esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In semiconductor manufacturing, plasma etching is a key process in material removal and surface modification. The efficiency of these plasma-based processes significantly influences production yield and the quality of semiconductor devices [1, 2, 3, 4]. Plasma intensity, a vital parameter in these processes, substantially affects the rate of unused gas, quantified as 1-Ui (the process unused rate). Understanding the precise impact of plasma intensity on 1-Ui is a critical step in minimizing the amount of exhausted gas, thereby enhancing process efficiency and reducing environmental impacts [5, 6, 7, 8].  
Previous studies have examined the effects of plasma on the 1-Ui rate of unused gas, though only to a limited extent. For example, [PERSON] et al. [9] examined the role of plasma discharge characteristics, particularly in an Ar/CAF6 mixture, and its influence on etching precision and unused gas rates. [PERSON] et al. [10] monitored atomic layer etching in CI2/Ar plasmas, emphasizing the significance of plasma conditions, such as gas flow and plasma intensity, in determining etching rates and unused gas outcomes.  
Further studies, such as [PERSON] et al. [11], analyzed CF4/O2/N2 plasma mixtures in Si\({}_{3}\)N\({}_{4}\) dry etching, highlighting how gas composition variations impact etching precision and gas utilization. [PERSON] et al. [12] investigated SF6/O2/Ar plasma conditions for silicon trench etching, focusing on how plasma uniformity affects unused gas rates. Additionally, [PERSON] et al. [13] explored the effects of plasma intensity and chemical composition on high-aspect-ratio SiO\({}_{2}\) etching, offering insights into unused gas rates under different plasma intensities and gas combinations. [PERSON] et al. [14] comprehensively reviewed deep reactive ion etching techniques, emphasizing how plasma intensity influences unused gas rates and etch uniformity in advanced semiconductor manufacturing. However, these studies have fallen short on plasma intensity and process unused rates across varying plasma levels and gas types.  
We aim to contribute to this body of knowledge and reduce the identified research gaps by investigating the effects of varying plasma intensities on 1-Ui across seven gases (NF3, SF6, CH2F2, CHF3, C4F6, C4F8, and CF4). By categorizing plasma intensities into low (<100), medium (500-700), and high (>1000) groups, this research seeks to quantify how plasma intensity specifically influences 1-Ui and offer insights into optimal plasma settings to improve efficiency and minimize exhausted gases in semiconductor manufacturing processes.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
###### Abstract  
Aromatics, especially polycyclic aromatic hydrocarbons (PAHs), are important combustion pollutants known to be carcinogenic and mutagenic and are also precursors of soot and, consequently, combustion-generated particulate matters that can significantly threaten environmental security and human health. In engine combustion, the multi-component and broad-source feature of transportation fuels makes synergistic effects commonly exist and greatly enhances the formation of aromatics and soot. Understanding the synergistic effects of different fuel components on aromatic formation facilitates concrete guidance for controlling soot emissions. This review focuses specifically on the synergistic effects of aromatics formation, including benzene, indene, naphthalene, and larger PAHs, in combustion among hydrocarbon blends and hydrocarbons blended with oxygenated fuels. Progresses in experimental measurements, theoretical calculations of critical reactions, and kinetic modeling are reviewed in detail. Special attention is paid to blends of aromatics and linear fuels, which show pronounced synergistic effects in PAH formation. Furthermore, some prospects for future research on synergistic effects in aromatic formation are provided.  
