esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Intelligence
#### 4.3.2 Machine Learning (ML)  
Machine Learning (ML) is an AI application offered on computer systems that can automatically learn and improve from experience without explicitly programming. ML focuses on developing programs that can access data and use it to learn on their own, without human intervention or assistance.  
In the medical field, a major problem in hospitals is the monitoring and detection of nosocomial infections. The goal with the help of ML will be to identify patients with infection, based on the clinical data collected by the patient. This will allow a patient to be diagnosed with the disease and their progression will indicate an effective monitoring system. The monitoring will be held by a supervised ML algorithm integrated into a clinical decision support system to diagnose infection during hospital presentation [45]. The parameters to be determined by doctors, who must be in order to know that the patient is sick, will be listed under supervised ML algorithm. This provides an automated evaluation of diagnostic probability of infection. The available data should provide personalized pollution management recommendations to doctors. In this way, we achieve optimal recovery of the patient and prevent the non-proliferation of the virus. A supervisedalgorithm ML proves that it can be effective and is accepted by most scientists to be incorporated into a tool to support clinical decisions to be able to support the diagnosis of infection in hospital patients [46]. In recent years, the continuous development of ML algorithms in the medical field focuses on exploring the potential impact of systems, such as decision making by clinicians who manage infections with various decision support.  
The use of ML algorithms can improve the identification of different types of viruses in order to cope with it and prevent it as soon as possible. This will also reduce the spread of vulnerable populations [47]. During the period of prevention and control of the epidemic, ML can be useful in the prognosis, diagnosis, and control of patients infected with some type of flu based on the characteristics of the air in the environment. According to the latest clinical research [48], the respiratory pattern is different from any type of infection that a person receives and the common cold. However, the benefits of implementing ML techniques in risk management will be quite effective with the increased performance of these algorithms, as well as the ability to exploit large volumes of data with different characteristics [49]. ML algorithm should provide for deployment of an epidemic, analyzing conditions in areas where people are affected [50]. This technology focuses on statistics about the behavior of the virus and the uncontrolled spread likely caused by exposure to infected individuals [51]. Provision will be made to analyze the spread of the disease on the basis of movement through the population by implementing ML techniques to predict the spread.
# Partial Energy Transfer Model of Lamb Waves Scattering in Materially Isotropic Waveguides
## 1 Introduction
In this study, we investigated the potential of the MM technique within the proposed PET approach, which enables control of the energy transfer for selected waveguide(s). This feature was implemented using the material interface of particular thickness and material parameters. The verification of the PET approach was realized on a surface breaking vertical notch, for which a section of the waveguide under the notch enables the partial energy transfer. The analysis of the scattering process involved the incident fundamental antisymmetric Lamb wave mode (A\({}_{0}\)) mainly due to its easy applicability in practical measurements. The energy transmission and reflection coefficients with respect to the incident mode were obtained using two methods--the PET approach and a hybrid method combining FEM and the general orthogonality relation [16]. Obtained results from both approaches show very good mutual agreement and thus provide promising applicability to many complex geometries in terms of their shape, including the study of the effect of multipoint contact between the crack faces or even partially opened/closed cracks.
###### Abstract
The main objective of the current study is to analyze the influences of the inclined diffuser vanes on the transient interaction between the impeller and diffuser blades as one of the main sources of the unsteadiness in the centrifugal compressors. Therefore, a numerical approach using the unsteady Reynolds-averaged Navier-Stokes (URANS) method was conducted to investigate the turbulent flow unsteadiness and characteristics of the generated noise of the centrifugal compressor by applying the inclined diffuser vanes. Finally, the inclined leading edge effects on the stall margin improvement were analyzed. The numerical data evaluated in the present study can be utilized to enhance the compressor efficiency and also to control the interaction strength between the impeller and diffuser, which leads to reduce the centrifugal compressor loss and level of the generated noise. The present work shows the numerical results of applying the inclined leading edges in the diffuser vanes of a transonic centrifugal compressor. Section 2 describes the numerical methodology including the compressor geometry, computational domain, governing equations, and numerical validation. The influences of the inclined leading edges on the aerodynamic performance, stall margin, and flow unsteadiness are extensively investigated in Section 3. Finally, Section 4 reports the conclusions.
