esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 2 Related Works
### Research on Behavior-Based Cyber-Attack Detection
[41] proposed a user behavior analysis system by combining big data analysis of application layer logs with RF algorithms. They collected application layer logs generated by web servers and web applications for a total of 12 months from January 2019 to December 2019. The system involved data preprocessing, data transformation, data analysis using RF algorithm, and result visualization. This approach could distinguish malicious users from normal users more accurately than existing signature-based methods and had the advantage of real-time detection and blocking. However, there were limitations in data collection, machine learning algorithms, and results visualization. In related studies, there are research papers that propose behavior-based detection methods for detecting cyber-attacks, as shown in Table 1. However, most of these studies have focused on detecting anomalous behaviors in host data using system call data, and machine learning algorithms such as KNN and SVM are commonly used for detection ([PERSON], et al. [10]; [PERSON] [25]; [PERSON], et al. [35]; [PERSON], et al. [38]). To make these detection methods more robust, cyber-attacks should be detected based on process data of applications used in hosts. Therefore, in this study, anomalous behavior is detected in real-time based on application process data, which is a different approach from that used in other studies. These studies have laid the groundwork for behavior-based cyber-attack detection using various data sources, machine learning algorithms, and techniques. However, there are some limitations that can be addressed in future research, such as improving data collection, validating algorithm performance, enhancing result visualization, and expanding the scope of detection methods to cover a wider range of cyber threats. By addressing these limitations, this paper presents a more efficient and resilient behavior-based detection algorithm that offers enhanced protection against cyber-attacks.
## 4 Simulation and Experiment Results
### Room Simulations
By analyzing the results in Tables 1 and 2, it can be found that when the SNR was 50 dB, the SD of the robot's position in the \(x\)-direction was 0.651 m, and the SD of the landmark's position in the \(x\)-direction was 0.452 m. If the SNR continued to decrease, the standard deviation would be greater than 0.651 and 0.452, and there would be little significance to the study of this paper. Also, from Tables 1 and 2, it can be observed that the errors for the robot's position and map estimation increase as the noises increase, but to a certain extent, the method can ensure the accuracy of the localization and mapping. The results show the robustness of the method proposed in this paper under environments with an SNR higher than 50 dB. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR** & \(\mathbf{x}\)**-Direction** & \(\mathbf{y}\)**-Direction** \\ \hline No noise & 0.214 m & 0.106 m \\
65 dB & 0.365 m & 0.206 m \\
50 dB & 0.651 m & 0.378 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Standard deviation of the robot’s position in the \(x\)- and \(y\)-directions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR** & \(\mathbf{x}\)**-Direction** & \(\mathbf{y}\)**-Direction** \\ \hline No noise & 0.098 m & 0.152 m \\
65 dB & 0.105 m & 0.165 m \\
50 dB & 0.452 m & 0.219 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Standard deviation of the landmarks’ positions in \(x\)- and \(y\)-directions. Figure 10: Simulation results for SLAM with a signal-to-noise (SNR) of 65 (dB) in the distance data: (**a**) EKF-SLAM for a mobile robot based on microphone landmark observations; (**b**) robot position errors in the \(x\)- and \(y\)-directions; and (**c**) landmark position errors in the \(x\)- and \(y\)-directions. Figure 11: Simulation results for SLAM with an SNR of 50(dB) in the distance data: (**a**) EKF-SLAM for the mobile robot based on microphone landmark observations; (**b**) robot position errors in the \(x\)- and \(y\)-directions; and (**c**) landmark position errors in the \(x\)- and \(y\)-directions.
