esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### False Positives in ESM  
We first analyze the queries predicted by the models along with their gold standard counterparts that are considered equivalent by \(\mathsf{ESM}\) but not by \(\mathsf{EXE}\). Since \(\mathsf{ESM}\) is a more stringent metric, it is expected that no query pair considered a mismatch by \(\mathsf{EXE}\) would be considered a match by \(\mathsf{ESM}\). Upon closer inspection, however, it becomes evident that \(\mathsf{ESM}\) has several shortcomings in its evaluation approach.  
One major issue is that \(\mathsf{ESM}\) does not account for \(\mathsf{JOIN}\) conditions, which are essential parts of many \(\mathsf{SQL}\) queries. In Figure 2, the two queries produce different outputs such that \(\mathsf{EXE}\) correctly considers them a mismatch. \(\mathsf{ESM}\) mistakenly considers them a match, however, because it ignores the \(\mathsf{JOIN}\) conditions (t2.breed_code vs. t2.breed_name).  
Another issue arises when evaluating queries with the \(\mathsf{DISTINCT}\) keyword. Even when distinct checks are enabled in the \(\mathsf{ESM}\) script (Section 2.2), it considers \(\mathsf{DISTINCT}\) only within aggregate keywords, like \(\mathsf{COUNT}\) or \(\mathsf{AVE}\), failing to recognize it in simpler and more commonly used cases (Figure 3).  
Additionally, the \(\mathsf{ESM}\) script ignores specified \(\mathsf{LIMIT}\) values even when value checks are enabled (Figure 4).
# NMR and LC-MS-Based Metabolomics to Study the Effect of Surfactin on the Metabolome of Flax
###### Abstract  
Fax (_Linum usitatssimum_) is a versatile plant used in a range of applications, from textiles to nutrition. Surfactin, a cyclic lipopeptide biosurfactant produced by bacteria such as _Bacillus subtilis_, has potential as a biocontrol agent or as a plant defense inducer in agriculture. This work aims to determine the effects of surfactin treatment at two kinetic points on the metabolism of flax hydroponic cultures, using advanced metabolomic techniques, including \({}^{1}\)H NMR and LC-MS analyses. Surfactin, detected in the roots, has a significant local impact on the metabolic profiles of flax roots, leading mainly to a higher content of cyanogenic compounds and amino acids and a lower content of carbohydrates. Surfactin, which is not detected in the aerial parts, also induces contrasted changes in amino acids, sugars, and secondary metabolite accumulation between stems and leaves. Surfactin treatment of flax leads to both a local and systemic effect on flax metabolism. These changes suggest that plant response to surfactin treatment could induce an enhanced plant defense. This could suggest potential applications of surfactin in the agricultural field as a biostimulant or biocontrol agent, to limit the use of chemical compounds in culture, and to limit their negative impact on both health and the environment.  
L _Linux usitatssimum_; surfactin; metabolomics; \({}^{1}\)H NMR; LC-MS; systemic effects; local effects
## 3 Results
### Functional Annotation and Classification of L.__pubescens_ Unigenes  
To obtain information regarding the function of _L.__pubescens_ unigenes, we performed annotations based on sequence alignments with those in multiple public databases (NR, NT, SWISS-PROT, BRAD, TAIR, COG, and GO) with an e-value cutoff of 1 \(\times\) 10\({}^{-5}\). A total of 68,695 unigenes (55.30% of the 124,233 unigenes) were successfully matched with sequences in the databases: 64,993 (52.32%), 20,947 (16.86%), 45,750 (36.83%), 57,951 (46.65%), 56,604(45.56 %), 16,019 (12.89%), and 44,814 (36.07%) unigenes in the NR, NT, SWISS-PROT, BRAD, TAIR, COG, and GO databases, respectively (Table S2). The e-value distribution in NR annotation indicated that 37.3% of the _L.__pubescens_ unigenes showed strong homology (e-value < 1 \(\times\) 10\({}^{-60}\)) (Figure 3A) with database sequences, and percentage similarity distributions indicated that 41% of the given unigenes had a high similarity (>80%), whereas the remaining 59% of the matched sequences showed a similarity between 18% and 60% (Figure 3B). In terms of species distribution (Figure 3C), 50.05% of the _L.__pubescens_ unigenes had matches with genes in _Sesamum indicum_, followed by _Erythhranthe guttata_ (21.56%), _Vitis vinifera_ (2.12%), _Coffea canehora_ (1.64%), _Nicotiana sylvestris_ (1.46%), _Beta vulgaris_ (1.45%), and _Salvia miltiorrhiza_ (1.30%).  
