esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Main Constituents of BC Profiles of Plants in Pots  
We start validating our GC-MS method for solid-phase microextraction (SPME).  
Firstly, we compared the profile of a citral analytical standard sample (a mixture of neral and geranial from AMCO International SA de C.V. Mexico) against the GC-MS profile obtained from fresh leaves of an adult plant of lemongrass grown in the field around (Supplementary Figure S10). Neral (\(\beta\)-citral) and geranial (\(\alpha\)-citral) mass spectra and retention times (RT) obtained from SPME/GC-MS analysis of citral analytical standard were identical to those of _C. citratus_ leaves (Table 2). The SPME/GC-MS analysis of _C. citratus_ essential oil from the different CK treatments identified many constituents, though most were present in small quantities (less than 1%). About fourteen typical components of _C. citratus_ EO were identified: hexenol, citronell-like monoterenes, terpinene, cadinene, limonene, ocinene, cynene, _cis_-geraniol (nerol), _trans_-geraniol (geraniol), neral (\(\beta\)-citral), geranial (\(\alpha\)-citral), geranial (\(\alpha\)-citral), geranial acetate, and sesquiterpenes such as \(\beta\)-caryophyllene and \(\alpha\)-humulene [33]. In Table 2, we summarize the most constant marker components present in the essential oil of established plants of _C. citratus_.  
Values represent the average area % of the 0.5 mg L\({}^{-1}\)concentration obtained for each terpenoid among the different treatments. Area % represents the total sum of the abundance of all terpenoids in the EO profile divided by the average quantity of each constituent \(\times\) 100.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{_C. citratus_ Essential Oil Composition Obtained by SPME/GC-MS} \\ \hline
**RT** & **Main Constituents** & \multicolumn{4}{c}{**MS-N (\%)**} & \multicolumn{4}{c}{**MS-S (\%)**} \\ \hline  & & **2 iP** & **tZ** & **BAP** & **KIN** & **2 iP** & **tZ** & **BAP** & **KIN** \\ \hline
7.50 & Limonene & 41.61 & 4.20 & 3.94 & 3.89 & 4.26 & 1.14 & 1.08 & 2.61 \\
10.20 & Citronellal & 0.04 & - & - & - & 0.28 & - & - & - \\
12.00 & \(\beta\)-citral (Neral) & 27.52 & 42.99 & 37.33 & 29.56 & 36.28 & 30.57 & 14.89 & 23.04 \\
12.20 & Geraniol & - & 0.10 & 1.57 & - & 1.04 & 7.49 & 0.09 & 1.54 \\
12.50 & \(\alpha\)-citral (Geranial) & 25.62 & 47.71 & 50.56 & 64.96 & 59.48 & 66.23 & 80.04 & 70.23 \\
14.40 & Geranyl acetate & 0.23 & 0.20 & 3.34 & 0.16 & 25.79 & 0.58 & 0.31 & 1.22 \\
15.20 & \(\beta\)-caryophyllene & 3.42 & 4.80 & 2.53 & 1.43 & 2.16 & 1.99 & 2.89 & 1.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the main volatile oils obtained from lyophilized (freeze-dried) leaves of established plants in pots. RT = retention time; (%) = maximum content.  
In all treatments, the monoterpene fraction was dominated by \(\beta\)- and \(\alpha\)-citral, ranging from 15% to 80% of the total terpenes' EO profiles. Limonene ranged from 1 to 41%, and the sesquiterpene \(\beta\)-caryophyllene ranged between 1 and 5% of the full content. Other monoterpenes were less present than 5% considered altogether.
## 5 Implementation of the Receiver
It is evident by considering the interference of the current symbol on many successive symbols only the number of LOTs increases. The main algorithm could still be implemented through matrix subtractions and finding the indices of the minimum value at every time. To conclude this section, it is essential to highlight three key aspects of this research. Firstly, as per the aforementioned process, the receiver provides overlapping patterns at each time epoch, with the number of patterns equating to the constellation size. Only one pattern occurs at a time. We employ feedback from the decoder to select the pattern corresponding to the constellation symbol with the highest probability of occurrence. Secondly, the average signal power at the receiver determines the noise variance. However, when compensating for reflected components in the means of the Gaussian observations, it is crucial to remove the power of those components from the noise variance. We simplify this adjustment using the LOTs provided for the receiver. Finally, the Gaussian multiplication is employed, whenever it is needed, in the message passing algorithm.