## 3 Generalized (\(\alpha\), \(\beta\))-Nonexpansive Multi-Valued Mapping  
**Definition 6**. : _Consider a multi-valued mapping \(\mathbb{J}:B\to S(B)\). Then, \(\mathbb{J}\) is called generalized (\(\alpha\), \(\beta\))-nonexpansive if there exists two positive real constants \(\alpha,\beta\) with \(\alpha+\beta<1\) and for all \(a^{\prime},b^{\prime}\in B\), we have_  
\[\frac{1}{2}d(a^{\prime},\mathbb{J}a^{\prime}) \leq ||a^{\prime}-b^{\prime}||\;\;\text{implies}\;\;\text{that}\] \[H(\mathbb{J}a^{\prime},\mathbb{J}b^{\prime}) \leq \alpha d(a^{\prime},\mathbb{J}b^{\prime})+\alpha d(b^{\prime}, \mathbb{J}a^{\prime})+\beta d(a^{\prime},\mathbb{J}a^{\prime})+\beta d(b^{ \prime},\mathbb{J}b^{\prime})+(1-\] \[2\alpha-2\beta)||a^{\prime}-b^{\prime}||.\]  
**Remark 1**. : _Obviously, the class of multi-valued generalized (\(\alpha,\beta\))-nonexpansive maps includes both the multi-valued generalized \(\alpha\)-nonexpansive and multi-valued Riech-Suzuki type nonexpansive maps classes._  
**Lemma 2**. : _Suppose a Banach space \(X\) and \(\emptyset\
eq B\subset X\) and also consider a multi-valued mapping \(\mathbb{J}:B\to S_{cb}(B)\). If \(\mathbb{J}\) is (\(\alpha,\beta\))-nonexpansive with a fixed point \(p_{0}\in F_{ix}(\mathbb{J})\) and satisfies the endpoint condition, then \(\mathbb{J}\) is quasi-nonexpansive._  
Proof. : Assume that \(p_{0}\in F_{ix}(\mathbb{J})\). Then \(\frac{1}{2}d(p_{0},\mathbb{J}p_{0})=0\leq||p_{0}-b^{\prime}||\) for any \(b^{\prime}\in B\), so  
\[H(\mathbb{J}b^{\prime},\mathbb{J}p_{0}) \leq \alpha d(b^{\prime},\mathbb{J}p_{0})+\alpha d(p_{0},\mathbb{J}b^{ \prime})+\beta d(p_{0},\mathbb{J}p_{0})+\beta d(b^{\prime},\mathbb{J}b^{ \prime})\] \[+(1-2\alpha-2\beta)||b^{\prime}-p_{0}||\] \[\leq \alpha||b^{\prime}-p_{0}||+\alpha H(\mathbb{J}p_{0},\mathbb{J}b^{ \prime})+\beta(||b^{\prime}-p_{0}||+H(\mathbb{J}p_{0},\mathbb{J}b^{\prime}))\] \[+(1-2\alpha-2\beta)||b^{\prime}-p_{0}||.\] \[\leq \alpha H(\mathbb{J}b^{\prime},\mathbb{J}p_{0})+\beta H(\mathbb{J }b^{\prime},\mathbb{J}p_{0})+(1-\alpha-\beta)||b^{\prime}-p_{0}||.\]  
This provides  
\[(1-\alpha-\beta)H(\mathbb{J}b^{\prime},\mathbb{J}p_{0})\leq(1-\alpha-\beta)||b ^{\prime}-p_{0}||.\]  
Since \((1-\alpha-\beta)>0\), we obtain our desired result. **Lemma 3**. : _Let \(B\) be a nonempty subset of a Banach space \(X\) and \(\mathbb{J}:B\to S_{cb}(B)\) be a multi-valued mapping satisfying the endpoint condition. Then, \(F_{ix}(\mathbb{J})\) is closed provided that \(\mathbb{J}\) is generalized (\(\alpha,\beta\))-nonexpansive._  
Proof. : Let \(\{k_{n}\}\subseteq F_{ix}(\mathbb{J})\) such that \(k_{n}\to p_{0}\in B\).
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 3 Changes and Trends
### Antarctic and Greenland Ice  
The Antarctic and Greenland ice sheets are the largest ice bodies on Earth and are significantly affected by changing air temperatures and solar radiation. If completely melted, the polar ice sheets would raise global sea level by 70 m [245]. Recent studies show that the magnitude of recent melting of the Greenland ice sheet is exceptional over at least the last 350 years [246]. Greenland's ice is melting so fast that it could become a major contributor to sea-level rise within two decades. Greenland ice loss mainly occurred in the southeast and northwest margins of the ice sheet in the 2000-2010 period; while the largest sustained acceleration (~10 years) in ice loss was detected in southwest Greenland from GRACE (Gravity Recovery and Climate Experiment) observations [247]. The overall transformation of ice into liquid water appears to be accelerating and Greenland loses an average of 270 billion tons of ice each year [248].  
