esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effects of Urea-FA Solution Combined Application on Photosynthetic Enzyme Concentrations  
Maize is a C\({}_{4}\) plant with high photosynthetic efficiency, with 80% of the grain dry matter being leaf photosynthetic. The PEPC and PPDK in maize leave are the key enzymes involved in photosynthetic carbon reduction [69; 70; 71]. In mesophyll cells, PEPC immobilizes CO\({}_{2}\) into oxaloacetic acid [72], providing a carbon skeleton for carbohydrate biosynthesis and nitrogen assimilation. The enzyme PPDK phosphorylates pyruvate into phosphoenolpyruvate, which is the primary CO\({}_{2}\) acceptor, promoting the tricarboxylic acid cycle. Studies showed that FA directly affects CO\({}_{2}\) assimilation and promotes carbohydrate accumulation by increasing the mRNA transcription and translation of PEPC and PPDK in cells [73; 74]. In this study, the concentrations of PEPC and PPDK in the U+FA treatment were significantly higher than in the U treatment, explaining the higher aboveground dry biomass and grain yield in the former.  
Significantly positively correlated with chlorophyll concentration, SPAD values can be used to estimate chlorophyll concentrations [1]. According to [PERSON] and [PERSON] [75], FA can up-regulate the ALAD gene (AFW69390.1) and induce the expression of H\({}^{+}\)-ATPase subtypes (Mha1 and Mha2) in maize, significantly improving the photoreaction of photosynthesis and expediting carbon cycling. In this study, the U+FA treatment significantly increased the SPAD value and photosynthetic rate at the jointing stage compared with the U treatment. In summary, the activities of carbon cycle-related enzymes and SPAD value were significantly increased in the U+FA treatment, which explains the higher aboveground dry biomass and grain yield in this treatment.  
As we all know, applying fertilizer can improve crop growth, and applying urea combined with FA solution can partially improve crop growth because the treatment is more efficient. Through this experiment, we found that the nutrient availability was increased by combining urea with FA solution and could meet the absorption of nutrients by plants. At the same time, by measuring various physiological and biochemical indicators, we found that the content of endogenous hormones and nitrogen cycle-related enzymes in urea combined with FA solution was significantly higher than that in urea. At present, we have come to the conclusion that the combined application of urea and FA solution significantly improved soil nutrient availability, increased the concentrations of endogenous hormones in maize, stimulated the activities of enzymes related to nitrogen metabolism, promoted the photosynthetic carbon assimilation efficiency, and ultimately improved the crop yield and NUE. At the same time, the mechanism of the significant expression of genes related to the nitrogen cycle in urea and FA solution will be continued in the future.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Evaluation Metrics  
In this research, several evaluation metrics were utilized to ascertain the effectiveness of the model. These metrics included the Dice similarity coefficient (DSC), the 95 th percentile Hausdorff distance (HD95), and the intersection over union (IoU) coefficient. Each metric is elaborated upon in the subsequent sections.
## 6 Experimental Results
### Practical Realization Considerations  
Figure 21a shows a realization of an ultrasonic receiver using a low cost microcontroller, a MEMS microphone and a ZigBee controller for data communication. The suggested software architecture is shown in Figure 21b. The input signal is amplified and bandpass filtered. Further processing is performed 1-bit only. Two operating modes are possible. In a low power mode the ultrasonic receiver  
Figure 19: **(a)** Mean error from reference points; **(b)** Size of groups at different positions.  
Figure 20: **(a)** Repeated measurement at different date and temperature; **(b)** Temperature obtained by using reference position was 29.1 \(\:\mathrm{C}\).  
is activated by the startframe available in the _LOSNUS_ sequence. If a lead-in is received, the lower comparator triggers the input capture register storing the current time of the free running clock and starts clocking the shift register. Storing of a frame stops if no further input condition is present and the total frame length has elapsed. A typical length of the frame including lead-in, chirp and transmitter coding at 1 MS is small and requires 740 Bit. These frames can then be processed offline.  
The other operating mode is when the shift register is permanently clocked and correlation with the chirp is always performed. In this case neither the starting frame nor the comparator for enabling reception is necessary. This mode is useful when power consumption is not of concern and the system should operate at very low SNRs as pulse compression allows detection of frames with an SNR close to zero.
