esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Limitations and Future Directions  
The limitations and the future directions for the proposed work are as follows:  
* In the current study, the malware samples were limited. It relied on samples from MalwareBazaar and GitHub, but including malware from varied sources and of various types, especially advanced and emerging strains, would enhance the model's robustness and adaptability. Therefore, the dataset's diversity could be expanded.
* The current methodology focused on dynamic analysis for capturing malware behaviour in an execution environment, and it might have missed static properties that are crucial for a comprehensive malware profile. Therefore, comparing the current API dataset with other available API-based datasets could improve its relevance and effectiveness.
* While the research successfully implemented traditional (baseline) ML models, there is an opportunity to explore more advanced neural network architectures. These advanced models could potentially offer a deeper understanding and more accurate detection of complex malware behaviours that traditional models might miss.
* In future extensions of this work, a hybrid analytical approach could be implemented by using a variety of Neural Networks [53], such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, with the inclusion of diverse big datasets. This expansion would improve the robustness and adaptability of the models to new and sophisticated malware threats. The hybrid strategy would focus on both static and dynamic malware attributes and behaviours. It would also provide a more holistic view of malware, capturing both its behavioural patterns during execution and its inherent static properties.
* Furthermore, the utilisation of CNNs for their exceptional pattern recognition capabilities and RNNs and LSTMs for analysing sequential data makes them ideal for identifying complex malware signatures and their evolution over a period. This multifaceted approach would aim to enhance the precision of malware detection and develop defence mechanisms that are both robust and adaptable to the constantly evolving nature of cyber threats.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 1 Introduction
In addition, there have been various previous studies on the effect of ASR on the mechanical properties of concrete, such as its compressive strength [9; 10; 11; 12], elastic modulus [9; 12], and fracture energy [9], and the relationship with the decline in mechanical properties has been clarified. Thus, ASR expansion is known to cause a reduction in the mechanical properties of concrete materials. However, bending loading tests on RC beams showed that ASR expansion caused visible cracks in the RC beams but had little effect on structural performance. The literature differs on whether or not ASR expansion reduces the shear capacity of RC beams, and a systematic relationship between the amount of ASR expansion and load-carrying capacity is needed to clarify the effect of ASR on structural performance. The ultimate goal of this research is to quantitatively evaluate the remaining load-bearing capacity of reinforced concrete structural members or structures due to ASR deterioration and to develop rational maintenance methods based on that. To achieve this, it is necessary to propose realistic analysis methods and analysis models, and this research focuses on that point. At the same time, it will contribute to the clarification of the relationship between ASR expansion and the load-carrying capacity and fracture behavior of RC beam members, which has not been firmly established by previous studies. To clarify the relationship between ASR expansion and load-carrying capacity through achieving the above goal, this study proposes an ASR expansion model that takes into account the effects of external restraints and reinforcing bars. Furthermore, by quantifying the amount of cracking (width, direction, density, etc. ), the relationships between the amount of expansion, the amount of cracking, and load-bearing capacity will be systematized, with the ultimate goal of achieving efficient load-bearing capacity evaluation. Specifically, in this study, uniaxial compression tests of concrete deteriorated by accelerated expansion were first conducted (Section 2), and observations of crack width and crack density were made to examine the state of internal crack damage in concrete with the same degree of ASR. Using these experimental results, the effects of ASR internal crack damage on mechanical properties are experimentally discussed. In Section 3, a concrete mesoscale model was developed to reproduce ASR degradation, and the material model was identified and validated based on the experimental results in Section 2. In Section 4, based on the mesoscale model developed in Section 3, a mesoscale model of RC beams was developed to reproduce ASR expansion mechanism based on a moisture -crack coupling theory. The ASR expansion theory and the coupled moisture-cracking theory were developed and applied based on the RC beam model. Section 5 summarizes and describes the findings of this study.
## 3 Results
### Data Preprocessing Results  
Based on the principle of locally weighted robust regression, the original data were pre-processed and the interval length of the data set was chosen as seven based on previous experience. The results are shown in Figure 3.  
