esa-sceva/satcom-chunk-collection huggingface dataset:
On the contrary, NLPs from rice were reported to activate the expression of _OsNR_ and _OsNiR_ genes and thus regulate the nitrogen use efficiency. OsNLP4 bind to the promoter of _OsNiR_ gene and transactivate [36] while OsNLP1 and OsNLP3 bind to the promoter of _OsNR_ genes and increase their abundance [37; 38]. NR activity was elevated in over expressing plants of _OsNLP1_ and _OsNLP3_ but reduced in _nlp1_ and _nlp3_ mutants. Moreover, both nitrate-promoted seed germination and stimulating effect on seedling growth by nitrate were blocked by application of NO-scavenger, 2-(4-carboxyphenyl)4,4,5,5-tetramethylimidazololine-1-oxyl-3-oxide (c-PTIO) [39; 40]. In addition, NR-dependent NO production was involved in promoting salt tolerance during seed germination in soybean [41] and in cucumber root [42] implying that nitrate promote plant development and improve stress tolerance is dependent on the NR-associated NO production. These results suggest that the regulating mechanism of seed germination under salt stress by NR-associated NO and nitrate in rice is still unclear. In this study, we generated a triple mutant of three _OsNR_ genes, which displayed a delayed germination phenotype and was much more sensitive to salt stress than seeds of WT plant. qPCR analysis revealed that expressions of ABA catabolism genes, _OsABA8 ox1_ and _OsABA8 ox2_, were suppressed in triple mutants, especially under salt stress, resulting in an elevated ABA content. Similar to SNP, application of nitrate also promoted seed germination under salt stress, which, however, was blocked in the triple mutants. Further study revealed that a nitrate responsive transcript factor, _OsNLP2_, was induced by salt stress, which thus up-regulates the expression of _OsNR_s and NR activity, resulting in promoted salt tolerance during seed germination. In addition, nitrate-mediated salt tolerance was impaired in mutants of _ababox1_ and _abab8 ox2_, two target gene for NLP2, suggesting NLP2 might activate expression of ABA catabolism gene directly or indirectly via NR-associated NO. Altogether, our results demonstrate that NLP2-NR associated NO was involved in salt response by increasing ABA catabolism during seed germination and highlight the importance of NO for stress tolerance of plants.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 2 Fundamentals of Compressed Sensing
### Sparse Representation  
In compressed sensing, the sparsity of the signal means that the signal can be represented on a suitable basis with fewer significant coefficients than the signal length. Examples of the bases include discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete wavelet transform (DWT), etc.  
In mathematical, the sparse representation of the signal can be expressed in the following form  
\[\mathbf{x}=\mathbf{\Psi}\mathbf{\alpha}=\left(\begin{array}{cccc}\psi_{11}&\psi_{12}& \cdots&\psi_{1N}\\ \psi_{21}&\psi_{22}&\cdots&\psi_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ \psi_{N1}&\psi_{N2}&\cdots&\psi_{NN}\end{array}\right)\left(\begin{array}{ ccccc}\alpha_{1}\\ \alpha_{2}\\ \vdots\\ \alpha_{N}\end{array}\right)=\left(\begin{array}{cccc}\Psi_{1}&\Psi_{2}& \cdots&\Psi_{N}\\ \vdots\\ \alpha_{N}\end{array}\right)=\sum_{i=1}^{N}\Psi_{i}\alpha_{i} \tag{1}\]  
where \(\mathbf{\Psi}\) is a sparsifying matrix, the size is \(N\times N\), \(\psi_{i}\) is the column of matrix \(\mathbf{\Psi}\), \(\Psi_{i}\) is the \(i\)-th column, \(i=1,2,\cdots,N\), \(\mathbf{\alpha}\) is the sparse coefficients whose has only \(K\) non-zeros (i.e., sparsity) or significant entries with \(K<<N\).
