esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Extract Spatial Features  
Videos are sequential data, in which frames representing spatial representations are listed according to time information, and the sequence of frames is the time information required to recognize the context of the action contained in the video. The video from the UCF-101 dataset processes frames at 25 fps. When all frame sequences per unit time are entered into the model, the processing time according to the computational cost increases and has a lot of redundant data. Considering processing complexity according to the data, the proposed system extracts N frames at random intervals and converts the image sequence of input video into \(FS^{N}\).  
\(FS^{N}\) is inputted to the CNN structure \(VGGNet\)[45], which is pretrained with ImageNet dataset and is extracted to feature maps. Moreover, the resolution of the images in \(FS^{N}\) is converted to (224, 224, 3) according to the kernel size of \(VGGNet\). \(VGGNet\) is a CNN structure that has deeply stacked convolutional layers through (3, 3) filters, with fewer parameters than convolutional layers, which were shallowly stacked through a larger sized filter. Therefore, although \(VGGNet\) is a deep layer structure, it is a CNN structure that has been proven to extract feature maps at high speed from input data and proven to extract significant features, which reduce classification errors as the layer deepens. In addition, \(VGGNet\) has the size (7, 7, 512) of a feature map generated from the convolutional layer prior to the FC layer, which is smaller than other CNN structures, such as \(ResNet\) and \(Inception\). The proposed system is light weighted using \(VGGNet\), which extracts relatively small size feature maps to build HAR models that process data at high speed. \(MobileNet\), a CNN structure that could be used in embedded systems with relatively low performance  
Figure 3: Action recognition process of the proposed system.  
GPUs and memory, was also proposed. However, the size of the final feature map was \((7,\,7,\,1024)\), which was not suitable for the purpose of the system to be built in this paper, which requires faster data processing by outputting a larger feature map than the \(VGGNet\) used in this paper.
## 4 Computational Results and Analysis
### Influence of the Bolt Diameter on Mechanical Properties of Bolts Support
At the failure of the anchored rock mass, the peak stress intensity, displacement of the bolt head, and the number of fractures in the anchored rock mass are summarized in Figure 8. The peak stress intensity of bolts declines, while the displacement of the bolt head and the number of fractures both increase with increasing bolt diameter. This indicates that, in a given type of rock mass, enlarging the bolt diameter can reduce the peak stress on bolts, avoid breakage of the bolts, and, at the same time, enhance the support capacity of bolts so that bolts can withstand greater deformation of surrounding rocks and avoid premature failure.
# Vector Beams with Only Transverse Intensity at Focus
## 3 Results
### Azimuthally Polarized Light
By substituting (8) into (6), we can show that the longitudinal component of the SAM in focus is equal to zero. To obtain azimuthally polarized light, there are currently commercially available vector wave plates (we used LBTEK VR1). Linearly polarized light incident on such an element along the \(x\)-axis is converted into an azimuthally polarized beam, and linearly polarized along the \(y\)-axis into a radially polarized beam, thus creating the Jones matrix:  
\[J_{CVB-1}= \begin{pmatrix}-\sin\varphi&\cos\varphi\\ \cos\varphi&\sin\varphi\end{pmatrix} . \tag{10}\]  
Figure 4 shows an image of a beam obtained by propagating linearly polarized light (from laser MGL-F-532-700 with a wavelength \(\lambda\) of 532 nm and an output power of 700 mW) through a vector wave plate (VWP, LBTEK VR1) at different positions of the linear polarizer-analyzer P in front of the recording CCD camera (UCMOS10000 KPA). Figure 4 shows that the beam polarization corresponds to the azimuthal one. The intensity distribution in Figure 4 corresponds to the distribution in Figure 3.  
Figure 4: Image of an azimuthally polarized beam on a camera without an analyzer (**a**) and with an analyzer rotated by angle of \(0^{\circ}\) (**b**) and \(90^{\circ}\) (**c**). Experimental setup used to obtain the images (**d**): laser—MGL-F-532-700 (\(\lambda=532\) nm, output power 700 mW); P—linear polarizer; VWP—vector wave plate (LBTEK VR1); CCD—CCD camera (UCMOS10000 KPA).
