esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of 450 MeV Kr Swift Heavy Ion Irradiation on GaN-Based Terahertz Schottky Barrier Diodes
## 3 Results and Discussion
### Kr SHI Irradiation Simulation  
The mechanisms of energy loss, 450 MeV Kr ion-irradiation distribution, stopping range, ionization, and displacement in GaN-based THz SBDs caused by Kr SHI impacts are analyzed using the SRIM software (SRIM-2013). SRIM is based on the Monte Carlo algorithm and employs a detailed full-damage cascades model to simulate and track the trajectories of a large number of incident particles in solid materials. The trajectories of 450 MeV Kr ions into the GaN-based THz SBDs were shown in Figure 5a. The Kr ions penetrate the anode metal and GaN epitaxial layer and stop in the SiC substrate. The penetration depth of Kr ions is approximately 28 \(\upmu\)m. As illustrated in Figure 5b, ionization energy deposition accounts for over 99.9% of the total energy loss of 450 MeV Kr ions. The energy deposition of Kr ions varies through the layers of Au-Ni/GaN/SiC structures, with ionization energy losses ranging from 32 to 19 keV/nm at the Au-Ni/GaN interface and from 19 to 14 keV/nm at the GaN/SiC interface.  
The energy transferred to the target atoms via elastic collisions between Kr ions and target atomic nuclei significantly exceeds the displacement damage threshold of target atoms, leading to the formation of vacancies due to atomic displacement. According to SRIM calculations, the collision of a Kr ion in GaN-based THz SBDs generates approximately 1534 vacancies. Figure 5c displays the vacancy densities of 0.2/nm in the GaN epitaxial layer and 0.1/nm in the SiC substrate by per Kr ion irradiation. The performance degradation of GaN-based THz SBDs caused by Kr ion irradiation is attributed to displacement damage within the GaN epitaxial layer. Consequently, Kr SHI irradiation introduces Ga vacancies (\(V_{Ga}\)) at a concentration of 0.13/nm and N vacancies (\(V_{N}\)) at 0.065/nm in the GaN epitaxial layers, as shown in Figure 5d. The high \(V_{Ga}\) concentration is primarily attributed to the low threshold energy required for Ga atom departure in GaN materials.  
Figure 4: (**a**) _C-V_ characteristics of GaN SBDs before and after irradiation, and (**b**) net doping concentration profile as a function of depth derived from these _C-V_ measurements.
# A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
## 3 Conclusions  
In this paper, we demonstrated an antenna with a multiple input, multiple output (MIMO) configuration for mm-wave 5G applications. The two antenna elements are sequenced in series to form a two-port antenna system such that significant field decorrelation is achieved. Moreover, a dielectric layer is backed by the proposed MIMO antenna to improve and analyze the radiation characteristics. The overall size of the MIMO configuration is 14 mm \(\times\) 20 mm, with an operation bandwidth in the range of 16.7-25.4 GHz, considering the \(-\)10 dB criteria with a maximum isolation of more than \(-\)30 dB within the operating band. The peak gain offered by the proposed antenna system is nearly 5.48 dB, and deploying a dielectric layer increases the gain value to 8.47 dB. A total efficiency of more than 80% is achieved within the operating band, and several MIMO performance metrics were analyzed, revealing satisfactory characteristics, making the proposed design a suitable candidate for mm-wave-based 5G applications. In the future, beam-steering techniques will be incorporated to achieve satisfactory cell transmission coverage.  
Conceptualization, D.A.S.; Data curation, M.A. ([PERSON]); Formal analysis, W.A.S.; Funding acquisition, N.U.; Investigation, J.K.; Project administration, S.A.; Supervision, M.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank Taif University Researchers, Taif University, Taif, Saudi Arabia, for supporting project number TURSP-2020/228.  
The authors declare that they have no conflict of interest to report regarding the present study.
