esa-sceva/satcom-chunk-collection huggingface dataset:
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 2 Related Work
### Machine Learning for Securing IoT--An Overview
The proposed model has achieved an overall accuracy of 94.5%, detection probability of 0.933, and false alarm probability of 0.040. The performance evaluation has indicated that the proposed model obtains better results as compared to other methods used in previous relevant studies. In another study, [44], have used ML as part of analytical data algorithm proposed for the identification of two False Data Injection (FDI) attacks in Industrial Control Systems (ICSs), i.e., measurement injection attack and control variable tampering attack. In [45], the authors have used a random-forest-based ML model to classify four different types of botnet attacks in an IoT-enabled smart factory environment. An average accuracy of 96.67%, 0.241 FPR, and model reliability of 94.6% is achieved which is calculated using kappa coefficient. In [46], the authors have proposed an ameliorated ANN-based model that employs dimensionality reduction technique to improve the DDoS attack detection process in IoT. The authors of [47] have proposed a deep reinforcement-learning-based strategy for securing the mobile edge computing systems against interference and jamming attacks. Similarly, Ref. [48] have used reinforcement learning for the development of a secure data collection strategy in the domain of IoT. The reviewed literature demonstrates the potential of ML approaches for developing security solutions. Table 1 presents the summary of recent literature related to the IoT security and attack detection. From the review of the literature, it is identified that the PS-R and SN-W attacks are active and internal routing attacks, while an SN-W attack can also be launched using resources external to the network. Each of them belongs to one of the two attack classes that the RPL-based IoT is vulnerable to. Both attacks cause severe security issues and are capable of instigating other attacks. They may collaborate with each other to increase the attack impact and occur concurrently in an RPL-based IoT network causing twofold or multifold deterioration of the IoT network, RPL mechanism, and routing resources which are naturally constrained. Table 2 presents the routing attacks considered in this paper along with their impact on Confidentiality, Integrity, Availability (CIA) security triad and effects on the IoT network.
## 2 Results
### Genome-Wide Identification of AS  
AS events were identified using ASTALAVISTA with default options [73]. Results showed that 27,907 AS events were predicted, including 9773 intron retention (IntronR), 3404 exon skipping (ExonS), 15,970 alternative 3' last exon (AltA) and 6388 alternative 5' first exon (AltD) (Table S4) [74]. These results were consistent with the four AS types that account for the majority in plants [75]. To validate the authenticity of the predicted AS, five AS events were verified by RT-PCR. The electrophoresis results were in concordance with the RNA-Seq data (Figure S1).  
Figure 2: Proposed fatty acid biosynthetic pathway in _C. sativa_[20]. PDHC: pyruvate dehydrogenase complex; ACCase: acetyl-CoA carboxylase; MCAAT: malonyl-CoA: ACP transacylasse; KAS: \(\beta\)-ketoacyl-ACP synthase; KAR: \(\beta\)-ketoacyl-ACP reductase; HAD: hydroxyacyl-ACP dehydrase; EAR: enoyl-ACP reductase; SAD: stearoyl-ACP desaturase; FAD6: oleate desaturase (chloroplast-type); FAD7/8: linoleate desaturase (chloroplast-type); FATA/B: acyl-ACP thioesterase A/B; PCH: palmitoyl-CoA hydrolase.  
Then, the AS events that occurred in structural genes related to cannabinoids, FA and cellulose were analyzed. Results showed that AS occurred in the cannabinoid biosynthetic structural genes _LOX_, _AAE_, _PT_, _DXR_, _MCT_ and _HDR_ (Table S5). AS also occurred in the FA biosynthetic structural genes, including _PDHC_ and _ACCase_ occurred AS (Table S6) as well as the cellulose biosynthetic structural genes, including _CESA_, _CSL_, UDP-galactose transporter, UDP-glucose transporter, kinesin-like protein, kinesin-4, COBRA-like protein, _CTL_, katanin-like protein, _FRA_, _SuSy_ and _MAPKKK_ (Table S7).