## 3 Physical Layer
### Spectrum Sensing Techniques  
A number of techniques have been proposed to deliver such fundamental functionality [14, 15, 16, 17, 18, 19, 20]. Table 1 provides a summary of the most common spectrum-sensing techniques and their characteristics, including prerequisites and limitations that should be considered when choosing the most suitable methods for specific use cases.  
Matched filter, cyclostationary and eigenvalue-based detection tend to be most suitable where there is a need to distinguish the source of particular target signals. When this is not required for typical _IoT_ scenarios, the energy detection, which has the least prerequisites and incurs the lowest computational overhead, is particularly appropriate for _things_ with limited power budget and computational ability.  
\begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline Technique & Prior signal info & Synchronization & Multiple Target Detection & Sensing Duration & Transmission Disruption & Computational Complexity \\ \hline Matched Filter [14, 15, 21, 22, 23] & Required & With target signal & With targeted signal & With dedicated antennas & Short & None (with dedicated antennas) & Medium \\ \hline Cyclostationary Feature [15, 17, 20, 24] & Required & Not required & Yes & Long & None & High \\ \hline Energy Detection [23, 25, 26, 27] & Not required & With adaptive peers & Not supported & Medium & Suspended during sensing periods & Low \\ \hline Eigenvalue-based Detection [28, 29, 30, 31] & Not required & Not required & Yes & Flexible & None & High \\ \hline \end{tabular}
\end{table}
Table 1: A summary of main spectrum sensing techniques.
## 2 A Short Review of the Contributions in This Special Issue
The threats that scientists believe have the most significant impact can be identified as follows: denial-of-service (DoS) attacks, satellite signal spoofing, external eavesdropping, spamming, broadcast tampering, and man-in-the-middle attacks. The idea presented in the paper was to show the possibility of using a communication model from transportation, which is adapted for robots (robot-to-infrastructure (R2I) model). A risk analysis was carried out, determining the likelihood of potential threats occurring, their consequences, and the ability to detect these threads. Finally, specific methods of responding to the occurring threats are proposed, considering the cybersecurity aspects. Their critical new approach was the proposal to use communication and protocols that were previously dedicated to transport (IEEE 802.11p WAVE, dedicated short-range communications (DSRC)).
## 2 Research Design
### Step 1: Define research aim  
Justify review and highlight contribution  
**Step 2: Craft inclusion and/or exclusion criteria**  
Determine required characteristics of primary studies  
**Step 3: Retrieve 'baseline sample'**  
Determine search procedures and keywords  
**Step 4: Select pertinent literature ('synthesis sample')**  
Apply inclusion and/or exclusion criteria  
**Step 5: Synthesize literature**  
Apply coding schemes to extract pertinent information  
**Step 6: Report the results**  
Provide descriptive overview and discuss findings  
_Step 3: Retrieve' Baseline Sample'_  
The third step includes the retrieval of a 'baseline sample' to identify relevant articles. Following [PERSON] et al. [21], the selection for the search comprised two databases: The Elton Bryson Stephens Company (EBSCO) (Business Source complete) and the Intercollegiate Studies Institute (ISI) Web of Knowledge (Social Sciences Citation Index (SSCI)-Database). EBSCO, as well as SCCI-databases, comprise extensive and comprehensive repositories in business and management research [22; 23]. A team of four senior validated the initial search terms based on the research aim and the inclusion criteria.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Rationale** \\ \hline \multirow{2}{*}{Peer-reviewed articles} & Published peer-reviewed articles increase the quality of the \\  & manuscript ([PERSON] and [PERSON], 2009) and enhance the quality \\  & control ([PERSON] and [PERSON], 1984) \\ \hline \multirow{2}{*}{Selection of papers published 2010–2020} & Given its very recent emergence, academic research in blockchain \\  & can still be regarded as in its infancy ([PERSON], 2018). \\ \hline \multirow{2}{*}{The summary must include an  organization theory in a blockchain  article in the context of LSCM } & The aim of the review is to analyze and synthesize the \\ \cline{1-1}  & organizational theories that are used to investigate blockchain \\ \hline \multirow{2}{*}{Different type of article considered (e.g., empirical, conceptual)} & The focus of the study is to evaluate and to synthesize the \\ \cline{1-1}  & various theories and methods in blockchain in LSCM \\ \hline \multirow{2}{*}{Article must be written in English  blockchain } & English is the dominant research language in the field of \\ \cline{1-1}  & blockchain in LSCM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion Criteria.  
