esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
### The Drivers of ET  
Studying the driving factors of \(ET\) is of great significance in understanding the change mechanism of agroecosystem \(ET\) and formulating agricultural water saving policies. Previous studies on the driving factors of agroecosystem \(ET\) are relatively limited, with a focus on the driving factors of _WUE_. [PERSON] [57] showed that the \(ET\) of maize during its growth period is mainly influenced by meteorological factors, while, in this study, only temperature, wind speed and net radiation were considered. [PERSON] [58] pointed out that net radiation, \(VPD\), and temperature are the three main driving factors leading to changes in \(ET\), but this study lacks consideration of physiological factors. [PERSON] [59] considered that net radiation dominates the changes in crop \(ET\) during the summer growth period in the Huai River Basin, while wind speed dominates the changes in \(ET\) during non-summer and plant growth seasons. [PERSON] [60] evaluated the contributions of environmental and physiological factors on the agroecosystem scale to soil evaporation and vegetation transpiration based on observations of EC systems, believing that \(VPD\) is the main factor driving soil evaporation changes while available energy above the canopy is the main factor driving vegetation transpiration changes. In summary, in existing studies on driving factors of agroecosystem \(ET\), most studies have included a relatively small number of driving factors. The agroecosystem-scale studies mostly focus on grain crops such as wheat and maize while paying little attention to the driving factors of changes in cabbage \(ET\). This study considered physiological factors including _WUE_, \(GPP\), and _LAI_, as well as environmental factors such as \(Rn\), \(Ta\), \(SWC\), and \(VPD\). Compared with previous studies, this study has considered the effects of multiple factors on \(ET\) changes. The results show that \(GPP\) is the main driving factor for \(ET\) changes during the growth period of cabbage.
## 4 Experimental Evaluation of Performances
### Community Anomaly Detection in Synthesis Data
#### 4.3.2 Second Experiment  
The model evaluation with the increased size network is conducted with networks of 500, 1000, 2000, 3500, 6000, and 10,000 nodes. The AUC of the CAnom model starts from 0.89 in the smallest network to 0.96 in the largest network. In fact, CAnom has the highest AUC compared with the other models (see Figure 9). The evaluation of the runtime with the increasing size of the network is shown in Figure 10. While ConSub shows faster runtimes overall in comparison with the other models, the CAnom has a better runtime than CODA.
## 3 TanSat Simulations and Retrieval Assumptions
### The State Vector Defination  
The state vector \(\mathbf{x}\) for CO\({}_{2}\) retrieval consists of the CO\({}_{2}\) mixing ratio profile (\(\mathbf{u}\)) and non-CO\({}_{2}\) elements (\(\mathbf{e}\)), including the H\({}_{2}\)O scaling factor, surface pressure, aerosol parameters, and surface BRDF factors. The H\({}_{2}\)O mixing ratio profile is scaled by a factor to describe the variation in the H\({}_{2}\)O column amount while ignoring changes in profile shape, as in [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2017). Table 4 summarizes all possible elements included in the state vector, where the aerosol parameters were composed of the 14 parameters used in our previous analysis ([PERSON] et al., 2018) and two further aerosol profile parameters. In fact, for different aerosol models, different parts of these aerosol parameters are chosen for the state vector, while the remainder serve as the forward model parameters.  
As is consistent with previous analyses, the wavelength dependence of the real and imaginary parts of the refractive index (\(m_{r}\), \(m_{i}\)) are also assumed to be exponent functions, following [PERSON] and [PERSON] (2010):  
\[m_{r}(\lambda)=a_{r}\star\lambda^{b_{r}}, \tag{12}\]  
\[m_{i}(\lambda)=a_{i}\star\lambda^{b_{i}}. \tag{13}\]  
where the \(a_{r},b_{r},a_{i}\) and \(b_{i}\) values are the fitting coefficients and were included in the state vector. The derivations of the Jacobians were presented in a previous study ([PERSON] and [PERSON], 2010). The Jacobians of these coefficients, rather than the Jacobians of the refractive index, were involved in CO\({}_{2}\) retrieval.