## 3 Results and Discussion
### Correlations between SSA and Contents of Ultimate Analysis and Proximate Analysis  
After the sample was burned out, the aerosol in the room mixed sufficiently and became approximately steady as the fresh smoke emitted in the field measurements. We picked the averaged values of last 5 min as the characteristics of a well-mixed stage. SSA of smoldering smoke from burning of biomass samples is similar to the result of smoldering smoke from biomass burning [53; 54; 55]. [PERSON] et al. [21] have given the correlations between inherent optical properties of carbonaceous  
Figure 5: The correlation coefficients between extinction coefficients (**a**) scattering coefficients (**b**) and particle number concentrations of different sizes for smoke of biomass samples.  
aerosols generated from various coals and the thermochemical or energetic properties of the bulk coal samples. The smoldering combustion of biomass consists of two fundamental stages [56; 57]. Tar (also as VM) is released from biomass in the first stage. Following that, the remaining char (FC) is burned in the second stage. The two stages could take place simultaneously or subsequently. There could be a correlation relationship between the optical properties of smoke generated from smoldering combustion and the inherent thermochemical properties of biomass samples. Table 3. reveals the correlation coefficients between SSA and the contents of ultimate analysis and proximate analysis of biomass samples, including elements (C, N, and S), and thermochemical properties (FC, VM/FC). It is obvious that the correlations between SSA and VM/FC (or FC, C, N and S) are significantly strong at the significance level of 0.05. The relationship between SSA and N, S, or FC shows a positive correlation, while that of SSA and VM/FC or C shows a negative correlation. However, the correlation between SSA and volatile (or ash) is weak, which is not displayed in the table.  
We found a strong linear correlation between SSA and VM/FC (or N) (r\({}^{2}\) = 0.88 for VM/FC and 0.80 for N), as seen in Figure 6. It can be viewed as follows,  
\[\text{SSA}=-0.04\text{VM/FC}+1.087, \tag{3}\]  
\[\text{SSA}=0.12473\text{N}+0.84431, \tag{4}\]  
in which VM is the fraction of volatile matter in mass (wt.%), FC is the fraction of fixed carbon in mass (wt.%), and N is the fraction of element N in mass (wt.%).
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 3 DNNs in CPS-Based Spatial-Temporal Data
### CPS Application Domains
#### 3.2.2 Manufacturing CPS  
It is also known as smart manufacturing or Industry 4.0, which integrates physical processes with advanced computing and communication technologies to optimize manufacturing production and operation processes. The systems monitor and regulate physical processes using sensors and actuators while using data analytics and machine learning algorithms to streamline operations, boost productivity, and reduce costs. It applies to various manufacturing processes, including assembly lines, material handling, quality control, maintenance, and supply chain management. The Industrial Internet of Things (IIoT) and machine learning are the critical enabling blocks of manufacturing CPS. The former connects machines, sensors, and other devices to a network to collect and share data. This allows for real-time monitoring and control of equipment and the ability to control and adjust machines remotely. At the same time, the latter makes use of the data generated by the system. This includes predictive analytics, which can forecast equipment failures and maintenance needs, and prescriptive analytics, which can optimize manufacturing processes and improve product quality. With real-time data and analytics, industrial-based manufacturing CPS helps manufacturers optimize their production processes, reduce energy consumption, minimize waste, as well as enhance product quality.
# Combined Optimization of Maintenance Works and Crews in Railway Networks
## 3 Methodological Framework and Problem Formulation
### Sets  
* PA--set of projects;
* RA--set of routine works;
* L--set of network links;
* \(A=PA\cup RA\)--set of all maintenance works;
* T--planning horizon (time);
* \(K=\{1,2,3\}\)--set of options for execution times;
* \(Comb\)\(\{(m,\ n,\ l)\}\)--set of combinable works \(m\) and \(n\) at link \(l\), \(\forall m,n\in A\) and \(l\in L\);
* \(max_{l}\)--maximum length of the train-free period on link \(l\in L\).  
It must be noted here that projects and routine works have different frequencies and therefore different scheduling. First, the link on which the selected project will be executed must be defined and then its earliest and latest start determined. Further, the duration needed to complete the project is defined for three options of \(k\in K\), and each option corresponds to a different duration. The \(k=1\) option allows the works to run at the maximum length of the free train period (once a day) on several consecutive days, the \(k=2\) option allows the project works to run on weekends by closing the link for 48 h, and the \(k=3\) option closes the link for several days or weeks. In addition, based on these three options, three different costs are generated: one fixed cost for each time the link is used for maintenance and two additional costs, which refer to the penalties for the cancelled routes with option \(k=2\) or \(k=3\). The advantage of this option is the efficient use of resources by the crews. The third cost refers to the inefficient use of resources with option \(k=1\) or \(k=2\), since activities in these two scenarios may be interrupted and equipment must be moved by the crews. Routine works are executed with a higher frequency and their frequency should be determined upfront. Based on the frequency and the scheduling period of a year, tasks can be allocated within the planning time horizon.  
