esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[14], they realized an image encryptor based on the high-dimension Lorenz chaotic system and a perceptron model within a neural network, and proved through experimental work the high security of this algorithm and also its strong resistance to the known attack vectors. [PERSON] et al. [15] also introduced a similar encryption system, but they added a position shuffler and a pixel value changer in the grey-mode of an image to strengthen the security of their method. In addition, [PERSON] et al. [16] treated the case of image coloring encryption, where they introduced a bit-level permutation and high-dimension chaotic map to encrypt colored images. They showed afterward through experimental results and security analysis that the scheme is very effective regarding the security of the encryption. [PERSON] et al. [17] introduced an encryption system that has the same goal as the previous one, but uses DNA sequences for the encryption in addition to the chaotic system. Relying on the logic in the DNA sequence, they established a scheme to encode the different color layers of the image in the process. The chaotic map was mainly used to scramble the locations of the different elements. Subsequently, chaotic systems play a major role in enhancing the cryptography field. In our previous work [18], we developed a new way to generate secure symmetric encryption keys for the IMDs in order to cipher the wireless communication of IMDs. This scheme relies on Henon Map to generate the Pseudo-Random keys, relying on synchronized initial conditions and the cycle seed. As an extension of that work, we designed in this work the full encryption scheme for IMDs and analyzed its performances. Therefore, the main contributions in this paper can be summarized as follows:  
* Analysis of the new symmetric key generation scheme for wireless cryptography based on PRNG derived from a low-dimensional chaotic system. * Design of an efficient light-weight block cipher encryption scheme that uses fewer rounds than conventional encryption schemes with short-length keys. * Investigation of the encryption scheme within an on-body to off-body communication channel.
# UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
## 1 Introduction
### Contribution  
The main contributions of this paper are:  
* An unsupervised feature selection for online dynamic multi-views (UFODMV) method is proposed that includes clustering-based technique along with an innovative merging mechanism that provides an efficient stream dynamic feature selection process with multi-views. UFODMV proposes a novel merging process that rapidly improves the dynamic feature selection process while significantly reducing the number of selected representative features in a hierarchical manner with a predefined reduction size.
* UFODMV is evaluated and compared with three well-known unsupervised feature selection methods, namely OMVFS, UFSSF, and SPEC. In this evaluation, three real and well-known multi-view datasets were used, including the Fox News, Caltech-7, and Multiple Features Handwritten datasets. UFODMV significantly outperformed the benchmarked methods when selecting a set of representative features that was able to classify the data accurately. UFODMV shows a significant classification accuracy of around 50% compared with the benchmarked methods.  
The rest of the paper is organized as follows. Section 2 discusses the related works. The proposed method is presented in detail in Section 3. Experimental results and performance evaluations are shown in Sections 4-6. Finally, Section 7 concludes the paper.  
Figure 1: Healthcare application that (**a**) demonstrates two clinics, namely heart and physical therapy, with their own features and (**b**) demonstrates a new healthcare clinic, namely diabetes, which has an additional group of features (i.e., view).
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 4 Cloud Security Challenges and Proposed Solutions
### Managed Security Threats
#### 4.1.2 Malicious Insider  
Malicious insider threats in cloud computing relate to the hazards posed by persons who have authorized access to cloud resources but purposefully abuse their rights for malevolent objectives. Employees, contractors, or service providers having lawful access to the cloud environment may be among these insiders. Here are some important features of hostile insider attacks in cloud computing [39, 40]:  
* Data theft or leakage
* Unauthorized Access
* Sabotage or data manipulation  
**Malicious Insider Threat Prevention and Mitigation:**  
Implement strict access restrictions, least privilege principles, and strong identity and access management (IAM) policies. Review and adjust user access credentials based on job positions and responsibilities on a regular basis.  
Implement robust monitoring and auditing systems to track user activity, system events, and data access. To detect suspicious behavior, use real-time alerting and anomaly detection technologies.  
