esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Presentation of the Averagely Weighted Iteration Approach
### Effectiveness of the DI Approach in Improving the Stability  
According to the common view, the DI approach helps to improve the numerical stability. To verify whether this view is true on IBM, a stretched pressurized membrane immersed in a viscous fluid is simulated [13; 22]. The resting shape of the membrane before inflation is a circular elastic curve with radius \(R=51.491\), and the inflated and stretched shape is defined as an ellipse whose major and minor radii are \(R_{a}=75\) and \(R_{b}=50\), respectively (Figure 1). The membrane is located at the center of a square (width: \(L=200\)), and composed of \(N_{b}=1200\) Lagrange points. The fluid viscosity is set as \(\
u=0.001\). Only the tension stiffness is considered in the simulation and non-dimensionalized as \(\overline{K}_{s}\) (following Equation (15)) to scale the stability of the iteration approach. In this case, the fluid density is set as \(\rho_{f}=1\), and the characteristic length is set as the resting radius. Additionally, to avoid the transient velocity and facilitate a quantitative comparison, the characteristic velocity is prescribed uniformly as \(U=0.1\).  
All of the above parameters are lattice units.  
When \(\overline{K}_{s}\) is gradually tuned, the following results are obtained:  
1. With the explicit method, the executable program begins to run stably only at \(\overline{K}_{s}\leq 13.6\), when the membrane relaxes to a circular curve.  
Figure 1: Schematic diagram of the membrane relaxation.  
With the DI approach (the iteration time per evolution step is set as 10), the critical value is \(\overline{K}_{s}\leq 5.82\). In addition, the stability rapidly decreases with increasing iteration time (in Figure 2 the IB knots in four diagonal regions).  
To further study this issue, a test simulation with \(\overline{K}_{s}=13.6\) and \(N=20\) is conducted. The simulation breaks down because of numerical overflow, and the external forces at probe points N and S in time step 1 are shown in Figure 3. Since probe points N and S locate at the endpoints of the ellipsoid, and their displacements are mainly in the y direction, the external forces at these points are thus mainly along the y direction. Severe fluctuation occurs after \(N=8\) when the DI approach is applied. When the iteration continues, the fluctuation is amplified, which causes the simulation to diverge; thus, instead of improving the stability, the DI approach accelerates the divergence process.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 4 Testing and In-Orbit Results  
This section presents testing results after software development was largely finished. These tests are used to determine the valid ranges and combinations of the capture parameters shown in Table 3. In-orbit results are presented in Section 4.4. Details on software-testing strategies, materials and lessons learned were presented in [72].
## Abbreviations  
CH, core hopping: LCH, ligand-based core hopping: RCH, receptor-based core hopping; HTVS, high-throughput virtual screening; SH3, Src homology 3; SH2, Src homology 2; ADMET, adsorption, distribution, metabolism, excretion and toxicity; MD, molecular dynamics; SP, standard precision; XP, extra precision; MDCK, Madin-Darby Canine Kidney; ERG, human ether-a-go-go-related gene; RMSD, root-mean-square deviation; TK, tyrosine kinase.
# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 4 Variables and Data Description
### Data Processing and Basic Characteristic Analysis
#### 4.2.1 Interpreted Variables
The results are shown in Table 2.  
The stationarity of each high-frequency original sequence is then tested, and it is found that they are all first-order single integer sequences. As the time series put into the GARCH or ARMA model must be a stationary series, this article calculates the logarithmic difference of each series according to the processing method of the crude oil futures return sequence, that is, the return rate is modeled. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Futures Price (FP)** & **Spot Price (PS)** & **Futures Return (RF)** \\ \hline Mean & 82.71533 & 81.49046 & \(-0.000147\) \\ Median & 81.47500 & 79.82500 & 0 \\ Maximum & 146.0800 & 143.9500 & 0.147017 \\ Minimum & 27.79000 & 26.01000 & \(-0.109455\) \\ Std. Dev. & 27.05621 & 28.29182 & 0.020872 \\ Skewness & \(-0.038434\) & \(-0.044386\) & 0.177744 \\ Kurtosis & 1.630533 & 1.582079 & 6.520205 \\ CV & 0.3271 & 0.34718 & \(-141.9864\) \\ Jarque–Bera & 192.8384 & 206.8840 & 1359.791 \\  & [0] & [0] & [0] \\ \hline \hline \end{tabular} Note: JB (Jarque–Bera) normal test statistics are: JB \(=\frac{T}{6}\left[S^{2}+\frac{1}{4}(K-3)^{2}\right]\), where S is skewness and K is kurtosis; [ ] the value in brackets is the probability value (p value); coefficient of variation (CV) = Std. Dev./Mean.  
