esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 2 Mathematical Model
### Motion Equations  
The wave-exciting force, added mass, and damping coefficients were substituted into the motion equations as follows:  
\[\sum\limits_{j=1}^{6}\left[-\omega^{2}\Big{(}M_{ij}+A_{ij}\Big{)}-i\omega B_{ ij}+C_{ij}\right]\xi_{j}=F_{i}i=1,\ 2,\mathrm{l}\mathrm{l}, \tag{11}\]  
where \(M_{ij}\) is the mass matrix of the hull, \(A_{ij}\) is the added mass matrix, \(C_{ij}\) is the restoring matrix, \(\xi_{j}\) is the displacement corresponding to each degree of freedom, and \(F_{i}\) is the wave-exciting force.  
Because the vessel is assumed to be a slender body, the surging motion was minimal and could be excluded from the calculations. Additionally, because the hull shape was symmetrical along the centerline, the horizontal and vertical motion planes did not have coupled motion relationships. The coupled motion relationships of the hull body were only observed for the coupled equations of vertical motion (i.e., heaving and pitching) and those of horizontal motion (i.e., swaying, rolling, and yawing). The coupled equations are listed as follows:  
\[\left[\begin{array}{cc}-\omega^{2}(M_{0}+A_{33})+C_{33}-i\omega B_{33}&- \omega^{2}A_{35}+C_{35}-i\omega B_{35}\\ \omega^{2}A_{53}+C_{53}-i\omega B_{53}&-\omega^{2}(I_{55}+A_{55})+C_{55}-i \omega B_{55}\end{array}\right]\left[\begin{array}{c}\frac{\xi_{3}}{a}\\ \frac{\xi_{5}}{a}\end{array}\right]\] \[=\left[\begin{array}{cc}\frac{F_{33}^{(c)}}{a}\\ \frac{M_{5}^{(c)}}{a}\end{array}\right] \tag{12}\]  
\[\left[\begin{array}{cc}-\omega^{2}(M_{0}+A_{22})-i\omega_{22}&-\omega^{2} \Big{(}A_{24}-\overline{OG}M_{0}\Big{)}-i\omega B_{24}&-\omega^{2}A_{26}-i \omega B_{26}\\ -\omega^{2}\Big{(}A_{42}-\overline{OG}M_{0}\Big{)}-i\omega B_{45}&-\omega^{2} (I_{44}+A_{44})+C_{44}-i\omega B_{44}&-\omega^{2}A_{46}-i\omega B_{46}\\ -\omega^{2}A_{62}-i\omega B_{62}&-\omega^{2}A_{64}-i\omega_{64}&-\omega^{2}(I_ {66}+A_{66})-i\omega B_{66}\end{array}\right] \tag{13}\]  
where \(M_{0}\) is the hull mass, \(I_{44}\) is the moment of inertia along the \(X\) axis, \(I_{55}\) is the moment of inertia along the \(Y\) axis, and \(I_{66}\) is the moment of inertia along the \(Z\) axis.
## 4 Dataset Generation and Simulation Environment
### Parameter Sensitivity Analysis  
To further assess the robustness of the MHMM's trained parameters, a sensitivity analysis was conducted. This involved systematically perturbing key emission parameters (means and standard deviations of HRV, TCT, and NASA-TLX for specific states) by \(\pm\)10% or \(\pm\)15%. For each perturbation, the MHMM (Expert System) was re-evaluated on the test dataset, and its performance (accuracy and macro-averaged F1-Score) was compared against the original model. This analysis quantifies the model's stability under variations in empirical estimates, providing critical insight into its reliability in practical applications where precise parameter values may vary.
## Appendix A  
A simple integrator in the continuous-time domain and its corresponding numerical implementations using SI, II, and BL are given by:  
\[y(t)=\int\limits_{t=0}^{T}x(t)dt \tag{1}\]and:  
\[y_{\text{SI}}(k) =y(k-1)+T\cdot x(k-1) \tag{10}\] \[y_{\text{II}}(k) =y(k-1)+T\cdot x(k)\] \[y_{\text{BL}}(k) =y(k-1)+\frac{T}{2}[x(k)+x(k-1)]\]  
where \(x\) and \(y\) are the input and output of the integrator, respectively, as in Table 2, and the subscripts represent each numerical integration method.  