Duty segregation: Enforce duty segregation to guarantee that no single employee has disproportionate control or privileges. Separation of roles decreases the possibility of collaboration and aids in the prevention of unauthorized acts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threats** & **Affected Cloud Services** & **Effects** & **Solutions** \\ \hline Abuse of Cloud Computing & PaaS and IaaS & \begin{tabular}{c} Validation Loss, Service Fraud, \\ Strong attacks due to unidentified \\ sign-up \\ \end{tabular} & \begin{tabular}{c} Network analysis, Robust \\ registration and multifactor \\ authentication \\ \end{tabular} \\ \hline Insecure API & PaaS, SaaS, IaaS & \begin{tabular}{c} Improper authentication and \\ authorization, the wrong \\ transmission of Content \\ \end{tabular} & \begin{tabular}{c} Data Encryption, Strong access \\ control and multi-factor \\ authentication \\ \end{tabular} \\ \hline Malicious Insider & PaaS, SaaS and IaaS & \begin{tabular}{c} Assets damage, productivity loss \\ and confidentiality break \\ \end{tabular} & Duty Segregation, IAM policies \\ \hline Data Loss & PaaS, SaaS and IaaS & \begin{tabular}{c} Removal, modification and stealing \\ of confidential and personal data \\ \end{tabular} & \begin{tabular}{c} Disaster, backup and recovery \\ management \\ \end{tabular} \\ \hline Service and Account & PaaS, SaaS and IaaS & \
\begin{tabular}{c} Breaching into critical areas of cloud \\ and server, access of root account \\ \end{tabular} &
\begin{tabular}{c} Adoption of strong \\ authentication, and security \\ policies. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative Study of Cloud security problems.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 4 The Proposed Algorithm
### Signal Representation Sub-Module  
The original one-dimensional electromagnetic signal needs to be processed as a fixed time-frequency matrix through energy detection, short-time Fourier transform, and other operations. The short-time Fourier transform usually aims to use a fixed-size window to intercept a small signal sequentially and convert each small signal into a one-dimensional matrix by discrete Fourier transform. Further, all the obtained matrices are spliced into a time-frequency matrix of a well-designed size. The problem of signal recognition can be considered as the task of image classification. From the generation process of a signal sequence as a time-frequency matrix, it can be found that the sequential relationship naturally exists between each column of the matrix. It has been proven that the model with a high recognition rate in the closed set scene has a high positive correlation with the recognition rate in the open set scene. As a result, it would be better to employ some feature extraction models with the global feature extraction ability for open-set scenarios. The transformer [41] was first proposed in natural language processing, which uses the self-attention mechanism to encode global information about the sequence data. The vision transformer, i.e., ViT [42], can be considered the first transformer-based model for computer vision. ViT has a strong global feature extraction ability to extract the features of time-frequency matrices. However, the ViT model network has extreme structural parameters in the training process, requiring data enhancement and regularization techniques to prevent over-fitting with relatively low computational efficiency. In this signal representation sub-module, we employ the lightweight mobile ViT model [43], instead of typical CNN-based models, as the important feature extractor to extract the global features. The mobile ViT combines both the traditional convolution layer and transformer model to learn local and  
Figure 1: Main algorithm framework containing three key sub-modules: _the signal representation_ sub-module based on transformer-based ViT structure; _the set distance metric_ sub-module based on Wasserstein distance; abd _the class space compression_ sub-module based on reciprocal point separation and central loss.  
global features, respectively. On the basis of the reduction in parameters to facilitate the training, it has the ability to extract global information.
## 3 Modeling Parameters
### Materials  
The aluminum and CFRP are both assumed to be uniform throughout the rotor and free of defects. Referring to [28], the aluminum exhibits negligible viscoelastic response at temperatures below 50 \({}^{\circ}\)C, therefore viscoelastic behavior in the aluminum is not considered. The material properties of the chosen 7075-T6 aluminum are found in [29].  
The composite considered in this study is IM7 carbon fiber (Hexcel Corp., Stamford, CT, USA) with an 8552 epoxy resin system (Hexcel Corp., Stamford, CT, USA), as described by [PERSON] et al. [30]. The filament winding process employed for fabricating CFRP flywheel rotors utilizes continuous unidirectional fiber reinforcement, which creates a transversely isotropic behavior [31].  