In addition, our enhanced model includes different links L corresponding to a crew E each time a task is scheduled. To include the set of crews E, we introduce their available number in our model and a set of links for the crews, in which we have added the depot as their reference point. In this way, when a crew is not performing a task, it returns to the depot, and thus the exact locations of the crews are known. The added sets are presented below:  
* E--set of crews;
* LL--set of links for crews.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 6 Performance Evaluation
#### Comparison of Encryption and Decryption Time  
We have already introduced the IKGSR [31] algorithm to enhance the security of asymmetric algorithms in cloud environments. In the proposed environment, we tested IKGSR against the traditional RSA algorithm. Figures 10 and 11 clearly show that IKGSR requires more time to encode and decode input health data. However, the IKGSR algorithm offers high security as well as security against chosen ciphertext and timing attacks, as shown in [31].  
Figure 10: Encryption Time Comparison against Traditional RSA.  
Figure 9: Time Required to Access the Data on the Cloud versus the Blockchain.
## 2 Research Problem
### Channel Estimation Error and Pointing Error  
In this paper, we take the channel estimation and pointing errors into account. Since the transmitters and receivers are fluctuant in the underwater environment, the pointing errors can result in the channel estimation error. As shown in Figure 2, the actual position of the receiver varies over time around the estimated location; therefore, due to the central limit theorem (CLT), we can model the channel uncertainty, \(\Delta h\), caused by pointing error as a Gaussian distributed variable. Thus, the overall channel loss can be modeled as \(h=\tilde{h}h_{o}(d)+\Delta h\).  
Considering a turbulence scenario, \(\tilde{h}\) represents the fading coefficient of the channel loss, which can be modeled with the log-normal distribution as [29]:  
\[f_{\tilde{h}}(\tilde{t})=\frac{1}{\tilde{h}\sqrt{2\pi\sigma_{x}^{2}}}\exp\left( \frac{-(\ln(\tilde{h})-\mu_{x})^{2}}{8\sigma_{x}^{2}}\right), \tag{4}\]  
Figure 1: Absorbing and scattering coefficients for different wavelengths in pure sea water [28].  
where \(\mu_{x}\) and \(\sigma_{x}^{2}\) are the mean and variance of the log-normal distribution factor \(x=\frac{1}{2}\ln(h)\)[30]. In this paper, we assume the pointing error is independent between LEDs and users, and this uncertainty can be modeled as a Gaussian random process with zero mean and variance \(\sigma_{h}^{2}\).
###### Abstract  
Healthcare remote devices are recognized as a promising technology for treating health related issues. Among them are the wireless Implantable Medical Devices (IMDs): These electronic devices are manufactured to treat, monitor, support or replace defected vital organs while being implanted in the human body. Thus, they play a critical role in healing and even saving lives. Current IMDs research trends concentrate on their medical reliability. However, deploying wireless technology in such applications without considering security measures may offer adversaries an easy way to compromise them. With the aim to secure these devices, we explore a new scheme that creates symmetric encryption keys to encrypt the wireless communication portion. We will rely on chaotic systems to obtain a synchronized Pseudo-Random key. The latter will be generated separately in the system in such a way that avoids a wireless key exchange, thus protecting patients from the key theft. Once the key is defined, a simple encryption system that we propose in this paper will be used. We analyze the performance of this system from a cryptographic point of view to ensure that it offers a better safety and protection for patients.  
