esa-sceva/satcom-chunk-collection huggingface dataset:
# The Gradient and the Hessian of the Distance between Point and Triangle in 3D
## 4 Algorithm and Testing
### Discussion  
The highest errors among all test cases are shown in Table 1. The values come from separate test cases: \(E_{0_{max}}\) is the maximum error for \(s\), \(E_{1_{max}}\) is the maximum error for \(\frac{\partial s_{c}}{\partial x_{i}}\) and \(E_{2_{max}}\) is the maximum error for \(\frac{\partial^{2}s_{c}}{\partial x_{i}\partial x_{j}}\). \(E_{2_{max}}\) is higher than \(E_{0_{max}}\) by two orders of magnitude, which is a good result, considering that it is the least accurate of 10 million tests. The tests show that the precision is adequate for all cases, including the ones form the collision simulation.  
Cases with the lowest accuracy usually correspond to the variables whose absolute values are very small, and computer simulations are often robust against such cases. For example, results of the grain interaction simulation where the current algorithm is applied are shown on Figure 4. The simulation advances with large time steps even when multiple fragments interact with each other.  
The ratio between the distance and the largest edge of the triangle is one of the factors that affect the precision. When this ratio drops below \(10^{-10}\), the accuracy of the result is likely to deteriorate. The problem of the round-off error is common in numerical analysis and should be addressed properly. If the influence of the round-off error is suspected when applying this algorithm, additional testing should be performed. In some cases, calculations can be performed with arbitrary-precision arithmetic to yield accurate results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Error Measure** & **Value** \\ \hline \(E_{0_{max}}\) & \(3.78\times 10^{-5}\) \\ \(E_{1_{max}}\) & \(2.75\times 10^{-5}\) \\ \(E_{2_{max}}\) & \(1.27\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative errors for squared distance and its first and second derivatives. The maximum values from 10 million test cases are shown.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 5 Machine Learning for CML-Based Rainfall Measurement
### Application of Machine Learning
[86] compared the accuracy of rainfall measurement between the terrestrial links (18, 38, 75 GHz) and the satellite links (12.25 and 20.74 GHz). The accuracy of rainfall measurement by applying ANN and LSTM at 11 GHz terrestrial link is studied, and the results show that LSTM is better than ANN. In addition, machine learning can also be used to distinguish different types of rainfall (convective or stratiform) [87], determine the baseline [88], predict the short-term attenuation [50], and so on.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 9 Related Work
### Static and Dynamic Vulnerability Analysis  
Most security analyzers treat reverts as a signal of vulnerability. Symbolic-execution-based tools like Oyente [29] and Mythril [30] detect missing or unreachable reverts by exploring feasible paths. The static analyzer Slither [23] also finds missing or unreachable reverts to diagnose re-entrancy, integer overflow, or access-control bugs. SmartState [25] extends this approach by searching for state-reverting patterns that attackers can exploit through atomic execution. It finds the assertion-related state dependencies through data-flow analysis of the smart contract source code and the temporal dependencies from historical transaction traces. ContractFuzzer [26] guides input generation by using revert as a signal in extending fuzzing coverage.  
In contrast, ErrorExplainer starts after a revert has already been triggered and explains the failure to users. Rather than detecting new bug patterns, it identifies a matched transaction-reverting statement to the user-reported error from the source code and builds an error context.
## 7 Conclusions  
The optimization of frequency regulation parameters of WTG based on VSG control is investigated, which comprehensively considers the available frequency regulation capabilities under different operating states. Moreover, this paper proposes an optimization method for the VSG control parameter of WTG considering the PFR dead zone and the frequency regulation physical constraint boundary, which could improve the frequency stability of the system. The main conclusions are as follows:  
* The frequency regulation capability of WTG can be evaluated more comprehensively and accurately by quantifying the influence of the PFR dead zone on the system frequency response. Additionally, the frequency response model of the WTG based  
Figure 14: Comparison results for Case 6. (**a**) Frequency. (**b**) Rotor speed. (**c**) Output active power of WTG.  
