esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Engineering Application
### Monitoring Data Analysis  
The stress monitoring of the bolt body is an important part of roadway support monitoring. Through the FBG online monitoring system of ground pressure, it is convenient to extract the data of each sensing equipment at each underground measuring station. An early warning value of the bolt force as preset in the FBG online monitoring system. When the bolt force reaches or exceeds this warning value, the system automatically triggers an alarm. This early warning value is set as the maximum force value in the elastic deformation stage of the bolt.  
Figure 15 shows the stress monitoring data of the rock bolt body on the roadway roof, solid coal side, and narrow coal pillar side at positions 5, 20, 50, 70, 90, and 110 m from the head of the tunnel face.  
From Figure 15, it can be analyzed that the roadway is in the excavation influence period, from 5-20 m away from the head, the tensile force of the roof bolts and the 2 sets of bolts are small, and the force distribution of the bolt body is relatively uniform. The stress of the roof bolt reaches the maximum at 70 m from the head and the maximum stress of the bolt occurs at 800 mm from the periphery of the roadway. The stress of the bolt for the solid coal side reaches the maximum at 90 m from the head, and the maximum stress occurs at about 500 mm from the surrounding of the roadway. The stress of the bolt for the  
Figure 14: The on-site connection and installation of the dynamometry bolts. (**a**) Device connection diagram; (**b**) on-site installation drawing.  
Figure 13: The roadway section deformation monitoring station layout in the return airway 81303.  
narrow coal pillar side reaches the maximum at 90 m from the head, and the maximum stress occurs at about 1000 mm from the periphery of the roadway.
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 4 Response Mechanism
### Data Link Layer  
The response system to switch infected vehicles into safe mode without delay was introduced on the data link layer. It is responsible for transferring control data and information between VANETs entities and has the ability to identify and correct physical layer errors. There are many factors in the data link layer that facilitate this [44]:  
* The data link layer provides basic addressing and access control to the physical layer on VANETs of self-driving and semi self-driving vehicles.  
Figure 6: System model.  
* It facilitates vehicle communication among the subnet, which is done through a reconfiguration requirement.  
These factors are often seen as crucial for choosing this layer amongst others on VANETs. Various kinds of communication protocol are used in this layer, such as the point-to-point protocol (PPP), Advanced Data Communication Control and High-Level Data Link Control (HDLC) [45]. Our suggested reaction system depends on PPP.
###### Abstract  
Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.  
ADAS; AI; CNN; computer vision; deep learning; embedded system; neural networks; PyTorch; real-time object detection; transfer learning +
Footnote †: journal: Applied Sciences
# A New Algorithm for Computing the Distance and the Diameter in Circulant Graphs
## 4 Diameter Formulas for Circulant Graphs
### Diameter of \(C_{n}(1,s)\) When Both \(n\) and \(s\) Are Odd
**\(\gamma=\frac{s+3}{2}\)  
In this case, \(d=\frac{\lambda+1}{2}+\frac{s-1}{4}\). Let \(i=\frac{\lambda+1}{2}s+\frac{s-1}{4}\) be a vertex of \(C_{n}(1,s)\). Note that \(s-\frac{s-1}{4}>\frac{s-1}{4}\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4J}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{1}(i)=d\). **Case 2.2. **\(\gamma\leq\frac{s+1}{2}\)  
If \(s=3\), then \(\gamma=2\), \(\lambda\geq 3\), and \(d=\frac{\lambda+1}{2}\). Let \(i=\frac{\lambda+1}{2}s\) be a vertex of \(C_{n}(1,s)\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4J}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{1}(i)=d\). If \(s>3\), then \(\gamma\geq 2\), \(\lambda\geq 3\), and \(d=\frac{\lambda+1}{2}+\frac{s+1}{2}-\frac{\gamma+2}{2}\). Let \(i=\frac{\lambda-1}{2}s+\frac{s-1}{2}+\frac{\gamma+2}{2}\) be a vertex of \(C_{n}(1,s)\). Since \(s>3\) and \(\gamma\leq\frac{s+1}{2}\), we get \(\frac{s-1}{2}+\frac{\gamma+2}{2}<s\). Note that \(\frac{\gamma+2}{2}>-\frac{\gamma+2}{2}+2\) and \(-\frac{\gamma-2}{2}>-\frac{\gamma+2}{2}+1\) because \(\gamma\geq 2\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4J}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{2}(i)=d\). **Case 3. **\(\gamma\in R_{3}\)In this case, \(\gamma\geq 3\), \(\lambda\geq 4\), \(s\geq 3\), and \(d=\frac{\lambda}{2}+\frac{s+1}{2}-\frac{s-\gamma+2}{2}.\) Let \(i=(\frac{\lambda}{2}-\frac{s-\gamma+2}{2})s+\frac{s+1}{2}\) be a vertex of \(C_{n}(1,s)\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4,i}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{1}(i)=\ell^{2}(i)=d\). **Case 4. \(\gamma\in R_{4}\)**  
