esa-sceva/satcom-chunk-collection huggingface dataset:
# BGP Neighbor Trust Establishment Mechanism Based on the Bargaining Game
## 4 The Detection Mechanism of the BGP Malicious/Inactive Neighbors
#### Detection Process  
Let us assume \(AS_{1}\) and \(AS_{2}\) have established a trusted neighbor relationship through the process described in Section 3. \(X^{succ}=\left\{x_{1}^{succ},x_{2}^{succ},x_{3}^{succ},x_{4}^{succ},x_{5}^{succ}\right\}\) represents their agreement on bandwidth, packet loss rate, jitter, delay, and price. At this time, \(AS_{1}\) needs to calculate \(AS_{2}\)'s behavioral trusts and checks whether it is a malicious/inactive neighbor. The specific process is as follows:  
1. \(AS_{1}\) collects the data set of bandwidth, packet loss rate, jitter, and delay when \(AS_{2}\) forwards \(AS_{1}\) traffic \(T\) times. The data set is marked as \(\left[X\right]_{\text{T}}=\left\{X^{1},X^{2}\ldots X^{T}\right\}\).
2. \(AS_{1}\) draw on the gray correlation algorithm to calculate the \(AS_{2}\)'s behavioral trust \(\gamma_{T}\). Since \(x_{2}\), \(x_{3}\), and \(x_{4}\) are the cost attributes, to facilitate calculation, we use the worst packet loss rate R, the largest jitter J, and the longest delay D in the actual network to process data with the same attributes in the data set \(\left[X\right]_{\text{T}}\). The detection algorithm is Algorithm 2.
3. If behavioral trusts are all within the normal range, \(AS_{1}\) and \(AS_{2}\) continue to maintain the trusted neighbor relationship. If the behavioral trust \(\gamma_{T}\) appears abnormal, go to Step4.
4. \(AS_{1}\) sends a warning to \(AS_{2}\) and sets the number of forwarding \(\Delta\) T. \(AS_{1}\) continues to calculate the \(AS_{2}\)'s behavioral trusts when it forwards \(\Delta T\) times.
5. If behavioral trusts are all within the normal range, \(AS_{1}\) and \(AS_{2}\) continue to maintain the trusted neighbor relationship. If \(\gamma_{\Delta T}\) still appears abnormal, \(AS_{1}\) judges \(AS_{2}\) as the malicious/inactive neighbor. Then, \(AS_{1}\) stops paying \(AS_{2}\) and filters the routing information announced/forwarded by \(AS_{2}\).  
```
Input:\(X^{succ}\), \(\left[X\right]_{T}\), R, J, D Output:\(\gamma_{AS_{2}}\)
1:\(x_{1}^{succ1}\)=\(x_{1}^{succ}\); \(x_{2}^{succ1}\)=R-\(x_{2}^{succ}\); \(x_{3}^{succ1}\)=J-\(x_{3}^{succ}\); \(x_{4}^{succ1}\)=D-\(x_{4}^{succ}\);
2:for (i=1;i<=T;i++)
3:\(\{\)
4:if (\(x_{1}^{i}\geq x_{1}^{succ}\)&& \(x_{2}^{i}\leq x_{3}^{succ}\)&& \(x_{3}^{i}\leq x_{3}^{succ}\)&& \(x_{4}^{i}\leq x_{4}^{succ}\))
5:output \(\gamma_{AS_{2}}^{j}\)=1;
6:else
7:\(\{\)
8:\(x_{2}^{i}\)= R-\(x_{2}^{i}\); \(x_{3}^{i}\)=J-\(x_{3}^{i}\); \(x_{4}^{i}\)=D-\(x_{4}^{i}\);
9:for (j=1;j<=4;j++)
10:\(\{\)
11:\(\gamma_{AS_{2}}^{ij}\)=\(\frac{\underset{j}{\minmin}\left|x_{j}^{succ1}-x_{j}^{j}\right|+\underset{j}{ \min}\left|x_{j}^{succ1}-x_{j}^{j}\right|}{\left|x_{j}^{succ1}-x_{j}^{i} \right|+\underset{j}{\min}\left|x_{j}^{succ1}-x_{j}^{i}\right|}\)\(\underset{j}{\min}\left|x_{j}^{succ1}-x_{j}^{i}\right|}\)
12:\(\}\)
13:output \(\gamma_{AS_{2}}^{i}\)=\(\frac{1}{4}\sum\)\(\gamma_{AS_{2}}^{ij}\);
14:\(\}\)
15:\(\}\)
```  
**Algorithm 2**Detection algorithm
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work
### Basic Concepts and Problem Description
#### 2.1.2 Trajectory Dictionary  
The trajectory dictionary generates a series of standard trajectories that satisfy the road network topology rules based on the network topology of the road, using the road network and road segment dictionaries as the basis. The generated standard trajectories are added to the dictionary, where each standard trajectory is represented as a set of points ST = [P1, P2, P3,...]. The dictionary is in the form of [ST1, ST2, ST3,... STN].
