esa-sceva/satcom-chunk-collection huggingface dataset:
# Quasi-Static Sliding Wear Analysis of 3D Rough Surface Considering Changes in the Point of Contact
## 1 Introduction
[PERSON]'s model has the great advantage that it does not require a wear coefficient to be measured experimentally, but it has not been sufficiently verified due to the lack of detailed experimental data. In this study, we performed a deterministic quasi-static wear analysis considering the point of contact that changed according to the sliding of asperities on two rough surfaces in relative motion, and also analyzed the evolution of surface topography and parameters representing the surface. We used the continuous convolution Fourier transform (CC-FT) for contact analysis of periodic rough surfaces and the Archard wear model to calculate the wear depth. In addition, we compared the differences in the analysis results between the truncation model, the deterministic fixed wear model that does not consider the change in the point of contact (referred to as the fixed model), and the deterministic quasi-static wear model that considers the change in the point of contact (referred to as the quasi-static model).
# An Efficient Network Resource Management in SDN for Cloud Services
## 1 Introduction  
Various services have been provided, based on recent advancements in cloud computing technologies [1], and their types and targets have been continuously expanding. Early cloud-based services mainly focused on storage services, whereas various current services include big data analysis, games, and multimedia processing. Cloud-based services are expected to achieve the fastest rate of growth over the next few years. However, owing to advancements in these technologies and their market growth, processing procedures have become more complex and the amount of data required in the service provisioning procedures has drastically increased in symmetry. Therefore, an efficient management method for the internal resources of the cloud has symmetrically become essential for providing services more smoothly while maintaining the quality of experience (QoE).  
The traditional network structure currently used on the Internet cannot flexibly deal with dynamically requested services. Software defined networking (SDN) [2] technology has emerged to address this issue. SDN symmetrically separates the network structure into a control plane and a data plane and manages network switches through the Openflow [3] protocol. At the control plane, the controller conducts network control functions such as routing, flow table management, and topology management. At the data plane, the switches forward packets using the flow rules received from the controller in symmetry. The overhead of the control plane in SDN may increase a little. However, SDN can efficiently manage network resources because it enables a logical access to the network and dynamically adds user-desired functions to the controller.  
Therefore, in this study, we propose a method for efficiently managing resources when providing cloud services in SDN. The proposed method collects and analyzes the traffic pattern of the services currently being transmitted to the network using SDN. Based on the analyzed information, the priority of services is symmetrically determined by predicting the usage of services requested from the user. The proposed method dynamically allocates the network resources for each service based on the priority of services in symmetry.
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking  
UAHWs employ active sonar for target detection. Initially, UAHWs utilize their array of sensors to emit frequency-modulated signals, which propagate through the ocean channel, reach the target, and reflect as echoes. These target echoes, after transmission through the ocean channel, are received by the sensor array of UAHWs and undergo array signal processing and correlation detection. In addition to interference from marine environmental noise, they are also influenced via reverberation. After receiving the target echoes through the sensor array, UAHWs initiate beamforming to achieve spatial filtering. Subsequently, the signal, post-beamforming, undergoes correlation detection. To mitigate the impact of reverberation, a time-varying gain control method is applied. Following correlation detection, the highlights corresponding to the target echoes are obtained. Feature extraction is then conducted based on these highlights. Due to a significant disparity in the quantity of submarine target echoes compared to decoy target echoes in the dataset, SMOTE is employed in the feature domain. Ultimately, target recognition is accomplished through the utilization of the stacking ensemble method. An entire workflow of the target recognition algorithm is depicted in Figure 2.
## 3 Results
### Models with Topography
#### 3.3.2 Steep Hill Model  
In this part, a steep hill model, as shown in Figure 9, is designed with both height and width of 1 km. The background conductivity is \(10^{-2}\) S/m. Frequencies considered are \(10^{1}\), \(10^{2}\), and \(5\times 10^{2}\) Hz.  
Similarly, solutions for cases based on the quasi-static approximation are compared with those for cases using the complete wavenumber along one survey line.  
Figure 10 shows the comparison of modeling results at different frequencies for the steep hill model, along with the relative errors as calculated in Figure 7. Similar to the results indicated in Figure 7, the deviation between the two solutions increases with frequency. At a frequency of \(10^{1}\) Hz, the two modeling results are in good agreement, as indicated in Figure 10a-d, for both real and imaginary parts of the \(x\)-component electrical field with relative errors much smaller than 1%. As frequency increases to \(10^{2}\) Hz, as shown in Figure 10e-h, the relative errors are mostly larger than 1% for both the real part and the imaginary part (with largest respective relative errors 2% and 11%). At \(5\times 10^{2}\) Hz, as shown in Figure 10i-l, for both the real and imaginary parts, their deviations from the reference solution are substantial. The largest relative error reaches 13% for the real part and 28% for the imaginary part.  