aromatic formation; synergistic effect; hydrocarbon fuels; oxygenated fuels; benzene
## 2 Materials and Methods
### Product Extractions and Material Analysis  
Solid phase microextraction (SPME) was used to collect volatile and semi-volatile components of the irradiated water mixtures. Supelco\({}^{\text{TM}}\) (Supelco, Inc., Bellefonte, PA, USA) 50/30 um DVB/CAR/PDMS fibers were exposed to the headspace of the vials containing the PE-water mixtures for 30 min at 80-85 \({}^{\circ}\)C. The SPME fiber was then set in the injection port of an Agilent (Agilent Technologies, Inc., Santa Clara, CA, USA) 7890A/5975C gas chromatography mass spectrometer (GCMS), where the collected compounds were released and then separated using an Agilent HP-5 MS column (19091S-433 UI, 30 m \(\times\) 0.250 mm). After maintaining the column temperature at 50 \({}^{\circ}\)C for 5 min, it was ramped from 50 to 260 \({}^{\circ}\)C at 8\({}^{\circ}\)/min. The single quadrupole GC/MS generated the total ion chromatogram over the range of 50-500 at a scan speed of 3.2 scans/s. The MS source was set at 230 \({}^{\circ}\)C in positive ionization mode. Compounds were originally identified by GCMS using the National Institute of Science and Technology (NIST) database of compounds [41], followed by the purchase and testing of suspected compounds. The extracted compounds in the PE-water mixtures were then matched with the retention times and mass spectra of known compounds.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **\(K_{L}\) (LR)** & **\(K_{L}\) (PS)** & **Total Stiffness** & \(K_{S}\) **of LR Limb** & \(K_{S}\) **of PS Limb** & **Pp** \\ \hline Con & 16.2 (4.9) & 8.6 (3.5) & 24.8 (6.9) & 65.3\% (12.4) & 34.7\% (12.4) & \textless{}0.01 \({}^{\ddagger}\) \\ Pre-OP & 11.3 (8.9) & 11.4 (9.8) & 22.6 (17.6) & 50.8\% (14.6) & 49.2\% (14.6) & 0.86 \\ Post-OP & 11.9 (7.3) & 8.5 (4.9) & 20.4 (12.0) & 57.8\% (8.2) & 42.2\% (8.2) & 0.01 \({}^{\ddagger}\) \\ \(p\) (Con vs. Pre-OP) & 0.14 & 0.41 & 0.71 & 0.03 * & 0.03 * & \\ \(p\) (Con vs. Post-OP) & 0.14 & 0.96 & 0.33 & 0.13 & 0.13 & \\ \(p\) (Pre-OP vs. Post-OP) & 0.83 & 0.35 & 0.61 & 0.29 & 0.29 & \\ \hline \hline \end{tabular}  
*: significant difference between groups \({}^{\ddagger}\): significant difference in \(K_{S}\) of a limb between LR and PS.  
\end{table} 
Table 2: Means (standard deviations) of the leg stiffness (\(K_{L}\)) (N/m) and proportional share of total leg stiffness (% total stiffness) during double-limb support in the CP group (Pre-OP: before surgery; Post-OP: after surgery) and the Control group. LR indicates the loading response phase. PS indicates the pre-swing phase. Pp: \(p\)-values of differences in \(K_{S}\) of a limb between LR and PS. \(p\): \(p\)-values of between-group differences.
# Crop Prediction Model Using Machine Learning Algorithms
## 6 Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **N, P, K** & **K, P, Rainfall** & \
\begin{tabular}{c} **Temperature,** \\ **Humidity, pH**, \\ **Rainfall** \\ \end{tabular} &
\begin{tabular}{c} **N, Temperature,** \\ **Humidity, pH** \\ \end{tabular} \\ \hline Bayes Net & 67.64 & 85.69 & 97.05 & 89.70 \\ \hline Naive Bayes Classifier & 65.37 & 85.16 & 96.39 & 87.03 \\ \hline Logistic & 66.17 & 74.19 & 85.42 & 76.07 \\ \hline Multilayer Perception & 66.84 & 80.34 & 89.17 & 82.88 \\ \hline Simple Logistic & 66.84 & 72.86 & 85.16 & 74.73 \\ \hline IBK & 66.57 & 79.27 & 91.04 & 81.02 \\ \hline KSTAR & 65.10 & 81.14 & 91.71 & 80.74 \\ \hline LWL & 42.11 & 46.39 & 61.23 & 50.26 \\ \hline Ada BoostM1 & 6.81 & 6.81 & 6.81 & 6.81 \\ \hline Regression & 65.37 & 84.22 & 95.98 & 86.49 \\ \hline Decision Table & 63.77 & 79.27 & 74.73 & 72.19 \\ \hline Hoeffding Tree & 65.37 & 85.29 & 96.52 & 86.89 \\ \hline J48 & 65.10 & 83.55 & 94.65 & 84.49 \\ \hline Random Forest & 66.57 & 82.88 & 97.32 & 87.03 \\ \hline Random Tree & 68.04 & 79.27 & 94.92 & 83.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy (%) for different feature sets. labels in crop classification and provides insights into which classification methods may be most effective for this task. Further research could explore the potential benefits of using a combination of classifications (multi-labeling) and investigate the potential of using more granular or specific labels that may impact the accuracy of classification algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
###### Abstract
An AI-assisted phishing detection technology can be progressively trained to detect phishing emails of increasing sophistication. Concurrently, a customizable, evolving phishing awareness training and testing solution can be tailored to different business models and effectively equip users with the experience and knowledge to identify and deal with harmful phishing attacks. Our paper presents a proposed hybrid security framework prototype that supports a customizable, evolving phishing awareness training and testing solution reinforced by an AI-assisted phishing detection platform. The prototype is currently used by a government agency and can be employed by other organizations to train and test its employees. The prototype solution offers the following benefits:  
