esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change
Many studies [29; 30; 31; 32] have indeed shown that climate change will impact both the phenology by changing the date for the beginning and the end of the growing season, as well as by changing the shape of the Leaf Area Index (LAI) distribution over the year, which, at the same time, will influence the way, among the other things, when photosynthesis can start and how recent and old photosynthates are partitioned and used to build new tissues and to replenish the reserves used for the metabolism, as well as carbon allocation [33] and the C-dynamic. Process-based models are valuable tools to understand how and to what extent future climate change will impact these two fluxes (GPP and R\({}_{\text{eco}}\)) in the MSC, both processes being controlled by warming and changes in precipitation regime and atmospheric CO\({}_{2}\) concentration [34; 35]. Here, we at first applied and validated, under current observed climate conditions, the 'Three Dimensional-Coupled Model Carbon Cycle-Forest Ecosystem Module' (3D-CMCC-FEM), a biogeochemical, biophysical process-based forest ecosystem model designed to simulate carbon, nitrogen, and water cycle in forest ecosystems and, secondly, under climate change conditions. Specifically, we question and analyze: (1) the capability of the 3D-CMCC-FEM to represent under the current climate the main C-fluxes governing the C-cycle in terms of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (R\({}_{\text{eco}}\)), by validating the model against independent data from the Fluxnet network; and,provided that the model is close to the observed data; (2) how, and if, the sink/source mean seasonality will be influenced under two locally bias-corrected scenarios of warming (RCP 2.6 and 6.0) and atmospheric CO\({}_{2}\) enrichment from three CMIP5 Earth System Models, within ISIMIP-PROFOUND initiative, in five well-studied and long-monitored contrasting forest sites (three evergreens and two deciduous) on a longitudinal transect through Europe up to the end of the century.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 4 Performance Evaluation  
In this section, the performance of SDNPS is evaluated through comparison with an SDN with traditional switches. The performance measures include the average throughput, packet loss ratio, and packet delay for mMTC and URLLC network slices.
## 5 Challenges and Open Research Directions
### Scalability in Large-Scale Networks  
While Holochain offers scalability improvements over blockchain, challenges remain when scaling it across vast IoT networks, (e.g., smart cities with 10,000+ devices or large industrial applications) which might involve millions of devices generating terabytes of data daily. Future research directions include the following:  
* Optimizing DHT performance to efficiently manage high-throughput environments and reduce lookup latency.
* Implementing adaptive sharding mechanisms to distribute workload dynamically and improve query efficiency, for instance, by dynamically partitioning the DHT by location or device type.
* Edge computing integration to process data locally, reducing latency and bandwidth consumption before storing data in the DHT.
* Developing load-balancing techniques to prevent hotspot nodes and ensure uniform data distribution across the network.
* Exploring hybrid approaches that combine Holochain with edge and cloud computing for efficient large-scale deployment.
# Recent Updates on the Synthesis of Bioactive Quinoxaline-Containing Sulfonamides
## 2 Synthesis and Biological Activities
### Synthesis of Quinoxaline Sulfonamide Derivative with Neuropharmacological Activity  
The activity which determines how drugs or therapeutic agents affect the cellular functions in the nervous system is called neuropharmacological activity. The neuropharmacological effects, such as analgesia, sedation, convulsion, anxiety, memory and psychosis, and the neural mechanisms through which the drugs influence behavior were studied in neuropharmacology.  
[PERSON] et al. reported the green microwave synthetic route to quinonoxaline sulfonamide with neuropharmacological activity. The 2,3-quinoxalinedione **71** was furnished in an excellent yield (99%) by the condensation of **34** and oxal acid dihydrate (**70**) using microwave radiations. The scaffold **71** was treated with **47** to obtain quinoxaline-6-sulfonyl chloride **72** at 88% yield, followed by the reaction with dibenzyloamine in anhydrous dimethylformamide (DMF) to give corresponding quinoxaline sulfonamide **84** at 75% yield (Scheme 7) [133].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Antimicrobial Activity**} \\ \cline{2-7}  & \multicolumn{6}{c}{**Zone of Inhibition (mm)**} \\ \cline{2-7}
**Compound** & \multicolumn{2}{c}{**Gram-Positive Bacteria**} \\ \cline{2-7}  & \multicolumn{2}{c}{_S. aureus_} & \multicolumn{2}{c}{_Enterobacteria_} & _V. cholera_ & _E. coli_ & _P. vulgaris_ \\ \hline
81 & 19 & 24 & 2 & 22 & 30 \\
82 & 18 & 18 & 16 & 16 & 16 \\
83 & 8 & 8 & 6 & 6 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Antimicrobial activity of quinoxaline derivatives **81–83**.  
Figure 12: Substituted quinaxoline sulfonamide derivatives **81–83**.  
