esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Reactive Oxygen Species Accumulation  
Reactive oxygen species (ROS) are naturally produced in plant cells involved in photosynthesis, and they participate in signal transduction and in response to the adverse effects of abiotic factors [35], such as pathogen attack [36], or exposure to ozone [37]. Oxidative stress, occurring as a consequence of such stressors, is induced by the imbalance between the increased production of, and the ability to rapidly detoxify, ROS in plant cells [38].  
In the present study, the ability of _Kalanchoe daigremontiana_ plants to produce ROS varied depending on the conditions of the process (Figure 4). After 1 min of the ozone treatment, carried out at a rate of both 5 and 10 ppm, there was a decrease in the concentration of ROS in plant tissue, compared to the control sample (not exposed to ozone). The longer duration of ozone treatment led to an increased production of ROS in cells, and this effect was most visible when ozone was applied at a rate of 10 ppm. The increased production of ROS in plant cells treated with ozone at higher rates may be linked to the fact that a greater amount of the gas entered the mesophyll through the stomata, and then penetrated the cell wall and plasmalemma, where it was converted into ROS [39]. Furthermore, ozone entering through the stomata may have impaired the respiratory chain (gas exchange), and consequently may have affected the respiratory process by increasing the speed of ROS production in the mitochondria of plants exposed to higher doses of the gas [35]. The activation of this loop, enhancing the production of cellular ROS in plants as a result of exposure to higher doses of gaseous ozone, has also been shown by other researchers [40]. [PERSON] et al. [41] demonstrated that ozone treatment applied to raspberries that had been kept in storage led to higher levels of \(\mathrm{O_{2}}^{\bullet-}\) and \(\mathrm{H_{2}O_{2}}\) (ROS) in the mitochondria of fruit treated with ozone, compared to a control sample (untreated fruit). On the other hand, [PERSON] et al. [42] showed that cells of _Betula pendula_ leaves exposed to ozone contained a significant accumulation of \(\mathrm{H_{2}O_{2}}\), which was a direct symptom of oxidative stress induced by ozone.
## 3 Measurement Campaign  
The harmonic current emission of a LV network mainly depends on the number and the type of electronic equipment, which is determined by the type of connected consumers. In order to obtain representative results, different uniform consumer configurations, such as residential areas with single- or multi-family houses, office areas, and commercial areas were distinguished in the site selection. Additionally, mixed areas were selected, which rep  
Figure 1: Classification of variation types and major impact factors in public LV networks [8].  
resent typical combinations of the three consumer configurations. In total 40 measurements of different public LV networks in Germany were selected:  
* 18 measurements in residential areas,
* 6 measurements in commercial areas,
* 6 measurements in office areas and
* 10 measurements in mixed areas.  
The measurement equipment (Dewe-561-PNA from Dewetron and G4500 Blackbox from Elspec) complies with IEC 61000-4-30 [15] class A and provides all major power quality parameters for voltage and current, including the harmonics up to 2.5 kHz. The measurement data was aggregated to 10 min. The individual harmonics of odd order 3 to 15 and the total harmonic current (THC) were analyzed in order to determine the typical harmonic emission of public LV networks. Currently, harmonics are usually studied as absolute values, because the expression related to the fundamental might not properly reflect the impact on the network. This is mainly due to the high variation of fundamental current compared, for example, to voltages. This variation can result in very high relative values in cases of low fundamental currents, as well as variations of the relative harmonic current, just because of the variation in the fundamental, even at a constant harmonic current. Consequently, harmonic currents, with regard to their impact on the grid, should always be analysed as absolute values. This equation represents the total harmonic currents, \(I^{(h)}\), independent of changes in the fundamental current magnitude, \(I^{(1)}\):  
\[\text{THC}=\text{THD}\cdot I^{(1)}=\sqrt{\sum_{h=2}^{50}\left(I^{(h)}\right)^{ 2}} \tag{1}\]  
The accuracy of the measurement equipment for harmonic currents was additionally validated in order to ensure meaningful results. Values of harmonic currents above 600 mA have a relative error of less than 10% with respect to the measured value. The measurement duration ranged between three months and up to more than one year for each of the four different consumer configurations.  
Usually, the harmonic current spectra contain only odd harmonics, which decrease in magnitude significantly with increasing order. Considering the required accuracy, usually only harmonic current magnitudes up to the 15 th order remain for an analysis. In this paper the 3 rd harmonic order serves as an exemplar for the description and illustration of the method.