COG annotation is considered a standard procedure for determining the function and evolution of coding proteins at the genome scale. On the basis of COG annotation, a total of 16,019 unigenes were functionally predicted and classified into 26 categories, the largest of which was \"Translation, ribosomal structure and biogenesis\" (1799 COG-annotated unigenes, 11.36%), followed by \"Carbohydrate transport and metabolism\" (1590, 10.04%), \"Post-translational modification, protein turnover, chaperones\" (1464, 9.24%), \"General functional prediction only\" (1441, 9.10%), and \"Signal transduction mechanisms\" (1330, 8.40%) (Figure S2).  
Figure 3: e-value distribution (**A**), percentage similarity (**B**), and species distribution (**C**) of the nonredundant (NR) annotation results for _Lavandula pubsccens_ unigenes.  
GO standard systems have been introduced to comprehensively delineate the functions of the predicted unigenes and their products. In the present study, we grouped _L. pubescens_ unigenes into the three major GO categories; namely, cellular component, molecular function, and biological process, with 51 function sections, as shown in Figure S3. With respect to the cellular component category, the unigenes matched 14 terms, among which most unigenes were found to be associated with cells and cell parts. Similarly, unigenes were matched to 14 terms in the molecular function category, the majority of which were found to be associated with binding and catalytic processes. In biological process category, unigenes were assigned to 23 terms, with most being associated with cellular and metabolic processes (Figure S3).
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## Appendix A Measuring and Modelling RFI  
### Radio Frequency Interferences in L Band  
#### a.1.1 SMOS Satellite RFI Data  
SMOS satellite is an L band (1413.5 MHz central frequency) instrument to monitor the soil moisture and ocean salinity [4]. Importantly, SMOS can provide the land brightness temperature file which can be used to estimate the radiation of the RFI on the ground.  
We can utilize the SMOS data to evaluate the impacts of RFI in China. The SMOS L3 products, the RFI probability product and the Level 1C Browse product over Land (BWLDIC), are utilized [51]. In Figure 11a, RFI probability in parts of China during 15 February to 16 March 2011 shows that China suffers high probability RFI impact. Most parts of China are interfered by the RFI (with the probability 1) and only a small part in the north of China has a relative lower RFI probability. L1C browse products contain brightness temperature values per pixel at a fixed incidence angle of 42.5, which are identical to the full L1C products. The SMOS BWLDIC on 28 February 2011 is utilized in the following analysis.  
On the basis of the above data, we study the SINR. The detected distribution of the RFI sources based on the cluster selection RFI sources positioning [2] is given in Figure A1b, in which we have detected 161 RFI sources. We select the RFI sources located within the mainlobe of GEO SAR and obtain the average brightness temperature, which is 5203 K. Based on the brightness temperature and a 2250 w average transmitted power of GEO SAR, the calculated SINR is given in Figure A2. The maximum SINR will be 9.5 dB, which suggests the RFI make the system performance deteriorate slightly (compared with a 10 dB SINR requirement).
