esa-sceva/satcom-chunk-collection huggingface dataset:
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 5 Vulnerability Analysis of CRN Under Emergency
### Disruption of CRN Edges
#### 5.2.2 Deliberate Attack  
In the case of deliberate attacks, this section selects edge weights based on network characteristics and conducts deliberate attacks on the top twelve main edges in terms of edge betweenness.  
As shown in Figure 13, intentional attacks on the edges with high weights or betweenness rankings in the network can lead to network cascade failure. Some edge failures trigger severe cascading failures, indicating that they play a key role in the CRN, connecting multiple important nodes or subnetworks. However, some edges with high weights or betweenness rankings have almost no impact on the network, possibly because their positions in the network are relatively unimportant, or the connections between the nodes they link have weak dependencies. The topology of the network determines the connection methods and importance between edges and nodes. Edges with high weights or betweenness rankings do not necessarily always cause severe cascading failures; their impact also depends on the overall layout of the network and the dependencies between nodes.  
Therefore, we changed the strategy of deliberately attacking edges to further analyze the impact of deliberate attacks on edges in the network. As can be seen from Table 2, among the edges with high edge betweenness and weight rankings, many connect to the same node. However, as shown in Figure 1, some edges have a minimal impact on the network, while others have a significant impact. Therefore, we chose to remove edges connecting the same nodes in the network to observe the effect of deliberate attacks on edges in the network. As shown in Figure 14a, when removing other edges that share common points with the Xi'an-Chongqing edge, some edges accelerated the propagation of cascading failures, while others slowed it down. This suggests that the removed collinear edges may be a key connection for failure propagation and could potentially disrupt cascading failure  
Figure 12: The cascading failure propagation process of CRN point failure under random failure.  
propagation. As shown in Figure 13b, when removing two edges with high betweenness rankings (Yiwu-Nanjing-Suzhou) or two edges with high weight rankings (Dezhou-Jinan-Xuzhou), there was almost no impact on the network. According to Figure 3, these edges are located in more peripheral areas of the network. Comparing Figure 14b, after removing the common point edge of Nanchang-Ganzhou, a larger-scale cascading failure occurred in the network; removing three common point edges of Taiyuan also had little impact on the network. Therefore, removing one or more edges with a high betweenness or weight may not cause serious damage to the network. Edges may play different roles in the network; removing certain edges may accelerate the propagation of cascading failures, while removing other edges may slow this propagation down.
## 2 Related Work
### MMSE Combining  
MMSE combining is a signal processing technique used to combine the signals received from multiple antennas before decoding them. The goal of MMSE combining is to minimize the mean squared error between the sender signal and the received signal, subject to some constraints on the channel model and noise. It can be used to enhance the efficiency of the system by reducing the effects of interference and noise [15].  
\[\mathrm{V}_{k}=\left(\sum\
olimits_{l=1}^{K}\ \left(\mathrm{\hat{f}}_{l} \mathrm{\hat{F}}_{l}\mathrm{\hat{H}}_{l}^{H}+\mathrm{C}_{l}^{\prime}\right)+ \sigma^{2}\mathrm{I}_{ML}\right)^{-1}\mathrm{\hat{f}}_{k}\mathrm{F}_{k} \tag{9}\]
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Wireless Network
#### 3.1.1 Physical Layer  
The physical layer is the only layer in the OSI model interacts with actual hardware, transmission, and signaling mechanisms. The physical layer transmits raw bits over a physical data link connecting network nodes by converting them to electrical pulses, representing the binary data. The electric pulses are then converted to electromagnetic waves to be transmitted wirelessly. On the other hand, the physical layer specifies the data transmission mechanism and how data can move between devices.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 1 Introduction
This convolution layer extracts the trend of crowd flow within the neighboring time slices, filters the data noise, and enhances the correlation between the neighboring time slice data and the prediction results. * Finally, the above modules are integrated with the dynamic graph convolution module to generate a traffic prediction model. Extensive experiments were run on two real datasets, and the results demonstrate that the model works better than existing baseline prediction methods. The remainder of the paper is structured as follows. In Section 2, we introduce the current state of national and international research in the field of crowd flow prediction, as well as graph convolution and attention mechanisms. Section 3 gives a description of the prediction problem and the details of the model implementation. In Section 4, we introduce the data sources, and then analyze the experimental results and compare their performance with other baseline models. This paper is summarized in Section 5.
