esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model
### Semi-Markov Process Model for 3Z
#### 3.2.5 Calculation of State Transition Probabilities  
Note that, in the state transition diagram, every transition probability contains \(m\) or \(m^{\prime}\). The first \(m\) is the probability that the UE is in a zone and will move to the neighboring zone before a call to/from the UE occurs. \(m\) can be obtained using the following equation:  
\[m=P(T_{c}>T_{m})=\int_{t_{m}=0}^{\infty}\int_{t_{c}=t_{m}}^{\infty}\lambda e^{ \lambda t_{c}}f_{m}(t_{m})dt_{m}dt_{c}=f_{m}^{*}(\lambda) \tag{1}\]  
where \(f_{m}^{*}(s)\) is the Laplace-Stieltjes transform for \(T_{m}\) (=\(\int_{t=0}^{\infty}e^{-st}f_{m}(t)dt\)).  
Next, let us derive \(m^{\prime}=P(T_{c}>R_{m})\), which is the probability that the UE, whose state changed due to a call without movement to a new zone, will move to the neighboring zone before another call from/to the UE occurs.  
The density function of \(R_{m},f_{r}(t)\) comes from the random observer property [1],  
\[f_{r}(t)=\mu\int_{\tau=t}^{\infty}f_{m}(\tau)d\tau=\mu(1-F_{m}(t)) \tag{2}\]  
The Laplace-Stieltjes transform for the distribution is as follows:  
\[\begin{array}{ll}f_{r}^{*}(s)&=\int_{t=0}^{\infty}e^{-st}f_{r}(t)dt=\int_{ t=0}^{\infty}e^{-st}\mu(1-F_{m}(t))dt=\frac{\mu}{s}-\int_{t=0}^{\infty}e^{-st} \mu F_{m}(t)dt\\ &=\frac{\mu}{s}+\left[\frac{\mu}{s}e^{-st}\mu F_{m}(t)\right]_{t=0}^{\infty}- \frac{\mu}{s}\int_{t=0}^{\infty}e^{-st}f_{m}(t)dt=\frac{\mu}{s}(1-f_{m}^{*}( \lambda))\end{array} \tag{3}\]  
Then, we have:  
\[m^{\prime}=P(T_{c}>R_{m})=\int_{\tau_{m}}^{\infty}\int_{t_{c}=\tau_{m}}^{ \infty}\lambda e^{-\lambda t_{c}}f_{r}(r_{m})dt_{c}dr_{m}=\int_{\tau_{m}=0}^{ \infty}f_{r}(r_{m})e^{-\lambda t_{c}}dr_{m}=\frac{\mu}{\lambda}(1-f_{m}^{*}( \lambda)). \tag{4}\]
## 3 Results and Discussion
### Stability of NCs  
The in vitro stability of the developed ZnSO\({}_{4}\)/SP NCs was evaluated in DW, a pH 1.2 medium, and a pH 6.8 buffer (Figure 6). The amphiphilic property of SP can be attributed to the homogeneous dispersion of ZnSO\({}_{4}\) in the aqueous medium. However, the presence of salts and enzymes and the variable pH values can affect the stabilization and maintenance of NC dispersion. Generally, the media of pH 1.2 and 6.8 are used to indicate artificial gastric and intestinal environment in disintegration and dissolution studies. The structural stability of fabricated ZnSO\({}_{4}\)/SP NCs was estimated by measuring their mean diameters after incubation in each medium. As shown in Figure 6, the mean diameter was not altered, even after incubation for 6 h in DW and pH 1.2 medium. In the pH 6.8 buffer, the mean diameter of ZnSO\({}_{4}\)/SP NCs was higher than those in DW and the pH 1.2 medium at 0 h (\(p<\) 0.05). However, it was reduced to approximately 100 nm after incubation for 6 h. The zeta potential values of ZnSO\({}_{4}\)/SP NC dispersion in DW, the pH 1.2 medium, and pH 6.8 buffer were \(-\)1.0 \(\pm\) 1.3 (Table 1), 43.8 \(\pm\) 6.3, and \(-\)3.0 \(\pm\) 0.2 mV, respectively. In acidic pH, a more positive zeta potential value was exhibited. Pharmaceutical salts (i.e., phosphate salts) included in a pH 6.8 buffer may interact with ZnSO\({}_{4}\) and/or SP, and the aggregation of NCs seems to be induced by that interaction. However, its influence was weakened as the incubation time went by. Although the initial mean diameter of ZnSO\({}_{4}\)/SP NCs in the pH 6.8 buffer (at 0 h) was approximately 224 nm, this value can be also regarded as suitable for the mucosal delivery as a nanocarrier. The polydispersity index values of ZnSO\({}_{4}\)/SP NC dispersion in DW, the pH 1.2 medium, and the pH 6.8 buffer after 6 h incubation were 0.14 \(\pm\) 0.04, 0.09 \(\pm\) 0.04, and 0.15 \(\pm\) 0.04, respectively. Those values indicate the maintenance of a narrow size distribution of NCs after 6 h of incubation. The maintenance of the nano-size of the developed NCs in  
Figure 5: FT-IR spectra of ZnSO\({}_{4}\), SP, and ZnSO\({}_{4}\)/SP NCs. Transmittance according to the wavenumber is shown.  