We have \(\gamma\geq 4\), \(\lambda\geq 5\), \(s\geq 3\), and \(d=\frac{\lambda+1}{2}+\frac{s+1}{2}-\frac{s-\gamma+3}{2}.\) Let \(i=(\frac{\lambda+1}{2}-\frac{s-\gamma+3}{2})s+\frac{s-1}{2}\) be a vertex of \(C_{n}(1,s)\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4,i}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{1}(i)=\ell^{2}(i)=d\). Hence for all \(\gamma\in R\), there exists a vertex \(i\) of \(C_{n}(1,s)\) such that, \(d(i)=\lceil\frac{\lambda}{2}\rceil+\frac{s+1}{2}-M\). Consequently, \(diam(C_{n}(1,s))=\lceil\frac{\lambda}{2}\rceil+\frac{s+1}{2}-\min(\lceil\frac {\gamma+1}{2}\rceil,\lceil\frac{s-\gamma+2}{2}\rceil).\)
## 4 Presumptions and Network Structuring
### State of the Art  
Transmission of data between the cluster head and the members of the network is a type of single-hop communication. Periodically, the member nodes collect the data and send it to their appropriate cluster head. They have two phases that can diminish the redundant data induced by the nodes at each period. The sensor nodes sense the environment periodically to collect the data measures. Recently, the clustering method has emerged as an efficient technique to control topology due to its scalability and network maintenance nature. However, most of the existing techniques focus on the selection of the cluster head where the processing of the data is performed only at the cluster level, not at a particular node level. Here, we focus on the processing of the data both at the node level and the aggregator level. We have compared our proposed protocol STCDRR with two existing techniques, KAB and ED.
## 4 Discussion and Conclusions  
The authors achieved two goals in the presented research. Firstly, using two different experimental test benches, it was possible to observe the effect described in papers [7; 8; 9], including a decrease in the energy flux density of the shock wave, with an increase in the frequency with which pulses of this wave are generated. The results of the experiments showed, unequivocally, that such an effect exists and is not the result of a poorly constructed measurement test bench or a measurement error. In paper [9], a hypothesis was put forward that the operation of the pneumatic solenoid valve of the apparatus resulted in the occurrence of this phenomenon. The authors decided to check it out and tested two shock wave generators with completely different designs. The first apparatus included a pneumatic system, and the second was an electromagnetic version. It turned out that the described effect occurs in both cases. The cause must, therefore, be sought elsewhere. According to paper [7], the possible cause of this may be the construction of the transmitter itself and, more precisely, the elastomer responsible for slowing down the movement of the  
Figure 9: Energy flux density (expressed as quadrature of pulse) and the operating frequency for electromagnetic applicator—measured by hydrophone.  
transmitter. It requires further research and experimentation. As to the test benches issue, in further studies, they require improvement because, for example, the tank size should be increased enough to eliminate the problem of reflection of waves from the walls and edges of the tank. Another problem with the measurements is the intensity with which cavitation occurs at higher pulse generation frequencies. The same problem was noticed in paper [23]. The paper also gives a concise physical description of the operation of the two most popular solutions of radial wave generators: pneumatic and electromagnetic. Nevertheless, in the future, this description should be modified because, currently, the concept of a material point was used to describe the motion of the device elements, and, in fact, the motion of rigid bodies should be defined. Of course, this will complicate the calculation. Still, at the same time, it will be possible to consider such phenomena as friction, stresses, projectile spin, and turbulence of the projectile flight in the closed guiding tube. Perhaps the whole structure of the applicator should be subjected to finite element simulation.  
