esa-sceva/satcom-chunk-collection huggingface dataset:
# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 5 A Neural-Symbolic Implementation of T-Pattern
### The T-Pattern Architecture  
The _T-Pattern Architecture_ (TPA) is a multi-SO architecture designed to manage a T-Pattern Network \(NET=\langle O,G,\Delta,\Gamma,P\rangle\), deriving the information needed to update T-patterns by observing the behavior of smart IoT objects and where \(O\) is the set of smart objects, \(G\) is a set of groups \((\Omega_{1},\Omega_{2},\cdots,\Omega_{n})\), \(Delta\) is the set of trust issues, \(\Gamma\) is a mapping on a confidence \(\gamma_{\alpha_{1},\alpha_{2},\Delta}\) which gives a value ranging in \([1,5]\), and \(P\) is a set of T-patterns on \(\Delta\), such that there are not two T-patterns belonging to \(P\) associated with the same ordered triplet \((o_{1},o_{2},\Delta)\).  
The TPA is distributed across three logical levels (see Figure 2), where each level follows the automated rules described in Section 4 (see also [11] for additional details):  
* A _smart object level_, consisting of \(n\)_trust manager smart objects_\(tm_{1},tm_{2},\ldots,tm_{n}\), where each \(tm_{i}\) is associated with a corresponding smart object \(o_{i}\in O\) of \(NET\) and is capable of updating the trust patterns associated with all edges originating from \(s_{i}\) in \(NET\);
* A _group level_, consisting of \(l\)_group manager smart objects_\(gm_{1},gm_{2},\ldots,gm_{l}\), where each \(gm_{i}\) is associated with the corresponding group \(\Omega_{i}\in G\) of \(NET\) and is capable of calculating the group reputation \(\gamma_{o,\rho,\Omega_{i}}(t)\) for each smart object \(o\in\Omega_{i}\) and each trust aspect \(k\in K\);
* A _community level_, consisting of a single _community manager_\(CM\) of smart objects, which is capable of calculating the community reputation \(\gamma_{o,\rho,\mathcal{S}}(t)\) for each smart object \(o\in O\) and each trust aspect \(\delta\in\Delta\).  
The three layers can be managed at three different computational layers: (i) the smart object level is represented by the smart object (IoT), and (ii) the group level can be managed into the Fog [50] and the community level can be mapped into the Cloud.  
We highlight that the presence of the three layers gives the possibility of managing each layer with different and independent computational resources as follows: (i) the smart object level is managed by the smart objects, (ii) the group level is managed by  
Figure 2: The three-layer TPA architecture.  
the computational power of the Fog, and, finally, the community level is managed into the Cloud. This way, increasing the number of smart objects or object groups do not significantly impact system performance. In particular, the scalability of the upper service will be easily ensured by the elasticity of Cloud resources.
## 6 Results and Discussions
### Simulation of the Monitoring and Control of Parking Lots in the North of a City
#### 6.2.2 Computerized M/M/c/N Submodel Verification  
In this section, we describe the verification of M/M/c/N submodel to assess its accuracy. The degree of precision of the submodel was calculated by analyzing the steady-state measures of performance of the M/M/c/N queue system. Thus, we made a comparison between the measures of performance obtained from the simulation and the mathematical model. These measures are the expected number of customers in the system (Ls), expected number of customers in the queue (Lq), expected waiting time in the system (Ws), and expected waiting time in the queue (Wq).  
The mathematical model of an M/M/c/N queue system depicts the specialized Poisson queuing situation with c parallel servers. The arrival rate at the system is \(\lambda\) customers per unit time. All parallel servers are identical, meaning that the service rate for any server is \(\mu\) customers per unit time. A waiting customer is selected from the queue to start service with the first available. The system limit is finite and equal to N. This means that the maximum queue size is N-c [39].  
