esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Abiotic Stress Treatments Induce TrhSFB2a Expression  
The expression pattern of _TrHSFB2a_ was investigated under different treatments of abiotic stresses, including high temperature, PEG and salt stress. The expression of _TrHSFB2a_ was strongly induced under drought stress, which is in accordance with our previous study of transcriptome analysis of white clover under drought stress [28]. Moreover, _TrHSFB2a_ is strongly upregulated under heat stress like other HSFs members, as well as strongly expressed in response to NaCl stress. These observations are well in agreement with  
Figure 9: Comparison of salt stress tolerance of overexpressing _TrHSFB2a_ lines (OE3 and OE4) and RNA interference T-DNA (T-77 and T-95) lines with wild type grown in nutrient soil for 28 days in normal conditions, irrigated with saline water gradients of 100 mM, 200 mM and 300 mM NaCl, each gradient for two times for two days. (**a,b**) Show phenotypic differences of wild + overexpression and wild + TDNA lines, respectively; (**c–f**) indicate relative water contents (%), electrolyte leakage (%), maximum quantum yield of PSII photochemistry (Fv/Fm) and performance index on absorption basis (PI\({}_{\text{ABS}}\)), respectively, under salt stress condition. Bars represent mean \(\pm\) SD values, and different small letters (a, b and c) represent significant statistical difference among means (one-way ANOVA, followed by Tukey test) between different lines.  
previous studies that HSFB2 candidates expressed under high temperature, high salinity and drought conditions [23; 40; 41]. Like stress-related cis-acting elements, including HSE, DRE, ABRE, MYCRS and MYBRS, present in the promoter region of the _HSFB2_ genes in _Arabidopsis_ and other plant species [5; 43; 44], they might also be involved in inducing response and the expression of _TrHSFB2a_ in white clover under drought, heat and high salinity stresses, but it needs to be further verified.
Optimization of Acute Kidney Injury (AKI) Time Definitions Using the Electronic Health Record: A First Step in Automating In-Hospital AKI Detection  
[PERSON]  
1 Department of Surgery, Houston Methodist Hospital, Houston, TX 77030, USA;  
[PERSON]  
1 Department of Surgery, Houston Methodist Hospital, Houston, TX 77030, USA;  
[PERSON]ADDRESS] (L.W.M.); [PERSON]ADDRESS] (A.O.G.)  
Harlan G. Sparrow  
Center for Outcomes Research, Houston Methodist Hospital, Houston, TX 77030, USA;  
[PERSON]  
1 Department of Surgery, Houston Methodist Hospital, Houston, TX 77030, USA;  
[PERSON]ADDRESS] (L.W.M.); [PERSON]ADDRESS] (A.O.G.)  
[PERSON]  
1 Department of Surgery, Houston Methodist Hospital, Houston, TX 77030, USA;  
[PERSON]ADDRESS] (L.W.M.); [PERSON]ADDRESS] (A.O.G.)  
(AKIN) in 2007 [8]. The KDIGO guidelines define AKI as an increase in serum creatinine (SCr) by \(\geq\)0.3 mg/dL (\(\geq\)26.5 umol/L) within 48 h (KDIGO 1a) or an increase in Scr to \(\geq\)1.5 times baseline within the prior 7 days (KDIGO 1b) [9] or urine volume < 0.5 mL/kg/h for 6 h [6]. As urine volume is poorly documented in the EHR during routine in-hospital care, the present investigation focuses exclusively on Scr criteria. Several guideline-making groups have critiqued the KDIGO AKI criteria and have requested additional research to evaluate these criteria [10; 11; 12; 13]. The present study is intended to address two issues: first, the recognized arbitrariness of the choice of 48 h for the change of \(\geq\)0.3 mg/dL in creatinine (KDIGO 1a) since this change could conceivably occur earlier or later with different outcome implications; second, due to differences in the timing of blood draws between index Scr and follow up, 7 calendar days may not encompass the 168 h implied in KDIGO 1b for an increase of \(\geq\)50% from the index Scr. The primary objective of this study is to explore the impact (in terms of numbers of patients with AKI detected and their respective outcomes) of the \(\geq\)0.3 mg/dL increase in creatinine over 24-h intervals up to 168 h to identify cases of AKI that may occur earlier or later than the currently prescribed time. The secondary objective is to similarly explore the impact of changing the time frame for KDIGO 1b criteria for AKI and the \(\geq\)50% increase in Scr from index value to 168 h rather than 7 days. If this analysis reveals meaningful increases in the numbers of patients detected with AKI with associated meaningful differences in their outcomes, the case can be made for programming the EHR to automatically identify the thus defined AKI and to alert the health care team.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
## 5 Conclusions  
In this paper, we proposed a joint user scheduling, tree-based search power allocation and link selection algorithm in building a NOMA-based D2D enhanced cellular network. Firstly, we introduced the D2D communication into the cellular network to increase the system capacity by reusing the spectrum of CUs. Secondly, the proposed algorithm jointly solved the user scheduling, power allocation and link selection problems to increase the sum data rate of the network. The user scheduling method and the tree-based search power allocation scheme were proposed not only to achieve the maximum data rate of the CUs and DUs, but also reduce the cost of computing compared with ESA. The distance-aware  
Figure 11: Plot of the sum data rate of CUs on the same SC with respect to the number of CUs. On each SC, the number of CUs changes from 1 to 7.  