the pH 1.2 and pH 6.8 media can contribute to the improved delivery to the intestinal epithelium after oral administration.
Therefore, apart from maintaining optimum power consumption, some features to ensure robust communication and secure stimulation are also desirable for efficiently monitoring and treating diseases. In this context, we have summarized significant electroceuticals; pacemaker, neural stimulation, artificial retina, and vagus nerve stimulation. We have discussed smart implants development challenges and opportunities. Furthermore, micro and nanoscale features and material aspects, electromagnetic field effects on innovative biomedical implants, their potential consequences have been presented. The report highlights the advancement in micro/nanoscale fabrication technologies and their use in fabricating electroceuticals for tar gated clinical needs. Finally, the importance of wireless power transfer and trigger-enabled functional electronic circuits with ultra-low power consumption has been discussed.
# Green Synthesis Method and Application of NaP
## Results and Discussion
### BET Result Analysis  
Table 2 shows the results of specific surface area, external surface area, total pore volume and average pore diameter of CGCS and different NaP zeolites after pretreatment at 200 \({}^{\circ}\)C. The specific surface areas of the SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 5.20 and 3.40 samples were 161.06 m\({}^{2}\)/g and 98.24 m\({}^{2}\)/g, respectively, and the pore volumes were 0.0021 cm\({}^{3}\)/g and 0.0036 cm\({}^{3}\)/g, respectively. The sample with a molar ratio of 5.20 has a large specific surface area and a low pore volume, which is confirmed by XRD and SEM because of the small particle size of the sample [21, 32].  
The isotherm of the aperture type and distribution is shown in Figure 6. The pore size distribution of the NaP zeolite samples synthesized at different molar ratios was calculated using the N\({}_{2}\)-DFT model. All samples exhibited a type IV isotherm with a H3 hysteresis loop (relative pressure range 0.45-0.99), which is a typical feature of mesoporous materials, where the hysteresis loop is related to the occurrence of pore condensation [36, 37, 38]. As can be seen from the dV/dW curve, samples were present as micropores and mesopores. According to SEM morphology analysis, the zeolite mesopores are caused by intercrystalline voids and intracrystalline mesopores in the crystal. There is a certain difference between the calculated area of the external surface area and the specific surface area, which further proves that the micropores and the mesopores coexist in the obtained zeolite [39]. The experimental results show that it has good consistency with typical NaP zeolite [40, 41].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **Surface Area** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} & \begin{tabular}{c} **External Surface** \\ **Area (m\({}^{2}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Pore Volume** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} &
\begin{tabular}{c} **Average Aperture** \\ **(nm)** \\ \end{tabular} \\ \hline CGCS & 0.88 & 0.87 & 0.0013 & 18.05 \\ SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 3.40 & 98.24 & 88.99 & 0.0036 & 8.35 \\ SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 5.20 & 161.06 & 153.35 & 0.0021 & 5.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters of NaP zeolite with CGCS and different SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratios.  
Figure 5: TG and DSC curves of different SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratio NaP zeolites (a) 5.20, (b) 3.40.  