The simulation was carried out with the following parameters:  
\(\lambda=\text{interarrival rate}=20\text{ cars/hour}\);  
\(\mu=1/\text{average service time}=1/3=0.333\text{ cars/hour}\);  
c = total number of parking spaces = 50;  
QC = queue capacity = 10.  
Table 9 shows some measures of a parking lot obtained in 1000 h of operation. The theoretical values of the performance measures obtained from the mathematical model were: Lq: 6.04420, Wq: 0.36579, Is: 55.66485, Ws: 3.3687.  
Table 10 summarizes the degree of accuracy of the model for 1000 and 5000 h of operation. The degree of accuracy of the measure obtained by simulation was estimated using the degree of similarity with the theoretical value through the following formula:  
Accuracy of the measure = 100 - (1 Simulated value \(-\) Theoretical value \(\mid\times\) 100)/Theoretical value  
We can observe that the simulation results were very close to the steady-state performance measures of the mathematical model. Thus, we had a submodel M/M/c/N whose measures of performance help to analyze the behavior of the system in a steady-state. Thisalso allows estimating that the parking lot model responds to the dynamics of parking space requests under normal conditions.  
The M/M/c/N submodel can be used as a layer of user behavior. In addition, other measures can be extracted from the simulation: number of not waiting customers, number of lost customers, the average number of busy lots, etc. Other user behaviors can be modeled by replacing this submodel with another or by modifying the current submodel.
## 7 Results
### C-V2X Communication Performance  
To understand the behavior of the C-V2X C-EEDR application, we investigated the performance of the C-V2X communication system. We measured the average PRR value during the whole simulation in all of the simulations we ran. Figure 17 shows the PRR values at different market penetration rates for the four traffic demand levels (25, 50, 75, and 100%, respectively).  
There are two observations to note here. First, as the traffic demand increases, the performance of the C-V2X communication system decreases. This is clear as the PRR value decreases with the different traffic demand levels at the same market penetration rate. Second, as the market penetration rate increases, the performance of the C-V2X communication system decreases. It is clear that as more connected probe vehicles enter the system, the traffic density increases and the traffic system becomes more congested, and, consequently, the communication system becomes more congested, which leads to more lost packets.  
The C-V2X communication protocol provides reliability to the C-EEDR application with a minimum of 70% of the messages delivered in the case of 100% traffic demand and 100% market penetration and 80% of the messages received in the case of the 25% and 100% market penetration rate.  
Figure 16: Average network-wide Fuel Savings of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with different traffic demand scale factors.  
Figure 17: Average Packet Reception Ratio using C-V2X-based Energy-Efficient dynamic routing application in LA road network with different traffic demand scale factors.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 5 Conclusions  
Harsh environments may lead to electrical connection faults in RF circuits. In this work, the characteristics of three specific kinds of electrical connection faults in the frequency domain, connector, bonding wire, and solder ball were analyzed. Machine learning algorithms were used to detect the electrical connection fault in an example filter circuit, and it has been demonstrated that he application of artificial intelligence classifiers to circuit fault diagnosis can significantly reduce the complexity of fault location, which can save time and reduce the requirements of testing equipment.  
SVM, LR and GBDT classifiers can accurately detect and diagnose electrical connection faults accurately. From the perspective of fault detection capability and practicality, the performance of the GBDT is the best of the three classifiers, with the highest classification accuracy and steepest ROC curve. From the perspective of processing speed, the LR has the shortest processing time. However, the GBDT improves classification accuracy without sacrificing substantial processing time. From the perspective of learning cost, compared with SVM and LR, the performance of the GBDT is more predictable and controllable. The error rate of the GBDT decreases with increasing training data. Compared with using the S\({}_{21}\) parameter, S\({}_{11}\) is more representative of electrical connection fault characteristics. Thus, it is a better choice for training and testing a classifier and the predictive algorithms tend to behave better when using the S\({}_{11}\) parameter. In addition, the classification accuracy is related with the fault location. The two faults that are close to each other in terms of their impact on the electrical performance of the circuit may cause some classification algorithms to confuse the two fault modes.  