Implantable Medical Devices; symmetric encryption; chaotic systems; pseudo-random keys; wireless communication +
Footnote †: journal: Sensors. Actuator Netw. **2018**, 7, 21; doi:10.3390/jsan7020021
## 4 Simulation Results and Discussion  
### Kinematics Simulation  
The inverse kinematic model is used to analyze the upper platform motion in three axes driven by four cables of the proposed CDPS mechanism. Since the mechanism needs specific stiffness to ensure a fixed and steady state, the compression spring is compressed through the force provided by the cables. The pretightening forces of the cables are set at 23.5 N in the original state. The effect of \(\theta_{m}\) on the forces of the four cables is analyzed when \(\theta_{b}\) = \(\pi\)/30 and \(\theta_{r}\) = 0. The range of \(\theta_{m}\) is 0-2\(\pi\).  
Figure 5 indicates that the forces in four cables periodically vary with \(\theta_{m}\) because the trajectory of the upper platform is a circle when \(\theta_{m}\) increases from 0 to 2\(\pi\).  
The effect of \(\theta_{b}\) on the forces of the four cables is analyzed when \(\theta_{m}\) = 0 and \(\theta_{r}\) = 0. The range of \(\theta_{b}\) is 0-\(\pi\)/10.  
Figure 6 shows that the forces of Cables 2 and 4 significantly increase with the increase in \(\theta_{b}\), while the forces of Cables 1 and 3 gradually decrease with the increase in \(\theta_{b}\). Cables 2 and 4 provide increasing proportion of the force to bend the spring from the initial state to the \(\pi\)/10 state. The nonlinear increasing tendency of the force is consistent with the characteristics of the spring.  
Figure 5: The cable forces when \(\theta_{m}\) increases from 0 to 2\(\pi\).  
Figure 6: The cable forces when \(\theta_{b}\) increases from 0 to \(\pi\)/10.  
The effect of \(\theta_{r}\) on the forces of the four cables is analyzed when \(\theta_{m}\) = 0 and \(\theta_{b}\) = 0. The range of \(\theta_{r}\) is 0-\(\pi\)/4.  
The yaw angle motion of the mechanism is analyzed by changing \(\theta_{r}\) from 0 to \(\pi\)/4. Figure 7 indicates that the forces in Cables 1 and 2 are identical to those in Cables 3 and 4 due to the axis symmetrical geometry of the mechanism.
## 3 Results
Figure 9: A graphical method for determining the distance of the measured point to the designed route, if the straight lines perpendicular to the hydrographic profiles could be drawn (**a**) and it is not possible to determine the perpendicular line (**b**). where \(\chi\) is the scale parameter (\(\chi>0\)), \(\varepsilon\) is the shape parameter (\(\varepsilon>0\)), and \(\phi\) is the shape parameter (\(\phi>0\)), where \(\Gamma(\phi)\) is the Euler's Gamma function with the following form:  
\[\Gamma(\phi)=\int\limits_{0}^{\infty}u^{\phi-1}\cdot e^{-u}du, \tag{11}\]  
and the cumulative distribution function with the following form:  
\[F(XTE;\chi,\varepsilon,\phi)=\frac{\Gamma_{n}\Big{[}\phi,\big{(}\frac{XTE}{ \chi}\big{)}^{\varepsilon}\Big{]}}{\Gamma(\phi)}, \tag{12}\]  
where the introduced auxiliary variable amounts to:  
\[\gamma=\left(\frac{XTE}{\chi}\right)^{\varepsilon}, \tag{13}\]  
and the incomplete gamma function is presented by the relationship:  
\[\Gamma_{n}(\phi,\gamma)=\int\limits_{0}^{\gamma}u^{\phi-1}\cdot e^{-u}du. \tag{14}\]  
Figure 10 shows empirical and theoretical functions of the XTE variables: probability density function and cumulative distribution function. Based on Figure 10, it can be concluded that the quantile of an order of 0.68 (percentile) in the analysed population amounted to 1.21 m, while the quantile of an order of 0.95 amounted to 2.34 m. This should be interpreted as meaning that 68% of the distance between the route recorded by geodetic GNSS receiver and the designed route was no greater than 1.21 m and, analogously, 95% of elements of this population did not exceed the value of 2.34 m. Next, the values of empirical and theoretical cumulative distribution function of the generalised gamma distribution of the XTE variable were compared using the following formula:  
\[\Delta p(XTE)=F_{n}(XTE)-F(XTE), \tag{15}\]  
where \(F_{n}(XTE)\) is the empirical cumulative distribution function of the XTE variable, and \(F(XTE)\) is the theoretical cumulative distribution function of the XTE variable. Figure 10: Probability density function (**a**) and cumulative distribution function (**b**) of the generalised gamma distribution of the XTE variable. A graph of the \(\Delta p(XTE)\) values is shown in Figure 11. At the same time, it was compared with the theoretical normal distribution. Based on Figure 11, it can be observed that the empirical distribution was almost identically fitted to the theoretical generalised gamma distribution. The maximum difference between the probabilities of the occurrence of variable XTE for the empirical and theoretical generalised gamma distribution was less than \(\pm\)0.01-0.015. On the other hand, for the normal (Gaussian) distribution, which was the most important probability distribution applied in practice, the analysed measure was several times greater and amounted to approximately \(\pm\)0.08.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 18 Dispatching of Mobile Sensor Nodes in a WSN to Sense a Region of Interest  
