esa-sceva/satcom-chunk-collection huggingface dataset:
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 4 Systems for Rail Defect Detection Based on Ultrasonic Guided Waves: Architecture
### Land-Based Systems  
The UBRD is a _broken rail detection_ (BRD) system, designed to continuously detect full breaks in rails [50]. Its transducers are tied to the rail and allow for monitoring a span of up to 1 km of track in pitch-catch mode. This system has been already installed on 841 km of tracks in the high-haul Oreline network in South Africa [51; 52]. The system architecture is represented in Figure 11; its operation is based on a simple transmit-receive confirmation protocol. As long as a reliable reception of the ultrasonic signal generated by the transmitter is possible, the integrity of the rail is verified; otherwise, an alarm is set [50; 53].  
The developers of the UBRD system are studying how UGWs can be exploited to detect and monitor a growing defect, and how specific guided modes can be excited in a rail. The use of techniques for the excitation of guided waves different from those based on piezoelectric transducers have been investigated. For example, the use of _electromagnetic acoustic transducers_ (EMATs) has been taken into consideration [55]. In the meantime,  
Figure 11: UBRD system concept [51].  
the reflection characteristics of defects and their dependence on time and environmental conditions are under evaluation [9; 56; 57; 65].  
In principle, UGWs have the potential to monitor a long span of a waveguide using permanently installed transducers in pulse-echo mode. The same transducer array is used to generate the ultrasonic wave and then to measure the echo generated by the wave reflection on a defect. A continuously welded rail is a perfect example of such a waveguide and, as a matter of fact, defects (and welds) act as reflectors for guided waves. A system based on this approach is able to detect the _presence_ of a defect, and to estimate its _magnitude_ and _location_. These results are achieved by monitoring the echoes coming from defects (defect presence), measuring their amplitude (defect magnitude) and the time-of-flight required by the transmitted wave to travel back and forth from the transducer to the defect (defect location) [51].  
An important advantage offered by land-based solutions is represented by the fact that monitoring can be accomplished in near real-time, so that defect growth can be analyzed over time and an alarm set only when a crack assumes a potentially critical size [9; 65].  
Further details about the architecture of the UBRD system are provided in Section 6. In Section 7, its evolution towards an early defect detection system is described. Other projects or studies about land-based systems are illustrated in Section 8.
## 2 Simulation Models of Human Body and HBC Devices
### Whole Human Body Model  
The whole human body model had a height of 1710 mm. The transmitter was placed at the wrist and the off-body receiver was placed in front of the human body as shown in Figure 1a. The floor was not modeled in the present simulation, because the influence of the floor on the transmission is small [12]. The human body model was a homogeneous muscle model with uniform electrical properties. Although the actual human body is composed of various tissues with various electric properties, a qualitative analysis of the transmission characteristics and electric field distribution can be performed using only muscle models. For example, [PERSON] et al. compared electric field distribution using a realistic high-resolution whole-body model and a homogeneous muscle model at 10 MHz and concluded that the simplified model closely resembled that of the realistic model [20]. [PERSON] et al. used the same realistic model and calculated electric field distribution and impedance between the transmitter electrodes by replacing several body tissues by other tissues and proposed a three-layered model composed of skin, fat, and muscle [23]. [PERSON] further compared this three-layered model with a homogeneous muscle model and concluded that the patterns of electric field distribution were similar, whereas there were some differences in quantitative values [24]. Based on these past findings, it was concluded that the homogeneous muscle model was appropriate for analysis of the signal transmission path. The electric properties of the human body model were equal to that of the muscle at 10 MHz. The relative permittivity \(\varepsilon_{\mathrm{r}}\) was 170, and the electrical conductivity \(\sigma\) was 0.62 S/m [25].
## 3 Results and Discussion
### The Relationship between Flowability and Entrapped Air Content  
The relationship of flowability and entrapped air content of UHPC mixtures for every steel fiber addition is plotted in Figure 8. It can be seen that the air content generally decreased with the increase of flowability. It was believed that the nature of changes in air content for non-air-entrained concrete mixtures mainly depend on rheological parameters. Previous experiments showed that when yield stress and plastic viscosity decrease, the diameter of slump flow increases and time of slump flow decreases, and the air content in the SCC (Self Compacting Concrete) mixture also decreases [26]. This can be used to explain the tendency of change in entrapped air content with flowability for the UHPC mixtures in this study.  