Let us consider a second-order PLL, for example. Figure 11 shows the block diagram of the linearized model of the second-order PLL in the continuous-time domain, where the analog integrator is represented by \(1/s\)[2]. In the figure, \(\varepsilon(t)\) represents the discriminator output at \(t\), which is obtained by subtracting the phase estimate at the previous loop update epoch, \(\hat{\phi}^{-}(t)\), from the phase measurement at \(t\), \(\phi(t)\). Similarly, the discriminator output is calculated in the discrete-time domain as follows:  
\[\varepsilon(k)=\phi(k)-\hat{\phi}^{-}(k)=\phi(k)-\hat{\phi}(k-1) \tag{11}\]  
The discriminator output is multiplied by the multiplier coefficients, which are based on the natural frequency of the system (i.e., \(\omega_{0}\)). The output is then processed as shown in the figure.  
The digital implementation of the integrator in \(F(z)\) is obtained by applying (10) to \(\hat{\phi}\):  
\[\hat{\hat{\phi}}_{\text{g}}(k) =\hat{\hat{\phi}}(k-1)+T\cdot\omega_{0}^{2}\varepsilon(k-1) \tag{12}\] \[\hat{\hat{\phi}}_{\text{II}}(k) =\hat{\hat{\phi}}(k-1)+T\cdot\omega_{0}^{2}\varepsilon(k)\] \[\hat{\hat{\phi}}_{\text{BL}}(k) =\hat{\hat{\phi}}(k-1)+\frac{T}{2}\cdot\omega_{0}^{2}[\varepsilon (k)+\varepsilon(k-1)]\]  
The control signal of the NCO, which is the estimate of the phase rate, is obtained by the summation of \(\hat{\phi}(k)\) and the output from the component with \(a_{2}\omega_{0}\):  
\[\hat{\phi}_{\text{in}}(k)=\hat{\hat{\phi}}(k)+a_{2}\omega_{0}\varepsilon(k) \tag{13}\]  
where \(a_{2}\) is the filter coefficient of the second-order PLL.  
The digital implementation of \(N(z)\) is obtained by applying (A2) and (A5) to \(\hat{\phi}\):  
\[\hat{\phi}_{\text{SI}}(k) =\hat{\phi}(k-1)+T\cdot\hat{\phi}_{\text{in}}(k-1)\] (A6) \[=\hat{\phi}(k-1)+T\cdot\hat{\phi}(k-1)+T\cdot q_{2}\omega_{0}\varepsilon (k-1)\] \[\hat{\phi}_{\text{II}}(k) =\hat{\phi}(k-1)+T\cdot\hat{\phi}_{\text{in}}(k)\] \[=\hat{\phi}(k-1)+T\cdot\hat{\phi}(k)+T\cdot q_{2}\omega_{0} \varepsilon(k)\] \[\hat{\phi}_{\text{BL}}(k) =\hat{\phi}(k-1)+\frac{T}{2}\Big{[}\hat{\phi}_{\text{in}}(k)+\hat {\phi}_{\text{in}}(k-1)\Big{]}\] \[=\hat{\phi}(k-1)+\frac{T}{2}\Big{[}\hat{\phi}(k)+\hat{\phi}(k-1) \Big{]}+\frac{T}{2}\cdot q_{2}\omega_{0}[\varepsilon(k)+\varepsilon(k-1)]\]  
Similarly, the digital implementation of \(F(z)\) and \(N(z)\) for the first-/third-order cases can be obtained. Finally, the digital implementation of the overall DPLL for the combinations of the three integration methods is listed in Table A1.