## 4 Results of Experiment and Simulation
### Disturbance Rejecting Ability Simulations and Experimental Results
#### 4.3.2 Experimental Results  
Four different methods are applied to the control system of MSA. From the experimental results in Figure 15, the following conclusions can be obtained: the \(p\)-\(p\) values of \(\Delta\theta_{a}\) are \(0.52^{\circ}\) and \(1.01^{\circ}\) when the disturbances are added to PID controller at time 70 s and 130 s, respectively; whereas the tracking values for NFTSM are \(0.072^{\circ}\) and \(0.14^{\circ}\) at time 70 s and 130 s, respectively; the tracking values for RBFNN based NFTSM are \(0.03^{\circ}\) and \(0.082^{\circ}\) at time 70 s and 130 s, respectively; when BSNN based NFTSM is applied, the \(p\)-\(p\) values of are \(0.013^{\circ}\) at time 70 s and \(0.059^{\circ}\) at time 130 s.  
Figure 14: Form change of the B-spline activation function: (a) initial forms of the curves; (b) final forms of the curves after training.  
Figure 15: Comparative azimuth gimbal absolute angle experimental results with four different methods: (a) PID method; (b) NFTSM method; (c) NFTSM-RBFNN method; (d) the proposed method.  
According to the simulations and experiments, it can be seen that the proposed BNN based NFTSM is good at nonlinear approximate and has strong self-adaptability.
# Breeding Tomato Hybrids for Flavour: Comparison of GWAS Results Obtained on Lines and F1 Hybrids
## 2 Materials and Methods
### GWAS Analysis  
The core collection, FTMV and the Gautier sentences plant materials were genotyped and filtered on quality modalities for 7442 common SNP markers (Table S2), using the Infinium assay developed by the Solanaceae Coordinated Agricultural Project ('Solan'P) ([PERSON] _et al._, 2013; [PERSON] _et al._, 2015). Each genotypic matrix was first filtered with the _plink_ software ([PERSON] _et al._, 2013) with: MAF \(\leq\)4%, missing genotype per accession \(\leq\)20% and missing information per marker \(\leq\)15%. As a result, genotypic matrices counted 5996 identical markers for CCI and TCI, and 6058 SNP markers for TCT. We conducted the GWAS using the multi-locus mixed model ('MLMM') proposed by ([PERSON] _et al._, 2013). Structure and Kinship matrices were re-evaluated for each trait in the GWAS analyses with the R package _FactomineR_ and the function 'emma.kinship' (Identity By State method), respectively ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013). The detection threshold was set at pval \(<\)10\({}^{-4}\) with up to 15 cofactors allowed. For any significant association, we first computed the marker pairwise linkage disequilibrium ('LD') with each neighbour marker in a 2 Mbp interval (1 Mb on each chromosome end) with the _LDcorSV_ package ([PERSON] _et al._, 2013). This package took both structure and kinship matrices into account to compute local r\({}^{2}\) between pairs of markers on a given chromosome. Thus, the confidence interval ('CI') around each associated SNP was defined as the interval where the computed r\({}^{2}\) was higher than 0.5 in a 2 Mbp region. The SL4.0 tomato genome version was used for the analysis along with the annotation ITAG4.0. We excluded genes that did not map on any of the 12 chromosomes, and focused on the 33,562 remaining genes. From these CIs, we extracted the list of CGs comprised in each interval. We retrieved CGs when their functional annotations matched functions involved in VOC synthesis as reported in the bibliography, irrespective of their expression in tomato fruits. Lists of CGs per metabolic pathway were thus constituted (Table S3) and then matched to the genes found in each association CI. We also assessed the number of transcription factors ('TFs') in each CI, based on the list of TFs presented in Table S3. We reported genes expressed in fruits based on transcriptome analysis of fruits from the eight parental lines of the MAGIC population described in ([PERSON] _et al._, 2013). Two SLC they studied are included in our panels at the line (CCI) and hybrid (TCI and TCT) levels.  
We performed a \(\chi^{2}\) test on each metabolic pathway and each panel separately to assess the ability of the MLMM method combined with the CI definition to detect CGs. We compared the number of CGs identified in the CI of associations for a specific metabolic pathway and a given panel, to the number of CGs in the whole genome for a specific metabolic pathway (Table S3). The \(\chi^{2}\) test was computed with a 5% error threshold.
## 6 Conclusions  
The main contribution of the present work is the design, modeling, and characteristic analysis of the new CRPDC. The effects of the duty cycle, pulse frequency, energy-storage capacitance, and power voltage on the output current are investigated by a mathematical model and a simulation model. The current control performance of the proposed CRPDC is compared to the traditional RDPDC. Five main conclusions can be drawn as follows:  
1. By adding an energy-storage capacitance to the proportional valve coil in a parallel manner, the designed CRPDC is able to drive the coil current with high accuracy and a fast response.