There was nearly a linear relationship between flowability and entrapped air content when no fiber was added. Additionally, this relationship became worse when a higher dosage of fiber was used. This is related to the influence of fiber on air content, and other possible reasons should be further investigated.
## 7 Conclusions  
In this paper, the capabilities of QMUX were discussed, and the techniques known to date were outlined. The appropriate placement of the quantum channel with respect to Raman noise was also discussed. The main part was the introduction of the attenuation method, the functionality of which was experimentally tested. The experiment consisted of three successive phases. First, a prototype QKD polygon was designed, and the safety of all optical elements was tested. In the next phase, a service channel power limit was determined by means of successive attenuation. Based on this measurement, additional optical elements were added to the scheme in the third phase. Thus, a functional polygon with a variable length of the shared route was created.  
Conceptualization, O.K.; validation, T.H. and P.M.; writing--original draft preparation, O.K.; visualization, O.K.; supervision, P.M.; project administration, T.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Ministry of the Interior of the Czech Republic, program IMPAKT1, under grant VJ01010008, project Network Cybersecurity in the Post-Quantum Era.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Connection** & **Raman (dBm)** &
\begin{tabular}{c} **Key Rate** \\ **(kbit/s)** \\ \end{tabular} & **QBER (\%)** & **Visibility (\%)** \\ \hline Direct & — & 2.52 & 2.78 & 99.30 \\
5 km & \(-112.75\) & 2.19 & 3.57 & 98.00 \\
10 km & \(-109.61\) & 2.04 & 3.67 & 97.50 \\
15 km & \(-107.63\) & 1.69 & 3.92 & 97.50 \\
20 km & \(-106.08\) & 0.45 & 4.61 & 97.30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement results for five QKD configuration of the polygon. The table contains the Raman noise optical power values and basic system parameters.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 5 Conclusions  
In this article, we analyze the problem of user grouping and power allocation in the downlink of a multi-carrier NOMA system. We establish a mathematical model that optimizes the system sum rate. The complexity of directly solving the optimal solution is higher; therefore, we adopt a step-by-step optimization approach. Users are grouped first and then power is allocated.  
1. Aiming to fix the shortcomings of the user grouping based on the maximum channel gain difference, we propose a new user grouping method to avoid users with similar channel gains being divided into one group. The proposed method improves the system sum rate.  
Figure 8: System sum rate comparison under different numbers of users.  
Figure 7: System sum rate comparison under different transmit powers.  
* In terms of power allocation, the DNN in DL is proposed for the first time to obtain the power for each subcarrier. The simulation results illustrate DLPA improves the sum rate by about 2.2% and 19% compared with FTPA and FPA, respectively.
* After determining the power of the subcarriers, we derive the power allocation CFE of superimposed users according to the minimum transmission rate constraint. The simulation results illustrate that when the DLPA method is used among subcarriers and the CFE method is used between users, the rate increases by about 10% compared with the FTPA method.  
Conceptualization, T.G.; validation, B.H. and W.Z.; writing--original draft preparation, T.G.; writing--review and editing, J.L.; supervision, J.L. and F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant number 12005108), and the Natural Science Foundation of Shandong Province (grant number ZR2020 QF016).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results  
### Classificatory Machine Learning: Random Forest  
To assess the quality of the classification, the Kappa index and overall accuracy were used. The Kappa index is widely recognized for measuring the agreement between the obtained classification and the reference data, taking into account the possibility of random agreement. This index can range from \(-1\) to \(1\), with values closer to \(1\) indicating excellent agreement. A more detailed explanation of the Kappa index can be found in studies such as [45; 46; 47; 48]. Overall accuracy, on the other hand, measures the proportion of correct classifications relative to the total number of samples evaluated and is frequently used to provide an overall view of classification performance. Additional details on overall accuracy can be found in studies by [49; 50; 51].  
The resulting map (Figure 4), generated using the Random Forest classifier, achieved a Kappa index of 0.993 and an overall accuracy of 0.995, indicating a high-quality classification, as confirmed by the confusion matrix (Table 1).  
In Table 1, we notice some minor confusion by the RF classifier, as expected. There was a small confusion between forest, eucalyptus, and savanna, which is due to their spectral signatures being similar. Additionally, there was a slight confusion between mining and bare soil; in mining areas, there is a predominance of sand extraction for construction; hence, the spectral signatures are alike. Another minor confusion occurred between savanna and mining, especially where savannization areas result from deforestation for commercial activities, later abandoned, and subsequently undergoing natural regeneration.