# Experimental Study on the Mechanical Properties of Xinyang Red Clay Improved by Lime and Fly Ash
## 3 Test Results and Analysis
### Compaction Test  
The change law of optimal water content and maximum dry density of modified soil with the addition of lime and fly ash is shown in Figure 3. As can be found from the figure, the maximum dry density of modified red clay decreases with the increase of the added lime and fly ash, but the overall decrease is small, while the optimal water content gradually increases. This is because red clay has an aggregate structure, while fly ash and lime have a granular structure. Fly ash and lime particles fill the pores between the red clay particles. In this way, the active substances in fly ash and lime, and the particles in the red clay, have an ion displacement effect. The particles of fly ash and lime are fine, the specific surface area is large, and they have strong hydrophilicity, so the optimal water content increases.  
Figure 2: Change curve of limit moisture content and plasticity index with the content of modified materials: (**a**) single lime, (**b**) single fly ash, and (**c**) mixed lime and fly ash.
## 3 Results  
### Low EBOV Replication Rates in Most Primary Cells from M. condylurus  
To investigate a potential correlation between EBOV replication rates and NPC1 receptor expression levels (Table 1), 15 unique cell isolates were infected with EBOV and analyzed. EBOV replication differed significantly depending on the cell isolate (Supplementary Figure S1). For comparison of virus replication rates, the fold-amplification of viral RNA copy numbers between 24 and 96 hpi was determined. Replication rates were classified as very low/no replication (lg(x) \(<\) 1.0), low (lg(x) = 1.0\(-\)1.5), moderate (lg(x) \(>\) 1.5-2.5), or high (lg(x) \(>\) 2.5) (Table 1; Figure 1).  
HEK293\(\Delta\)NPC1 and MoLi Prim cells were refractory to EBOV infection; no virus replication was detectable within the first 96 hpi. Low virus replication rates (lg(x) = 1.02-1.42) were determined in four primary cell isolates from _M. condylurus_ (MoTra Prim, MoLu Prim, MoSp Prim Early, and MoKi Prim). MoSp Prim Late, MoTes Prim, MoSk Prim, and MoBra Prim from _M. condylurus_ as well as NyKi Prim from _N. notula_ showed moderate replication rates. The human and monkey cell lines (HeLa, HEK293, and Vero) and the immortalized MoKi cells had the highest replication rates, with detected amplification increases of up to approximately lg(x) = 4.  
To determine the correlation between viral RNA copy numbers and viable virus, cell culture supernatants at 96 hpi from Vero, MoKi, MoBra Prim, MoTra Prim, MoLu Prim, HEK93\(\Delta\)NPC1, and MoLi Prim cells were titrated (Supplementary Figure S2). Overall, infectious virus titers were congruent with a virus/RNA ratio between about 1:15 (MoTra Prim) and 1:2000 (MoBra Prim). No infectious virus could be detected in supernatants of HEK293\(\Delta\)NPC1 and MoLi Prim cells.
### Conceptual Framework and Related Work  
Figure 1 illustrates the conceptual framework which is used to structure the related work. Based on this framework, the research streams relevant to this paper become visible. First, we discuss research on the association between personality and TA (Research Stream 1). Second, we outline studies which indicate that gender differences in attitude toward and usage of information and communication technologies exist. Thus, TA decisions are influenced by user gender (Research Stream 2). Third, we summarize important insights on gender differences in personality (Research Stream 3). These three research streams together constitute the literature basis of the present study.  
With respect to the possible moderating influence of gender on the personality-TA relationship, we only identified one finding in the scientific literature. In a study on the role of personality in TA decisions, [PERSON] et al. [11] only write: \"A one-way analysis of variance (ANOVA) of differences in responses between males and females in the sample supports the conclusion that gender does not play a significant part in our research model and results.\" (p. 98). Further analyses (e.g., on an actual moderation effects of gender), results, or comments are not provided in the paper. What follows is that we know very little about the possible moderating influence of gender on the personality-TA relationship (see the question mark in Figure 1).
# Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
## 5 Conclusions  
The current technological level allows the CubeSat to be effectively used in complex interplanetary missions. This work illustrates a simplified thrust model that can be used to analyze the optimal transfer trajectory of a CubeSat modeled on the M-ARGO spacecraft in a heliocentric scenario. The thrust vector description, which is a refinement of a recently proposed mathematical model, was used to simulate both a classical circle-to-circle orbit transfer and a phasing maneuver along a heliocentric circular orbit of assigned radius.  
Simulation results indicate that a phasing maneuver along a circular orbit, which is consistent with the heliocentric trajectory of the Earth, is possible even when the phasing angle is on the order of 60 degrees. In particular, considering the typical spacecraft constraints related to the propellant mass, this work analyzes the propellant-constrained optimal transfers towards one of the two triangular Lagrangian points of the Sun-Earth system. In this context, simulations show that a rapid transfer is possible when a coasting arc appears near the middle of the heliocentric transfer. The proposed refined thrust model can be easily extended to a general three-dimensional case in order to obtain a simple tool for the CubeSat trajectory optimization, which can be used to explore the spacecraft performance in more complex mission scenarios.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### Establishing Goals and Questions  
The GQM approach was utilized as a structured framework to systematically direct and assess the progression of this research. Initially, comprehensive goals were established to accurately encapsulate the fundamental objectives of the study. Each overarching goal was systematically deconstructed into sub-goals, offering more precise and targeted guidance.  
Figure 2: Overview of case study protocol.  
Aligned with these sub-goals, a structured set of precise inquiries was developed to systematically guide data collection and analytical processes. Ultimately, quantifiable metrics were established for each question, facilitating objective assessment and systematic evaluation. The GQM hierarchy used in this study is concisely outlined in the subsequent Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Goal** & **Sub-Goal** & **Question(s)** & **Metric(s)** \\ \hline \multirow{6}{*}{**G1:** Analysis of the discovered process model quality and runtime} & & Q1.1.1. How does applying a \(k\)-anonymization privacy model on event log data affect the discovered process & \\  & & log data affect the discovered process model & \\  & & model result quality and runtime & \\  & & performance? & \\  & &
# Modernising Receiver Operating Characteristic (ROC) Curves +
## Appendix B Explaining \(z_{ROC}\) Curve Parameters  
From Equation (2), variation of \(\delta\) (classification difficulty) with \(x\) on either axis (TPR or FPR) of a \(z_{ROC}\) curve, would yield a straight line against the marker value, \(x\), with slope \(m=\frac{1}{u}\), according to A3. As exemplified in Figure 4, in the present case the slopes of these straight lines change little, meaning that the classification uncertainty, \(u\), is largely the same for the different device types.  
There are two intercepts of interest which characterise the straight line of \(z\) against \(x\) for each axis of the \(z_{ROC}\) curve separately (Figure 4):  
1. The straight line intercepts the vertical axis, where \(x=0\) (and \(\delta=0\))), at a place \(z=-\frac{SL}{u}=\theta\) which shifts with the ability, \(\theta\), of each device;
2. The straight line crosses the horizontal (\(x\)-) axis at \(z=0\), where \(\theta=\delta\) and \(x=SL\). The different values of \(SL\) obtained in this fashion match the different intercepts on the vertical axis, that are due to the varying device ability.  
