esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
However, these session-based methods regard user preference for items as a stationary property and therefore cannot capture the changing contextual interactions between user and items. To solve the problem of the static user preference and intent, [PERSON] et al. [30] focused on the variation in user intent at different times and proposed an LSTM-based \"Just-In-Time\" approach to recommend the right item at the right time. [PERSON] et al. [31] proposed an LSTM-based approach that implicitly captures various known temporal patterns in movie ratings data without explicit inclusion in the model, to learn dynamic embeddings of users and movies. The next-basket recommendation model proposed in [32] learned the varying representation of a user and captured global sequential features among baskets to reflect the user's dynamic intent at different times and interactions of all baskets of the user over time. [PERSON] et al. [7] improved the next-item recommendation and cold-start problem by implementing two RNNs to extract user preference from recent sessions and the current session, respectively. [PERSON] et al. [29] designed an efficient personalized session-based recommender system with shallow wide-in-wide-out networks over relaxed ordered user-session contexts. These methods assume that each item in one session has the same influence. However, this assumption is usually invalid, especially when considering a user's interactions with items of diverse characteristics. In the meantime, not only his preference for items changes among different sessions, but also his intent on items within one session. To deal with the problem of the static weights being assigned to each item, [PERSON] et al. [34] developed an attention-based model to capture the changing attention that a user pays to different text reviews and ratings. [PERSON] et al. [35] designed an attention-based transaction embedding model to weight each item in a transaction without assuming order. To solve these two issues at the same time in a unified way, we follow the idea in [7] but make the following contributions: (1) We propose an Attention-based Short-term Intent Layer to assign different weights for items in the current session to capture the contextual property of user intent. (2) We propose a Long-term Preference Layer to take the user's previous sessions into account, to provide information to the attention-based short-term intent layer and enable it to combine both the user's short-term intent and long-term preference in the attention process.
## 5 Conclusions  
DH36 steel is often used in polar ships at low temperature. In this study, low temperature fatigue tests of T-welded and cruciform welded joints were performed, and the difference in the fatigue properties of the T-welded and cruciform welded joints was examined by analyzing the stress concentration, microstructure, Vickers hardness, and fractography. In addition, the effect of low temperature on DH36 welded joints was also analyzed in this study. The following conclusions can be drawn:  
1. The fatigue properties of T-welded joints were almost 2-7% better than those of the cruciform welded joints at \(-60\)\({}^{\circ}\)C. In this paper, the difference in fatigue properties between T-welded and cruciform welded joints can be attributed to the difference in hardness and stress range level of the failure zone.
2. The Vickers hardness at \(-60\)\({}^{\circ}\)C was 12.17 HV higher than that of room temperature. Furthermore, the difference in hardness between T- and cruciform welded joints also showed different situations at \(-60\)\({}^{\circ}\)C and room temperature. The Vickers hardness in the failure zone of T-welded joints was close to that in the failure zone of cruciform welded joints at room temperature. However, the Vickers hardness in the failure zone of the T-welded joints (168-174 HV) was higher than that in the failure zone of the cruciform welded joints (159-165 HV) at \(-60\)\({}^{\circ}\)C.
3. The spacing of fatigue striations of cruciform welded joints in the HAZ was larger than that of T-welded joints in the crack propagation zone which means the fatigue crack growth rate of the T-welded joints was lower than that of the cruciform welded joints. There were no significant differences in the crack initiation zone and instantaneous fracture zone.
4. Low temperature had a significant effect on the fatigue performance of welded DH36. The fatigue properties of cruciform welded joints at \(-60\)\({}^{\circ}\)C were 57.215% better than those at room temperature.  
Data curation, W.L.; Supervision, H.R.; Writing--original draft, W.Z.; Writing--review & editing, G.F. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Natural Science Foundation of China, Fund No. 51679050.  
The authors declare no conflict of interest.