## 4 Experiment Environment
### Capacity Test of Battery Energy Storage System  
Due to the limitation of its own capacity, the maximum output power of BESS at a certain sampling time has certain constraints. Thus, the maximum output power is related to the capacity of BESS. The maximum output power is reflected in the control output constraint \(u_{i}\in[-u_{lim},u_{lim}]\). When the maximum output power of BESS increases, \(u_{lim}\) also increases.  
Therefore, this section discusses the influence of BESS capacity on the control results by adjusting \(u_{lim}\). Assuming \(u_{lim}=2.0\) and \(u_{lim}=0.8\), the influence of the equipment capacity of the battery energy storage system on the frequency control process is discussed by obtaining the control results of the frequency deviation of the IEEE14 bus testing system under the ADES controller and the change of the control output \(u_{i}\) in these two cases.  
Assuming \(\eta_{ji}=0.05\), the attack period is 1-1.3 s, the system parameters of the IEEE14 bus testing system are shown in Table 1, and the parameters of the ADES controller are shown in Table 3. The experimental results are shown in Figure 17, where (a) and (b) represent the frequency deviation curve at \(u_{lim}=2.0\) and the control output curve of the corresponding ADES controller, (c) and (d) represent the frequency deviation curve at \(u_{lim}=0.8\) and the control output curve of the corresponding ADES controller.  
Comparing Figure 17a with Figure 17c, \(u_{lim}\) decreases, the adjustment range of the frequency deviation decreases, the convergence time increases, and the ability to suppress disturbances is weakened. As shown in Figure 17a,b, when \(u_{lim}=2.0\), the control output is constrained within the range of \([-2,2]\). At this time, BESS has sufficient output power to participate in the frequency control process of power grids to suppress the fluctuation of power grid frequency caused by load change disturbances.  
As shown in Figure 17c,d, when \(u_{lim}=0.8\) (the maximum output power of BESS is small), the control output is constrained within \([-0.8,0.8]\), the adjustment time required for frequency deviation is long, and the fluctuation range of the frequency deviation in the stable interval is large. Therefore, the capacity of BESS equipment affects the performance  
Figure 17: Frequency deviation curve and control output curve, \(u_{lim}=2.0\) and \(u_{lim}=0.8\). (**a**) Frequency deviation curves, \(u_{lim}=2.0\). (**b**) Control output, \(u_{lim}=2.0\). (**c**) Frequency deviation curves, \(u_{lim}=0.8\). (**d**) Control output, \(u_{lim}=0.8\).  
of power grid frequency control. When the equipment capacity increases, The control efficiency of ADES on frequency deviation is positively related to the capacity of BESS.  
According to the above experimental results, the proposed frequency control strategy can complete the frequency control of power grids under load change disturbances and DoS attacks. Compared with other methods, the control efficiency of the proposed method is higher.
###### Abstract  
Communication between the primary and secondary sides is pivotal to the wireless power transfer (WPT) system. The system control commands and feedback information need simultaneous wireless information and power transfer (SWIPT). In this paper, a PSK-based SWIPT system with full-duplex communication via a shared channel is provided. Considering the complexity of the coupling relationship in this kind of full-duplex SWIPT system, this paper proposes an analysis method based on the transmission channel, studies the crosstalk between the power channel and the information channel, and between the forward and reverse transfer of information. A design method of full-duplex communication SWIPT system based on shared coupling channels is provided. A 60 WSWIPT prototype with a full-duplex communication rate of 20 kbps is built to verify the proposed method.  
bidirectional power transmission; crosstalk; FSK; full-duplex information transfer; information transfer channel gain; SWIPT
# Evaluating Optical Clock Performance for GNSS Positioning
## 5 Results
III satellites are the most stable amongst the other GPS clocks having ADEV of 1.8 \(\times\) 10\({}^{-13}\) at 30 s and 2.4 \(\times\) 10\({}^{-14}\) after 900 s. In contrast, the three Cs clocks on GLONASS M type satellites have shown ADEV of 1.38 \(\times\) 10\({}^{-12}\) at 30 s and 2.4 \(\times\) 10\({}^{-13}\) at 900 s, which seem the least stable clocks of all four constellations overall, although the short-term stability of the clocks at 30 s is slightly better than the GPS Block IIR and IIR-M clocks with 1.41 \(\times\) 10\({}^{-12}\) and 1.57 \(\times\) 10\({}^{-12}\), respectively. The estimated ADEV values of the satellite clocks as per the satellite types are summarised in Table 3.  