## 5 Numerical Study
### Uncertainty Analysis  
A third-order gPC expansion is employed to represent the uncertain output, in which \(N=9\). Since the random parameters follow a normal and uniform distribution, the orthogonal polynomials of the system output are determined as the tensor products of Hermite and Legendre polynomials. The roots of the fourth-order Hermite polynomial are \(\{-2.3,-0.74,0.74,2.3\}\), while those of the Legendre polynomial are \(\{-0.86,-0.34,0.34,0.86\}\). By adding zero to the roots, 25 collocation points are produced with which to calculate the responses. These responses are used to determine the ten unknown coefficients by the least squares method. For uncertain parameters varying with the eccentricity ratios, such as the maximum pressure and dynamic coefficients, the responses and coefficients of the gPC expansion are calculated at different eccentricity ratios. To validate the accuracy of the constructed gPC expansion, the uncertainty analysis is also carried out by way of an MC simulation with 1000 samples. The convergence of the MC simulation is shown in Figure 7.  
It can be seen that the mean value and the variance of the stability threshold are well converged after adopting 500 samples, which shows that these results are suitable for validation use.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Description** & & \multicolumn{3}{c}{\(\boldsymbol{\alpha}=\boldsymbol{0}^{\circ}\)} & \multicolumn{3}{c}{\(\boldsymbol{\alpha}=\boldsymbol{90}^{\circ}\)} \\ \(\boldsymbol{\gamma}\)/o & **0** & **0.004** & **0.005** & **0.006** & **0.004** & **0.005** & **0.006** \\ \hline Threshold (rad/s) & 212.38 & 212.51 & 212.56 & 212.62 & 213.54 & 214.27 & 215.12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Threshold for various misaligned angles.  
Figure 6: The influence of the misalignment effect on run-up curve and borderline.
# VOCs Are Relevant Biomarkers of Elicitor-Induced Defences in Grapevine
## 2 Results and Discussion
### Comparison of VOC Emission Elicited by Other Elicitors  
In addition to Bastid(r), two other commercial elicitors--Redeli(r) and Romeo(r)--were tested on grapevine plants. VOC analysis was performed, and the results were compared. Redeli(r) is a phosphonate-based product with anti-oomycete and elicitor activities, used to control grapevine downy mildew [29; 30]. Romeo(r) is made up of cerevisiae extracted from yeast cell walls [31] and is marketed to control grapevine diseases like downy mildew, powdery mildew, and grey mould. For each elicitor, time point, and replicate, the data obtained from the scan mode analysis were filtered according to the fold change (FC \(>\) 1.5) relative to the H2O control. This procedure provided a list of 43 VOCs induced at least by one elicitor and in two replicates of the experiment at one time point/in one replicate at two time points (Table 2). Among them, 23 fitted these criteria for the three elicitors (Table 2 and Figure 3): 4_,_4\({}^{\prime}\)_-bi-4_H_-pyran, 2_,_2\({}^{\prime}\)_,6_\({}^{\prime}\)_-tetrakis(1,1-dimethylethyl)-4,4_\({}^{\prime}\)_-dimethyl-; decane, 3,8-dimethyl-; oxime-, methoxy-phenyl-; cyclobutanol; toluene; 2-oxo-4-phenyl-6-(4-chlorophenyl)-l-1,2-dihydropyrimidine; \(\beta\)-caryophyllene; \(\alpha\)-farnesene; carbazole, 2,4,6-trimethyl-; 4-hexen-1-ol-acetate; 1,3-benzenedicarboxylic acid, 5-(dimethylamino)-; borane, diethyl(decyloxy)-; dodecane; sulphurous acid, 2-ethylhexyl hexyl ester; benzene, (1-ethyldecyl)-; undecane, 3,5-dimethyl-; benzene, (1-butyloctyl)-; decane, 2,3,8-trimethyl-; -D-limonene; benzaldehyde; 4-chlorobenzic acid, 