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 4 PSIJ Sensitivity Study of Two-Stage Driver
### Two-Tone Jitter Superposition
#### 4.4.1 Simulation Setting  
_Pre-driver case_: Firstly, the pre-driver is simulated for different frequencies with a single tone of amplitude \(a_{P}\) of the \(V_{DD}\) supply voltage, while the last-stage supply is kept constant. Secondly, two tones with different amplitudes (e.g., \(a_{P1}\) and \(a_{P2}\)) are applied at \(V_{DD}\), as shown in Figure 8a. The output jitter is measured for both cases and compared against the superposition formula of the output jitter for different amplitude variations.  
\[S_{P}(f_{P1}+f_{P2})\cong S_{P}(f_{P1})+S_{P}(f_{P2})\ if\ \frac{a_{P1}+a_{P2}}{V_{DD}}\ll 1 \tag{9}\]  
_Last-stage case_: Firstly, the last stage is simulated for different frequencies with a single tone of amplitude \(a_{L}\) of the \(V_{DDQ}\) supply voltage, while the pre-driver supply is kept constant. Secondly, two tones with different amplitudes (e.g., \(a_{L1}\) and \(a_{L2}\)) are applied at \(V_{DDQ}\), as shown in Figure 8b. The output jitter is measured for both cases and compared against the superposition formula of the output jitter for different amplitude variations.  
\[S_{L}(f_{L1}+f_{L2})\cong S_{L}(f_{L1})+S_{L}(f_{L2})\ if\ \frac{a_{L1}+a_{L2}}{V_{DDQ}}\ll 1 \tag{10}\]  
The six cases studied for both IO buffer stages are summarized in Table 8. The frequency of the two-tone supply voltage waveform is selected as a mixture between the low MHz range (e.g., 57 MHz, 123 MHz), where the driver's IO stage is sensitive to noise, and the higher-frequency range (e.g., 507 MHz, 907 MHz), at which the driver's IO stage is less sensitive to jitter.  
Figure 14: Pre-driver-stage parametric plot of the IO buffer amplitude-dependent jitter TF as the pre-driver stage amplitude is swept for different \(V_{ddq}(t)\) supply noise.
## 1 Introduction
### Preliminaries  
We represent a _graph_ as a pair of finite sets, \((V,E)\). The elements of \(V\) are known as _vertices_. The elements of \(E\) are _edges_, represented as pairs, \((v_{1},v_{2})\in V\times V\). We call vertices \(v_{1}\) and \(v_{2}\)_adjacent_ if \((v_{1},v_{2})\in E\). Throughout, we assume that our graphs are _undirected_, that is, \((v_{1},v_{2})\in E\Rightarrow(v_{2},v_{1})\in E\), and contain no _loops_, that is, for all \((v_{1},v_{2})\in E\), we have \(v_{1}\
eq v_{2}\).  
The _neighbourhood_ of a vertex, \(v\), in a graph, \(G\), is the set of vertices adjacent to \(v\), denoted \(\mathrm{N}(G,v)\). The _degree_ of a vertex is the size of its neighbourhood.  
A graph \(G^{\prime}=(V^{\prime},E^{\prime})\) is called a _subgraph_ of a graph \(G=(V,E)\) if \(V^{\prime}\subseteq V\) and \(E^{\prime}\subseteq E\). The subgraph _induced by \(V^{\prime}\)_ is the subgraph with vertices, \(V^{\prime}\), and all edges between those vertices. A graph \(G=(V,E)\) is called _complete_ if all its vertices are adjacent--that is, for every distinct pair of vertices, \(v_{1}\) and \(v_{2}\), we have \((v_{1},v_{2})\in E\). A complete subgraph is known as a _clique_. We may represent a clique by the vertex set that induces it, and we define the size of the clique to be the size of this vertex set. A clique is _maximum_ if there is no clique with a larger size. We denote the size of a maximum clique in a graph by \(\omega\).  
A _colouring_ of a graph is an assignment of colours to vertices, such that adjacent vertices are given different colours. Computing a minimal colouring is \(\mathsf{NP}\)-hard, but a greedy colouring may be obtained in polynomial time.