2. The current ripple of the CRPDC can be regulated by modulating the frequency of the PWM signal. While the frequencies are set to be 2 kHz, 3 kHz, 4 kHz, and 5 kHz, the resulting current ripples are 140.11 mA, 64.29 mA, 36.25 mA, and 23.10 mA.
3. The current amplitude of the CRPDC can be adjusted by changing the duty cycle of the PWM signal. While the duty cycles are set to be 20%, 40%, 60%, and 80%, the resulting coil currents are 0.81 A, 1.33 A, 1.83 A, and 2.32 A.
4. The coil current control characteristic of the CRPDC can also be improved by choosing the appropriate energy-storage capacitance and power voltage.
5. Future research efforts will be focused on studying the influence of other parameters on the coil current such as the inlet pressure, comparing the simulation results and the experimental results, and controlling the proposed CRPDC by closed-loop control algorithms [35,36].  
Conceptualization, X.L., X.Y. and S.L.; methodology, X.L. and X.Y.; validation, X.L. and X.Y.; writing, X.L., X.Y. and D.L. All authors have read and agreed to the published version of the manuscript.  
Figure 15: The coil currents of proposed CRPDC and traditional RDPDC under different duty cycles: (**a**) 10%; (**b**) 30%; (**c**) 50%; (**d**) 70%.  
**Funding:** This research was funded by the National Key R&D Program of China under Grant 2019 YFB2005303, the Jiangsu Province Marine Science and Technology Innovation Project under Grant JSZRHYJKJ202215, and the National Natural Science Foundation of China under Grant 52175018.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
In summary, the strategy only setting different \(N_{t}\) and \(T_{t}\) for corresponding QoS requirements has a very limited optimization performance on Diffserv networks, proposing an efficient differentiated services strategy in WSNs is a challenging issue. For the above problems, a Differentiated Data Aggregation Routing (DDAR) scheme is proposed to reduce energy consumption and ensure that all kinds of data meet their service requirements. Salient features of the proposed work are given as follows:* The DDAR scheme is a novel data aggregation routing framework. In this framework, each node configures only one set of parameters to satisfy a certain QoS requirement. When a node performs aggregation, it searches an aggregator whose service most closely matches its QoS requirement for next hop. The most closely matching refers to the nodes which have the smallest difference of QoS requirement with the sender. DDAR scheme ameliorates the high energy consumption, complex storage and poor service guarantee in previous strategies. Thus, DDAR scheme realizes the differentiated data aggregation routing in the true sense, and is able to significantly reduce energy consumption while ensuring that data transmission of data packets meets service requirement. * Based on DDAR scheme, we propose an improved DDAR scheme to reduce delay and improve energy efficiency by utilizing the residual energy in the nodes far from the sink. Whatever routing strategy is adopted, the data volume a node transmits decreases with the increase of distance to the sink. This phenomenon illustrates that the energy consumption of the nodes near the sink is larger than the nodes far from the sink, there is residual energy in the nodes when the network dies. In this paper, improved DDAR enhances the performance by increasing the frequency of aggregation. * In this paper, we propose the differentiated data aggregation routing scheme. Simulation results demonstrate that DDAR can improve the service guarantee rate by 25.1%, network lifetime by 55.45% and energy efficiency by 83.99%. The rest of this paper is organized as follows: in Section 2, related works are reviewed. The system model and problem statement are described in Section 3. In Section 4, design details on DDAR scheme are presented. Section 5 is simulation results and comparisons for the optimization performance of DDAR scheme. We conclude in Section 6.
## 5 Performance Evaluation
### Experiment Setup
#### 5.1.1 Average Execution Time  
Figure 7 plots the execution time of 1000 transactions experimented on various topologies for the four functions. Commonly, the average execution time decrease as the number of TNs increases (thanks to increase in parallel, simultaneous execution of transactions), whereas the change with increasing SN is not striking. This is an interesting result as we were concerned that a large number of SNs might result in slower execution time due to validation overhead of DDNC. It turns out that, thanks to parallel execution, the overhead of DDNC does not scale proportionally with the number of SNs. The outliers for _GetNonce_ and _GetBalance_ are from packet drops between SN and BN due to  
Figure 6: Test flow: Test client runs a batch program that executes a predefined transaction and records UNIX timestamps of every moment when packets are transmitted and received between nodes.  
authentication failure. For example, if they wait for a _nonce_ that has already expired or disappeared, the transaction will wait until timeout before it retransmits. This is an obvious problem observed in the experiments and worth a future work. _SendCoin_, on the other hand, shows similar aspects overall with no noticeable singularities, although it has the largest _average execution time_ in every experiment due to its complexity (in terms of query steps required).