Figure 1: Steps for conducting a systematic literature review (adapted from [PERSON] et al. [18]).  
Similar to other systematic literature reviews, we searched for organizational theories in blockchain articles in the field of LSCM by using the keyword \"blockchain\", combined with \"logistics\" or \"supply chain\" (see Table 2). Furthermore, we changed the search string and replaced \"logistics\" with words possessing a similar meaning, including \"transport\", \"infrastructure\", or we added another category named \"theory\" or \"theories\" to consolidate the findings. These searches took place in September 2019, and were carried out again in January 2020.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 3 Cluster Analysis
### Distribution-Based Clustering  
One of the problems with clustering based on distance is that the shapes are limited. The solution to this problem was shown in the previous section using density-based methods. However, in some situations, it may be beneficial to retain the flexibility of algorithms, such as K-means, i.e., requiring only the target number of clusters, rather than setting up more complicated values for radii and point amount thresholds. Distribution-based clustering answers this by employing a similar technique to K-means while allowing not only circular but also elliptical shapes.  
One of the most used algorithms of this type is the Gaussian mixture model (GMM). The way the algorithm operates is that it estimates the center and standard deviation for each cluster. These two values, together with the number of K clusters, form the algorithm's parameters. After initialization of these values, the algorithm follows these steps:  
1. For every data point, calculate the probability that it belongs to each of the clusters, based on the distance to the cluster centers.
2. Recalculate the parameters to maximize the probabilities.
3. Repeat the previous steps until (1) maximization of the probabilities or (2) stagnation.  
The initial values for the centers of clusters and standard deviations may be chosen randomly at first. The values are optimized with each algorithm iteration. The final result is similar to K-means but with more flexibility in the cluster shape.  
The experimental solution utilizes algorithms--K-Means, DBSCAN, and OPTICS. K-mean provides a way to cluster data into a specified amount of groups based on distance, which proves to be a simple and efficient way of cluster analysis. For more specific cases with irregular data shapes, DBSCAN and OPTICS are utilized.
## 3 Physics-Based Particle Simulation Approach
### Simulation Model Configuration and IFT Calculation  
Each emulsification simulation was executed using a periodic cubic box with dimensions of \(100\times 100\times 100R_{c}^{3}\). Systems contained water, oil, and emulsifier beads, mixed at 70% or 80% water content while keeping the emulsifier-to-oil mass ratio at 1:1. Configurations were randomly initialized to mimic high-shear dispersion. A slab model was used to evaluate IFT which consisted of two oil layers (25 \(R_{c}^{1}\)) and a central water layer (50 \(R_{c}^{1}\)), with 20% emulsifier to ensure sub-monolayer coverage. Simulations ran in the NVT ensemble for 1,000,000 steps at 65 \({}^{\circ}\)C with a 0.005 tc timestep. Analyses included Radial Distribution Functions (RDFs), visual morphology, and phase classification. Table 3 below lists the parameters for the purpose of replication.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(a_{ij}\) & \(W\) & \(C\) & \(B\) & \(N\) & \(EO\) & \(T\) \\ \hline W & 78 & & & & & \\ C & 129.78 & 78 & & & & \\ B & 131.02 & 79.55 & 78 & & & \\ N & 88.95 & 101.94 & 95.24 & 78 & & \\ EO & 101.12 & 82.69 & 79.55 & 83.79 & 78 & \\ T & 138.95 & 79.58 & 78 & 97.34 & 79.84 & 78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conservative force parameters of the beads employed in the present study. Bead types are defined as: W (water, representing three water molecules), C (polyaromatic core of emulsifiers), B (aliphatic side chains of emulsifiers), N (nitrogen-containing heteroatom side chains), EO (ether functional groups in oils 8–10), and T (aromatic groups in oils 11–14, e.g., toluene).  
Analyses included visual morphology, phase classification, and radial distribution functions (RDFs). The RDF was computed to quantify the spatial arrangement of molecules, particularly focusing on the distribution of emulsifier molecules around each other and around oil molecules at the interface. RDFs were calculated using center-of-mass distances between coarse-grained beads, with a cutoff radius of 10 Rc. The results were averaged over the final 200,000 steps of the simulation to ensure equilibrium sampling. The computed RDFs help to identify stacking behavior, interfacial adsorption, and dispersion tendencies of different emulsifiers across the simulated systems [41; 42; 43; 44; 45]. These descriptors have previously been explored in machine learning contexts and remain valuable for future data-driven investigations [46]. In this work, RDFs were analyzed strictly as physical indicators of interfacial organization and packing efficiency [47].