\end{table} 
Table 2: Basic statistical characteristics of Brent oil futures price, spot price and futures return. Figure 2: Price return trend chart of Brent oil futures. In the extraction of volatility, this article adopts the [PERSON] (1989) method, using explanatory variable logarithmic difference sequence to construct an AR(p) model, and the residuals obtained are then squared and used as the volatility of each variable. The wave dynamic potential of each variable is captured, and the AIC information criterion and the SC information criterion should be comprehensively considered as for the lag order p of the AR(p) model.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 6 Conclusions  
In this study, we implemented a botnet detection approach using network flow data, leveraging both Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs). The project involved several stages, including data pre-processing and the extraction of statistical, autocorrelation, and network topology features, followed by model training, testing, and evaluation.  
Our results indicated that the PHMM outperformed the HMM, demonstrating superior performance in classifying botnet families. In contrast, the HMM delivered average performance. While the PHMM demonstrated superior performance in classifying botnet families, the HMM yielded only average results. The limitations of the HMM may stem from its inherent assumptions (e.g., Markovian behavior) and simplicity compared to more complex models. It is important to acknowledge that both models have their limitations, and further research can explore hybrid approaches or ensemble methods.  
Although the HMM and PHMM were effective, other models, such as neural networks, have been reported to achieve better outcomes in similar studies [3]. Another limitation is due to relying on network flow data only. Future work can explore additional data sources or incorporate more diverse features to enhance model robustness. We also propose considering additional topological features, such as betweenness centrality, clustering coefficient, and PageRank. Furthermore, other models like Support Vector Machines (SVMs) and Random Forest can be explored to potentially achieve improved results. Additionally, exploring advanced techniques beyond SMOTE, such as Generative Artificial Intelligence models to generate synthetic data and balance botnet and normal traffic samples, can prove to be even more effective.  
Conceptualization, F.D.T.; Software, R.M.; Investigation, F.D.T.; Resources, F.D.T.; Data curation, R.M.; Writing--original draft, R.M.; Writing--review & editing, F.D.T.; Supervision, F.D.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The dataset used in this article can be found at [[https://www.stratosphereips.org/datasets-ctu13/](https://www.stratosphereips.org/datasets-ctu13/)]([https://www.stratosphereips.org/datasets-ctu13/](https://www.stratosphereips.org/datasets-ctu13/)) (accessed on 6 May 2024).  
The authors declare no conflict of interests.
# Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
## 2 System Model
### Blockage Models  
We consider the scenario where the signal from the transmitter located at height \(h_{tx}\) to the receiver, located at height \(h_{rx}\), can be blocked by both large static blockers, such as buildings, and small dynamic blockers, such as people. We consider different models for both blockers and then calculate the total blockage probability.  
There are a number of blockage models proposed to date including those related to human body blockage [21] and LoS/NLoS link behavior [22; 23]. To calculate the human body blockage probability by user blockers, we use the human blockage model from [21], where human blockers are modeled as cylinders with radius and height \(r_{B}\) and \(h_{B}\), respectively. They are placed with density \(\lambda\) units/m\({}^{2}\) and the blockage probability is given by (1)  
\[p(l)=1-exp\left(-2\lambda r_{B}\bigg{[}\sqrt{l^{2}-(h_{tx}-h_{rx})^{2}}\frac{h_ {B}-h_{rx}}{h_{tx}-h_{rx}}+r_{B}\bigg{]}\right). \tag{1}\]  
Here, \(h_{tx}\) is the height of the transmitter, \(h_{rx}\) is the height of the receiver, and \(l\) is the three-dimensional distance between the transmitter and the receiver.  
Figure 1: The considered deployment scenarios.  
