esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 5 Malware Detection Techniques
### Signature-Based Malware Detection  
Malware has a definable feature that is evident in its structure and can be utilized to pinpoint a single instance of an infection. When discussing the application of antiviral software in a business context, the signature-based identification approach is generally the method employed [47]. This technique is fast and accurate at finding common viruses, but it cannot catch anything novel. Malware belonging to the same family can easily evade signature-based identification by employing obfuscation techniques. Executables are the source of the first three signature recognition characteristics. After that, a signature is created by the signature creation system and added to the signature directory. This contrast helps establish whether or not the example program is malicious. Since signature-based detection algorithms can recognize previously found aware so swiftly and reliably, they have been heavily depended upon by antivirus firms. This strategy is frequently employed for the detection of adverse belonging to a specific family. Unfortunately, it cannot detect ransomware of the next generation that relies on stealth and code modifications. In addition, it is susceptible to numerous FPs, and signature extraction necessitates substantial human effort [47; 48; 49]. The schema for the signature-based malware detection technique is given in Figure 7.  
Figure 6: Categorization of Malware Analysis.  
Assessment of Signature-based Malware Detection  
The following methods should be adopted and taken into consideration while building an effective signature.  
1. Identifies should be as brief as possible and can include multiple virus types under a single signature.
2. A reliable system for automatically generating signatures needs to be built.
3. At some point in the signature technology, data mining and ML tactics should be used more.
4. The signature should be immune to sorting and mystification techniques [50].
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 1 Introduction
The main contribution of this paper is to propose methods that can deliver continuous latency monitoring over existing networks without the disruption or distortion of the original content. A timecode method is used to measure the short distance video transmission delay. Remote online measurement is used to measure the remote video transmission delay over a network. Lossless remote video online measurement realizes lossless online measurement without content distortion. A time delay measurement device is proposed and implemented. The performance was validated over a typical network. This paper is organized as follows. In Section 2, the latency caused by video compression, decompression, and network transmission is analyzed. In Section 3, we propose three methods for measuring video end-to-end latency. Then, we present some practical experience with video latency measurements in Section 4. Finally, we conclude the paper in Section 5.
## 1 Introduction  
Genomic databases are widely used not only to extract information about genes, but they also provide the gene position on chromosomes, which is useful when entire regions of chromosomes are analyzed. Neighboring genes can be studied as a whole entity in several circumstances. For example, many gene clusters have been described and their analysis suggests that a set of adjacent genes can be functional. Benefits for gene clustering include co-inheritance, co-transcriptional regulation in the presence of a similar chromatin environment as well as a coordinated handling of post-transcriptional processes such as export for protein synthesis and compartmentalization [1, 2, 3]. Other examples of analysis that consider large regions of chromosomes are the genome-wide association studies (GWAS) and the linkage disequilibrium studies, which have identified loci related to disorders or pathologies [4, 5, 6, 7].  
In all cases, it is important to know the mapping of genes in clusters, loci, or specific regions of the chromosome, in order to analyze their relationships. Unfortunately, the same ordered list of the corresponding codified proteins is not easily retrieved from protein databases. The most used protein database, UniProt Knowledgebase (Uniprot), gives information on human proteins without ordering based on gene position. This lack means that without the ordered list of proteins codified by gene clusters, loci, or other specific regions, it is difficult (and impossible on a large scale) to analyze common features of the products of genes, such as amino acid content. Indeed, we have recently demonstrated that the proteins codified by two gene clusters show a significantly lower ratio glutamate/glutamine compared with the nearby regions of the same chromosome [8].  
The aim of this work was to create a new procedure that interrogates two databases (genomic and protein) and matches the results in order to produce a novel dataset in which the human proteins are ordered following the mapping of the corresponding genes on the chromosome, together with their amino acid composition. The novel dataset provides details on the position of the codifying gene and on the amino acid (AA) sequence and length of the translated protein.  