This process identifies and processes local anomalies in the raw data. It accomplishes two main tasks: one is to remove outliers and the other is to smooth the data noise while maintaining the original data fluctuation trend. The window length in the local regression is seven, meaning that the window traverses the entire data set with each piece of data smoothed seven times. The aberrant wind speed hazard is analyzed based on the frequency data identified as outliers. In total, 81 pieces of data were identified as outliers in the calculation process and \(n\) is used to represent the identification number. There are 56 pieces of data with \(n\leq 2\), accounting for 69.1%, which can be considered normal fluctuations and no risk. There are 25 pieces of data with \(n\geq 3\), accounting for 30.9%, with which it is believed that there are certain risks. The data with \(n=3  
Comparing the results in Table 2 with Figure 3, the evaluation table provides an objective assessment of the risk of the data. High-risk outliers occurred five times or 1% of the total data; medium-risk outliers occurred six times or 1.2% of the entire data, and low-risk happened 14 times or 2.8% of the whole data.  
Sample 284 is the only one identified as an abnormal value, which is not listed in Table 2. This is because the outlier detection algorithm in this step depends on the change in the surrounding data. The data before and after the 284 th sample have large fluctuations, which makes the MAD value larger and the robustness weight larger, therefore they cannot be recognized as an outlier.  
Constant abnormal values were detected at samples 180 and 181, 208 and 209, 441, and 442, and the three groups of abnormal values appeared at the fluctuation of the overall wind speed state. For example, in samples 180 and 181, the wind speed jumps from a lower state to a higher state and the algorithm identifies it as an abnormal value at the junction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Values/(m/s)** & **Times** & **Risk** & **No.** & **Values/(m/s)** & **Times** & **Risk** \\ \hline
26 & 3.03 & 3 & & 442 & 1.10 & 3 & low \\ \hline
72 & 2.93 & 3 & & 107 & 3.22 & 4 & \\ \hline
134 & 3.08 & 3 & & 296 & 3.09 & 4 & \\ \hline
180 & 2.43 & 3 & & 461 & 1.11 & 4 & medium \\ \hline
181 & 0.46 & 3 & & 466 & 1.29 & 4 & \\ \hline
208 & 0.51 & 3 & & 114 & 2.64 & 5 & \\ \hline
209 & 2.48 & 3 & & 462 & 1.01 & 5 & \\ \hline
252 & 2.67 & 3 & & 392 & 3.29 & 6 & \\ \hline
261 & 2.47 & 3 & & 481 & 2.61 & 6 & \\ \hline
266 & 2.68 & 3 & & 47 & 4.35 & 7 & \\ \hline
267 & 2.38 & 3 & & 274 & 3.16 & 7 & \\ \hline
301 & 5.21 & 3 & & 373 & 3.20 & 7 & \\ \hline
441 & 4.71 & 3 & & — & — & — \\ \hline \end{tabular}
\end{table}
Table 2: Risk table of monitoring data.  
Figure 3: Outliers Detection and Processing Results.
## 2 Materials and Methods
### Training Procedure Using \(D_{\textit{strong}}\) Subset  
The Faster-RCNN model is used for object detection tasks, which is detecting lesions in BUS images. Faster-RCNN is a two stage object detector, where a RPN is trained to specifically perform region proposals on feature maps. Region of interest (RoI) obtained from the RPN is then fed to the RCNN-top layer for classification and additional bounding box regression. Bounding box information is only given by images in \(D_{\textit{strong}}\) subset. This information is used for bounding box regression in both the RPN and RCNN-top, and for foreground background classification in the RPN. The overall dataflow is shown in Figure 1. The loss is comprised of four terms, \(L_{\textit{rgg}}^{\textit{RPN}}\), \(L_{\textit{cls}}^{\textit{RPN}}\), \(L_{\textit{rgg}}^{\textit{RCNN-top}}\), \(L_{\textit{cls}}^{\textit{RCNN-top}}\). \(L_{\textit{rgg}}^{\textit{RPN}}\) and \(L_{\textit{rgg}}^{\textit{RCNN-top}}\), which are regression losses for the RPN and the RCNN-top, respectively, are obtained by calculating the smooth l1 loss between the ground truth box and the predicted box coordinates. \(L_{\textit{cls}}^{\textit{RPN}}\) and \(L_{\textit{cls}}^{\textit{RCNN-top}}\), which are classification losses for the RPN and the RCNN-top, respectively, are obtained by calculating the crossentropy loss between the ground truth label and the predicted label. Corresponding ground truth label and coordinates are assigned when the intersection over union (IoU) between the boxes are over 0.5. Details of calculating the four terms remain same as the method that was proposed in [11].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{3}{c}{**SNNBH**} & \multicolumn{3}{c}{**Stanford Dog**} \\ \hline Role & Supervision & Mal. & Ben. & Total & Blk. & Eng. & Total \\ \hline \multirow{3}{*}{Train} & Strong & 400 & 400 & 800 & 20 & 20 & 40 \\ \cline{2-7}  & Weak & 933 & 3291 & 4224 & 107 & 77 & 184 \\ \hline Test & Strong & 200 & 200 & 400 & 60 & 60 & 120 \\ \hline \multicolumn{2}{c}{Total} & \multicolumn{3}{c}{5424} & \multicolumn{3}{c}{354} \\ \hline Mal., Ben., Blk., Eng., denote malignant, benign, Blackhound, English Foxhound respectively. & & & & & \\ \end{tabular}
\end{table}
Table 1: Cardinality of SNUBH and Stanford Dog Dataset.  