## 2 Materials and Methods
### Methods
#### 2.3.5 Correlation Analysis  
In order to select the optimal combination of drought and heatwave indices for constructing the CDHI for rubber plantations on Hainan Island, we employed two correlation analysis methods--Pearson and Spearman correlations [60,61]--and selected the drought index that exhibited the strongest and most significant negative correlation with STI-1 as the drought component of the CDHI.
# Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades
## 3 Laboratory Investigation of Damage Accumulation in Steel Due to Cyclic Loading
This observation is based on a 10-hertz modulated frequency calculated with a time window of 10 s, as presented in Figure 16a,b. In Figure 17, the level of the DEMON component is shown in relation to the normalized number of cycles. This normalized load cycle number can be considered to be the sample's lifetime. This diagram demonstrates two phases of the sample's lifetime. In the first phase,  
Figure 16: Spectral display via the DEMON technique within the 50-hertz frequency window for the steel sample subjected to varying load cycles: (**a**) 5000 load cycles; (**b**) 48,004 load cycles [58]. Figure 15: Ratio of the number of cycles relative to MI for a given frequency: \(f=189\) kHz [58]. there is no damage to the sample that shows a low value of SNR. In the second phase, the damage is not visible; however, the elevated level of the SNR using the 10-hertz component signifies the existence of a crack. The accuracy levels of the conducted measurements of SNR and MI parameters were estimated via the processing of the recorded signal in 10 windows. This processing was performed for a 48,004-cycle load. The average value of SNR was 16.4 dB, with the standard deviation being 0.7 dB. The accuracy of the MI measurements was higher, as the average MI was \(-\)23 dB, with the standard deviation being 0.04 dB. Comparing the plots in Figures 15 and 17 reveals that both methods exhibit similar levels of sensitivity regarding crack detection. The wideband signal method is more convenient for real WT blade SHM, as it does not require a probe wave emitter that transmits sinusoidal waves. The conducted test demonstrated the applicability of the VAM to crack detection in the steel sample, and we hope that a similar demonstration for the WT scaled model and the real turbine will be conducted in the near future.
## 4 Experimental Results for the Light Field Capturing and Reconstruction  
### Experiments for One-Shot 360-Degree Light Field Capturing  
An optical structure for 360-degree light field capturing was implemented, as shown in Figure 8a. Two plane mirrors and one light field camera were used to compose the overall system. To align each optical component, we adjusted the positions of light field camera and the stage of the subject first. The positions of two mirrors were controlled with a linear translation stage. A simple laser pointer is utilized to adjust orientation of the mirrors. With the composition of the optical elements, the position for subjects was fixed on the stage. The stage for the subject and background of the studio were wrapped with solid color sheet, which was green color in our system. To remove the background data, the chroma-key technique was used. Figure 8b,c show the captured light field data and its depth map information, respectively. The background noise was removed in the raw light field and the light field was cropped to calculate accurate depth map. The resolution of processed light field was 3864 \(\times\) 2681. Since the resolution of each lens image was \(7\times 7\), the resolutions of each sub-image and depth map were 552 \(\times\) 383.  
Figure 7: (**a**) Simulated light field image in the proposed optical setup; (**b**) extracted depth information of the light field; (**c**) refocused image of left perspective of the light field; (**d**) refocused image of the center perspective; and, (**e**) reconstructed three-dimensional (3D) triangular mesh from the color point cloud.  
Figure 8d,e show left and center perspective images, which were digitally refocused to extract clear color information. In the comparison of Figure 8d,e, the center perspective had the higher resolution than the left and right one. The original resolutions of the center and left or right perspective were 1020 \(\times\) 1020 and 510 \(\times\) 510, respectively. To acquire proper refocused images, involving enough depth of field was essential, according to the size of subjects. Since the f-number of the light field was 2, which provided rather low depth of field for most of objects, we used a synthetic aperture technique whose _f_-number is to reduce converging rays onto sensor and increase depth of field of the synthetic image. Figure 8f shows 3D color information of red box region in Figure 8e. Triangular mesh was used to illustrate the 3D structure and the location of virtual camera for rendering was adjusted to emphasize the peak of the penguin. As to the simulation results, the color point cloud was located on virtual space in the PC environment, bound altogether, and reconstructed as 3D meshes.