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 2 EUV-Induced Plasma
### Scaling Laws
However, nowadays EUV scanners operate together with LPP sources at a repetition rate of up to 50 kHz [7; 15]. Upscaling the repetition rate alters EUV-induced plasma dynamics significantly, as was already demonstrated in Ref. [16]. At 500 Hz repetition rate, the plasma that was created with a certain pulse of EUV photons has fully vanished at the moment the next pulse of EUV photons is applied. Experiments using a tin-based EUV source at repetition rates of 1, 5, and 10 kHz, however, show that more background plasma remains between two consecutive pulses of irradiation, at higher repetition rates [16]. This effect will also occur at higher pressures, as higher pressures slow down the plasma decay. In the end, the build-up of the plasma background depends on the balance between the plasma decay time (as determined by pressure and geometry) and the source repetition rate. * Different gases  
EUV-induced plasmas have been investigated in several different gases (e.g., Ar, He, H\({}_{2}\), residual air). Of main importance in the governing plasma dynamics are the (photo-and electron-impact) ionization cross sections, which impact the maximum plasma density, the ion mass of the specific gas, which impacts the rate of plasma expansion and decay and the possibility to excite specific molecular and/or atomic energy levels. Very recently, EUV-induced plasmas in N\({}_{2}\)-diluted H\({}_{2}\) gas have been studied [63]. From that study, it was concluded that the addition of even a small amount of N\({}_{2}\) (in the order of 0.1%) to H\({}_{2}\) resulted in a significant alteration of the ionic balance of the system and a rich chemistry producing NH-containing ions.
## 4 Valorization Technologies of Avocado-Derived Biomass through Obtaining Bioenergy and Biofuels
### Biodiesel  
Biodiesel is primarily produced from oilseeds, which are limited and relatively expensive. It also competes with food for human consumption. As a consequence, it is necessary to investigate inedible options for the production of second-generation biofuels, so as not to compete with food uses [59]. In this context, several documents highlight the potential of different type of avocado oils for biodiesel production through, mainly, catalyst-assisted alcohols of its oil fraction [60; 61; 62; 63; 64]. It is also known as the transesterification reaction of triglycerides and the esterification of free fatty acid [65].  
Table 4 shows methods applied to produce biodiesel and the characteristics of the biodiesel produced using oils from avocado waste. [PERSON] et al. [60] reported that avocado seed contains triglycerides and it has a low content of free fatty acids (around 0.37%); hence, this oil can be processed to obtain biodiesel through transesterification. They found that the highest content of methyl ester for biodiesel from avocado seed oil is obtained at 60 \({}^{\circ}\)C, with a 1:6 molar ratios of oil to methanol, and that the best washing method is dry washing [60]. Other studies have applied catalyzed methanolysis and ethanolysis to produce biodiesel from avocado oils at temperatures of 65-75 \({}^{\circ}\)C, in a time of 1-2 h, and with an alcohol-to-oil ratio of 6:1 or 9:1, and 1-7% catalysts [62; 63]. Their results show the technical viability (Table 4) and environmental friendliness potential of avocado oil for diesel engines, and thus underused avocados can be collected for this use. In this way, using rotten avocados, [PERSON] et al. [66] applied catalyzed (with KOH) and non-catalyzed (with silica) methanolysis and dichloromethane for transesterification to obtain biodiesel. The latter experiment with dichloromethane provided a yield of 93%, avoiding the use methanol as an acyl acceptor, but it requires the use of high temperatures (up to 380 \({}^{\circ}\)C). Furthermore, the peel also contains lipids (up to 10.4%, Table 1) and thus it could be a potential source of biodiesel. Even the wastewater from the CPAO process has around 54% of lipids, but a feasible recovery is firstly required.
# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 4 Results and Discussion
### Result Discussion  
In this section, we first present the results for our three datasets NSI-KDD, ToN-IoT network, and CICIDS2017. A cross validation with 10 factors was used for all ML methods. In summary, all the applied techniques obtained significant results due to the different feature engineering methods that were employed on the datasets. First, we imputed missing values and converted categorical values, solving the imbalanced data problem with the SMOTE technique, and then applying the Min-Max normalization technique.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 2 Power Semiconductor Devices
In contrast to the structure of the IGBT, the emitter is replaced with the source electrode, while the gate electrode remains separated at the top by the interlayer insulator without a trench. Like Si IGBT, the channel of SiC MOSFET is located in the p region, between the \(n^{+}\) source and the \(n\)-layer. Although both devices' structure is similar to the MOS-gated structure, there is no parasitic body diode in Si IGBT, and thus it requires an antiparallel Si \(p\)-\(i\)-\(n\) freewheeling diode for practical applications [33; 34]. From the electrothermal behavior viewpoint, both devices are conducting to the top or bottom surface of the die when there is a current flow. This causes variation in the temperature distribution within the device, and thermal modeling of these devices under the same current and voltage rating has shown that the junction temperature and the temperature swing of IGBT are higher than that of SiC MOSFET, since the on-state resistance of the IGBT is independent of junction temperature [35]. Meanwhile, in case of short circuit failure, the junction temperature rises faster in SiC MOSFET than in IGBT, which results in a lower short circuit holding time. This is because the heat generation rate in SiC is three times higher than the conduction rate, when compared to Si IGETs. Hence, during a short circuit, junction temperature will be dominated by the heat generation rate [36], suggesting that the magnitude of the junction temperature for both devices depends on their operation state. The structure of both devices is similar to traditional MOSFET, and since both are Si-based semiconductors, they are suitable for the TSOP junction temperature sensing approach, as discussed in the subsequent Section 3. Moreover, since FBG could be bonded on SiC and IGBT devices while IRC can detect temperature distribution on their respective surfaces, both allow measurement of device junction temperature, thus making the OBS technique an attractive technology.