By comparing the results of SEM, FT-IR, TG-DSC, and BET, we found that, without adding a silicon aluminum source (SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) = 5.20), the crystal morphology is more regular, the thermal stability is better, and the specific surface area is larger.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
###### Abstract
More recently, in order to adjust and/or control the effective CAP dosages, some researchers begin to focus on machine learning and neural network [18, 19], which further proves the complexity and difficulty of the plasma dosage definition. In our opinion, since the concept of \"dosage\" is frequently used in pharmaceuticals, the definition of plasma dosage can refer to the basic knowledge in pharmacy. The median lethal dose (LD\({}_{50}\)) is one of the most typical expressions to describe the drug toxicity which represents the exact dose that can kill half of the experimental subjects (e.g., cells, animals, etc.). The measurement of the LD\({}_{50}\) values usually serves as an important and basic step for the systematic assessment and evaluation of the drug toxic characteristics [20, 21, 22]. Inspired by  
Figure 1: Schematic of the complex interactions between CAPs and treated biological targets (cells). this methodology in pharmacy, we intend to introduce a novel method to estimate the plasma dosage: treating the in vitro cells with different CAP intensities to find the value of LD\({}_{50}\) which corresponds to the situation under which the CAP treatment can kill a half number of the cells. The value of LD\({}_{50}\) can serve as an indicator to help the users determine and control the plasma dosage used in their practical studies. Distinguished from the physics- and/or chemistry-based method to define or control the plasma dosage [8,18,19,23,24,25, 23], this biology-based approach has the advantages of being versatile to various CAP sources and easy to be conducted without considering the specific and complex interaction processes between the CAPs and the treated biological targets.
## 3 Results
### Procedural Information Intake
#### 3.2.3 Interaction Effect of Phase/Step x Expertise  
Regarding the interaction effect between phase and expertise, the results showed no significant interaction effect for execution or the percentage of time spent on documentation. However, a significant interaction effect was observed for the consultation duration (\(F(2,30)=23.6,p<0.001\)) and absolute duration (\(F(2,30)=22.1,p<0.05\)). In a post hoc analysis of the two variables, significant differences in document consultation duration emerged between the novice and expert groups for both the removal and installation phases (\(p<0.001\)). During the removal phase, experts showed an average document consultation duration of 5.6 min (\(SD=2.7\) min), while novices showed a significantly longer average of 24.7 min (\(SD=9.7\) min). Similarly, in the installation phase, experts showed an average document consultation duration of 4.3 min (\(SD=2.8\) min), while novices showed an average duration of 12.9 min (\(SD=3.3\) min). Furthermore, a significant difference was observed within the novice group across all phases (\(p<0.001\)). This was not observed in the expert group.  
When examining the interaction effect of step x expertise, the results show a significant effect on the duration of the consultation (\(F(8,120)=14.6,p<0.001\)), as well as a significant effect on the percentage of time spent on documentation (\(F(8,120)=8.3,p<0.001\)). No interaction effect is found for absolute and execution duration.  
Post hoc analyses were conducted for consultation duration and the percentage of time spent on documentation. In relation to the duration of document consultation during Step I (Figure 5), a significant difference was found between the novice and expert groups at this point (\(p<0.001\)). Additionally, in terms of the percentage of time spent on documentation during Step I of the task (as shown in Figure 5), both novices (\(\sigma=55.6\%,SD=14.3\%\)) and experts (\(\sigma=22.1\%,SD=11.8\%\)) allocated the highest percentage of time to document consultation. In particular, Step I stands out as significantly higher than all other steps for novices (all \(p<0.001\)) and, with the exception of Step II, for experts (all \(p<0.05\)).  
In this section, we investigated how expertise influences the variables that compose absolute duration. The results demonstrate that expertise has a significant impact on absolute duration, with a greater impact on consultation than on execution. More specifically, experts perform the maintenance task at an average of 45.8% faster than novices. This distinction becomes more apparent when consulting instructions, where experts are 71.6% faster than novices. For execution, the difference is 35.9%.
# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 3 Caching-Aware Intelligent Handover Strategy
### Intelligent Handover Strategy with Multiple Attributes
To break the time correlation between the sequences, the replay buffer is used to store the experience, and a random sample of minibatch is used for learning. The detailed algorithm is described as Algorithm 1. ```
Initialize replay buffer \(D\) Initialize action-value function \(Q\) with random weights \(\theta\) Initialize target action-value function \(\hat{Q}\) with weights \(\theta^{-}=\theta\) Repeat Initializeepisode\(=1\) and \(p=1\) Initialize the start state \(s_{1}\) with connection relationship. for\(p=1,T\)do  Select a random action \(a_{1}\) With probability \(\varepsilon\) otherwise select \(a_{p}=\arg\max_{a}Q(s_{p},a;\theta)\). Execute action \(a_{p}\) in LEO environment emulator. Receive a reward \(R(s_{p},a_{p})\) and next state \(s_{p+1}\)  Store transition \((s_{p},a_{p},r_{p},s_{p+1})\) in \(D\). Sample minibatch of transition \((s_{p},a_{p},r_{p},s_{p+1})\) randomly from \(D\) when \(D\) is full. Update the main network parameters \(\theta\) using gradient descent with the goal of minimizing the loss function \(L(\theta)\) defined in Equation (20)  Every \(C\) steps reset \(\hat{Q}=Q\). endfor Untilepisode\(>\)episode_max. ```  
**Algorithm 1** Intelligent handover algorithm based on DRL.
## 5 Conclusions  
In this paper, the GPP time series data from 2000 to 2018 were estimated by the VPM based on 500-m MODIS MOD09A1 data. The multi-year mean, the Theil-Sen Median trend analysis, the MK test and the Hurst exponent methods were used to explore the CLP level, the trend and the amplitude of change, and the future sustainability and future direction of change of the regional CLP. The following conclusions were drawn:  
(1) Overall, the CLP level in Jilin Province was characterized during 2000 and 2018 by a medium level GPP. The CLP of Jilin was generally high in the southeast and low in the northwest. Therefore, when the cultivated land protection and land improvement measures were implemented, the northwest region with low productivity should be given more consideration.  
(2) Most of the regions showed an increasing trend throughout 2000-2018, which was larger than that of the decreasing trend. Most changes were slight. Higher productivity had a lower rate of increase and lower productivity had a higher rate of increase.  
(3) The area shown to be uncertain in the future is located in Changchun City and Siping City in the central part. These areas should be monitored dynamically. The improving areas were located in Baicheng City and Songyuan City, and should be given protection measures. The deteriorating area was mainly located in the southeastern cities and the central part, and should undergo comprehensive quality improvement measures.  
(4) We propose that the temporal analysis methods are suited for exploring the trend and forecasting the direction of change in the future. Because they are universal, the methods could also be applicable for a finer data source and for other areas in the world to explore future trends.  
Conceptualization, C.Z. and W.Y.; methodology, J.M.; software, J.M. and Y.L.; validation, W.C. and J.M.; formal analysis, J.M.; investigation, J.M. and W.C.; resources, W.Y. and D.Z.; data curation, Y.L.; writing--original draft preparation, J.M.; writing--review and editing, J.M. and C.Z.; visualization, J.M. and W.C.; project administration, D.Z.; funding acquisition, C.Z. All authors have read and agree to the published version of the manuscript.  
This research was supported by the National Key R&D Program of China, grant number 2017 YFF0206801-2.  
Thanks for research assistance from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Insightful and constructive comments of the anonymous reviewers are appreciated.  
The authors declare no conflict of interest.
###### Abstract  
Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologies are being widely applied to the development of intelligent analysis services based on image data and streaming video data. As such, in the port yard, these object recognition technologies are being used to develop port safety services in smart ports. Accidents are a frequent occurrence in port yards due to misaligned loading of ship containers. In order to prevent such accidents, various studies using artificial intelligence technology are underway. In this paper, we propose a real-time shipping container accident inference edge system that can analyze the ship container's loading status from a safety point of view to prevent accidents in advance. The proposed system includes the collection of video data of the ship container, inferring the safety level of the alignment status of the ship container, and transmitting the inference results for the safety level. In this paper, the proposed inference model is implemented with YOLOv3, YOLOv4 and YOLOv7 networks and can be used in video monitoring to realize the accurate classification and positioning of three different safety levels (safe, caution, and danger) in real time. In the performance evaluation, the detection accuracy of the inference model implemented with the YOLOv4 network was greater than 0.95. Its performance was also significantly better than that of the inference model implemented with the YOLOv3 and YOLOv7 networks. Although it was slightly inferior to the YOLOv4 network in terms of the accuracy, the inference model implemented with the YOLOv3 network had a faster inference speed than the model implemented with the YOLOv4 and YOLOv7 networks. Because of the port safety scenario, in which the inference accuracy is more important than the inference speed, we applied the YOLOv4 algorithm to the inference model of the system.  