# A Redactable Blockchain Scheme Supporting Quantum-Resistance and Trapdoor Updates
## 1 Introduction
### Problem and Motivation  
The implementation of redactable blockchain allows for the modification or removal of specific data entries within the ledger when encountering illegal data, privacy breaches, and other situations. On the other hand, some application scenarios require frequent data modification, such as mobile networks [12], medical applications [13], and so on.  
Second, we review the key issues that need to be taken care of in the design of redactable schemes, mainly including:  
* How to balance variability and security;
* How to balance decentralization and assigning edit permissions;
* How to consider the compatibility between variability and existing blockchain systems.  
Nonetheless, designing a redactable scheme that meets all these requirements simultaneously is a huge challenge, and few existing schemes can accommodate all requirements. Therefore, we focus on some work to balance the above issues.  
Furthermore, the emergence of quantum computing poses a distinct threat to conventional public key cryptosystems and hash algorithms, consequently impacting blockchain security. In the realm of post-quantum encryption techniques for blockchain applications, lattice ciphers have emerged as a mature and efficient cryptographic solution, as demonstrate in [14]. The work of [15] designed quantum-resistant redactable blockchain schemes using latticed cryptography which provide insights into the realization of more secure redactable technologies. Nevertheless, existing lattice-based redactable blockchain is rare and there is no solution realistically deployed in blockchain for experimentation. For example, [PERSON]'s work proposes schemes only to implement chameleon hash functions combined with blockchain, and has no practical application [15].  
Chameleon hashing enables physical modifications to the blockchain, but the key problem is that it can lead to trapdoor exposure. Therefore, to protect against trapdoor exposure, [PERSON] et al. proposed to manage trapdoor exposure by using the secret sharing and the multiparty computation (MPC) protocol [16]. However, trapdoor sharing potentially results in reduced efficiency.  
The hierarchical identity structure combined with blockchain can provide strong support in data management and user verification [17; 18]. Therefore, this structure is consistent with the goals of our work. In the existing hierarchical identity cryptographic structure, we have the problem of parent nodes forging the identity of child nodes as well as a linear or even exponential growth of the delegated key dimension.  
Based on the abovementioned issues, to harmonize key management and chameleon hash functionality while mitigating concerns such as key exposure, we propose the development of a chameleon hash function grounded in hierarchical identity. Our objective is to construct a redactable scheme characterized by compatibility, quantum resistance, and updatable trapdoor functionality, all the while adhering to the decentralized attributes intrinsic to blockchain technology.
## 3 Computational Modeling
### Model Setup
#### 3.2.1 Geometry and Boundary Conditions  
It should be highlighted that the dynamic behavior of the Aluminum plate under the combined effect of blast wave and fragment impact is out of scope for the present numerical study and will be addressed in future work. Consequently, only the EDST, the air, the explosive charge, and the projectile are modeled. The tube wall is modeled as a perfectly reflective and rigid wall. Therefore, it is simulated by constraining the flow in the Arbitrary Lagrangian Eulerian ALE mesh in the normal direction of the 1200 mm edge elements. The segments selected in the wall of the EDST were set as \"no flow through normal direction\" using \"ALE essential boundary of the \"ALE card. An \"outflow\" of 300 mm is considered at the entrance of the EDST where the spherical explosive charge is detonated. There were no set conditions on the segments of the free air mesh at the entrance of the tube. The total meshed area has dimensions of 1550 mm by 93.5 mm. At the tube's exit, an outflow of 50 mm by 93.5 mm is considered. It serves to simulate the environment where the projectile enters in contact with free air. The detonation is set at time zero and located in the center of the explosive charge.  
A mesh convergence study is conducted to determine an adequate mesh size. It is important to choose a mesh size that allows to adequately simulate the explosive energy release. Additionally, it has to accurately capture the blast wave front at the tube's exit. The pressures as a function of the inverse of the mesh sizes were recorded for mesh sizes ranging from 4 mm to 0.25 mm. Results reveal that convergence is obtained as from a 0.5 mm mesh size. This optimal mesh size also fits into the recommendation given by [PERSON] and [PERSON] [37] for the simulation of energy release.