## 2 Development of a Numerical Model
### Boundary and Initial Condition  
In order to solve Equation (1), reasonable boundary conditions should be defined. The energy losses due to convective and radiation heat transfer were considered at the top surface of the computational domain. The boundary condition of the top surface is as follows:  
\[-k\frac{\partial T}{\partial\overrightarrow{n}}=h(T-T_{amb})+\varepsilon \sigma\Big{(}T^{4}-T_{amb}^{4}\Big{)} \tag{2}\]  
where \(\overrightarrow{n}\) is the surface normal vector, \(h\) is the convective heat transfer coefficient (10 W/m\({}^{2}\cdot\)K), \(T_{amb}\) is the ambient temperature (303.15 K), \(\sigma\) is the Stefan-Boltzmann  
Figure 1: 3D finite element model used in this study.  
Figure 2: Scanning patterns used in simulation: (**a**) zig-zag pattern and (**b**) bidirectional pattern.  
constant (\(5.67\times 10^{-8}\) W/m\({}^{2}\).K\({}^{4}\)), and \(\varepsilon\) is the surface emissivity (0.7), respectively. In this study, it was assumed that no heat transfer occurred through any of the side walls of the computational domain. Therefore, an adiabatic condition was applied to all side walls of the computational domain, as follows:  
\[-k\frac{\partial T}{\partial n}=0 \tag{3}\]  
In the simulation, it was also assumed that wafer was uniformly preheated. In general, wafer preheating is used to enhance the absorption rate of the laser beam in wafer (see Section 2.5. to check the effect of preheating in detail). A preheat temperature of 400 \({}^{\circ}\)C was applied in the model. Thus, the initial condition of the simulation is expressed as follows:  
\[T|_{t=0}=T_{0}=400\ ^{\circ}\text{C} \tag{4}\]  
where \(T_{0}\) is the initial temperature of the wafer.
## 4 Results and Discussion
### Validation and interrogation of 2-stage NN models  
The 2-stage NN models based on data from experimental harvests could be validated with commercial harvests both in living and in butter lettuce. Living lettuce (with trimmed roots) and butter lettuce (with no roots) were expected to be different, and different models were established accordingly. For living lettuce, this study illustrated that the chosen experimental NN model could be validated with commercial harvests (Table 1), having R\({}^{2}\) of 0.66 and average error of 154%. The 2-stage NN models were used for interrogation. With all the data of living lettuce combined, storage temperatures recorded ranged from 1\({}^{\circ}\)C to 21\({}^{\circ}\)C. When _fa_ of 1\({}^{\circ}\)C was assigned, most predicted RSL were longer than those actually observed (data not shown), and resulted in R\({}^{2}\) of 0.53 and average error of 212%. When _fa_ of 21\({}^{\circ}\)C was assigned, most of the errors fell within 2 days of actual (Fig. 4), and predictions resulted in R\({}^{2}\) of 0.65 and average error of 23%. Although there has never been any commercial practice to store lettuce at 21\({}^{\circ}\)C, this maximum temperature of 21\({}^{\circ}\)C did allow us to generate a reasonable range of potential RSL. Of course, any temperatures within the range of 1 - 21\({}^{\circ}\)C can be assigned to model and predict the RSL. As to butter lettuce, 2-stage NN models based on data from experimental harvests were validated by using a commercial harvest, having R\({}^{2}\) of 0.96 and average error of -34% (Table 1). When _fa_ of 1\({}^{\circ}\)C was assigned, the R\({}^{2}\) was 0.95 and average error was -23%. When _fa_ of 21\({}^{\circ}\)C was assigned, R\({}^{2}\) was 0.96 with average error of -66%. If we considered a predicted RSL that is within 2 days of its observed RSL as a useful criteria, 1\({}^{\circ}\)C resulted in a good prediction 6 out of 9 times, while 21\({}^{\circ}\)C generated only 3 good predictions out of 9 times. Two (2)stage NNs confirmed the importance of a broad data base being used for training NN models, but with a penalty of having lower R2. Living lettuce models were established with data from 66 cases covering 6 experiments over a 13-month period (Table 1), and butter lettuce models were established with 38 cases covering 2 experiments conducted in 2 months. The wider data space of living lettuce resulted in models with lower R2 in commercial validation, whereas the NN models based on the narrow data range of butter lettuce resulted in models with higher R2 in commercial validation.
