esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Major and Trace Element Assessment in Crops  
The study of toxic element contamination in crop plants is crucial for understanding the transfer of pollutants through the soil-plant system in agricultural areas located near industrial platforms. Knowing the concentrations of chemical elements in plants intended for human and animal consumption can help plan and implement measures to reduce the risk of contamination through the food chain.  
Heavy metal (HM) bioavailability and accumulation in plants are determined by the soil characteristics [1]. Parameters such as pH, clay content, CaCO\({}_{3}\), and OM concentration influence the ion mobility of chemical elements [62; 87]. Moreover, the metal bioaccumulation in plants is contingent upon the stage of vegetation, species, and the ecologic characteristics of the region where the plants thrive. Certain plants exhibit the capacity to accumulate and process hazardous elements, thereby establishing their significance as bioindicators.  
In recent years, experiments have been carried out on phytoremediation, which is the process of using plants to remove, degrade, or immobilize environmental contaminants [88; 89; 90]. Sunflower has shown to be a good hyperaccumulator for toxic or potentially toxic elements such as Cr, Ni, Pb, Zn, Cu, and Cd. Under these conditions, crops have to be monitored in terms of nutritional composition and potential elements with health risks, even in smaller quantities.  
The analytical results obtained for the level of heavy metals in wheat and corn grains and sunflower seeds (expressed in mg kg\({}^{-1}\) dry weight (d.w.)) (Table 11) comply with the maximum values provided in [92], Codex Alimentarius [93], and [94] for certain contaminants in foodstuffs (Table 12). For Co, Cr, Cu, Ni, Pb, and Zn, European regulations do not provide a limit threshold in cereals and oilseeds. Similarly, no limit values are provided for heavy metals in the vegetative organs of cereals and sunflower. If these plants are used in mixtures for animal feed, the concentrations of the mixture should not exceed the maximum permitted values provided in Directive 2002/32/EC [95] on undesirable substances in feed, as amended.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Plant**} & \multirow{2}{*}{**Plant**} & \multicolumn{8}{c}{**Element Concentration (mg kg\({}^{-1}\)\(\pm\)\(\sigma\))**} \\ \cline{3-10}  & & **Cd** & **Co** & **Cr** & **Cu** & **Ni** & **Pb** & **Zn** \\ \hline T-1-1\({}^{1}\) & & 0.007 \(\pm\) 0.001 & 0.161 \(\pm\) 0.010 & 0.649 \(\pm\) 0.055 & 29.911 \(\pm\) 0.209 & 4.130 \(\pm\) 0.235 & 0.799 \(\pm\) 0.05
## 2 Mathematical Dynamic Model of the IRW  
As shown in Figure 1, a concept model of the IRW is introduced to deduce mathematical models and study its dynamic characteristics. For lateral dynamics analysis, an IRW can be simplified into three main parts: a left wheel, a right wheel, and a carrier of the two wheels. Under constraints of the bearings between the carrier and wheels, only relative rotation is allowed. Under constraints of contact patches between rail and wheels, the contact position of each wheel should obey the geometric profile in three dimensions. Additionally, under the constraint of the rail gauge, two sides of the wheel-rail contact patches have a positional relationship between each other. Then, the IRW's degrees of freedom (DOF) can be obtained as  
\[\text{DOF}=6\times 3-5\times 2-1\times 2-1=5 \tag{1}\]  
Considering the situation of constant longitudinal velocity, four DOFs are taken into account to describe the lateral motion of an IRW on a straight rail track. We choose four generalized states, including the lateral displacement \(y\), yaw angle \(\psi\), rotational speed of the left wheel \(\omega_{\text{L}}\), and rotational speed of the right wheel \(\omega_{\text{R}}\). To substitute for the connection between the wheelset carrier and the two-axle bogie's frame and to study the IRW's dynamic characteristics separately, a 'virtual joint' is set moving with the IRW along the center of the rail track, shown in Figure 1b. The pitch angle of the wheelset is assumed to be constrained to zero with respect to this 'virtual joint'. In addition, a yaw suspension system with springs and dampers is also installed between the wheelset and this 'virtual joint'.  