## Materials and Methods
### qPCR Analysis  
qPCR reactions were performed with previously designed pairs of primers for the expression analysis of genes for the metabolism of PAs (_odc_, _samdc_, _spds_, and _oaz_) and LOV biosynthesis (_lovA_, _lovB_, _lovC_, _lovD_, _lovE_, _lovF_, and _lovG_) (Table 1) [10; 12]. Reactions and processing of the results were carried out in accordance with the protocol [26]. To normalize the data of mRNA expression levels, we used two reference genes, actin and the ubiquitin-conjugating enzyme E2 6 (Ube2 6; EC 2.3.2.23). Pairs of primers for these housekeeping genes were previously selected [10].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Primer** & **Gene** & **Product**, **Function** & **Oligonucleotide (Sequence 5–3)** & **Source Sequence** \\ \hline Act\_RT\_F & _actin_ & A major component of the cytoskeleton & CCACCTTACCACTITCAAATCC & XM\_O01209659.1, [10] \\ \hline E2\_6\_RT\_F & _uk2 6_ & Ubiquitin-conjugating enzyme & TGACCAGCAAGAATGACA & XM\_O01211932.1, [10] \\ E2\_6\_RT\_R & & E2 6 & TTATCTTTCATCCATTTCCA & XM\_O01211932.1, [10] \\ \hline LovA\_F & _levA_ & Dihydromanocolin L & GCGATTCACAACCACTCTTCTG & AH007774.2, [12] \\ LovA\_R & _levA_ & monooxygenase & AGACCCCAAGCTCCAAGTCCCAAG & AH151722, [12] \\ \hline LovB\_F1 & _levB_ & Lovastatin nonaketable synthase & GCCCCTATTATTAAAACCCTGAGATT & AH151722, [12] \\ \hline LovC\_F1 & _levC_ & & AGCTGCCAAGAGCTTCTTCATCATCTG & \\ \hline LovC\_F1 & _levC_ & Enoyl reductase & GACTCCGGTGTATACACTGCG & \\ \hline LovD\_F1 & & 2-Methylbutylryn/monocolin J & GGATCTGAGAGAGAGAGAGAG & AH0077742, [10] \\ \hline LovD\_R1 & _levD_ & transesterase & CAGGCTTCCAAGTTGAAAAC & AH0077742, [10] \\ \hline LovE\_F & _levE_ & GAL4-like transcription factor & TGCATACCGTCTACAGTGAGC & \\ \hline LovF\_F & _levF_ & Lovastatin diabetic synthase & TGCTACTTTG
Sensors_**2012**, _12_, 16498-16521; doi:10.3390/s121216498  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Autonomous Manoeuvring Systems for Collision Avoidance on Single Carriageway Roads**  
**[PERSON] *, [PERSON] and [PERSON]**  
University Institute for Automobile Research (INSIA), Technical University of Madrid (UPM), 28040 Madrid, Spain; E-Mails: [EMAIL_ADDRESS] (J.E.N.); [EMAIL_ADDRESS] (O.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-913-365-300.  
_Received: 13 August 2012; in revised form: 19 November 2012 / Accepted: 27 November 2012 / Published: 29 November 2012_  
**Abstract:** The accurate perception of the surroundings of a vehicle has been the subject of study of numerous automotive researchers for many years. Although several projects in this area have been successfully completed, very few prototypes have actually been industrialized and installed in mass produced cars. This indicates that these research efforts must continue in order to improve the present systems. Moreover, the trend to include communication systems in vehicles extends the potential of these perception systems transmitting their information via wireless to other vehicles that may be affected by the surveyed environment. In this paper we present a forward collision warning system based on a laser scanner that is able to detect several potential danger situations. Decision algorithms try to determine the most convenient manoeuvre when evaluating the obstacles' positions and speeds, road geometry, _etc._ Once detected, the presented system can act on the actuators of the ego-vehicle as well as transmit this information to other vehicles circulating in the same area using vehicle-to-vehicle communications. The system has been tested for overtaking manoeuvres under different scenarios and the correct actions have been performed.  