The recent loss of continental ice includes both the northern and southern hemispheres. An estimate of the mass balance of the entire Antarctic ice sheet over a 25-year record (1992 to 2017) shows that the Antarctic Peninsula, the smallest ice sheet in Antarctica, has lost an average of 20 Gigatonnes (Gt) of ice per year. The loss rate increased during the study period especially after the year 2000. The West Antarctic Ice Sheet lost 53 \(\pm\) 29 \(\mathrm{Gt}\) yr\({}^{-1}\) from 1992-1997, and the loss rate accelerated to 159 \(\pm\) 26 \(\mathrm{Gt}\) yr\({}^{-1}\) from 2012-2017. The East Antarctic Ice Sheet is relatively stable, with small gains over the study period [249]. The changes of polar mass balance are associated with snow and ice surface darkening [250], warmer atmosphere and ice surface conditions [251; 252; 253], and increased surface melt duration and extent [253; 254; 255] as observed by satellite optical and microwave sensors.  
The velocity of ice flow in the Antarctic has been closely monitored using optical and radar remote sensing due to its importance in determining ice discharge and sea level rise [60; 256]. The velocity map derived from the MODIS-based Mosaic of Antarctica data showed the general flow patterns of glaciers and ice sheets moving from interior Antarctica toward the ocean for the periods from 2003-2004 and 2008-2009 (Figure 6). Continuous monitoring of the widespread ice flow over the entire continent is highly needed for improving our understanding of ice sheet dynamics and evolution in a warming climate [60; 256].  
Figure 6: Antarctic surface ice velocity map. The floating velocity field of the Drygalski Ice Tongue is enlarged in the close-up below. The figure was reproduced with permission from [60].
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 2 Effect of Background Gain/Loss
### Cubic Background Gain  
Next, we consider the combined effect of cubic background gain and linear background loss (\(\beta<0\)), or analytically, \(\alpha(x,t)=\alpha_{0}\) and \(\gamma\equiv 0\). When the cubic gain is small, a rogue wave can still be observed and attains a maximum greater than that of the case with linear loss only (Figure 2a). However, for larger cubic gain \(\alpha_{0}\), the growing peaks will eventually cause the solution to break down. This highlights the difference between linear and cubic amplification rates.  
For specific values of amplitude, plane wave solutions exist but are unstable under the presence of dissipation. Mathematically,  
\[A_{cw}=\sqrt{\frac{-\beta}{\alpha_{0}}}\exp\left(i\frac{-\beta\sigma}{\alpha_ {0}}x\right)\]  
is a stationary plane wave in the focusing regime. From linear stability analysis, it can be shown that the plane wave is modulationally unstable (Figure 2b). Recently, the role of baseband modulation instability in the generation of rogue waves was established for several integrable systems [35; 36; 37]. In other words, rogue waves can be triggered from a plane wave by a long wave disturbance [36; 37]. Starting from the following initial condition, (\(A_{cw}\) is the background continuous wave)  
\[A(x,0)=A_{cw}+b_{0}\exp(iKx)A_{cw}\]  
localized modes can be observed (Figure 2c). With a larger baseband increment (Figure 2b), rogue wave modes can be generated in a shorter time period (Figure 2d) [36]. Some structures with waveforms similar to a second order rogue wave can be observed at approximately \(t=6.6\).
## 1 Introduction
Anodization studies have previously incorporated calcium (Ca) or phosphorus (P) oxide dopants individually to increase bone-to-implant contact (BIC), implant screw removal torques, and osteoblast mineralization compared to non-anodized titanium control surfaces [15; 20; 23; 24; 25; 26]. Other studies have simultaneously incorporated Ca and P oxide dopants to achieve calcium-tosphorus (Ca/P) ratios in the range of human bone [15; 27; 28]. A few research groups have recently achieved the formation of hydroxyapatite on titanium using a single-step anodization process [15; 24; 29; 30]. Interestingly, these anodized hydroxyapatite coatings have shown improved adhesion and interfacial bonding compared to their plasma-sprayed counterparts [24; 29]. Magnesium (Mg) is the fourth most abundant element in the body and is the most abundant in cells in its ionic form [31; 32]. Mg is important for bone growth as it inhibits osteoclast differentiation and bone resorption [31; 33]. It also regulates cellular functions like growth and proliferation, signaling, and metabolism [34]. Mg-dopant additions incorporated onto titanium surfaces have been shown to enhance osseointegration by promoting osteogenesis, cell adhesion, and angiogenesis through pathways such as PI3K, ERK, and BMP-4 [35]. Anodized coatings on titanium implant surfaces, followed by subsequent Mg incorporation by hydrothermal treatment, have been shown to enhance osteogenic differentiation in bone [36]. In the present study, we developed novel Mg-doped carbonated hydroxyapatite and tricalcium phosphate anodization coatings that can be implemented in a single-step process using a novel electrolyte composition. These coatings are focused on the incorporation of dopant elements that are naturally found in bone, such as Ca, P, and Mg, with the goal of improving osseointegration outcomes on titanium implants. To the best of our knowledge, the use of this electrolyte composition for the purpose of implant anodization coatings has not been explored before. The present study aimed to implement these promising anodization coatings on alpha-, beta-, and duplex-phase titanium alloy substrates to demonstrate their applicability to a wide range of implant applications.