on VSG control is established, and the time domain analytic expression of the whole frequency response process is obtained;
* The trajectory sensitivity of each frequency regulation parameter is calculated, and the intensity of influence for each parameter on the dynamic frequency process is analyzed. As a result, the virtual damping parameter that plays a key role in the frequency modulation process is determined;
* Considering the different operating conditions and the dynamic frequency response process comprehensively, the optimization value of the virtual damping coefficient is determined based on the constraints of rotor speed and converter capacity so that WTG can make full use of available frequency regulation capacity and improve the frequency support capability. Hence, the proposed scheme has a certain engineering value for the optimization of the frequency regulation control parameter of WTG based on VSG control.  
There is still some research in this paper that needs further consideration and improvement. This paper discussed the frequency regulation control strategy of WTG, but less consideration was given to the interaction characteristics of WTG and SG during the frequency regulation process. Considering the difference in frequency regulation characteristics between WTG and SG, the collaborative frequency regulation control strategy of WTG and SG can be further studied.  
Conceptualization, P.S. and J.C.; data curation, Z.Q.; methodology, Z.C. and J.C.; validation, P.S., Z.C. and J.C.; formal analysis, P.S.; investigation, J.C.; project administration, P.S.; software, Z.C. and J.C.; supervision, Z.C. and Z.Q.; writing--original draft, J.C.; writing--review and editing, P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Please contact the corresponding author for available data support.
## 2 Results
### AtMYB2 Improves Antioxidant Capacity under Salt Stress  
Electrolyte leakage (EL) and total chlorophyll content were measured after 7 days of salt stress treatment. In comparison to the WT and pCAMBIA1301 lines, all transgenic _AtMYB2_ lines exhibited significantly lower EL, with the OE-AtMYB2-L2 line showing a reduction of one third compared to the WT and pCAMBIA1301 lines (Figure 4A). Furthermore, the transgenic _AtMYB2_ lines showed higher total chlorophyll content than that in the WT, with the _OE-AtMYB2-L2_ line showing the highest value (Figure 4B). Furthermore, there was a decrease in MDA levels across all transgenic lines compared to the WT, indicating a mitigation of cell membrane damage in the _AtMYB2_ transgenic plants (Figure 4C).  
Given that the accumulation of ROS was reduced in _AtMYB2_ transgenic plants under salt stress conditions, we wondered whether the activities of enzymatic ROS scavengers were affected. We noticed that the enzymatic activities of SOD, POD, and CAT were significantly elevated in all _AtMYB2_ transgenic lines compared to the WT, with increases ranging from 29.7% to 74.2% (Figure 4D-F). The data indicated that under salt stress conditions, the enzymatic scavenging activities were significantly enhanced in the transgenic lines, and _AtMYB2_ improved the ability to resist salt stress by enhancing the antioxidant capacity of transgenic _S. milliorhiza_ under salt stress.  
Figure 2: Morphological changes under salt stress. Scale bar = 5 cm; CK, control group; DAT, days after treatment; WT, wild type; pCAMBIA1301, empty vector control pCAMBIA1301 line; _OE-AtMYB2-L2/L5/L7, AtMYB2_ transgenic lines 2/5/7.
## 2 Materials and Methods
### Preparation of P(_bis_MEP)/BC Nanocomposite Membranes  
The P(_bis_MEP)/BC nanocomposite membranes were prepared following an established procedure based on the in-situ free radical polymerization of a monomer inside the BC three-dimensional porous network [24]. In brief, never-dried BC membranes with ca. 40% water content (400 mg dry weight) were placed in Erlenmeyer's stoppered with rubber septa and purged with nitrogen. At the same time, aqueous solutions of monomer (_bis_MEP) and radical initiator (AAPH, 2.0% \(w/w\) relative to the monomer) were prepared according to Table 1 and transferred to the Erlenmeyer's containing the drained BC membranes. After the total incorporation of the solutions into the BC membranes during 1 h in ice, the reaction mixtures were placed in an oil bath at 70 \({}^{\circ}\)C for 6 h. The nanocomposite membranes were then thoroughly washed with distilled water and dried at 40 \({}^{\circ}\)C in a ventilated oven for 12 h. The experiments were performed in triplicate and the samples analyzed as thin dried membranes. For comparison purposes, P(_bis_MEP) homopolymer was prepared in the absence of BC.