Figure 17: Fault detection error rate vs. number of training samples for different algorithms trained on (**a**) S\({}_{11}\), (**b**) S\({}_{21}\).  
Author Contributions:Conceptualization, Z.W.; data curation, J.L.; funding acquisition, J.G.; investigation, Z.W. and G.T.F.; project administration, J.G. and Z.C.; software, J.L., K.S. and W.Y.; supervision, J.G.; writing--original draft, Z.W.; writing--review and editing, G.T.F. and Z.C. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China (No.51877010). This work was supported in part by the National Natural Science Foundation of China (No.51877010) and in part by the NSF Center for Advanced Vehicle and Extreme Environment Electronics (CAVE3) at Auburn University. Conflicts of Interest: The authors declare no conflict of interest.
## 6 Conclusion  
The limited power supply at the sensor side of a wireless EEG sensor network necessitates a careful management of the power consumption by the different components of the sensor node. In this paper, we present different data reduction methods that could be used in a wireless seizure detection system to reduce the power consumption on the sensor node. We found that by transmitting only the EEG features that are pertinent to seizure detection, significant power can be saved in the wireless transmission. Our results show that by only extracting and transmitting the nonlinear autocorrelation features, we are able to reduce the combined power consumption of the microprocessor and wireless transmitter at the sensor side to 7% of that required by the conventional approach of transmitting the entire EEG signals to the data server. This extends the battery life of the sensor node by a factor of 14.3. At the same time, we maintain the same seizure detection performance as the conventional approach with a seizure sensitivity (_i.e._, the seizure detection rate) of 95%.
## 3 Results and Discussion
### Cytocompatibility Experiment  
WST-1 based cytotoxicity study was performed on HEK-293 cells by incubating with AuNPs synthesized by a sonochemical approach in a range of concentration 0.1-0.5 mg/mL. In this study, human embryonic kidney cells were picked for two major reasons: (a) according to the previous studies, kidney is one of the organs, after spleen and liver, where AuNPs were accumulated as well as an excretion route [63]; (b) the cytotoxicity reaction may also be associated with the exposure concentration of the AuNPs. The type of cell has no impact on the cytotoxicity of the studies [64]. The results are presented in Figure 6. In the control group (HEK-293 cells without AuNPs) of all test replications, cell viability was defined at the constant rate of 100% with no fluctuations (zero standard deviation). The Mann-Whitney test results depict the mean cell viability values as significantly reduced at a concentration of 0.5 mg/mL after 24 h of incubation time at concentrations of 0.4 and 0.5 mg/mL after 48 h (\(p<0.01\)). The Kruskal-Wallis test shows no significant differences between independent experimental groups for 24 h (\(p=0.176\)) and 48 h (\(p=0.553\)). A 10% to 20% reduction in the number of HEK-293 cells treated with 0.1-0.5 mg/mL AuNPs after incubation time (48 h) was observed in comparison to the 24 h incubation time. Despite that, as demonstrated in Figure 6, the percentage of cell prolonged was more than 70% at all concentrations and different treatment periods. The results of this study show that there were no cytotoxicity effects on HEK-293 cells at a range of concentration 0.1-0.5 mg/mL AuNP. Nanoparticles with cell viability above 70% have been reported to be suitable as biocompatible material [65]. Therefore, the synthesized AuNPs have the potential to be used as biocompatible diagnostic nanoprobes.  
Figure 5: Polydispersity index (PDI) and hydrodynamic size of AuNPs prepared through sonochemical method. AuNPs dissolved in water (green solid line) and dissolved in PBS (red solid line).
## 5 Existing Cybersecurity Solutions for Smart Manufacturing
### Other Security Solutions  
#### 5.5.1 Solutions by Security Firms  
Two organizations, IBM and ABB have collaborated in 2020 to focus on the cybersecurity of operation technology and developed a security event monitoring service for smart industrial operations [23]. This is a combination of the process control system provided by ABB and the security event monitoring platform of IBM known as QRadar. They jointly developed a reference architecture that is designed to react to the security incidents of complex industrial environments. As a result of this collaboration, the process control system collects event log details and shares them with IBM QRader which identifies security anomalies and threats using Artificial Intelligence (AI). This reduces the number of security events causing disruption in production which leads to financial loss to the manufacturing company.  