The overall \(z_{ROC}\) curve would thereafter be a line with slope \(M\) and intercept \(C\) according to the equation: \(Y=M\cdot X+C\) of a \(z_{ROC}\) plot, expressed in terms of the various parameters occurring in the formulae for the straight lines associated with each axis (Figure 5):  
\[M=\frac{y_{a}-y_{b}}{x_{a}-x_{b}}=\frac{\frac{SL_{tp}-x_{a}}{u_{tp}}-\frac{SL _{tp}-x_{b}}{u_{fp}}}{\frac{SL_{fp}-x_{a}}{u_{fp}}-\frac{SL_{fp}-x_{b}}{u_{fp}} }=\frac{u_{fp}}{u_{tp}}\]  
and  
\[C=-\left[\frac{SL_{tp}-x}{u_{tp}}\right]\bar{\omega}z_{fpr}=0\Rightarrow C=- \frac{SL_{tp}-SL_{fp}}{u_{tp}}\]
## 3 Results  
### ET Comparison among the Five ET Products  
The fluctuations of the annual ET are obvious, where the minimum values appeared in 2006, and the maximum values appeared in 2011 (except for the TerraClimate ET) (Figure 3a). Although the change trends of all ET products are similar, the differences between their extreme annual values are large. For example, the largest is up to 455.3 mm for the TerraClimate ET while the minimal is only 214.7 mm for the MODIS ET. The minimum and maximum annual ET values are 324.7 mm and 617.8 mm (AWAP); 364.3 mm and 611.2 mm (CSIRO); 366.7 mm and 732.5 mm (GLDAS); 249.6 mm and 464.3 mm (MODIS); and 257.8 mm and 713.1 mm (TerraClimate), respectively. Their ratios of the maximum to the minimum annual ET values are 1.9, 1.7, 2.0, 1.9 and 2.8, respectively. Moreover, there are differences among their average values (Table 2). The average value of GLDAS ET is 14% more than the AWAP ET, while MODIS ET is 24% less than the AWAP ET. The CSIRO ET and TerraClimate ET are very close to the AWAP ET. Their RMSE and MAE were smaller as RSR's values were around 0.5, and the PBIAS values were close to 0.  
Figure 3: The comparison of annual and monthly ET products: (**a**) the annual ET, (**b**) mean monthly ET with the maximum and minimum values, and (**c**) the linear fit curves between AWAP ET and the other four ET products.  
The mean, maximum, and minimum values of the monthly ET each year are shown in Figure 3b, and the variations of the mean monthly ET are the same as the yearly ET, but the changes of maximum and minimum values are very different. Compared with the AWAP ET, both the maximum and minimum values of GLDAS ET are larger, while the maximum values of MODIS ET are less, and its minimum values are less before 2010 and then almost the same since 2010. For the CSIRO and TerraClimate ET, their maximum values are similar to the AWAP ET, but the minimum values are less than the AWAP ET, and the TerraClimate ET is more obvious. The range of the monthly values are from 14.0 mm to 99.2 mm (AWAP), 11.2 mm to 87.2 mm (CSIRO), 15.0 mm to 103.1 mm (GLDAS), 9.8 mm to 77.3 mm (MODIS), and 5.1 mm to 105.3 mm (TerraClimate). Their ratios of the maximum to the minimum monthly ET values are up to 7.1, 7.8, 6.9, 7.9, and 20.6, respectively. Figure 3c shows the scatter diagram between the monthly AWAP ET with the other ET products from 2001 to 2016, and the performances of the fitting relationship between the GLDAS ET and CSIRO ET and AWAP ET are better than the other two ET products, which can be reflected by the slopes of fitting lines around 1 and correlation coefficients of more than 0.75 (Table 2). According to the other evaluation indicators, the CSIRO ET is the best among the four ET products, because its NSE is the largest, while the RMSE, RSR, and MAE are the smallest.