## 3 Results
### Comparative Results  
Figure 6 is a comparison of the accuracy of our models, with the optimization method color-coded: Grid Search (blue), Random Search (green), and Bayesian search (purple). The baseline value is shown as a dashed line at approximately 0.611. Based on our experimental study, we found that the SVM model outperformed the other models, except for the pre-processing approach. The MICE imputation method with SVM yielded the highest accuracy, 0.6294, showing marginal effectiveness over the other imputation techniques. In contrast, RF, which underperformed the others, only displayed good performance when using mean imputation and Grid Search to determine the ideal configuration of its hyper-parameter, which ranged from 0.595 to 0.609. Unlike SVM and RF, the XGBoost models achieved varying levels of accuracy based on the imputation technique, between 0.577 and 0.619.  
Figure 7 shows the sensitivity of each model. The baseline performance, approximately 0.575, is shown as a dashed line. The SVM models consistently demonstrated superior performance across all imputation methods, achieving sensitivity scores above 0.61. Notably, performance was consistent across all three optimization methods, with minimal variation of less than 0.005. The RF models showed sensitivity scores ranging from 0.52 to 0.60, while XGBoost models achieved intermediate performance levels of 0.55 and 0.59. Examination of the imputation methods showed that MICE resulted in marginally higher sensitivity scores compared to mean imputation, with improvements of approximately 0.002-0.005 across most models.  
Figure 6: Comparison of the accuracy of our 36 models.  
A comparison of the AUC scores is shown in Figure 8. As with the other performance comparisons, the SVM models consistently demonstrated superior performance, achieving AUC scores exceeding 0.66 across all the imputation methods. They remained stable across the optimization methods, with a variation of less than 0.001. The RF models exhibited AUC scores ranging from 0.60 to 0.63, while the XGBoost models achieved intermediate performance levels between 0.61 and 0.65. MICE showed marginally higher AUC scores than the other imputation methods, with improvements ranging from 0.001 to 0.003 across most model configurations when compared to mean imputation.
## 5 Analysis and Evaluation
### Resolver Count  
We have calculated the HM count deployed to satisfy the transmission latency constraints under different node sizes, and the simlation result is shown in Figure 6. From Figure 6 we can learn that the count of deployed HMs grows exponentially along with the increase of node size. With more distributed nodes to serve, more resolvers are demanded to meet the latency constraints. When the node size is 100, the HM count is 15, and increases to 178 when the node size increases to 3200. To improve the resource utilization rate, we can use the curve in Figure 6 to estimate the number of resolvers required for deployment when the node size is in a large scale, to balance the transmission latency threshold and the computing storage resources required.
## 4 Simulation and Evaluation
### Performance Evaluation
#### 4.2.3 Data Delivery Efficiency  
The success rate is the ratio of number of successfully received data packets at a sink to the total number of data packets generated by a source. This metric shows how effective the data delivery is. Also, it is one of most important parameters of QoS. In some applications such as target tracking, data  
Figure 3: Average Energy Consumption of MHFR, LRER and MCR  
Figure 4: Difference of Average Energy Consumption in MHFR and LQER  
Figure 5: Difference of Average Energy Consumption in MCR and LQER  
delivery efficiency outweighs energy efficiency. Unless enough reliable data is transmitted to the base station, the target can not be well tracked and controlled.  
Figure 6 shows the comparison of success rate in LQER, MHFR and MCR with different node number, where \(k\) equals to 9. It is clear that the success rate in LQER is higher than that in MHFR and MCR, and when node number increases, the variation is small, which indicates a good scalability of data delivery efficiency. The success rate of MHFR and MCR decline as the node number increases and that of MCR declines more quickly. Particularly, when number of nodes equals to 1000, LQER (95.01%) results in percent of success rate that are more than 21.23% higher than those in MHFR (78.37%), and 86.99% higher than those in MCR (50.81%)
## 3 Methodology
### Model Architecture  
Our model, as shown in Figure 2, consists of four modules. In the first module, we conduct entity identification using a Conditional Random Field (CRF) [35]. In the second module, we create a heterogeneous graph to model the interactions among the document, sentences, and entities, utilizing GCN to learn document-level representation. In the final two modules, we detect event types and extract their corresponding arguments, employing the Tracker module for continuous global tracking and memorizing candidate entities.