## 3 Results and Discussion
### Identification and Quantitative Analysis of Volatile Compounds in YCSP, YMMP, and YEMP
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & \begin{tabular}{c} **Retention Time** \\ **(min)** \\ \end{tabular} & **Volatile Compounds** & **Formula** & **CAS ID** & \begin{tabular}{c} **Yak Colostrum** \\ **Powder** \\ \end{tabular} & \
\begin{tabular}{c} **Yak Mature Milk** \\ **Powder** \\ \end{tabular} &
\begin{tabular}{c} **Yak Ending Milk** \\ **Powder** \\ \end{tabular} \\ \hline
31 & 22.997 & bubyric acid & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\) & 107-92-6 & ND & 302 \(\pm\) 23 a & ND \\
32 & 23.407 & 2,6,10,14-Tetramethylpentadecane & C\({}_{13}\)H\({}_{40}\) & 1921-70-6 & 166 \(\pm\) 68 a & ND & 186 \(\pm\) 35 a \\
33 & 23.939 & Heptadecane & C\({}_{17}\)H\({}_{36}\) & 629-78-7 & 392 \(\pm\) 107 a & 23.8 \(\pm\) 0.6 b & 343 \(\pm\) 32 a \\
34 & 26.095 & octadecane & C\({}_{18}\)H\({}_{38}\) & 593-45-3 & 44.3 \(\pm\) 148 a & ND & ND \\
35 & 26.704 & Ethoxyacetylene & C\({}_{4}\)H\({}_{4}\)O & 927-80-0 & ND & 13.7 \(\pm\) 0.7 b & 33.4 \(\pm\) 30 a \\
36 & 26.712 & 1-Ox-3-4-diazacyclopentadiene & C\({}_{4}\)H\({}_{2}\)N\({}_{2}\)O & 288-99-3 & 9.29 \(\pm\) 452 b & 13.8 \(\pm\) 12 b & 34.5 \(\pm\) 48 a \\
37 & 27.682 & Pentancho acid & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\) & 109-524 & 53.0 \(\pm\) 57 b & 240 \(\pm\) 13 a & 69.3 \(\pm\) 20 b \\
38 & 27.683 & caprole acid & C\({}_{4}\)H\({}_{2}\)O\({}_{2}\) & 142-62-1 & ND & 234 \(\pm\) 13 a & ND \\
39 & 28.331 & phenylmethanol & C\({}_{2}\)H\({}_{3}\)O & 100-51-6 & 40.2 \(\pm\) 84 a & ND & ND \\
40 & 28.705 & methylcyclohexa-2,5-dien-1-one & C\({}_{18}\)H\({}_{2}\)O & 19687-22-0 & 46.2 \(\pm\) 11.1 a & ND & 58.8 \(\pm\) 33 a \\
41 & 29.134 & dimethyl sulphone & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\)S & 67-710 & 35.5 \(\pm\) 37 b & 271 \(\pm\) 22 a & ND \\
42 & 29.899 & 1-Decanol & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\) & 112-30-1 & 22.5 \(\pm\) 79 a & ND & 30.9 \(\pm\) 67 a \\
43 & 31.863 & octanoic acid & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\) & 124-07-2 & 67.4 \(\pm\) 22 a & 29.2 \(\pm\) 85 b & ND \\
44 & 33.497 & Ethanol, 2-phenoxy & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\) & 122-96 & 122 \(\pm\) 167 a & 102 \(\pm\) 11 b & 117 \(\pm\) 155 a \\
45 & 34.441 & 2H-Pyran-2-one, tetrahydro-6-propyl- & C\({}_{4}\)H\({}_{3}\)O\({}_{2}\) & 698-70-1 & 13.0 \(\pm\) 14.4 & 15.9 \(\pm\) 50 a & ND \\
46 & 35.313 & Adipic acid, di(but-2 en-1-y) ester & C\({}_{4}\)H\({}_{2}\)O\({}_{4}\) & 1000324-71-1 & ND & 14.6 \(\pm\) 21 a & ND \\
47 & 38.476 & 2-Chlorethyl benzoate & C\({}_{4}\)H\({}_{3}\)CO\({}_{2}\) & 939-55-9 & 17.2 \(\pm\) 84 b & 34.5 \(\pm\) 0.5 a & ND \\
48 & 40.075 & Disability phthalate & C\({}_{18}\)H\({}_{2}\)O\({}_{4}\) & 84-69-5 & 54.9 \(\pm\) 10.7 b & 50.9 \(\pm\) 19 b & 106 \(\pm\) 21 a \\ \hline \hline \end{tabular} \(\text{ND}=\text{not}\) detected. Concentrations are expressed in ug/kg. The values labeled with different lowercase letters indicate significant differences (\(\sigma\) = 0.05)  
\end{table} 
Table 1: Cont.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 6 Results
The detailed reasons behind this are that at higher frequencies, the free space path loss increases significantly due to the shorter wavelengths. This results in higher signal attenuation and worse coverage. In addition, at frequencies above 100 GHz, molecular absorption due to oxygen, water vapor, and other gases starts to become significant. This leads to additional signal attenuation and reduces the coverage area. This effect is most prominent in the THz band. Higher-frequency signals have more difficulty penetrating common solid materials like walls, windows, foliage, etc. This results in more blockages and reduced coverage probability. This effect worsens with increasing frequency. Furthermore, to compensate for the higher path loss, transmitters at higher frequencies tend to use more directional antennas with narrower beamwidths. This reduces the coverage area of each transmitter. Simulation results follow the analytical solution. Figure 5 shows the normalized throughput comparison (high data, low