Figure 12: Plot of the sum data rate of the system versus the number of users.  
link selection algorithm limits the interference for the DUs, saves energy for the CUs and offloads part of the heavy traffic for the BS. Finally, we conducted simulations to evaluate the performance of our proposed algorithm. Numerical results demonstrate that our algorithm improves data rate, has a low computational complexity cost and has a good convergence performance. Thus, the algorithm also has significant advantages compared with traditional algorithms.  
Conceptualization, [PERSON] and X.S.; methodology, [PERSON]; software, Y.M.; validation, [PERSON], X.S. and Y.M.; formal analysis, [PERSON]; investigation, X.S.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"the National Nature Science Foundation of China, grant number no. 61601109\" and \"The APC was funded by the Fundamental Research Funds for the Central Universities under Grant No. N152305001\".  
This work was supported by the National Nature Science Foundation of China under Grant no. 61473066 and no. 61601109, and the Fundamental Research Funds for the Central Universities under Grant No. N152305001. The authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Simulation Results and Model Validation  
The validation of the numerical model is an important stage in ensuring the reliability of the results obtained. To that end, numerical results must be compared with experimental data. Figure 6 shows an example of comparison of the numerical (blue line) and experimental (orange line with circles) frequency response qualitative curves for the bottom magnet (Figure 5(a)) and induced current (Figure 5(b)), respectively. The experimental curves were obtained by testing the harvester prototype on the dynamic workbench described in Section 2.1. The frequency response was measured for a sinusoidal excitation on the shaker with an amplitude of \(A=0.6\,g\), where \(g\) is the gravitational acceleration. The test was made for forward or backward frequency sweeps with a step 0.5 of Hz and an interval of 5 s.  
Generally, the numerical model successfully predicted the nonlinear frequency response curves, and it is observed that the experimental and simulation results are consistent. The presence of two resonance peaks is confirmed, although the experimental peak values are considerably lower.  
These differences could be explained by the estimates of damping and the assumptions of constant magnet friction and fixed electromechanical coupling. These assumptions are responsible for errors in the calculation of the numerical resonance curves. Moreover, the resonant frequencies of the experimental results are slightly shifted to the left compared to their numerical equivalents (a detuning phenomenon). This effect may be related to the small rotations of the magnet and the contact of the magnet with the bumper.
## 4 Overview of GNSSs
### Quasi-Zenith Satellite System
#### 4.5.2 QZSS Space Segment  
The QZSS consists of IGSO and GEO satellites with an orbital period of 23 h and 56 min, synchronized with the Earth's rotation. The IGSO satellites are located in three orbital planes, offset by 120\({}^{\circ}\) in RAAN and latitude arguments, providing an identical ground track and continuous availability of one satellite every 8 h.  
To optimize visibility around Japan, the orbits have an inclination of 43\({}^{\circ}\) and a slight eccentricity, with an apogee in the north and a perigee argument of 270\({}^{\circ}\). The central longitude of the ground track is 135\({}^{\circ}\) east. The asymmetric orbit provides a good compromise between service availability, link properties, and resilience, compared to a fully symmetric orbit and a teardrop-shaped orbit [92].
