esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis
### The Attacking Process after ACD Migration
#### 4.3.1 Stochastic Walk  
In the previous subsection, we obtained the probability of compromising a single node over a migration period denoted as \(\mu\). Defenders randomly reconfigure the allocation of each node with probability \(\omega\) in each migration period to prevent attacks. An attacker's movement is described as stochastic walks along an attack chain. The corresponding Markov chain is built as Figure 6. The Markov chain has \(L+1\) states where \(L\) indicates the length of the attack chain. The state \(i\) denotes that \(i\) nodes have been compromised after the last migration, and the attacker is currently attacking \((i+1)^{th}\) node. The successful state of attacks is represented by state \(L\) (i.e., node G shown in Figure 1). As mentioned in Section 3.2, we concentrate on the active attack. In this scenario, while the attacker aims to shut down the functionalities of nodes, the defender attempts to resume functions using VM migration.  
Similar to [18; 19], we slightly modify the game by giving the adversary an extra advantage. The defender can only migrate the nodes from where the attack occurred. For this defenders' restriction game, only if the attacker has compromised \(i^{th}\) node then the attacker and defender are allowed to play at \(j^{th}\) node \((j\geq i)\).  
We describe the stochastic walk with a transition matrix \(G^{(L+1)\times(L+1)}\), where \(L+1\) columns (and rows) denote the \(L+1\) states. The cell \(G_{i,j}\) denotes the transition probability from the \(i^{th}\) node to the \(j^{th}\) node. Compared with the last period, the attack will move in three possible directions: going to the next node, going back to the previous node, or staying at the same node. The transition probabilities are shown as follows:  
(1). \(G_{i,i-1}=\omega\). Once the node from which the attacks originated (i.e., node \(i\)) migrates, attacks cannot be carried on. The attacker must return to the previous node and attack node \(i\) again.  
(2). \(G_{i,i+1}=(1-\omega)\mu\). No effective migration occurred before the node \(i+1\) was compromised, so the attacker moves to the next node. The probability is \(G_{i,i+1}=(1-\omega)\mu\), with probability \(\mu\) attacking node \(i+1\) successfully and probability \((1-\omega)\) means that node \(i\) does not migrate.  
(3). \(G_{i,i}=(1-\omega)(1-\mu)\). No successful attack or effective migration has occurred, which are expressed with probabilities \((1-\mu)\) and \((1-\omega)\) respectively.
## 3 Results
### Cytotoxicity Assay
#### 3.2.2 MTT Assay  
To explore the possible biological activity of the plant extracts, it was further evaluated by _in vitro_ antiproliferative characteristics on two normal cell lines; human fibroblast cell lines and monkey Vero cell lines. This study showed no toxicity at the tested concentrations on both Vero and fibroblast cell lines (Figure 2A,B).  