In 2019, Deloitte and the Manufacturers Alliance for Productivity and Innovation (MAPI) have produced guidelines to build effective manufacturing security programs to identify, protect, respond, and recover from cyberattacks on smart manufacturing industries [24]. As per them, the operational technology (OT) organizations should invest in cybersecurity maturity assessment, establishing security governance programs for ICS, Identifying and prioritizing actions based on the assessment results, and finally building security. Important aspects to consider while building security controls are network segmentation models, passive monitoring solutions, securing remote access to the industrial network, privileged access management, and backup resources for fault recovery.
# A New Kinematic Model of Portable Articulated Coordinate Measuring Machine
## 3 Error Model and Calibration Algorithm  
### Error Model  
To ensure the high accuracy measurement results of PACMM, the generalized geometric error parameters vector of Equation (6) must be accurately calibrated except for the nominal values \(\theta_{i0},\alpha_{i},l_{i},d_{i}\left(i=1,2,\cdots,6\right)\) and \(d_{7}\). In this paper, an Invar length gauge which has the coefficient of low-thermal expansion is employed as the gauge for calibrating the generalized geometric error parameters of PACMM. For ease of use the Invar length gauge is placed on the three DOFs support platform shown in Figure 4. The pose of the Invar length gauge with regard to PACMM is changed by rotating the three DOFs support platform to collect six joint angle values of PACMM in different poses. The Invar length gauge has three cone-shaped holes on one surface, the length between cone-shaped hole 1 and 2 is \(L_{12}=514.371\) mm and the length between cone-shaped hole 1 and 3 is \(L_{13}~{}=~{}1015.962\) mm. In this paper, \(L_{12}\) is selected as the standard value for calibrating the generalized geometric error parameters of PACMM. Therefore, the error model of the generalized geometric error parameters of PACMM is established based on the spatial distance. The measuring points \(P_{k}^{1}\) and \(P_{k}^{2}\) are obtained when the probe is placed in cone-shaped hole 1 and 2, respectively. Therefore, the error model of PACMM is indicated by Equation (7):  
\[E_{k}\left(\epsilon\right)=L_{12}-\left\|P_{k}^{1}-P_{k}^{2}\right\| \tag{7}\]  
where the symbol \(\left\|\bullet\right\|\) indicates the norm of the vector. \(k\) indicates the serial number of measurement points.  
According to the least square principle, the objective function is established by Equation (8):  
\[F\left(\epsilon\right)=\sum_{k=1}^{m}E_{k}^{2}\left(\epsilon\right)=\sum_{k=1 }^{m}\left(L_{12}-\left\|P_{k}^{1}-P_{k}^{2}\right\|\right)^{2} \tag{8}\]  
where \(m\) indicates the number of measurement points.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 3 Results  
### Adapting Dynamical Noise Changes in Simulations  
In the simulations, the proposed ATTD illustrated in the red box of Figure 3c maintained a high correlation relative to the clairvoyant benchmark reference across noise levels, with robustness measuring around \(-\)30 dB, outperforming the fixed truncation (\(k_{\text{f}}\)) and SURE (as depicted in Figure 3d and Figure 3e, respectively). \(k_{\text{f}}\) achieved consistent but lower performance as noise increased, whereas SURE with a known noise ratio exhibited high fidelity at \(-\)70 dB but suffered a drastic performance decline at higher noise levels, indicating limited effectiveness in noisy environments.