## 2 Materials and Methods
### Volatile Compounds  
The extraction of volatile compounds was done by HS-SPME technique with 1 cm DVB/CAR/PDMS fiber (Supelco, Bellegonte, PA, USA). Briefly, 10 mL of sample were placed into 20 mL headspace vial together with 5 \(\upmu\)L of benzyl acetate (1000 mg L\({}^{-1}\)) as internal standard and 1 g of NaCl. Thereafter, the sample was placed on autosampler (AOC-600 Plus, Shimadzu) at 250 rpm and 40 \({}^{\circ}\)C, for 40 min. The identification and quantification of the volatile compounds was carried out using a Shimadzu GC2030 gas chromatograph and a TQ8040 NX triple quadrupole mass spectrometer as detector (Shimadzu Scientific Instruments, Inc., Columbia, MD, USA) equipped with an AOC-6000 Plus autosampler. Only the single quadrupole acquisition mode was exploited on the TQ8040 NX (Q3 Scan). Data were acquired by using the GCMS solution software ver. 4.4 (Shimadzu). The column used was an X5 MS (silphenylene polymer; Teknokroma, Barcelona, Spain) with dimensions of 30 m (length), 0.25 mm (internal diameter), and 0.25 \(\upmu\)m (film thickness). The GC temperature program was, as follows: 50 \({}^{\circ}\)C hold for 2 min, then +3 \({}^{\circ}\)C min\({}^{-1}\) ramp up to 170 \({}^{\circ}\)C, and an increment of 20 \({}^{\circ}\)C min\({}^{-1}\) up to 230 \({}^{\circ}\)C. Helium head pressure was 18 kPa (constant linear velocity mode 30 cm s\({}^{-1}\)). Injector, ion source, and interface were at 230, 230, and 280 \({}^{\circ}\)C, respectively. Helium with flow 0.6 mL min\({}^{-1}\) was used as carrier gas with a split ratio of 1:10. Volatile compounds were identified by comparison of: (i) experimentally obtained mass spectra with those available in NIST 17 Mass Spectral, and (ii) linear retention indices calculated using the C6-C20 n-alkane mix (sigma-Aldrich, Steinheim, Germany). Only compounds with spectra similarity >90% were considered as correct hits; besides, the linear retention similarity threshold was set up at \(\pm\)10 units. Analyses were conducted in triplicate and results were expressed as mg L\({}^{-1}\).
## 3 Results
### Doping of HA Nanoparticles
#### 3.3.5 Selenium-Doped Nano-Hydroxypaatite (Se-Nano-HA)  
Selenium (Se) is an essential microelement known for its antioxidant properties, which play a critical role in protecting the body against free radicals and carcinogens [48]. Selenium ions are integral to selenoproteins, which contribute to immune function, catalyze the production of active thyroid hormone, and promote cellular proliferation. Selenium deficiency has been linked to increased risks of various cancers, including those affecting the prostate, colon, lungs, liver, and thyroid. Insufficient selenium intake can elevate reactive oxygen species (ROS) levels, leading to reduced type-I collagen synthesis, suppressed alkaline phosphatase expression, and impaired osteoblast differentiation--all of which are closely tied to selenoprotein expression.  
In the context of biomaterials, selenium has been explored extensively for orthopedic implants due to its antimicrobial, anticancer, and osteoinductive properties. Selenium-doped hydroxyapatite nanoparticles (Se-nano-HA) are typically synthesized through co-precipitation or hydrothermal methods, with the latter involving temperatures up to 160 \({}^{\circ}\)C [55]. These doping processes enable the incorporation of selenium into the hydroxyapatite matrix, imparting unique biological activities.  
Se-nano-HA has shown significant potential in combating osteosarcoma. For example, studies by [PERSON] et al. demonstrated that selenium-doped HA induces apoptosis in MG-63 osteosarcoma cells by activating the intrinsic mitochondrial apoptotic pathway [56]. This mechanism involves oxidative damage to DNA and mitochondria, leading to mitochondrial dysfunction and cell death. These findings suggest selenium's potential as an anti-osteosarcoma agent, offering dual benefits of anticancer and osteoinductive activity.  