Figure 1: Toxicity effects of selected plant extracts against human erythrocytes.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Plant Extracts**} & \multicolumn{3}{c}{**Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) (mg/mL)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**MRSA**} & \multicolumn{2}{c}{**MSSA**} & \multicolumn{2}{c}{_P. aeruginosa_} & \multicolumn{2}{c}{_E. coli_} \\ \cline{2-9}  & **MIC** & **MBC** & **MIC** & **MBC** & **MIC** & **MBC** & **MIC** & **MBC** \\ \hline _Aloysia citrodota_ & 1.3 \(\pm\) 0.7 & 2.5 \(\pm\) 1.4 & 1.5 \(\pm\) 1.0 & 2.8 \(\pm\) 1.7 & 5.3 \(\pm\) 1.9 & 9.4 \(\pm\) 4.0 & 7.5 \(\pm\) 3.5 & 15.0 \(\pm\) 7.0 \\ _Camellia sinensis_ & 0.5 \(\pm\) 0.2 & 0.8 \(\pm\) 0.4 & 0.5 \(\pm\) 0.5 & 1.1 \(\pm\) 0.9 & 3.1 \(\pm\)1.7 & 5.6 \(\pm\) 3.6 & NT & NT \\ _Hibiscus sabdariffia_ & 4.0 \(\pm\) 2.0 & 9.2 \(\pm\) 4.1 & 5.2 \(\pm\) 1.5 & 9.9 \(\pm\) 3.1 & 3.1 \(\pm\) 0 & 6.3 \(\pm\) 0 & 9.4 \(\pm\) 3.1 & 19.9 \(\pm\) 6.9 \\ _Glycyrrhiza glabra_ & 0.9 \(\pm\) 0.5 & 1.8 \(\pm\) 1.0 & 0.7 \(\pm\) 0.2 & 1.4 \(\pm\) 0.3 & NT 1 & NT & NT & NT \\ _Thymus vulgaris_ & 5.5 \(\pm\) 4.9 & 10.9 \(\pm\) 5.2 & 4.0 \(\pm\) 1.6 & 8.1 \(\pm\) 6.25 & NT & NT & NT & NT \\ Vancomycin \(\mu\)g/mL & 1.56 \(\pm\) 0.50 & 2.2 \(\pm\) 0.63 & 1.9 \(\pm\) 0.93 & 2.6 \(\pm\) 1.11 & NT & NT & NT & NT \\ Gentamycin \(\mu\)g/mL & NT & NT & NT & NT & 5.6 \(\pm\) 1.96 & 6.4 \(\pm\) 1.96 & 4.6 \(\pm\) 0.92 & 4.2 \(\pm\) 0.86 \\ \hline \hline \end{tabular} Values are mean \(\pm\) SD; 1 NT: not tested because zone of inhibition was \(<\)14 mm in disc diffusion.  
\end{table}
Table 3: MIC and MBC results of plant extracts against selected bacterial species.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 3 Preliminaries
### SVM Regression  
SVM is a supervised learning algorithm for regression and classification based on the optimal separating hyperplane generated by a fixed nonlinear transformation. As described by [12], the details of SVM regression (SVR) are as follows:  
Given \(\{x_{n},\ t_{n}\}_{n=1}^{N}\) as pairs of input vectors and target values, we obtain a solution in the following form:  
\[y(x)=w^{T}\phi(x)+b, \tag{13}\]  
where \(w\) is the weight vectors, \(\phi(x)\) is a nonlinear mapping function that maps the input vectors into high-dimension feature space, and \(b\) is the bias parameter. As sparse solutions, the \(\epsilon\)-insensitive loss function is as follows.  
\[\left|y(x)-t\right|_{\epsilon}=\begin{cases}0,&\text{if }|y(x)-t|\leq \epsilon\\ \left|y(x)-t\right|-\epsilon,&\text{otherwise.}\end{cases} \tag{14}\]  
Therefore, the regularized objective function can be represented as follows.  
\[C\sum_{n=1}^{N}\left|y(x_{n})-t_{n}\right|_{\epsilon}+\frac{1}{2}\parallel w \parallel^{2}, \tag{15}\]  
where \(C\) denotes the relative importance of the loss and penalty terms. This helps to avoid overfitting. This objective function can be transformed into the following function with slack variables \(\xi_{n}\) and \(\xi_{n}^{*}\) for each data point.  