# Numerical Simulation of Non-Linear Models of Reaction--Diffusion for a DGT Sensor
## 2 The Model
### Mathematical Model
Let _numeq_ be the number of species of the problem, which, of course, coincides with the number of equations. In the first place two vectors of coefficients are considered, one for the region \((0;r)\), hereinafter referred to as: \(\vec{D}^{\text{R}}=(D_{1}^{\text{R}},D_{2}^{\text{R}},\ldots,D_{numeq}^{\text{ R}})\), which contains the diffusion coefficient for each species inside the resin phase, and the other denoted by \(\vec{D}=(D_{1},D_{2},\ldots,D_{numeq})\), which corresponds to diffusion coefficients inside the gel (\(r<x<r+g\)). Diffusion coefficients of the resin and its complexes are zero since they are considered to be fixed in the gel matrix of the resin disc. The _mreac_ and _kreac_ matrices, that were used for solving the equilibrium problem, must be extended incorporating the reactions corresponding to the resin and its complexes. These two new matrices contain the information to express the general problem. For each species \(i\), where \(i=1,\ldots,numeq\), the corresponding reaction-diffusion equation will be as:  
\[\frac{\partial c_{i}}{\partial t}= \begin{cases}D_{i}^{\text{R}}\\ D_{i}\end{cases} \frac{\partial^{2}c_{i}}{\partial x^{2}}+\sum_{j=1}^{numreac} \left(A_{i,j}c_{mreac(j,1)}c_{mreac(j,2)}+B_{i,j}c_{mreac(j,3)}\right)\,, \tag{14}\]  
where \(D_{i}^{\text{R}}\) and \(D_{i}\) act as a diffusion coefficients in \((0,r)\) and \((r,r+g)\), respectively. In Equation (14), for a given value of index \(i\) (which runs across chemical species), the coefficients \(A_{i,j}\) and \(B_{i,j}\) can be calculated using the following algorithm: if in the reaction \(j\) does not participate the species \(i\), \(A_{i,j}=B_{i,j}=0\). Otherwise, the absolute value of \(A_{i,j}\) is equal to \(k_{\text{a},j}\) and the absolute value of \(B_{i,j}\) is equal to \(k_{\text{d},j}\). Finally, it must be taken into account that the coefficient (\(A_{i,j}\) or \(B_{i,j}\)) of the term that contains the substance \(i\)-th must be negative.
# Motion Characteristics of Self-Sensing Piezoelectric Actuator for Yarn Micro-Gripper
## 3 Performance and Self-Sensing Testing of BPBA
### Study of Bending Motion Characteristics
#### 3.4.2 Measurement Result  
As shown in Figure 12a, the actuator end velocity profile is used to describe the motion process of the BPBA. By comparing the velocity profile graphs of two consecutive cycles, it was found that the end velocity profile of the actuator driven by different excitation voltages has the same periodicity. Also, we can see the response time of the BPBA is about 24 ms, which is shorter than our target. Meanwhile, the end velocity profile in each cycle is divided into two stages, which are divided into acceleration and deceleration in two directions, corresponding to each other with the direction of the applied square wave excitation voltage. The velocity at the end of the actuator increases during the deformation process, and when it reaches the bending equilibrium position for the first time, the internal stress generated by the excitation voltage is equal to the elastic force generated by the deformation, and the velocity reaches the maximum value. However, after the actuator passes the equilibrium position, the elastic force generated by deformation is greater than the internal stress generated by the excitation voltage, and the actuator begins to decelerate until the maximum swing position. The actuator then oscillates near the bending equilibrium position and finally stops at the equilibrium position. This is also true when a reverse excitation voltage is applied.  