**Keywords:** intelligent transport systems; laser scanner; cooperative warning; wireless communications
###### Abstract  
The characteristic of the satellite repeat shift time can reflect the status of the satellite operation, and is also one of the key factors of the sidereal filtering multipath correction. Although some methods have been developed to calculate the repeat shift time, few efforts have been made to analyze and compare the performance of this feature for the GPS (Global Positioning System), BDS (BeiDou System), and Galileo in depth. Hence, three methods used for calculating the repeat shift time are presented, and used to compare and analyze the three global systems in depth, named the broadcast ephemeris method (BEM), correlation coefficient method (CCM), and aspect repeat time method (ARTM). The experiment results show that the repeat shift time of each satellite is different. Also, the difference between the maximum and minimum varies from different systems. The maximum difference is about 25 s for the BDS IGSO (Inclined Geosynchronous Orbit) and the minimum is merely 10 s for the GPS system. Furthermore, for the same satellite, the shift time calculated by the three methods is almost identical, and the maximum difference is only about 7 s between the CCM and the ARTM method for the BDS MEO (Medium Earth Orbit) satellite. Although the repeat shift time is different daily for the same satellite and the same method, the changes are very small. Moreover, in terms of the STD (Standard Deviation) of the BS (between satellites) and MS (mean shift for the same satellite), the GPS system is the best, the performance of the BDS system is medium, and the Galileo performs slightly worse than the GPS and BDS.  
satellite repeat period; satellite repeat shift time; GPS; BDS; Galileo +
Footnote †: journal:
## 1 Introduction
The economic and environmental parameters are secondary influencing elements in many cases. In some papers, novel solar constructions are investigated without using them because it is clear that they would affect the justification of the presented concept. Since the thermal performances of the SCs depend on design (geometric parameters and material choices), working conditions, meteorological data, market factors and ecological aspects, analyses should be comprehensive and multidisciplinary. Thermal management plays a crucial role in ensuring efficiency and reliability in energy conversion systems, especially in high-temperature operating conditions. Recent studies, such as those on power converters, emphasize the importance of considering the temperature-dependent thermal properties of materials to enhance prediction accuracy and system performance [41]. In particular, temperature-dependent thermal networks, such as the improved Cauer-type thermal model for IGBT modules proposed in [42], enable a more precise estimation of thermal impedance, significantly reducing computational costs while improving heat transfer modeling accuracy [43]. Such methodologies can be effectively adapted to solar thermal applications, where accurate thermal modeling is essential for optimizing energy conversion efficiency [44]. After presenting (in [30]) the simple and fast mathematical method (based on an iterative calculation algorithm with a thermal resistance model and multi-criteria analysis, i.e., MCA) for glass tube collector (GTC) dimensioning and selection, this paper continues to investigate the single-glazed tube collector (SGTC) with a VL--the non-conventional solar structure composed of three components: GT (the main feature of the ETCs), the selective (using SnAl\({}_{2}\)O\({}_{3}\) coating) flat absorber plate, i.e., FAP (the main feature of the FPCs), and a VL between them. The flow channel is integrated on the underside of the absorber, so that the water flows directly (single-stage) using the force of gravity. The main goal of the paper is the new method for the geometric (dimensional) optimization of the mentioned solar concept based on two steps: the development of a unique (supplemented with heat power and thermal efficiency models--in comparison with [30]) iterative calculation algorithm (step one) and the application of a double MCA (step two). The comprehensive and multidisciplinary approach of this paper is reflected in the following facts: (1) the iterative algorithm with useful heat power and thermal efficiency models uses three variables (absorber temperature, ambient temperature and wind speed), and (2) the MCA uses eight variables divided into three groups: geometric (mass, surface occupation, total surface occupation and volume occupation), economic (manufacturing costs and exploitation costs) and ecological (embodied energy and greenhouse gas emission). The developed mathematical method can be applied to any other solar device. The authors hope that this paper will be helpful in future investigations in the solar energy field.