Figure 1: Illustration of the dataflow in the presented model. All of the four losses are used when training with \(D_{\textit{strong}}\) images, while only the classification loss in RCNN-top is calculated for \(D_{\textit{weak}}\) images. Refer to [11] and Section 2.3 for detailed methods for choosing target RoIs and calculating losses.
## 2 Methods
### Basic Principle of Encryption Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symmetric Key Length** & **RSA Key Length** & **SM2 Key Length** & **Period of Confidentiality** \\ \hline
80 & 1024 & 160 & 2010 \\
128 & 3072 & 256 & 2040 \\
256 & 15,360 & 512 & 2120 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table of key strength values. Figure 2: SM2 encryption algorithm. The experiment was completed on a calibrated local unencrypted device to obtain the time interval measurement data \(T_{LR}\). It changed the data to zero. Then, the zero value was sent to the remote device to complete the time synchronization test. It was found that the time at the remote site existed \(T_{RL}/2\) of the lead offset. The optical time transfer link before encryption can complete the time synchronization function independently. However, an attacker can tamper with the plaintext of the intuitive time-delayed data. In this paper, the optical fiber security time transfer equipment with an encryption unit was used in the experiment. The data transmitted to the remote site were ciphertext, which is not easy to tamper with. In order to verify the feasibility of the algorithm in this paper, by means of external injection, the data encrypted by the SM2 algorithm were transmitted to the decryption unit of the remote site. Through the external connection line, the security optical fiber time transfer equipment of the local site is connected with the remote site. After the normal operation of the encryption and decryption units, the secure optical time transfer system can perform time delay compensation to achieve synchronization. If the optical fiber link is disconnected or the data are not the same, the transmission system defaults to an external attack. At this time, the security optical fiber time transfer equipment fails synchronously. The equipment cannot complete the compensation function of the optical fiber link. At this time, the alarm function is enabled. Security personnel performs a security check of the link and the device. The alarm flow of abnormal operation of safety equipment is shown in Figure 4.  
Figure 3: SM2 decryption algorithm.
###### Abstract  
The automation and continuous operation of coal production are fundamental to the construction of high-yielding and efficient mines. Underground coal bunkers, serving as the pivotal link between various production and transportation segments, are vital for the seamless operation of mines. Nonetheless, the adhesive properties of raw coal can lead to increasingly severe issues, such as the adhesive clogging of coal bunkers. To address this issue, this paper first employs a self-designed raw coal shear testing apparatus to conduct experiments under varying conditions of shear interfaces, moisture content in raw coal, and compaction forces. Obtaining the adhesion behavior characteristics and adhesion parameter variation patterns of raw coal at coal-coal and coal-wall interfaces under various influencing factors. Subsequently, leveraging the adhesion property parameters of raw coal and the engineering conditions of the 1011 roadway coal bunker in Taoyuan Coal Mine II, a numerical model for coal bunker discharge using irregular particles was developed with the PFC2D numerical simulation software. Based on these, we obtained the influence patterns of various factors, such as coal bunker convergence angle, coal storage height, and coal moisture content, on the coal particle flow pattern, bunker wall pressure, and adhesive clogging distribution characteristics of the coal bunker during the discharge process, thereby revealing the mechanisms underlying the adhesive clogging phenomenon. The findings offer significant insights for optimizing solutions to the adhesive clogging issues in underground coal bunkers and ensuring their safe and efficient operation.  
