esa-sceva/satcom-chunk-collection huggingface dataset:
Therefore, in order to cultivate berries with more commercially relevant fruit characteristics, treatments with gibberellic acid (GA\({}_{3}\)) and thidiazuron (TDZ) can be applied at one or more times at different stages of grape berries during their fruit development [15; 16]. GA\({}_{3}\) has been widely used in the cultivation of table grapes due to its multifunctional ability, including the prevention of fruit drop, berry enlargement and the development of seedless fruit [17]. TDZ is a novel phenylurea plant growth regulator with dual auxin and cytokinin like activities, which are capable of inducing cell division and promoting fruit enlargement, thus improving the fruit quality and commodity values [18; 19]. In apple, treatments with TDZ have been shown to alter the apple shape index and cause a reduction in the red surface of fruit and TDZ treatment also increased the percentage of fruit that were asymmetrical and exhibited calyx-end rot [20]. Plant endogenous hormone levels are regulated by TDZ and floriculture applications have shown a benefit for the inhibition of leaf yellowing of cut stock flowers by protecting leaf chlorophyll from degradation after transfer to vaes [21]. Similarly, the immersion of grape berries in a TDZ solution resulted in higher quality fruit and increased firmness of fruit flesh [22]. Collectively, these findings show promise for using TDZ in grape cultivation and warrant additional study for the utility of this compound at a commercial scale. In recent years, researchers focused on the effect of the exogenous application of hormones, environmental conditions and cultural practices on the volatile compounds of grape berries [23; 24]. Limited studies have been conducted to characterize the effect of TDZ application on the volatile compounds, physicochemical composition and taste evaluation of grape berries--especially volatile monoterpneoids [25; 26; 27; 28]. The novel work presented in this study was performed to explore the effect of TDZ application on the production of aroma volatile compounds in grape under field conditions and to identify the key genes involved in the terpenoids biosynthesis pathways that were suppressed by the elicitors. Findings from this study will help to provide a deeper understanding of the volatile composition after TDZ application in Shine Muscat berries, and they will ultimately serve as a foundation for future improvement of berry flavor and overall quality of table grapes and also to establish the relationship between volatile levels, berry flavor and consumer preference.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 3 PM2.5 Time Series Prediction Model  
In order to accurately predict the concentration of PM2.5, this paper proposes a PM2.5 time series prediction model. To handle high-dimensional input variables, the mRMR method is utilized to select optimal subset. Then, the phase space is reconstructed from the subset to obtain evolution information of time series. Finally, we predict PM2.5 time series based on supplemental leaky integrator echo state network (SLI-ESN) model, which is proposed in this paper.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### High-Definition Mapping
#### 3.2.1 Automotive Requirements  
A basic requirement for this use case is that a vehicle needs to be able to accurately determine its position. It also needs to be equipped with sensors (e.g., cameras or lidar) that can identify objects, road signs, etc. Finally, the vehicle needs connectivity to transmit the collected information to the map service provider. On the automotive side, the requirements for this use case are thus rather modest. All the needed functionality is anyway required for autonomous cars, and systems realizing some parts of it--for example traffic-sign recognition--have been commercially available for almost a decade.
## 3 Materials and Methods
### Performance Measures  
The data extracted from the CSV files was analyzed to assess the performance of the LLMs. Two key performance metrics were used in this study: hit rate (i.e., accuracy) and the fallback index. The hit rate represents the proportion of correct answers (based on the official answer key), relative to the total number of questions (as shown in Equation (1)). In addition to calculating this metric, we conducted a descriptive statistical analysis to summarize the results and highlight patterns in model performance.  
\[Accuracy=\frac{Number\;of\;Correct\;Ansvers}{Number\;of\;Questions} \tag{1}\]  
A fallback trigger occurs when an LLM fails to understand the input and provides a response that does not match any of the answer options presented in the question [39]. This type of response can also be interpreted as a form of hallucination [40; 41], where the model generates information that is irrelevant or inconsistent with the prompt. A fallback trigger may also take the form of a task rejection [42], where the model explicitly states that it cannot answer the question or lacks the necessary knowledge (i.e., honesty). The fallback index is defined as the proportion of fallback triggers relative to the total number of questions (as shown in Equation (2)).  
\[Fallback\;Index=\frac{Number\;of\;Fallback\;Triggers}{Number\;of\;Questions} \tag{2}\]  
[PERSON]'s Kappa coefficient [43] was used to assess the level of agreement between the responses generated by the LLMs when the same questions were independently submitted by two researchers. This analysis was performed only for the commercial LLMs, as they were the ones evaluated through manual input by both researchers. Cohen's Kappa is a widely used statistical measure that quantifies the degree of agreement between two raters or methods while accounting for agreement that may occur by chance. Data analysis and result visualization were performed using both Python 3.11.13 and R 4.5.0 languages.  
