esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Applicability of Bio-Based Fertilisers in a Full-Scale Field Trial
After harvest, the NH\({}_{4}\)-N and NO\({}_{3}\)-N residues in the soil were analysed (Figure 7). The potential for residual nitrate leaching to the ground and surface water is one of the important aspects of the safe application of BBFs. The residual nitrate in the soil profile (0-90 cm) in the post-harvest period showed no significant difference in leaching risk for BBFs in comparison with the used mineral fertiliser. However, all the treatments (including the unfertilised control) resulted in relatively high nitrate residues. In the Netherlands, there is no legal limit on maximum allowable nitrate residue. On the other hand, in Flanders, in the neighbouring region, nitrate residue in potato cultivation should not exceed (depending on the location of the field) the maximum limit of 165 kg NO\({}_{3}\)-N ha\({}^{-1}\) according to current Flemish environmental standards (VLM, 2021). In general, high nitrate residue is considered common in the case of potatoes due to their ability to uptake only 50-60% of the applied N ([PERSON] et al., 2018), and also their shorter root depth ([PERSON] et al., 2018). Additionally, the effect of rainfall during the growing season is also found to be inversely proportional to the nitrate residue in 0-90 cm soil depth ([PERSON] et al., 2018). The high application of N (310 kg ha\({}^{-1}\)) resulted in high residue concentrations in the soil. The NO\({}_{3}\)-N residue in the soil profile was on average highest in the plots that only received AS (264 kg ha\({}^{-1}\)), but the residues in plots that received mineral fertiliser and manure (256 kg ha\({}^{-1}\)), LFD and manure (258 kg ha\({}^{-1}\)), and AS and manure (245 kg ha\({}^{-1}\)) were almost as high. The NO\({}_{3}\)-N residue in the soil profile was, except from the control, lowest in the plots that only received LFD.
## 5 Conclusions  
The following are our primary findings:  
* Under the studied drying conditions, vacuum drying provoked more cell damage and tissue collapse than convective air drying at MR of 0.2 and 0.1. Under complete dehydration (aw = 0.05) damage seemed to be similar for both drying methods irrespective of if the leaves were PEF pretreated or not.
* The protective effect of resting after reversible PEF application on metabolic and ionic integrity was only detected at high water activities and suppressed upon full dehydration (aw = 0.5).
* Samples dried under vacuum showed less color degradation upon rehydration when compared to convective dried samples.
* When dried with either convective or vacuum drying, reversible electroporation followed by resting resulted in higher trichome preservation than in samples that were not PEF-treated. When the PEF-rested treatment was combined with convective drying, the area of the trichomes was found to be similar to that of the fresh leaf sample.  
Conceptualization, G.T. and F.G.G.; methodology, G.T.; software, D.O.; validation, G.T., F.G.G., A.G.R., D.O.; formal analysis, G.T.; investigation, G.T.; writing--original draft preparation, G.T.; writing--review and editing, F.G.G.; visualization, G.T.; supervision, F.G.G.; project administration, F.G.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Royal Thai Government scholarship.  
Not applicable.  
Data availability StatementData will be available upon request.  