object detection; port safety; YOLOv4 network; edge system +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Comprehensive Evaluation of DE and GD
#### 2.2.2 Combined Assignment Method  
Index empowerment is an important link in comprehensive evaluation. The entropy weight model (EWM) and criteria importance through inter-criteria correlation (CRITIC) method, as objective weighting methods, effectively eliminate the bias introduced by individual preferences in subjective weighting processes. The EWM relies on entropy to evaluate the informational content of indicators, guaranteeing objective weight allocation. CRITIC incorporates the conflict among indicators and their standard deviation, thereby offering a more holistic assessment of indicator importance [51]. Additionally, combined weighting methods have been widely applied across various fields in recent years and can effectively solve the problems of contradiction and poor differentiation in the evaluation results of the single assignment method [52]. Therefore, we opted to combine these two methods for weight determination. A comprehensive weight is calculated as follows:  
\[W_{j}=\frac{w_{j}^{1}\times w_{j}^{2}}{\sum\limits_{j=1}^{m}w_{j}^{1}\times w _{j}^{2}}, \tag{1}\]  
where the index weights \(w_{j}^{1}\) and \(w_{j}^{2}\) were calculated by the EWM and CRITIC model, respectively.  
The principle of EWM is as follows. The initial data are standardized, with positive indices computed via Equation (1), and negative indices via Equation (2).  
\[{x^{\prime}}_{ij}=\frac{x_{ij}-x_{\min}}{x_{\max}-x_{\min}}, \tag{2}\]  
\[{x^{\prime}}_{ij}=\frac{x_{\max}-x_{ij}}{x_{\max}-x_{\min}}, \tag{3}\]  
where \(x_{ij}\) denotes the statistics of province \(i\) for indicator \(j\), \(i=1,2,\cdots,n\), \(j=1,2,\cdots,m\).  
The weight \(w_{j}^{1}\) of indicator \(j\):  
\[w_{j}^{1}=\frac{h_{j}}{\sum\limits_{j=1}^{m}h_{j}}, \tag{4}\]  
\[h_{j}=1-e_{j}, \tag{5}\]  
\[e_{j}=-K\sum\limits_{i=1}^{n}M_{ij}\ln M_{ij}, \tag{6}\]  
\[M_{ij}=\frac{{x^{\prime}}_{ij}}{\sum\limits_{i=1}^{n}{x^{\prime}}_{ij}}, \tag{7}\]  
where \(K=\ln(n)^{-1}\), \(h_{j}\), \(e_{j}\), and \(M_{ij}\) denote the information entropy redundancy, the entropy value, and the characteristic weight, respectively.  
Similarly, the principle of the CRITIC model is as follows, where the standard deviation \(\sigma_{j}\) is used to express the comparability of indicator \(j\):  
\[\sigma_{j}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left({x^{\prime}}_{ij}-\overline {x}^{\prime}_{j}\right)}{m-1}}, \tag{8}\]  
Let the size of the contradiction between indicator \(j\) and the remaining indicators be:  
\[f_{j}=\sum\limits_{i=1}^{m}\left(1-r_{ij}\right), \tag{9}\]where \(r_{ij}\) denotes the correlation coefficient between indicator \(i\) and indicator \(j\).  
The information carrying capacity of indicator \(j\) is calculated as \(C_{j}\):  
\[C_{j}=\sigma_{j}\times f_{j}, \tag{10}\]  
The information carrying capacity of the indicators is normalized to obtain the CRITIC weights:  
\[w_{j}^{2}=\frac{C_{j}}{\sum\limits_{j=1}^{m}C_{j}}, \tag{11}\]
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Control Overhead  
To analyze and evaluate performance of control overhead for discovering and establishing node-disjoint paths, the number of control messages transmitted and re-transmitted in the network is used as a metric for the performance analysis.  
Unlike AODVM, which uses many types of control messages during the route discovery process, MP-AODV and NDMR use two: request messages (RREQ/PREQ) and reply messages (RREP/PREP). Additionally, intermediate nodes under MP-AODV and NDMR discard duplicates of RREQs/PREQs instead of re-transmitting them as under the AODVM.