Figure 8: Comparison plots of the apparent resistivity (**a**,**c**) and phase (**b**,**d**) from the case based on the quasi-static approximation (upward-pointing triangle lines)) with \(10^{-6}\) S/m for air conductivity, and the one with the complete wavenumber (circle lines) at a frequency of \(5\times 10^{2}\) Hz for the canyon model. The relative errors are referenced to the results based on the use of the complete wavenumber.  
Figure 9: Sketch of the steep hill model. (**a**) \(x\)-\(z\) section view along \(y=0\), (**b**) \(x\)-\(y\) plan view at \(z=0\).  
Figure 10: Comparison of the \(E_{x}\) numerical solutions from the case based on the quasi-static approximation (upward-pointing triangle lines) with \(10^{-6}\) S/m for air conductivity, and the one with the complete wavenumber (circle lines) at frequencies of (**a**–**d**) \(10^{1}\), (**e**–**h**) \(10^{2}\), and (**i**–**l**) \(5\times 10^{2}\) Hz for the steep hill model. The relative errors are referenced to the results based on the use of the complete wavenumber.  
Due to the existence of the steep hill, similar to the canyon model case, large relative errors tend to happen at considerably lower frequencies for the quasi-static approximation cases than the corresponding flat model. Similar to the results indicated in the previous case, the steep hill also makes the upper limit of frequency for valid quasi-static approximation, substantially lower than expected (as low as several hundred Hz). This error might be caused by the inaccurate EM description in the air. The comparison of apparent resistivity and phase shown in Figure 11 also indicates that the quasi-static approximation has little effect on the accuracy of apparent resistivity and phase calculations.
## 3 Results
### Cost Analysis  
Table 8 shows the results of the determination of the process cost rate. The tariffs for the cost-type groups in years 2012-2016 and the resulting mean values are listed. The table also contains the calculated mean weightings. The weighting for personnel costs of medical activity in the ENT surgery cost center group can be verified using the data from the MCC documentation. On average, 1.7 physicians operated in non-navigated surgery, and 1.8 in navigated operations. These values correspond to the established weighting from the data of the business department. \begin{table}
\begin{tabular}{c} **Mean Operating** \\ **Time** \\ \end{tabular} & **Median** & **SD** \\ \hline
**Junior Doctors** & **21** & **93.71** & **80.00** & **42.41** & **20** & **139.40** & **117.00** & **48.10** \\ \hline JNR3 & 8 & 78.75 & 72 & 31.52 & 6 & 162.17 & 141 & 69.36 \\ \hline JNR17 & 13 & 102.92 & 116 & 46.65 & 14 & 129.64 & 117.5 & 34.49 \\ \hline
**Specialists** & **62** & **113.21** & **109.00** & **57.65** & **21** & **113.29** & **114.00** & **35.43** \\ \hline SP2 & 34 & 114.32 & 105 & 56.49 & 7 & 127.71 & 122.00 & 36.07 \\ \hline SP3 & 28 & 111.86 & 113 & 60.05 & 14 & 106.07 & 112 & 34.10 \\ \hline
**Consultants** & **76** & **76.29** & **67.50** & **38.53** & **51** & **80.41** & **80.00** & **35.11** \\ \hline CON7 & 59 & 83.22 & 81.5 & 38.66 & 23 & 92.87 & 104 & 33.65 \\ \hline CON9 & 17 & 52.24 & 45.5 & 27.38 & 28 & 70.18 & 69 & 33.46 \\ \hline
**Head of** & **151** & **55.32** & **49.00** & **29.10** & **183** & **60.29** & **57.00** & **28.52** \\ \hline
**Overall result** & **310** & **74.64** & **59.00** & **45.28** & **275** & **73.82** & **69.00** & **39.76** \\ \hline \end{tabular}
\end{table} 
Table 7: Operations performed, reduced to those physicians most frequently operating. Overall, a process cost rate of EUR 11.79/min is calculated. The personnel costs, which are made up of the medical activity and medical technical activity/functional activity for ENT surgery and anesthesia, are EUR 6.24/min. Based on an analysis of operating times and the process cost rate, three possible calculations or cases according to the bottom-up approach can be implemented:  