latency, and reliability) across the three bands (RF, mmW, and THz). High-data users prioritize high data rates, so they require more throughput to support their demands. However, the system may allocate resources preferentially to users with less stringent throughput needs like low latency and reliability. This could explain why high-data users see the lowest relative throughput. Low-latency users have tight latency constraints that the network aims to satisfy, potentially at the expense of maximizing throughput. Their intermediate values suggest that resources are balanced between latency and throughput goals. Reliability users are the most tolerant of throughput fluctuations. The system can prioritize their connections to maintain service quality. This likely leads to reliability users achieving the highest normalized throughput. The RF band provides the least available bandwidth, limiting maximum the achievable throughput. mmW offers a bandwidth improvement but is more prone to blockage effects. THz has the highest bandwidth to maximize throughput, though it may face greater atmospheric attenuation challenges. Figure 3: SINR distribution comparison for the three bands. (**a**) max-SINR algorithm. (**b**) Proposed algorithm. Together, the user category priorities and band characteristics provide context for the observed trends of relatively lower throughput for high-data users sensitive to resource allocation, intermediate values for latency-focused associations, and the highest ones for reliability optimized connections across bands with increasing spectrum resources.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 1 Introduction
[25] achieve automatic online calibration of radar pose using the multi-lidar odometer based on the sliding window approach. They determine relative motion between two lidar frames through line and surface feature frame matching, enabling calculation of initial states for each radar's coordinate system pose. The hand-eye calibration problem is then employed to complete coarse external parameter calibration. The initial outcomes of the algorithm's rough external parameter calibration serve as the foundation for constructing an optimization problem. By associating and matching the feature point cloud within the sliding window with the reference radar's local map, accurate calibration results are attained through continuous iterations until convergence. Current research calls for enhancements in efficiency and accuracy. Many multi-sensor, external parameter calibration strategies are carried out offline and often in a controlled calibration environment. Prolonged driving can lead to shifts in sensor position and orientation, rendering prior offline calibration outcomes invalid and affecting the usability of autonomous driving functions. The online calibration of external parameters among multiple lidars within a varying environment also bears significant research importance. The innovation aspects of this paper are outlined as follows:  
1. The registration method based on line and surface features is used for point cloud matching, and the LM algorithm is used for nonlinear optimization. A refined root mean square error (RMSE) method is introduced to evaluate the point cloud registration accuracy. 2. A feature fusion strategy, grounded in feature point cloud smoothness and distribution, enables frame-to-frame matching using the Ceres library and LM nonlinear optimization method. This prevents an excessive number of multi-lidar feature point clouds from impairing the real-time performance of the system. 3. The GTSAM library is employed to formulate a graph optimization model, supplemented by a loop closure detection constraint factor grounded in the Scan Context descriptor and a ground constraint factor relying on plane feature matching. These elements collectively achieve global trajectory optimization and z-axis error limitation. 4. A real vehicle experimental platform is established to validate the robustness, real-time performance, and accuracy of the online calibration algorithm presented in this paper. This study also verifies that the multi-radar SLAM system has a more pronounced accuracy improvement than the single radar system.