# Bioassays with _Allium cepa_ for the Monitoring of Toxicity in the Groundwater of Yucatan, Mexico
## 3 Results and Discussion
### Bioassay
Colors indicate its nearness to the coast; inland samples are presented from red to green, and the sites nearest to the coast are blue. The studies in which _A. cepa_ has been used to evaluate water quality are primarily from sites with high pollution, such as industrial effluents, water from household waste, and agriculture. Generally, this bioassay has been conducted in water from surface sources such as rivers or lakes [11; 15; 16]; thus, the contaminants are not attenuated by groundwater filtration and are not in sufficient concentration to cause damage to cells. Interestingly, in some lakes, there have been reports of no effect on growth, MI, and nuclear abnormalities [15], where only the positive controls exposed to dipyrrone were affected. Other studies use _A. cepa_ to evaluate the genotoxic and cytotoxic effects of specific pollutants at known concentrations rather than considering a sample of ambient water. Most of these studies assess concentrations, in general, much higher than those environmentally found in the present study. For example, previous works evaluated the effect of the pesticide etoxazole. They found a dose-dependent decrease in growth and an increase in mitotic index, nuclear aberration index, CAT, SOD, and MDA (malondialdehydedehyde, a subproduct of lipid peroxidation) [41]. A meta-analytical review of the _Allium cepa_ test and pesticides showed that the number of micronucle is higher in organisms exposed to pesticides than in controls and that pesticides reduce the mitotic index [42]. Other authors evaluated benzo(a) anthracene and benzo(a)pyrene at 1.5 \(\mu\)g/L, finding significant inhibition in root development [43]. Still, in the present work, the highest concentration of total PAHs found in the wells was 14.26 ng/L, a concentration approximately 100 times lower than the one used in that previous study. This could explain the lack of toxicity in the bioassays. The results of the _A. cepa_ bioassay in the present study indicated no nuclear abnormalities. Miotic index, root length, biomarkers of oxidative stress, and inhibition of B-esterases indicated no statistical differences between sites and controls. [PERSON]'s non-parametric correlation analysis revealed no significant correlation between the concentration of pesticides, caffeine, or PAHs and the biomarkers evaluated at the cellular and biochemical levels.
# Design and Performance Evaluation of eLoran Monitoring System
## 1 Introduction  
The structural components of the eLoran system generally include a broadcasting station, monitoring station, and differential enhancement station. The monitoring system is used to monitor the accuracy, reliability, and integrity of the eLoran system, and is an indispensable part of the eLoran system. The traditional monitoring station is designed for the Loran-C system, which cannot monitor the status of the eLoran system completely [1; 2; 3].  
Compared with the traditional Loran-C system, the eLoran system has two major changes: firstly, the data channel is increased by way of phase modulation, which can broadcast time code information and emergency information; secondly, the time synchronization between each broadcasting station is carried out by comparing the link, which improves the control precision of broadcasting [4; 5; 6; 7].  
China's BPL time service system adopts the Loran-C signaling system, and it was up-graded in 2008, with eLoran signaling capability, but its monitoring system has not been established completely. In this paper, a monitoring system is designed for the eLoran system characteristics, and tested and verified based on the BPL time service system, and the test results show that the monitoring system can monitor the BPL time signal and analyze and evaluate the status of the BPL time service system in all weather in real time [8; 9; 10; 11; 12; 13].
# Algorithms for Calculating Generalized Trigonometric Functions
## 6 Construction of Discrete _Ateb_-Transforms  
### One-Dimensional Discrete Ateb-Transform  
Let us consider discrete _Ateb_-transforms (DATs). Let the signal be given in the form of a discrete sequence \(S(p)\). Let us consider the functions \(A(m,n,k)\) and \(B(n,m,k)\) given by the following formulas  
\[A(m,n,k)=\sum_{p=0}^{N-1}S(p)ca^{m}(m,n,-i\frac{2\Pi pk}{N}),k=0,...,N-1, \tag{40}\]  
\[B(n,m,k)=\sum_{p=0}^{N-1}S(p)sa^{n}(n,m,-i\frac{2\Pi pk}{N}),k=0,...,N-1. \tag{41}\]  
where \(p\) is the harmonic number, \(N\) is the sample size, \(ca(m,n,\overline{\omega})\) is the _Ateb_-cosine function, \(sa(n,m,\overline{\omega})\) is the _Ateb_-sine function.  