# Ultrashort Vortex Pulses with Controlled Spectral Gouy Rotation
## 4 Experimental Results and Discussion
### Spectral Control by Tuning Center Wavelength and Bandwidth  
To study the influence of a variation of spectral input parameters on the spectral moments, slight modifications of center wavelengths and FWHM bandwidth were induced (Figure 4a,b). Three closely neighboring central wavelengths were generated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Spiral Phase** & **Type of Orbital** & **Grating** & **Aperture** & **Depth of** & **Fresnel** & **1 st Order** \\
**Gratings (SPG)** & **Angular Momentum** & **Period** & **Diameter** & **Focal Zone** & **Number** & **Diffraction** \\
**Con-Figuration** & **(OAM) Shaper** & **(\(\mu\)m)** & **(mm)** & **(mm)** & **(Aperture)\({}^{1}\)** & **Angle (\(\zeta\))** \\ \hline
1 & SLM\(\_\)32 & 32 & 2 & 40 & 250 & 1.43 \\
2 & SLM\(\_\)16 & 16 & 2 & 20 & 500 & 2.87 \\
3 & DOE\(\,\)4-step & 16 & 2 & 20 & 500 & 2.87 \\
4 & DOE\(\,\)2-step & 16 & 0.4 & 4 & 100 & 2.87 \\
5 & DOE\(\,\)2-step & 16 & 0.2 & 2 & 50 & 2.87 \\
6 & DOE\(\,\)2-step & 32 & 0.4 & 8 & 50 & 1.43 \\ \hline \end{tabular}  
\({}^{1}\) Data related to half depth of each focal zone at a central wavelength of 800 nm.  
\end{table}
Table 1: Geometrical parameters of spiral phase gratings used as orbital angular momentum (OAM) beam shapers.  
Figure 3: Parts of selected spiral phase gratings (SPGs) used as vortex beam shapers in the experimental studies: (**a**–**c**) fixed diffractive elements (left) and (**d**) spiral structure programmed into the SLM grey scale map (right). The elements correspond to configurations 3, 4, 6, and 2 in Table 1, respectively.  
As expected, an increasing input spectral bandwidth results in an extended bandwidth in the spectral eyes as demonstrated in Figure 5. The transfer characteristics also enable one to identify the ranges for channel separation without spectral overlap, which may be relevant for practical applications. To avoid misinterpretations, it has to be addressed that the essential information in the curves is not a bandwidth transfer. The pairs of curves are related to the spectral eyes, each upper curve of a pair corresponds to the red-shifted part, and each lower curve to the blue-shifted one, respectively. The ordinate values represent the spectral coordinates of the spectral eyes. Their distance is not directly a measure of the output bandwidth.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 5 Protocol Verification Logic  
### Message Exchange  
The process below signifies the message exchange between the sender and receiver:  
\[A\to B:\ K_{a},T_{1}\] \[B\to A:\ \left\{K_{b,a},M_{8\times 8},A_{b},K_{b},T_{s} \right\}_{K_{a}}\] \[A\to B:\ \left\{K_{a,b},\lambda_{a},T_{2},P_{a}\right\}_{K_{b}}\] \[B\to A:\ \left\{\right\}_{K_{ab}^{\prime}}\]  
This process notations can be elaborated as \(K_{a,b}\) and \(K_{b,a}\) as session keys for the protocol process, and \(K_{ab}^{\prime}\) as an event session key with limited validity used before and after the protocol authentication with cryptography. During the protocol process initiation, the public key and time-stamps of sender \(A\) are used. Later, receiver \(B\) starts challenge 1 with public keys as temporal session keys \(K^{\prime}_{ab}\) in the successive steps. The challenges solved by both the communicating parties and approved response results in the last step of protocol success as authentication.
# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
## 3 Results
### Description of Runtime Adaptation
The anti-affinity policy ensuresthe deployment of target components on different cloud providers for fault tolerance or compliance purposes. The precedence policy ensures sequential deployment of fragments in a specified order. Device exclusion policies optimize edge processing by marking unsuitable devices for deployment. Placement policies, modeled using fragment entities in the TOSCA extension, provide a clear visualization of important deployment constraints, offering advantages over native TOSCA relationships. Furthermore, there are occurrences where certain extensions of the TOSCA standard are deficient in their ability to represent workflows. A case in point is TOSCA Light [30], which is incapable of depicting workflows due to its reliance on semantics from both TOSCA and EDDM [7]. Unfortunately, the EDDM framework lacks a dedicated semantic entity to describe the intricacies of workflows. Workflows are a common concept employed in various works. For instance, [PERSON] et al. [29] introduce a language that allows users to describe the execution flow as interconnected tasks with task dependencies. In their work [12], [PERSON] et al. discuss that SMADA-Fog incorporates an additional model, apart from the deployment model, to describe runtime adaptation. This model represents runtime adaptation as dynamic behavioral events associated with deployed applications. The authors emphasize that within the SMADA-Fog framework, runtime adaptation can be performed in response to changes in the execution environment, such as variations in traffic load, alterations in QoS parameters, and the number of connected users. Similarly, [PERSON] et al. propose a language in [21] where workflows are used to depict runtime adaptation based on event-driven logic triggered by QoS conditions. Section 3.9 provides further insight into how workflows and event-driven logic are associated with the syntax of QoS and QoE conditions in CMLs. High-level languages like GENTL [9] have the potential to combine the capabilities of multiple existing languages to describe runtime adaptation. For instance, GENTL could leverage both TOSCA and Blueprints for describing complex system adaptations at runtime. TOSCA is well suited for describing workflows and conditional logic, while Blueprints offer the ability to describe QoS characteristics and provision resources. This makes GENTL valuable for cloud platforms that require runtime adaptation of applications.
### _M. tuberculosis_  
Toxins from type II TA systems are generally activated following degradation of their cognate antiotoxins. The major stress proteases Lon and/or ClpP together with its AAA\({}^{+}\) subunit ClpX, ClpA or ClpC, were identified as key players in this process [86, 87, 88, 89, 90, 91]. Although it clearly appears that certain antioxins possess intrinsically flexible domains sensitive to proteolysis that are protected when bound to the toxin, very little is known about specific degradation signals within antiotoxins that determine selective proteolysis [92, 93, 94]. Likewise, the cellular cascades that trigger activation of proteases towards TA systems are not well understood. In several cases, it has been shown that TA systems are induced in response to nutritional stress [86, 87]. Such conditions are known to induce the stringent response characterized by the synthesis of the small alarmone molecule (p)ppGpp, which causes growth arrest, activation of the stress response, and bacterial persistence [95]. Remarkably, increased persistence in _E. coli_ strongly relies on both the activation of Lon by inorganic polyphosphates known to accumulate in response to (p)ppGpp synthesis, and the presence of TA systems, as shown recently [8]. Although it has been shown that the stringent response is important for _M. tuberculosis_ persistence, it is not known whether activation of TA systems by specific stress proteases contributes to such phenomenon [44].  
In contrast with _E. coli_, _M. tuberculosis_ and closely related species do not have the proteases Lon. However, they do have two _clpP_ genes, _clpP1_ and _clpP2_, encoding the proteolytic subunits and three predicted ClpP-associated regulatory subunits encoding genes, _clpX_, _clpC1_, and _clpC2_, involved in substrate recognition, unfolding and translocation into the proteolytic chamber [96]. Note that in contrast with ClpC1 and ClpX, the ClpC2 sequence does not contain any AAA\({}^{+}\) domain and it is thus not clear whether it can cooperate with ClpP proteases. Yet, the most striking difference with _E. coli_ is that _M. tuberculosis_ possesses a 20S proteasome-like degradation machinery to which proteins are addressed by addition of an ubiquitin-like tag called Pup (Prokaryotic ubiquitin-like protein), giving the name of \"Pupylation\" to this pathway [97]. Analysis of the Pup proteome of _M. tuberculosis_ revealed that at least 4 toxins, namely Rv0059 (unclassified), Rv0749 (VapC31), Rv2035 (Unclassified) and Rv2527 (VapC17) are pupylated, while no pupylated antitoxin could be detected [98]. Although there is no evidence for a role of the proteasome in TA activation, the observation that pupylation seems to concern only toxins suggest that an additional, yet unexplored level of regulation of TA systems by proteolysis could exist.