* Adaptable to different business models and operations;
* Assess an organization's security posture and identify at-risk employees;
* Able to generate test cases to scale with increasing phishing email deceptiveness;
* Offers better privacy for an organization's security posture and vulnerable employees;
* Reinforces an organization's security posture with AI-based phishing URL detection. The remainder of this paper is organized as follows: Section 2 reviews related AI research work in the phishing domain; Section 3 introduces the 3 rd generation neural network machine learning model that can be used to support realistic human-like text production; Section 4 provides a background on the practice of designing and refining prompts that are used to elicit responses from AI models; Section 5 discusses the accuracy of the large language model used in the generative AI component in our framework; Section 6 presents the phishing education solution prototype architecture; Sections 7 and 8 introduce and detail the functionality of the phishing campaign formulation portal of the phishing education solution prototype; Sections 9-11 present the training dataset collection and data pre-processing considerations and deep learning model architecture specifications for the AI-assisted phishing detection platform; Section 12 compares the performances of the different deep learning architectures considered for the AI-based phishing URL detection platform; and Section 13 concludes the paper with a view for future research.
## 1 Introduction
### Review of Data Aggregation
During the diffusion update step, a diffusion matrix is incorporated which has a direct influence on the performance. One realization of diffusion matrix is by Covariance Intersection (CI) method using the information of error covariance [45; 46].
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 2 System Design  
The telecentric surface metrology system in this study is designed for the measurement of micro-scale surfaces, such as industrial inspection for circuit boards. The system consists of a single camera with a telecentric lens, a micro line laser generator and a one-dimensional (1D) displacement platform. As shown in Figure 1, all components are completely fixed on a workbench to reduce the measurement uncertainty. During the measurement procedure, the object measured is fixed on the motor desk of the displacement platform. The precise movement of the desk along the guideway leads to the relative movement between the object and laser, which realizes the linear pushbroom imaging for the measured surface.  
To achieve the measurement, the single telecentric camera is supposed to be calibrated, and the location of line laser plane and the motor direction of the motor desk in the world coordinate system are required. Hence, these issues are studied and proposed in Section 3.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 2 Materials and Methods
### Fractal Antenna: Step-by-Step Design Development  
The monopole Koch fractal antenna's step-by-step development is shown in Figure 5. The design begins with an evaluation of a circular patch on an FR-4 substrate of size 4a\({}_{\text{e}}\)\(\times\) 4a\({}_{\text{e}}\), where a\({}_{\text{e}}\) is the effective radius of the circular patch evaluated at a frequency of 3.5 GHz [36]. Then, in step 2, a semi-circular patch is removed from step 1, and another circle with a four-line segment (rhombus shape is attached at the same center of the circular patch), and binary addition is used to add the two sections, as displayed in step 3. In steps 4 and 5, Koch iteration-1 and iteration-2 fractals are created. Throughout these steps, the ground length is kept equal to a quarter of the guided wavelength, i.e., 10.21 mm (9.7 mm optimized value). In step 6, an SRR is designed at a frequency of 3.5 GHz and introduced near the monopole antenna ground at the bottom surface. This helps to enhance the bandwidth of the overall antenna.  
Figure 3: Fractal antenna patch development: (**a**) iteration 0, (**b**) iteration 1, and (**c**) iteration 2.  
Figure 2: Development of Koch fractal iteration 0 stage.
## 4 Conclusions  
It is shown that the low temperature plasma of DC glow discharge which burns in the active or inert gases at the medium pressures is the perspective source of surface heating in the processes of diffusion bonding and ion treatment of metals. At the same time, a number of factors on the cathode surface can emerge, which leads to the transition of a glow discharge into an electric arc as a more stable form of gas discharges. In this study, the impact of macro and micro relief of the samples on the stability of a glow discharge while diffusion bonding and treatment of metal has been analyzed and analytical equation of a glow discharge energy stability boundary has been obtained. The main conclusions of this study can be summarized, as follows:  
1. The emergence of oxide films on the surfaces of specimens during welding or metals treatment does not lead to a significant disruption of a glow discharge stability as long as electrical field strength does not exceed the breakdown values.
