esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Results  
In this section, the modeling methodologies presented in the previous sections are compared. In Section 7.1, the surface charge accumulation trend for a 2D axisymmetric HVDC-GIS test chamber is examined. In Section 7.2, the ionization effect upon the insulating gas of a multi-conductor TL for the AGPS is evaluated.  
The models are implemented within the commercial Finite Element Software (FEM) COMSOL(r)Multiphysics 6.2. In particular, the _Electric Currents (ec)_ module is applied for the whole computational domain, with appropriate boundary conditions along the HV and LV electrodes. To add the DDR model, different possibilities are proposed in the literature. One is to use the _Weak Form (w)_ as described in [31], or the _Transport of Diluted Species (tds)_ module as pointed out in [30]. The latter approach is advantageous for the implementation of the boundary conditions to be applied to \(n^{+}\) and \(n^{-}\) since they are directly available within the _Open Boundary_ options.  
As described in [30], the _tds_ is coupled with the _ec_ for the electric field evaluation. In principle, this step requires a fictitious \(\sigma_{G}\) to be specified in the gas, usually set to be a very small constant value. The coupling between the _tds_ model and the _ec_ occurs at the gas-solid insulator interface where the charge accumulation phenomenon proceeds. Therein, a _Boundary Current Source_ realizing  
\[\mathbf{n}\cdot(\mathbf{J}_{I}-\mathbf{J}_{G}), \tag{19}\]  
with \(\mathbf{J}_{G}\) given by Equation (16), is added to the model.  
It is worth noting that, even though \(\sigma_{G}\) is set to a very small value, a post-processing analysis of the electric field/current characteristic of the gas clearly shows that this assumption is not fulfilled and the gas conductivity follows a nonlinear field-dependent pattern, supposing that temperature and gas pressure are kept constant, even when steady state is reached in a transient time-domain simulation.
# RBC Storage Lesion Studies in Humans and Experimental Models of Shock
## 10 The Effects of Intravenous Iron versus Blood Transfusion in Sepsis  
We next compared the effects of infusion of intravenous (IV) iron to transfusion of blood in our canine model of sepsis [17]. Clinically, intravenous iron is widely utilized in intensive care units and has been marketed as a safer alternative to RBC transfusion [18; 19]. We reasoned that if longer stored RBC hemolyzed in vivo and released iron which augmented the growth of the bacterial challenge, a similar effect might result from direct intravenous iron administration. We compared transfusion of fresh RBCs to two forms of commercially available intravenous iron: iron sucrose, which is an older preparation and the most widely used preparation worldwide, and ferumoxytol, a novel preparation utilizing an iron oxide nanoparticle with a polyglucose sorbitol carboxymethylester coating designed to minimize immunological sensitivity. In our canine model of mild anemia and pneumonia, both iron preparations resulted in similar statistically significantly increased mortality and pulmonary toxicity in comparison to transfusion of fresh 7-day-old RBC [17]. These data suggest that in states of established infection both transfusion of old stored RBC and intravenous iron infusion should be used with caution.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 4 Proposed MS-FL Framework
### Inter-Server Model Aggregation
Distribute current model \(\text{w}^{(\text{s})}(\text{t})\) to the selected clients
#Collect and aggregate clients' local models
9. \(\text{w}^{(\text{s})}(\text{t}+1)\leftarrow\) UpdateWeights(\(\text{w}_{\text{i}}(\text{t}+1)\))
10. endfor
```  
**Algorithm 2** FL server operation in MS-FL using SPAA/DWAA  
Table 2 summarizes the key strengths and limitations of each proposed aggregation method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proposed** & & & \\
**Aggregation Method** & **Core Mechanism** & **Strengths** & **Weaknesses** \\ \hline BA & Evaluates all peer models on the local validation set and selects the single best-performing model to multicast. & No need for client feedback or fine-grained weighting. & Discards all but one update—loses potentially useful information. Can be brittle if the “best” model is an outlier. \\ \hline SA & Iteratively incorporates each peer model: computes a weighted average with the current model, accepts the update only if it improves validation accuracy, then moves on to the next peer. & Guards against degradations—only beneficial updates are adopted. & Order-dependent: later peers may never be applied if an early update saturates performance. Can be slow, since each candidate requires a full validation pass. \\ \hline DWAA & Assigns each peer model a weight proportional to its validation accuracy and performs a single weighted average over all peer updates. & Utilizes information from every peer—more robust than picking just one. & No explicit outlier filtering—poor or malicious models can still influence the result. \\ \hline SPAA & Extends DWAA by computing a z-score on validation loss (or accuracy), converting large deviations into a sigmoid “penalty,” and then weighting each model. & Guards against outliers or malicious updates. Yields faster convergence, especially when model quality varies widely. & In highly non-IID settings, legitimately diverse models may be over-penalized. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative summary of aggregation strategies in MS-FL, highlighting each method’s core mechanism, key advantages, and potential limitations.