The obtained derivative **84** was analyzed for its neuropharmacological effects, such as anxiolytic, anticonvulsant and total locomotor activity, in mice (Table 7). It showed maximum inhibition of locomotor activity (sedative action) at 40 mg/kg. As for anxiolytic activity, **84** exhibited a maximum action at 2.5 mg/kg, having 67.3% time in open arm and 81.0% entry into open arm, and the index of open arm avoidance was 25.9 when compared with diazepam at 1 mg/kg (53.3% = time in open arm, 82.4% = entry into open arm and 32.2 = index of open arm avoidance). As for anticonvulsant activity, **84** displayed a protection effect of 100% against both leptazol at 80 mg/kg and strychnine at 2.0 mg/kg after 60 min at 25 mg/kg dose, when compared with phenobarbittone sodium, having a 90% protection effect at 20 mg/kg dose against both leptazol at 80 mg/kg and strychnine at 2.0 mg/kg after 60 min [133].
# Seismic Hazard Evaluation and Strain Dynamics in the
###### Abstract  
This study aims to determine the earthquake hazard of the Simav Fault Zone (SFZ), one of the key tectonic features of Western Anatolia--a region that serves as a natural laboratory for geoscientists due to its complex tectonic structure and swarm-type seismic activity generated by multiple active graben systems. For this purpose, the \(a\) (6.33) and \(b\) (0.92) seismic parameters based on the Gutenberg-Richter magnitude-frequency relationship were first calculated using M \(\geq\) 3 earthquakes that occurred between 1900 and 2024 along the SFZ. Moreover, the recurrence periods of events with magnitudes between M = 5 and M = 7.1 were determined (ranging 32 and 982 years), and the seismic hazard levels in the region were identified. The spatial density of the earthquakes and the regional distribution of the energy they released were analyzed, and the variations in seismic activity along the fault and energy flow in the region were investigated. For the evaluation of past earthquakes with the current strain field of the region, using geodetic velocity data, the strain rates of each segment of SFZ were calculated (reaching 90 ns/year) and findings related to stress accumulation processes were obtained. The findings, along with previous events, indicate that the region is susceptible to seismic hazards and that even moderate earthquakes pose a significant threat to both infrastructure and the population. This study, which seeks to enhance the understanding of seismic hazards and regional strain accumulation in the SFZ, is expected to serve as a valuable tool in seismic hazard assessments and local disaster management strategies, and to provide a critical reference for decision-makers in effective earthquake risk management.  
Academic Editor: [PERSON]  
11 February 2025  
Revised: 6 March 2025  
Accepted: 8 March 2025  
Published: 11 March 2025  
**Citation**: [PERSON],[PERSON].  
Seismic Hazard Evaluation and Strain Dynamics in the Simav Fault Zone: A Comprehensive Analysis of  
Earthquake Recurrence and Energy Release Patterns. _Appl. Sci._**2025**, 15, 3039. [[https://doi.org/10.3390/app15063039](https://doi.org/10.3390/app15063039)]([https://doi.org/10.3390/app15063039](https://doi.org/10.3390/app15063039))  
**Copyright**: 62 2025 by the authors.  
Unionse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Experimental Results  
### Calibration Experiments  
Figure 7 shows the experimental configuration used for calibration experiments. The optical freeform surface is fixed on a stationary platform and the slope sensor is fixed on the spindle of linear moving stages. The slope sensor moves along the \(x\) or \(y\) direction above the rotating paraboloid by the driving of the spindle, and the relative displacement is measured by a laser interferometer (Renishaw, Gloucestershire, UK) with linear measurement precision of \(\pm\)0.05 ppm.  
Because the freeform surface is rotationally symmetric, it can be calibrated in any direction along the freeform surface. The slope detector selects any point on the paraboloid as the starting point. Then, the spindle of linear moving stages drives the slope sensor to move along a fixed direction with a step size of 20 \(\upmu\)m, and the relative displacement of the slope sensor is detected by a laser interferometer. Figure 8 shows the relationship between the relative displacements of the light spot detected by the image sensor and the values measured by the laser interferometer.  
Figure 8: Parameter calibration.  
Figure 7: The experiment of displacement measurement.  
In actual experiments, the slope sensor was calibrated in four different directions on the rotating paraboloid. The experimental results show that the relative displacement of the center point of the imaging spot is linear with the relative displacement of the slope sensor. The relationship between the relative displacement value of the slope sensor and the relative displacement value of the center point of the imaging spot could be obtained by linear fitting of the measured data. The results of linear fitting are shown in Table 2.  
This can be expressed as the following equation:  
\[l=0.68l^{\prime} \tag{15}\]  
where \(l^{\prime}\) is the relative displacement of the center point of the imaging spot and the unit in pixels, and \(l\) is the relative displacement of the slope sensor and the units are in micrometers.  