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design
### Environment Configuration
#### 3.4.3 Action  
In order to conduct reliable communications with satellites and eliminate potential risks to obstacles such as buildings, it is assumed that UAVs maintain their altitude for stable flight. Therefore, the actions of UAVs simply include moving in four directions, left, right, bottom, and up on the traffic map. In other words, UAVs can move east, west, south, or north in the real world as shown by the yellow arrows in Figure 1. In addition to these 4 directions, a UAV has a total of five actions by adding one more action, staying in the position.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 5 Conclusions  
Building upon receiver signal processing principles, this study establishes a comprehensive interference evaluation model capable of quantitatively evaluating B2a signal performance degradation in DME interference scenarios. The model extends the conven  
Figure 11: Heatmap of the B2a signal detection probability \(Pd\) (**a**), carrier tracking error \(\delta_{PLL}\) (**b**), and the demodulation bit error rate \(Pe\) (**c**) degradation at FL400 altitude.  
tional RTCA assessment framework and explicitly formulates the computational method for cumulative interference effects in multi-DME scenarios. The evaluation systematically analyses the interference effects and hotspot distributions through four key metrics: \(C/N_{0}\), signal detection probability, carrier tracking accuracy, and demodulation bit error rate (BER). The analysis utilizes empirical data from the DME beacon network in the Chinese mainland combined with an interference budget model.  
The evaluation results reveal substantial degradation in the B2a signal reception performance within regions characterized by dense DME beacon deployments. Moreover, interference hotspots show significant overlap with regions that have high air traffic density. Under the worst-case interference conditions, the effective \(C/N_{0}\) of B2a signal decreases by 3.25 dB, leading to a 33% reduction in signal detection probability, a 2.5\(\sigma^{\circ}\) increase in carrier tracking error, and a 5.1% increase in demodulation bit error rate. The degradation in navigation signal reception performance adversely impacts the accuracy, availability, continuity, and integrity of the navigation system, jeopardizing the flight safety of civil aviation aircraft. Furthermore, the hotspot mapping evaluation method proposed in this study is also applicable to assessing the interference effects of other L-band signals (e.g., TACAN, JTIDS/MIDS, and ADS-B) on the BeiDou navigation system.  
Conceptualization, Y.L. and J.C.; methodology, Y.L.; software, Z.M.; validation, J.C., Z.M. and Z.D.; formal analysis, Y.L. and Z.M.; investigation, Y.L. and J.C.; resources, Z.M.; data curation, Z.D.; writing--original draft preparation, J.C.; writing--review and editing, Y.L. and Z.M.; visualization, Z.M. and J.C.; supervision, Z.D.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Civil Aviation Administration of China (contract no. 61 (2023)).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflicts of interest.
## 4 In-Loop Testing of the Control Model
### Results and Analysis of Fuzzy Control Type Control Strategy  
Using the development process in the article [41], the load demand power, the hydrogen fuel cell's output power, the hydrogen fuel cell, and the power fluctuation ratio of the hydrogen fuel cell are input variables of the fuzzy controller, and each energy output situation is used as the output of the fuzzy controller. Through the control model in-loop test, some of its results are shown in Figures 13 and 14.  
Figure 13 shows the power output of each energy source in the fuzzy control strategy experiment. It can be seen that the hydrogen fuel cell takes up the main load power, and the power fluctuation of the hydrogen fuel cell under the fuzzy control strategy is small and large across. The highest output power of the hydrogen fuel cell based on fuzzy control rules is 18 kW. This control strategy can meet the demand of the load power.  
As can be seen from Figure 14, because the fuzzy control type control strategy only considers the change of load power fluctuation and suppresses the fluctuation, it does not consider the optimization of the working point of the PEMFC system, so the working point  
Figure 11: (**a**) The power output of each energy part; (**b**) the power output of the hydrogen fuel cell.  
Figure 12: The actual working point of the hydrogen fuel cell.  
of PEMFC is more scattered during the working process, and some working points deviate from its efficient working interval.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 2 Damage Estimation Method Based on CWT  
The proposed damage estimation method describes a color-mapped image for each actuator-sensor pair, which indicates the possibility of the presence of damage in the discretized monitoring region. The region of maximum possibilities in the aggregate image of all the pairs represents the damage features. In time-of-flight (ToF)-based triangulation of a point damage \(D(x,\ y)\), when actuator \(A(x_{i},y_{i})\) excites the Lamb waves, sensor \(S\big{(}x_{j},y_{j}\big{)}\) receives two signals: a direct signal from \(A\) to \(S\), which travels a distance, \(L_{A-S}\), and a damage-scattered signal, which travels a longer distance, \(L_{A-D}+L_{D-S}\), as shown in Figure 1.  