Conceptualization, M.S. and M.M.; methodology, M.S.; software, M.M.; validation, M.M.; formal analysis, M.S.; investigation, M.S. and M.P.; resources, M.M.; writing--original draft preparation, M.S.; writing--review and editing, M.M.; visualization, M.M.; supervision, M.S.; project administration, M.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# In Silico Modeling Study of Curcumin Diffusion and Cellular Growth
## 2 Methodology
### Curcumin Diffusion Model  
According to several studies, the threshold of curcumin's therapeutic properties was defined at 10 \(\upmu\)M as it showed minimal effect on cellular growth. Doses above this concentration proved to have inhibitory effects on cellular migration and proliferation [14; 46; 47; 48; 49], while doses below this concentration were more effective in stimulating cell proliferation [14; 46; 50]. Therefore, in this study, concentrations below 10 \(\upmu\)M were considered low doses, while concentrations above it were classified as high doses. After considering the doses and their effects shown in [14] and this threshold, six different concentrations were selected to investigate both the positive and negative effects of curcumin on fibroblast proliferation. These concentrations were 0.25, 0.5, 1, 5, 10 and 20 \(\upmu\)M.  
To model curcumin diffusion in this study, the experimental values obtained by [PERSON] et al. were used [17]. In that experiment, the authors modeled the release of curcumin from polyhydroxybutyrate (PHB) microparticles using three different curcumin concentrations: 5%, 10%, and 15%.  
These percentages were used to calculate the concentration of curcumin released for each of the six concentrations selected with the same time step as the authors [17]. The obtained values were fitted to the Korsmeyer-Peppas (K-P) equation (Equation (3)) and [PERSON]'s hyperbolic (HB) model [51; 52] (Equation (4)). Various studies have found a high  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & \multicolumn{4}{c}{**Concentration (\(\upmu\)M)**} \\ \hline
**Model** & **0.25** & **0.5** & **1** & **5** & **10** & **20** \\ \hline K-P \({}^{1}\) & 42.75–75.84\% & 37.52–66.57\% & 34.61–61.40\% & 32.46–57.59\% & 30.65–54.37\% & 24.59–43.63\% \\ \hline HB \({}^{2}\) & 42.70–75.74\% & 42.70–75.74\% & 34.11–60.52\% & 33.29–59.05\% & 30.65–54.37\% & 24.59–43.63\% \\ \hline \end{tabular} \({}^{1}\) Korsmeyer–Peppas. \({}^{2}\) [PERSON]’s hyperbolic.  
\end{table}
Table 1: Confluence range values for different curcumin concentrations.  
correlation between the K-P model and the experimental results of curcumin release [53; 54], as well as [PERSON]'s hyperbolic model.  
\[\frac{M_{t}}{M_{\infty}}=K_{m}t^{n}, \tag{8}\]  
\[\frac{M_{t}}{M_{\infty}}=\frac{C_{1}t}{1+C_{2}t}. \tag{9}\]  
In both equations, \(M_{t}\) is the amount of curcumin (ug) released at time \(t\) (hours), \(M_{\infty}\) is the maximum amount of curcumin released (ug), \(K_{m}\) is a rate constant for the exponential model, and \(n\) is the diffusional exponent of the model. For the hyperbolic model, \(C_{1}\) is the release rate at the very beginning (1/h), and \(C_{2}\) is the constant related to the maximum release (1/h). \(C_{1}/C_{2}\) is also known as the capacity constant [52].