In the CFRP rim, the fibers run circumferentially and display only subtle viscoelastic characteristics. The long-term behavior of a CFRP in the transverse direction is often described using a time-temperature superposition (TTSP) master curve. This curve is created by measuring short-term creep data at various elevated temperatures. Then, a shift factor is applied to the elevated temperature experimental data to shift them temporally, increasing the time axis while decreasing the temperature. Shifting all elevated temperature experimental data creates a smooth master curve representing the lifetime strain and compliance behavior, provided the applied stress from experimentation is known. Finally, curve fitting is performed on the master curve to generate empirical equations for the creep compliance. TTSP is applicable for modeling linear viscoelasticity, which is acceptable for this application as permanent damage, material aging, and other higher order effects are excluded from this simulation. The transverse compliance equations published by [PERSON] et al. [30] are given in Table 1 and Equation (14), as are the aluminum properties, where variable \(t\) indicates time. The tensile strengths of the CFRP and yield strength of the aluminum necessary for the Tsai-Wu criteria are given in Table 2.  
\[S_{11}^{0}=9.0\times 10^{-12}\;\mathrm{Pa}^{-1},\;S_{22}^{0}=S_{33}^{0}=1.1 \times 10^{-10}\;\mathrm{Pa}^{-1},\;S_{66}^{0}=2.0\times 10^{-10}\;\mathrm{Pa}^{-1}. \tag{14}\]
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Effectiveness and Robustness of the AEs in Novelty Detection
#### 5.1.2 Evaluating the Novelty Detection Task with Anomalies  
Description. This experiment extends the one in Section 5.1.1, by including C4 in the scenarios under evaluation. C4 describes a novel operating condition, but it differs from the other ones for the existence of anomalies in the signals. As in Section 5.1.1, we consider all possible scenarios created from all k-permutations of the time series describing the operating conditions (see Table 5).  
Table 5 reports the results of the experiments for each scenario in terms of F1 score, by considering a window size equal to 200 (100 s) and the problem of detecting an existing operating condition (Known column) or  
than 33%). Only the SVM was demonstrated to be resilient to the anomalies, showing a small increment of the F1 score (from 40.8% to 41.37%). The breakdown of the results in Table 6 shows that for each scenario, almost all AEs outperform the baselines in both the detections of known and novel conditions.
## 4 Discussion
The model was displayed as three-dimensional plots, a three-dimensional depiction of the response for chosen components, to help understand the influence of the factors on the antifungal metabolite yield. These charts show how two variables interact, which makes it possible to determine the optimum experimental setup directly. As shown in the color code, a red zone indicates maximum inhibition zones, and thus, maximum production, while a blue zone indicates the minimum production of the antifungal metabolite. These 3D plots, together with the numerical optimization function in the software, determine the optimum conditions for maximum antifungal metabolite production, and these conditions were verified experimentally. Our design and optimization model were validated because the actual experimental results were in good agreement with the projected outcomes from the software for this experiment. The metabolite produced a maximal inhibition zone of 21.3 mm, which is equal to 109.92 \(\upmu\)g/mL. Therefore, compared to unoptimized conditions (31.475 \(\upmu\)g/mL), optimization in our investigation was able to increase the _B. toyonensis_ OQ071612 production of the metabolite by approximately 3.49-fold. Graphical model diagnostics are plotted by the software (Design Expert(r) v. 11.0, Minneapolis, MN, USA) to verify ANOVA results. These plots include normal plots of residuals (Figure 7a), which reveal whether the residuals (difference between an actual and predicted value) follow a normal distribution, i.e., a straight line or not. The Box-Cox plot recommends the correct power transformation, based on the best lambda value obtained at the lowest point of the curve. In our case, the software recommended a transformation to the inverse square root, as shown in Figure 7b. Another diagnostic plot is the predicted vs. actual plot, which displays the values not easily predicted by the model (Figure 7c). Finally, the residual vs. run plot (Figure 7d) identifies trends that indicate lurking variables that influence the response and ruin the analysis.