Regarding optical clocks, the Sr Lattice optical clock is the most stable optical clock with exceptional short-term stability of 4.1 \(\times\) 10\({}^{-16}\) at 1 s, and 4.1 \(\times\) 10\({}^{-18}\) over 10,000 s [13]. As opposed to the Galileo FOC satellite clocks at 30 s averaging time, the Sr Lattice optical clock is significantly better, and the corresponding ADEV value is 7.5 \(\times\) 10\({}^{-17}\). The second most stable optical clock is the Iodine MTS optical clock, which has also shown better short-term stability of 6 \(\times\) 10\({}^{-15}\) at 1 s and 2 \(\times\) 10\({}^{-15}\) at 900 s. Though, the steady trend in ADEV values around 3.2 \(\times\) 10\({}^{-15}\) over the averaging time until 10,000 s results in lower stability because the clock has seemingly experienced a flicker noise. Lastly, with the CROC, its overall stability is almost two times better than the Galileo FOC satellite clocks in the first  
Figure 3: Calculated overlapping ADEV values of the satellite clocks based on IGS daily clock products with 30 s intervals in 2022. Each line denotes the ADEV values of the satellite clocks. 900 s, and it is changed thereafter to three times better stability up to 10,000 s averaging time. So, the CROC ADEV values at 30 s and 900 s are 7.3 \(\times\) 10\({}^{-14}\) and 1.3 \(\times\) 10\({}^{-14}\), respectively.
## 3 Results and Discussion
### Comparison of the Optimum Results with RSM, ANN-PSO and ANN-GA  
The coefficient of determination (\(R^{2}\)) is used as a criterion in relation to the confirmatory and predicted results, which gives a clear insight into the performance of the models. The value of \(R^{2}\) (0.9999) manifests that the BP-ANN model has a better capability for predicting the removal efficiency of MG than the RSM model (\(R^{2}\) = 0.9813). The predicted optimum conditions by using the RSM, ANN-PSO and ANN-GA models are clearly different. The predicted maximum removal efficiency of MG by using the ANN-GA model is higher than the RSM model by 4.62% and higher than the  
Figure 16: Evolvement of fitness with 100 generations.  
Figure 15: The removal efficiency vs. iterations.  
ANN-PSO model by 1.57%, while the confirmatory experiments show that the relatively higher removal efficiency of MG is obtained by using the ANN-GA model (Table 6). The absolute errors of removal efficiency between the confirmatory and predicted are 12.13% (RSM), 7.68% (ANN-PSO) and 3.40% (ANN-GA), which indicate that the ANN-GA model outperformed the other models presented in this study.
## 3 Numerical Modelling
### Water and Soil Pressure  
The loading state of the shield tunnel in the stratum is simulated by applying water and soil pressure and foundation spring. The foundation spring is a nonlinear spring under uniaxial compression in the radius direction of the tunnel. The calculation method of spring stiffness is as follows:  
\[K_{R}=\left\{\begin{array}{ccc}k\cdot n\cdot S_{i}&,\ R^{+}\\ 0&,\ R^{-}\end{array}\right. \tag{1}\]  
In the above formula, \(K_{R}\) is the foundation spring stiffness, \(k\) is the foundation bed coefficient, \(n\) is the number of grids represented by the foundation spring, \(S_{i}\) is the unit area divided by the outer segment surface, \(R^{+}\) denotes tunnel radius direction is positive, while \(R^{-}\) means tunnel radius direction is reversed, and the coordinate system is shown in Figure 6. The foundation spring stiffness in the \(Z\) direction and T direction is taken as 1/3 of \(R\) positive spring stiffness in the radius direction of the tunnel.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Uniaxial Compressive** & **Uniaxial Inelastic** & **Damage** & **Uniaxial Tensile** & **Tensile Strain Due** & **Tensile Damage** \\
**Stress/(MPa)** & **Compressive Strain** & **Parameter** & **Stress/(MPa)** & **to Uniaxial Cracking** & **Factor** \\ \hline
5.769 & 0 & 0 & 2.640 & 0 & 0 \\
21.107 & \(5.96\times 10^{-5}\) & 0.037 & 2.168 & \(7.93\times 10^{-5}\) & 0.275 \\
30.056 & \(3.03\times 10^{-4}\) & 0.122 & 1.677 & \(1.26\times 10^{-4}\) & 0.438 \\