Figure 12b shows the end acceleration curve of the BPBA in one motion cycle with Figure 12a, which exhibits the acceleration change process corresponding to the velocity change process analyzed in the previous section. There is no obvious difference in the overall trend of the acceleration change process under different excitation voltages. We can see that the larger the excitation voltage, the faster the acceleration increases, the faster the piezoelectric actuator responds, and also the faster the acceleration change process ends.  
Figure 11: Photo of output force test experimental setup. The camera is at the bottom of the image.
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
## 3 Hybrid Arctic Puffin Optimization (APO) with JADE
### Hybrid APO-JADE Algorithm  
The Hybrid APO-JADE algorithm (See Algorithm 1) enhances the optimization process by dynamically adjusting the crossover rate (\(CR\)) and scaling factor (\(F\)) using normal and Cauchy distributions, respectively. Additionally, it employs an external archive to maintain diversity and mitigate premature convergence. The algorithm alternates between the JADE and APO mechanisms in two distinct phases, ensuring a robust balance between exploration and exploitation, thereby efficiently navigating complex optimization landscapes and converging to high-quality solutions, the steps of the algorithm has been shown in Figure 1.  
```
1:Initialize: Set parameters \(uCR=0.5\), \(uF=0.5\), \(p0=0.05\), \(top=1\)
2: Initialize archive \(A\) as empty
3: Initialize iteration counter \(l=1\)
4: Generate initial population \(P\) of size \(pop\) with dimensions \(dim\) using bounds \(ub\) and \(lb\)
5: Evaluate fitness of initial population
6:while\(l\leq Max\_iter\)do
7:if\(l\leq Max\_iter/2\)then
8:JADE Mechanism:
9:for each individual \(i\) in population do
10: Compute \(CR(i)=\mathcal{N}(uCR,0.1)\) and ensure \(0\leq CR(i)\leq 1\)
11: Compute \(F(i)=\mathcal{C}(uF,0.1)\) and ensure \(F(i)>0\)
12:endfor
13: Identify the best individual \(X_{best}\) and select top \(p\%\) as \(X_{pbest}\)
14:for each individual \(i\)do
15: Generate mutant vector \(V_{i}\) using Equation (27)
16: Generate trial vector \(U_{i}\) using crossover (Equation (28))
17:endfor
18:for each individual \(i\)do
19: Bound \(U_{i}\) using Equation (36)
20: Evaluate fitness of \(U_{i}\)
21: Update population and archive if \(U_{i}\) improves the fitness
22:endfor
23: Update control parameters \(uCR\) and \(uF\)
24:else
25:APO Mechanism:
26:for each individual \(i\)do
27: Compute behavioral conversion factor \(B\) (Equation (30))
28:if\(B>0.5\)then
29: Perform Levy flight using Equation (34)
30:else
31: Apply dynamic search strategies
32:endif
33: Bound positions using Equation (36)
34: Update population based on fitness
35:endfor
36:endif
37: Update the best solution found so far
38: Increment iteration counter \(l\)
39:endwhile
40:Output: Best solution and its fitness value
```  
**Algorithm 1** Hybrid APO-JADE algorithm.  
Figure 1: Flowchart of the Hybrid APO-JADE algorithm.
## 4 Implementation and Results  
In this test, 55 prosumers (each one is equipped with 20 KW RES) and 503 normal customers are distributed in a general DN, where the proposed cloud-fog architecture is deployed to implement energy management and decision-making optimization. The location and number of fog nodes are deployed according to the amount of generated data and the location of prosumers and customers. The prosumers are normally connected to a bus of the underlying DN by the point of common coupling (PCC) in the distribution grid. Among prosumers, the generators mainly include WTs and PVs. The storage refers to the Tesla Powerwall, the capital cost of storage is 5500$, and the lifetime is 15 years [34].  
As the flow of energy management, the regression and forecasting of RES and loads in prosumers and customers are first implemented at the fog layers, the real-time consumption characteristics \(\omega\) can be calculated. Furthermore, the retail price of the DN and the total purchase energy quantity from the wholesale market can be optimized at the cloud layer.