## 4 Performance Evaluation  
### Simulation Setup  
We evaluate the offloading efficiency of DOPS based on the simulation data from ONE, a simulation software for opportunistic network environments. In the simulator, the nodes were vehicles that traveled along the roads and the mobility model of these nodes was RandomWayPoint. The road map used in the simulation is Helsinki's city area. The nodes density nearby the main streets was higher than in the suburb. The content was supposed to be music files and those files are from 2 M to 5 MB in size. The parameters we used in the simulation are listed in Table 1. Nodes communicate with each other through WiFi connections. Every 25 to 75 s, a randomly selected node downloaded a new music through the cellular networks. The subscribers were randomly selected from all nodes. The subscriber ratio in each round of simulation was constant. The subscriber ratios were 1%, 2%,..., 10%. We ran the simulation for 10 rounds and the results were the average of the simulation data of 10 rounds. The deadline of all the subscriptions is after 12 h. The opportunistic routing was Maxprop that is provided by the simulator and its delivery performance is very high.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 180 \\ TTL & 60 min \\ Wi-Fi transmission range & 100 m \\ Size of the content & 2 M–5 M \\ Simulation time & 1 day \\ Transmission speed & 10 Mbps \\ Simulation area & 15.3 km\({}^{2}\) \\ Nodes travel speed & 7 to 10 m/s \\ Node’s buffer & 100 M \\ Opportunistic routing & Maxprop \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters in the simulation.  
Figure 4: When a relay receives its subscribed content, the relay removes itself from the subscription table.
## 6 Discussion
### Real-Life Applications  
The model proposed to predict the states of resources and their deadlines was adopted for epidemiological modeling and, thus, can be mapped to different types of real-life problems in which any epidemiological mathematical model can be adopted. Foraging with online deadlines can also be scaled to a large spectrum of real-life problems, as long as it can be mapped to biological foraging behavior. [PERSON] [18] discussed different possible applications of this, such as cleaning pollution that needs to be collected at a specific time, operating factory-recycling systems, or constructing data mules that are used for collecting wireless sensor nodes' stored data. All these applications can also be undertaken using ED_Foraging.  
One example of real-life application, in which the proposed approach can be used, is for a COVID-19 search-and-rescue mission. This mission can be about searching for infected individuals and predicting possible exposure around these individuals to collect the victims before they die. Multiple robots can be used to collaboratively accomplish the task, which is about moving objects from one place to another. Collected objects (resources) can represent individuals (or patients), and clinics are represented by the collection zones. In general, the task is about searching for patients, and when there is a suspicion of COVID-19 infection (captured by temperature sensors for example), individuals are moved to checkup points in order to be tested if they are positively infected. When the patients result is positive, they are moved to a general hospital to receive treatment, which is represented by the central collection zone. Finally, when it comes to the deadlines, it is important to move infected people before they die. Hence, the infection status can be mapped to the time constraint model, which can be updated online using the proposed DD_Model. The ultimate objective is to move as many patients as possible, while considering the time constraint to give priority to individuals that are more critical. Hence, the efficiency of the model is not only concerned with the number of resources it collects (foraging rate) but also about the death probability (expiry rate). Having the foraging and expiry rate are very important metrics to give an intuition about how well the system perform and to what extent the task is accomplished.  
From all of the above, it can be concluded that any search-and-rescue mission with online deadlines can also be carried out using the proposed method. All that is required is to decide on the sensors that will be used to identify the resources and update the DD_Model parameters to model the task requirements. The ultimate objective is to prioritize critical individuals rather than to collect a large number of resources.
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 3 The Architecture of Software-Defined Vehicular Cloud
### SDN Controller Assistance for Cloud-Based Vehicular Applications
#### 3.2.1 Cloud Vehicular Application Based on Computation Resource Sharing  
Real-time navigation with computation resource sharing serves as a hypothetical use case to illustrate the potential benefits of applications of cloud-based vehicular networks [4]. A vehicle is using real-time navigation controlled from the central cloud. It will first request cloud service from the central cloud and MEC cloud. The SDN OpenFlow controller controls virtualization infrastructure of MEC micro-data center towards the driving direction. SDN OpenFlow controller also controls the VM cluster from the cloud to provide the navigation cloud service by suggesting several routes based on the current traffic conditions. The SDN communication infrastructure controller acts as an agent to push control traffic to the vehicle considered as a SDN device. The active VM controller updates the vehicle with navigation paths conditions on the road and controls the migration of dedicated VM to the next MEC micro-data center. After the vehicle moves out of the radio range of the current serving MEC, the vehicle resumes dedicated VM independently of the radio access at the next MEC micro-data center. During the trip, traffic congestion or an accident could happen, which requires the SDN OpenFlow controller to suggest routing decisions. Consequently, the VM cluster in the central cloud updates new navigation directions before vehicle entering the congestion area.  