Then, the direct DAP is given by the formula  
\[X(m,n,k)=A(m,n,k)-iB(n,m,k). \tag{42}\]  
We obtain an expression for the inverse transform in the form  
\[S(m,n,p) =\frac{1}{N}\sum_{k=0}^{N-1}\{A(m,n,k)ca(m,n,-i\frac{2\Pi pk}{N})+\] \[+B(n,m,k)sa(m,n,-i\frac{2\Pi pk}{N})\},p=0,...,N-1. \tag{43}\]  
The input signal \(S(p)\) is formally transformed into the signal \(S(m,n,p)\) under the action of the direct and inverse DAT. However, for fixed values of the parameters \(m,n\), the value of the signal \(S(p)\) can be reproduced.
###### Abstract  
In underwater wireless sensor networks (UWSNs), localization and time synchronization are vital services that have been tackled independently. By combining localization and time synchronization, could save nodes energy and improve accuracy jointly. Therefore, it is of great significance to study joint synchronization and localization of underwater sensors with low energy consumption. In this paper, we propose the energy-efficient joint framework of localization and time synchronization, in which the stratification effect is considered by using a ray-tracing approach. Based on [PERSON]'s law, ray tracing is applied to compensate for the variation of sound speed, this is one of the contributions of this article. Another contribution of this article is the iteration process which is used to improve the accuracy of localization and time synchronization. Simulation results show that the proposed joint approach outperforms the existing approaches in both energy efficiency and accuracy. This study also calculates Cramer-Rao lower bound to prove the convergence of the proposed technique along with the calculation of complexity of the proposed algorithm to show that the provided study takes less running time compared to the existing techniques.  
localization; time synchronization; energy efficiency; ray tracing; stratification effect +
Footnote †: journal: Sensors
## 2 On-Site Measurements
### Accelerations of Running Trains and Bridge  
In order to investigate the correlations between temperature-change-caused deformation of the arch bridge and the responses of the bridge and train, the accelerations of the bridge and train as measured during the winter (February 2016) and summer (July and August 2016) seasons by [PERSON] et al. (2022) were assessed.  
The method of measuring the acceleration of a train is based on the simplified method described in UIC 518 OR ([PERSON] et al., 2018). Acceleration sensors and train speed sensors were installed in the train body and bogie as shown in Figure 3. Train acceleration was filtered by the ride-comfort filter shown in Figure 4([PERSON] et al., 2018) according to UIC 518 OR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **Date and Time** & **Track** & \
\begin{tabular}{c} **Ambient Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} &
\begin{tabular}{c} **Average Temperature of** \\ **Main Arch Ribs (\({}^{\circ}\)C)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Winter} & February 03 2016 & \multirow{3}{*}{South bound} & \(-5\) & \(-1\) \\  & 01:00 \(\sim\) 04:30 & & \(-2\) & \(-2\) \\ \cline{1-1}  & February 02 2016 & \multirow{3}{*}{North bound} & \(-2\) & \(-2\) \\  & 01:00 \(\sim\) 04:30 & & \(+25\) & \(+30\) \\ \hline \multirow{3}{*}{Summer} & August 10 2016 & \multirow{3}{*}{North bound} & \(+23\) & \(+28\) \\  & 01:00 \(\sim\) 04:30 & & \(+28\) & \(+28\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Temperatures at track-geometrical measurement points.  
Figure 4: Ride-comfort filter for train acceleration (UIC 518 OR).  
Figure 3: Locations of accelerometers installed in test train (from [PERSON] et al. (2022)).  
Bridge accelerations were measured through six accelerometers, one for each crown of the main arch. Table 2 summarizes the ambient temperatures and the train passing speeds at the measurement dates [22].