To calculate the LOS probability for large stationary buildings, we implement 3 GPP path-loss propagation models for UMi (2), UMa (3), with parameters given in (4), and RMa (5) scenarios ([8])  
\[p_{LOS}(l)=\begin{cases}\left[\frac{18}{l}+exp(-\frac{l}{36})(1-\frac{18}{l}) \right]&18\ m<l\\ 1&\text{otherwise}\end{cases}, \tag{2}\]  
\[p_{LOS}(l)=\begin{cases}\left[\frac{18}{l}+exp(-\frac{l}{63})(1-\frac{18}{l}) \right]\left(1+C^{\prime}(h_{rx})\frac{5}{4}(\frac{l}{100})^{3}exp(-\frac{l}{1 50})\right)&18\ m<l\\ 1&\text{otherwise}\end{cases}, \tag{3}\]  
\[C^{\prime}(h)=\begin{cases}\left(\frac{h-13}{10}\right)^{1.5}&13\ m<h\leq 23 \ m\\ 0&h\leq 13\ m\end{cases}, \tag{4}\]  
\[p_{LOS}(l)=\begin{cases}exp\left(-\frac{l-10}{1000}\right)&18\ m<l\\ 1&\text{otherwise}\end{cases}. \tag{5}\]  
According to the 3 GPP Urban Micro model (UMi) [8], the path loss of a signal transmitted at a carrier frequency \(f\), at a given three-dimensional distance \(l\), can be calculated as  
\[PL(l,\alpha,\beta)=10\alpha log_{10}(l)+10\beta+20 log_{10}(f), \tag{6}\]  
where the coefficients \(\alpha\) and \(\beta\) are responsible for human and building blockage, respectively, are given in Table 1. In addition, we consider the signal to be blocked by both small mobile blockers and large stationary blockers with a certain probability in all cases, except the case of a BS-to-UAV connection. In that case, since the UAV is located high above the ground and the BS antenna is also located higher than a human height, for this link, we assume that human blockage is not possible and only consider blockage by large stationary buildings; thus, in this case, we only use value \(\beta_{1}=3.24\) from Table 1.
## 4 Experiment and Results
### Training Label Generation  
In the label generation stage, two labels need to be generated according to the training data annotation: the scale prediction training label and the spatial information prediction training label. According to the annotation information of the training data set, the scale prediction training label is first generated. Its vertex coordinates \(\{v_{1},v_{2},v_{3},v_{4}\}=\{[x_{1}\ y_{1}],[x_{2}\ y_{2}],[x_{3}\ y_{3}],[x_{4 }\ y_{4}]\}\) can be obtained for a character area \(t\), and the shortest edge \(M=min(\|v_{1}-v_{2}\|_{1},\|v_{2}-v_{3}\|_{1})\) of the character area can be  
Figure 8: Examples of high-definition calligraphy database.  
Figure 7: Generation of the simulation database.  
obtained according to the vertex coordinates. In this chapter, the pixels in the character area of the training label are assigned according to the Formula (5).  
\[label\left\{\begin{array}{l}1,\ M\geq\frac{I_{h}}{8}\\ 2,\frac{I_{h}}{32}\leq M<\frac{I_{h}}{8}\\ 3,M<\frac{I_{h}}{32}\end{array}\right. \tag{5}\]  
In the formula, \(I_{h}\) represents the height of the image. The generated scale prediction training label is shown in Figure 8(b). The green area represents the large area marked \"1\", the blue area represents the medium area marked \"2\", and the white area represents the small area marked \"3\".  
After the scale prediction training label is generated, the offset \(d=\gamma\times W\) is calculated according to the short side length M of the character area obtained in the scale prediction training label stage, where \(\gamma\) is set to 0.1. For a small character area with a short edge of fewer than 20 pixels, when \(d<2\), \(d\) is set to 2. Then, the original area is reduced according to the offset, and the reduced area is marked as the character boundary area. The new vertex of the character area is defined as \(\{[x_{1}+d][y_{1}+d][x_{2}-d][y_{2}+d][x_{3}-d][y_{3}-d][x_{4}+d][y_{4}-d]\}\). Finally, values are assigned to the pixels in the character core area and boundary area, respectively. The generated spatial information prediction training label is shown in Figure 8(c). The green area represents the character area marked \"1\", and the orange area represents the boundary area marked \"2\".