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 2 Main Volatile Components  
Plant aroma VOCs are diverse in nature and can be classified into terpen (Figure 1a), benzene rings (Figure 1b), and fatty acid derivatives based on their structure [9]. Terpenes are the largest class of VOCs and consist of over 550 compounds [14] derived from two common C5 precursor compounds, isopentenyl diphosphate (IPP) and its allyl isomer dimethylallyl diphosphate (DMAPP) [15]. Phenylpropanoids, containing the aromatic amino acid L-phenylalanine (Phe), are the second most common VOCs in plants. Phe is produced via two branching pathways, the shikimic acid pathway and the Phe pathway, and is linked to the central carbon metabolism. Fatty acid derivatives, including methyl jasmonate and (Z)-3-hexenol, are the third class of plant VOCs. These VOCs are all derived from C18 unsaturated fats, linolenic acid, or linoleic acid. There were significant differences in the types of aroma components and the main aroma-active components among the fruits of different species (Table 1). For example, the main aroma components of 'Fujii' apples at maturity are n-butyl 2-methylacetate and hexyl acetate [16]; those of bananas are 2-methylpropyl butyrate and orthontrophenyl-beta-d-fucopyranoside [17]; those of kivirruit are ethyl butyrate, (E)-2-hexene-1-ol, and (e)-2-hexene aldehyde [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Main Aroma Component** & **Variety of Aroma** & **Reference** \\ \hline Apple & N-butyl 2-methylacetate, hexyl acetate & 300 & [PERSON] et al. [16] \\ Kiwifruit & ethyl butyrate, (E)-2-hexene-1-ol, (E)-2-hexenal & 172 & [PERSON] et al. [18] \\ Grape & ethyl butyrate, ethyl hexanoate, octanal, nonanal, limonene & 26 & [PERSON] et al. [19] \\ Peach & hexanal, (Z)-3-hexene-1-ol, (E)-2-hexenal, & 100 & [PERSON] et al. [20] \\  & 3-mercaptoethanol, nonanal & & \\ Banana & 2-methylpropyl butyrate, & & \\  & orthontrophenyl-beta-d-fucopyranoside & 48 & Pino and Febles [17] \\ Tomato & cis-3-hexenal, trans-2-hexenal, cis-3-hexenol & 400 & Buttery and Takeoka [21] \\ \hline \hline \end{tabular}
\end{table}
Table 1: 10 Aroma components of representative fruits.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 4 Protocol Description  
The network initialization process is defined first, followed by a detailed explanation of data forwarding.  
The network initialization is achieved by the sink broadcasting Hello packets, and the nodes gradually establish connectivity through the process of updating their hop counts and retransmitting data packets upon receiving the Hello packets. A detailed description of this process is provided in Section 4.1. Figure 2 shows a flowchart for the network initialization process.  
Following the successful initialization of the network, the data forwarding process commences with a preliminary handshake phase. Assuming node x is tasked with data forwarding, the initial step entails engaging in a handshake with its adjacent nodes. Node x initiates the process by dispatching HANDSHAKE-S packets to its neighboring counterparts. Upon receipt of these packets, the neighboring nodes respond by generating HANDSHAKE-R packets, which are then transmitted back to node x.  
Subsequently, the completion of the handshake phase between node x and its neighboring nodes is realized. A detailed description of this process can be found in Section 4.2. A visual representation of the handshake phase is presented in Figures 3 and 4. Figure 3 shows the process of sending HANDSHAKE-S and receiving HANDSHAKE-R when a node needs to forward data, and Figure 4 shows the process by which a neighbor node receives HANDSHAKE-S and sends HANDSHAKE-S.  
Figure 2: Flowchart for network initialization process.  
The subsequent phase of data forwarding unfolds with the execution of the data transmission process. Upon reception of HANDSHAKE-R packets from its neighbors, node x computes the link quality metric with each neighboring node using the prescribed Equation (8). Subsequently, node x selects the neighbor with the highest computed link quality to facilitate data transmission. Acknowledgment (ACK) packets are relayed back to node x by the recipients of the transmitted data. It is crucial to emphasize that only those nodes are relayed back to node x.  
Figure 4: Flowchart for the neighbor node.  