By integrating selenium into hydroxyapatite, Se-nano-HA provides a multifunctional scaffold for bone regeneration that combines antioxidant, antimicrobial, and anticancer properties, making it a promising material for advanced orthopedic applications.
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 5 Experimental Validation and Analysis
### Risk Level Classification  
To apply the collision risk R to the car-following risk prediction, it is necessary to first classify the risk levels. In Section 2, the CNN-LSTM model is used to predict the future trajectories of target vehicles in the dataset. By calculating the displacement and time difference between points along the trajectory, the speed and acceleration of the target vehicle are derived. The calculated data are then input into the deep-learning model to obtain the threshold values for the collision risk R. The threshold value for the collision risk R is \(\text{R}=0.2219,0.4284,0.8347\). The classification of risk levels is outlined in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Risk Level Classification** & **Threshold** \\ \hline High risk & \(\text{R}>0.8347\) \\ Medium risk & \(0.4284<\text{R}<0.8347\) \\ Low risk & \(0.2219<\text{R}<0.4284\) \\ Safe & \(\text{R}<0.2219\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Risk level classification.  
This means that, when \(R<0.2219\), the parameters such as the distance between vehicles and their speed are within a safe range, and the target vehicle can maintain its current driving state. When \(0.2219<R<0.4284\), it may indicate certain potential safety hazards, but the overall risk is still controllable, and the target vehicle can take preventive measures. When \(0.4284<R<0.8347\), the driving risk is classified as medium, indicating a higher risk during driving, requiring close monitoring and appropriate actions to mitigate the risk. When \(R>0.8347\), the vehicle is in a high-risk state, meaning there is a significant safety risk, and the target vehicle should immediately take emergency measures such as decelerating or changing lanes to avoid a potential accident.
## 1 Introduction
### Literature Review and Contribution
[PERSON] and [PERSON] (2017) studied the impact of financing-pollution mitigation policy instruments on corporate performance using the DID estimation methods based on data from manufacturing enterprises in Jiangsu Province. They found that financing and emission reduction policies have a penalty effect on the performance of high-polluting enterprises, including total factor productivity, profitability and sales growth. Throughout the above research, scholars at home and abroad mainly focus on the impact of environmental regulation on regional productivity development, and provide a lot of relevant theoretical and empirical research methods on the relationship between environmental policy and regional sustainable development. However, there are still few studies on the sustainable development effect of the APCP Action Plan. Some scholars have analyzed the impact of _GTFP_ on various industries, but little attention is given to its impact on the sustainable development of China's provinces. The goal of a comprehensive environmental policy is not only to improve air quality and improve the environmental level, but also to promote regional sustainable development. Therefore, based on the existing research, this paper studies the impact and mechanism of the APCP Action Plan on the _GTFP_ by using panel data from 30 provinces in China from 2004 to 2017. Compared with previous studies, the marginal contribution of this paper is mainly reflected in the following three aspects. Firstly, by analyzing the impact of the APCP Action Plan on the _GTFP_ of Chinese provinces, this paper enriches the research theme in this field, rather than simply exploring the impact of environmental policies on productivity and air quality. In this paper, the SBM-DEA model is used to measure the _GTFP_ of the provinces. The Panel OLS-DID model and the fixed effect model are used to explain that the APCP Action Plan can enhance the improvement of regional _GTFP_, which provides a useful reference for other countries to formulate environmental policies. Moreover, since there are three pilot regions of the APCP Action Plan in China, and there are significant differences among the three pilot regions, this paper analyzes the heterogeneity of the impacts of the environmental policy on different pilot regions through sub-sample regression. Finally, by adding the rationalization of industrial structure and the optimization of industrial structure as moderating variables, this paper further studies whether there is a heterogeneous moderating effect in different moderating variables on the policy effect. This paper is divided into five sections: Section 2 introduces the theory introduction and the research method explanation; Section 3 illustrates the empirical results and the analysis; Section 4 conducts a further discussion; the fifth section draws the conclusion.
## 4 Environmental Parameters Measurement and Discussion of Results
### Impact of Artificial Seawater Factor on UIWOC  
In addition to the variation of water temperature, this experiment also studies the influence of seawater in UWOC by putting an amount of salt into the water tank. The actual seawater was simulated by adding artificial seawater into the water tank. In this experiment, the artificial seawater was poured into the water tank in portions. The seawater density of 1.03 g/cm\({}^{3}\) is used as a standard value to ensure the similarity between the lab saltwater and the actual seawater. First, the prepared seawater is rest for a while so that the impurities in the water are sunk to the bottom of the water tank, then the BER against BER was measured. After the measurement, the submerged motor is turned on to measure the optical power and BER values again.  