## 3 Results and Discussion
### Optimized AChE Assay Application in Lettuce and Strawberry  
As carbofuran was the strongest AChE inhibitor tested, it was chosen for assay performance demonstration in real life samples. Carbofuran screening is also important because despite being an unauthorized pesticide in the EU, it still remains a problem, which can potentially impact consumer's health. For example, carbofuran detection in goji berries from China resulted in the 2018 amendments of Regulation (EC) No 669/2009, which prescribes more frequent official controls at the point of entry into the EU territory [6]. During the latest European Pesticide Residue Workshop (EPRW) in 2018, carbofuran was detected in the so called \"superfoods\" such as goji berries and matcha tea [35], a group of \"nutrient-rich\" trending foodstuffs without a clear legal definition. Usually, these products are imported from countries where legislative regulatory mechanisms, requirements and official control systems are different than those in the European countries.  
The assay was validated also for the detection of carbofuran-3-hydroxy (a carbofuran toxic metabolite, which is included also in carbofuran residue definition, [[https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/public/?event=activesubstance.detail&language=EN&](https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/public/?event=activesubstance.detail&language=EN&)]([https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/public/?event=activesubstance.detail&language=EN&](https://ec.europa.eu/food/plant/pesticides/eu-pesticides-database/public/?event=activesubstance.detail&language=EN&)) selectedID=1082, last accessed 03/12/2019) and dichlorvos (an OP) since they share a common inhibitory effect against AChE (see Supplementary Materials). To investigate the cumulative inhibitory effect in food extracts, the TU approach was applied and matrix matched calibration curves containing two of the analytes were measured. Regarding the tested matrices, lettuce and strawberry were selected since they are crops in which carbofuran and dichlorvos were earlier reported to reach the toxicological reference value [36]. Another reason for selecting these matrices is their different composition mainly meaning different natural pigments transferred into extracts, which can potentially affect the AChE assay performance.
# Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
## 2 Tracking of UAVs Using a Kalman Filter Algorithm with Error and Uncertainty Sensitivity  
Here, we present the UAV tracking model with the modified Kalman filter algorithm (A method for tracking unmanned aerial vehicles using KFUEA). First, the problem of UAV target tracking without prior knowledge using the existing Kalman filter-based UAV tracking model is discussed. Then, a modified Kalman filter, namely KFUEA, is presented to improve the accuracy of UAV tracking in uncertain and noisy environments. Table 1 shows the list of terms and symbols used in this study.
## 4 Discussion
Conversely, if model N was tested on data from another hospital, we would expect its accuracy to be lower. Because each hospital has different patient backgrounds and each anesteliologist administers propofol at slightly different doses, the propofol dose prediction model may be valid only for the hospital where it was created. Although it has been reported that the dose of propofol required to induce the loss of consciousness with anesthesia induction decreases with age, this is the first study to quantify this relationship utilizing a neural network model. In the present simulation with model N, the induction propofol dose was decreased by 0.1 mg/kg for every 10 years of aging. This result is consistent with the results of simulations with model P and may be widely adaptable. Some limitations exist in terms of this study. First, this is a single-center, backward-looking cohort study; therefore, sampling bias may exist in the data collected. A multicenter study is a future challenge. Second, the propofol doses were given at the discretion of the anesteliologist so that the dose was sufficient to cause loss of consciousness, but it was not necessarily the minimum required. The retrospective nature of this study has limited the data collection. To more accurately examine the propofol doses that result in the loss of consciousness, BIS should be used, as previously reported [25]. Third, there may be confounding factors not measured in this study. Patients with cardiac disease or other comorbidities are more likely to develop hypotension after the induction of anesthesia [24]; therefore, anesthesiologists may reduce the dose of propofol. However, since we use midazolam in high-risk cases of heart disease at our hospital, high-risk cases were not included in this study, and we believe that the influence of confounding factors regarding heart disease has been avoided. Fourth, the MAE of model N was about 10 mg, a non-negligible error. We believe that the error was caused by the fact that the propofol dose administered varied from anesteliologist to anesteliologist, even for the same patient. There may be several reasons for the variation, but the major reason may be that the propofol dose required to achieve the loss of consciousness varies from patient to patient [26]; therefore, the dose cannot be determined exactly. Titration of the propofol dose based on the patient's response is also considered necessary in actual clinical practice.