Figure 3: Flowchart for the node.  
neighbors whose link quality surpasses a designated threshold can successfully receive data packets. In instances where all neighbors possess link quality values beneath the threshold, the node temporarily stores the packet in a cache queue for the next send. Additionally, in cases where node x fails to receive ACK packets and retransmission, it proceeds to select the neighbor with the second-highest link quality for data forwarding, and this iterative process continues accordingly. Section 4.3 provides a detailed description of this process. The procedural depiction of the data forwarding phase is graphically presented in Figure 5.  
Figure 5: Flowchart for the data forwarding phase.
# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 3 AgriEnt: Architecture
### Semantic Layer  
The semantic layer relies on the AgriEnt-Ontology (Agricultural Entomology) and a rule-based inference engine. On the one hand, the role of the AgriEnt-ontology is to capture expert's knowledge about a crop insect pest's management as well as to provide an agreed-upon understanding of such a domain. On the other hand, the rule-based inference engine examines the crop's symptoms provided by the user against a set of rules to provide a diagnosis about the insect pest affecting the crop. Both ontology and rule-based engine components are widely discussed below.
###### Abstract  
In numerous applications, such as the correction of grid voltage during voltage sag or swell events caused by system faults, it is necessary to ensure the stabilization of the output voltage with in-phase and out-phase features. This feature can also be employed in high-voltage-gain AC to DC conversion. AC voltage control schemes with one-stage conversion are viable approaches in this regard as only voltage regulation is needed. This conversion approach has strong potential for such applications as it is simple to realize. The voltage-correcting compensators are connected in series with the lines. The inputs of the AC voltage controllers employed in the voltage-correcting compensators may be connected to the faulty phase or other healthy phases. The number of AC voltage controllers used in a voltage compensator are one and two if its input power is drawn from the faulty and non-faulty phases, respectively. In the latter case, voltage gains and phase adjustment are major problems. There is no such issue in the first approach, where the voltage-correcting controller is connected to the line where the voltage variation is to be corrected. A high voltage gain more than the unity of both voltage polarities is required if the depth level of the correcting voltage is around 50% or more. The size and cost of a voltage-correcting controller depend on the number of switching transistors, as an isolated DC source with a gate-controlling circuit is a mandatory requirement for the switching operation of each transistor. Therefore, in the suggested research, an AC voltage controller having bipolar voltage gain is realized only with four switching transistors and six diodes, which reduces the overall size and cost significantly. The verification of the suggested topology is ensured by obtaining the simulation and real results from Simulink-based and practical-based platforms, respectively.  
Voltage sag; voltage controller; voltage gain; switching transistors; non-inverting features; inverting features; faulty and non-faulty phase
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 2 Communication Architectures
### Decentralized Communication Architecture
#### 2.2.2 Multi-Group Swarm Ad hoc Network  
To address the shortcomings of a \"single-group swarm Ad hoc network\", the \"multi-group swarm Ad hoc network\" (Figure 4) integrates both a centralized architecture and a \"single-group swarm Ad hoc network\" architecture. Different types of groups have different applications depending on the mission. Overall, the architecture is organized in a centralized manner as described in Section 2.1. While the difference is that the intra-group UAVs communicate with each other in an Ad hoc manner. The intra-group communication architectures are the same as the intra-swarm communication architectures described in Section 2.2.1. Inter-group communications, i.e., the Group-to-Group (G-T-G) communications, are performed through the infrastructure, so the gateway UAVs are still responsible for communicating with the infrastructure. When mission scenarios require a variety of UAVs, a \"multi-group swarm Ad hoc network\" architecture can be employed. However, attention must be paid to the robustness of this architecture, as the \"multi-group swarm Ad hoc network\" architecture is semi-centralized. At the same time, for G-T-G communication between two UAVs in different groups, \"multi-group swarm Ad hoc network\" architecture still suffers from high latency. One specific application of multi-group architecture is multi-theater joint operations for military operations. The central control center communicates with different UAV swarms, and the swarms rush to the mission area from various directions, according to the control instructions [41].
# On Ad Hoc Communication in Industrial Environments
## 3 Comparing Methods for the Extraction of NEPs
### Numerical Comparison  
The proposed models for \(N_{\varnothing}^{NT}\), \(N_{\varnothing}^{TP}\), \(D_{\varnothing}^{NT}\), and \(D_{\varnothing}^{TP}\) were compared to a numerical simulation of an ad hoc network. In this simulation, the nodes use the Random WayPoint Model [32] to move within the confined area. The results of the proposed models and the numerical simulation are presented in Appendix A.  