The results of transmission loss measured in this experiment are shown in Table 3, from which it was found that the transmission loss is more significant than that in tap water. Using 450 nm laser diode for the BER performance test, the optical loss of 1.5 m in simulated seawater is close to 6 m in tap water. When the transmission distance is 4.5 m, the transmission loss is as high as 23.23 dB. The optical loss after 4.5 m transmission is huge, so the BER performance is not detectable. Thus, the seawater simulation experiment for the BER test is only 3 m, and the results are shown in Figure 10. The optical power loss may be even huge if the experiment is conducted in the open sea under the sun. It is because, in the daytime, there is background light to interfere with transmitted light [19]. It can be seen from Figure 10 that the bit error rate performance after turning on the submerged motor is worse than that after precipitating impurities in the water. When the submerged motor is turned on, the light loss increases by about 1.5 dB compared to when the motor is not turned on. Comparing the eye diagram of seawater after precipitation with those of seawater during the disturbance, one may see that precipitating impurities in seawater dramatically influence the light-wave transmission quality, as is shown in Figure 11.  
Therefore, it can be seen that the transmission effect of the underwater OWC system in seawater is worse than that in tap water. The reason is that there are many minerals in seawater, and the water quality becomes turbid after artificial seawater is added. Therefore, the loss of signal light in the transmission process increases, and the BER performance decreases. Besides Equations (1)-(4), the BER is also affected by the power level of SNR in digital optical communications. The BER value decreases with the increase of SNR, as is shown in Equation (6).  
\[\text{BER }=Q\bigg{(}\frac{1}{\sigma}\bigg{)}=Q(sqrt(SNR)) \tag{6}\]  
where \(Q\) is the \(Q\) function, and sqrt is the square root function.
# Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring
## 3 Results and Discussion
### Differential Signal with Motion Removed  
The method for capturing signals associated with swallowing was developed using two accelerometers. External movement was eliminated and vibration transmitted from within was confirmed. Figure 3a is a signal acquired four times during swallowing when the body was swaying side to side. The light blue and red solid lines are signals acquired from each accelerometer (IMU1, IMU2). The wave moving once every two seconds is observed due to the body movement of swaying. The black solid line indicates the differential signal which is the difference between the two accelerometer signals. The waveform in which the body sways side to side is removed from the original accelerometer signal. Additionally, the differential signal has a more distinct internal biometric signal caused by swallowing and heartbeat.  
\begin{table}
\begin{tabular}{c c c} \hline
**Specific Action** & **Time Interval between** & **Number of** \\  & **Swallowing (s)** & **Swallowing** \\ \hline Swallowing saliva & 30 & 5 \\ Sitting + SW & 15 & 10 \\ Swaying body back and force + SW & 15 & 10 \\ Swaying body from side to side + SW & 15 & 10 \\ Walking + SW & 15 & 10 \\ Cycling + SW & 15 & 10 \\ Running + SW & 15 & 10 \\ Confounding action & - & - \\ Chewing gum 1 & - & - \\ \hline \end{tabular} \({}^{1}\)Swallowing saliva can occur while chewing gum.  
\end{table}
Table 2: Scenario of swallowing detection test.  
Figure 3b is the waveform acquired when swallowing while walking. It was confirmed that changes due to body movement while walking were observed in the signals acquired from each accelerometer, but the walking-related signals were eliminated in the differential signal represented by the black solid line. Additionally, swallowing signals were also observed more clearly.
# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 2 Data Observation and Analysis
### Spatial Domain  
Figure 3 shows the spatial distribution of Call traffic over a certain period of time. We can easily find that the traffic distribution of the whole city was very uneven. Intensive traffic was concentrated in the downtown area, while urban suburbs had very sparse traffic distributions. Moreover, it can be clearly seen from Figure 3 that a few areas covered traffic much higher than other areas. These areas were usually the bustling areas of the city and were often the most burdensome areas for wireless networks, and accurate predictions of traffic in these areas are important. However, these areas carrying large amounts of traffic also pose great difficulties for traffic prediction modeling. The existence of such singular values was very detrimental to the fitting of the model. Moreover, the trend of traffic fluctuations in these areas was often much larger than in other areas, and more research is needed to capture its space-time characteristics well.  
