esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Bacterial Strains and Culture Conditions
Coagulase positive staphylococci were enumerated according to the standard procedure ISO 6888-2 [15]. The absence of _E. coli_ O157 and _L. monocytogenes_ in milk was tested according to UNI EN ISO 16654:2017 [16] and UNI EN ISO 11290-1:2017 [17], respectively. _E. coli_ O157 and _L. monocytogenes_ were enumerated according to the ISO 16649-1 [18] and UNI EN ISO 11290-2:2017 [19], respectively. Tolerance to low pH was tested for strains of lactobacilli grown in MRS broth at 37 \({}^{\circ}\)C for 24 h. A 10 \(\upmu\)L aliquot of culture was inoculated into 990 \(\upmu\)L of phosphate-buffered saline (PBS) (8 g/L NaCl, 2.9 g/L Na\({}_{2}\)HPO\({}_{4}\) -2H\({}_{2}\)O, 0.2 g/L KH\({}_{2}\)PO\({}_{4}\) and 0.2 g/L KCl) with pH adjusted to 2.5, 3.0 or 6.2 (control). Cell suspensions were incubated for 2 h at 37 \({}^{\circ}\)C. Counts were carried out on MRS agar before and after incubation, and the survival rate was expressed as percentage of Log CFU/mL at the tested pH compared to the initial count. For the determination of tolerance to bile salts, strains were grown in MRS broth at 37 \({}^{\circ}\)C for 24 h and then inoculated at 2% (\(v/v\)) ratio into MRS broth containing 0.3%, 0.5% or 1% (\(w/v\)) bile salts (Ox-bile, Merck S.P.A., Serono, RM, Italy) and incubated at 37 \({}^{\circ}\)C. Optical density at 600 nm (OD\({}_{600}\)) was measured after 24 h using a BioPhotometer D30 (Eppendorf S.r.l., Milan, Italy) and compared to a control culture grown in MRS broth without bile salts. The results were determined as percentage of OD\({}_{600}\) in the presence of bile salts compared to the control. All experiments were performed in triplicate. The deferred antagonism test was used to evaluate the in vitro production of inhibitory compounds by LAB isolates against pathogenic bacteria. Briefly, 2 \(\upmu\)L of a bacterial culture grown for 16-18 h at 37 \({}^{\circ}\)C were spotted onto the surface of BHI agar (BHI broth + Bacto agar 1.5% \(w/v\)) and incubated at 30 \({}^{\circ}\)C for 18-24 h. The plates were than overlaid with 8 mL of BHI soft agar (BHI broth + Bacto agar 0.6% \(w/v\)) seeded with 8 \(\upmu\)L of indicator strain grown for 16 h at 37 \({}^{\circ}\)C in BHI broth. After incubation for 16-18 h at 37 \({}^{\circ}\)C, the plates were screened for the presence of growth inhibition zones in the indicator layer.
## 3 Experiments and Results
### Comparative Experiments  
To evaluate the performance of the proposed method, comparative experiments were conducted on the standard MNIST dataset and a self-made fastener dataset. GANomaly [47], f-AnoGAN [48], MemAE [36], and SQUID [37] were selected as comparative models.
## 2 SRE Empowered by RISs
Accurate RIS models are lacking as in most cases they are assumed to be perfect reflectors, which is not completely true; a validated channel model is palso lacking [17]. The most important challenge in implementing an SRE is the maintenance of a perfect balance between cost and effectiveness. As network designers work with limited resources, creating the ideal configuration is a more complex problem than prior designs. The first and most critical issue is that the nature of the design optimization problem for the creation of energy efficient SREs is non-convex [18]; thus, the solution may be computationally demanding. Another important issue is the ever-expansive complexity that is being introduced by the nature of the IoE. The number of devices and their presence and movement is more fluid than ever, as an increased number of static sensors and a large number of highly dynamic drones come into play. There have been studies contributing to the simplification of setting up the RISs [19; 20]. While the aforementioned methods simplify the use of the surfaces to constitute the technology usable, the design of a complete SRE is far more complex [21]. The number of parameters that we need to consider, and the fluidity of contemporary and future communication networks render such calculations at least computationally expensive if not impossible. Apart from the greater number of devices that are on the move and need to be connected constantly, the increasing number of obstacles has to be considered, too. As higher frequencies are used to increase the channels' data throughput, we come across greater attenuation and other signal degradation phenomena, because higher frequencies are more susceptible to the effects caused by physical objects. As there is no effortless way to solve this design issue, engineers have steered their attention towards the use of AI to design the SREs. Figure 2 presents the simplest implementation of RIS-aided communications controlled by AI. The \"AI Control\" unit of Figure 2 is actually an AI-based joint optimizer; the latter performs the total optimization of the system by intervening to the transmission channel through RIS (which is the main objective of a SRE), by taking into account both  
Figure 1: A general representation of a Smart Radio Environment. the base station (BS) and the mobile device. The necessary Channel State Information (CSI) may be provided to \"AI Control\" either from the BS or from certain cell units of RIS that operate as sensors.