# Nighttime Image Dehazing Based on Point Light Sources
## 3 Proposed Method  
### Problem Analysis  
Firstly, the illumination of the light source is uneven. In the nighttime environment, the illumination intensity of the artificial light source is much greater than that of the ambient light, resulting in excessive illumination in the area close to the light source and insufficient illumination in the area far away from the light source, which is why the illumination of the image we see is uneven. Accordingly, the non-constant atmospheric light value plays a significant role in nighttime image dehazing.  
Secondly, the color shift of the light source. The intensity of artificial light sources is not like sunlight in the daytime. The color of the light source determines the naturalness of scene objects in the image, and the color of the light source will also affect the final dehazing effect.  
Third, the details of the image after dehazing. Since the whole image is in a night scene, the lack of brightness will lead to high noise and serious loss of image details after dehazing. However, if the exposure of the entire image is simply increased, the glow portion will also increase.
## 5 Conclusions  
After the encryption function was introduced into the self-developed secure optical fiber time transfer equipment, it had little impact on the stability of the system. Additionally, the stability of the optical fiber time transfer system is still higher than GNSS and other systems. The secure high-precision optical fiber time transfer method in this paper strengthens the security protection of time information, which is a very necessary and promising line of research.  
The next step is to carry out a secure and high-precision optical fiber time transfer network experiment on the field optical fiber link. The system will be optimized in terms of the TIC unit to improve the time transfer accuracy. Furthermore, the DWDM scheme will replace the cascade mode for experimental verification. Last but not least, we will explore a more secure quantum encryption mechanism to enhance the construction of a secure optical fiber time transfer network, which can further strengthen the security of the time service system.  
Conceptualization, X.G.; formal analysis, W.K.; Investigation, B.L.; project administration, T.L. and R.D.; resources, S.Z.; software, X.G.; supervision, T.L.; validation, Y.Q.; writing--original draft, X.G.; writing--review & editing, X.G. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 12033007, 61875205, 91836301, 12103058, and 61801458) and the Key Research and Development Plan of Guangdong Province (Grant No. 2018B030325001).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The C programs and case analysis data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 12: Cascade experiment of ring network using three equipment.
## 2 Numerical Model: Material Region
### Pyrolysis  
The material is assumed to be composed of \(N_{p}\) solid phases. For example, in the wood-cell walls the main components are cellulose, hemicellulose, and lignin, which are modeled as three phases. Each solid phase \(s_{i}\) may decompose following several pyrolysis kinetics in the absence of oxygen. For this reason, each phase \(i\) is split into \(P_{i}\) sub-phases to model the different degradation mechanisms. A generic decomposition of a sub-phase \(j\) (from solid phase \(i\)) leads to the production of species \(A_{k}\) according to the stoichiometric coefficients \(\xi_{i,j,k}\) as follows  
\[s_{i,j}\longrightarrow\sum_{k=1}^{N_{g}}\xi_{i,j,k}A_{k},\quad\forall i\in[1, N_{p}],\ \forall j\in[1,P_{i}] \tag{1}\]  
where \(N_{g}\) is the total number of the gaseous species accounted for in the gas mixture. Pyrolysis reactions are modeled by Arrhenius laws. In this way, the advancement of the pyrolysis reaction \(\chi_{i,j}\) of sub-phase \(j\) within phase \(i\) can be defined as  
\[\partial_{t}\chi_{i,j}=(1-\chi_{i,j})^{m_{ij}}T_{g}^{n_{ij}}A_{i,j}exp\Big{(}- \frac{\mathcal{E}_{i,j}}{RT_{g}}\Big{)},\quad\forall i\in[1,N_{p}],\ \forall j\in[1,P_{i}] \tag{2}\]  
where \(R\) is the perfect gas constant, \(T_{g}\) the temperature of the gas, \(\mathcal{A}\) the Arrhenius law pre-exponential factor, \(\mathcal{E}\) the Arrhenius law activation energy, and \(m\) and \(n\) the Arrhenius law parameters. The total advancement in the pyrolysis process, \(\tau\), is evaluated as follows  
Figure 2: Illustration of the problem: interaction between a multi-phase reactive material with a multi-species reactive gas mixture. The model characterizes this problem at the macroscopic scale under the assumption of scale separation (\(L>>l\)).  