The correlation between traffic changes in different cells cannot be seen intuitively through Figure 3. To better show the spatial correlation of cellular traffic, we extracted \(11\times 11\) cells in the Call  
Figure 3: Spatial distribution of cellular network traffic.  
Figure 2: Daily traffic change statistics.  
dataset for correlation analysis. Figure 4 shows the Pearson correlation coefficient \(\rho\) for the spatial correlation between target cell \(x^{(i_{j})}\) and its neighboring cells \(x^{(i^{\prime},j^{\prime})}\). The Pearson correlation coefficient is a widely used metric [16; 17]. Its definition is expressed as follows:  
\[\rho=\frac{\text{cov}\left(x^{(i_{j})},x^{(i^{\prime},j^{\prime})}\right)}{ \sigma_{x^{(i_{j})}}\sigma_{x^{(i^{\prime},j^{\prime})}}} \tag{2}\]  
where _cov_ represents the covariance operator and \(\sigma\) represents the standard deviation. Figure 4 shows that the spatial correlation between different urban areas depended not only on the their proximity, but also on many external factors. For example, Cell (3,4) was the same distance from cell (4,3) to target cell (5,5), but the correlation coefficient was very different (0.35 and 0.96). Generally, the change in traffic was not necessarily highly related to neighboring cells and may also be strongly related to non-adjacent cells. However, the traditional convolution can only extract the information of neighboring cells. Therefore, we needed to find new ways to get potentially relevant information.
## 3 Background
### Machine Learning
In addition, the authors in [47] demonstrated how machine learning techniques can improve routing strategies in vehicular networks, including implementing a software-defined networking (SDN) architecture that incorporates a neural network (NN)-based mobility prediction. This approach guarantees uninterrupted connectivity and reduces latency [48], as well as using Q-learning-based hierarchical routing instead of traditional routing tables. This uses self-constructed adaptive Q-value tables that are based on local traffic flow. This approach enables them to achieve high delivery rates and balance network load [49]. These examples demonstrate how machine learning can effectively handle the many forms of communication and Quality of Service (QoS) needs in automotive networks, beyond the performance limitations of conventional networking systems. Moreover, the authors in [50] demonstrated the effectiveness of machine learning in predicting traffic flows, a typical time-series challenge. Because traffic flows are stochastic and nonlinear, conventional methods that rely on autoregressive moving averages frequently fail to account for these characteristics. Effectively enhancing the accuracy of predictions has been accomplished with the help of ML techniques such as kNN and support vector regression (SVR). A fundamental endeavor in intelligent transportation systems, behavior prediction is another area in which ML performs well. The authors in [51] showed that SVMs can predict lane changes. Overall, machine learning in the environment of IoV has shown its adaptability and efficacy in solving a wide range of challenges associated with predicting traffic patterns, optimizing routes, and forecasting behavior in vehicular networks.
## 1 Introduction
In this paper, we present an investigation of impact damage detection using Carbon Fiber Sensors (CFSs). Publications dealing with CFSs have presented good results on mechanical strain monitoring [23; 24; 25], microcracks monitoring, and delamination monitoring [26]. Our previous published work has also shown good prospects for impact damage monitoring [27]. Compared to other SHM methods CFS detects damages along the sensor fiber. Therefore, this method has a high potential to be used for impact loaded leading edges, etc. [PERSON] et al. used CFS to detect microcracking along the length of the fiber, which was wrapped around pressure vessels [26]. An advantage of this type of sensor is also the possibility to integrate the sensor into the material during manufacturing. The sensor does not compromise the integrity of the material. Another aspect compared to other monitoring system are the costs of the measuring systems. Since the CFS is a piezoresistive sensor cost effective electronic devices can be applied compared to optical systems (i.e., fiber Bragg grating) or piezoelectric SHM systems. CFSs can be possibly used for remote monitoring. The CFSs should work as an indicator of impact damage. The work presented here reports on a comparative study of CFSs made of three different carbon fiber tow. The CFSs were integrated into specimens made of standard prepreg material and were manufactured using autoclave technology. Flat specimens made of 8 layers of woven glass prepreg sheets were manufactured with embedded CFSs. The CFSs were embedded in three different positions (close to the top of the specimen, close to the bottom of the specimen, and in the middle of the lay-up). The specimens were exposed to cyclic flexural loading before and after impact loading. An inspection of the specimens was carried out using thermographic inspection and computer tomography (CT). The influence of the ultimate elongation of the material on impact damage detection is studied. The relationship between the mechanical loading and the measured electrical resistance of the CFSs is also investigated. The effects of the arrangement of the CFS in the composite lay-up are discussed.