4-nitrophenyl ester and succinic acid, 2,4,6-trichlorophenyl 2-naphthyllester. Among those, 12 commonly induced VOCs (listed in Figure 3) constitute a VOC blend marker of the grapevine response to elicitor treatment. Toluene may have been present in the greenhouse atmosphere, as mentioned above. As indicated in Figure 3, nine VOCs were specifically induced in response to Bastid(r) (nonanal; nonane, 2,2,4,4,6,8,8-heptamethyl-; 3-pentanamine; 2,5-cyclohexadiene; 1,4-diethyl-1,4-dimethyl-; \(n\)-hexadecanoic acid; decanal; methyl methacrylate; 3,4-dihydroisoquinin-7-ol, 1-[4-hydroxybenzyl]-6-methoxy-; \(\alpha\)-pinene); 6 in response to Redeli(r) (unde-cane, 2,6-dimethyl-; pyrimidine-2_A_(1\(H\),3_H_)-dione, 5-amino-6-nitroos-; _cis_-muurola-(41),5-diene; 3-ethyl-3-methylheptane; butanoic acid, 2-methyl-; 1,2-dimethylpropyl ester, and 3-benzoyl-2-t-butyl-4-isopropyloxazolidin-5-one); and 5 in response to [PERSON](r) (1,2-benzene-dicarboxylic acid; bis(2-methylpropyl) ester; 4-ethylbenzoic acid, 2-formyl-4,6-dichlorophenyl ester; methanamine; tetradecane, and methyl salicate). However, we cannot conclude that they are markers of the grapevine response to the corresponding elicitor because they were not induced (FC \(>\) 1.5) in the three replicates of the experiment.
# Global Formulation of the Synthetic Storm Technique Oriented to Satellite Link-Budget Design
## 4 Results  
Because of its reliability, we assume \(A_{SST}(t)\) as experimental, calculate its annual average probability \(P_{SST}(A)\) and compare (test) to it the probability \(P_{SST,glo}(A)\), calculated according to the modelling of Section 3. The test is explicitly conducted for the elevation angle in the range \(20^{\circ}\leq\theta\leq 60^{\circ}\) (\(5^{\circ}\) steps) and for the frequency (\(5^{\circ}\) GHz steps) in the range \(10\leq f\leq 100\) GHz (\(5\) GHz steps). At the boundary between any two ranges modelled in Section 3, we suggest considering the average of the two limit values.  
Figures 2 and 3 show \(P_{SST}(A)\) and \(P_{SST,glo}(A)\) obtained at Spino d'Adda, for different elevation angles and frequencies (circular polarization in the following). We can see that the global SST tends to underestimate the full SST.  
Figure 1: Normalized value of the average exponent of the global SST, between 10 GHz and 100 GHz. The thin lines give the average value for each elevation angle. The thick red line gives the model \(m_{n}(f)\). Notice that the normalized value of each elevation angle is practically the same, regardless of frequency and locality, a fundamental result that allows the modelling reported in step (3) of range (b). The superposition of the thin colored lines indicates that the global SST model can be applied to every site.  
Figure 2: \(P_{SST}(A)\) (black lines) and \(P_{SST,glo}(A)\) (blue lines) at 20 GHz (**a**) and at 30 GHz (**b**) at Spino d’Adda, for \(\theta=20^{\circ}\) (continuous lines) and \(\theta=60^{\circ}\) (dashed lines).  
We define a synthetic index of the test the error \(\varepsilon(\mathrm{dB})\), defined at equal average annual probability \(P\)  
\[\varepsilon(P)=A_{SST,glo}(P)-A_{SST}(P) \tag{10}\]  
The relative error is given by  
\[\varepsilon_{\mathbf{r}}(P)=\mathbf{100}\times\frac{\mathbf{A_{SST,glo}(P)-A_{SST}(P)}}{ \mathbf{A_{SST}(P)}} \tag{11}\]  
For Spino d'Adda, Figure 4 shows the scatter plots of \(\varepsilon\), in the indicated range of elevation angles \(\theta\leq 30^{\circ}\) and \(\theta>30^{\circ}\) without distinguishing frequency, probability and elevation angle. In Appendix A, we report the scatter plots for the other sites.  