# The Study of Thermal Properties of Blackberry, Chokeberry and Raspberry Seeds and Oils
## 3 Results and Discussion
### Study of Oil Obtained from Seeds  
#### 3.2.1 Oil Extraction Efficiency  
Fat extraction yields from milled seeds were as follows: 12.43% for blackberry seed oil, 8.19% for chokeberry seed oil, 14.25% for raspberry seed oil. According to [PERSON] et al. [34], oil yield obtained by extraction in a Soxhlet apparatus using hexane as a solvent ranged from 15.68% for blackberry and 10.55% for raspberry seed oils. [PERSON] et al. [35] yielded 11.8% blackberry seed oil in that method, although the process was carried for 8 h. In the same study, ethanol was used as a solvent in a comprehensive run and resulted in a higher oil yield than hexane.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
###### Abstract  
In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit attackers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.  
connected and autonomous vehicles; vehicular communication; Wi-Fi 6; 5G; connected vehicles applications; V2X; challenges; opportunities +
Footnote †: journal: Sensors  
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Footnote †: journal: Sensors  
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Footnote 1: email: {[[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))}
# Telehealth Secure Solution to Provide Childhood Obesity Monitoring
## 5 Experimental Results
The recovered plain text of the five users is shown in Table 5. The plain text cannot be recovered correctly for users 1 and 4, whereas the plain text is recovered correctly for users 2, 3, and 5, even with noise added in the cryptogram. Figure 4: Cryptogram: (**a**) complete cryptogram with noise, (**b**) first 1,000 data points of cryptogram, and (**c**) AWGN noise added. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(a\) & \(b\) & \(x_{0}\) & \(y_{0}\) \\ \hline User 1 & 1.400112233445566 & 0.300112233445566 & 0.556677889900112 & 0.667788990011223 \\ User 2 & 1.400223344556677 & 0.300223344556677 & 0.66778899001122334 & 0.778899001122334 \\ User 3 & 1.400334455667788 & 0.300223344556677 & 0.778899001122334345 & 0.88990011223344556 \\ User 4 & 1.400445566778899 & 0.30033445566778899 & 0.990011223344556 & 0.00112233445567 \\ User 5 & 1.400556677889900 & 0.30044556678899 & 0.990011223344556 & 0.00112233445567 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Secret key definition for encryption process of the five users. Therefore, the plain text can be recovered by the physician even if the transmitted cryptogram has noise interference. This is due to the advantages of using spread spectrum technology and codification based on chaos. [PERSON] et al. present schemes for monitoring childhood obesity [PERSON] et al. (2018), but these do not consider data security.
## 4 Deformation Force Monitoring Based on Adaptive Adjustment of Clamping Force
### Clumping Force Decision and Deformation Force Calculation  
In addition to the probability distribution of deformation forces, the equivalent flexibility is required to ensure that the deformation force is monitorable. The equivalent flexibility is \(C=\sum_{1}^{n}C_{\cdot,i}\), where \(C_{i,j}\) denotes the equivalent flexibility of the force acting normal to the clamping point \(i\) at the clamping point \(j\). [PERSON] et al. [39] proposed a finite element method-based approach to obtain the equivalent flexibility of a part. Basically, the equivalent flexibility is calculated according to (22):  
\[C_{i}=\frac{1}{F_{i}} \tag{22}\]  
where \(C_{i}\) is the equivalent flexibility of the part for each machining step, and \(F_{i}\) is the node force obtained from the simulation.  
