esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture  
### System Model  
Secure communication requires the generation of session keys, and mutual authentication between both parties is needed before the generation of the keys. However, authentication protocols typically require both parties to store some information for authentication, which means that each drone in the fleet needs to store the identity information of the entire fleet. The number of drones directly affects the storage cost. The existing key generation algorithms, such as RSA, ECC, etc., are considered to be quite secure, and they pose a great challenge to the computing power and storage of drones when there are a large number of drones involved in P2P communication. Generally speaking, in the entire networked drone system, drones are the most vulnerable to attacks, especially when they are executing missions in the field and are susceptible to physical capture. If the program stored in the non-volatile read-only memory of drones is compromised, the identity information (local and fleet information) and simple key generation algorithms stored in ROM for authentication will be cracked, posing a great threat to the entire network of drones. This study proposes using the ground station as the authentication core to guide drones to read their ID for authentication. Other than a one-time key, the drones store minimal information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
# Detection of Rock Mass Defects Using Seismic Tomography
## 4 Application
### Results
ized veins in this section: Vein 68, which encompasses stopes 68232, 68234, 68234E, 68238W, 68238E, 68240, 68242W, 68242, 68244, 68246W, 68246, 68261 down, 68263 Edown, 68263 Wdown, 68261 up, 68263 Eup, and 68263 Wup; Vein 45, which includes stopes such as 45265E, 5263N, 45263, 45263 NE, 45261W, and 45261E; and Vein 63, which is represented by stope 63234E. Furthermore, statistical data indicate that there are a total of twenty-four voids distributed throughout this section. 3. Figure 10c represents the physical detection results for the third section. The statistical analysis reveals three mineralized veins in this section: Vein 68, encompassing stopes 68334, 68336, 68336E, 68338, 68338, 68340, 68342W, 68342E, 68344, and 68346W; Vein 45, which includes stopes such as 45365 Sup, 45365 Nup, 45365 Sdown, 45365 Ndown, 45363 Sup, 45363 Nup, 45363 Sdown, 45361 Sdown, 45361 Ndown, 45361 Sdown, 45361 Sdown, 45361 Ndown, 45361 Ndown, 45361 Eup, 45361 Edown, 45361 Edown, 45360 up, 45332, and 45332E; and, finally, the singularly identified Vein 63, containing stope 63334. Furthermore, statistical data indicate a total of thirty goafs distributed throughout the third section. 4. Figure 10d represents the physical detection results for the fourth section. The statistical analysis reveals four distinct mineralized veins within this section: Vein 68, which encompasses stopes 68434, 68436, 68436N, 68438W, 68438, 68440, 68442W, 68442E, 68444, and 68442E; Vein 45, comprising stopes 45465E, 45463, 45461N, 45461S, 45461E, 45460, and 45432; the third vein, identified as the Vein 43, that includes stopes 43438 Wup, 43438 Wdown, 43438 Eup, 43438 Edown, 43440 up, and, finally, the designated area known as Vein 63, which consists of stopes 63440, 63442W, and 63442E. Furthermore, statistical data indicate a total of twenty-six goafs distributed throughout the fourth section. 5. Figure 10e represents the physical detection results for the fifth section. The statistical analysis reveals three distinct mineralized veins within this section: Vein 68, comprising stopes 68534, 68536, 68538W, 68538, 68540, 68542W, 68542E, 68544, and 6546; Vein 63, including stopes 63538E, 63540, 63542, and 63542W; and Vein 55, encompassing stopes 55540 and 55540E. Furthermore, it is noted that a total of fifteen goafs are distributed throughout the fifth section. 6. Figure 10f represents the physical detection results for the sixth section. The statistical analysis reveals the presence of three mineralized veins within this section: Vein 68, encompassing stopes 68636E, 68638, 68640, 68642S, 68642N, 68642E, 68644, 68646, and 68646W; Vein 63, with its associated stope 63642; and Vein 55, which includes stopes 55640 and 55640E. Furthermore, the statistical data indicate a total of twelve goafs distributed throughout the sixth section. Figure 10: _Cont._