## 3 Results: The Theoretical Geometrical Model and Its Application for Budapest
### The Theoretical Geometrical Model  
First, the theoretical geometrical model of the city logistics system of concentrated sets of delivery locations (in this case, primarily shopping malls) is defined. This theoretical model can be seen in Figure 11.  
The notation for the theoretical model can be seen below:  
* \(n\): number of vertices (CSDLs) on the central ring (given as polygons)
* \(m_{i}\): number of vertices (CSDLs) on ray \"\(i\)\"
* \(V(i)\): CSDL No. \"\(i\)\" on the central ring; its sum demand is \(Q_{V(i)}\)
* \(W(i,j)\): CSDL No. \"\(j\)\" on ray \"\(i\)\"; its sum demand is \(Q_{W(i,j)}\) (based on this logic, the CSDL on the central ring can be marked as \(W(i,0)\)
* \(I(i,i+1)\): the edge (road section) between \(V(i)\) and \(V(i+1)\) on the central ring; its length is \(D[I(i,i+1)]\), \(i=1\dots n\); \(I(n,n+1)=I(n,1)\)  
Figure 11: Theoretical geometrical model of the city logistics system of the concentrated sets of delivery locations.  
Figure 10: Designation of the radial structure-based geometric model in the case of different urban structures.  
* \(E(i,j-1,j)\): the edge (road section) between \(W(i,j-1)\) and \(W(i,j)\) on ray \"\(i\)\"; its length is \(D[E(i,j-1,j)],j=1\,\ldots\,\,m_{i}\)
* \(R\): the ray of the best fitting circle of the polygon of the central ring In the theoretical model, the length of ray \"\(i\)\" can be calculated by the Formula (1): \[d_{i}=\,\sum_{j=1}^{m_{i}}D[E(i,j-1,j)]\] (1) If the length of all rays is known, the average length of a ray in the examined structure can be calculated by the Formula (2): \[D=\,\,\frac{\sum_{i=1}^{n}d_{i}}{n}\] (2) The average number of CSDLs on one average ray can be calculated by the Formula (3): \[\overline{m}=\frac{\sum_{i=1}^{n}m_{i}}{n}\] (3) The average demand of an average CSDL on the central ring can be calculated by the Formula (4): \[\overline{Q}_{V}=\frac{\sum_{i=1}^{n}Q_{V(i)}}{n}\] (4) The average demand of an average CSDL on ray \"\(i\)\" can be calculated by the Formula (5): \[\overline{Q}_{W(i)}=\frac{\sum_{j=1}^{m_{i}}Q_{W(i,j)}}{m_{i}}\] (5) The average demand of an average CSDL of the examined geometrical model can be calculated by the Formula (6): \[\overline{Q}=\,\frac{\sum_{i=1}^{n}Q_{V(i)}+\sum_{i=1}^{n}\sum_{j=1}^{m_{i}}Q _{W(i,j)}}{n+\sum_{i=1}^{n}m_{i}}\] (6)
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## 4 Discussions
### Yield Variations in Southern US Tiles  
As expected, the modeled GPP did not show much variations for these tiles (14 SKB and 15 SYT), showing no obvious variations in cotton yield among counties either. Within tile 17 RKQ, the modeled GPP explained 50% of variations in cotton yield within a narrow range. For tile 14 SKB with variations in irrigation conditions (Figure 2), there were large variations in modeled GPP ranging from 500 g-C\(\cdot\)m\({}^{-2}\)-year\({}^{-1}\) to 2500 g-C\(\cdot\)m\({}^{-2}\)-year\({}^{-1}\), although these variations were averaged at the county level; the area with center-pivot irrigation (circles) was characterized by high GPP values up to  
Figure 5: The linear relationship between cotton GPP and lint yield for 17 counties in the three Sentinel-2 tiles in 2017.  
2500 g-C-m\({}^{-2}\)-year\({}^{-1}\). Although our study showed the important climatological effect on cotton yield, the region with highest yield was not located in GA where the condition is most ideal because of the short cotton growing season due to crop rotations; instead, the highest yield in our study was shown in MS due to their longer growing season and irrigation. This suggests that our model captured the role of crop management, in addition to the climatological effect.