Figure 6: Multi-access edge cloud-enabled SDN (left) and MEC server (right) for VM migration.
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 3 Principle of the Proposed Method
### Threshold Segmentation  
The image processing results of threshold segmentation is shown in the Figure 6, and, with the image enhancement processing, the target contour in the mini-crack feature image is more prominent. The threshold segmentation method is adopted to extract the contour information of the damaged area in the mini-crack feature image. The threshold value is selected by the maximum category variance method [43]. Image binarization [44] is used to divide the image into two fields: target object and background. The mini-crack is separated from the background of conveyor belt by threshold segmentation. After image binarization, defects such as graphic fracture and edge burr may occur. In order to eliminate the noise influence on damage judgment, the binary image will be morphologically processed before the mini-crack interrogation.
## 5 Transmission Power Management  
As mentioned earlier, each of BSs in a CCS together serves its own users and the CEUs of other collaborative BSs under a power constraint. Therefore, it is necessary to develop a method of distributing transmitter power among intra-cell CCUs, intra-cell CEUs, and inter-cell CEUs. To the best of our knowledge, however, there has been no literature dealing with this issue. The purpose of power management is to achieve a uniform capacity regardless of user position. Because of the cooperation with the neighboring BSs, the design of power management may involve the information exchange among the cooperative-based stations. Due to the time-variant characteristics of channel, this continuing need for information exchange will consume a lot of radio resources. In the absence of information exchange, our method of power management for MU-network MIMO cellular systems only needs the number of different types of users to realize an independent power adjustment among BSs.  
Let \(P_{C}\), \(P_{E}\) and \(P_{E}^{\prime}\) denote the sub-channel power allocated for an intra-cell CCU, intra-cell CEU, and inter-cell CEU, respectively. Let \(P_{b}\) denote a sub-channel baseline power and \(P_{\text{max}}\) represent the maximum transmission power constraint in a sector antenna. In our method, a CCU and a CEU are respectively allotted the initial power \(P_{b}\) and \(\alpha P_{b}\), where the power coefficient \(\alpha>0\), i.e., we initially set \(P_{C}=P_{b}\) and \(P_{E}=P_{E}^{\prime}=\alpha P_{b}\).  
The power management is conducted in a per-sector basis. Let \(x\), \(y\) and \(z\) denote the number of intra-cell CCUs, intra-cell CEUs, and the inter-cell CEUs served by a particular sector antenna, respectively. Thus, the total power requirement is \(P_{T}=xP_{C}+yP_{E}+zP_{E}^{\prime}\), where the power required to serve other cells' CEUs is \(zP_{E}^{\prime}\). In the subsequent sub-sections, we present transmission power management for SU- and MU-network MIMO, respectively.
## 4 Evaluation  
In this section, we execute watermark encryption and compression on each ECG signal with length 4,096 sampled from 47 datasets in the MIT-BIH arrhythmia database. Each ECG signal is first adjusted to have zero mean to eliminate the DC offset and then the Haar wavelet transform is applied to each signal with 7-level decomposition. Evaluation of the watermarked and compressed ECG signals is presented in the following sections.