# Promotion Effects of High-Speed Rail on Urban Development: Evidence from Three Lines in China
###### Abstract  
Amid the vigorous development of the high-speed rail (HSR) network, local governments in China generally consider the construction of HSR as a crucial task in their regional development strategies. Currently, most provincial capitals and prefecture-level cities in the eastern and central regions of China already have operational HSR services. This study aims to examine a key question: has the objective of local governments to promote urban development through the construction of HSR been effective? The research selects cities along the Beijing-Shanghai, Beijing-Guangzhou, and Harbin-Dalian HSR lines as the study subjects. Based on the principles of proximity and similarity, cities with operational HSR and those without are chosen as the experimental group and the control group, respectively. Following the double difference (difference-in-differences) approach, an advantage index is proposed to systematically evaluate the impact of HSR operation on urban development from three dimensions: population aggregation, economic development, and expansion of construction land. Furthermore, the evaluation results are systematically clustered to identify city types that exhibit different promotional effects in various dimensions. The research findings indicate the following: (1) The promotion effect of HSR on the development of small to medium-sized cities is more reflected in economic growth and construction land growth. (2) The promotion effect of HSR on the development of large cities is more reflected in the growth of the employment population. (3) For smaller or economically less-developed cities, HSR may be detrimental to the aggregation of resident and employment populations. (4) Cities with moderate size and good economic development have the opportunity to use HSR to promote population aggregation. On this basis, combined with the findings above, strategies to promote the coordinated development of high-speed rail construction and urban systems are discussed.  
high-speed rail; urban development; promotion effect; advantage index; China +
Footnote †: journal: Applied
## 2 Nutritional Characteristics
### Minerals  
Artichoke and its by-products represent a good source of minerals necessary for organisms [38]. Compared to the mineral composition of other vegetables reported in the literature, artichoke represents a good source of K and mainly Fe and Zn (Table 2). Deficiency of these minerals can lead to various disorders, such as anemia [39] or neurological [40] and immunological alterations [41]. The concentration of macro- and microelements varies depending on the botanical part of _C. scolymus_ L. [42], but, generally, all parts of artichoke are able to cover the mineral requirements. Also, the genotype and environmental factors play an important role in the mineral content [43]. On the other hand, the Na/K ratio of artichoke (0.1) [43] is one of the lowest among other vegetables such as carrot, cabbage or spinach. Increased values of this parameter are associated with increased blood pressure and cardiovascular disease [44]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nutrient** & **Value** & **Ref.** \\ \hline Energy & (kcal/100 g FW) & 79.73 \(\pm\) 0.04 & [24] \\ Moisture & (g/100 g FW) & 74.54 \(\pm\) 0.21 & [27] \\ Ash & (g/100 g DW) & 6.88 \(\pm\) 0.14 & [27] \\ Proteins & (g/100 g DW) & 24.27 \(\pm\) 0.12 & [26] \\ Carbohydrates & (g/100 g DW) & 56.62 \(\pm\) 1.41 & [31] \\ Fats & (g/100 g DW) & 2.06 \(\pm\) 0.05 & [27] \\ SFAs & (\%) & 53.2 \(\pm\) 0.5 & [24] \\ MUFAs & (\%) & 2.26 \(\pm\) 0.05 & [24] \\ PUFAs & (\%) & 44.5 \(\pm\) 0.6 & [24] \\ \hline \hline \end{tabular} DW: dry weight FW: fresh weight; MUFAs: monounsaturated fatty acids; SFAs: saturated fatty acids; PUFAs: polyunsaturated fatty acids.  
\end{table} 
Table 1: Approximate composition of the edible part of artichoke. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Composition** & **Head** & **Leaves** & **Bracts** & **Stem** \\ \hline Minerals & & & & \\ Macroelements & & & & \\ \hline K & (mg/100 g DW) & 2160 [1] & 506.3 [2] & 1540 [1] & 2100 [3] \\ Ca & (mg/100 g DW) & 380 [1] & 386.9 [2] & 310 [1] & 290 [3] \\ Mg & (mg/100 g DW) & 200 [1] & 220 [2] & 130 [1] & 80 [3] \\ Na & (mg/100 g DW) & 100 [1] & 194.4 [2] & 80 [1] & 130 [3] \\ \hline \hline \multicolumn{5}{l}{Microelements} \\ \hline Zn & (mg/100 g DW) & 3.14 [1] & 2.1 [2] & 2.09 [1] & 4.16 [4] \\ Fe & (mg/100 g DW) & 3.69 [1] & 1.6 [2] & 4.81 [1] & 3.1 [3] \\ \hline \hline \end{tabular} DW: dry weight FW: fresh weight; MUFAs: monounsaturated fatty acids; SFAs: saturated fatty acids; PUFAs: polyunsaturated fatty acids.  