In the next section we calculate and discuss the overall error.  
Figure 4: Scatter plots of the error \(\varepsilon=A_{SST,glo}-A_{SST}\) versus \(A_{SST}\): (**a**) \(\theta>30^{\circ}\); (**b**) \(\theta\leq 30^{\circ}\). Spino d’Adda.
## 2 Methodology
### Mask Generator
As the Wav2 CLIP model aligns visual and audio embeddings within the same space, the relevance of audio and visual information is determined by computing the normalized cosine similarity between the embeddings. The second process derives the final mask based on the scoring formula, which can be mathematically represented by the following formula:  
\[U=\bigcup_{i=1}^{n}x_{\delta(i)} \tag{1}\]  
\[\delta:\forall\text{j},\text{k }\in\{1,\text{...,m}\},\text{j}\text{<k}\Rightarrow \text{s(x}_{\delta(j)})\geq\text{s(x}_{\delta(k)}) \tag{2}\]  
\[\text{s(x}_{i}\text{)=}\lambda_{1}*S_{i}+\lambda_{2}*A_{i}\centerdot V_{i} \tag{3}\]  
In Equation (1), \(n\) indicates the total number of masks, \(x_{\text{i}}\) denotes each image block, and \(\delta\) represents the ranking function; the index of the ranking corresponds to the result computed by the scoring function, as presented in Equation (2). In Equation (3), \(\lambda_{1}\) and \(\lambda_{2}\) denote the weighting parameters for area and similarity, respectively, \(S_{\text{i}}\) represents the normalized area, and \(A_{\text{i}}\) and \(V_{\text{i}}\) correspond to the audio embedding and image embedding. The specific processing flow of the mask-scoring module is structured as follows: First, the cosine similarity between the video embedding and the audio embedding is computed, with the results normalized and ranked in descending order. Second, the area of each image block is calculated, normalized, and sorted in descending order. These normalized similarity and area values are then assigned different weights, summed, and ranked in descending order to produce the final scores. Finally, the \(n\) masks with the highest scores are selected and concatenated to generate the final mask. In order to determine the optimal combination of the parameters \(\lambda_{1}\), \(\lambda_{2}\), and \(n\) for the mask-scoring mechanism, we iteratively fine-tune these parameter values based on the output mask map until optimal results are achieved. Specifically, the parameter \(n\) is assigned values of 3, 4, and 5 across three testing rounds. In each round, the variable \(\lambda_{1}\) begins at 0 and incrementally increases to 1 in steps of 0.1. Given that \(\lambda_{1}+\lambda_{2}=1\), the variable \(\lambda_{2}\) consequently starts at 1 and decrementally decreases to 0 in steps of 0.1.
## 2 Materials and Methods
### Experimental Design  
The experimental area was divided into four blocks (replicates), and, in each block, three fertilization treatments were randomized. They included a mineral N treatment (MIN) and other two treatments with sewage sludges (SSs), which followed an N threshold criterion (SNC) or a P threshold criterion (SPC). The MIN treatment was used as a reference; it received 100 kg ha\({}^{-1}\) of N, P\({}_{2}\)O\({}_{5}\) and K\({}_{2}\)O at maize sowing, applied as a triple 15 complex fertilizer, and it was complemented by an additional application of 70 kg N ha\({}^{-1}\) at topdressing as ammonium nitrate. In the SPC and SNC treatments, sludge was applied at a rate of 170 kg N ha\({}^{-1}\) with a mineral supplementation of 100 kg K\({}_{2}\)O ha\({}^{-1}\). However, in the SPC treatment, when soil Olsen-P levels exceeded 40-60 mg P kg\({}^{-1}\), no sludge was applied the following season; instead, N and K mineral fertilizers were applied at similar rates to the MIN treatment. In accordance with this criterion, no sludge was applied from 2006 to 2009 in SPC, nor in 2014. The plots undergoing sludge fertilization had a size of 41 m\({}^{2}\) (9.6 m long \(\times\) 4.25 m wide), while MIN plots had an area of 81.6 m\({}^{2}\) (9.6 m long \(\times\) 8.5 m wide).  