## 3 Results and Discussion
### Statistical Analysis of the VOCs Profile in the Sabrina and Elide Strawberry Samples
#### 3.2.1 Linear Mixed-Effect Modelling (LME) on Elide Data Set
On the contrary, 15 (E16, E20, E21, E30, E32, Ald6, Ac5, Ac6, Ac9, T3, T8, F3, and L1-L3) were more abundant in fruit produced using the N5 film, while the amount of the remaining 11 volatile constituents was comparable between the treatments (N5 and N18) (Figure 1; Table S1). Among the 43 volatiles predominant in Elide N18 fruit, methyl butyrate (E3), methyl 2-methylbutyrate (E4), ethyl 3-methylbutyrate (E8), butyl acetate (E9), ethyl pentanoate (E11), pentyl acetate (E13), methyl hexanoate (E14), ethyl hexanoate (E15), hexyl butyrate (E24), ethyl octanoate (E25), hexyl hexanoate (E31), nonanal (Ald3), benzaldehyde (Ald4), 2-methylbutanoic acid (Ac4), _cis_-linaloid oxide (T2), linalool (T4), mesifuran (F1), and furaneol (F2) have been indicated as positively correlated with the attributes \"sweet\" and/or \"aromatic\" [10; 17; 18]. Some of these volatiles, including E3, E11, E14, E15, Ald1, Ald3, Ald4, Al5, Ac4, and F1 and F2, are crucial to the characteristic fruity notes of mature strawberries [10; 23]. On the contrary, ethyl 2-methylbutyrate (E7) and 2-hexenal (Ald2) have shown negative correlations with \"acceptance\" and \"aromatic\" retronasal sensation, respectively [17]. Among the 15 VOCs (E16, E20, E21, E30, E32, Ald6, Ac5, Ac6, Ac9, T3, T8, F3, and L1-L3) more abundant in N5 fruit, only \(\gamma\)-decalactone (L2) and \(\gamma\)-dodecalactone (L3) have been indicated directly correlated with both sweetness and liking independently of sugars, while isoamyl butyrate (E16) and _trans_-2-hexen-1-ol-acetate (E20) are inversely associated to consumers' acceptance [10; 17]. According to these results, the N18 strawberries could be preferred by the consumers compared to the N5 counterparts, as it induces the development of a larger number of volatiles with a positive impact on the consumers' preference. Moreover, the higher number of VOCs associated with ripe fruit in N18 than in N5 suggests that N18 film anticipates the ripening time of the fruit.
## 5 Robustness Analysis
### Analysis of Navigation and Positioning Errors under Different Noise Intensities  
The RINEX2.1 format was used to describe the signal strength. According to the official RINEX2.1 format documentation, the signal strength value range of the observation value is 1-9 [38], with better the observation quality at larger values, where 5 is the critical point for a good quality observation value. We assume that the noise intensity corresponding to Gaussian white noise is 1, then according to Equation (35):  
Figure 9: Error statistics.  
\[SNR_{0}=\frac{1}{\textit{Noise intensity}} \tag{39}\]  
for other noisy environments, there are:  
\[SNR=\frac{1}{k\times\textit{Noise intensity}}=\frac{1}{k}SNR_{0} \tag{40}\]  
where \(k\) is the noise intensity coefficient. Here, we take MSR = 0.55 and take \(k\) = 1, \(k\) = 5, and \(k\) = 9 for simulation according to the reference value given in the RINEX2.1 format. The results are shown in Figure 12.  
Figure 10: Error under different MSR.  
Figure 11: Error statistics.  
Figure 12: Errors under Different Noise Intensities.  
From Figure 12, we find that when noise is added, various algorithms have certain fluctuations, where the smaller the k, the smaller the fluctuation, which is consistent with Formula (40). We calculate the errors in these cases separately, and the results are shown in Figure 13.  
Figure 13 shows that even when the SNR is very low, that is, when \(k=1\), and we adopt the algorithm based on adjacent orbits, the algorithm's longitude, latitude, and altitude mean and standard deviation performance are \(-0.1081\times 10^{-4\circ}\), \(0.5142\times 10^{-4\circ}\), \(0.8353\) m, and \(1.4920\times 10^{-4\circ}\), \(1.9787\times 10^{-4\circ}\), \(3.9934\) m, respectively, which can also meet the location service needs of most users.