adhesion properties of raw coal; shear test; coal particle flow pattern; adhesive clogging situation of coal bunker; coal bunker wall pressure
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Beamforming Setup and Grid Definition  
To be able to calculate the spacing for the PSF-shape-based beamforming grid, the PSF dimensions were determined for both the simulation and experimental setup (see Table 3). Field II was used to simulate the response of multiple point scatterers placed at spatial positions relevant for carotid artery imaging (Figure 3a). Simulated element data were beamformed on a high resolution grid and intensity curves for each individual scatterer were generated (Figure 3b). The axial (PSF\({}_{\text{ax}}\)) and lateral (PSF\({}_{\text{lat}}\)) size of the PSFs were determined based on the \(-6\) dB values. The PSF\({}_{\text{ratio}}\) was determined according to \(\text{PSF}_{\text{ratio}}=\text{PSF}_{\text{lat}}/\text{PSF}_{\text{ax}}\). The resulting ratios were averaged for all simulated scatterers. The PSF dimensions were determined experimentally using a wire phantom (60 \(\upmu\)m diameter) placed in a water tank. Plane wave data were acquired of the wire phantom and similar processing was performed to determine the PSF\({}_{\text{ratio}}\).  
Delay-and-sum beamforming was used to generate RF data for every plane wave transmission at pre-specified data sampling points [39]. The data were beamformed on the PSF-shape-based grid using the axial and lateral sampling distance as specified (Section 2.1.2) for the RF data (d\({}_{\text{ax,RF}}\) and d\({}_{\text{lat,RF}}\)).  
Figure 2: (**a**) Experimental setup rotating disk experiments. The cylindrical phantom is attached to the motor unit, using the fixating structure (**b**) and placed in a water tank. The transducer is positioned perpendicular to the phantom, at 3.4 cm from the center of the phantom.  
Consequently, the envelope data were obtained by demodulation and down sampling of the RF data in the axial direction. Furthermore, the element data were also beamformed on a conventionally spaced grid, with a lateral sampling distance equal to the pitch and 4 samples per wavelength in axial direction. Details on the grid spacing and beamforming parameters can be found in Table 3.
# Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
## 3 Results
### Results of Carbon Emission Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Amount** & **Unit** & **Recycled Material** &
\begin{tabular}{c} **Carbon** \\ **Emission** \\ **Reduction** \\ **(tCO\({}_{2}\)e)** \\ \end{tabular} \\ \hline Concrete & 10,995.276 & \(\rm{m^{3}}\) & Recycled aggregate & 2270.524 \\ Glass & 188.4 & t & Glass & 149.024 \\ Steel & 3801.573 & t & Crude steel & 7013.902 \\ Aluminum & 22.464 & t & Crude aluminum & 46.725 \\ Wood & 8.218 & \(\rm{m^{3}}\) & Wood & 0.951 \\ Doors and windows & 1162.61 & \(\rm{m^{2}}\) & Doors and windows & 176.252 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reduction in carbon emissions during recycling stage. \begin{table}
\begin{tabular}{c c} \hline \hline
**Stage** & **Carbon Emission (tCO\({}_{2}\)e)** \\ \hline Production & 13,214.531 \\ Transportation & 358.587 \\ Construction & 764.785 \\ Recycling & \(-\)9657.378 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Carbon emissions of main stages in life cycle of building. Figure 7: Carbon emission reduction shares during recycling stage. As shown in Figure 8, a comparison of carbon emissions across different main stages is illustrated. The calculation results for the carbon emissions during the materialization stage indicate that the production stage accounts for the majority, with 92.4% of the total emissions. In contrast, the carbon emissions from the transportation stage and the construction stage are relatively low. Therefore, reducing carbon emissions during the production of the materials is crucial for overall carbon emission reduction. Additionally, the recycling stage demonstrates substantial reductions in carbon emissions according to the results, thereby playing an important role in overall carbon emission reduction efforts.