## 4 Use Cases
### Spatiotemporal Learning Dataset  
The dataset used in this study was derived from the Conceptual Model-Based Problem-Solving (COMPS) program [41]. The COMPS program developed an online mathematical problem-solving platform designed to enhance students' mathematical understanding and problem-solving skills. The platform consists of two modules and fourteen lessons. Module A focuses on using counting strategies to solve mathematical problems. While module B emphasizes model-based problem-solving, teaching students to abstract and structure mathematical problems into equations and solve them systematically.  
A group of 16 students participated in this program, dedicating 30 min per school day to solving mathematical problems on the platform over the course of one academic semester. The computer labs used for the program were designed with consistent environmental settings, illuminated by both natural and artificial light. All students worked on Dell Precision 3520 workstations equipped with 15.6-inch displays and a resolution of 1920 \(\times\) 1080 pixels (Dell Technologies, Round Rock, TX, USA).  
To capture students' eye movements, a Tobii Pro X3-120 eye tracker (Tobii AB, Stockholm, Sweden) was mounted at the bottom of each laptop screen. The Tobii Pro X3-120 eye tracker operated at a frequency of 120 Hz and featured an ultra-slim design (dimensions: \(115\times 111\times 32.7\) mm), which minimized interference with students' interactions and reduced potential distractions. Students' eye movement data, including fixation positions (\"x_position' and 'y_position' in pixels), timestamps (\"timestamp'), and fixation durations ('fixation_duration' in milliseconds), were recorded using Tobii Pro Studio 3.4.7 and imported into a MySQL 5.7.18 database for storage, retrieval, and analysis. Additionally, students' mouse movements and clicks were captured by the COMPS program and stored in the database with the fields'mouse_x','mouse_y' (mouse coordinates in pixels), 'click_type' (left or right click), and 'timestamp'. Task performance data, including students' answers to problems ('answer'), correctness ('is_correct'), and time spent on each problem ('time_spent'), were also stored in the database. Each dataset was organized in separate tables, enabling efficient storage and analysis.  
Together, the spatiotemporal learning dataset provided rich information about students' learning behaviors and cognitive engagement. For the use cases analysis, the learning data were provided to the five educators. Due to time constraints, the educators were only required to analyze five specific tasks of Module B.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 2 Methodology
### Visualization Analysis of Different Metrics  
In MBBM, the interaction between the anomaly score calculation function and feature engineering ultimately affects the calculation of anomaly scores. In existing research on image anomaly detection, the evaluation of anomaly score calculation functions is typically manifested as performance metrics on specific datasets. However, this is far from sufficient [49]. Considering the inherent flaws of real datasets and the significant impact of complex variable interactions on results, we simulate sample distributions in two-dimensional space to facilitate the visual analysis of anomaly score functions.  