The ToF from \(A\) to \(S\) for the damaged-scattered signal, \(\mathrm{T}_{D}\), can be written as,  
\[\mathrm{T}_{D}(x,y)=\ \frac{L_{A-D}+L_{D-S}}{V} \tag{1}\]  
where, \(V\) is group velocity of the incident Lamb wave and,  
\[L_{A-D}=\ \sqrt{\big{(}x-x_{i}\big{)}^{2}+\big{(}y-y_{i}\big{)}^{2}}\ ;\ L_{D-S}=\sqrt{\big{(}x-x_{i}\big{)}^{2}+\big{(}y-y_{j} \big{)}^{2}}\]  
The theoretical solution of Equation (1) represents an elliptical path locus with focal points at \(A\) and \(S\), indicating the possible damage locations on it, as shown in Figure 1. When transducers are arranged as an active sensor network, the actual damage location falls at the intersection of multiple elliptical path loci from different actuator-sensor pairs. For damage localization using Equation (1), the accurate extraction of ToF is important therefore, the CWT is introduced to process the signals due to its high time-frequency resolution.
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 2 Materials and Methods
### Intercomparison Statistics  
As mentioned in the Introduction, direct comparisons of each precipitation product with the others were carried out. Therefore, the present analysis represents more of a comparative analysis among the three products aimed at detecting similarities and differences, rather than an \"absolute\" validation with respect to an independent reference dataset assumed as \"truth\".  
Dichotomous statistics was exploited in order to evaluate the precipitation detection capacity. The statistical parameters used to discriminate precipitating and non-precipitating days are summarized in Table 1 according to [PERSON]'s definitions [36]. A precipitation threshold of 1 mm day-1 was selected to identify precipitating days, as recommended by the Expert Team on Sector-Specific Climate Indices (ET-SCI, [[https://climpact-sci.org](https://climpact-sci.org)]([https://climpact-sci.org](https://climpact-sci.org)), accessed on 21 October 2021).  
In Table 1, hits (H) is the number of precipitating events (i.e., 0.25\({}^{\circ}\) cells with daily precipitation \(\geq\) 1 mm day-1) observed by both products, misses (M) represents the number of precipitating events observed by the product used as a basis for comparison (reference product) but not by the other product (compared product), false alarms (FA) is the number of precipitating events identified by the compared product but not by the reference product, and the correct negatives (CN) is the number of non-precipitating days according to both products. The dichotomous statistics was applied both considering the whole daily precipitation time series from 1983 to 2017 for each 0.25\({}^{\circ}\) grid cell, and year by year considering the yearly statistical parameters averaged over each study region. This twofold approach allowed for simultaneously investigating spatial patterns and the temporal evolution (temporal stability) of the products' capability to discriminate precipitating and non-precipitating events.  
For the quantitative evaluation, the exploited statistical parameters were the linear correlation coefficient (CC) measuring the strength of the linear relationship between the two compared products, the mean error (ME) evaluating the tendency toward over- or underestimation of one product compared to the other, and the mean absolute error (MAE) quantifying the average difference between the precipitation estimates of the two compared products. The expressions of these statistical parameters are presented in Table 2, where \(\mathit{SPE_{1}}\) and \(\mathit{SPE_{2}}\) are the satellite precipitation estimates of the two compared products, \(\mathit{\overline{SPE_{i}}}\) represent the mean values of the satellite data, and N is the number of considered data pairs. CC, ME, and MAE have been computed at the daily and monthly (only ME and MAE) time scales and year by year, considering the yearly statistical parameters averaged over each study region as for the dichotomous statistics.
# On the Semi-Local Convergence of Two Competing Sixth Order Methods for Equations in Banach Space
## 4 Numerical Example  
Let us apply methods (2) and (3) with \(\alpha=\frac{2}{3}\) to solve the following nonlinear problems.  