# Fully Open-Source Meeting Minutes Generation Tool
## 5 Evaluations
### Performance of Split Video Function  
This section evaluates the practical impact of _SSIM_'s algorithm. It categorizes video frames based on slide transitions as \"change\" or \"same\" to accurately segment the video. Video resolution was reduced to \(426\times 240\), and the algorithm was applied to one frame per second instead of every frame to optimize processing time. This approach significantly lowered the execution time, enabling _SSIM_ to segment videos within one minute while maintaining a perfect _F1_ score, as shown in Table 7. These results indicate that each detected frame change accurately corresponds to the actual scene transition in the video.
###### Abstract  
Coded Aperture Compressive Temporal Imaging (CACTI) can afford low-cost temporal super-resolution (SR), but limits are imposed by noise and compression ratio on reconstruction quality. To utilize inter-frame redundant information from multiple observations and sparsity in multi-transform domains, a robust reconstruction approach based on maximum a posteriori probability and Markov random field (MAP-MRF) model for CACTI is proposed. The proposed approach adopts a weighted 3D neighbor system (WNS) and the coordinate descent method to perform joint estimation of model parameters, to achieve the robust super-resolution reconstruction. The proposed multi-reconstruction algorithm considers both total variation (TV) and \(\ell_{2,1}\) norm in wavelet domain to address the minimization problem for compressive sensing, and solves it using an accelerated generalized alternating projection algorithm. The weighting coefficient for different regularizations and frames is resolved by the motion characteristics of pixels. The proposed approach can provide high visual quality in the foreground and background of a scene simultaneously and enhance the fidelity of the reconstruction results. Simulation results have verified the efficacy of our new optimization framework and the proposed reconstruction approach.  
CACTI; TV; super-resolution; MAP-MRF; Belief Propagation (BP); WNS +
Footnote †: journal: Applied Physics Letters  
**2018**, \(8\), 338; doi:10.3390/app8030338
## 6 Conclusions  
The present climatic and economic circumstances of the world necessitate an increased role of HES and EVs at domestic levels, and a significant research work is devoted to effectively accommodate their role. Adding to the existing literature, this work defined and modeled an IHEMS in the framework of modern SHs to optimize their economic operation using RCGA. To meet the thermal and electrical demands, the developed SH model included a HES which (in an all-inclusive case) consisted of natural gas-based micro-CHP system and electric power from a utility, a BESS, a wind turbine, and a PV system. In this study, a modern utility grid which allows a bidirectional power flow was considered where the customer can sell their surplus energy to the utility. Keeping in view the high loading impacts of the EV, typical EV charging methods were incorporated, and their impact on the SH's economy was discussed. The developed IHEMS optimally scheduled the energy resources and EV charging simultaneously, and resulted in a cost-effective economic operation of the SH as indicated by the simulation results. A comparison of costs and savings of SH devices was summarized to show the features of the developed model. Moreover, a comparison of different EVSEs showed that flexible charging mechanism was beneficial for both the customer and the utility. The work revealed that optimal use of HES improved the economy of SHs while reducing the utility loading in the presence of EVs. This work may be extended in future to include stochastic behavior of various components of SH. Similarly considering thermal energy storage devices and a bidirectional flow of thermal energy in the proposed model may yield interesting results.  
Conceptualization, M.K.R.; Resources, S.U.K., S.B.A.B. and C.-H.K.; Software, K.K.M. and Z.M.H.; Writing--original draft, M.K.R. and M.S.U.Z.; Writing-- review & editing, M.K.R., S.U.K. and M.S.U.Z.  