Generally, in the force analysis process of conventional wheelsets for lateral dynamics research, only creep, spring, damping, and inertia forces are considered. Under the considerable influence of longitudinal creep forces, the effects of gravitational restoring forces are often simplified or omitted. However, it is widely regarded that the effects of longitudinal creep forces on the IRW's lateral dynamics are negligible. Then, some forces simplified or omitted before may regain dominance in the IRW's motion. In this study, the influences of gravitational restoring forces and gyroscopic moment are considered. They are introduced in Sections 2.2 and 2.3, respectively.
## 4 Conclusions  
In this paper, three different glass metallization methods, including conductive copper foil (CCF), conductive silver paste (CSP) and indium tin oxide (ITO) thin film, were employed for the design of X-band glass antennas. Four glass antennas with the combination of these metallization methods were fabricated for analysis. The fabrication technique of each metallization scheme was described, and the performance of the metallization technique-based glass antennas were compared and analyzed. The maximum available gains of the case one antenna (CCF as the radiator and ground plane) and case four antenna (CSP as the radiator and ground plane) are 4.60 dB and 2.10 dB, respectively, while both of the case two (ITO as the radiator) and case three (ITO as the ground plane) antennas had much lower radiation gains due to the lossy property of ITO films [27]. This study demonstrates the feasibility of CCF and CSP as well as ITO films in the field of glass antennas. Generally, the processing accuracy of CSP needs to be improved in order to achieve better antenna design and application. The high optical transparency of ITO films is beneficial for realizing optically transparent antenna, but it is essential in enhancing antenna performance by using appropriate optimization techniques, such as multilayer film or hybrid metallization scheme.  
Conceptualization, L.C.; methodology, L.C. and Q.Y.; validation, L.C. and Q.Y.; draft and revision, L.C. and Q.Y.; supervision, L.C.; project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (Grant No. 61901108) and in part by the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20180364).  
Data Wariability Statement: Data sharing is not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors thank the anonymous reviewers for the constructive comments and suggestions.  
The authors declare no conflict of interest.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 5 Simulation Results
### Frequency Synchronization Performance  
In the simulations, a ZC sequence with length \(L=128\) and a root index \(u=127\) is employed. The symbol timing synchronization is assumed to be perfect. The AGC gain is adjusted with a gain step \(\lambda=2\) dB for every \(\tau=16\) samples. The comparison of EVM for the preamble with frequency offsets 300 Hz and 450 Hz is illustrated in Figure 14. The simulation results allow us to assess the effectiveness of the proposed AGC compensation method in reducing distortion and improving signal quality.  
The comparison of NMSE for the frequency estimation with and without AGC compensation is illustrated in Figure 15. In the simulations, the frequency offset is set to 300 Hz and 450 Hz, and the AWGN channel is considered. The simulation results show that the proposed frequency synchronization scheme improves the performance by approximately 2 dB.  
Figure 13: Comparison of symbol timing synchronization performance over AWGN channel with different quantization methods.
# Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming
## 2 Materials and Methods
### Nark-Nn and Tsf-Arx Models  
A nonlinear autoregressive neural network with exogenous inputs (NARX-NN) and Takagi-Sugeno fuzzy (TSF-ARX) models has been chosen in this study for a comparative analysis. The NARX-NN is a recurrent dynamic network with output feedback. The architecture of the NARX-NN model of the crane dynamic used for the comparative analysis is shown in Figure 5, where \(l\) and \(m\) are, respectively, the rope length and the mass of the payload transported by a crane, which indicate the current operating point of a crane system, while \(u\) and \(y\) are the input signal and the model response, respectively. The time-delayed normalized inputs and outputs are fed into the hidden layer, where a linear combination of inputs passes through an activation function. Throughout this work, the activation function has been chosen as the tangent-sigmoid function, while the output function is linear. The weights \(w\) and biases \(b\) have been optimized using the Levenberg-Marquardt algorithm. The optimization process is halted if the maximum number of epochs exceeds 1000, the gradient is less than \(10^{-7}\), or the MSE on the validation data set increases six times consecutively.  
Figure 4: Flowchart of G3P-SR.  