\[C\sum_{n=1}^{N}(\xi_{n}+\xi_{n}^{*})+\frac{1}{2}\parallel w\parallel^{2} \tag{16}\]  
subject to the constraints  
\[t_{n}-y(x_{n})\leq\epsilon+\xi_{n}^{*},\ \xi_{n}^{*}\geq 0, \tag{17}\]  
\[y(x_{n})-t_{n}\leq\epsilon+\xi_{n},\ \xi_{n}\geq 0. \tag{18}\]  
This optimization problem can be solved by the Lagrange multipliers \(\alpha_{n}\geq 0\), \(\alpha_{n}^{*}\geq 0\), \(\beta_{n}\geq 0\), \(\beta_{n}^{*}\geq 0\) for each data point. The Lagrangian can be represented as follows:  
\[L=\frac{1}{2}\parallel w\parallel^{2}+C\sum_{n=1}^{N}(\xi_{n}+\xi_{n}^{*})- \sum_{n=1}^{N}\alpha_{n}(t_{n}-y(x_{n})+\epsilon+\xi_{n})-\sum_{n=1}^{N} \alpha_{n}^{*}(y(x_{n})-t_{n}+\epsilon+\xi_{n}^{*})-\sum_{n=1}^{N}(\beta_{n} \xi_{n}+\beta_{n}^{*}\xi_{n}^{*}). \tag{19}\]  
This Lagrangian's minimum over \(w\), \(\xi_{n}\), \(\xi_{n}^{*}\) leads to the following equations  
\[w=\sum_{n=1}^{N}(\alpha_{n}^{*}-\alpha_{n})\phi(x_{n}), \tag{20}\]  
\[\sum_{n=1}^{N}(\alpha_{n}-\alpha_{n}^{*})=0, \tag{21}\]  
\[\alpha_{n}^{*}+\beta_{n}^{*}=C,\ \alpha_{n}+\beta_{n}=C. \tag{22}\]  
Substituting Equation (20) into Equation (13),  
\[y(x)=\sum_{n=1}^{N}(\alpha_{n}^{*}-\alpha_{n})K(x,x_{n})+b, \tag{23}\]  
where \(K(x,x_{n})\) denotes the kernel function that generates inner products in the high-dimensional feature space.
## 5 Experimental Analysis and Discussion
### QoS in Different Routing Protocols Corresponding to Mobility Models
#### 5.3.1 Packet Delivery Rate  
Figure 4 demonstrates the behavior of different routing protocols for both mobility models. AOMDV performs better with increasing scalability with respect to nodes because of storing multiple paths for a destination. The graph shows the better performance of AOMDV with respect to the other two routing protocols and shows 48.91% betterment in the case of RDM and 50.94% improvement in the case of RWM for a varying number of nodes. This graph is taking this shape because of the AOMDV nature of storing multiple paths for a destination and providing the alternate option in the case of failure of one path.
# Evolution towards Smart and Software-Defined Internet of Things
## 4 Smart Software-Defined IoT
### Smart SDN/NFV  
With the advent of 5 th generation mobile technologies, problems and limitations faced by prior SDN and NFV based IoT models can be addressed with promising improvements. An SDN environment infused with NFV is shown in Figure 3 where the network function virtualization infrastructure (NFVI) layer is responsible to provide interoperability in a multivendor environment. [PERSON] et al. present a network slicing concept in 5G technology that can enhance the IoT architectures focusing on 5G applications [61]. The authors believe that network slicing can address the problem of accommodating multiple applications and users in a single channel to increase resource usage efficiency and cater to rising demands.  
[PERSON] et al. in [62] present a solution for advanced 5G connectivity in an SDN-based IoT system enhanced by virtualization. The solution presents a flexible and centralized platform with mobility management and scheduling. The load balancing is made mobility aware through virtualization, which also makes resource utilization efficient. However, the solution still needs reliable security.  
Extending the 5G works to enhance SDN and NFV based IoT systems, Ref. [63] discussed a 5G management architecture that can provide distributed and on-demand deployment of network functions, provision of network slicing, flexible orchestration of network functions, and allocation of optimal workload [64]. The authors believe that the traditional mobile networks lack the flexibility and scalability required to offer the customized services of the current era. The authors in their work show how a 5G mobile network architecture based on the SDN and NFV based IoT systems can enhance the working of the entire model and produce the desired outcomes.  
Authors in [65] present a Q-learning-based, QoS-aware robust resource management scheme for industrial IoT systems using a non-orthogonal multiple access scheme. The method is very effective in maintaining the diverse QoS requirements in future generations of IoT networks. Authors in [66] discuss a new approach of infusing SDN and NFV with IoT infrastructures by slicing end-to-end multiple networks segments and building an application that holds the capability to recover IoT services once out of order. In this model, applications are deployed as SDN controllers on top of the SDN network to bring about a high degree of interoperability, security, privacy, quality, flexibility, network slicing, energy efficiency, and virtualization [67, 68]. Edge computing and AI can improve performance by reducing the bandwidth requirement in the backbone network.  