## 2 Machine Learning Methods
### Quality Assessment  
In this study, various methods, i.e., mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (\(R\)), were selected for validation of the proposed models. MAE is a metric which is used for assessment and summary of the quality of a soft computing model. It measures the difference between the predicted and actual values. RMSE refers to the square root of the average of squared differences between the actual and predicted values [48]. \(R\) is a statistic measurement which is used to measure the degree of relationship between the predicted and actual values [49]. In prediction problems, MAE, RMSE, and \(R\) are popular statistical validation methods for machine learning models [50]. Lower RMSE and MAE values indicate better performance of the models [48]. In contrast, higher \(R\) values indicate better performance of the models [49]. Formulas of these indexes are listed below.  
\[MAE=\frac{\sum_{i=1}^{n}\left|p_{i}-v_{i}\right|}{n}, \tag{1}\]  
\[RMSE=\sqrt{\sum_{i=1}^{n}\left(\left(p_{i}-v_{i}\right)^{2}/n\right)}, \tag{2}\]  
\[R=\sqrt{\frac{\sum_{i=1}^{n}\left(p_{i}-\overline{p}\right)\left(v_{i}- \overline{v}\right)}{\sqrt{\sum_{i=1}^{n}\left(p_{i}-\overline{p}\right)^{2} }\sum_{i=1}^{n}\left(v_{i}-\overline{v}\right)^{2}}}, \tag{3}\]  
where \(n\) is the number of the samples, \(p_{i}\) and \(\overline{p}\) are predicted and mean predicted values, and \(v_{i}\) and \(\overline{v}\) are measures and mean values of the compressive strength, respectively.
# Spelt Grass Juice: Phytochemicals and Antiproliferative Activity
## 2 Materials and Methods
### Determination of the Antiproliferative Activity of Spelt Grass Juice Extracts
#### 2.6.2 MTT Assay with Extracts of Spelt Grass Juice  
Cell growth was assessed using the MTT assay [21]. Cell lines were seeded into 96-well microtiter plates (Sarstedt, Newton, MA, USA) at varying densities: 5 \(\times\) 10\({}^{3}\) cells per well for MRC-5 and A-549, and 4 \(\times\) 10\({}^{3}\) cells per well for HeLa in a volume of 180 \(\upmu\)L. The cells were pre-incubated in complete medium supplemented with 5% FCS at 37 \({}^{\circ}\)C for 24 h.  
For evaluation of antiproliferative activity, extracts of spelt grass juice were dissolved in DMSO and 0.9% NaCl. The final concentration of DMSO was under 0.2%, while the concentration of aqueous extracts of spelt grass juice ranged between 633.6 and 6700 \(\upmu\)g/mL, and the concentration of acetone-water and methanol-water extracts 100-1000 \(\upmu\)g/mL. Additionally, the antineoplastic drug doxorubicin for co-treatment was used at a final concentration of 2 \(\upmu\)M. Microplates were incubated at 37 \({}^{\circ}\)C for an additional 48 h. Color development was measured using a Multiscan Ascent (Labsystems; Helsinki, Finland) photometer at 540 nm, with 620 nm as the background. Results of antiproliferative activity were expressed as the mean \(\pm\) SD of two independent experiments, each performed in quadruplicate. All values presented in the figures and text represent the percentage of cell growth relative to the control, according to the following formula:  
\[\text{Cell growth}(\%)=(\text{At}/\text{Ac})\times 100, \tag{7}\]  
where At represents the absorbance of the test sample and Ac represents the absorbance of the control.  
Based on concentration-cell growth curves, IC50 values (the concentration that inhibits cell growth by 50%) were determined using CalcuSyn Version 1.1 ([PERSON] Copyright Biosoft, 1996, St. Louis, MO, USA). Using IC50 values obtained from a non-tumor cell line and the respective tumor cell line, non-tumor/tumor IC50 ratios (NT/T) were calculated for the extract. The NT/T ratio (or selectivity index) indicates the selectivity of the tested samples towards tumor cells compared to a healthy cell line.