As mentioned earlier, the deformation force is assumed to satisfy the Gaussian distribution, which is determined by two parameters \(\theta=(\mu,\sigma)\). The probability distribution function of Gaussian distribution is then given, as in (23):  
\[F(X,u,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}\int_{-\infty}^{X}exp\Bigg{(}-\frac{ (x-\mu)^{2}}{2\sigma^{2}}\Bigg{)}dx=P(X<x) \tag{23}\]  
The above formula represents the probability when the predicted value of deformation force \(\hat{F}_{b}\) is larger than the true value of deformation force \(F_{b}\). Let us set the confidence degree \(F(X)=\alpha\) and calculate the predicted value of deformation force \(\hat{F}_{b}\) according to (23). If \(\hat{F}_{b}\leq 0\), it is considered that the direction of deformation within the current machining step is the same as that of the support element arrangement under the confidence \(\alpha\). In this case, a smaller clamping force can be used according to our past experiences. Conversely, if \(\hat{F}_{b}>0\), it is considered that the deformation direction in the machining step is opposite to the support element arrangement at the confidence level \(\alpha\). In this case, the clamping force of each machining step is calculated according to (24):  
\[F_{i}=\frac{\big{(}|\hat{F}_{bi}|\big{)}C_{i}}{C_{i-1}} \tag{24}\]  
where \(F_{i}\) is the \(i\)-th machining step support force before the machining, \(\hat{F}_{bi}\) is the predicted value of the \(i\)-th machining step deformation force, \(C_{i}\) is the \(i\)-th machining step equivalent flexibility, and \(C_{i-1}\) is the equivalent flexibility at the \((i-1)\)-th machining step.  
Adaptive adjustment of the clamping force according to the above method ensures a high accuracy of monitoring of the deformation force. According to (6) and (8), the deformation force at machining step \(i\) can be calculated as in (25):  
\[F_{b}=F_{i}-F_{i}^{\prime}-\frac{F_{i}\times(C-C^{\prime})}{C^{\prime}} \tag{25}\]  
where \(C\) is the equivalent flexibility of the part before the machining, \(C^{\prime}\) is the equivalent flexibility of the machined part, \(F_{i}\) is the \(i\)-th machining step support force before machining, and \(F_{i}^{\prime}\) is the \(i\)-th machining step support force after the machining.
## 2 Background Study
### Building Blocks of CNN
#### 2.1.1 R-CNN  
R-CNN [8] in the Figure 4 comprises three parts: a region proposal, a feature extractor, and a classifier. A region proposal is a tool that generates and extracts region proposals. The feature extractor's job is to extract the features out of each candidate region. The classifier then classifies the features as one of the known classes.  
Figure 3: Detection pipeline using CNN.  
Figure 2: Classification pipeline using CNN.  
A bounding box region is proposed for the objects in the image with the help of a selective search. One of the simple examples of a CNN is object localization and recognition. Its disadvantage is that it is time-consuming because each of the candidate regions generated by the region proposal algorithm requires a CNN-based extraction pass.
# Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models
## 4 Result and Discussion
### Numerical Study
#### 4.1.2 Varying Parameters  
A series of simulation is performed with varying input parameters \(\mathbb{P}_{I}\), \(\mathbb{P}_{\beta}\) and \(\mathbb{P}_{\gamma}\). The percentage of initially infected agents \(\mathbb{P}_{I}\) is considered to lie between 0.0002 and 0.1, while \(\mathbb{P}_{\beta}\) and \(\mathbb{P}_{\gamma}\) vary between 0 and 1 with an increment of 5%. For each triple \((\mathbb{P}_{I,i},\mathbb{P}_{\beta,j},\mathbb{P}_{\gamma,k})\) six simulations are performed, resulting in a total amount of 21,600 simulations. To analyze these simulation results, the mean value of infected agents and the 50% and 90% confidence intervals are used.  