# Performance-Based Evaluation of Courtyard Design in China's Cold-Winter Hot-Summer Climate Regions
## 5 Conclusions  
This work studied the traditional courtyards in the southern Jiangsu Province based on qualitative analysis of quantitative simulation using the wind environment simulation software, PHOENICS, and the energy simulation software, DesignBuilder. This study proposes three strategies, including adjusting the spatial layout of courtyards, the aspect ratio, and building an ecological buffer space. The proposed improved design can increase ventilation in summer--wind speed at the opening can reach 1.75 m/s while keeping the average wind speed from 0.25 to 0.5 m/s to maintain thermal comfort. Further, adjusting the aspect ratio increases natural ventilation driven by thermal pressure in summer, while the northern enclosing wall is raised and improved to resist cold wind in winter. Considering the ecological buffer areas, enclosing the buffer space in winter can achieve the overall energy saving of the courtyard building. Results show that the average natural room temperature in Room 1 can be reduced by about 1.8 \({}^{\circ}\)C in summer and increased by about 1.1 \({}^{\circ}\)C in winter. The 12.4% of peak cooling load and 25.1% of total cooling load in summer in Room 1 is significantly reduced, and 35.7% of peak heating load and 36.1% of total heating load can be saved in winter. Based on the overall performance in the study's three rooms, results show that the proposed design of a courtyard can save about 19.6% of cooling loads in summer and about 22.3% of heating loads in winter. These findings provide insights into the optimal design of a courtyard to maximize its benefit in regulating the microclimate and saving energy during both winter and summer.  
**Author Contributions:** The following statements are provided for individual contributions: Conceptualization, X.X. F.L. and T.H.; Methodology, X.X. and F.L.; Software, F.L. and X.F.; Validation, X.X. and W.W.; Formal Analysis, X.X. and W.W.; Investigation, F.L. and X.F.; Resources, X.X.; Data Curation, X.X. and F.L.; Writing-Original Draft Preparation, X.X. and F.L.; Writing-Review & Editing, X.X., W.W., and T.H.; Visualization, F.L.; Supervision, X.X., W.W. and T.H.; Project Administration, X.X.; Funding Acquisition, X.X.  
**Funding:** The work described in this study was sponsored by the projects of the National Natural Science Foundation of China (NSFC#51678127), the National Scientific and Technological Support during the 12 th Five-Year Plan Period (No.: 2013 BAJ10B13), Beijing Advanced Innovation Center for Future Urban Design (UDC# 016010100), and China Scholarship Council (CSC#201706095035). Any opinions, findings, conclusions, or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of the National Scientific and Technological Support committee and NSFC.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Study on the Effect of Rock Mass Structure on CO2 Transient Fissure Excavation
## 2 Theoretical Analysis
### Analysis of Cracking Process
#### 2.2.1 Impact Effect of Dynamic Load  
The transient cracking of carbon dioxide mainly acts on the inside of the rock. After crushing the cracking pipe, the shock airstream formed by high-pressure gas directly acts on the rock contacted by the cracking pipe. The rock mass is fractured by fracturing when the fracturing pipe is broken, and the shock wave produced by the impact gas flow exceeds the dynamic compressive strength of the rock mass. The areas showing different distances with the cracking hole present an obvious division scope of cracking characteristics at the macro level [18]. As shown in Figure 1, the action zone of a dynamic load of transient cracking of liquid carbon dioxide could mainly be divided into three parts: 1-crushed zone close to the cracking hole, 2- fissure zone formed due to fissure expansion in the crushed zone, 3- vibration zone away from the cracking zone.
# IoT Technologies during and Beyond COVID-19: A Comprehensive Review
## 2 Related Work  
The COVID-19 pandemic has given rise to various predictions on the future. It is envisaged that IoT will play a significant role in the new normal. This article presents, analyses, and discusses IoT-inspired solutions during the pandemic in different sectors, challenges, and opportunities beyond the pandemic.  
The large number of interconnected devices used by the IoT that can track and alert different types of illness help in creating a smart network for health management systems. Patient information is captured without the need for human interaction, which could be beneficial in decision-making methods [2].  