Furthermore, we propose a novel analysis of amino acid content of proteins mapped on chromosomes by our new procedure. As an example of application, we investigated the proteins codified by chromosome 15, which contains a locus harboring candidate dyslexia susceptibility genes.
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 5 Simulation and Analysis
### Performance of Data Enhancement in the Model  
In this section, the DCGAN algorithm is compared with the data enhancement algorithm proposed in this article. Figure 6 displays real samples of six types of jamming signals at a JNR of 10 dB, along with samples generated by WDCGAN-SA and DCGAN. Analysis of Figure 6 indicates that both generative adversarial network models can produce synthetic samples that mimic the true distribution. However, upon detailed examination, it is observed that the multi-tone jamming and comb frequency jamming signals generated by the DCGAN model exhibit very blurry features; the signal feature outlines in single-tone jamming and linear sweeping frequency jamming are extremely coarse. Conversely, the samples produced by the WDCGAN-SA model provide a clearer and more precise description of the signal features with smoother outlines, achieving a closer approximation to the real samples.
## 3 Experimental Results and Discussion  
### Changes in the Slump  
The evaluation of changes in the shotcrete under different ball-milling durations and rates of cement replacement is conducted to explore the influences of the two on the conveying performance of shotcrete. This is critical for determining the appropriate ball-milling duration and rate of cement replacement. Measuring the slump of shotcrete under different conditions provides an important basis for optimizing the proportions of raw materials of the shotcrete, thus ensuring the optimal conveying performance and working efficiency of the shotcrete in practice. Experimental results of the slump testing of the shotcrete with the partial replacement of cement with gangue powder are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Ball-Milling Duration**} & \multirow{2}{*}{**Gange/kg\(\cdot\)m\({}^{-3}\)**} & \multirow{2}{*}{**Sand/kg\(\cdot\)m\({}^{-3}\)**} & \multicolumn{2}{c}{**Gange**} & \multirow{2}{*}{**Cement/kg\(\cdot\)m\({}^{-3}\)**} & \multirow{2}{*}{**Water/kg\(\cdot\)m\({}^{-3}\)**} & \multirow{2}{*}{**Accelerator/kg\(\cdot\)m\({}^{-3}\)**} \\  & **(for Gangue)/h** & & & & & \\ \hline A\({}_{1}\) & \(1\) & 801.25 & 801.25 & 135 & 315 & 247.5 & 18 \\ A\({}_{2}\) & \(  
The analysis of Figure 7 shows that the slump of the slurry decreases as the rate of cement replacement increases. The slump is large at a rate of cement replacement of 30%. Taking gangue powder ball-milled for 1 h as an example, the slump reduces by 55.3% from 103 mm to 46 mm as the rate of cement replacement rises from 30% to 50%. This is mainly because ball-milled gangue particles are generally larger and more irregular than cement particles, and they have a higher surface roughness. Due to these characteristics, the gangue particles do not fill the interstices in the shotcrete as tightly and uniformly as cement particles, so the slump of the corresponding shotcrete declines.  
With the extension of ball-milling duration, the slump of the slurry reduces. The slump is large when the ball-milling duration is 1 h. For instance, at the rate of cement replacement of 30%, the slump is decreased by 33.0% from 103 mm to 69 mm as the ball-milling is extended from 1 h to 5 h. This occurs mainly because with the prolonging ball-mining duration, gangue particles are gradually refined after being treated by the ball mill, and some particles are aggregated. The refined particles and the aggregates are poorly dispersed in the shotcrete slurry, so the slump of the slurry decreases.
## 2 Methodology  
### Overview  
The network structure of our proposed method is depicted in Figure 1 and comprises five key components: global feature extraction module, feature coding module, feature aggregation module, information decoding module, and lightweight convolution module.  
Here's a brief overview of the functions of each module:  
* **Global feature extraction module**: This module's primary function is to extract global contextual information from large-format images. It takes a downsampled low-resolution image as input and produces global semantic features with a consistent size as the output. The training dataset for this module includes low-resolution images and their corresponding labels. In the subsequent sections of this paper, global semantic features refer to the features output by this module.