## 2 Materials and Methods
### Calculation of Chain Measurement Uncertainty
This interval,where the true value is expected to lie, is calculated by multiplying the combined standard uncertainty by a coverage factor, denoted as \"k\", which is selected based on the probability distribution of the uncertainty and the desired level of confidence. Its selection is crucial, as it directly impacts the reliability and interpretation of the uncertainty interval. A higher value of k increases the confidence level but results in a wider interval. On the other hand, a lower value of k provides a narrower interval, albeit with a reduced confidence level.
## 4 Discussion
Future research must broaden the context of this case study to track sEMG data and qualitative responses on recovery and muscle monitoring over time in a larger and more diverse sample of participants. Additionally, repeated-measure monitoring at multiple points in the same day could be useful to allow the direct comparison of muscle activity and sensor reliability. As stroke recovery is a highly complex process, equally complex analyses should also be implemented in future work that explore the role of sEMG in monitoring recovery. For example, coherence analysis, which has been used to investigate neural mechanisms of muscle activity through linear correlation, has been examined in sEMG-intramuscular EMG and sEMG and electroencephalogram pairings, or in sEMG rectification processes [46; 47]. This same analysis could be applied to muscle firing rates, co-contraction, or coordination pattern pairings during functional movement in recovery. Similarly, frequency domain analysis is used to make inferences on motor unit performance, assessing parameters such as the conduction velocity of muscle fibers and muscle fatigue, which are also useful to understand the trajectory of clinical recovery [48; 49]. While these metrics were outside the scope of this case study exploring the proof-of-concept of sEMG monitoring combined with the participant perception of muscle activity monitoring in acute and subacute recovery, it is important to consider these analyses to more deeply understand the progression of recovery in future work with new or current datasets. Finally, as new sEMG technologies emerge and existing systems are refined, it is important to assess these within real-time clinical environments, to further understand their acceptability to patients and medical care teams and their utility in clinical decision making during stroke recovery.
## 4 Discussion
### Total Energy Expenditure (TEE)  
The total energy expenditure (TEE) was markedly greater in running tests compared to cycling. This finding aligns with the hypothesis that running requires a broader engagement of muscle groups, leading to higher overall energy expenditure. The comparison of these outcomes with existing studies reveals that, while cycling may be less demanding in terms of energy expenditure, it is crucial for assessing specific aspects of anaerobic power and endurance. Running and cycling elicit distinct central and peripheral sensations with respect to localised perceived effort [40]. In the context of this study, central and peripheral sensations refer to the subjective feelings of exertion experienced by the athletes, which can be influenced by both central factors (such as cardiovascular and respiratory strain) and peripheral factors (including muscle fatigue and metabolic accumulation in the limbs). Localised perceived effort specifically denotes the subjective exertion felt in particular muscle groups engaged during the exercise, reflecting localised muscle fatigue and stress more directly associated with the specific activity (running or cycling).  
Given that running demands a higher oxygen consumption [37], it is not surprising that measures such as energy demand, TEE, PCr_Breaks (kJ), and PCr_Epocfast (kJ) were significantly greater in running compared to cycling, as illustrated in Table 3. Additionally, running involves the engagement of postural and upper extremity muscles, contributing to its higher energy demands. This engagement results in a more substantial overall contribution of VO2 from the upper body muscles in running, as opposed to cycling, which shows a comparatively lower VO2 contribution from the metabolic expenditure of upper body activities [40; 41]. Electromyographic studies further reveal significant phasic activity in the arm and trunk muscles during running, indicating that the upper body musculature plays a crucial role in oxygen utilisation [42]. The diminished energy demand observed in cycling can be attributed to the significant intramuscular tension during repeated sprints and the strong correlation between the recruitment of type II motor units and the necessity for muscle force generation [41]. Elevated intramuscular pressures may also lead to the partial occlusion of the femoral arterial blood flow, subsequently reducing oxygen delivery and increasing the recruitment of type II motor units [41]. When contrasting running with cycling, the overall VO2 response measured at the mouth predominantly reflects the oxygen consumption in the legs [43].