The authors declare no conflict of interest.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
### C-glycosflavone Derivatives
#### 3.1.5 Phenethyl Derivatives  
In Cluster H (Figure 7), the node with a precursor ion [M + NH4]\({}^{+}\) at \(m/z\) 434.202 was assigned as phenethyl-1-O-\(\beta\)-D-apiofuranosyl (1\(\rightarrow\)2)-\(\beta\)-D-glucopyranoside (**32**) based on GNPS spectral library matching. The compound, also known as sayaendoside, was previously isolated from young seedpods of _Pisum sativum_ L. [32]. From the analysis of its MS/MS spectral data, it was found that **32** exhibited the characteristic fragment ion of 2-phenylethanol as the aglycone unit at \(m/z\) 123 for [(M+NH4)-NH3-294]\({}^{+}\), resulting from direct losses of ammonia and the disaccharide moiety [\(\beta\)-D-apiofuranosyl (1\(\rightarrow\)2)-\(\beta\)-D-glucopyranoside]. This observation verified that the disaccharide moiety was \(O\)-linked to the hydroxyl group of the 2-phenylethanol chain. Fragment ions attributable to losses of the disaccharide moiety [\(\beta\)-D-apiofuranosyl (1\(\rightarrow\)2)-\(\beta\)-D-glucopyranoside] and \(\beta\)-D-apiofuranosyl were also observed at \(m/z\) 295 and 133, respectively. Another node in Cluster H had a precursor ion [M + NH4]\({}^{+}\) at \(m/z\) 476.213, a 42 Da (C3H6) mass difference from that of **32**. Its MS/MS data showed similar sugar moiety fragment ions to that of  
Figure 7: Molecular families of benzyltetrahydrodoiquinoline alkaloids (Cluster F: positive ion), hydroxyjasmonic acid derivatives (Cluster G: positive ion, and Cluster R: negative ion), and phenethyl derivatives (Cluster H: positive ion), extracted from the full MN of _Christia respertilionis_ leaf. Red dotted circles represent proposed new structures.  
**32**, at \(m/z\) 295 and 133, indicating that the additional substituent was not attached to the hydroxyl group of the disaccharide moiety. Based on the 2-phenylethylal structure, there was only one active site of the hydroxyl group, which was already linked to the disaccharide moiety. Therefore, it is feasible to suggest that the additional substituent was attached to the 2-phenylethanol chain, either on the phenyl ring or on the saturated carbon chain. Furthermore, a fragment ion that was in agreement with the proposed substituted aglycone structure was observed at \(m/z\) 165 for [(2-phenylethanol)+42 Da]\({}^{+}\) (Figure S15). This fragment ion, resulting from the losses of the disaccharide moiety (294 Da) and ammonia, further supported the putative identity of compound **45** as an isopropyl derivative of **32**.
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 6 OPM Query Implementation
### Neighborhood Queries
#### 6.2.2 Neighborhood Queries Implementation  
All the queries in the neighborhood category are native, noncomplex Cypher queries. The main difference between the three query types in this category, listed in Code 5, Code 6, and Code 7, is the link direction between the start node, which is provided as input from the user, and the other elements in the OPM model. The max depth parameter, also provided by the user, is part of the relationship search configuration.  
**Code 5**. Unidirectional query.  
```
1:MATCH p = (source: $ROOT_NODE_TYPE [name:\"$ROOT_NODE_TEXT\")] -[\"1..$MAX_DEPTH]-(child)
2:RETURN p
```  
**Code 6**. Directional query--outgoing links.  
```
1:MATCH p = (source: $ROOT_NODE_TYPE [name:\"$ROOT_NODE_TEXT\")]-[\"1..$MAX_DEPTH]->(child)
2:RETURN p
```  
**Code 7**. Directional query--incoming links.  
```
1:MATCH p = (child) -[\"1..$MAX_DEPTH]->(source: $ROOT_NODE_TYPE [name:\"$ROOT_NODE_TEXT\"])
2:RETURN p
```  
Code 8 is the conditional statement that is added to the query prior the \"RETURN\" directive if the user wants to specify that there is a need to filter the OPM procedural links.  
**Code 8**. Conditional statement in neighborhood queries to filter procedural links  
```
WHERE ALL (r IN relationships(p)WHERE type(r)=\"OpmProceduralRelation\")
```
## 6 Inertia Estimation of the Southeast Zone of Mexico
### Load Increment  
In this case study is simulated an increase in the value of the electrical load at bus 9 of 32.5 MW, in \(t=1\) s.  
Figure 11 illustrates the response of the active power at bus 2 generated by Chicoasen. It can be noted that due to the increment of the electrical load, there was an increase from 0.33 p.u.-0.3392 in p.u. in \(t=1.02\) s, and this increment represents 22.5 MW of active power. The peak value was used for the inertia estimation for this generator. Furthermore, there was a minimum of oscillations when the load was increased, as a result mainly of the actions of the control systems of the generation units.  