## 4 Simulation and Numerical Results
### Radar Performance
#### 4.1.2 Range and Velocity Estimation Performance  
In order to quantify the target range and velocity estimation performance of the proposed method, Monte Carlo experiments with \(10^{4}\) repetitions were carried out under different SNR settings. In the simulation, \(K=4\). The other parameters are as given in Table 1. The four range and velocity estimation methods used for simulation comparisons are as follows: (1) the MUSIC algorithm under the MIMO-OFDM system, (2) the improved parameter estimation method (IPEM) under the MIMO-OFDM system in [15], (3) the FFT algorithm under the MIMO-OFDM-IM system, and (4) the proposed method under the MIMO-OFDM-IM system. Figure 7 shows the variation in the mean-square errors (MSEs) of range and velocity estimation with the SNR under different methods. It can be seen that the MSE of range and velocity estimations both decrease with the increasing SNR. The estimation accuracies of the MUSIC algorithm under the MIMO-OFDM system are basically similar to the published IPEM method in [15], and both are higher than that of the FFT algorithm. In contrast, the proposed method can obtain better estimation accuracy. This is because the proposed method screens the effective data under the designed integrated waveform condition based on MIMO-OFDM-IM. In addition, although the velocity estimation accuracy of the proposed method is better than the methods in the literature and the MUSIC algorithm under the MIMO-OFDM system, its improvement is not as significant as the range estimation improvement, mainly because the proposed method performs FFT on the velocity dimension after dimensionality reduction.  
Figure 5: The PSLR performance. (**a**) The variation in PSLR with SNR. (**b**) The variation in PSLR with the number of activated subcarriers (SNR = 10 dB).  
Figure 6: The variation in PSLR with SNR under different number of activated subcarriers.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 4 Test Results and Discussion
### Ultimate Flexural Strength of Donut-Type Voided Slab
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**Considering Strain of**} & \multirow{2}{*}{**Park Method \({}^{\ddagger}\)**} & \multirow{2}{*}{**Offset Method \({}^{\ddagger}\)**} & \multicolumn{3}{c}{**Displacement Ductility**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Bottom Rebar \({}^{\ast}\)**} & & & & & & **\(\langle\mu\rangle\) §** & \\ \cline{2-10}  & \(P_{y}\) & \(\delta_{y}\) & \(P_{y}\) & \(\delta_{y}\) & \(P_{y}\) & \(\delta_{y}\) & \(\delta_{y}\) & **Strain of Bottom Rebar** & **Park Method** & **Offset Method** \\ \hline Solid & 134.01 & 9.75 & 153.46 & 12.71 & 152.90 & 12.54 & 6.64 & 5.10 & 5.17 \\ OF-V-S-D & 139.81 & 11.76 & 154.10 & 15.56 & 150.95 & 14.01 & 5.67 & 4.29 & 4.76 \\ \hline OF-V-M-D & 138.56 & 10.70 & 165.99 & 14.58 & 153.69 & 12.07 & 7.63 & 5.60 & 6.77 \\ \hline OF-V-S-R & 129.04 & 11.37 & 127.00 & 10.56 & 119.21 & 9.51 & 1.45 & 1.57 & 1.74 \\ \hline \hline \end{tabular}  
* Values of \(P_{y}\) and \(\delta_{y}\) when strain gauges placed at the tensile bars at the center of the slab reach 0.24%, \({}^{\ddagger}\) The first straight line corresponds to the initial stiffness (K), defined between 0% and 75% of the peak load. The second line defined as horizontal line at peak load. The yield point on the load-deformation curve defined the projected line from the intersection point of these two lines. \({}^{\ddagger}\) A line is constructed parallel to the initial portion of the stress–strain curve but offset by 0.002 in/in (0.2%) from the origin. The 0.2% offset yield strength is the stress at which the constructed line intersects the stress–strain; \({}^{\ddagger}\)\(\mu=\delta_{y}/\delta_{max}\).  