* **Feature coding module**: The feature coding module processes cropped small image patches. It generates multi-scale convolution features from the input, with the highest and smallest features being used for fusion with the output feature from the global feature extraction module, while other multi-scale features are utilized for skip connections. In the subsequent sections of this paper, local semantic features refer to the features output by this module.
* **Feature aggregation module**: This module is responsible for combining the global features produced by the global feature extraction module with the local features generated by the feature coding module. Initially, these two features are concatenated, and then a series of Transformer units process the concatenated features to obtain new aggregated features that incorporate both global and local information.
* **Information decoding module**: The information decoding module's primary purpose is to generate a prediction map consistent with the original image size. It takes as input the new features produced by the feature aggregation module and the multi-scale shallow features generated by the feature coding module. The feature map gradually returns to its original size through a series of upsampling, convolution, and skip connection operations, resulting in a prediction map consistent with the original image size.
* **Lightweight convolution module**: This module primarily stitches the feature map at the original resolution. This stitching corresponds to the slicing operation in the feature coding module, which is used to create a feature map of the same size as the original image. Subsequently, the prediction map with the same size as the original image is restored through a simple combination of lightweight convolution.  
Figure 1: Network structure of the proposed method.
## 3 Results and Discussion
### Temperature Dependence of the Polarisation Resistances
However, this is likely to not be the case in the current electrodes, due to the presence of the proton-conducting phase of BCY10 in the composite electrode. This behaviour can indeed favour the \(R_{\text{p}}\) by extending the electroactive surface area for water formation. Conversely, in low humidity conditions, the activation energy for the \(Y_{0}\)-parameter is 1.38 eV (Table 3), close to that determined for oxygen self-diffusion in LNO (\(E_{\text{a}}=1.44\) eV [14]), thus, suggesting that the oxide-ion conductivity can be more relevant in such conditions. Concerning the electrode reaction step, i.e., the low-frequency term, \(R_{\text{low,eon}}\), shows a strong \(p_{\text{H2O}}{}^{-1}\) dependence, thus, the rate determining step of this term can be assigned to either water formation (\(m=1\)):  
\[2\text{OH}^{\bullet}_{\text{O(cathode)}}\to\text{H}_{\text{2}}\text{O}_{\text {(ads)}}+\text{V}^{\bullet\bullet}_{\text{O}}\to\text{O}^{\text{x}}_{\text{ O(cathode)}} \tag{25}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{\(p_{\text{H2O}}\)/atm} & \multicolumn{4}{c}{**Activation Energy/eV**} \\ \hline  & & \(R_{\text{high}}\) & \(R_{\text{G}}\) & \(R_{\text{low}}\) & \(R_{\text{p}}\) \\ Wet & \(10^{-2}\) & 1.26 & 1.24 & - & 1.24 \\  & \(R_{\text{high,eon}}\) & \(R_{\text{G,eon}}\) & \(R_{\text{low,eon}}\) & \(R_{\text{p,eon}}\) \\  & & 1.12 & 1.09 & - & 1.09 \\ \hline Low & & \(R_{\text{high}}\) & \(R_{\text{G}}\) & \(R_{\text{low}}\) & \(R_{\text{p}}\) \\ humidity & \(10^{-7}\) & 0.97 & 1.63 & 1.66 & 1.28 \\  & & \(R_{\text{high,eon}}\) & \(R_{\text{G,eon}}\) & \(R_{\text{low,eon}}\) & \(R_{\text{p,eon}}\) \\  & & 0.69 & 1.40 & 1.40 & 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculated values for the activation energy obtained in both wet and low humidity conditions. and/or to steam release (\(m=1\)) :  
\[\mathrm{H_{2}O_{(ads)}}\rightarrow\mathrm{H_{2}O_{(g)}} \tag{26}\]  
The very high capacitance values found for this term (close to 1 F cm\({}^{-2}\)) are characteristic of diffusion processes, hence, possibly suggesting that the molecular gaseous water diffusion is rate limiting. Indeed, the presence of this low-frequency process only in low humidity conditions is an indication that the adsorbed water may be involved in the process, where the decreased concentration of protons is affecting the formation of water at the surface. In this regard, the relatively open microstructure of the current electrode should play an important role, due to the increased surface area, thus, providing an increase in active sites all over the surface of the grains. Such observations are in agreement with those previously reported by [PERSON] et al. [17; 18] for lower order _Ruddlesden Popper_ nickelates and also the results of [PERSON] et al. [37] and [PERSON] et al. [38], where the authors reported that improved performance can be obtained for more porous cathodes.