IoT stemmed from a concept that prevailed in 2010 known as the Web of Things [69] where APIs such as REST and SOAP made interaction through interfaces feasible. It was in the same year that advancements began to make the transition from the Intranet of Things to the IoT [70].  
Figure 3: AIoT Architecture with SDN and NFV.
Sensor networks, in conjunction with UASs, are often implemented in research settings to expedite the process of data collection and to increase the breadth of data that may be collected over a geographical area. This conjunction is applied normally to monitor marine-coastal environments for environmental metrics data collection, such as water temperature, salinity and pH. Moreover, it is potentially useful to monitor agrometeorological data in remote areas (i.e., vineyard and olive cultivation in steep slopes, forests on cliff, large trees in urban environment, etc. ), and also to monitor greenhouse atmospheric gas concentrations and many other examined applications in mapping, feature detection, and monitoring wildlife. IoT is defined as the worldwide network of interconnected objects (devices, mechanical and digital machines, animals or people) uniquely addressable based on standard communication protocols, that are provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction [16; 17]. In the agricultural field, IoT allows us to increase the monitoring points of agro-meteorological parameters and to remotely control the agro-actuators, with reduced costs compared to the traditional approach. It owes much of its success to the widespread distribution of Internet communication networks (without this, it would not be applicable), even in agricultural areas. In Italy, there are still large portions of territory, mostly in the Apennines mountain chain, not served by communication networks due to low population density. These areas host agricultural and forestry activities located at different altitudes, which is difficult to monitor. The use of WSN for monitoring climatic changes such as temperature degree and relative humidity in agricultural fields has been studied considerably but substantial applications are still rare [18; 19; 20]. Therefore, new solutions are needed in order to monitor constantly and remotely the data of interest, overcoming the barriers given by lacking communication networks and difficulties to reach hostile territories. For this reason, this work focuses on the properties of a new sensor called \"AgriLogger\", coupled with an UAS system, capable to collect, store for long periods and transmit agro-meteorological data, presented by temperature and relative humidity, in remote areas that are hard to reach and not served by telecommunication networks.
## 3 Authentication Model and Authorization Model
### Proposed Authorization Model: Attribute-Based Encryption for Access Control  
To fill the gap in the security requirements between the IoT and fog layers [5; 6; 7], we propose a fine-grained data access control model based on the ABE of the IoT-Fog-Cloud architecture to limit access to sensor data and achieve the authorization aim of this study.  
Figure 3 illustrates the several operations comprising the model, which are as follows: (1) attributes are generated for each sensor data type; (2) keys containing a set of attributes or corresponding to attributes are generated; (3) the generated sensor data type values are encrypted with the corresponding key that contains their attributes; (4) the encrypted message is published to the fog device using a secure communication channel; (5) the access tree that specifies the policy of the set of attributes is generated; (6) the ciphertext is decrypted if the key containing a set of attributes satisfies the access policy tree; and (7) the decrypted message is published to the AWS cloud. The model is designed such that each data type in an IoT device is associated with attributes, which represent the topic of each sensor data type. For example, the DHT1 sensor attached to the Raspberry Pi is considered an IoT device [6; 7] and generates two types of data: (A) temperature degree and (B) humidity degree. The temperature value is encrypted according to the key that contains a set of attributes and then published to the fog device.  
Figure 2: Blockchain-based certificate model applied to the IoT–Fog–Cloud architecture.  
The fog device then generates an access policy tree according to the attributes of each data type in the IoT device, an example of which is presented in Figure 4. Once the fog device receives the ciphertext from the IoT device, it decrypts it if the ciphertext key that contains the attributes satisfies the access policy tree. Otherwise, it will decline the decryption request. Then, the temperature degree value will be published to the AWS cloud. Figure 5 shows how the ABE for the access control model was applied to the IoT-Fog-Cloud architecture.  
Figure 3: Access control model for the IoT–Fog–Cloud architecture.  
Figure 4: Access policy tree for the access control model for the IoT-Fog–Cloud architecture.  
Figure 5: Access control model applied to the IoT-Fog–Cloud architecture.