## 3 Results and Discussion
### Extraction Process Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Point** & \multicolumn{2}{c}{**Independent Variables**} & \multicolumn{2}{c}{**Response**} \\ \hline  & **X\({}_{1}\) (S\({}_{\text{S}}\), rpm)** & **X\({}_{2}\) (\(C_{\text{DES}}\), \% _w/v_)** & **X\({}_{3}\) (\(t_{\text{US}}\), min)** & **Y\({}_{\text{TP}}\) (mg GAE g\({}^{-1}\) dm)** \\ \hline  & & & & **Measured** & **Predicted** \\ \hline
1 & \(-1\) (300) & \(-1\) (55) & 0 (10) & 70.67 & 71.24 \\
2 & \(-1\) (300) & \(1\) (85) & 0 (10) & 73.83 & 73.17 \\
3 & \(1\) (900) & \(-1\) (55) & 0 (10) & 71.72 & 72.38 \\
4 & \(  
In certain cases, such as polyphenol extraction from hop [16], saffron processing wastes [22] and onion solid wastes [30], relatively high S\({}_{\text{S}}\) (>650 rpm) were demonstrated to provide Y\({}_{\text{TP}}\) maximization. To the contrary, the requirement in S\({}_{\text{S}}\) for optimum extraction of olive leaf polyphenol was shown to be either low (300 rpm) [31] or moderate (500 rpm) [21]. In another study on the extraction of polyphenols from _M. oleifera_ leaves, the optimum S\({}_{\text{S}}\) was determined to be 800 rpm, but when ultrasonication was integrated as pretreatment, the optimum S\({}_{\text{S}}\) was 200 rpm [23]. In general, S\({}_{\text{S}}\) is considered to play important role in solid-liquid extraction, and its careful adjustment may end up in significantly higher yields [32; 33]. It has been supported that a sufficient level of S\({}_{\text{S}}\) results in turbulence in the extraction tank, which is appropriate to boost mass transfer rate, and increases in S\({}_{\text{S}}\) have been correlated to higher polyphenol diffusivity [33]. C\({}_{\text{DES}}\) had also a significant positive effect on Y\({}_{\text{TP}}\), and the optimum value estimated was 77% (_w/v_). This level lies between 75 and 80% (_w/v_) found for polyphenol extraction with DES from _M. oleifera_ leaves [23; 34], and 78 and 80% (_w/v_) from olive leaves [21; 35]. Other investigations reported 80% (_w/v_) for tartary buckwheat hull [36], 80% (_w/v_) for sea buckthorn leaves [37], 74% (_w/w_) for _Cymbidium kunran_[38] and 76.2% (_w/w_) for grape skin [39]. In all these optimization studies, the appropriate adjustment of water amount was shown to be critical for the extraction efficiency, because the DES/water proportion regulates features such as viscosity and polarity [40], which profoundly affect solute solubility, hence extraction performance. Such hypothesis has been well exemplified by a recent examination, which demonstrated that the higher the lipophilicity of the HBA in a DES, the higher the water amount required to achieve polyphenol extraction maximization from _O. dictamus_[41].
# The RND Efflux Pump Gene Expression in the Biofilm Formation of _Acinetobacter baumannii_
###### Abstract  
Multidrug resistant (MDR) _Acinetobacter baumannii_ is a critical opportunistic pathogen in healthcare-associated infections (HAI). This is attributed to several factors, including its ability to develop biofilms that can enhance antimicrobial resistance (AMR) in addition to creating an environment for horizontal transfer of antibiotic resistance genes. The role of the efflux pump in biofilm formation is important for studies on alternative treatments for biofilms. One of the significant efflux pump families is the RND efflux pump family, which is common in Gram negative bacteria. The aim is to study the role of the RND efflux pump in biofilm formation by _A. baumannii_. The biofilm formation potential of thirty-four MDR _A. baumannii_ isolates was evaluated by crystal violet assays. The effect of efflux pump inhibition and activation was studied using the efflux pump inhibitor carbonyl cyanide 3-chlorophenylenylhydrazzone (CCCP) and the RND efflux pump substrate levofloxacin (at sub-MIC), respectively. The isolates were genotypically grouped by enterobacterial repetitive intergenic consensus (ERIC) typing and the expression of _adeABC_, _adeFGH_, and _adelJK_ efflux pump genes was measured by qPCR. Overall, 88.2% (30/34) of isolates were biofilm producers (the phenotype was variable including strong and weak producers). Efflux pump inhibition by CCCP reduced the biofilm formation significantly (\(p<0.05\)) in 17.6% (6/34) of some isolates, whereas sub-MICs of the substrate levofloxacin increased biofilm formation in 20.5% (7/34) of other isolates. Overexpression of the three RND efflux pump genes was detected in five out of eleven selected isolates for qPCR with remarkable overexpression in the _ade_ gene. No correlation was detected between the biofilm phenotype pattern and the RND efflux pump gene expression in biofilm cells relative to planktonic cells. In conclusion, the role of the RND efflux pumps AdeABC, AdeFGH, and AdelJK in biofilm formation does not appear to be pivotal and the expression differs according to the genetic background of each strain. Thus, these pumps may not be a promising target for biofilm inhibition.  