Figure 1: Initial prompt used in commercial LLMs, translated from PT-BR into English with maximum possible fidelity.
###### Abstract  
The comparison of output powers between self-Raman Nd:YVO\({}_{4}\) lasers and Nd:YVO\({}_{4}\)/KGW Raman lasers operating at lime and orange wavelengths is presented. We exploit the LBO crystal with cutting angle \(\theta=90^{\circ}\) and \(\varphi=8^{\circ}\) for the lime wavelengths, and then we change the angle to \(\theta=90^{\circ}\) and \(\varphi=3.9^{\circ}\) for the orange wavelengths. In self-Raman Nd:YVO\({}_{4}\) lasers, experimental results reveal that thermal loading can impact on the output performances, especially at the high pump power. However, by using a KGW crystal as Raman medium can remarkably share the thermal loading from gain medium. Besides, the designed coating for high reflectively at the Stokes field on the surface of KGW also improved the beam quality and reduced the lasing threshold. For self-Raman Nd:YVO\({}_{4}\) lasers, we have achieved the output powers of 6.54 W and 5.12 W at 559 nm and 588 nm, respectively. For Nd:YVO\({}_{4}\)/KGW Raman lasers, the output powers at 559 nm and 589 nm have been increased to 9.1 W and 7.54 W, respectively. All lasers operate at a quasi-CW regime with the repetition rate 50 Hz and the duty cycle 50%.  
Raman laser; visible light; solid-state laser +
Footnote †: journal: Applied Sciences
## 1 Introduction
Second, the tomography area was narrowed to ensure that side rays could pass from the side of the narrowing region, and the scale factor model was established by analyzing the proportional relationship between the part slant water vapor (part SWV) and height. Consequently, both models fully utilized GNSS signals and improved the accuracy of tomographic results. However, the accuracy of the initial water vapor field is key to computing the high-precision SWD of inside rays, and was neglected in both studies. In this work, to make full use of side observations and obtain the high-accuracy SWD of inside signals, we investigate the isotropic and anisotropic part of the GNSS SWD and propose the height factor model (HFM), where isotropic and anisotropic height factors are innovatively introduced. The former, revealing the relationship between the height and the isotropic zenith wet delay (ZWD), is modeled by 30-year radiosonde data from different months. The latter, representing the ratio of the height to anisotropic gradient delay, is determined by formula deduction. The SWD of inside signals is calculated using the HFM from two components and added into the tomography system. Four experimental schemes are performed separately to test and assess the improved method. The water vapor profiles obtained by the traditional approach and proposed model are compared with those provided by radiosonde data, and the statistical results demonstrate the benefit of including the side signals. In addition, a further comparison of the proposed optimized model and the existing correction model is performed to illustrate the advantages of the proposed HFM in tomography modeling.
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 5 Conclusions  
The proposed AEF scheme demonstrates significant improvements over network lifetimes while maintaining detection capacity and energy efficiency. As false report attacks increase, the relative margin of gain in network lifetime lowers. Moreover, lifetime over a large number of rounds (i.e., FCO, 10 PCO, and LCO) decreases the relative margin of improvement. Generally with an increase in FTR, a smaller network size, and an increase in number of rounds, we observe less improvements in network lifetime; however, this is not a fixed trend.  
In AEF, we used different methods to increase network lifetime which resulted in even distribution of energy consumption loads over the larger group of participating sensor nodes. It can dynamically give matching responses according to the current attack ratio. We calculated current FTR without causing extra messages on sensor nodes. Further, it can be stated that, by carefully designing en-route filtering schemes, not only effective filtering but also network lifetime gain can be achieved.
# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 2 Related Work
According to the criteria from prior work [15], the number of nodes (i.e., about 1000 hospitals) and the number of links (i.e., about 2000 patient transfers) in our study were in the range for a force-directed layout. Nonetheless, we placed the nodes reflecting their actual locations on a map as it was necessary to support the analysis considering the geographical location of hospitals. Further, grouping was visualized through superimposition among the methods summarized in a previous study [16] to demonstrate the group structure derived through community detection. The map was designed to reduce the scope of analysis through various interactions. There were many overlapping nodes with many groups, which cannot be distinguished by color when visualizing them on the map. [PERSON] et al. [17] surveyed visual analytic research of movement and transportation systems. Patient transfers in this study are origin-destination (OD) travel data without start and end times. According to the survey, one commonly aggregates OD data into matrices or flows for visual analysis. While visualizing flows as lines on a map has been widely used [18], the following research worked on resolving visual clutters for more significant numbers of flow. For instance, [PERSON] et al. [19] proposed an OD map in which each cell represents an OD vector as in an OD matrix. It preserved the spatial layout by introducing a spatial treemap, and reduced visual clutters caused by intersecting links. [PERSON] et al. [20] worked on long-term flow data and proposed OD flow aggregation by direction and distance ranges. They used the aggregated flow to cluster time intervals and presented the flow with diagram maps. [PERSON] et al. [21] also adopted spatial simplification of locations to avoid visual clutter in analyzing mass mo bility data. [PERSON] et al. [22] proposed direction-based filtering to address the problem. They introduced an interactive visualization technique called Flowcube, which presents flows as three-dimensional arcs, and offered direction-based filtering to reduce the clutter. [PERSON] et al. [23] proposed an interactive visual analytics system for epidemic control and introduced mouse hover interaction to focus on a specific region instead of visualizing the whole network. We adopted both approaches in visualizing patient transfers. As spatial information was crucial for the target users, we presented flows as lines on a map without any additional visual encodings, which might hinder the perception of spatial information. However, we provided interactive filters to narrow the analysis scope and dedicated views for force-directed node-link diagrams where each node represents aggregated locations (i.e., province).
## 5 Comparison of Different Topologies
### Topology Comparison Based on Overhead and Efficiency  
Flat topology produces the maximum number of packets for routing. In a flat topology, because of flooding, a node can receive multiple copies of same data from different nodes. On the other hand, in cluster-based topology, the cluster heads receive data from all members of the cluster. In tree topology, a parent node receives data from its children node(s). However, in a chain-based topology, a node in a chain receives data from only one node. Thus, in terms of communication overhead, chain-based topology is the best, tree-based topology is the second best, cluster-based topology is good, and flat topology is bad.  
In terms of control overhead, which measures the ratio between control and data messages in the network, flat topology is the best because it does not need to maintain any structure. Thus, this topology does not need to disseminate topology control messages. Tree-based topology, on the other hand, has the largest number of control message overhead to maintain the tree structure. Cluster-based and chain-based topologies also have control message overheads, but much less than that of the tree-based topology.
## 6 Simulation and Experiments  
The Distributed Building Evacuation Simulator (DBES) [2] that we use is an agent-based discrete event simulator, where each entity is modelled as a process, and the physical world is represented by a set of \"points of interest\" (PoI), which are significant locations, such as doorways and corridors, and links, which are physical paths. To improve the level of realism, we employ the congestion model introduced in [50; 51] to simulate the chaotic collective motion. Each PoI where evacuees can congregate is modelled as a single server with one queue. This mimics a narrow space where only one civilian can depart at a specific time. The service time represents the time cost for a civilian to obtain the suggestion from portable devices or visual indicators. In the simulations, we set the service time to one second. When an evacuee arrives a the PoI, it will not be served until all the evacuees that came earlier have departed.  
The environment is the lower floors of Imperial College's EEEbuilding shown in Figure 3. SNs and DNs are deployed in proximity to the PoI. SNs collect hazard information, and DNs execute the decision support algorithms. Real-time decisions can be transformed to evacuees with visual indicators that are installed at the POI or portable devices carried by the civilians. To evaluate the performance and adaptability of decision algorithms, we assume a fire outbreaks near the eastern staircase on the ground floor that blocks a main channel. Initially, evacuees are randomly scattered in the building, and they can hear or see fire alarms and evacuate immediately. Five routing algorithms are employed: [PERSON]'s shortest path algorithm, the CPN-based algorithm with the distance metric, CPN with the time metric, CPN with the safety metric and CPN with the energy metric. In first scenario, when the fire spreads, global hazard information will be synchronized in the network, and [PERSON]'s shortest path algorithm will be executed at each decision node to obtain the up-to-date shortest path. The other scenarios are similar to the first one, but use the CPN based algorithm to collect information and make decisions.  
Figure 2: The average number of survivors out of 120 evacuees with different movement depths in the distance-based system. The error bars show the standard deviation of 10 experiments.
# Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch
## 5 Simulations
### Comparison Study  
In this section, we focus on the performance comparison of the algorithms, covering aspects of the number of iterations and execution time. The comparison counterparts include the centralized version of Algorithm 1, termed centralized, and the widely used decentralized ADMM algorithm, termed DADMM. We utilized a modified IEEE 118-bus system with 54 generators. To facilitate the performance comparison, we focused on a single time period dispatch problem, considering only generators with quadratic cost functions while omitting factors such as power losses. In the following simulations, the residual is defined as \((1/N)\sum_{i=1}^{N}\left\|\lambda_{i}^{k}-\lambda^{*}\right\|\) to reflect the gap between the optimal result and the one obtained by the three methods. We employed the same communication network for Algorithm 1 and DADMM, ensuring a fair comparison. In this network, each edge has a probability of existence of 0.5. Both algorithms were manually adjusted to achieve optimal convergence performance.  