Linear models offer several advantages such as ease of implementation in addition to increased interpretability but lack the flexibility required to model complex systems. One method to overcome this has been to use local linear methods by partitioning the input space into several operating points in which the system can be represented by a parameter varying (LPV) linear model. An LPV-ARX system is described by  
\[y_{k}+\sum_{i=1}^{n_{a}}\!a_{i}(l,m)y_{k-i}=\sum_{j=0}^{n_{b}}\!b_{i}(l,m)u_{k-j }+e_{k} \tag{7}\]  
where the coefficients of the ARX model are dependent on the scheduling variables, rope length \(l\), and mass of a payload \(m\), which indicate the current operating point of a crane system. The parameters of the ARX model for a given operating point are found using the least squares method, and a Takagi-Sugeno fuzzy system is applied to interpolate between the locally linear models. Figure 6 shows a description of the TSF-ARX model used for our comparative analysis.
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 4 Current Status of Applying FBG Systems for Geohazard Monitoring
### Earth Fissure, Fault and Soil Cracking Monitoring  
Earth fissure is a geological phenomenon which is often closely related to land subsidence caused by engineering activities, especially the excessive exploitation of groundwater. Leveling Survey, photo-geological analysis, crackmeters, tape extensometers, GPS and InSAR have been extensively deployed to monitor earth fissure hazards all around the world [80]. [PERSON] et al. investigated the feasibility of using fixed-point distributed optical fiber sensor in earth fissure monitoring [30]. Both FBG and BOTDR fibers were embedded in aluminum tubes with equally spaced fixed points (Figure 12). In this way, they became long gauge displacement sensors. This installation method can provide the fiber with better protection and the measuring accuracy and ranges of cracking width are adjustable according to specific instrumentation requirements. The sensor system was successfully used in an earth fissure site in Wuxi, China for capturing the trends of crack opening.  
[PERSON] et al. investigated the feasibility of crack detection in expensive soil using embedded FBG strain sensors [81]. They performed a one-dimensional simulation test in laboratory and successfully captured the whole process of soil shrinkage and cracking due to dehydration. The tempo-spatial strain distributions and strain rates clearly show that before crack occ  
Figure 11: Comparison of tunnel heaves from finite element analysis, FBG sensors and Hydrostatic Level [79].  
Figure 12: Fixed-point distributed optical fiber sensor for earth fissure monitoring.  
shrinkage when the water content reduced. The strains at the desiccation crack location converted from compressive to tensile and slippage occured at the soil-fiber interface if the overburden pressure was very low.  
Another interesting applicaiton was reported by [PERSON] et al. for using FBG sensors to monitor large fault zones [82]. The sensors were installed in a research borehole in Greece for long-term monitoring of strains and stresses in active geologic faults.
## 4 Numerical Analysis according to Tunnel Depth
### Analysis Conditions
#### 4.1.3 Analysis Case and Measurement Position  
Analysis was performed under the same conditions as the site at a tunnel depth of 40.0 m, and compared with the field vibration measurement data. For the case of NATM half section (top) excavation and that using TBM and NATM parallel construction method, analyses were performed according to the depth, as shown in Figure 9. The measurement position on the ground surface for comparison with field data is shown in Figure 10. For the TBM and NATM parallel construction method, the impact of vibration according to the depth was analysed by measuring at the top of the pilot tunnel, the TBM, and the TBM excavation surface.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 1 Introduction
### Notations  
In this paper, the main notations are shown as follows: \(\left(\cdot\right)^{*}\) is the operator of the conjugate. The notation \(f\left(x\right)\) represents the probability density function (PDF) of the independent variable \(x\), while \(F\left(x\right)\) represents the cumulative density function (CDF) of \(x\). \(E\left(\cdot\right)\) is the expectation operator. \(\sum\left(\cdot\right)\) represents the operation of summation, and \(I_{0}\left(\cdot\right)\) is the zeroth order modified Bessel function of the first kind. \(x^{*}\) represents the conjugation of \(x\). \(x\sim N\left(0,\sigma^{2}\right)\) represents that \(x\) follow Gaussian distributions. In addition, the list of acronyms used in this paper is listed in Table 1.