2. On the other hand, the roughness of a cathode's surface affects the glow discharge stability at the working gas pressures when the height of the protrusions roughness becomes comparable to the length of the cathode potential drop region \(\mathrm{d_{c}}\).
3. The direct dependence of the \(\mathrm{d_{c}}\) length on the gas pressure allows to determine the limit values of the latter based on the given characteristics of the samples surface microrelief which ensures the stable glow discharge existence during ion treatment and welding of metals. In our experiments the increase of the cathode surface roughness from 10-15 \(\mathrm{\SIUnitSymbolMicro m}\) to 60-80 \(\mathrm{\SIUnitSymbolMicro m}\) led to a rapid decrease of the region of the limiting  
Figure 9: The experimental dependence of the working gas pressure (\(P\)) on the surface roughness parameters (Rz) according to the conditions of glow discharge stability: (\(d_{c}\)—cathodic potential drop region).  
pressure of the stable glow discharge existence from 1.33-13.3 kPa to a 1.33-5.3 kPa, respectively.  
G.B.: conceptualization and methodology; M.B.: experiments, results validation, formal analysis, writing--original draft preparation; S.S.: literature review, main glow discharge's instabilities related with surface state of cathode, writing--review and editing; P.I.: investigation, data curation, getting profilograms. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Ministry of Education and Science of Ukraine (Grant 0117/U007259 \"New high-tech energy-efficient heating source for precision welding, brazing and surface treatment of metals\").  
The authors declare no conflict of interest.
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 4 Numerically Verification
### The Effect of Vehicle Mass  
To study the effect of vehicle mass on the proposed approach, two more cases are considered, referring to a 'lighter' vehicle and a 'heavier' vehicle, respectively. For the lighter vehicle, \(m_{s}=9300\) kg, and for the heavier one, \(m_{s}=19,300\) kg. Other properties of the vehicle are kept the same as in Table 1. For comparison, the vehicle in Table 1 (i.e., \(m_{s}=14,300\) kg) is labeled as the'regular' vehicle. The lower vehicle velocity of \(v=2.5\) m/s is considered in the VBI system, and the bridge health condition is the same as in the previous case. The proposed approach is used to analyze these two cases and the changes in wavelet energy are shown in Figure 15. It should be noted that since the vehicle mass changes, the vehicle frequency changes as well. As a result, for different vehicle masses, the peak energy of the vehicle responses is at different scales. It was found that heavier vehicles can improve the feasibility of the proposed bridge damage detection method because of the greater variation in wavelet energy. The wavelet energy change rates for lighter, regular, and heavier vehicles are 1%, 3%, and 4%, respectively. Therefore, it is recommended to use the proposed method to implement relatively heavy vehicles for bridge health monitoring.  
Figure 14: Bridge damage identification at higher velocity. (**a**) wavelet energy; (**b**) damage index.
###### Abstract  
This study aimed to investigate the effects of the solvent type, extraction time, extraction temperature and solvent-to-material ratio on the recovery of the total saponin content (TSC), the total phenolic content (TPC) and the antioxidant activity from the rhizomes of _Curculigo orchioides_ Gaertn. The extraction efficiency of the bioactive compounds by ultrasound-assisted extraction (UAE) was also evaluated. Extraction with 80% ethanol achieved the highest extraction yields, and the optimal conditions for the extraction of TSC were 60 min, 57 \({}^{\circ}\)C and an 80 mL/g solvent-to-material ratio. The optimal conditions for the recovery of TPC were 178 min, 45 \({}^{\circ}\)C and a solvent-to-material ratio of 68 mL/g. The highest antioxidant activity of the extracts from _Curculigo orchioides_ Gaertn rhizomes was obtained with the optimal conditions of 180 min, 40 \({}^{\circ}\)C and an 80 mL/g solvent-to-material ratio. The actual extraction yields obtained from the optimal conditions were 11.33 mg aescin equivalents (AE)/g dry weight (DW) for TSC, 23.58 mg gallic acid equivalents (GAE)/g DW for TPC and DPPH antioxidant activity of 133.45 \(\upmu\)M Trolox equivalents (TE)/g DW. UAE using the same type and amount of solvent for only 10 min could result in comparable extraction yields of TSC, TPC and DPPH antioxidant activity to the 180 min conventional extraction process. These promising results suggest the potential for the development of effective extraction processes to recover bioactive compounds from _Curculigo orchioides_ Gaertn rhizomes in practical production.  