## 3 Multilateration Estimation Resolution  
Gradient descent (GD) is one of the most used algorithms for many recent applications [35; 36] due to its simplicity and computational efficiency in optimizing an objective function. We consider in this work two variants of GD methods to solve multilateration problems (3)-(6) based on fixed or variable rates in the gradient descent step. We also introduce a revisited formulation of the variable step descent. Indeed, it is well known that choosing a proper descent step can be tricky. Too small a descent step leads to slow convergence, while a too large descent step can hinder convergence and cause the utility function to fluctuate around the minimum or even diverge. Some approaches [37] use Line Search methods or variants, solving a second iterative optimization problem under specific conditions to find an appropriate fixed step length.  
To solve problems (3)-(6), starting from an initial guess \(\mathbf{x}_{i,0}\in\mathbb{R}^{3}\) of a node position \(\mathbf{x}_{i}\), a sequence of solutions is produced iteratively by:  
\[\mathbf{x}_{i,k+1}=\mathbf{x}_{i,k}-\alpha_{k}\mathbf{g}_{k} \tag{7}\]  
where \(\mathbf{x}_{i,k}\) is the position estimate at iteration \(k\), \(\mathbf{g}_{k}\) is the gradient of the utility function, and \(\alpha_{k}\) is the gradient descent step defined by Equation (8). \(\alpha_{k}\) could be constant in a fixed step descent or, depending on \(H\), the Hessian matrix of the utility function. The proposed revisited formulation introduces a damping parameter \(0<\beta\leq 1\) in the variable step rate:  
\[\alpha_{k}=\begin{cases}\alpha&\text{using the fixed rate step},\\ \frac{\mathbf{g}_{k}^{T}\mathbf{g}}{\mathbf{g}_{k}^{T}H\mathbf{g}_{k}}&\text{ using the variable rate step},\\ \beta\frac{\mathbf{g}_{k}^{T}\mathbf{g}}{\mathbf{g}_{k}^{T}H\mathbf{g}_{k}}& \text{using the revisited formulation.}\end{cases} \tag{8}\]  
These methods are implemented and evaluated in the next section, with the objective of finding the optimal parameters and conditions necessary to solve the cases presented by the localization scenarios. Table 1 summarizes the different combinations with the evaluated parameters.
## 3 Results  
### Participants' Background  
Participants' mean age, height, body weight, and BMI were 13.6 years (range, 12-15 years), 166.4 cm (range, 140-182 cm), 57.7 kg (range, 35-85 kg), and 20.7 kg/m\({}^{2}\) (range, 16.4-28.0 kg/m\({}^{2}\)) (Table 1). Forty-eight players were right-handed and 15 were left-handed. The mean career duration of the baseball players was 6.3 years (range, 2-11 years). Participants' background characteristics in each group are summarized in Table 1. Age (r = 0.65, \(p\) < 0.001), height (r = 0.83, \(p\) < 0.001), body weight (r = 0.82, \(p\) < 0.001), and BMI (r = 0.60, \(p\) < 0.001) were significantly positively correlated with elbow stress (Table 2, Figure 6). In a between-group comparison, age (HT, 14.1 \(\pm\) 0.6 years; LT, 13.2 \(\pm\) 0.6 years; \(p\) < 0.001), height (HT, 170.7 \(\pm\) 4.9 cm; LT, 161.5 \(\pm\) 6.6 cm; \(p\) < 0.001), body weight (HT, 63.6 \(\pm\) 8.8 kg; LT, 51.2 \(\pm\) 7.1 kg; \(p\) < 0.001), and BMI (HT, 21.8 \(\pm\) 2.5 kg/m\({}^{2}\); LT, 19.6 \(\pm\) 2.0 kg/m\({}^{2}\); \(p\) < 0.001) were significantly higher in the HT group than in the LT group (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **All Players** & **HT** & **LT** & \(p\)**-Value** \\ \hline Age & 13.6 \(\pm\) 0.8 & 14.1 \(\pm\) 0.6 & 13.2 \(\pm\) 0.6 & \textless{}0.001 * \\ (years) & Height & 166.4 \(\pm\) 7.4 & 170.7 \(\pm\) 4.9 & 161.5 \(\pm\) 6.6 & \textless{}0.001 * \\ (cm) & & & & \\ Body weight & 57.7 \(\pm\) 10.1 & 63.6 \(\pm\) 8.8 & 51.2 \(\pm\) 7.1 & \textless{}0.001 * \\ (kg) & & & & \\ BMI & 20.7 \(\pm\) 2.5 & 21.8 \(\pm\) 2.5 & 19.6 \(\pm\) 2.0 & \textless{}0.001 * \\ (kg/m\({}^{2}\)) & & & & \\ \hline \hline \end{tabular} Mean values are expressed as the mean \(\pm\) SD. The independent \(t\)-test was used to determine significant differences (* \(p\) < 0.05). N = 33 and 30 in the high torque (HT) and low torque (LT) groups, respectively.  