# Controller-Pilot Data Link Communication Security
## 5 Potential Countermeasures
### Elliptical Curve Cryptography  
Elliptical curve cryptography [10] uses relatively small keys while providing the same level of security as encryption techniques based on factoring of large prime numbers. As a result, less computational power is needed to encrypt and decrypt the information. According to [21], the computational overhead of ECC was significantly lower than the overhead associated with the widely used RSA (Rivest-Shamir-Adleman) cryptosystem. Table 2 presents the comparison for the message sizes encrypted with these two algorithms.  
According to [22], a 161-bit ECC is roughly 5 to 10 times as fast as a 1024-bit RSA private key operation. Further comparisons show that as the key size increases so does the performance difference. Unfortunately, there is a problem with ECC certification. In 2015, the National Security Agency (NSA) announced its intentions to move to the new set of cryptographic algorithms to post-quantum cryptography (PQC). During the transition, the NSA advised the vendors, who decided to adopt the Suite B which includes ECC, to wait for the next upcoming set instead. The reason for this behavior has been a question within the cryptography community [23] which is still unanswered.  
The latest SHA-3 hashing standard is based on a subset of the Keccak cryptographic family with sponge construction. It can be also extended to cover a stream cipher and authenticated encryption combining previously distinct functions in a single efficient implementation.
## 2 Related Work
### Federated Learning
[26] proposed an approach to control the state of the edge servers to minimize the long-term cumulative cost of FL while satisfying the convergence of the training model. However, these works have mainly focused on centralized FL and client computation/communication capabilities or local data size/distribution, but little consideration has been given to hierarchical FL, data quality of individual clients, and how they affect the learning performance of the global model, which is critical for FL performance. In addition, some recent studies have also made new progress in the field of federated learning. For example, [PERSON] et al. [27] proposed a federated learning algorithm based on adaptive weight adjustment, which is able to dynamically adjust the weights of model aggregation according to the computational power and data quality of the device, improving the training efficiency and accuracy of the model. [PERSON] et al. [28] proposed a federated evaluation through the federation mechanism for adaptive weighting for domain adaptation on edge devices. The method assigns reasonable weights to different devices and data samples by comprehensively evaluating the device states as well as data features, which improves the performance and convergence speed of federated learning in cross-domain scenarios to a certain extent. However, in the face of extreme heterogeneous scenarios where the number of devices increases dramatically and the performance of devices varies too much, the dynamic adjustment of weights in this method may not be able to adapt to the changes in a timely manner, which requires the accuracy of weight assignment and the overall performance to be further optimized. In this paper, we will comprehensively analyze the impact of device heterogeneity, complex data distribution, and other factors on federated learning and strive to build a more universal and efficient federated learning framework.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 6 Analysis and Discussion
### RMS Delay Spread  
The power delay profile (PDP), \(P_{\tau}\), can be calculated as the marginal expectation of the local scattering function Equation (13) with respect to the Doppler domain as follows:  
\[\hat{P}_{\tau}[k_{t};n^{\prime}]=\frac{1}{M}\sum_{p}\left[C^{(hh^{\prime})} \right]_{k;n^{\prime},p} \tag{17}\]  
From this, we can calculate the first and second moments \(\tau\) and \(\sigma_{\tau}\), respectively, as follows:  
\[\sigma_{\tau}[k_{t}]=\sqrt{\frac{\sum_{n^{\prime}=0}^{N-1}(n^{\prime}\tau_{s} )^{2}\hat{P}_{\tau}[k_{t};n^{\prime}]}{\sum_{n^{\prime}=0}^{N-1}\hat{P}_{\tau} [k_{t};n^{\prime}]}-\tau[k_{t}]^{2}},\qquad\tau[k_{t}]=\frac{\sum_{n^{\prime} =0}^{N-1}(n^{\prime}\tau_{s})\hat{P}_{\tau}[k_{t};n^{\prime}]}{\sum_{n^{ \prime}=0}^{N-1}\hat{P}_{\tau}[k_{t};n^{\prime}]}, \tag{18}\]  
where \(\tau_{s}=1/(N\Delta f)\). Figure 14 shows the rms delay spread for the different antennas over the two routes, and Figure 15 shows the corresponding CDFs. In calculating the moments in Equation (18), only contributions from the PDP that satisfied certain power thresholds were taken into account [38]. The power threshold was selected as 5 dB above the noise floor to mitigate any spurious component, and 30 dB below the peak to only  
Figure 12: The time-varying stationarity region (in m) with a threshold of 0.7.  