Anaerobically digested sludge came from a village with a population of around 18,000 inhabitants, mainly devoted to tourism activities. Sewage sludges were spread over the soil surface one month before maize sowing, and they were buried by disk-ploughing (\(\sim\)0.2 m depth). Heavy-metal concentrations in sludge for use in agriculture were always below the allowed concentrations [12].  
The present study was performed in 2019, 23 years after the experiment's establishment. The historical average grain yield (14% humidity) was 14 Mg ha\({}^{-1}\) (coefficient of variation of 7%), without significant differences between treatments [27]. In the previous harvest (10 October 2018), maize stalks were chopped and incorporated into the soil through ploughing. In 2019, maize (P1570Y--Pioneer, Corteva; Madrid, Spain) was sowed on 12 April (80,000 seeds ha\({}^{-1}\)), and it was harvested on 10 October. Plots were irrigated by a high-frequency system (with drippers separating every 0.40 m in the line, and one line was established every two rows of maize). The amount of water applied between early June and mid-September was 3936 m\({}^{3}\) ha\({}^{-1}\). Daily irrigation was calculated from a soil water balance. The evapotranspiration of a reference crop (ETo, Penman-Monteith formula) was obtained from an automatic meteorological station located 200 m from the experimental field. ETo values were adjusted according to the phenology of the maize crop by multiplying by a coefficient that ranged from 0.3 to 1.1. If the daily recorded precipitation was higher than 3 mm, the effective precipitation was obtained by multiplying the recorded precipitation by 0.8.
## 3 Example Applications and Results
### Results  
The optimization results of SMPO are compared with the results of the manual optimization of CMOS thermal parameters by engineers as presently performed in industry to verify the performance of SMPO. Additionally, the optimized results are substituted into finite element software to verify the performance of the parameters in the high- and low-temperature cases. The results are shown in Figure 14. In the low-temperature case, CMOS components essentially fail to work, there is no internal heat source, the CMOS temperature basically does not fluctuate, and the results obtained through SMPO optimization and those obtained through manual optimization both meet the requirements of the CMOS thermal control index, however, the overall temperature in the results obtained through SMPO is 0.5 \({}^{\circ}\)C higher than that in the results obtained through manual optimization, leaving a greater temperature margin in the low-temperature case. In the high-temperature case, the internal heat source works intermittently. Compared with the results obtained through manual optimization, the temperature derived through SMPO is 5.33 \({}^{\circ}\)C lower  
Figure 12: Temperature clouds of CMOS components in the high-temperature case ((**a**). Heat dissipation component; (**b**). CMOS).  
Figure 13: CMOS temperature fluctuations in the high- and low-temperature cases.  