# Metal-Organic Frameworks Characterization via Inverse Pulse Gas Chromatography
## 3 Applications of IGC for the Characterization of MOFs
### Zeolitic Imidazole Frameworks (ZIFs)
In another study [70], the adsorption and separation of ethanol/ethyl acetate (EA)/water mixtures, formed during an industrial process for the synthesis of EA, were studied on ZIF-8. Single- and multicomponent isotherms of adsorption, acquired via gravimetric and breakthrough experiments, showed high adsorption ability and selectivity to EA. The higher interaction strength between EA and ZIF-8 was verified by IGC experiments. At zero coverage, the EA adsorption enthalpy was equivalent to 38.6 kJ mol\({}^{-1}\), while ethanol sorption released less energy (23.4 kJ mol\({}^{-1}\)). The larger polarizability of \(\pi\)-electrons containing ester carbonyl groups than hydroxyl groups may further enhance adsorption [119]. ZIF-68, which has a **gme** topology, consists of ZnN\({}_{4}\) tetrahedra connected by two derivatives of imidazole linkers; the first is a polar 2-nitroimidazolate linker, and the second is a nonpolar benzimidazole linker, in a 1:1 ratio. ZIF-68 consists of two diverse channels and three types of cages, which are large, medium, and small [120]. A six-membered channel (diameter approximately 3.4-3.8 A) forms alternating small and medium cages, where nitro groups are projected within the cages, maintaining the polar character. The larger channel (12-membered with a diameter of approximately 7.5-8.6 A) is predominantly constructed from imidazole linkers substituted with benzene, resulting in this channel's pronounced nonpolar character. Since the large cages occupy 74% of the total volume, ZIF-68 was assigned an overall nonpolar behavior [121]. An IGC investigation of ZIF-68 [72] demonstrated a lack of shape-selective behavior with the complex pore structure of this material. The logarithm of the Henry constant was plotted as a function of the logarithm of vapor pressure for n-alkanes and isoalkanes to evaluate the shape selectivity (Figure 14). The same linear correlation for linear and branched alkanes indicated the absence of shape selectivity [122; 123]. The zero-coverage adsorption enthalpies of the branched alkanes were approximately 5-8 kJ mol\({}^{-1}\) lower than those of the linear chains. This is typical of materials that are not shape selective. The structure demonstrated an overall nonpolar framework with limited polar centers. In contrast to alkanes, alkenes, and aromatics, small polar probes exhibited relatively high adsorption enthalpies. Due to the presence of nitro moieties, the cages in the small channel served as favorable adsorption positions for such polar molecules.
# Identification of Key Performance Indicators for T20--A Novel Hybrid Analytical Approach
## 2 Materials and Methods
### Data Processing  
Ball-by-ball data from www.ESPNcriinfo.com included the number of overs, balls bowled, bowling style (spin vs. seam), partnership scores, match venues (home vs. away), and coin toss outcomes (winning vs. losing the coin toss). The data was checked for errors and missing values, and then cleaned and restructured using R Studio software (RStudio) version 4.4.1. The key packages used within R were dplyr and tidyr, which were applied to clean and structure the raw dataset [32]. Pre-recorded data may contain noise or random variations that distort the accuracy of information. Due to error(s) in recording, sometimes there may be missing values for different columns [33]. This results in improper data classification [33]. Hence, columns with missing values were removed from the final dataset in this study. With regard to data processing, the data was initially aggregated into a set of PIs that represented various aspects of a team's performance in each innings of a match. Thereafter, the data was divided into specific play intervals to examine match dynamics and identify KPIs. The following match divisions or play intervals were commonly used: (1) the opening six overs (1 st-6 th)--this interval is known as the powerplay overs, where only two fielders are allowed outside the 27 m (30-yard) circle, and the focus during this phase is on aggressive batting to maximise the run rate; (2) the lower middle overs (7 th-10 th)--these overs are when captains and batsmen strategies to consolidate their innings, rotate the strike or batsmen and build a solid batting foundation for the latter part of the match--(3) the upper middle overs (11 th-14 th)--this interval is sometimes called the acceleration phase, when batsmen aim to increase the run rate, take calculated risks and build momentum leading into the final overs; and, lastly, (4) the last six overs (15 th-20 th)--this is the final stage of the innings, also known as the \"death-overs\", when batsmen typically target a higher run rate and, often, play more aggressively to maximise the total score. This interval is crucial for determining the team's overall score and setting competitive targets for the opposition. Therefore, the focus during this interval is on hitting boundaries and scoring runs quickly [11; 13; 14; 15]. This study examined 136 PIs, including some PIs drawn from the existing literature [13; 15], and also introduced some novel PIs that had not been evaluated previously. Notably, the innovative approach used in the current study involved 68 PIs for each team per innings. Due to the COVID-19 pandemic, some contextual features (such as the match venue) were excluded from the data analyses because the pandemic affected the travel of players and the scheduling of matches; consequently, many matches were moved to neutral venues or played without spectators. A summary of these PIs and their definitions is listed in Table 1.