x+h_{8}y+h_{9}},\ y^{\prime}=\frac{h _{4}x+h_{5}y+h_{6}}{h_{7}x+h_{8}y+h_{9}} \tag{6}\]  
Figure 9 shows the result of correcting the perspective distortion using Equation (6).  
The input image of Figure 10a shows severe inclination. If this is corrected using a perspective transformation, the position of the license plate becomes constant to favor character recognition (Figure 10b).
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 2 Theoretical Analysis  
This section verifies that the model updating method using FRFs based on Sherman-Morrison formula can obtain the sensitivity of the FRFs to the parameters only by initial FRFs and parameter perturbations, which can avoid repeated finite element analyses and calculations.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 3 Hydraulic Criteria  
Hydraulic criteria are critical factors in assessing whether internal erosion will occur in soil under seepage conditions. These assessment criteria are usually derived from the force balance analysis of soil microelements. The critical hydraulic gradient and critical seepage velocity are two hydraulic parameters commonly used to establish these criteria [24; 44; 45; 46]. Therefore, hydraulic conditions must always be considered in all seepage failure issues to ensure an accurate evaluation of internal soil erosion.  
The hydraulic gradient criteria are typically derived through force balance analysis, focusing on changes in the effective stress of soil particles under seepage. When water flows through soil, seepage force will affect the effective stress of the soil particles. The seepage velocity criterion is derived through the principle of energy balance, primarily considering the impact of seepage kinetic energy on soil particles.
## 4 Results and Discussion
### RQ1: What Are the Trend and Types of Studies on DL-Based Web Attacks Detection?  
This question aims at reviewing bibliometric studies in the DL-based web attacks detection domain; the answers reflect publication information of Primary Studies.  
**RQ1-1: What is the annual number of studies on DL-based web attacks detection?** Figure 7 is the year-by-year presentation of selected studies. The year started in 2010 and ended in September 2021, and we have shown ten years of data of 63 articles. The figure shows the disparate distribution of papers according to the years. In 2012, we found the first and only one research article on the DL-based web attacks detection. Since that day, several research articles have been published on the topic. The number of papers published reaches the maximum in 2019 and then decreased in 2020. We finished the SLR by September 2021. Thus, the 12 papers published in 2021 are not representative of the papers published in that year. This scenario suggests that the distribution of published research papers is not equal, and web vulnerability detection topics using Deep Learning will gain more attention in coming years.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Attribute Name** & **Research Question** \\ \hline
01. & Study identifier & \\
02. & Year of publication & RQ1 \\
03. & Type of study & RQ2 \\
04. & Datasets & RQ3 \\
05. & Frameworks & RQ4 \\
06. & Performance measures & RQ5 \\
07. & Feature extraction & RQ6 \\
08. & Classification models & RQ7 \\
09. & Type of web attacks & RQ8 \\
10. & Experimental performance of proposed models & RQ9 \\
11. & Limitations & RQ10 \\
12. & Main objective & RQ11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data extraction form.  
**RQ1-2: What is the percentage of studies published in journals and conferences?** Figure 8 shows that 57% of PS are published in conferences while 43% are published in journals.  
The RQ1 answer indicates that interest in detecting web vulnerabilities using Deep Learning models is very recent; since 2019, the number of articles published in this research field has increased significantly. In addition, the number of journal articles is less than the number of conference papers.