\[v_{x} =\bigg{\{}x\in\mathbb{R}\ |\ x=x_{0}+0.005n,\,n\in\mathbb{N},\,0\leq n <\frac{x_{1}-x_{0}}{0.005}\bigg{\}},\] \[v_{y} =\bigg{\{}y\in\mathbb{R}\ |\ x=y_{0}+0.005n,\,n\in\mathbb{N},\,0\leq n< \frac{y_{1}-y_{0}}{0.005}\bigg{\}}, \tag{13}\] \[Grid =v\times v,\quad\text{where }Grid\in\mathbb{R}^{\left\lfloor(x_{1}-x_{ 0})(y_{1}-y_{0})\right\rfloor/0.00005\times 2}.\]As shown in Equation (13), this study uses \(v_{x}\) and \(v_{y}\) to denote one-dimensional grid vectors along the \(x\) and \(y\) axes within the visualization region (\(x_{0}<x<x_{1},y_{0}<y<y_{1}\)) and employs \(Grid\) to represent the two-dimensional spatial vector for visualization. The values \(x_{0}\), \(x_{1}\), \(y_{0}\), and \(y_{1}\) are manually input to define the visualization range. We employ random sampling within the grid to generate a core set \(\in\mathbb{R}^{n\times 2}\), and subsequently, visualize the gradient of anomaly scores within the \(Grid\) range.  
The set \(dists=\{(\|x-c\|_{2})\mid x\in Grid,c\in\textit{coreset}\}\) denotes the Euclidean distance between the points \((x,y)\) in \(Grid\) and points \(c\) in \(\textit{coreset}\). By substituting \(dists\) into the corresponding formula, one can obtain the anomaly score or anomaly score weights \(Z\), which are ultimately used to draw contours. Both \(dists\) and the anomaly scores can be obtained through parallel computation.
## 5 Conclusions  
The proposed circuit topology for a high-frequency induction heating system reduces the cost, overall size, and circuit complexity by reducing the number of solid-state switching devices. The suggested topology is realized with cascading of full-bridge diode rectifier circuit and full-wave H-bride inverter. The first stage rectifier converts the ac input voltage into its absolute value, while the second stage inverter converts it into high-frequency ac voltage. Therefore, the proposed circuit topology only requires four diodes and four transistors less than the existing topologies implemented with a similar principle. The reduction in switching transistors also lowers the use of many gate driving circuits. The overall impact of reducing solid-state switching devices and gate driving circuits is the reduction in overall size and circuit cost. There is no requirement to synchronize the gating  
Figure 10: Practically recorded waveforms: (**a**) output voltage; (**b**) output voltage (blue) and output current (red); (**c**) output voltage (blue) and output current (red) in magnified form; (**d**) input voltage (blue) and input current (red).  
signals with an ac input voltage that lowers the control effort of the PWM generator. As only two control signals are required for the realization of the overall operation so a simple control scheme can be generated with any low-cost PWM controller. The validation of the suggested circuit is carried out by simulating this topology in Simulink based platform and through the practical results obtained by developing a practical prototype.  
Conceptualization, N.A. and G.A.; data curation, G.A. and N.U.; formal analysis, N.A., A.B.A. and U.F.; funding acquisition, N.U. and A.A.A.; investigation, N.A. and M.Z.; methodology, G.A. and A.A.A.; project administration, G.A.; resources, A.B.A. and N.U.; software, U.F. and M.Z.; validation, U.F. and M.Z.; writing--original draft, N.A. and G.A.; writing--review and editing, G.A., A.B.A. and N.U. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by Taif University Researchers Supporting Project Number (TURSP-2020/121), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The authors acknowledge the financial support from Taif University Researchers Supporting Project Number (TURSP-2020/121), Taif University, Saudi Arabia. The authors are also thankful to the Department of Electrical Engineering, The University of Lahore--Pakistan for providing facilities to conduct this research.  
The authors declare no conflict of interest.