### System Model and Design Objectives  
In [13], a sensing field \(A\), an area of interest \(I\) inside \(A\), and a set of mobile sensors \(S\) resident in \(A\) were considered. The dispatch problem considered finding a subset \(S^{\prime}\subseteq S\) of the sensors to be moved to \(I\) such that the coverage and connectivity requirements are satisfied, and the movement energy cost is minimized. The positions where the set of sensors \(S^{{}^{\prime}}\) are to be located in \(I\) were obtained by the placements methods that were proposed in the same paper. Those placement methods are not mentioned here as they do not contain the optimization component we are interested in. Two solutions were proposed, a centralized one and a distributed one.
## 2 Results and Discussion  
Here is a representative example of the interactions. The sample was selected to illustrate the consultations. Hence, this illustration is a foundation for the statements in the interviews. The collaborative work in the videoconferences starts with the GP describing a problem (utterance 1), here regarding the rising level of a CRP (C-reactive protein). The GP asks a question about how to treat the patient, outlining a solution for the problem (2). The specialist supports the GP by confirming the plan to change to intravenous treatment. The specialist also recommends a treatment: that the patient should be transferred if she becomes worse (3). The specialist describes a diagnosis, what may happen if resistant bacteria have been created, and comments that the patient may become very ill quickly. This requires a close follow up (4). The GP confirms this, acknowledging and agreeing with the recommendation from the specialist (5). The specialist goes into detail, recommending that the GP check the CRP at noon and make new judgments if the patient does not respond. Table 2 illustrates the talk in the consultations, representing medical decision support and problem solving.  
This is a medical discussion in a professional collaboration and illustrates the content of the videoconferences. The results also aim to illuminate how this collaboration between professionals using videoconferencing may affect the delivery of treatment. Here, I will present the collaboration in relation to the four telemedicine contexts: medical, technological, spatial, and benefits [1]. These results are presented according to the professionals' own assumptions regarding the collaborative work, as exemplified in Table 2.
## 4 Results and Discussion
### Vertical Interlayer Stratum Model  
The results of Vertical Interlayer Stratum as shown in Figure 14. In the vertical interlayer stratum, there were gradation zones under the interlayer. When the interlayer was a relatively high resistance region, the deeper interlayer could be identified clearly. The closer the R1 was to the R2 value, the lower the identifiable depth of the interlayer, as shown in Figure 14a. When the interlayer was a relatively high resistance region, the identifiable depth increased with interlayer thickness. However, when the interlayer was a relatively low resistance region, the identifiable depth remained constant as the interlayer thickness increased, and the interlayer and the region outside the interlayer had jumping phenomena, as shown in Figure 14b. When there was noise interference in the detection process, the resistivity profile had a random jump; the random jump worsened as the noise increased, but the identifiable depth of the interlayer was free from the effect of noise, as shown in Figure 14c.  
Figure 13: Results of the single vertical layer stratum: (**a**) different resistivities and (**b**) different noises.  
value, the detected area of the material was larger than the set value, as shown in Figure 15a. When the material center position descended, as the gradation zone expanded the material was still identifiable, but obscure, as shown in Figure 15b. When there was noise interference in the detection process, the resistivity profile had a random jump; the random jump worsened as the noise increased, and the area of material varied with the noise intensity, as shown in Figure 15c.  
Figure 15: Results of composite stratum: (**a**) different resistivities, (**b**) different material center depths, and (**c**) different noises.