## 3 Positioning Method
Benefiting from the sequential exclusion algorithm and the fix-and-hold strategy applied for ambiguity resolution, the time epochs achieving successful FAR has increased from about 80% to 95% in the suburban scenario compared to the case without using the sequential exclusion algorithm. For the time epochs with partial ambiguity resolution (PAR), the average fix-rate, i.e., the averaged number of the fixed ambiguities among all the possible ambiguities, has also increased from about 55% to 91% upon applying the sequential exclusion algorithm. In the open-sky scenario, the FAR was achieved in almost all epochs, i.e., above 99.8%, whether or not the sequential exclusion algorithm is used. Note that the PAR aims to resolve a part of the ambiguities in case that the FAR cannot be achieved. Different from the PAR based on a pre-defined formal ambiguity success rate (ASR) [(40; 41)], the PAR in this study refers to the resolution of a subset of the remaining float ambiguities, which allows the ratio test to be passed. The selection of the subset was determined based on the sequential exclusion algorithm discussed before and given in Appendices A and B for the baseline-known and -unknown cases, respectively. The algorithm is developed to deal with the miss-modelled effects that are difficult to be covered by the model and the formal ASR.
## 3 Results
### Effect of Nano-Silica Additive under Different Ambient Pressures and Constant Inlet Pressure  
Figure 8 shows the influence of ambient pressure on the rock erosion characteristics of the jets under constant inlet pressure. In this group of experiments, the ambient pressure increased from 5 MPa to 25 MPa. The inlet pressure was set at 50 MPa, and the fluid temperature was set at a constant value of 340 K. The standoff distances were three and five for the pure and nano-silica SC-CO\({}_{2}\) jet, respectively. The mass fractions of nano-silica additive were 0 wt %, 3 wt %, 6 wt %, 12 wt %, and 18 wt %, respectively.  
In the figure, it can be observed that, under a constant inlet pressure, the rock erosion volume for all of the jets decreases with the increase of ambient pressure. It is suggested that the increase in ambient pressure can weaken the erosion capability of all of the jets. This is because, when the inlet pressure is constant, the increase of ambient pressure can cause a dramatic reduction in the impact energy of the jet, due to the decrease of the pressure drop across the nozzles. Moreover, the effect of the ambient pressure under constant inlet pressure on the erosion ability of the nano-silica SC-CO\({}_{2}\) jet, largely depends on the mass fraction of the nano-silica. To be more specific, the erosion volume is most significantly increased by a mass fraction of 12 wt %, at an ambient pressure of 5 MPa. The maximum erosion volume is 997.81 mm\({}^{3}\), which is about 1.6 times larger than that of the pure jet. Furthermore, with the increase of ambient pressure, the optimum mass fraction is 12 wt %. In addition, the order of the erosion ability of the jets with different mass fractions of the nano-silica additive remains unchanged at each ambient pressure, so the jet with a mass fraction of 12 wt % is always the best, and the 18 wt % mass fraction always performs the worst.
## 4 \"Multistage Anchorage System + CFST\" Combined Support Technology  
### Establishment of the New Support Method  
According to the above analysis, the disadvantages of the initial support are as follows:  
1. The U-shaped steel strength is insufficient under broken rock and high ground stress, as the steel tended to bend and even began to break, especially on the straight wall foot.
2. Owing to the large fractured zone and zonal disintegration, the surrounding rock was not pierced completely for coupling as the bolts were not sufficiently long, which resulted in a low anchorage force and easy failure.
3. There is no feasible measure to handle the floor deformation where the roadway deformation occurs and gradually develops.  
Therefore, to make up for the defects of the initial support method, a \"multistage anchorage system + CFST\" joint support scheme is presented herein. The compound supporting structure includes a multistage anchorage system (bolt, cable, mesh, shotcrete) and, later, a high-strength rigid supporting structure (CFST). The design of the new support is displayed in Figure 12.
## 3 Methodology
### Pseudocode and Flow Chart  
The methodology outlined previously can be precisely articulated through pseudocode in Algorithm 1 and a flow chart in Figure 1.  
The operational flow of the RRAPS algorithm is shown in Figure 1. The RRAPS functioning is as follows. When a new process enters the ready queue, the CPU loads a high-priority process for execution. If the ready queue is empty, the time quantum will equal process burst time. If multiple processes exist in the queue, the time quantum is determined by averaging their burst times. If a process completes within this time, its priority is decreased and added back to the ready queue with an updated burst time. When a process finishes executing, it is marked as completed. The scheduler examines the available processes in the ready queue and picks the one with the highest priority for execution. This process repeats until all the processes in the ready queue have been executed.  