## 4 Experiment and Result Analysis  
According to Figure 1, the proposed experimental design requires the execution of the number of components: (I) feature selection, (II) oversampling following by (III) comparison between synthetic oversampling techniques and machine learning classifiers for prediction models of the smoking cessation intervention. Moreover, non-parametric Friedman's test is applied to ensure the differences between the results of the prediction models are significant. In addition, the best representative features were provided, which were used to build the best ranked predictive models.
## 2 Experimental Details
### Full Cone Twin-Fluid Atomizer  
In the present experimental study, two full cone twin-fluid atomizers of different sizes (Spraying Systems Co., Illinois, United States of America), namely large and small atomizers, were prepared to examine the effect of the discharge area in the twin-fluid atomizer on the fire-extinguishing performance. Figure 2 shows schematic diagrams of both the twin-fluid atomizers with their dimensions. Typically, large and small atomizers are comprised of a liquid discharge hole at the center and a gas discharge annular gap around the liquid discharge hole. The large and small atomizers were identical except for the center hole size for discharge of water and annular gap size for discharge of atomizing gas. The discharge areas (e.g., center hole and annular gap areas in Figure 2b) of atomizer have a great impact on the characteristics of water mist and discharged flow, which can affect the fire-extinguishing performance and mechanism. The discharge areas of center  
Figure 1: Schematic diagrams of experimental setup and measurement locations for fire extinguishment: (**a**) experimental setup and (**b**) measurement locations.  
hole and annular gap for large atomizer were 1.824 and 2.230 mm\({}^{2}\), and those for small atomizer were 0.203 and 0.809 mm\({}^{2}\), respectively. The spray angles of large and small atomizers were similar and corresponded to approximately 17-19\({}^{\circ}\) and 18-19\({}^{\circ}\), respectively, as per the specifications.  
The flow rate conditions of large and small atomizers are summarized in Table 1. To investigate the discharge area effect, water and nitrogen were used as the liquid and gas, respectively, in large and small atomizers. The flow rates of water and nitrogen for both atomizers were determined, considering whether the water mist was successfully generated or not. For example, under high water flow rate and low gas flow rate conditions, water mist was not generated. To examine the effect of atomizing gas, nitrogen and air were used in the small atomizer.
## 7 Real-World Applications and Challenges
### Limitations and Open Research Questions  
Despite ElliottAgents demonstrated effectiveness in pattern recognition and market analysis, several limitations and areas for future investigation warrant consideration. The current implementation focuses on a subset of Elliott wave patterns, primarily addressing impulsive waves, corrective waves, and wave extensions. While this approach has proven effective, it represents only a portion of the thirteen distinct patterns identified in Elliott wave theory. This limitation presents an opportunity for expanding the system's pattern recognition capabilities to include more complex formations such as triangles, flats, and zigzags.  
The reliance on historical data for DRL, while effective, raises questions about the system's adaptability to unprecedented market conditions. Although backtesting has demonstrated significant improvements in pattern recognition accuracy, the approach may be limited by the assumption that future market behavior will reflect historical patterns.  
This limitation becomes particularly relevant during periods of market stress or structural changes in trading dynamics such as politcal events.