Because the surface of the rotating paraboloid is rotationally symmetric, the ratio of the relative displacement of the slope sensor and the relative displacement of the spot in the image sensor is a fixed value along any direction. It can be said that the above calibration results can be used in any direction of the rotating paraboloid.
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 4 Acute Life-Threatening Asthma Advanced Management in the ICU
### Mucolytics
#### 4.8.1 Nebulised Heparin  
Heparin is an endogenous glycosaminoglycan used primarily for its anticoagulant properties. Inhaled heparin has previously been proposed as an adjunct to treat severe asthma due to speculation regarding its possible anti-inflammatory effects. Heparin has been shown to bind and inhibit a variety of cytotoxic and inflammatory mediators, including inhibition of mast cell activation and mast cell mediators along with inhibition of eosinophil cell migration and cationic proteins [127]. Inhalation of heparin is likely to suppress the initial reaction to allergens and exercise-induced asthma by preventing the release of mast cell mediators [127; 128]. Although several early studies in the 1960s and later in the 1990s reported subjective improvement in asthma symptoms, objective evidence for clinical improvement has not been demonstrated [129; 130]. Two case studies have been reported showing a benefit of nebulised heparin administered early in the course of asthma exacerbations; however, little can be gleaned from these in terms of wider clinical effects [127]. A meta-analysis published in 2023 examined 23 studies in their initial qualitative analyses, reducing this to eight studies (220 patients) for the primary outcome measure of improvement in respiratory function (FEV1% and FEV1). A statistically significant improvement in FEV1 suggesting that a trial of inhaled heparin may prove beneficial in addition to standard therapy was found. Interestingly, upon subgroup analysis, it was found that unfractionated heparin was more effective than low-molecular-weight heparin for this indication [128]. Further large randomised controlled trials are needed to evaluate the prophylactic and therapeutic effect of nebulised heparin in mechanically ventilated asthmatic patients.
## 7 Related Work  
Sistla _et al_. [11] first identify the importance of the spatio-temporal queries and describe modeling methods and query languages for the expression of these queries. They further attempt to build a database of moving objects, called the DOMINO [12], on top of existing DBMS. The main focus of DOMINO is to support new kinds of spatio-temporal attributes and query language for moving objects. However, the issues of processing the spatio-temporal queries are not exclusively addressed by the above works.  
Figure 11: Effect of \(K\).  
Figure 10: Effect of \(T_{u}\).  
Recently, several methods have been designed to efficiently process the spatio-temporal queries over moving objects.  
Kalashnikov _et al_. [3] propose a method operated with R-tree and Quadtree to efficiently process range query. Tao _et al_. [4] propose a repetitive query processing approach with a TPR-tree for answering the range and _K_NN queries. Benetis _et al_. [13] develop an approach to find the nearest neighbor through a depth-first traversal of TPR-tree. To adapt to moving objects and reduce computational cost, Raptopoulou _et al_. [14] propose efficient methods that process the _K_NN query by tracing the change of the _K_th NN. Iwerks _et al_. [15] answer a _K_NN query through a method that a range query is first processed and then _K_NN result are determined among the objects qualifying the range query. If the number of objects that satisfy the range query is greater than \(K\), the answer to _K_NN query can be obtained from these objects. Song _et al_. [16] propose an approach in which the snapshot _K_NN query is re-evaluated at each time instant. Their approach is restricted in processing the _K_NN query over static objects. Nehme _et al_. [17] extend the approach in [16] to adapt to moving object datasets. They utilize the concepts of shared execution and incremental evaluation to process range query on moving objects. Recently, the technique of incremental evaluation has been further adopted by Mouratidis _et al_. [6], Xiong _et al_. [18] and Yu _et al_. [19] to answer the _K_NN query.  
The above _K_NN methods are developed for the moving objects, in which the locations of objects are obtained through the GPS technique, instead of the sensor network technique. Moreover, all the mentioned methods focus on processing the _K_NN query under the situation that moving objects have precise locations. This means that these methods are inapplicable to the _K_NN query with uncertainty as proposed in this paper (where a probabilistic method is needed). Cheng _et al_. [10] propose a PNNQ approach for processing the _K_NN query over objects with uncertainty, which is similar to the problem tackled in this paper. However, there are significant differences compared to our work, which are described as follows: (1) the uncertainty model in [10] is a _circle_ or a _line segment_; (2) no grid index is used in [10]; and (3) the probability model in [10] is based on numerical integration.