###### Abstract  
Based on the complex network theory, we established a topological network of the Beijing subway under Space L, Space P, and Space C. Then, we analyzed the complex characteristics of the subway network under each topological network, proposed the global impact indexes (including aggregation impact coefficient, path length impact coefficient, network efficiency impact coefficient, and connectivity impact coefficient), and interline impact indexes (including degree centrality impact coefficient, near-centrality impact coefficient and intermediate centrality impact coefficient, the higher the value, the more obvious the effect on other lines; degree centrality sensitivity coefficient, near-centrality sensitivity coefficient and intermediate centrality sensitivity coefficient, the higher the value, the more vulnerable to the impact of other lines). At the global and local levels, it is possible to analyze the effect of different lines on the global situation and other lines. The concept of the \"line importance index\" is proposed to identify the key lines in the Beijing subway network. The network is characterized by scale-free and small-world characteristics under Space P, and scale-free network characteristics but no small-world characteristics under Space L and Space C. Subway Line 10, Line 9, Line 1, Line 2, and Line 5 are the five subway lines with the highest importance. Subway Line S1, Chapping Line, Xijiao Line, Capital Airport Line, and Daxing International Airport Line are the five subway lines with the lowest importance.  
urban public transport; complex network theory; subway network; global influence index; local influence index; key lines identification
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 4 Conclusions  
In this study, the effect of hydrogen ions on the GAC and solution was tested on phosphate adsorption. The primary findings are summarized as follows:  
* The HCl condition for the GAC shifted the pH\({}_{\text{pzc}}\) from 4.5 to 6, but the modification of other physical properties, such as total surface area, pore volume, and pore size distribution, was negligible;
* The optimized solution pH of the phosphate removal using GAC was determined at the weak acid condition by electrostatic interaction and an increase of the pH\({}_{\text{pzc}}\) from 4.5 to 6 for the GAC, extending the segment to a neutral pH;
* At the base condition, the positive surface charge of the GAC, in spite of the species of \(\text{HPO}_{4}{}^{2-}\) and \(\text{PO}_{4}{}^{3-}\), led to a rapid decrease in phosphate removal, and the hydrogen  
Figure 10: Phosphate breakthrough profiles for GAC and GACA and the effluent solution pH for deionized water (DI) and phosphate.  
ions, excluded from the GAC in solution, deferred the reduction rate. Additionally, desorption of phosphate occurred with the increase of the solution pH;
* The PSO kinetic rate was more suitable than that of the PFO to describe the chemisorption of phosphate, and the rate constant (K\({}_{2}\)) was relatively faster at the acid condition than at other solution pH values;
* The fixed column tests confirmed that the phosphate was removed by adsorption because the hydrogen ion release onto the surface of the GAC was not due to an ion exchange process.  
The following are available online at [[https://www.mdpi.com/2076-3](https://www.mdpi.com/2076-3)]([https://www.mdpi.com/2076-3](https://www.mdpi.com/2076-3)) 417/11/6/2777/s1, Table S1: Experiment and optimized solution pH for the removal of phosphate at various adsorbents, Table S2: Variables of linear PFO and PSO, Figure S1: Linear PFO kinetic for GAC and GACA at initial pH of 4(a), 7(b), and 9(c).  
Conceptualization, T.K. and B.A.; methodology, T.K.; formal analysis, B.A.; writing--review and editing, T.K. and B.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a 2018 research Grant from Sangmyung University.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Discussion  
### Ablation Study  
We conducted an ablation study to explore the influence of the incident angle, as shown in Table 2. In the table, \(cos\lambda\) refers to whether incident angle correction was applied, and \"PE\" indicates whether positional encoding was used on the incident angle information. Without \(\cos\lambda\), UAV-NeRF degenerates into ordinary Sat-NeRF. When using \(\cos\lambda\) and PE, the rendering effect and 3D reconstruction accuracy were improved. Finally, when these two strategies were simultaneously applied, the model achieved the optimal performance, thus confirming the effectiveness of the various UAV-NeRF mechanisms.