## 6 Conclusions  
In this paper, we proposed a weather-conscious AMC method for satellite-related UNC. Firstly, the satellite-to-ground scenario was modeled and formulated into an MDP problem. Then, the proposed framework was depicted, which contained the DL-based estimation model and the DRL-based decision model. The estimation model was based on LSTM, which remembered historical information and was responsible for acquiring information from the environment and predicting satellite-to-ground channel states. The decision model was designed based on the actor-critic network. The actor-network in the decision model was responsible for selecting a proper coding method, and the critic network scored the selection of the actor-network. Within our proposed method, the real-time global weather and historical channel information were fully considered, and therefore, the accuracy of channel estimations could be improved. The designed decision model can intelligently switch coding schemes in advance, thus increasing the total throughput of satellite-to-ground communications. Simulations were carried out by using the LSTM network and actor-critic network to verify the performance of the proposed method. Results showed that  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Throughput** \\ \hline Baseline (BER) & 96,316 \\ Baseline (PER) & 114,763 \\ Proposed method & 118,358 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Throughput of Different Methods.  
Figure 8: Comparison of PER of different methods at different SNRs.  
our estimation model outperformed three existing ones, including SVR, linear soothing, and exponential smoothing. It was also demonstrated that the proposed method improved the throughput by 3.1% over the BER-based and PER-based look-up table method. This work can be helpful to realize the internet connectivity service everywhere in the UNC.  
Conceptualization, S.Y. and Y.Z. ([PERSON]); investigation, S.Z. and G.Y.; methodology, S.Z.; software, S.Z.; supervision, S.Y and Y.Z. ([PERSON]); validation, S.Z. and G.Y.; writing--original draft, S.Z.; writing--review and editing, S.Y. and Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Science and Technology of the People's Republic of China, for funding this research work through the project number 2020 YFB1806000.  
The data presented in this study are available in [[https://github.com/zsq95919/Starlink_Simulation_Dataset](https://github.com/zsq95919/Starlink_Simulation_Dataset)]([https://github.com/zsq95919/Starlink_Simulation_Dataset](https://github.com/zsq95919/Starlink_Simulation_Dataset)) (accessed on 16 March 2022).  
The authors declare no conflict of interest.
###### Abstract  
This paper addresses the challenge of synchronizing data acquisition from independent sensor systems in a local network. The network comprises microcontroller-based systems that collect data from physical sensors used for monitoring human gait. The synchronized data are transmitted to a PC or cloud storage through a central controller. The performed research proposes a solution for effectively synchronizing the data acquisition using two alternative data-synchronization approaches. Additionally, it explores techniques to handle varying amounts of data from different sensor types. The experimental research validates the proposed solution by providing trial results and stability evaluations and comparing them to the human-gait-monitoring system requirements. The alternative data-transmission method was used to compare the data-transmission quality and data-loss rate. The developed algorithm allows data acquisition from six pressure sensors and two accelerometer/gyroscope modules, ensuring a 24.6 Hz sampling rate and 1 ms synchronization accuracy. The obtained results prove the algorithm's suitability for human-gait monitoring under its regular activity. The paper concludes with discussions and key insights derived from the obtained results.  
sensor network; remote data transfer; latency; network synchronization; gait analysis Article
# Security-Oriented Cyber-Physical Risk Assessment for Cyberattacks on Distribution System
## 2 CPPDS Framework  
According to the types of connected information equipment and the distinct functions, CPPDS can be divided into three levels: distribution network backbone layer, access layer, and terminal layer. On this basis, the framework of CPPDS as shown in Figure 1 is constructed. The backbone layer includes the main distribution station system, supervisory control and data acquisition (SCADA) server, management information system (MIS) server, etc. The main distribution station and each slave distribution station are connected by synchronous digital hierarchy (SDH) optical communication technology. The access layer uses Ethernet passive optical network (EPON) technology to connect the slave distribution station and each distribution terminal to realize the real-time communication. EPON is composed of an optical line terminal (OLT), optical network unit (ONU) and optical distribution network (ODN). The terminal layer includes feeders, circuit breakers, section switches, and other power components, as well as the feeder remote terminal units (FRTUs) and other intelligent distribution terminals.  
From the perspective of cybersecurity, the control center of the power plants or substations usually operates in a closed way with physical isolation. Thus, there are relatively few attack access points that can be exploited by hackers in the control system. Considering the cybersecurity of CPPDS, the main distribution station is usually equipped with relatively complete defense measures, such as the intrusion detection system (IDS), firewalls, and data encryption. However, the terminal units controlled by the slave distribution station lack complete physical protection. In addition, the security of data transmission protocol in the information system of the slave station is relatively low. Consequently, the risk of a cyberattack faced by the slave distribution station is relatively large. Therefore, it is particularly important to accurately evaluate the impact of a cyberattack on it and improve the cybersecurity.