Figure 7a illustrates the reference scenario for the remaining five figures. The initially infected population lies between \(0.5\%\leq\mathbb{P}_{I}\leq 10\%\) of the total population, the probability of spreading the disease is in the range of \(25\%\leq\mathbb{P}_{\beta}\leq 50\%\), the probability to recover is in the range of \(0\%\leq\mathbb{P}_{\beta}\leq 35\%\). Similar to the simple SIR model in Section 2.1, the number of infected agents increase first, reach a peak and decline again. In Figure 7b, the range of the initially infected population is increased to \(5\%\leq\mathbb{P}_{I}\leq 10\%\), which results in a smooth curve with a clearly defined peak in an early phase of the epidemic. Increasing the infection probability (\(50\%\leq\mathbb{P}_{\beta}\leq 100\%\)), lead also to a peak in the early phase as illustrated in Figure 7c, but compared to Figure 7b the maximum number of infected agents appear for the mean value, the 50% and the 90% confidence interval at the same time around day 4. If the probability of getting recovered is decreased to the range of \(0\%\leq\mathbb{P}_{\gamma}\leq 10\%\) (see Figure 7d), \(1.3\%\) of the population is still not recovered after 80 days. Also, there is a shift in the mean peak to the 10 th day in the simulation. In contrast, increasing the recovery probability (\(10\%\leq\mathbb{P}_{\gamma}\leq 50\%\)) as illustrated in Figure 7e, results in a fast decay of the infected agents and the epidemic become extinct already after 15 days. Finally, Figure 7f illustrates the simulations where both the infection and the recovery probability lie in the range of 0% to 10%. In this case, no distinct peak of the influenza epidemic can be identified due to the low probability of infection. In combination with a similar recovery probability, the number of infected people is slowly declining since re-infection is not considered, i.e., agents get sick and recover but never at such a critical rate that an epidemic outbreak is observed.  
Figure 6: Temporal-spatial epidemic spread. Red dots indicate infected agents.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Displacement Transformation  
Consider a single subset pair and its two sets of in-plane displacements determined by temporal matching between the reference and deformed image pairs under consideration. First \(x_{s_{j}}^{d}=\left[\begin{array}{cc}x_{s_{j}}^{d}&y_{s_{j}}^{d}\end{array} \right]^{T}\) is calculated based on \(x_{s_{j}}^{o}=\left[\begin{array}{cc}x_{s_{j}}^{o}&y_{s_{j}}^{o}\end{array} \right]^{T}\) as  
\[\left[\begin{array}{c}x_{s_{j}}^{d}\\ y_{s_{j}}^{d}\end{array}\right]=\left[\begin{array}{c}x_{s_{j}}^{o}\\ y_{s_{j}}^{o}\end{array}\right]+\left[\begin{array}{c}u_{j}\\ v_{j}\end{array}\right]. \tag{49}\]  
Thereafter \(x_{s_{j}}^{o}\) and \(x_{s_{j}}^{d}\) are undistorted to obtain \(\hat{x}_{s_{j}}^{o}=\left[\begin{array}{cc}\hat{x}_{s_{j}}^{o}&\hat{y}_{s_{ j}}^{o}\end{array}\right]^{T}\) and \(\hat{x}_{s_{j}}^{d}=\left[\begin{array}{cc}\hat{x}_{s_{j}}^{d}&\hat{y}_{s_{ j}}^{d}\end{array}\right]^{T}\) respectively. This is performed using non-linear, least-squares optimization since an exact solution for the inverse of Equation (3) does not exist because it requires determining the roots of a polynomial of degree greater than four [74]. Triangulation, represented by function \(\Psi\), determines the 3D coordinates in the world CS to which the reference subset pair, \(\hat{x}_{w}^{o}=\left[\begin{array}{cc}\hat{x}_{w}^{o}&\hat{y}_{w}^{o}&\hat{ z}_{w}^{o}\end{array}\right]^{T}\), and investigated subset pair, \(\hat{x}_{w}^{d}=\left[\begin{array}{cc}\hat{x}_{w}^{d}&\hat{y}_{w}^{d}&\hat{ z}_{w}^{d}\end{array}\right]^{T}\), correspond as  
\[\hat{x}_{w}^{o}=\Psi\big{(}\hat{x}_{s_{1}}^{o},\hat{x}_{s_{2}}^{o}\big{)}\ \text{and}\ \hat{x}_{w}^{d}=\Psi\Big{(}\hat{x}_{s_{1}}^{d},\hat{x}_{s_{2}}^{d}\Big{)}. \tag{50}\]  
Finally, the displacements \(\hat{u}_{w}\), \(\hat{v}_{w}\), and \(\hat{w}_{w}\) are determined as  
\[\left[\begin{array}{c}\hat{u}_{w}\\ \hat{v}_{w}\\ \hat{w}_{w}\end{array}\right]=\left[\begin{array}{c}\hat{x}_{w}^{d}\\ \hat{y}_{w}^{d}\\ \hat{z}_{w}^{d}\end{array}\right]-\left[\begin{array}{c}\hat{x}_{w}^{o}\\ \hat{y}_{w}^{o}\\ \hat{z}_{w}^{o}\end{array}\right]. \tag{51}\]