Xian Mao; extraction; saponin; phenolic; antioxidant activity +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Definitions  
This section aims to provide an operational definition of \"an attack on CDFS\" and define three core concepts (trust, trustworthiness, and reputation) that are frequently used in this paper.  
An attack on a system is defined as \"a threat action that undesirably alters system operation by adversely modifying system functions or data\" [24]. For this study, an attack on CDFS is defined as systematically deviating the point forecasts and interval forecasts for \(k\)-step ahead aggregate demands from the actual values to a certain direction (upwards or downwards) over time by adversely modifying sensor data. The success of the attack is measured on the following two indicators: (1) the higher the deviations of the single-point demand forecast from the actual demand for a given period, the more successful the attack, and (2) the ultimate success of the attack is achieved when the actual demand falls outside of the forecasted bounds.  
Although the terms 'trust', 'trustworthiness', and'reputation' are widely used in the literature, there remains debate about the definitions of all three [11; 13; 22; 23; 25; 26; 27; 28; 29]. Different studies have different definitions of these terms. In this study, we adopt the following definitions for trust, trustworthiness, and reputation. We use 'trust' and 'trustworthiness' interchangeably. If a node needs to evaluate the trust score of another node, the evaluating node is called the subject node, and the evaluated node is called the object node [20]. 'Trust' or 'trustworthiness' of a node is the subjective probability assigned to an object node by a subject node at time step \(t\) based on various factors (e.g., the object node's long-term reputation, the similarity of its measurements with the measurements generated by other nodes received at the same time step etc.) that the measurement received from the object node is accurate [27; 30]. The trustworthiness of data provenance is the probability assigned by a subject node to the provenance that the measurement received from the data provenance is accurate. Data provenance refers to the metadata that captures the complete history of a piece of data from origin to destination. The data provenance of measurement consists of two basic components [31]. The first component is the route of the measurement. A route refers to the path a measurement travels on a network. The route includes every node that handles the measurement between its source and destination. The second component of the data provenance is the list of actions performed by participating nodes. The trustworthiness of a measurement value is the probability that the measurement is accurate [11]. The reputation of an object node is the overall probability estimated from the trust scores assigned to the object node by all subject nodes over time [26; 30].
## 2 Background and Related Work
### Number of Interrelated Attributes
There are \(l\) attributes in \(AT\) and there is a number \(m\)\((1<m<l)\) such that \(\rho(x,a_{1})\)= \(\ldots\) =\(\rho(x,a_{m})\)= \(1\) and \(\rho(x,a_{m+1})\)= \(\ldots\) =\(\rho(x,a_{l})\)= \(0\) hold, respectively. In other words, the item \(i_{x}\) has all \(m\) attributes \(a_{1}\), \(\ldots\), \(a_{m}\) and does not have any \(l\) - \(m\) attributes \(a_{m+1}\), \(\ldots\), \(a_{l}\). In this situation, the number of interrelated attributes with a value of \(1\) can easily be obtained. Thus, for every two attributes \(a_{i}a_{j}\)\((1\leq i<j\leq m)\), it is clear that the value of \(a_{i}=_{eq}a_{j}\) is \(1\) but the values of \(a_{i}>_{mo}a_{j}\) and \(a_{i}<_{le}a_{j}\) are \(0\). It can also be easily verified that the number of the interrelated attributes \(a_{i}=_{eq}a_{j}\) is \(C_{m}^{2}\). Similarly, for every two attributes \(a_{p}\), \(a_{q}\)\((1\leq p\leq m\) and \(m+1\leq q\leq l)\), it is also clear that the value \(a_{p}>_{mo}a_{q}\) is \(1\) but \(a_{p}=_{eq}a_{q}\) and \(a_{p}<_{le}a_{q}\) are \(0\), hence, the number of interrelated attributes \(a_{p}>_{mo}a_{q}\) is \(m\times(l-m)\). These results conclude the following theorem about the number of interrelated attributes with the value \(1\). Theorem 3.1: _Let \(AT\) be a set of attributes that comprises \(l\) attributes and \(i_{x}\in I\) be an item that owns \(m\) attributes in \(AT\). The number of interrelated attributes that item \(i_{x}\) owns, denoted by \(num(x,l,m)\), can be obtained using_  
\[num(x;l,m)=C_{m}^{2}+\ m(l-m). \tag{11}\]