1. Inclusion of all surgeons from Table 5.
2. Only selected surgeons from Table 6.
3. Only the head of the department. A Fiagon navigation device costs EUR 70,000, according to the list price.
## 3 Results
### Vegetation Indices  
NDVI was observed to be almost stable till April, whereas it started to increase in May, reached its peak in July, and then decreased till the measurement was conducted in October (Figures 3 and 4). For the CL site, the NDVI value got saturated around the peak of the vegetation season, i.e., in July and August, when the NDVI values reached around 0.85 (Figure 3). In CL, the manipulated plots show a slight increase in NDVI before it reached the saturating value (i.e., May and June), whereas for CR the slight increase in NDVI was observed for manipulated plots during the growing season. EVI and NIRv showed a very strong correlation in between for peatland vegetation over the whole vegetation season (Figure 5), although EVI takes higher values than NIRv (Figures 3 and 4). For EVI and NIRv, the manipulated plots were observed to have a slightly higher value than the control plots during the growing season.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 9 Case Study 2: Cisco System Breach by Yanluowang Ransomware Gang
### Future Directions
#### 9.7.8 Blockchain Security: Leveraging Distributed Ledgers for Enhanced Detection and Response  
The potential of blockchain technology in enhancing malware detection and incident response is an intriguing area of exploration.  
* **Decentralized Threat Intelligence Platforms:** Exploring opportunities for creating decentralized threat intelligence platforms using blockchain's distributed and immutable nature.
* **Addressing Challenges:** Giving careful attention to scalability, privacy, and performance challenges when integrating blockchain into intrusion detection systems.
## 3 Application to Virtual Fit Analysis Method to Pilot Oxygen Mask Design
### Acquisition of Information for Virtual Fit Analysis
#### 3.1.3 Identification of Mask-Wearing Positions  
The mask-wearing positions of the oxygen masks of the four size categories were identified based on the profile photos of the 85 KAF pilots while wearing their MBU-20/P oxygen masks. The identification of mask-wearing positions was conducted in a four-step procedure. First, the profile face of a pilot while wearing his/her own oxygen mask was photographed by a digital camera (Figure 8a). Second, the digital photo was printed on a transparent film and placed on a PC monitor (Figure 8b). Third, the 3D face scan of the pilot and the CAD model of the mask were aligned manually with the face and mask images of the printed film (Figure 8c) using RapidForm 2006 (INUS Technology, Inc., Seoul, South Korea). Lastly, the mask-wearing positions were identified by measuring the Euclidian distances between the facial landmarks (sellion and supramentale) and mask design landmarks (D1 and D8), as summarized in Table 1.  
Figure 6: Projection of design landmarks of the existing MBU-20/P mask onto a representative face of the Korean Air Force (KAF) pilots (red \(\blacksquare\): design landmarks of MBU-20/P specified by [PERSON] et al. (2015); blue \(\blacklozenge\): projected design landmarks on the face surface; \(\times\): facial landmarks).  
Figure 7: Boundaries of the existing and alternative oxygen masks. (**a**) The boundary of the existing MBU-20/P oxygen mask based on design landmarks specified by [PERSON] et al. (2015); (**b**) a boundary based on adjusted design landmarks identified by the projection of the existing design landmarks onto a representative face model of the Korean Air Force pilots; (**c**) the difference of the existing and alternative mask boundaries.
# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 3 Proposed Work
### Data Preparation
#### 3.1.1 Data Integration  
The considered dataset \"CSE-CIC-IDS2018-V2\" on AWS was assumed to capture all available network traffic during 10 consecutive days of activities under a controlled network environment. All appropriate base traffic that was conducted on specific attack scenarios was controlled. The dataset also captured benign network traffic and the most well-known attacks. The unusual dataset consisted of 10 raw-data files, containing 16 million (approximately) individual network flows that cover common and different types of attacks, including Brute Force attacks, DoS attacks, DDoS attacks, Heartbleed attacks, web attacks, intrusions, and botnets. For the subsequent training phase, we combined all these files into the dataset.
## 3 Stability Analysis for Upper Limit
### Step and Frequency Responses  
Figure 5 shows an example of the step response of the third-order closed loop function of DPLLs with the numerical integration combination of \(N(z)=\text{II}\) and \(F(z)=\text{SI}\) [Type A in Figure 5a] and II [Type C in Figure 5b] for three \(BT\) values. Since all the DPLL types are the same when the computational delay is included, zero computational delays are assumed here to observe the behaviors of the different types. The Type A system oscillates rapidly as \(BT\) increases and becomes unstable when \(BT=0.57\), which is the value of \(BT_{\text{osc}}\) for the II-SI combination when \(t_{\text{D}}=0\) (see Table 5), whereas the Type C system remains stable, even for \(BT=1\).  