Table 5 shows the results of the inertia estimation of the generation units. In particular, it can be observed that there were a few differences in the inertia estimation, and the percentage of error increased slightly in some power plants in comparison with the case study presented previously.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Power Plants** & **Generation** & \(H\) **(Input)** & \(H\) **(Estimated)** & **Absolute Error \%** \\ \hline Bombana & Hydropower & 4 & 3.85 & 3.75 \\ Chicoasen & Hydropower & 4.7 & 4.62 & 1.70 \\ Schpoina & Hydropower & 3.7 & 3.71 & 0.27 \\ Tapachula & Hydropower & 4 & 4.04 & 1.00 \\ Ixtepec & Wind power & 0.00237 & 0.00236 & 0.42 \\ Juchitan & Wind power & 0.00237 & 0.00233 & 1.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inertia estimation results, generation trip case.  
Figure 10: Active power in p.u. at bus 3 (Bombana): generation trip case.  
This is associated with the type of disturbance applied to the power system. For this case, it was a small disturbance. Nevertheless, the results are still good in comparison with the results reported in the literature, whose values were between 0.22 %, 41.11% [24; 25; 37], and 16%, reported in [11].
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 3 Proposed Methodology
### Motivation  
The demand for easy, smart, and effective methods of interacting with electronic devices has reached its peak owing to the massive improvements in modern technology. Vision-based methods, particularly human action recognition, have become very popular nowadays. Most of the methods described in Section 2 encoded the depth sequences into dynamic images to capture spatial and temporal changes in action. However, there can be several neighboring frames that are very similar in terms of spatial and motion information. These frames have less significance in the overall structure and texture information of action. Thus, it is important to define a method that can effectively sample more significant frames by keeping the spatial and temporal information unaltered. At the same time, most of the papers used hand-crafted features to recognize the actions by using traditional machine learning techniques such as NB-NN, MV, CRC, and ELM. By considering the above drawbacks, we suggest a novel approach to sample key-frames using the ranking method. In addition, we directly use the sampled frames to recognize the action using the 3D-CNN model.
## 3 Types of Scanning Patterns
### Square Spiral Scanning
#### 3.3.2 Mean Acquisition Time for Square Spiral  
The scan time \(T_{\rm ss}\) function for the square spiral path can be written as  
\[T_{\rm ss}(x,y)=\frac{L_{\rm ss}(x,y)}{n}\times\Delta t, \tag{34}\]  
where \(\Delta t\) is the dwell time at each scan spot, and \(n\) is the scan step size.  
To calculate the mean acquisition time \(E[T_{\rm ss}]\) to scan the uncertainty area, the total scan time must be weighted by the joint PDF of the receiver satellite's position in both the vertical and horizontal directions for each domain. The mean acquisition time can be derived as follows:  
The domain \(\Omega_{u}\), as illustrated in Figure 4, comprises the subdomains \((1)\) to \((5)\) in a direct union, leading to  
\[E[T_{\rm ss}]=\sum_{i=1}^{5}I_{i}, \tag{35}\]  
where  
\[I_{i}=\iint_{(i)}T_{\rm ss}(x,y)\ f(x,y)\ dxdy. \tag{36}\]  
The domain \(I_{1}\) itself consists of the subdomains \(I_{a}\), \(I_{b}\), and \(I_{c}\), forming a direct union.  
Hence,  
\[I_{1}=I_{a}+I_{b}+I_{c}, \tag{37}\]  
Figure 4: Decomposition of the uncertainty area domain \(\Omega_{u}\) into subdomains \(I_{1}\) to \(I_{5}\).  
where \(I_{a}\), \(I_{b}\), and \(I_{c}\) are defined in an obvious fashion.  