In Appendix A it can be seen that the numerical simulation behaved similarly to the proposed models. This validates that the proposed models are able to estimate the network performance and behavior without simulating the complete network.The plots of the performance predicted by observations from tracing protocol and from network traces, however, are highly dissimilar; this suggests that the assumptions of the NEP generation from network traces are not applicable.  
The numeric simulation confirms the behavior expected from the presented models. Models and simulations show the same behavior in regards to parameters and similar network performance. Figure A1 also shows the differences of the two methodologies \(TP\) and \(NT\). The two acquisition types showed clear behavioral differences, which are examined in detail in the following subsection. Only for specific configurations of \(N\), \(N_{AP}\), \(v\), and \(r\) can similar results in terms of \(N_{\emptyset}\) and \(D_{\emptyset}\) be obtained from both acquisition methods. All these observations were done under assumptions of random node mobility and random access point placement. More complex distributions of nodes and APs will lead to even more complex relations between all of these parameters and metrics, and even more pronounced differences between both acquisition methods.
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 2 Literature Review
### Factors That Affect UAV Accuracy  
Despite the capabilities of UAV in surface modeling, there are a lot of factors that affect the accuracy of UAV. Using UAV for getting ground imagery needs control points at the ground as a reference for post-processing and there are many factors that affect the accuracy of UAV like GCPs (Ground Control Points), image overlapping rates, earth surface materials, flight height from the ground surface and camera used for taking images, etc. [19; 20]. UAVs follow the photogrammetry process and output after the processing is from the point cloud and the position of each point affects the accuracy of the measurement taken from the point cloud. The flight height from the ground affects the ground sampling distance which is the center-to-center distance between two pixels measured on the surface. More center-to-center distance reduces the pixels of the image, which results in low resolution that affects the accuracy of the measurement. During the image processing, the material of the surface has impact on the quality of the output models. The overlapping of these images should be more to have more common area that increases its accuracy. All these factors should be kept in balance to get high accuracy because each one of them can affect spatial accuracy, but GCP is the most influential factor [19].
## Appendix B Interpolation of T/S Strains  
Each triangle \(\varpi_{e}\) in the physical NEM grid \(\Im_{\mathrm{C}}\) shown in Figure 4 is mapped to the three-node master element \(\mathbf{\hat{\omega}}\) in Figure A1. And, the non-singular displacement part is re-expressed using the computed triangle-wise nodal vectors \(\mathbf{b}_{\mathrm{K}}^{\prime}=\left(u_{0}^{e},v_{0}^{e},w_{0}^{e},\beta_{x}^{e},\beta_{y}^{e}\right)_{K}^{T}\) and the Lagrange-type bilinear shape functions \(\left\{N_{L}(\xi,\eta)\right\}_{L=1}^{3}\). Next, according to the MITC3+ shell approach, the triangle-wise T/S strains \(\gamma_{e}^{h}\) are indirectly interpolated as  
\[\left(\gamma_{e}^{h}\right)_{xz}=\frac{2}{3}\bigg{[}\gamma_{xz}^{(B)}-\frac{ 1}{2}\gamma_{yz}^{(B)}\bigg{]}+\frac{1}{2}\Big{[}\gamma_{xz}^{(C)}+\gamma_{yz }^{(C)}\Big{]}+\frac{\delta}{3}(3\eta-1)\] (A5)  
\[\left(\gamma_{e}^{h}\right)_{yz}=\frac{2}{3}\bigg{[}\gamma_{yz}^{(A)}-\frac{ 1}{2}\gamma_{xz}^{(A)}\bigg{]}+\frac{1}{2}\Big{[}\gamma_{yz}^{(C)}+\gamma_{ xz}^{(C)}\Big{]}+\frac{\delta}{3}(1-3\xi)\] (A6)  
with \(\delta=\gamma_{xz}^{(F)}-\gamma_{xz}^{(D)}+\gamma_{yz}^{(E)}-\gamma_{yz}^{(F)}\), where \(A\), \(B\), \(C\), \(D\), \(E\), and \(F\) indicate the six tying points, as shown in Figure A1.