## 3 Proposed Method
### Loss Function  
In the authentication phase, the Euclidean distance between two feature vectors determines whether the test subject matches the target object. Therefore, the selected loss function should make the samples of the same category close in the feature space and the samples of different categories far away in the feature space. The triplet loss function [19] is a function that can make the optimization direction of the network parameters meet this characteristic. Therefore, this paper will introduce the main idea of the triplet loss function.  
When using the triplet loss function to optimize the network, some triples need to be constructed for each batch of FV and IKP image pairs (referred to as \"samples\" hereafter) and corresponding labels from them first. A triplet consists of an anchor sample, a positive sample, and a negative sample. Among them, anchor samples can be selected arbitrarily, samples of the same category as the anchor samples can be regarded as positive samples, and samples of different categories from the anchor samples can be regarded as negative samples. For a batch of image pairs, multiple triples can be constructed.  
Figure 3: Dual-Branch-Net network structure.  
For each triplet, we first use Dual-Branch-Net to extract features to obtain the corresponding three feature vectors, as shown in Figure 4, and then calculate the loss of the network on the triplet according to formula (1):  
\[L(A,P,N)=max(\|f(A)-f(P)\|^{2}-\|f(A)-f(N)\|^{2}+\alpha,0) \tag{1}\]  
where \(A\) is the anchor sample, \(P\) is the positive sample, and \(N\) is the negative sample. \(f()\) represents the Dual-Branch-Net network, and \(f(x)\) represents the feature vector corresponding to the sample \(x\). \(\alpha\) is a fixed distance threshold. Only if the distance between the negative sample and the anchor sample and the distance between the positive sample and the anchor sample is not less than \(\alpha\) will the loss of the network on the corresponding triplet be 0; otherwise, the loss will be greater than 0. Then, the model will then optimize the network parameters to adjust the feature representation of the sample (the arrow roughly shows the adjustment direction in the Euclidean space in Figure 4).
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction
#### 2.2.7 Feature Scaling  
Feature scaling is a crucial step in data pre-processing aimed at normalizing data to a specific range. This process is essential for speeding up calculations within ML algorithms. When working with frameworks like Scikit-learn, feature scaling is necessary, especially when the dataset contains variables with differing scales. In this analysis, feature scaling was applied to adjust the dataset, making it more compatible with ML methods. Among various scaling techniques available, the StandardScaler() was utilized [42] exclusively to the training dataset to avoid any information leakage into the test dataset. This scaler standardizes the dataset by transforming it so that the resulting distribution has a mean of zero and an SD of one. The transformation is achieved by subtracting the mean from the original value and then dividing by the SD:  
\[z=\frac{x-\mu}{SD}. \tag{1}\]  
In the above equation, \(z\) represents the transformed feature value, \(x\) denotes the original value, \(\mu\) is the mean, and SD stands for the standard deviation of the training samples.
## 2 Air to Ground Channel Modelling
Furthermore, the probability of the line-of-sight (LoS) channel of a UAV is modelled utilizing a sigmoid function of the vertical angle between the UAV and the user, as presented in [28]. This model is leveraged to characterize the coverage radius for an aerial base station of the UAV as a function of path loss, alongside assessments of the optimal UAV altitude in [29; 30]. The WIRELESS Insite software version 2.5, program is employed to simulate a dense urban environment at varying altitudes and frequencies, further refined through analytical formulation using the knife edge refraction model [31]. A statistical model specific to high altitude, based on elevation, is delineated in [32]. The methodology for estimating coverage using the generalized UAV air-to-ground channel model for altitudes up to 100 m is explained in [33]. In comparison, for altitudes up to 500 m, it is delineated in [34]. Authors in [35] furnish a comprehensive channel model for UAV linkages based on the alpha-beta model framework. The existing body of literature concerning the generic channel model does not sufficiently investigate low-altitude communication links for unmanned aerial vehicles (UAVs) within the complex and varied urban landscapes of India. Previous research underscores the critical need for an elevation-based model, which can improve the precision of path loss calculations and account for shadowing variations that occur at different altitudes. The objective of this study is to introduce a comprehensive air-to-ground channel model that takes elevation angles into account. This model will utilize statistical parameters recommended by the International Telecommunication Union (ITU-R), specifically tailored to suit various environments, including suburban, urban, dense urban, and high-rise urban areas. By doing so, this study aims to support the effective expansion and implementation of 5G communication systems across the diverse urban settings in India. The proposed system aims to enhance the coverage area while managing the energy requirements of UAVs operating as aerial base stations (ABSs). It incorporates power reduction techniques such as trajectory optimization through efficient flight planning, flexible user assignment to nearby base stations, and dynamic adjustment of the coverage area based on demand. These strategies are designed to address the limited energy capacity of UAVs.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 5 Conclusions  
In this paper, we propose an improved Jacobi iterative algorithm in signal detection in the massive MIMO system. The performance improvement has been achieved with the fast convergence of the iteration process, which is conducted by integrating an SD method and the whole-correction method into the traditional Jacobi iterative algorithm. We evaluate the performance of the proposed algorithm by a theoretical analysis and Monte Carlo simulations. Based on the theoretical analysis, the proposed algorithm has been proved to have good convergence and low computational complexity. Simulation results show that the proposed Jacobi-based algorithm performs better than the conventional algorithms. Especially, with the small number of iterations, the improved Jacobi-based algorithm performs almost the same as the MMSE algorithm in terms of BER, but achieving a much lower computationally complexity compared to one of the latter. Future work includes the joint channel estimation and signal detection based on the proposed algorithm in the massive MIMO system and the related hardware design for a real application system.  