**Example 1**.: _Consider the system of nonlinear equations with \(F:\mathbb{R}^{m}\rightarrow\mathbb{R}^{m}\) defined by_  
\[F_{i}(x) = 3v_{i}^{3}+2v_{i+1}-5+\sin(v_{i}-v_{i+1})\sin(v_{i}+v_{i+1}), i=1,\] \[F_{i}(x) = 3v_{i}^{3}+2v_{i+1}-5+\sin(v_{i}-v_{i+1})\sin(v_{i}+v_{i+1})\] \[+4v_{i}-v_{i-1}\exp(v_{i-1}-v_{i})-3, 1<i<m,\] \[F_{i}(x) = 4v_{i}-v_{i-1}\exp(v_{i-1}-v_{i})-3, i=m.\]  
_Here \(x=(v_{1},\ldots,v_{m})^{T}\). The initial approximation is calculated by the formula \(x_{0}=(2s,\ldots,2s)^{T}\), where \(s\) is a real number. The exact solution is \(x_{*}=(1,\ldots,1)^{T}\). The iterative process is stopped if the condition holds_  
\[\|F(x_{n+1})\|_{\infty}\leq 10^{-10}.\]  
_Tables 1 and 2 show values of errors for different \(s\) and \(m=5\). Notice that the closer \(x_{0}\) is to \(x_{*}\) the faster the convergence._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(s=0.35\)} & \multicolumn{2}{c}{\(s=0.4\)} & \multicolumn{2}{c}{\(s=0.45\)} \\ \cline{2-7} \(n\) & **(2)** & **(3)** & **(2)** & **(3)** & **(2)** & **(3)** \\ \hline
0 & 3.0000e-01 & 3.0000e-01 & 2.0000e-01 & 2.0000e-01 & 1.0000e-01 & 1.0000e-01 \\
1 & 1.2680e-01 & 8.3460e-01 & 3.7373e-03 & 2.7441e-02 & 2.3940e-05 & 4.1343e-04 \\
2 & 2.0491e-05 & 6.6401e-02 & 3.3085e-14 & 6.3708e-07 & 0 & 4.1744e-14 \\
3 & 0 & 1.6444e-05 & & 0 & & \\
4 & & 0 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The values \(\|x_{n}-x_{*}\|_{\infty}\) at each iteration for \(s=0.35\), 0.4, 0.45.  
**Example 2**.: _Consider the boundary value problem_  
\[\left\{\begin{array}{ll}\vspace{0.2 cm}y^{\prime\prime}(t)-y^{\prime}(t)tg(t)+ \frac{2y^{2}(t)}{\sin(t)}=0,\,0<t<\frac{\pi}{2},\\ y(0)=0,\,y(\pi/2)=1.\end{array}\right.\]  
_Denote \(v_{i}=y(t_{i})\), \(i=0,\ldots,m+1\), where \(t_{i}=ih\) and \(h=\frac{\pi}{2(m+1)}\). Using the approximation for the first and second-order derivatives_  
\[v_{i}^{\prime\prime}\approx\frac{v_{i-1}-2v_{i}+v_{i+1}}{h^{2}},\quad v_{i}^{ \prime\prime}\approx\frac{v_{i+1}-v_{i-1}}{2h},\,\,i=1,\ldots,m,\]  
_the following system of the nonlinear equations_  
\[F_{i}(x) = -2v_{i}+v_{i+1}-\frac{h}{2}v_{i+1}tg(t_{i})+\frac{2h^{2}v_{i}^{2 }}{\sin(t_{i})}=0,\qquad\qquad\quad i=1,\] \[F_{i}(x) = v_{i-1}-2v_{i}+v_{i+1}-\frac{h}{2}(v_{i+1}-v_{i-1})tg(t_{i})+ \frac{2h^{2}v_{i}^{2}}{\sin(t_{i})}=0,\,\,\,i=2,\ldots,m-1,\] \[F_{i}(x) = 1-2v_{i}+v_{i+1}-\frac{h}{2}(1-v_{i-1})tg(t_{i})+\frac{2h^{2}v_{ i}^{2}}{\sin(t_{i})}=0,\qquad\quad i=m\]  
_with \(x=(v_{1},\ldots,v_{m})^{T}\) is obtained._  
_Figure 1 shows \(\|F(x_{n})\|_{\infty}\) at each iteration. The results are obtained for \(m=49\) and \(\varepsilon=10^{-10}\). The starting approximations \(x_{0}\) were given by formulas \(x_{0,i}=\sin(ih)+0.6\) (for the graphs on the left) and \(x_{0,i}=0.5\sin(ih)\) (for the graphs on the right). Notice that the method (2) convergences faster than (3) for both problems._
## 3 Experimental Results
### Mineral Characterization  
The chemical compositions of the aggregates were determined by EDX. In conjunction with the aggregate particles' examination with the SEM, the particles were subjected to an electron beam used to characterize the elemental composition of the samples. Table 4 provides the complete chemical composition of each aggregate used in the study with the total percent composition summing to 100% for each.