## 2 Materials and Methods
### Methods
#### 2.2.1 Temporal Expansion Model  
A.  Sinusoidal model  
According to [14], the \(R_{ni}\) under clear sky during daytime was thought to be theoretically conforming to the sinusoidal model as Equation (8):  
\[R_{n}(t)=R_{n\_max}sin\left[\left(\frac{t-t_{rise}}{t_{set}-t_{rise}}\right) \pi\right] \tag{8}\]  
where \(t_{rise}\) and \(t_{set}\) are the sunrise and sunset times, respectively, and \(R_{n\_max}\) is the maximum \(R_{n}\) in a day. Hence, the \(R_{nD}\) could be obtained by  
\[R_{nD}=\frac{\int_{t_{rise}}^{t_{set}}R_{n}(t)}{\int_{t_{rise}}^{t_{set}}dt}= \frac{\int_{t_{rise}}^{t_{set}}R_{n}(t)}{LDt}=\frac{2R_{n\_max}}{\pi} \tag{9}\]  
where \(LDt\) (unit: hour) is defined as the daytime lasting hours between sunset and sunrise, and it was calculated by  
\[LDt=2\times\frac{180}{15\pi}\times arccos(tan\delta tan\gamma) \tag{10}\]  
\[\delta=0.409 sin\left(\frac{2\pi doy}{365}-1.39\right) \tag{10a}\]  
where \(\gamma\) is the latitude (unit: rad) and \(\delta\) is the solar declination (unit: rad). B. Two existing \(C_{d}\) models (\(S\) Model and \(R\) Model) and new \(C_{d}\) model  
Aware of the limitations of setting \(C_{d}\) as a constant, [PERSON] et al. [17] proposed \(S\) Model to calculate \(C_{d}\) by linking it with the second-degree polynomial equation of _doy_ based on the ground measurements from the east coast of the Iberian Peninsula. \(S\) Model was designed for three specific local times including 12:00, 13:00, and 14:00 hours in a uniform format as Equation (11):  
\[C_{d}=a_{1}doy^{2}+a_{2}doy+a_{3} \tag{11}\]where \(a_{1}\), \(a_{2}\), and \(a_{3}\) are the empirical coefficients and should be defined separately for the three moments(see Section 3.1.1). This model could characterize the variations in clear-sky \(C_{d}\) in a year well, but with a heavy saturation issue. Afterwards, based on the ground measurements from Tandil Argentina during 2007-2009, [PERSON] and [PERSON] [19] found that a linear relationship existed between \(R_{nd}\) and \(R_{ni}\). Hence, they proposed \(R\) Model to calculate \(C_{d}\) as follows:  
\[C_{d}=b_{1}-\frac{b_{2}}{R_{ni}} \tag{12}\]  
where \(b_{1}\) and \(b_{2}\) are the empirical coefficients and were defined as 0.43 and 54 in the original study. However, being limited by the available ground measurements, \(R\) Model only works for the averaged \(R_{ni}\) during 10:00-11:00 h. The authors validated the \(C_{d}\) from \(R\) Model through the estimate \(R_{nd}\) with a Bias of 2.0 Wm\({}^{-2}\) and a root mean square error (RMSE) of 12.0 Wm\({}^{-2}\), respectively. However, the accuracy of the \(C_{d}\) calculated from this model heavily depends on \(R_{ni}\), especially when the value of \(R_{ni}\) was small. So far, the \(S\) and \(R\) model has not been widely validated. In this study, a new \(C_{d}\) model (namely New Model) that could work under clear sky at any moment during 9:30-14:30 hours has been proposed. In this model, the \(R_{nd}\) under clear sky could be regarded as consisting of \(R_{nD}\) and nighttime-averaged as \(R_{n}\) (\(R_{nN}\)).
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 5 Conclusions  
This paper presents the results from wave-flume experiments exploring the response of pile-seabed systems under regular wave action and discusses the effect of pile diameter, pile cross-section, pile stiffness and wave height. From the results of the experiments, the following conclusions can be drawn:  
1. During 640 s of regular wave loading, the pore pressure in Sample 1 fine sandy seabed varied slightly and its amplitude decreased with the increase in depth. The prior analytical method for predicting pore pressure agreed well with the measured values but led to a relatively large error for the fine sandy seabed embedded with piles. Around a pile, pore pressure increased by 15.7%-25.9% at different depths.  