\end{table} 
Table 8: Yield strength and displacement–ductility ratio. \[M_{n}=A_{s}f_{y}\Big{(}d-\frac{a}{2}\Big{)} \tag{2}\] \[M_{n}=(A_{s}+A_{legs})f_{y}(d-\frac{a}{2}) \tag{3}\]
* \(M_{n}\) : nominal flexural strength
* \(A_{s}\): area of non-prestressed tension reinforcement
* \(A_{legs}\): area of the legs of fixing steel cage
* \(f_{y}\): specified yield strength of non-prestressed reinforcement
* \(d\): effective depth of slab specimen
* \(a\): depth of equivalent rectangular stress block
## 3 Results
### Guided Wave Techniques Results
#### 3.2.3 Contact-to-Contact Transducer Guided Wave Result  
The scanning parameters were the same as that of the air-coupled to contact the GW method. However, in this case, since two contact transducers were used the transmission efficiency is higher in comparison to air-coupled transducers. Thus, the used static gain was 25 dB, and the dynamic gain was 10 dB. The A-scan and B-scan of the experiment with contact-to-contact transducers GW is shown in Figure 19a,b, respectively. The results in this method can be interpreted in a similar way to the results of the air-coupled to contact GW because the amplitude of the GW increases when it passes through the debonded region due to the constructive interference.  
Figure 17: Guided wave A-scan—numerically simulated with probe point across the bond.  
When compared to the air-coupled excitation method, where the incidence angle can be changed, here the angle of incidence is normal to the surface of the plate. Hence, the generated guided waves will have equal amplitudes in all directions. As a result, a strong boundary reflection was observed at the edge of the plate. This interference of the boundary reflection can hinder the possibility of detecting the defects near the edge of the plate boundary Figure 19b. The A-scans from the perfect bond path and the path with debonding is shown in Figure 19a.  
The amplitude of the A-scan along the path with the debonding shows a higher amplitude than the amplitude of the A-scan along the perfect bond path. A similar effect can also be observed in the simulation and in air-coupled excitation and contact reception GW technique.  
Figure 18: Air-coupled to contact guided waves results: (**a**) A-scan; (**b**) B-scan; (**c**) slice at time = 62.84 \(\upmu\)s.
## 3 Results
### LIPS-G Assays Detect Antibodies Elicited by Natural Infection  
Murine immune responses following sequential infections with either RSV-A2 or RSV-B1 viruses were evaluated using LIPS-G\({}_{\mathrm{A}}\) and -G\({}_{\mathrm{B}}\) assays (Figure 6). RSV-A2 infected mice undergoing primary infection developed a subtype-specific anti-G\({}_{\mathrm{A}}\) IgG immune response that was boosted following a second infection. In contrast, mice infected once with RSV-B1 failed to develop an anti-G IgG antibody response detected using either LIPS-G\({}_{\mathrm{A}}\) or LIPS-G\({}_{\mathrm{B}}\) assays and only responded weakly with a subtype-specific IgG response following the second exposure to RSV-B virus. Sera from mice obtained prior to RSV infection were negative in all assays and values obtained from these tests were used to set the cutoffs for a positive result in each test (2.83 log\({}_{10}\) RLU and 2.64 log\({}_{10}\) RLU for LIPS-G\({}_{\mathrm{A}}\) and LIPS-G\({}_{\mathrm{B}}\), respectively). Murine anti-FLAG antiserum gave a positive result as expected. Murine antisera did not bind to the Ruc-only antigen.  
Similarly, human serum samples from 30 adolescent children, 10-14 years of age, were tested in LIPS-G\({}_{\mathrm{A}}\) and -G\({}_{\mathrm{B}}\) assays (Figure 7). Serum samples were also tested in PRN assays and all samples had detectable neutralizing antibodies against both RSV-A2 (ND\({}_{50}\) range 1:35 to 1:10,000) and RSV-B1 (ND\({}_{50}\) range 1:31 to 1:1795). Human IgG\(\Delta\) was the negative control and used to set the cutoff for a positive result (cutoffs were 3.14 log\({}_{10}\) RLU for Ruc-G\({}_{\mathrm{A}}\) and 3.38 log\({}_{10}\) RLU for Ruc-G\({}_{\mathrm{B}}\)). Overall, 29 and 27 samples (out of 30) had detectable IgG antibodies when tested against Ruc-G\({}_{\mathrm{A}}\) and Ruc-G\({}_{\mathrm{B}}\) antigens, respectively. No correlation was seen when we compared the results of the LIPS testing with PRN assay results by RSV subtype.  
Figure 5: Purified RSV-G proteins inhibit binding of human immune globulin (hulgG) in LIPS-G assays. Soluble purified recombinant rRSV-G\({}_{\mathrm{A}}\) (red), rRSV-G\({}_{\mathrm{B}}\) (blue), or rRSV-N (light gray) proteins were incubated with an equal volume of hulgG diluted to 0.1%. Binding of hulgG in the absence of competitor protein (dark gray) was set at 100% in each assay.