## 5 Test Results and Evaluation
### The Amplitude of Shape Imperfections in the Circumferential Direction
The \(p\)-value of offsets are larger than 5%, indicating no statistical significance. The multi-collinearity was investigated by variance inflation factors (VIFs) using Equation (7). \(R_{j}{}^{2}\) is the square of the multiple correlation coefficient that results when the predictor variable is regressed against all the other predictor variables [28]. \[\text{VIF}_{j}=\frac{1}{1-R_{j}^{2}},j=j,1\cdots n \tag{7}\]  
The results of the VIFs are shown in Table 9 (first column). A VIF greater than 10 is often considered a signal that the data have collinearity problems [28]. For example, the \(\text{VIF}_{5,234678910}=10.4\) amplitudes of \(w_{5}/t\) have a high correlation with other amplitudes of \(w_{2}\)-\(w_{4}\) and \(w_{6}\)-\(w_{10}\). The VIF was recalculated after excluding \(w_{5}\), \(w_{6}\), \(w_{8}\), and \(w_{9}\), and the results are shown in Table 9 (second column). The VIFs smaller than 10 were investigated through ANOVA. The results are shown in Table 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**First**} & \multicolumn{2}{c}{**Second**} \\ \hline VIF2,345678910 & 4.8 & VIF2,34710 & 2.3 \\ VIF3,245678910 & 6.2 & VIF3,24710 & 2.3 \\ VIF4,235678910 & 8.0 & VIF4,23710 & 1.7 \\ VIF5,234678910 & 10.4 & VIF7,23410 & 1.5 \\ VIF6,234578910 & 10.0 & VIF10,2347 & 2.0 \\ VIF7,234568910 & 4.7 & & \\ VIF8,234567910 & 12.8 & & \\ VIF9,234567810 & 11.3 & & \\ VIF10,23456789 & 4.8 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: \(p\)-value of regression analyses. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **KDF6** & _w2/t_ & _w3/t_ & _w4/t_ & _w5/t_ & _w6/t_ & _w7/t_ \\ \hline \(p\)-value & \(1.83\times 10^{-80}\) & 0.00712 & \(4.99\times 10^{-06}\) & 2.36 \(\times 10^{-21}\) & 0.824 & \(7.21\times 10^{-06}\) & 0.0113 \\ slope (a) & 0.665 & \(-0.0363\) & 0.125 & \(-0.396\) & \(-0.0151\) & \(-0.368\) & 0.123 \\ \hline \(w_{8}/t\) & \(w_{9}/t\) & \(w_{10}/t\) & Offset 0\({}^{\circ}\) & Offset 90\({}^{\circ}\) & Offset 180\({}^{\circ}\) & Offset 270\({}^{\circ}\) \\ \hline \(p\)-value & \(1.43\times 10^{-12}\) & 0.161 & 0.692 & 0.657 & 0.877 & 0.733 & 0.711 \\ slope (a) & 0.308 & \(-0.127\) & 0.10397 & \(-0.00484\) & 0.00169 & \(-0.00372\) & \(-0.00405\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(p\)-value of regression analyses. The \(p\)-values of \(w_{2}/t\), \(w_{4}/t\), \(w_{7}/t\), and \(w_{10}/t\) are much smaller than 1%, indicating high statistical significance. The \(p\)-value of \(w_{3}/t\) is larger than 5%, indicating no statistical significance. The analysis of the KDF was conducted considering the range of \(w_{n}/t\), as shown in Table 11. Regarding the slope (a), \(w_{2}/t\), \(w_{4}/t\), and \(w_{10}/t\) are negative, i.e., these shape imperfections reduce the buckling load; \(w_{7}/t\) has the opposite effect. When \(w_{7}/t=0\), the KDF is 0.618. When \(w_{7}/t=0.6\), the KDF is 0.693. Thus, \(w_{7}/t\) has a small effect on improving the KDF.
## 3 Data and Methods
### SAR Data and Processing
First, we performed a preliminary exploratory spatial analysis of the DT displacements to recognize possible correlations, trends, and outliers. Then, we estimated the experimental variogram to identify the presence of possible anisotropies in the data. Finally, we fitted the experimental variogram with a Spherical plus nugget model defined by Equation (1) [56]:  
\[\gamma(h)= \begin{cases}c_{0}+c\left\{\frac{3h}{2r}-\frac{1}{2}\left(\frac{h}{ r}\right)^{3}\right\}&0<h\leq r\\ c_{0}+c&h>r\\ 0&h=0\end{cases} , \tag{1}\]  
where \(h\) is the lag distance, \(r\) is the range, \(c_{0}\) is the nugget, and \(c_{0}+c\) is the sill. The complete analysis was performed using the geostatistical package Gstat [59], and experimental data were interpolated with ordinary kriging, together with the associated variance (or standard deviation). InSAR measures the radar Line-of-Sight (LoS)-projected component of the surface displacements, a combination of horizontal and vertical ground movements. To retrieve the vertical component, we assumed that almost all the observed LoS displacements were only due to vertical ground movements, at least in the Sant'Agostino-Mirabello area. Hence, we neglected the horizontal component, and we calculated the vertical displacement at each DT according to Equation (2):  
\[d_{v}=\frac{d_{LoS}}{\cos(\theta)}, \tag{2}\]  
where \(d_{v}\) is the vertical displacement, \(d_{LoS}\) is the displacement measured along the satellite LoS, and \(\theta\) is the incidence angle of the satellite.