32.454 & \(7.37\times 10^{-4}\) & 0.239 & 1.344 & \(1.69\times 10^{-4}\) & 0.565 \\
27.726 & \(1.54\times 10^{-3}\) & 0.435 & 1.120 & \(2.08\times 10^{-4}\) & 0.658 \\
21.198 & \(2.40\times 10^{-3}\) & 0.610 & 0.962 & \(2.45\times 10^{-4}\) & 0.725 \\
16.413 & \(3.21\times 10^{-3}\) & 0.730 & 0.845 & \(2.82\times 10^{-4}\) & 0.775 \\
13.142 & \(3.97\times 10^{-3}\) & 0.807 & 0.756 & \(3.17\times 10^{-4}\) & 0.813 \\
10.858 & \(4.71\times 10^{-3}\) & 0.857 & 0.685 & \(3.52\times 10^{-4}\) & 0.842 \\
9.205 & \(5.43\times 10^{-3}\) & 0.891 & 0.628 & \(3.86\times 10^{-4}\) & 0.864 \\
7.966 & \(6.13\times 10^{-3}\) & 0.914 & 0.581 & \(4.21\times 10^{-4}\) & 0.882 \\
7.009 & \(6.82\times 10^{-3}\) & 0.931 & 0.541 & \(4.54\times 10^{-4}\) & 0.897 \\
5.636 & \(8.20\times 10^{-3}\) & 0.953 & 0.508 & \(4.88\times 10^{-4}\) & 0.909 \\ \hline \end{tabular}
\end{table}
Table 1: Compressive and tensile properties of C50 concrete.  
Figure 5: Boundary conditions.  
The typical stratum section of the shield tunnel is selected to calculate the water and soil pressure. The specific soil layer parameters are shown in Table 2. The buried depth of the tunnel is set at 9.9 m. The water and soil pressure are calculated by the soil and water integrated overburden earth pressure, and the distribution model is shown in Figure 7.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 4 MQTT Attack Analysis  
Various attacks, such as DoS, flood, SlowTfe, malformed, and brute-force attacks, can attack the MQTT protocol [6]. This section analyzes these attacks to extract features that can be used to identify malicious MQTT traffic.
# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
## 2 Overview of the System
### Working Flow of the System  
This section will go over the entire working flow, which is the procedure by which mobile devices in daily and industrial application situations complete transactions based on this system.  
Taking air quality sensors as a vivid example, they collect air quality indicators, such as the concentrations of carbon dioxide, the concentrations of PM 2.5 [35], etc. Then they analyze the collected data above to estimate the situation of surroundings. However, the sensors, cannot process the data themselves. They need to send it to edge computing servers so that servers would help conduct it.  
As shown in Figure 2, the complete procedure can be included as following:  
1. The air quality sensor, which is a mobile device, connects to the wallet to make sure the device itself links to the blockchain-based mobile edge computing system.
2. The wallet is used to send a request to the service node, along with a certain amount of crypto token for the cost of edge computing services.
3. The smart contract will detect this transaction and allocate computing resources to the data, which is uploaded by the air quality sensor, based on the current state of the system and the size of the uploaded data, and it is called the \"edge task\".
4. The allocated computing resources then start dealing with the \"edge task\", and the processed result is finally obtained. After that, the service node, or the miner, needs to finish the training of a specific neural network under the PoL consensus, which is the \"mining task\", for the preparation of block generation.
5. Once the \"mining task\" is finished, the service node is required to upload the training results, such as the accuracy of the training model and the neural network parameters. Furthermore, other miners can thus verify its correctness and make sure the service node is not doing malicious things. After everything is completed, a block is generated and stored in the blockchain system.
6. Finally, the result of \"edge task\" will be sent back to the air quality sensor.  
As the purpose of this article is to deal with the resource allocation problems, the implementation of this system will not be discussed. In the next part, the inner mechanism of allocating computing resources will be discussed in detail, which is also the main focus of this study.