There are many wearable lightweight IoT devices [10] that could be used to limit contagious viruses such as COVID-19 and improve healthcare offerings. Some symptoms can be effortlessly monitored with the use of IoT devices. If symptoms of the virus are detected, the device can notify both the user and the closest health department. IoT can also work as a mapping network that shows places with dense population or places that have an increased number of cases and a higher risk of infection. This can improve the intervention of health department performance to detect and save individuals in critical situations (e.g., the patient is unable to contact the health department at the right time due to symptoms) and share data with different departments to come up with a faster cure and to keep civilians safe. With the use of technologies such as 4/5 G and the cloud, it is much easier to monitor patients from a great distance, especially individuals who find it difficult to reach or access healthcare facilities.  
Another benefit of IoT devices in the healthcare sector is the reduction in human error, which is more frequent than machine or AI errors. IoT can provide more accurate diagnosis and patient reporting, taking into consideration that human mistakes will likely recur.  
IoT technology plays a significant role in detecting the virus by using fever screening to recognize some of the virus' symptoms, limiting spread by enforcing social distancing; and managing remote health monitoring, pollution and air quality control, occupancy control, and smart parking. The use of fever screening requires no contact between people and allows multi-targets detection. Sensors and cameras provide colour temperature scales and images. Pre-screening of employees, disaster evacuees, or patients is the first line of defense against the virus [5]. This does not guarantee virus detection but can be used to determine if the subject may have symptoms of the virus, and further inspections are made for final determination. Fever screening can be used in airports, schools, warehouses, and other crowded areas. Figure 1 illustrates the temperature screening output of a solution available in the market.
## 3 Related Work on the Usage of CNMs
In [1], the key challenges of CNMs are named as validity, reliability, and representativeness, which play an important role in all stages of a crowdsourcing campaign: in the design and methodology, the data capturing and storage, and the data analysis. Other challenges inherent to CNMs via smartphones were presented by [6]. Here, for example, end device related issues, resource consumption, and privacy versus reliability were discussed and shown by the example of a CNMs data set. While these two articles describe in detail various challenges in designing, collecting, and analyzing CNM data, they do not provide specific solutions or guidelines. To the best of our knowledge, a detailed discussion of the validity of crowdsourced data and a guideline on how to check data for validity is still missing.
## 3 Analysis of Optical OFDM for Underwater Communications  
This section describes and analyzes the principles and performances of DCO-, ACO-, and U-OFDM in underwater communication systems.
## 2 The Models
### A Modified Model
#### 3.1.1 Melnikov's Approach
The \(\beta\)-function \(B(\cdot,\cdot)\) is defined through the gamma function as  
\[B(x,y)=\frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}. \tag{23}\]  
Having in mind that the moment-generating functions of these distributions are  
\[ \begin{split}\psi^{\text{uniform}}(x)&=\frac{1-e^{ (K+1)x}}{K(1-e^{x})}\\ \psi^{\text{binomial}}(x)&=(1-p+pe^{x})^{K}\\ \psi^{\beta-\text{binomial}}(x)&={}_{2}F_{1}(-K, \alpha,\alpha+\beta,1-e^{x})\\ \psi^{\text{hypergeometric}}(x)&=\frac{(N-K)_{2}F_{ 1}(-M,-K,N-M-K+1,e^{x})}{\binom{N}{M}},\end{split}  \tag{24}\]  
we express the terms \(\psi(-2\ln|x|)\) from Equation (21) as \[\psi^{\text{uniform}}(-2\ln|x|) =\frac{x^{2(k+1)}-1}{Kx^{2k}(x^{2}-1)} \tag{25}\] \[\psi^{\text{binomial}}(-2\ln|x|) =\left(1-p+\frac{p}{x^{2}}\right)^{K}\] \[\psi^{\beta-\text{binomial}}(-2\ln|x|) ={}_{2}F_{1}\!\left(-K,\alpha,\alpha+\beta,1-\frac{1}{x^{2}}\right)\] \[\psi^{\text{hypergeometric}}(-2\ln|x|) =\frac{\binom{N-k}{M}{}_{2}F_{1}\!\left(-M,-K,N-M-k+1,\frac{1}{x ^{2}}\right)}{\binom{N}{M}}.\]  
Let us remind the reader that the symbol \({}_{2}F_{1}\) stands for the Gaussian hypergeometric function. We can now construct varieties of distribution-based oscillators combining terms (25) and estimating them in (21).