\[\tau=\sum_{i\in[1,N_{p}]}\sum_{j\in[1,P_{i}]}\frac{\epsilon_{i,0}\rho_{i,0}F_{i,j}} {\sum_{i\in[1,N_{p}]}\sum_{j\in[1,P_{i}]}\epsilon_{i,0}\rho_{i,0}F_{i,j}}\ (1-\chi_{i,j}) \tag{3}\]  
where \(\epsilon_{i},\rho_{i}\), and \(F_{i,j}\) denote, respectively, the volume fraction of phase \(i\), the intrinsic density of phase \(i\) and the mass fraction of sub-phase \(j\) within phase \(i\). The subscript \(0\) indicates the initial time (\(t=0\)).  
The total production rate of species \(\pi_{k}\) is given by  
\[\pi_{k}=\sum_{i\in[1,N_{p}]}\sum_{j\in[1,P_{i}]}\xi_{i,j,k}\epsilon_{i,0}\rho_ {i,0}F_{i,j}\partial_{t}\chi_{i,j} \tag{4}\]  
By summing all the contributions in the gas mixture, it is possible to obtain the overall pyrolysis-gas production rate  
\[\Pi=\sum_{k\in[1,N_{g}]}\pi_{k} \tag{5}\]
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Fec  
FEC techniques introduced for WSN are broadly divided into two areas: FEC for SHAS WSN and FEC for generic WSN. In this section, we review FEC introduced for SHAS WSN first and then FEC introduced for WSN in general.
## 2 Theory and Method
### Construction of Low-Rank Tensor Completion Model  
#### 2.2.1 Tensor Composition  
In the landslide monitoring system, the monitoring data of each on-site monitoring sensor is collected through the intelligent terminal, and then the on-site monitoring data are transmitted to the background data monitoring center. Considering that the collected monitoring data include date, time, displacement and other information, it can be considered that the acquired landslide displacement time series is high-dimensional. For the landslide time series, this paper constructs a three-dimensional missing tensor \(\mathcal{T}\in\mathbb{R}^{l_{1},l_{2},l_{3}}\), where \(l_{1}\) is the number of on-site sensors for landslide, \(l_{2}\) is days collected, and \(l_{3}\) is the frequency of sampling every day. Therefore, the tensor represents the displacement monitoring value of each sensor at the corresponding time. This paper aims to identify the missing data position in tensor \(\mathcal{T}\) and complete data completion and displacement prediction, respectively.  
Figure 1: Random missing. The yellow dots represent missing data.  
Figure 2: Non-random missing. The curves in the green area represent missing data.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 2 EEG Dataset
### Data Acquisition
#### 2.4.1 Subjective Rating Scale  
The subjective evaluation scale used in this experiment is the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) scale. In order to measure the value of the brain load from the actual feelings and viewpoints of the operators themselves, the subjects are evaluated in the six dimensions of mental power demand, physical strength demand, time demand, effort level, performance level, and frustration degree. The weights of each dimension are determined by a two-two comparison method, and the final mental load value is calculated by a weighted average method.  
The statistical results show that the NASA-TLX score gradually increases with the increase of mental load in the experimental design. Analysis of variance by SPSS repeated measurements shows that the main effect of NASA-TLX scores is significant for brain load factors (F (2, 44) = 51.651, \(p<0.001\), \(\eta^{2}{}_{P}\) = 0.701). Post-hoc least significance difference (LSD) analysis shows that the low-load NASA-TLX score is significantly lower than the medium load (\(p<0.001\)) and high load (\(p<0.001\)), while the medium-load NASA-TLX score is significantly lower than the high load (\(p<0.001\)). Considering the effectiveness of the NASA-TLX scale for assessing mental workload, it is shown that the workload task conditions in the experiment are well set-up.
# Medical Augmented-Reality Visualizer for Surgical Training and Education in Medicine
## 3 Results  
We performed various evaluations to validate the performance of the MARVIS system. The analyzed functionalities include angular accuracy of phantom tracking, quality of mutual calibrations at particular modules, and accuracy of head tracking. Finally, a test of the whole system was conducted.