Figure 6 presents the Bode plots of the same case as discussed above with the addition of \(F(z)=\text{BL}\) (Type B) and its continuous-time domain counterpart for small and large \(BT\). When \(BT\) is sufficiently small relative to \(BT_{\text{osc}}\), the frequency responses of the DPLLs accurately represent the system, and little distortion is observed for all numerical integration methods. However, the distortion causes the DPLLs to oscillate at maximum amplitudes at larger \(BT\) when \(BT\) approaches the system's resonant frequency \(BT_{\text{osc}}\). This clarifies the fact that the design parameter for the PLL is the bandwidth of the loop \(B\); however, the normalized bandwidth \(BT\) in DPLL does not reflect the true noise equivalent bandwidth of the loop as \(BT\) increases, and at the same time, the deviation of the actual bandwidth of the digital system is affected by the type of numerical integration method.  
Figure 4: Root location of the DPLL closed-loop transfer functions with three different integration methods for \(N(z)\) and \(F(z)\) (\(t_{\text{D}}=T\)). (**a**) First-order root loci. (**b**) First-order root magnitude. (**c**) Second-order root loci. (**d**) Second-order root magnitude. (**e**) Third-order root loci. (**f**) Third-order root magnitude.
## 4 Experiments
### Experimental Results  
The fine-tuning of the human segmentation model was undertaken using a rigorous method that incorporated a three-fold cross-validation process to ensure the robustness and generalizability of results; in addition, data shuffling was utilized during training. Through this procedure, the model achieved remarkable metrics, including a pixel global accuracy of 98.46%, a mean sensitivity of 92.78%, a mean Intersection Over Union (IoU) of 83.98%, and a mean weighted IoU of 97.24%, as shown in Table 3.  
The implementation of three-fold cross-validation served to reduce overfitting by dividing the dataset into three distinct subsets. Each subset was used in turn as a validation set, while the remaining two subsets formed the training set. This ensured that the model's performance was validated on different portions of the dataset, thus contributing to the generalizability of the model.  
The resulting model was highly effective in distinguishing between a person and their surroundings. This effectiveness is further demonstrated by experimental examples of human segmentation using the fine-tuned [83] model on each type of falling action as well as other actions (such as sitting), with post-processing, as illustrated in Figure 7. These examples highlight the model's accuracy in segmenting human subjects, further supporting its effectiveness and reliability, as proven by the three-fold cross-validation process.  
On average, the segmentation network took 0.21 s to segment one image.  
In the experiment with the entire proposed method for fall action classification, the prepared dataset included 120 videos, each containing fall action, and 57 videos without any falls. The method was applied to each video, and a binary result was reported based on the presence or absence of a fall situation. The classification results of the proposed method, as in Table 4, were highly accurate, achieving an accuracy of 99.44%, a sensitivity of 99.12%, a specificity of 99.12%, and a precision of 99.59%. The confusion matrix of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Global Accuracy** & **Mean Sensitivity** & **Mean IoU** & **Weighted IoU** \\ \hline Human Segmentation & 98.46\% & 92.78\% & 83.98\% & 97.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the fine-tuning of the human segmentation model.  
Figure 7: Examples of human segmentation using fine-tuning [83] with post-processing.  
proposed method's performance in classifying fall actions can be seen in Figure 8, providing a visual representation of the classification results. These impressive results indicate the effectiveness of the proposed method in accurately detecting fall actions in videos.
## 3 Reliable Cloud Data Storage: System Model and Problem Formulation
### Problem Formulation  
Cloud data storage is a system that refines storage and business accessibility. It arranges a vast number and various kinds of storage devices or machines, where the hardware or software components are considered. In the storage mechanism, the cloud is constituted by the data files \(D\), the number of VMs \(M\), and the number of components \(C\). In addition to this, the storage mechanism is accomplished with security and integrity enhancement. When the cloud data are outsourced, some critical issues are faced in the mechanism, which degrade the quality of the services. Hence, the challenging issues and problems are described as given below:  
1. Deploying the cloud storage: While deploying the cloud storage, it relies on the requirements and technologies. Performing the process requires the geographical location. Hence, the storage cost becomes greater.
2. Data virtualization: This is the process of mapping the data in the local storage to the physical storage, which includes the servers, storage, operating systems, and servers. This results in a higherruntime for storing the data.
3. Data organization: This becomes cumbersome while arranging the data or dividing them into chunks, files, or blocks. Since it contains a poor format for storing, it loses its efficiency.
4. Load balancing with data migration: When one of the machines faces a large load, some data are transferred to another machine. Yet, the challenges with the bandwidth or energy consumption exist.  
Figure 1: Architectural representation of proposed reliable cloud data storage using HP-BOA.  
Machine constraints: Due to such constraints in machines or servers, critical data storage problems are faced. Factors such as the structural or general traits affect the storage process.  
Therefore, the cloud network does not have the potential to control the stored data in its respective machines. Owing to this ineffective processes, the data can be destroyed, copied, altered, and so on. Moreover, the lack of control leads to imprecise results. While considering all the aforementioned problems, an effective, reliable cloud data storage was developed with the objective of heuristic development.