Figures 3 and 4 respectively illustrate the convergence with respect to the number of iterations and time for these three algorithms. The stopping criteria were set to reach a residual of \(10^{-5}\). In Figure 4, we observe that to achieve the same level of accuracy, the centralized algorithm requires the fewest iterations, while within the decentralized algorithms, the DADMM algorithm converges in fewer iterations compared to Algorithm 1. Figure 5 indicates that for achieving the same level of accuracy, the centralized algorithm still outperforms in terms of time, but Algorithm 1 is more time-efficient compared to the DADMM algorithm. Table 3 provides the specific number of iterations and time required for the three algorithms to reach a residual of \(10^{-5}\).  
Figure 4: Evolution of residuals with number of iterations.  
Figure 5: Evolution of residuals with time.
## 3 Problem Formulation
### Mathematical Formulation
An auxiliary variable, \(t_{slt}\), is used to represent the relationship between link \(l\) and path \(p\) for multicast group \(s\). That is, if link \(l\) is selected by destination \(d\) in multicast group \(s\), making \(t_{slt}\) equal to 1, it must also be on the path adopted by destination d in multicast group \(s\). Constraint (1.4) means that only one path can be selected for a destination \(d\) belonging to a multicast group \(s\). Constraint (1.5) confirms that if one path is selected for destination \(d\) belonging to multicast group \(s\), it must also be on the subtree adopted by multicast group \(s\). For example, if a link is selected for two different destinations in a multicast group, the left side of Constraint (1.5) should be 2. The right side of Constraint (1.5), is therefore used to make sure that the selected link must also be selected by \(y_{sl}\) at most for all destinations in the multicast group, which is equal to the right side of Constraint (1.5). The other auxiliary variable, \(g_{sl}\), is used to stand for the collocated traffic flow in the link \(l\) for multicast group \(s\). Therefore, Constraint (1.6) ensures that the maximal traffic flow passing through the link \(l\) in group \(s\) does not exceed the link capacity allocated for link \(l\) in multicast group \(s\). Constraint (1.7) gives a discrete range of traffic rates for each \(g_{sl}\), which is from 0 to the maximal bandwidth requested by a destination \(d\), which belongs to multicast group \(s\). Constraint (1.8) confines that for each link \(l\), the sum of the allocated capacity on the link \(l\) among all multicast groups does not exceed the link's allocated capacities \(c_{l}\). Constraint (1.9) confines that for each node \(v\), sum of the allocated capacity of all out-coming link \(l\) from node \(v\) does not exceed the node's physical capacities \(C_{v}\). Constraint (1.8) and Constraint (1.9) also shows the contenting relationship among groups in wireless sensor network. Constraint (1.10) confines that the total number of links in the multicast tree rooted at source s is at least the maximal value chosen from the height of the multicast tree, \(H_{s}\), and the number of destinations \(D_{s}\). It also ensures that the number of all the links in a multicast tree \(s\) should exceed the number of the destinations. Constraints (1.11) and (1.12) are both redundant constraints. Constraint (1.11) requires the number of selected incoming links \(y_{sl}\) to node is 1 or 0. Constraint (1.12) requires that there is no selected incoming links \(y_{sl}\) to node that is the source of multicast group \(s\). Constraints (1.13), (1.14) and (1.15) are the integer constraints.
# Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services
## 5 Conclusions  
In this paper, we have presented a novel routing algorithm for achieving fast internet access, thereby enabling users to enjoy a superior online experience. As the ISL is known to be less stable compared to ground network equipment, it is essential to avoid multi-hop transmissions via the ISL. To address this issue, we first established a formal model and calculated the theoretical minimum hops between two given satellites. Then, we proposed a linear programming to match the satellites between the landing satellite and the non-landing satellite. Finally, only the routes between the satellites in the matching set need to be calculated. In the simulation section, we leveraged the OPNET modeler to construct a large-scale LEO satellite network by referring to the Starlink network configuration, and used it to evaluate the performance of the proposed algorithm. The simulation results indicate that the IFAR algorithm can significantly enhance the internet access speed and substantially decrease the network packet loss rate.  
Conceptualization, Z.Y. and F.T.; methodology, Z.Y. and F.T.; software, Z.Y. and J.J.; validation, F.T. and H.L.; formal analysis, Z.Y.; investigation, Z.Y.; resources, F.T. and H.L.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, F.T.; visualization, Z.Y.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Innovation Promotion Association of Chinese Academy of Sciences (grant number 2021291).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 7: The average packet loss rate of users under different network loads.