# Effects of Velocity and Permeability on Tracer Dispersion in Porous Media
###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Researchers** & **Research Method** & **Findings** \\ \hline [PERSON] et al. [24] & Single solid phase for dimensionless fractional liquid volume fraction distributed between 0.5 and 0.9, 0.002 \(<u<\) 0.3 m/s & Dispersion coefficient is a sole function of liquid superficial velocity \(D=0.25u^{2}\), exponential parameter \(\beta\) was fixed at 2.2 \\ \hline [PERSON] et al. [31; 32; 33] & Experiments on two glass spheres of different particle sizes in a one-dimensional vertical soil column & The dispersion coefficient varies linearly with the interstitial velocity, i.e., \(D=au\) \\ \hline [PERSON] et al. [25] & Theoretical derivation and fluidization experiments verification in a Perspex tube, 50 mm in diameter & The dispersion coefficient varies linearly with the interstitial velocity—i.e., \(D=au/\phi\)—adjustable parameter \(a\) was fixed at 0.7 \\ \hline [PERSON] and [PERSON] [34] & Experiment in a packed bed & \(\beta\) is negatively correlated with the flow rate \\ \hline [PERSON] et al. [35] & Experiment in a two-dimensional porous medium & The longitudinal dispersion coefficient \(D_{\text{L}}\) does not vary linearly with water velocity \\ \hline [PERSON] and [PERSON] [36] & Measuring the dispersion coefficient in a series of porous media with different grain sizes and shapes & The closer the particle is to a spherical shape, the more significant the nonlinear relationship between the dispersion coefficient and the velocity of water flow is—that is, \(\beta\) approaches 1.0 as the particle curvature increases \\ \hline [PERSON] et al. [37; 38] & Mathematical model on solute transport and dispersion in soil & In a certain range, the dynamic dispersion coefficient is linearly related to the square of the soil pore water velocity and the dispersion coefficient is positively related to the coefficient of variation of the velocity \\ \hline [PERSON] et al. [30] & Mathematical model and numerical model (CFD-DEM) & Dispersion coefficient incorporating the mean free path of collision and interstitial fluid velocity as the characteristic velocity of collision \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main research findings on the relationship between the dispersion coefficient, structure of porous media, and fluid velocity. media. Thus far, the relationships between the dispersion coefficient, permeability, and fluid velocity in core samples are not clearly understood. To fill this gap, the present study applies two types of core samples to conduct experiments, exploring the influence of permeability and velocity on dispersion coefficients. Two sets of displacement experiments are conducted in natural and sand-filled cores, respectively, to obtain the tracer breakthrough concentrations. The experimental results will then be fitted using a correlation to ascertain the relationship between the dispersion coefficient and the fluid velocity and permeability. Finally, the underlying mechanisms behind these relations are discussed.