Figure 13: The CDF of the time-varying stationarity region (in m) with a threshold of 0.7.  
consider components that had a significant contribution. The median rms delay spread was in the range 38 ns to 54 ns, with significant variations between both antennas and over the routes. We see that the results are in agreement with previous measurements in industry environments [39; 40], where the spread was also found to be around 50 ns in a similar-sized environment. In [41], machine-type communication between robot arms was measured in an industry environment. Measurements were made with a bandwidth of 500 MHz and in a fixed position in the room due to the installation of the robot arm. In the their findings, the delay spread was somewhat lower, around 30 ns. Lastly, in [42], two wideband measurements were performed in what was classified as _indoor classroom_ and _industry_. The dimensions of the room where the industrial measurements were taken were approximately half those of ours. They reported results of around 70 ns in both LOS and NLOS situations in the _industry_ scenario.
## 3 Study Area and Data Processing  
### Study Area  
The study area was located in northern Yuncheng County, Shandong Province (35\({}^{\circ}\)19\({}^{\prime}\)\(\sim\)35\({}^{\circ}\)52\({}^{\prime}\) N, 115\({}^{\circ}\)40\({}^{\prime}\)-116\({}^{\circ}\)08\({}^{\prime}\) E) (Figure 2). As a major storage place in northern China, Yuncheng County is rich in coal reserves and contains four major coal mines, namely Lilou Coal Mine, Pengzhuang Coal Mine, Guotun Coal Mine, and Zhaolou Coal Mine, which show severe ground displacement due to extensive coal mining. The area of displacement in the ZhaoLou coal mine is approximately 11.68 km\({}^{2}\), the area of displacement in the Guotun coal mine is approximately 13.97 km\({}^{2}\), the area of displacement in the PengZhuang coal mine is approximately 9.15 km\({}^{2}\) and the area of displacement in the Yuncheng coal mine is 5.68 km\({}^{2}\). As coal mines continue to be mined in 2017-2018, the area of the mining area is expected to further expand [26]. The geotectonic in the study area belonged to the southeast marginal hollow zone of the Shandong anteklise of the China-Korea continental shelf. The surface was fully covered by Cenozoic without outcropping of bedrock [27]. Surface coverage mainly had cultivated land, urban industries, mines, and water conservancy facilities. Mining displacement in the study area had caused some damage to cultivated lands and infrastructures like traffic and water conservation projects. Moreover, it brought serious threats to the life and property safety of local people. Hence, it is crucial to provide fast and high-efficiency monitoring of ground displacement in the study area.  
Figure 1: Data processing procedures of the proposed method.
## 4 Geomagnetic Activities Affecting Ionospheric Scintillation
This pattern of solar activity persisted into 2020, with January recording a higher average F10.7 value of 72.23, compared to 70.12 in March and 69.57 in July. The variation between the highest and lowest monthly averages in 2020 was notably narrow, approximately 3 units, indicating a reduction in solar activity from 2015 levels, thereby highlighting diminished seasonal variability during periods of lower solar activity. The highest number of sunspots recorded during the observation period was in January 2015, with a peak of 153 and an average of 93.65, which exceeded counts from  
Figure 3: Values of geomagnetic parameters affecting the occurrence of ionospheric scintillations such as: international geomagnetic index Kp, solar flux, Dst index, and sunspot number (SSN) during the months of January, March, and July in 2015 and 2020.  
both March and July of the same year. In 2020, the trend in sunspot numbers significantly declined, yet the highest counts were observed in July and January, aligning with the trends in solar flux. January and March of 2015 were notable for elevated geomagnetic and solar activities, as evidenced by the Kp, Dst, SSN, and F10.7 indices. A significant geomagnetic storm in mid-March 2015 marked the highest recorded values for Dst and Kp within the observational range. However, July 2015 displayed a relative decline in geomagnetic activities, still surpassing those of similar periods in 2020. The pattern observed in 2020 was consistent, with January and March exhibiting the most activity, and July showing a subdued profile. January's activities notably exceeded those of March, with July being the least active month, highlighting a clear pattern of geomagnetic and solar variations across the studied years.