at the highest temperature of the CMOS and 0.39 \({}^{\circ}\)C lower at the lowest temperature of the CMOS. Additionally, the temperature fluctuation is reduced by a factor of 4, and the calculation time is reduced from several days to a few hours. The effectiveness and superiority of SMPO are thus demonstrated.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 2 Multidimensional Scaling Algorithm
#### The Ambiguity Problem  
In order to consider the ambiguity problem, we assume a set of n nodes in 2D Euclidean space. We fix a coordinate system, which, without a loss of generality, places the first node \(\mathbf{\mathsf{s}}_{1}\) at the origin: \(\mathbf{\mathsf{s}}_{1}=[0,0]^{T}\). We recall that knowledge of the distances provides an ambiguity up to a universal Euclidean transformation of the nodes. This fixing of node 1 at the origin removes the shift from this Euclidean transformation. Accordingly, the solution of the MDS, \(\mathbf{\mathcal{S}}^{*}\), is replaced by subtracting \(\mathbf{\mathsf{s}}_{1}^{*}\) from each column, so that, with some abuse of notation, \(\mathbf{\mathsf{s}}_{1}^{*}=[0,0]^{T}\), and the other \(\mathbf{\mathsf{s}}_{j}^{*}\) become \(\mathbf{\mathsf{s}}_{j}^{*}-\mathbf{\mathsf{s}}_{1}^{*}\). Once the shift is removed, the remaining ambiguity devoves to a rotation and a reflection (flip). We give the definitions of rotation and flip ambiguities, as follows.  
**Definition 1** (Rotation ambiguity).: _If there exists an angle \(\theta\
eq 2k\pi\), \(k\in\mathbb{Z}\) such that_  
\[\mathbf{\mathcal{S}}=\mathbf{M}(\theta)\mathbf{\mathcal{S}}^{*}, \tag{4}\]  
_where \(\mathbf{M}(\theta)\) is rotation matrix with angle \(\theta\) and it is defined by [38]_  
\[\mathbf{M}(\theta)=\begin{bmatrix}\cos(\theta)&\sin(\theta)\\ -\sin(\theta)&\cos(\theta)\end{bmatrix}, \tag{5}\]  
_then rotation ambiguity occurs._  
**Definition 2** (Flip ambiguity).: _Flip ambiguity occurs if_  
\[\mathbf{\mathcal{S}}=\mathbf{F}\mathbf{\mathcal{S}}^{*}\ \ \ \text{with}\ \ \ \mathbf{F}=\begin{bmatrix}-1&0\\ 0&1\end{bmatrix}. \tag{6}\]  
**Remark 1**.: _The matrix \(\mathbf{F}\) can be defined equivalently by \(\mathbf{F}=\begin{bmatrix}1&0\\ 0&-1\end{bmatrix}\). This definition can be obtained via simply rotating (6) by \(\pi\). In the following analysis, we use the definition of \(\mathbf{F}\) as in Definition 2._  
It can be seen that rotation ambiguity and flip ambiguity can occur simultaneously. If this is the case, then the true positions can be represented by  
\[\mathbf{\mathcal{S}}=\mathbf{M}(\theta)\mathbf{F}\mathbf{\mathcal{S}}^{*}. \tag{7}\]  
Figure 1 shows samples of these two ambiguities.
## 1 Introduction
### Problems in the Application of DE-XRT
#### 1.4.1. Overexposure and Underexposure  
There are two kinds of non-normal exposure effects of XRT, including overexposure and underexposure. Overexposure is represented by high transparency, low shading, low contrast of background against material images, which is caused by the high energy of the ray source, the small atomic number of materials, or smaller thickness, allowing most rays of the penetrated materials to reach the detector. Conversely, underexposure takes on low transparency, high shading, large contrast of background against material images, which is the result of low energy of the ray source, the large atomic number of the materials, or greater thickness, allowing few rays of the penetrated materials to reach the detector. Based on actual measurement and analysis, this study has found that both overexposure and underexposure have a great impact on random quantization errors and R_value calculation accuracy, which makes the R_value curves of various materials cross so as to affect material identification. It is more appropriate to use the threshold of low-energy shading to judge overexposure and underexposure. The relevant impacts are shown in Table 1.  
Due to their lighter weight, thinner wall, and being the more complicated parts of automobiles, home appliances and other products, Cu, SS, and brass parts still exist, so overexposure and underexposure are usually inevitable during a single measurement of DE-XRT. If that is neglected, misinformation will appear, resulting in the increase in measurement errors, which will affect the identification accuracy. Therefore, it is indispensable to resolve problems of overexposure and underexposure.