### Related Work  
While many survey and review papers on anomaly detection are available [15; 17; 18; 19; 20; 21; 22] there are only a few works that have compared different methods experimentally.  
[PERSON] et al. [7] conduct an experimental comparison of twelve anomaly detection methods, including Seasonal AutoRegressive Integrated Moving Average with exogenous variables (SARIMAX), Generalized Linear Model, Facebook Prophet [23], Matrix Profile [24], or Donut [25]. The comparison is made using a dataset compiled mainly from the Numenta benchmark [26] with a focus on different time series characteristics, such as seasonality and trend. They use the Youden Index [27] to determine a threshold for classifying anomaly scores and assess the quality of the analyzed methods using AUC ROC, Windowed-F1 and the NAB Score, which is the metric used in the Numenta benchmark.  
[PERSON] et al. [28] compare 15 anomaly detection methods in the context of collaborative robots. The analyzed methods are categorized as _instance-based_ methods, such as k-Nearest-Neighbors and Local Outlier Factor, and _explicit generalization models_, such as Principal Component Analysis, One-Class Support Vector Machine, or Autoencoder. They compare these methods on a dataset collected from different tasks performed by a robotic arm by using AUC ROC and Area under Precision-Recall Curve as quality measures.  
[PERSON] et al. [1] provide a comprehensive review of classical and deep-learning methods for anomaly detection. They group the presented methods into the three main classes: _Density Estimation and Probabilistic Models_, _One Class Classifications_ and _Reconstruction Models_, and present various classical and deep-learning methods from each category. They also give a unifying view of the anomaly detection problem by identifying specific anomaly detection modeling components to characterize the presented methods and exemplify the modeling and evaluation process on two real-world examples.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Signal Quality Analysis
#### 3.2.2 Quasar Scan  
Since the quasar signal is merged into the white noise of the receiving system, it is difficult to extract the quasar signal from raw data at one tracking station. However, the fringe of cross spectrum of different baseline (as shown in Figure 13) could reflect the relative quality of the signal at stations.  
Figure 12: Analysis of antenna pointing angle during the observation: (**a**) elevation angle of CEB varies from 12 to 33 degree; (**b**) elevation angle of NNO varies from 33 to 13 degree; (**c**) elevation angle of KSH varies from 61 to 68 degree.  
The SNR of the fringe could be evaluated with the equations:  
\[\rho_{inv}=k\cdot\sqrt{T_{int}\cdot F_{s}} \tag{22}\]  
\[SNR=\rho_{max}\cdot\rho_{inv} \tag{23}\]  
Figure 13: Interferometric fringe of quasar of all the baselines: (**a**) fringe of baseline CEB-NNO—the SNR is about 22.1 dB; (**b**) fringe of Baseline CEB-KSH—the SNR is about 11.9 dB; (**c**) fringe of Baseline NNO-KSH—the SNR is about 14.3 dB.  
where \(T_{int}\) is the integration time, \(F_{s}\) is the bandwidth, \(k\) is constant value, and \(\rho_{max}\) is the maximum value of the visibility function.  