## 1 Introduction
### The Japanese Highway Classification System (JH System)  
The Japanese Highway Classification System was first developed in Japan in the 1960s for large dam foundation and later extended to tunnel rock mass characterization [24]. This classification system commonly referred to as JH System, like many rock mass classification systems, has undergone several revisions since its introduction. The JH System relies primarily on seven rock mass parameters namely: intact rock strength (compressive strength), weathering, spacing of discontinuities, condition of discontinuities, effect of discontinuities orientation, groundwater condition, and degradation by water. Each of these parameters is further subdivided into subgrades and assigned a grade point corresponding to the level of the rock mass feature being characterized. For example, the intact rock property (UCS), is divided into six subgroups: less than 3 MPa, 3-10 MPa, 10-25 MPa, 25-50 MPa, 50-100 MPa, and greater than 100 MPa. Each of these subgroups is assigned a grade point reflecting the strength of the intact rock material. Once each rock mass parameter is graded/rated, the grade point for the intact rock property, weathering, joints spacing, condition of joints are added up and the grade points for groundwater conditions, deterioration due to water, and effect of discontinuities orientation are subtracted from the sum to obtain total grade points of the rock mass at that location. The total grade point ranges from 0 to 100 representing very poor rock to very good fresh rock respectively. The total grade point is then used to categorize the rock mass into classes. The system has six rock mass classes; A, B, CI, CI, D, and E. In terms of rock mass competence, it decreases from class A through class E, with class E been the least competent rock mass. In tunnel excavation, these rock mass groups are used to determine the ground support system required to stabilize the tunnel walls. Table 1 shows typical JH System data collection sheet used in the Pahang-Selangor Raw Water Tunnel (PSRWT) while Table 2 shows typical ground support systems for the different rock mass classes.
## 3 Theoretical Calculation of the Additional Stress Caused by Shield Construction
### Theoretical Calculation Method
#### 3.3.2 Frontal Additional Thrust  
The distribution curve of the frontal additional stress \(\sigma_{x}\) is shown in Figure 4 along the y direction, which is caused by the frontal stress in the center of the tunnel. The response of the additional stress field is the largest near the centerline of the tunnel, and the amplitude is also the largest, which is the main reason for the surface uplift in front of the shield. The farther the distance from the tunnel face is, the smaller the additional stress is. This results in slower attenuation to both sides of the tunnel from the centerline but a larger influence range. The maximum normal additional stress is 3.9 kPa at a distance of 1 m from the tunnel face, and the stress decreases rapidly towards both sides. At a distance of 8 m from the tunnel face, the additional stress is nearly zero, and the influence range is mainly within approximately one tunnel diameter from the tunnel centerline. The calculation parameters are shown in Table 2, where \(P_{1}\) is the frontal additional thrust, \(f\) is the friction coefficient between the shield shell and the soil, \(P_{2}\) is the friction between the shield shell and the soil, \(\mu\) is the Poisson ratio, \(H\) is the buried depth of the tunnel, and \(R\) is the radius of the shield machine.  
The distribution curve of the lateral additional stress \(\sigma_{y}\) distribution is shown in Figure 5 along the y direction. The closer the distance from the tunnel face is, the greater the additional stress is. The lateral additional stress increases from the centerline towards the two sides before gradually decreasing after reaching the peak. At a distance of 1.27 \(D\) from the tunnel face (where \(D\) is the diameter of the tunnel), the lateral additional stress approaches zero. The influence range is approximately 1.3 \(D\) on both sides of the centerline of the tunnel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(P_{1}\) (kPa) & \(f\) & \(P_{2}\) (kPa) & \(\mu\) & \(H\) (m) & \(R\) (m) \\ \hline
25 & 0.3 & 46.3 & 0.3 & 14 & 3.14 \\ \hline \end{tabular}
\end{table}
Table 2: Calculation parameters.
###### Abstract  
With the development of IoT technology and 5G massive machine-type communication, the 3 GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3 GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.  
In the IEEE Conference on Communications and Information Engineering, Vol., pp. 1-10. External Links: Document Cited by: [PERSON].  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], G. Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach Sensors2024 24 2004 10.3390/s24062004