[][PERSON], [PERSON] and [PERSON] contributed the idea generation; [PERSON], [PERSON] and [PERSON] conducted the study design and paper writing; [PERSON], [PERSON], and [PERSON] helped the analysis of the simulation results.  
[]This work is supported in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Provence (No. SJCX18_0646), the National Natural Science Foundation of China (No. 61571108 and No. 61701197 and No. 61801193), the open research fund of the National Mobile Communications Research Laboratory, Southeast University (No. 2018D15 and No. 2019D18), the open research fund of the National Key Laboratory of millimeter wave, Southeast University (No. K201918), the Open Foundation of Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunication (No. 2017 WICCOM01), the Project funded by the China Postdoctoral Science Foundation (No. 2018M641354), the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (No. KF-2018-03-065), and the Fundamental Research Funds for the Central Universities under Grant JUSRP11919.  
[]The authors declare no conflict of interest.
## 3 Case Study
### Dataset and Computation  
#### 3.2.1 Nitrate Concentration Data Collection  
In order to assess the vulnerability and validate the results, nitrate concentration in shallow groundwater from a previous study was referenced [33]. That study had previously described the sampling methodology employed in the study area. The ion balance errors for the nitrate concentrations of these samples are of less than 5%. The data were gathered from the literature for 168 samples that satisfied the information requirements of the vulnerability assessment. Additionally, nitrate concentration data from the literature for the region in 1998 were employed to validate the results of this study (Figure 1).  
Figure 1: Location of the study area and distribution of the groundwater samples. The nitrate data are cited from the work of [PERSON] et al. [33].  
The spatial distribution of the nitrate concentration was estimated by the OK. The mean error (ME) and krigged root-mean-square error (KRMSE) of the 1998 and 2018 data, which can be used to estimate the performance of the kriging model, are 0 and 0.98 and \(-\)0.02 and 1.13, respectively. The closer the ME is to 0 and the closer the KRMSE is to 1, the better the model is [51].
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
## 2 Advanced Sliding Mode Control Framework for UAV Security
### Adaptive and Fault-Tolerant Mechanisms
#### 2.3.5 Redundancy and Control Allocation  
The system can also leverage redundancy by redistributing control efforts among the remaining operational actuators. Consider the control input vector \(u\) split among \(n\) actuators, with a weighting matrix \(W\) that allocates the control effort:  
\[u=W\cdot u^{\prime}, \tag{19}\]  
where \(u^{\prime}\) is the original control input vector and \(W\) is a reconfiguration matrix that adjusts the distribution of control inputs based on the status of the actuators.  
If an actuator fault is detected, the corresponding row in \(W\) is modified to reduce or eliminate control input to the faulty actuator, reallocating it to the operational actuators:  
\[W=\begin{bmatrix}w_{11}&w_{12}&\dots&0&\dots&w_{1n}\\ w_{21}&w_{22}&\dots&0&\dots&w_{2n}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ w_{n1}&w_{n2}&\dots&0&\dots&w_{mn}\end{bmatrix}, \tag{20}\]  
where the zero entries correspond to the faulty actuator, effectively isolating it from the control loop.