# Situationally Sensitive Path Planning
## 2 Related Work
### Path Planning Heuristics  
Path planning models for pedestrian movement generally take a heuristic approach to treating both space and pedestrians as users of that space. In planners, space is usually represented using a graph that is mapped to the metric space of a design by absolute means: nodes on the graph represent discrete locations in the counterpart real space (or designed space), while the edges of the graph correspond to real metric distances (or the effort required to traverse them), following conventions from algorithm design in computational geometry [34]. Heuristic approaches allow for a greedy search of the graph to find an optimal solution to a problem that is confined within the space. Generally, the problem is formulated as finding the shortest (or least-effort) metric-distance _and_ graph-distance path from an origin to a destination [35]. Once this path is found, a model pedestrian is moved,node by node, along the graph edges of the resolved path, and the nodes may be weighted to produce different speeds of movement.  
[PERSON]'s path planning algorithm is among the most popular schemes (we use it partially in our approach later in the text (Section 4.1.1), and we provide a detailed explanation then). It will find the shortest path from an origin to every node in the graph space, and these paths then become available for guidance for model pedestrians as they move. The positions of existing pedestrians in the space can be embedded in the graph so that those locations are removed from consideration, and this can be achieved via an update cycle ahead of movement so that it resembles real-time collective movement patterns. The equally popular A* heuristic [35] adapts this approach primarily by allowing for the pruning of unlikely-to-be-visited paths from the search based on a reference to the currently estimated progress in advancing to the destination.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 4 Compensation Control Strategy for Delayed Information  
In this section, we present a compensation method for the time delay caused by the measurement and estimation process. By adopting this method to modify the guaranteed cost strategies given in the third section, we get the compensation control strategies.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 3 The Architecture Proposal
### Network Architecture Overview  
A well-established design principle is the end-to-end argument [24]. Based on this principle, the network complexity has to be concentrated in the network edges, making the core simple from the perspective of network management. Thus, the following proposed architecture applies this design principle, and the programmable devices (specifically OpenFlow switches) are positioned at the edges of the network. Hence, two options for the core might occur:  
* **Legacy network**: With the legacy network in the core, the SDN controller is the centralization logic that acts as _Route Server_ for the BGP routers. Figure 1 depicts a scheme for mixing OpenFlow switches and legacy network appliances. The legacy core is connected through internal BGP (iBGP) sessions to the OpenFlow Controller, which distributes external routes to the internal BGP routers. The infrastructure routes continue to be distributed with the interior gateway protocols (such as OSPF) executed in the _Legacy core_ perimeter.
* **OpenFlow network**: In the future, it is expected that all network devices will have some SDN technology embedded inside an AS. Thus, a domain with SDN technology (OpenFlow devices) is straightforward, and Figure 2 depicts this scenario. All OpenFlow switches are connected to the OpenFlow Controller, which manage the flows inside the AS domain.  
Figure 1: Scheme for backward compatibility with legacy network.
## 4 Experimental Results  
### Overview of Model Performance and SHAP Analysis  
#### 4.1.1 Model Comparison and Performance Metrics  
The first boxplot shows the Root Mean Squared Error (RMSE) results for each of the predictive models applied in this study. RMSE is a key performance metric that quantifies the difference between the predicted values and the actual values, with lower RMSE values indicating better predictive accuracy [32].  
As shown in Figure 3, the XGBoost (XGB) model achieves the lowest RMSE compared to the other models, which signifies that it has the highest predictive performance. Other models such as Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Linear Regression (LM), K-Nearest Neighbors (KNN), and Decision Tree (DT) were also evaluated. However, due to XGBoost's superior performance, it was selected for further analysis in this study.
## Appendix B Interpolation-Based Data-Augmentation Protocol for Expanding Sparse Process Parameters
The model's performance is then re-evaluated on the validation set by computing the updated MSE. If the inclusion of the new batch results in a lower validation error compared to the previous baseline, the batch is permanently incorporated into the labeled dataset; otherwise, it is discarded. This cycle continues until all candidate samples have been assessed or a predefined performance threshold is achieved. Ultimately, the model with the lowest validation MSE is selected as the final model, and the corresponding refined labeled dataset is output for further use. In active learning, selecting the evaluation standard is crucial. An analysis of several models--decision tree, linear regression, and MLPRegressor--revealed differences in feature importance and model reliability. Notably, the decision tree model proved most robust; its performance deteriorated when nearly any feature was removed, indicating effective utilization of the entire feature set. In contrast, linear regression exhibited minimal change with most feature removals, suggesting reliance on only a few key features, while the MLPRegressor experienced negative R\({}^{2}\) drops when certain features were omitted, implying suboptimal use of the full feature set. Consequently, the decision tree model is employed for active learning and further analysis due to its consistent and comprehensive performance. The entire process is implemented using the decision tree model with default parameters from the scikit-learn library. A total of 86 datasets were ultimately selected, and the MSE of the decision tree model for the validation set and the test set were 53.6106 and 54.5309, respectively.