## 1 Introduction
For higher-order nonlinear systems, large-signal stability analysis can be performed by the Lyapunov direct method [20], the key of which is to construct a suitable Lyapunov function. Various approaches have been proposed to produce Lyapunov functions, e.g., the Brayton-Moser mixed potential function method [21; 22], the Takagi-Sugeno multi-modeling method [23], genetic algorithm methods [24] and the reverse trajectory tracking method [25]. The literature [26] compares the above several methods. The Takagi-Sugeno multi-modeling method uses multiple linear models to fit nonlinear models, and is not suitable for higher-order nonlinear systems. The Brayton-Moser mixed potential function method needs to find the voltage and current potential functions, which is conservative. The genetic algorithm method requires an artificial neural network toolbox and selects appropriate parameters. The reverse trajectory tracking method can only be represented by an image, and the analytical expression cannot be obtained. In this paper, the mixed potential function method is applied to consider the effect of long-distance submarine cables on the power system large-signal stability, and the equivalent circuits of a long-distance submarine cable are built. Based on this, the equivalent topology model of a single-node CSO undersea DC power system is established. Considering the characteristics of long-distance submarine cables, the large-signal stability of the single-node CSO undersea DC power system is analyzed by the mixed potential function method, and the large-signal stability criterion is obtained. The large-signal stability criterion is verified by computer simulations and experiments, and the factors affecting the large-signal stability of the CSO undersea DC power system are analyzed by simulations and experiments, which is of great significance to the design, operation and future development of the CSO undersea DC power system. The remainder of the paper is divided as follows. Section 2 introduces the equivalent circuits of a long-distance submarine cable. Section 3 analyses the steady-state equilibrium point and the large-signal stability criterion of the CSO undersea DC power system. Section 4 simulates and experiments on the large-signal stability of CSO undersea DC power systems. Section 5 summarizes the conclusion and anticipates further research on this topic.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 4 Results
### Result of Hash Tree and Verification  
A hash tree is a data structure used for the efficient and secure verification of large datasets. The hash values generated by the logger are verified for integrity by the verifier, and the verified hash values are used as input for constructing a hash tree. The hash tree is used to verify the integrity of the data, which may be compromised due to external tampering or internal data management issues during the image storage process.  
According to the designed data verification process flow, it can detect whether data have been changed by using the hash value of the hash tree's root node (root hash) delivered from the logger, who is the digital content creator. Figure 12 shows a hash tree composed of four leaf nodes. Figure 13 shows the processing results after verification using the hash tree's root hash value, followed by integrity and authenticity verification.
## 3 Interoperability for Wide-Area Applications  
Wide-area applications include different areas such as WAMPAC and protection/control applications that require inter-substation communication. These applications use wide-area measurements and protection data collected from multiple IEDs that are distributed over large geographical areas. Interoperability in order to facilitate integration and information exchange is one of the main issues to be addressed for wide-area applications. IEC 61850 standards provide interoperability via a standardized data model and communication services (R-SV and R-GOOSE). These services can be applied in multicast communication to publish data to multiple receivers placed at different physical locations simultaneously. R-SV is a stream-based protocol while R-GOOSE is an event-driven protocol, and they are suitable for wide-area measurement and protection applications.