It could be identified that the SNR of CEB-NNO baseline is higher than the baselines CEB-KSH and NNO-KSH, which could reflect the signal quality at the stations, with a similar phenomenon when receiving the spacecraft signal.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 4 Evaluation
### Comparison with Existing Detectors
In addition, through comparison, it can be found that ClamAV, LOKI, and CloudWalker have a lower detection rate for web shells of the three families NST, NCC, and Crystal; the reason is that the web shells of these three families are not as popular as the previous ones, and the features are not well included in these detectors, which result in a low detection rate. Overall, the detection rates of VirusTotal, ClamAV, LOKI, and CloudWalker are 86.26%, 55.69%, 62.56%, and 73.46%, respectively. For the state-of-the-art methods, it can be found that the method proposed by [PERSON] et al. is much better than that of [PERSON] et al. The main reason is that (1) PHP opcode sequence features can represent the malicious web scripts effectively, and (2) the threshold defined by [PERSON] et al. is easily compromised by sophisticated attackers. Our method does not use the opcode sequence features because extra costs may be incurred to adopt the transformation tool (i.e., the PHP VLD extension used in [44]). Additionally, the detection rate of our method reaches 95.26%, which is much higher than other detectors. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **c99** & **r57** & **WSO** & **B374K** & **NST** & **NCC** & **Crystal** & **Overall** \\ \hline Our method & 96.92\% & 98.99\% & 88.89\% & 100\% & 97.44\% & 85.29\% & 87.88\% & 95.26\% \\ VirusTotal & 92.31\% & 93.94\% & 88.89\% & 97.62\% & 92.31\% & 52.94\% & 48.48\% & 86.26\% \\ ClamAV & 83.08\% & 83.84\% & 37.78\% & 26.19\% & 20.51\% & 17.65\% & 6.06\% & 55.69\% \\ LOKI & 86.15\% & 87.88\% & 51.11\% & 52.38\% & 17.95\% & 26.47\% & 12.12\% & 62.56\% \\ CloudWalker & 95.38\% & 93.94\% & 91.11\% & 90.48\% & 15.38\% & 17.65\% & 6.06\% & 73.46\% \\ [PERSON] et al. & 94.17\% & 96.76\% & 62.50\% & 54.72\% & 72.45\% & 54.76\% & 63.86\% & 79.92\% \\ [PERSON] et al. & 96.31\% & 87.57\% & 94.64\% & 93.40\% & 83.67\% & 82.14\% & 85.54\% & 92.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison average in TPR results of various advanced web shell detectors and different web shell families. All the results are based on the average of 100 independently repeated experiments.
## 3 Control Strategy
### Isolation Stage Control  
As shown in Figure 2, the isolation stage of the proposed novel PET enables the medium- and high-voltage AC/DC distribution network to flexibly connect with multiple medium- and low-voltage AC/DC distribution networks of significantly different voltage levels through a reasonable series or parallel connection of DAB modules. ISOS and ISOP connection are usually used for DAB modules in consideration of the difference between the voltage levels of the input and output stages. Benefitting from the output-series, ISOS is able to connect with the output port of relevantly high voltage levels and is applied for the part of the isolation stage that connects with the medium- and low-voltage  
Figure 6: DC output port: (a) Topology; (b) block diagram.  
AC output port, while ISOP is applied for the other part of the isolation stage that connects with the low-voltage DC output port due to the output-parallel being applicable for relevantly low voltage levels and high power levels.  
Figure 7a shows the topology of ISOS where \(U_{\mathrm{dc1}}\) and \(U_{\mathrm{dc1L}}\) are the input and output side voltages of ISOS, respectively. \(U_{\mathrm{dc1}}\) is determined by the DC side voltage (\(U_{\mathrm{dc}}\)) of the input stage and the input side voltage (\(U_{\mathrm{dc2}}\)) of ISOP at the isolation stage. The proper sharing of input and output voltages of DAB modules is the key factor to ensure reliable and stable operations for ISOS and PET. Therefore, a control strategy that decouples the input voltage sharing control loop and the output voltage control loop is utilized in this paper [21], as shown in Figure 7b. Where, \(U_{\mathrm{dc1\_in}}\) is the reference of the input voltage of each DAB module and is equal to \(U_{\mathrm{dc1}}\)/M, \(U_{\mathrm{dc1\_i}}\) is the actual value of the input voltage of the \(i^{\mathrm{th}}\) DAB module, \(U_{\mathrm{dc1L\_ref}}\) is the reference of the output voltage of ISOS.  
It can be learned from Figure 7b that the sum of the output signals from the input voltage sharing control loop is zero, which enables the decoupling between the output voltage control loop and the input voltage sharing control loop.  
Figure 8a shows the topology of ISOP where \(U_{\mathrm{dc2}}\) and \(U_{\mathrm{dc2L}}\) are the input and output side voltages, respectively. It can be seen that the output sides of all DAB module connect in parallel to the same DC bus, and they have equal output voltages. Since the output side voltage of ISOP is controlled by the DC port converter in the output stage, it is only necessary to control the input voltage of each  
Figure 7: Input-series output-series (ISOS) connection: (**a**) Topology; (**b**) Block diagram.  