## 3 Results and Discussion
### Biological Characterisation of Biosolids
The values obtained for MBC in biosolids at different times of the year were higher than those obtained in other agricultural soil studies with addition of biosolids where MBC values ranged from 20.1 to 820 mg C kg\({}^{-1}\) dry soil [107; 109]. [PERSON] et al. [109] observed that the application of biosolids to the soil presented a significant increase in MBC from the first 7 days to 60 days of application, and then a decrease to stabilize, so they considered the effect to be temporary. In addition, [PERSON] et al. [108] found that MBC presented important variations at different application doses of biosolids to the soil, being directly proportional the increase of MBC with the application dose. However, none of these studies evaluated the MBC content of biosolids before they were added; therefore, with the results obtained in this study and as stipulated in the literature, it can be established that biosolids provide active microorganisms and affordable organic matter that could have a \"positive synergy\" with active soil microorganisms. This was stated in this study since in previous studies of application of biosolids to soils a biostimulation of soil MBC has been observed when using biosolids as an organic amendment. In the same context, the results obtained for MBN in the evaluated biosolids ranged between 84.25 and 304.70 mg N\({}_{\text{mic}}\) kg\({}^{-1}\) dry biosolids, exhibiting very significant differences (F = 240.960, \(p\) = 0.000) between autumn biosolids compared to the biosolids of the rest of the year (Table 3). A content of MBN was observed that corresponded to a range of 0.13 to 0.46% of the NT present in biosolids for different times of the year. MBN in soils constitutes more than 5% of NT [107]. The MBN of biosolids was found to be below that reported in soils. The results reported in the literature for MBN of soil with added biosolids have ranged from 3.1 to 101 mg N\({}_{\text{mic}}\) kg\({}^{-1}\) dry soil [108; 109]. However, based on previous studies [108; 109], as with MBC, an increase in MBN would be expected with the addition of biosolids to soils. [PERSON] et al. [110] established that MBN in agricultural ecosystems controls the availability and loss of inorganic N from the soil, especially in systems with high N inputs such as agriculture. Therefore, adding an organic amendment, such as biosolids, with active microbial biomass (MBC and
# A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems
## 2 System Model  
In this section, the mmWave massive MIMO system model, channel model, and joint codebooks design scheme are established.
## 4 Security Analysis of LEACH-Based Secure Routing Protocols and ESPDA
### Security Analysis of LEACH-Based Secure Routing Protocols
#### 4.1.3 Security Analysis of MS-LEACH  
Below are the observed drawbacks of the MS-LEACH protocol:  
* Proper counter management is not provided. The counter \(c\) in the step 1.1 and 6 should be different from the one in steps 4 and 5 (readers should refer to Figure 6 for the MS-LEACH protocol).
* MS-LEACH does not provide authentication for join request messages.
* The non-authenticated join request messages invite intruders to send them to the CH freely. For this reason, even though the protocol provides confidentiality, integrity and reply attack protection for the **sched** message after the attack has been launched, an attacker can still disrupt the timeslot schedule. The CH cannot remove the timeslot already assigned to it or even to rebuild the timeslot. As found in SLEACH, there is no key update provisioning for key \(k_{j}\).
* Multiple unicasts for schedule messages can deplete the energy of a CH.  
These three protocols still trust CHs to do the data aggregation. Since BS is the only trusted party in the network, these schemes still lack secure data aggregation.
# The Subsea Micro-Drilling Vehicle's Dynamic Analysis during Landing
## 3 Simulation Analysis of Landing Dynamics
### SMDV Landing Response Analysis
In order to prevent falling too deep into the sediment, the SMDV's release height must be controlled within 5 m. The SMDV is outfitted with electronic components for sensing and control, and the acceleration response of the general instrumentation must not exceed ten gravitational accelerations. According to the simulation results, the SMDV's impact acceleration at a release height of 5 m is approximately 6.6 gravitational accelerations, which does not damage electronic components. Notably, these simulations are founded on the sediment substrate of a particular target sea area. The harder the sediment, the smaller the quantity of SMDV subsidence and the greater the distance from the critical value. However, the greater the impact acceleration of the SMDV, the greater the risk of electronic component damage. If the sediment is softer, the impact acceleration of the SMDV will be reduced, and the electronic components will be safer; however, the sinkage of the SMDV will increase, and the risk of sinking into the sediment will increase. On the one hand, there is reasonable selection of the SMDV release fall height according to the SMDV service sea substrate mechanical parameters. On the other hand, we should optimize the structural design of the SMDV based on the mechanical properties of the seabed in the service area and increase the width of the track as necessary to avoid potential safety hazards.