This research was funded by \"National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP), grant number 2018R1A2A1A05078680\" and \"The APC was funded by the authors\".  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Power Demand and Generation**} & \multicolumn{8}{c}{**Costs**} \\ \hline \(T_{l}\) & \(P_{D_{\lambda}}\) & \(P_{EV}\) & \(P_{W}+P_{PV}\) & \(P_{TC}\) & \(P_{
## 4 Discussion
### Discussion on Locked Segment Effect
Region B is located at the southwestern end of the Amanos FS Segment, which is a primary segment of the East Anatolian Main Fault. This region serves as the southernmost boundary of the Anatolian Fault. Centered around station 2718 in Islahiye city (coordinates 37.008\({}^{\circ}\) N, 36.627\({}^{\circ}\) E), the peak acceleration is 693.81 cm/s\({}^{2}\) at a distance of 48.30 km from the epicenter. Region C is situated in close proximity to the bending point of the Amanos FS Segment, more specifically known as the Islahiye releasing bend. Centered around station 3137 in Hatay city (coordinates 36.489\({}^{\circ}\) N, 36.693\({}^{\circ}\) E), the peak acceleration is 670.17 cm/s\({}^{2}\) at a distance of 82.46 km from the epicenter. Region D is near the left-lateral faulting area of the Amanos FS Segment, known as the Demreck restraining stepover. Centered around station 3129 in Defne city (coordinates 36.191\({}^{\circ}\) N, 36.134\({}^{\circ}\) E), the peak acceleration reaches 1351.50 cm/s\({}^{2}\) at a distance of 146.39 km from the epicenter. The four regions are situated in close proximity to local overlaps, bends, or terminations of the East Anatolian Fault. These areas frequently exhibit concentrated stress patterns and experience multiple reflections and refractions of seismic waves, which are further influenced by inertia forces. This combination of factors makes these regions prone to experience elevated ground motion [37; 40; 41]. The concentrated distribution of peak ground motion in these four regions signifies the locked segment effects and suggests higher ground motion intensity and severe seismic damage near these segments.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 3 Results and Discussion
### Analysis of 8-\(\upmu\)m Microparticle Counting at Normal Conditions (Ratio 1)  
Figure 6 presents the results for counting 8-\(\upmu\)m particles under normal conditions (ratio of 600:1) at various flow rates and channel sizes. The graph indicates that lower flow rates resulted in higher particle counts across all microchannel sizes. This suggests that at slower flow rates, particles have more time to interact with the detection mechanisms, enhancing counting efficiency. Additionally, narrower channels (150 \(\upmu\)m) consistently yielded the highest particle counts, implying better control over particle flow and improved contact with detection zones in these conditions. At all channel sizes, the highest flow rate (15 \(\upmu\)L/min) resulted in shear-gradient lift forces dominating over wall-induced lift forces, consistently leading to the lowest cell count. At a channel size of 200 \(\upmu\)m and a flow rate of 15 \(\upmu\)L/min, the combination of weakened inertial forces increased the dominance of shear gradients and reduced the residence time, creating conditions that reduced particle focusing.  
* Fluorescent vs. Inverted Microscopy: Under the Bioimager fluorescent microscope, the particle counts were slightly higher compared with the inverted microscope, likely due to enhanced visibility and contrast provided by fluorescent imaging, as shown in Figure 7. Despite this difference, both imaging methods showed a decline in counting efficiency at higher flow rates. The optimal condition for counting 8-\(\upmu\)m particles was observed at a 150 \(\upmu\)m channel size and a 1 \(\upmu\)L/min flow rate under the fluorescent microscope.
# Calculation of the Rearfoot Angle Representing Flatfoot from Comparison to the Cavanagh Arch Index
## 1 Introduction
Otherwise, if the ankle leans outward, it is called high arch or vurus foot. RFA \(\geq\) 5\({}^{\circ}\) valgus represents a flatfoot type, 4\({}^{\circ}\) valgus to 4\({}^{\circ}\) varus a neutral foot type, and RFA \(\geq\) 5\({}^{\circ}\) varus a supinated foot type [13]. Heel valgus > 5\({}^{\circ}\) is considered to shift the axis of the foot and the normal distribution of pressure on the sole, which triggers pain [4]. The RFA, however, has not been reported obviously as a method for flatfoot detection. This paper presents a calculation of the RFA as a simple alternative method to determine the flatfoot. It is motivated by the previous observation that shows flatfoot sufferers tend to have an abnormal RFA [12]. This study aims to examine whether RFA can be used to determine the condition of a person's flatfoot based on the comparison with the Cavanagh AI method [14]. Prior to the RFA measurement and analysis, the authors conducted studies to calculate Cavanagh's AI from confirmed flatfoot subjects. The AI result of the studies was then used as a comparable variable for the RFA measurement. Correlation methods such as linear correlation, ANOVA, and \(t\)-test were used as the analysis tools. The study revealed that the RFA is a potential simple method to determine the flatfoot. A more detailed description of the manual AI measurement method, digital AI measurement method, and the proposed method for flatfoot detection is presented in Section 2.