DAB module to enable reliable and stable operations of ISOP, and the control block diagram is shown in Figure 8b.
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## 2 Approach and Learning Objectives
### Methodology
#### 2.1.3. Section C: Layered Approach for the Different Standards and Technologies  
In the third section, we adopted a layered approach for the examination of the first five generations of both fixed and wireless networks from three different perspectives, namely the service perspective, the networking perspective, and the physical layer perspective [4]. We emphasized that each of the three layers has specific objectives in order to serve the end users seamlessly. In particular, the physical layer aims to enhance the capacity and reach of transmission. Subsequently, the network layer is tasked with enhancing connectivity and decreasing latency, to achieve adequate Quality of Service (QoS). Finally, the app/service layer aims to maximize the user's QoE. Figure 3 illustrates the key services, standards, characteristics, novelties, and limitations of the first five generations from three different perspectives, namely the app/service perspective, the networking perspective, and the physical layer perspective, adopting a layered approach. This approach was followed mainly for three reasons. First, a layered analysis minimizes the overall complexity by neatly categorizing the numerous current standards and technologies into a limited number of groupings. Second, it allows for modular engineering, which simplifies teaching and learning. Last, but not least, it seeks to describe the primary limits and innovations of each generation from a variety of perspectives. Finally, each of the three levels has certain goals in order to serve the end user effectively and efficiently. In essence, the approach we followed in this module for the five generations is dialectical since it follows an evolutionary path; that is, the limits of one generation become the novelty of the next generation. In this way, the constraints of each layer for each generation become more evident.  
In this section, the main learning objectives were (a) a deeper understanding of the standardization procedure, e.g., the need to adopt standards and the standards that are used today, (b) a deeper understanding of the rate of evolution for each layer and for each generation, (c) the examination of the state-of-the-art technologies used today, paving the way for the students to enter as professionals into the fifth-generation era, and (d) the limitations and novelties of each generation for the wider understanding the burdens that each generation poses to the services' evolution.
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 4 Experimental Results Discussion
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attack Index** & **Description** \\ \hline _Index_0 & No change in watermarked image \\ _Index_1 & Average filter taking a 3 \(\times\) 3 window size \\ _Index_2 & 90\({}^{\circ}\) anticlockwise rotation \\ _Index_3 & 25\% central region cropping \\ _Index_4 & Addition of Gaussian noise with mean 0 and variance 0.005. \\ _Index_5 & JPEG compression taking a quality factor of 75 \\ _Index_6 & Rescaling 512 \(\to\) 256 \(\to\) 512 \\ _Index_7 & Median filter taking a 3 \(\times\) 3 window size \\ _Index_8 & Salt and pepper noise addition with density of 0.005 \\ _Index_9 & 10 rows and 10 columns from arbitrary locations are deleted \\ _Index_10 & Low-pass Gaussian filter with a 3 \(\times\) 3 window \\ _Index_11 & Rows flip \\ _Index_12 & Columns flip \\ _Index_13 & Motion blur with a 3 \(\times\) 3 window \\ _Index_14 & Pixelation with a 2 \(\times\) 2 window \\ _Index_15 & Speckle noise with mean of zero and variance of 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attacks used to destroy watermarks. Figure 5: Watermark images: Watermark images labeled from \(W1\) to \(W4\). error ratio (BER) are employed for this purpose. These two measures were defined and utilized for the robustness analysis in the current study:  
\[BER(W,W^{\prime})=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}W_{i,j}\oplus W^{\prime}_{i,j }}{n\times n} \tag{25}\]  
\[NCC(W,W^{\prime})=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}W_{i,j}\times W^{\prime}_{i, j}}{\sqrt{\sum_{i=1}^{n}\sum_{j=1}^{n}W_{i,j}^{2}}\sqrt{\sum_{i=1}^{n}\sum_{j=1}^ {n}W_{i,j}^{\prime 2}}} \tag{26}\]  
where subscripts \(i\) and \(j\) indicate the pixel's position in the images, and '\(\bigoplus\)' refers to the XOR operation.