\end{table}
Table 1: Participants’ background characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **R-Value** & \(p\)**-Value** \\ \hline
**Patient background** & & \\ Age & 0.65 & \textless{}0.001 * \\ Height & 0.83 & \textless{}0.001 * \\ Body weight & 0.82 & \textless{}0.001 * \\ BMI & 0.6 & \textless{}0.001 * \\ \hline
**Pitch parameter** & & \\ Arm slot & \(-\)0.07 & 0.59 \\ Shoulder rotation & \(-\)0.004 & 0.97 \\ Arm speed & 0.04 & 0.74 \\ Ball velocity & 0.8 & \textless{}0.001 * \\ \hline
**Shoulder ROM** & & \\ Dominant & \(-\)0.12 & 0.36 \\ Abduction & Non-dominant & \(-\)0.15 & 0.24 \\ Difference & \(-\)0.06 & 0.65 \\ Dominant & 0.11 & 0.38 \\ Horizontal adduction & Non-dominant & 0.004 & 0.97 \\ Difference & \(-\)0.08 & 0.52 \\ \hline \hline \end{tabular} Pearson correlation coefficient (R-value) was used to determine significant correlations (* \(p\) < 0.05). Difference: non-dominant shoulders’ ROM—dominant shoulders’ ROM.  
\end{table}
Table 2: Correlations with elbow stress.
## 3 Results and Discussion
### Physicochemical Properties of RB-DF  
The physical and chemical properties of RB-DF at different stages of cellulase hydrolysis and HPH are shown in Table 1. With the removal of starch and protein by hydrolysis, RB-DF showed a significant change in oil and water holding capacity, expansion capacity, and cation-exchange capacity (\(p<\)0.05). Following cellulase digestion and HPH treatment, the water holding capacity, expansion capacity, and cation exchange capacity of RB-DF continuously increased to 5.81 g/g, 5.26 mL/g, and 0.38 mmol/g, respectively, and the oil holding capacity decreased from 4.23 g/g to 3.43 g/g. Compared with cellulase, xylanase and ball-milling RD-DF [33], the cellulase and high pressure homogenization treatments have significantly improved the water holding capacity, swelling capability, and cation exchange capacity of RB-DF. The reason is that enzymatic hydrolysis and HPH treatment can change the microstructure of RB-DF, making it loose and porous, and exposing hydrophilic groups. Enhanced hydration resulted in an increase in water holding and expansion capacities. Therefore, it was inferred that a higher ability to retain water is related to a decrease in particle size of DF, due to the resultant breakdown of DF after being induced by high pressure treatment [34; 35]. Additionally, the exposure of hydrophilic groups, such as carboxyl, hydroxyl, and amino groups, can produce a similar effect as anion exchange resins, allowing RB-DF to exchange cations such as Ca\({}^{2+}\), Zn\({}^{2+}\) and Cu\({}^{2+}\), maintaining stable pH and ion balance. Obviously, the chemical and structural natures of DF determine oil holding and swelling capacities. With the exposure of hydrophilic groups, the hydrophobic effect was weakened, reducing oil holding capacity. Therefore, the combination of cellulase hydrolysis and HPH treatment can micronize RB-DF, thereby improving the taste and nutrient absorption of DF foods.