Therefore, for subdomain \(I_{a}\):  
\[I_{a}=\int_{-u}^{n-u}\int_{-y}^{u}4x\Big{(}\frac{x}{n}+1\Big{)}\frac{\Delta t}{2 \pi n\sigma^{2}}\exp\biggl{(}-\frac{1}{2}\frac{x^{2}+y
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 7 Numerical Results
Figure 7: Bit error rate (BER) performance for proposed the relay space-time interference alignment (R-STIA) with perfect and limited channel state information at the relay (CSIR) knowledge. Figure 8: Sum-rate for the proposed relay space-time interference alignment (R-STIA) technique. Figure 9: Bit error rate (BER) performance of the proposed relay space-time interference alignment (R-STIA) with an minimum mean square error (MMSE) precoder.
## 4 Problem Formulations and Optimization Models
Finally, the data sent towards the suface sink travels through the following nodes in the path using hierarchical routing: \(s_{(i\cdot x)k\to 2}\), \(s_{(i\cdot x)k\to 1}\), \(s_{(i\cdot x+1)k\to 1}\), \(s_{(i\cdot x+1)k\to 1}\), \(s_{i\to 1}\), \(s_{i\to 2}\), \(s_{i}\), \(s_{i+1}\),..., and the surface sink. Generally speaking, the optimal location of the node \(s_{(i\cdot x)k\to 1}\) can be determined as follows. We define:  
* \(u_{(i\cdot x)k\to 1}=(x_{(i\cdot x)k}y_{(i\cdot x)k}\boldsymbol{z}_{(i\cdot x )k\to 1}),\forall k\in K,\ \forall x\in N\) is the new location of node \(s_{(i\cdot x)k\to 1}\). * \(u_{(i\cdot x+1)k\to 1}=(x_{(i\cdot x+1)k}y_{(i\cdot x+1)k}\boldsymbol{z}_{(i\cdot x +1)k\to 1})\) is the new already established location of node \(s_{(i\cdot x+1)k\to 1}\) for a particular \(k\in K\) outside the shadow zone. We consider that node \(s_{(i\cdot x+1)k}\) was located originally in the shadow zone. * \(u_{(i\cdot x-1)k}=(x_{(i\cdot x-1)k}y_{(i\cdot x)k}\boldsymbol{z}_{(i\cdot x -1)k}),\forall k\in K,\ \forall x\in N\) is the location of node \(s_{(i\cdot x-1)k}\). We consider that node \(s_{(i\cdot x-1)k}\) is not located in the shadow zone. **P3: Location Optimization Problem**  
\[\begin{array}{ll}\mbox{\it Given}:&x_{(i\cdot x)_{k}},x_{(i\cdot x+1)_{k}},x_{(i\cdot x-1)_{k}},y_{(i\cdot x)_{k}},y_{(i\cdot x+1)_{k}},y_{(i\cdot x-1)_ {k}}\\ \boldsymbol{z}_{(i\cdot x+1)_{k}\to 1},\boldsymbol{z}_{(i\cdot x-  
problem **P3** aims at finding the best location for \(s_{(i\cdot x)k\to 1}\)that minimizes the transmission loss of the link \(l_{(i\cdot x)k\to 1(i\cdot x+1)k\to 1}\) or the link \(l_{(i\cdot x\cdot 1)k\ (i\cdot x)k\to 1}\) with the highest transmission loss value and preserves the connectivity with node \(s_{(i\cdot x+1)k\to 1}\) and nodes \(s_{(i\cdot x-1)k}\) preventing from network partition. Objective 1 is valid when the nodes \(s_{(i\cdot x-1)k}\) are also located in the shadow zone. Otherwise objective 2 should be applied. Constraints (18) and (19) impose that the transmission range should be lower than a threshold to ensure connectivity. Constraints (20) and (21) express that the transmission loss should be lower than a transmission loss threshold. Constraints (19) and (21) are only valid for objective function 2. Constraint (22) imposes that the sensor node \(s_{(i\cdot x)k\to 1}\) should be located outside the shadow zone. Constraint (23) states that the energy consumed by the electronically controlled engine to move the sensor to the optimal position should be lower than a threshold.
# Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography
## 3 Experimental Setup and Procedure
### Impact Tests  
After each impact, thermal videos were acquired in time sequence at 60 Hz under the thermal stimulation. To better account for thermal phenomena, sequences of these thermal images were re-arranged and processed by subtracting the first thermal image for each subsequent image [42; 53; 57]. The first thermal image was taken before the impacts and the thermal stimulation. Therefore, we obtain a map of temperature difference \(\Delta T\):  
\[\Delta T=T(i,j,t)-T(i,j,0) \tag{1}\]  
where \(i\) and \(j\) represents rows and columns of the thermal image. Therefore, a sequence of \(\Delta T\) images was created [31]. \(\Delta T\) images supply quantitative information about location and strength of hot spots and, in turn, of the impact induced damage. The hot spots imply the visualization of thermal signatures that develop under impact damage. They represent the thermal perception of impact damage arose on the specimen and the information on damage location, which is important to detect the severity of the impact damage. They also provide useful information to understand the PVA-ECC impact damage mechanisms, such as initiation of cracks and their propagation, severity of damages. Defects like cracks  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cement** & **Sand** & **Fly Ash** & **Water** & **SP (\%)** & **PVA (\%)** \\ \hline
1.16 & 1.16 & 2.2 & 0.66 & 0.002 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of polyvinyl alcohol fiber reinforced engineering cementitious composite (PVA-ECC) under study [22]. (PVA-ECC: Polyvinyl alcohol fiber reinforced engineering cementitious composite; SP: Superplasticizer, a high-range water-reducing admixture).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nominal Strength** & **Apparent Strength** & **Diameter** & **Length** & **Young’s Modulus** & **Elongation** \\
**(MPa)** & **(MPa)** & **(mm)** & **(mm)** & **(GPa)** & **(\%)** \\ \hline
1620 & 1092 & 39 & 12 & 42.8 & 6.0 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of the polyvinyl alcohol (PVA) Fiber [22].  
in the structure, which have different density, heat capacity and heat conductivity in comparison to the intact area, could be localized and partly quantified.
## Appendix B The Calculation Steps of Index Weight  
Step 1: Data standardization. Different index dimensions will cause different data to affect the result in the calculation process. In order to eliminate the influence of different index dimensions, the initial data are first processed by forward (MMS), and the proportion of the \(i\)-th sample value of the \(j\)-th index is calculated \(p_{ij}\);  
\[p_{ij}=\frac{y_{ij}}{\sum_{i=1}^{n}y_{ij}} \tag{1}\]  
Step 2: Calculate the entropy value of each index \(e_{j}\);  
\[e_{j}=-k\sum_{i=1}^{n}p_{ij}\ln\left(p_{ij}\right) \tag{2}\]  
where \(k\) is the correction factor, \(k=\frac{1}{\ln n}\), \(k>0\), and guarantees \(0\ll e_{j}\gg 1\);  
Step 3: Calculate the information utility value of each index \(d_{j}\);  
\[d_{j}=1-e_{j} \tag{3}\]  
Step 4: Determine the weight coefficient of each index \(w_{j}\);  
\[w_{j}=\frac{d_{j}}{\sum_{j=1}^{n}d_{j}} \tag{4}\]
## 2 Basic Formulas of Gaussian Beams and Parameters of Optics  
By considering a slowly varying envelope (SVE) of the electric field, the Gaussian beam emerges as an analytical solution to the paraxial Helmholtz Equation [14]. In the context of indoor OWC, the transverse intensity distribution of single-mode fibers (SMFs) is presumed to adhere to an ideal Gaussian profile, with this Gaussian shape being preserved along its propagation axis in indoor environments [15]:  
\[I(r,z)=I_{0}exp\left(\frac{-2r^{2}}{\omega^{2}(z)}\right), \tag{1}\]  
where \(I_{0}\) is the peak intensity, \(r\) is the transverse distance concerning the propagation axis Z, and \(z\) is the axial distance. The expression indicates that the transverse intensity distribution of a Gaussian beam is circular and symmetric with no obvious boundaries and weakens with increasing transverse distance. More precisely, its intensity drops to \(1/e^{2}\) of the peak intensity \(I_{0}\) when \(r=\omega(z)\), and \(\omega(z)\) is typically referred to as the beam radius. The peak intensity \(I_{0}\) can be expressed as:  
\[I_{0}=\frac{2P_{o}}{\pi\omega^{2}(z)}, \tag{2}\]  
where \(P_{o}\) is the total optical power emitted to the free space. Due to diffraction, the beam radius \(\omega(z)\) keeps varying with \(z\) as:  
\[\omega(z)=\omega_{0}\sqrt{1+\left(\frac{z}{z_{R}}\right)^{2}}, \tag{3}\]  
where \(\omega_{0}\) is the minimum beam radius along the propagation axis named beam waist, and it appears at axial distance \(z=0\). The wavelength and beam waist dependent Rayleigh length can be calculated by Equation (4):  
\[z_{R}=\frac{\pi\omega_{0}^{2}}{\lambda}, \tag{4}\]  
where \(\lambda\) is the operating wavelength of the laser source, and the Rayleigh length represents the ability of optical beams to maintain collimation along their propagation direction. Equation (4) indicates that a larger divergent angle can be obtained by shrinking \(\omega_{0}\), and vice versa. Herein, the target divergent angle of the optical beam is obtained by focusing a collimated beam.  