## 6 Conclusions  
In conclusion, our results suggest that fast and accurate liver parenchyma and liver tumor segmentation from MRI can be achieved using the HighResNet-based deep learning method. Our approach got a Dice score of 0.944 \(\pm\) 0.009 for the liver and 0.780 \(\pm\) 0.119 for the tumor segmentation on the annotated dataset. The time required to create and correct a clinically accurate 3D model using our method was at an average of 23 min per volume. Compared to performed annotations starting from inference by the presented approach, the DL-based segmentation received a Dice score of 0.994 \(\pm\) 0.003 and 0.709 \(\pm\) 0.171 for the liver and tumor. The GUI we designed for the automatic deep learning-based segmentation can be used as an assisting tool for creating a patient-specific 3D model for radiologists, which surgeons could also use for surgery planning. In the future, work will be performed to create an open code application or integrate our method with already available tools such as a 3D Slicer or ITK snap.  
Conceptualization, Y.K., E.P. and R.P.K.; methodology, Y.K.; software, Y.K.; validation, Y.K., E.P. and R.P.K.; formal analysis, Y.K.; investigation, Y.K.; resources, E.P.; data curation, Y.K. and E.P.; writing--original draft preparation, Y.K. and E.P.; writing--review and editing, A.B., B.E., O.J.E. and R.P.K.; visualization, Y.K. and E.P.; supervision, A.B., B.E., O.J.E. and R.P.K.; project administration, O.J.E. and R.P.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** The study was performed in accordance with the ethical standards of the institutional and regional ethical committee (2011/1285/REK), the 1964 Helsinki declaration and its later amendments or comparable ethical standards.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was financially supported by the Department of Research and Development, Division of Emergencies and Critical Care, Oslo University Hospital, Rikshospitalet. The authors would like to thank [PERSON] and [PERSON] for helping with dataset creation and segmentation, and [PERSON] for the writing assistance for this paper. Special thanks to [PERSON] for help and discussions regarding the method implementations.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Scenario Analysis
### Study Solution and Result Analysis
#### 4.2.2 Sensitivity Analysis
Time-Window Sensitivity Analysis  
In order to further understand the impact of time windows on multi-path vehicle routing problems, the changes of four key indicators without time-window constraints were analyzed. Multi-path traffic conditions were also considered, but the time-window constraint from Section 4.2.1 was removed; the results are shown in Table 5.  
Table 5 shows four key performance indicators under different objective functions without time-window constraints. By comparing the data in Table 5 with the data in Table 3, we obtained the results shown in Figure 4.  
Through the analysis of Figure 4, it can be determined that the influence of the time window on the time index was the largest. In the absence of time-window constraints, time indicators showed better results, and other indicators were also optimized to varying degrees. This was mainly due to the further increase in the selectivity of the route after eliminating the time-window constraint, which also increased the possibility of choosing a better route during driving. Figure 4: Impact of time window on performance indicators. \begin{table}
\begin{tabular}{c c c c c} \hline
**Path and Key Indicators** & **Minimum Distance** & **Shortest Time** & **Minimal Fuel Consumption** & **Lowest Cost** \\ \hline  & 0-9-10-0 & 0-3-4-0 & 0-2-10-0 & 0-8-2-0 \\  & 0-8-7-0 & 0-5-9-0 & 0-4-9-0 & 0-7-6-0 \\ Tour plan & 0-4-3-0 & 0-6-7-0 & 0-6-8-0 & 0-9-5-0 \\  & 0-1-2-0 & 0-8-10-0 & 0-5-7-0 & 0-4-10-0 \\  & 0-6-5-0 & 0-2-10-0 & 0-3-1-0 & 0-3-1-0 \\ Total traveled distance (bm) & 314.6 & 318.99 & 320.25 & 321.35 \\ Total traveled time (minute) & 406 & 411 & 401 & 405 \\ Total fuel (liter) & 41.52 & 42.87 & 41.23 & 40.96 \\ Total cost (\#) & 501.98 & 508.56 & 512.31 & 507.49 \\ \hline \end{tabular}
\end{table} 
Table 5: Examples of different objective functions with no time-window constraints at 11 nodes.