2. In Sample 2 silty seabed, pore pressure gradually built up with wave action and reached higher values at deep locations. A rapidly increasing period of pore pressure around the piles appeared earlier than in pure silty seabed. Pile failure occurred soon, with liquefaction occurring at \(z=15\) cm (half the embedment) and being accompanied by higher oscillating pore pressure.  
3. As the pile diameter increased from 3 cm to 5 cm, residual pore pressure decreased by 57.0%, 35.2 % and 37.4 % with depths increasing from 5 cm to 25 cm in silty seabed. By comparing the calculated value to the measured value, it was found that the displacement response of small-diameter piles was more possibly enlarged by the induced pore pressure.  
4. Compared with normal piles, the use of fins led to a decrease in pore-pressure amplitude by 4.1%-10.4% in fine sandy seabed and the decrease in residual pore pressure was also apparent in silty seabed. The permanent displacement of the finned piles developed much more slowly and reached low peak values in both seabed types.  
5. Wave height was a major factor in the responses of piles and seabed. As wave height increased from 8 cm to 10 cm, pore pressure increased by 12.1%-17.3% in fine sandy seabed and rapid stage of displacement accumulation was apparent in silty seabed.  
Conceptualization, T.H. and Y.T.; methodology, T.H.; software, T.H.; validation, T.H., Y.T. and G.D.; formal analysis, T.H.; investigation, G.D.; resources, G.D.; data curation, A.J.; writing--original draft preparation, T.H.; writing--review and editing, Y.T. and G.D.; visualization, Y.T.; supervision, G.D.; project administration, G.D.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
This paper was financially supported by Fundamental Research Funds for the Central Universities (B200202050), China Communications Construction Company (2018-ZJKJ-01) and National Natural Science Foundation of China (No.51408185).  
Not applicable.  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# AI-For-Mobility--A New Research Platform for AI-Based Control Methods
### Related Work  
The autonomous vehicle Stanley [5] was developed by a research team from the Stanford University for the DARPA Grand Challenge. The vehicle platform is based on a Volkswagen Touareg R5 and includes a drive-by-wire system. As part of the perception layer, Stanley is equipped with several different sensors, including laser rangefinders, radars, and cameras to perceive its environment. The control layer enables steering, acceleration, and deceleration of the vehicle in order to follow a trajectory generated by a path planner. The steering controller is based on a nonlinear feedback function with the cross-track error as function input. The longitudinal motion is determined by a PI error metric which either applies the brakes or the throttle. Besides lateral and longitudinal guidance, the vehicle does not enable controlling actuators for other applications, such as a semi-active suspension.  
The company Five.ai [6] developed an autonomous vehicle based on a Ford Fusion platform. The vehicle includes a drive-by-wire system and is equipped with a rich set of perception sensors, including lidars, radars and cameras. Besides motion planning [7], Five.ai's main research topics are image classification [8], object tracking [9; 10] and segmentation [11; 12]. However, to the best of our knowledge, their research does not focus on the development of complex control algorithms for vehicle dynamics.  
The Virtual Vehicle Research Center [13] developed an Automated Drive Demonstrator which is based on a Ford Mondeo vehicle platform including a drive-by-wire kit. Their research topics, among many others, include real-time sensor fusion, validation and test of automated driving functions, novel human-machine interface concepts for increased passenger comfort and fail-operational active safety.  
In ref. [14], an autonomous vehicle based on a KIA Soul platform was presented with a minimal sensor suite. For perception tasks, two cameras, a radar and a lidar sensor were integrated into the vehicle and neural networks were trained for object detection tasks. For longitudinal control, a PID feedback controller is applied in order to minimize the error between the current values of the motion in the longitudinal direction and the target values. For lateral control, an approach based on classical Model-Predictive Control (MPC) is utilized.  
In contrast to the autonomous vehicle concepts introduced above, the main conceptualization of the AFM is based on enabling the development of a variety of AI-based control algorithms for different tasks throughout the entire vehicle. Further, it allows addressing all three application layers introduced in Figure 2. In addition to a rich sensor set, the AFM includes powerful computing platforms that enable testing and validating complex AI-based control methods for a wide range of applications, which is not only limited to longitudinal and lateral control, such as semi-active suspension or state estimation approaches.