Acinetobacter baumannii; biofilm; RND efflux pump; AdeABC; AdeFGH; AdelJK
## 2 System Reliability Evaluation Methods
### System Reliability Evaluation Method Based on the Modified AHP
#### 2.2.3 Constructing Judgment Matrix  
(1) Traditional Method  
The traditional AHP method combined expert experience and a 9-point scale (shown in Table 1) to construct a judgment matrix \(A\). Table 1 shows the definition of the element \(a_{ij}\) in the judgment matrix, in which \(a_{ij}\) indicates the relative importance of the \(i\)th factor over the \(j\)th factor.  
(2) The Modified Construction Method of Judgement Matrix  
The core content of the AHP method is to construct a judgement matrix. An objective construction method of a judgment matrix is proposed, which considers the subjectivity of the traditional AHP method in constructing the judgment matrix. The judgment matrix is constructed for the two cases of hinge joint damaged and hinge joint undamaged, respectively.  
a. Hinge joint undamaged  
The superstructure is composed of several slab segments, and the system failure probability is closely related to the failure probability of a single component. Equation (2) shows the relationship between the reliability index and the failure probability. Therefore, the elements of the judgment matrix \(a_{ij}\) can be represented by the reliability index of each slab segment, as shown in Equation (7):  
\[a_{ij}=\frac{\beta_{j}}{\beta_{i}} \tag{7}\]  
where \(\beta_{i}\) and \(\beta_{j}\) represent the reliability index of the \(i\)th slab segment and the \(j\)th slab segment, respectively.  
b. Hinge joint damaged  
The judgment matrices are constructed separately for different damage cases. In each damage case, the hinge joints have different damage degrees in the scope of (0%, 100%). The range analysis of the reliability of each slab segment is performed under the same damage case. Then, the elements of the judgment matrix \(a_{ij}\) can be represented by the range of each slab segment, as shown in Equation (8):  
\[a_{ij}=\frac{R_{\beta_{i}}}{R_{\beta_{j}}}=\frac{max(\beta_{i-0\%}:\beta_{i-1 00\%})-min(\beta_{i-0\%}:\beta_{i-100\%})}{max(\beta_{j-0\%}:\beta_{j-100 \%})-min(\beta_{j-0\%}:\beta_{j-100\%})} \tag{8}\]  
where \(R_{\beta_{i}}\) and \(R_{\beta_{j}}\) represent the range of reliability for slab segment \(i\) and \(j\). \(\beta_{i-0\%}\) and \(\beta_{i-100\%}\) mean the reliability indexes of slab segment \(i\) when the damage degree are 0% and 100%. Similarly, \(\beta_{j-0\%}\) and \(\beta_{j-100\%}\) have the same meaning.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Relative Importance**\(a_{ij}\) & **Verbal Judgement of Importance** \\ \hline
1 & Factor \(i\) is as important as \(j\) \\
3 & Factor \(i\) is slightly more important than \(j\) \\
5 & Factor \(i\) is more important than \(j\) \\
7 & Factor \(i\) is significantly more important than \(j\) \\
9 & Factor \(i\) is absolutely more important than \(j\) \\
2,4,6,8 & Between two importance levels \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of the element in the judgement matrix.