```
1:New process P Initialized
2:Process P arrives in ready queue (RQ)
3:Load high-priority Process P from the RQ into the CPU to be executed
4:IF (RQ is empty)  Time_Quantum (TQ) = Process_Burst_Time (p_BT) End if
5:IF (RQ is not empty)  TQ = Average (burst_times_of_all_processes_in_ready_queue) End if
6:CPU executes P by TQ time
7:IF (P is terminated)  Marked as completed End if
8:IF (P is not terminated)  IF (p.priority is greater than 1) // 1 is the lowest priority. Priority cannot be less than 1.  p.priority = p.priority - 1 Return p to the RQ with its updated burst time Else Return p to the RQ with its updated burst time
9:IF (RQ is empty)  End algorithm Else  Load the next high-priority Process P from RQ into the CPU to be executed
10:Repeat steps 3 to 9
```  
**Algorithm 1** Round Robin with Adaptive Priority Scheduling (RRAPS)  
Figure 1: RRAPS algorithm flow chart.
## 5 Verification and Discussion  
### Verification against Some Special Cases  
To verify the proposed solution, the following case study is used. The height of soil layer, coefficient of vertical permeability of soil mass and unite weight of water are assumed as follows: \(H\) = 6 m, \(\gamma_{\rm w}\) = 10 kN/m\({}^{3}\), \(k_{\rm v}\) = 1 \(\times\) 10\({}^{-7}\) m/s. The values of the rheological parameter and fractional order for each viscoelastic model are given in Table 2. Considering that external loading is applied instantly and total vertical stress in soil layer is uniform with depth, Figure 4 illustrates the results of excess pore-water pressure obtained from the proposed solutions and existing solutions of [PERSON] et al. [13], [PERSON] et al. [20] and [PERSON] [9]. Figure 4a gives the distribution of excess pore-water pressure with depth, whereas Figure 4b gives the change of excess pore-water pressure with time factor. It can be obviously observed that the results calculated from the proposed solution show good agreement with those from the existing solutions derived from standard four-element model [13], standard Merchant model [20], standard Maxwell model [13] and linear elastic model [9], respectively, for Case I, II, III and IV. Therefore, the results of the proposed solution and some existing theories are identical. Moreover, the proposed solution is more general to predict the consolidation behavior of viscoelastic soils.  
Figure 4: Results of excess pore-water pressure calculated from proposed and existing solutions: (**a**) Distribution with depth at \(T_{\rm v}\) = 0.1 and (**b**) Change with time factor at \(z/H\) = 0.5.
## 2 Background and Related Literature
Cartesian space mapping is used to map human hand fingertip positions to those of a robot hand and is useful for manipulation tasks where precision grasps are desired [36]. Cartesian mapping for telemanipulation can either be implemented using numerical\(-\)based [36; 37] or data\(-\)driven approaches [4; 11; 38]. Numerical methods are easy to implement but often require heuristic modeling to accommodate kinematic differences. In contrast, data\(-\)driven approaches need large labeled datasets for training but can leverage valuable features like approach vectors and fingertip positions. Although both joint and Cartesian space mapping can be adapted to a robot thumb with kinematic dissimilarities, they can yield robot thumb poses that are different from the human thumb, which makes telemanipulation less intuitive [39; 40]. Researchers have proposed hybrid mapping approaches that provide intuitive control for telemanipulation. [PERSON] et al. [39] developed a hybrid mapping between human and robot hands that switches from joint and Cartesian space mapping depending on the executed grasp type. They used joint space for power grasps and Cartesian space for precision grasps. [PERSON] et al. [40] developed a hybrid mapping approach that transitions from joint space to Cartesian space mapping depending on the distance between the thumb and fingertips. In our literature review, we identified various tracking systems and mapping approaches that can effectively track human thumb movements and telemanipulate a robot thumb with kinematic differences. However, employing these systems depends on factors such as cost, portability, and output quality. While wearable systems are less expensive and portable compared to vision systems, they generally do not track the entire CMC joint motion without the need of expensive hardware. As such, a cost\(-\)effective device capable of accurately capturing both the FE and AA motion of the human CMC joint along with a suitable kinematic mapping algorithm to the robot thumb will be desirable and beneficial for research purposes.