In order to analyze the propagation characteristics of the divergent Gaussian beam within the context of short-range indoor OWC, a comprehensive set of simulation and experimental results is provided in the subsequent sections. The optical parameters employed in both the simulations and experiments remain consistent, and they are detailed in Table 1. Specifically, the emitting collimator holds a fixed focal length, while the receiving collimator features an adjustable focal length.
## 4 Result Analysis
### Model Verification  
For the identification of river and lake water environment problems, a model named Water-YOLOv11n was trained using transfer learning based on the YOLOv11n architecture. As illustrated in Figure 7, the precision confidence of the water environment problem identification model reaches 1, while the average recall rate across all categories achieves 95.8% at a 0.5 threshold, indicating a high precision level. The model's F1-score is 0.9, reflecting a favorable balance between precision and recall and demonstrating excellent overall performance. Through verification, the model shows strong capability in accurately identifying diverse water environment issues.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Work Performance** & **Application Scenarios** \\ \hline \multirow{6}{*}{Supervised} & Automatically updates & \multirow{6}{*}{Team collaboration, large-scale data} \\  & annotations of existing & \multirow{6}{*}{Team collaboration, large-scale data} \\  & data using classification & \\  & tools; automatically & \multirow{6}{*}{Team collaboration, large-scale data} \\  & annotates new data with & \\  & augmentation tools & \\  & Category deletion, & \\  & merging, or addition often & \\  & requires manual inspection & \multirow{6}{*}{Fast model iteration, dataset management} \\ Roboflow & and annotation & \\  & adjustments; processes & \\  & fewer images per & \\  & augmentation batch & \\  & Deletion operations may & \\ Scale AI & affect existing annotations; & \multirow{6}{*}{Large-scale annotation services, industry-specific customization} \\  & significant variation in & \\  & augmentation behavior & \\  & Automatically updates & \\  & annotations of existing & \\ Our Method & data using the classification & \multirow{6}{*}{Team collaboration, localized management} \\  & annotates new data using & \\  & the augmentation tool & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with contemporary automated annotation platforms.  
Figure 7: _Cont._  
To further assess classification performance, the normalized confusion matrix is displayed in Figure 8. The matrix reveals that most categories are correctly identified with high confidence. However, some misclassification occurs between visually similar categories, such as dead animals and sewage, which reflects the inherent complexity of distinguishing closely related environmental features.  
Figure 7: Model training results, including precision–confidence and precision–recall curves.  
Given the inherent complexity of river and lake water environment problems, the experiment included multiple cycles involving data addition, classification modification, and data augmentation. These tasks were efficiently executed through the collaborative workflow and specialized tools developed in this study, allowing the experiments to proceed swiftly and effectively. Looking ahead, leveraging the collaborative process and tools proposed herein, additional real-world application datasets will be incorporated into the river and lake water environment problem dataset to further enhance the model's performance and adaptability.