\end{table} 
Table 2: Micronutrients and bioactive compounds of diverse botanical parts of artichoke.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 3 Simulation Environment and Settings
### Simulation Parameter Settings  
The parameter settings for our simulations remain the same across all four routing protocols. This ensures that comparisons of these routing protocols can be made adequately and accurately. We set the simulation parameters up and configure the network appropriately. To study the impact of heterogeneity on system performance, we consider the mobility of various node types, data transmission speed, and transmission ranges of all the nodes in the network. The following nodes are considered in this study.  
Figure 2: The network model comprises PDAs, laptops, and mobile phones.  
* Fixed PDAs (no mobility).
* Laptops (low mobility), speed ranging from 1 to 2 Mbps.
* Mobile phones (high mobility), speed from 10 to 15 Mbps
* Radio transmission ranges of all the nodes in the network (2 to 5 mW).
# A Web-Based Application for Smart City Data Analysis and Visualization
## 3 Methodology: System Architecture and Design
### Data Visualization  
The visualization layer is built by leveraging Leaflet [66] for interactive maps and Plotly [67] for dynamic, responsive charts. Time-series plots are rendered using smoothed lines and scatter formats to depict trends over time, with visual cues. Standard and stacked bar charts effectively visualize categorical variables, such as vehicle types or sensor zones, to communicate proportional distributions. Box plots are introduced to represent statistical ranges and highlight variability across sensor categories, while histograms illustrate frequency distributions. Correlation heatmaps are also generated to show the interrelationships between sensor variables, providing insights into how environmental and infrastructural parameters interact within urban zones. Every plot is customized with readable labels, intuitive legends, and responsive hover interactions to ensure accessibility and clarity for non-expert users (see Figure 2).  
These visualizations are integrated into the web application's front end, where JavaScript modules handle dynamic data fetching, real-time updates, and the interactive rendering of charts and maps, thus ensuring seamless synchronization between client-side elements and the processed datasets. This setup allows charts and interface components to refresh as new or updated data becomes available. Moreover, temporal values are transformed into browser-compatible date objects to ensure accurate rendering on time-based charts. Meanwhile, multiple visual elements are presented simultaneously on the interface, allowing users to cross-reference trends, distributions, and statistical summaries in a single, cohesive view. As a result, this multi-layered visualization approach enables intuitive, on-demand exploration of historical smart city datasets, offering valuable insights into urban dynamics without relying on real-time data streams.
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 3 Isfl-Sys Architecture
### Effl Federated Learning Mechanism
#### 3.3.4. Our ETFL Algorithm  
The function ModelUpdate (\(\omega\),\(D\),\(b\),\(\beta\),\(R\)) in Algorithm 1 is utilized for computing model parameter updates through R iterations of stochastic gradient descent with a local learning rate \(\beta\), as specifically referenced in Algorithm 2.  
```
0: Model update.
1:\(\omega_{0}\leftarrow\omega\);
2:for\(r=1,2,\cdots,R\)do
3: Randomly sample a batch \(D_{b}\) from \(D\);
4:\(\omega_{r}\leftarrow\omega_{r-1}-\beta\bigtriangledown Loss(D_{b};\omega)\);
5:endfor
6:return\(\omega_{R}-\omega\);
```  
**Algorithm 1** ModelUpdate(\(\omega\),\(D\),\(b\),\(\beta\),\(R\))  
```
0:\(n\) clients with local training datasets \(D_{i}\), \(i=1,2,\cdots,n\); a server with root dataset \(D_{0}\); global learning rate \(\alpha\); number of global iterations \(R_{g}\); number of clients \(\tau\) sampled in each iteration; local learning rate \(\beta\); number of local iterations \(R_{i}\); and batch size \(b\).
0: Global model \(\omega\).
1:\(\omega\leftarrow\) random initialization;
2:for\(r=1,2,\cdots,R_{g}\)do
3:// Step I: The server receives the initial model parameters and sends them to the client.
4: The server randomly samples \(\tau\) clients \(C_{1},C_{1},\cdots,C_{\tau}\) from \(\{1,2,\cdots,n\}\) and sends \(\omega\) to them;
5:
6:// Step II: Training client local models and server model.
7:// (1)client local model.
8:for\(i=C_{0},C_{1},\cdots,C_{\tau}\)do
9:\(g_{i}=ModelUpdate(\omega,D_{i},b,\beta,R_{i})\);
10: Send \(g_{i}\) to the server;
11:endfor
12:// (2)server model.
13:\(g_{0}=ModelUpdate(\omega,D_{0},b,\beta,R_{i})\);
14:
15:// Step III: Aggregating local model updates to update the global model.
16:for\(i=C_{0},C_{1},\cdots,C_{\tau}\)do
17:// A constant parameter based on empirical common sense.
18:\(\tilde{g}=\) K;
19:// Calculate the Euclidean distance.
20:\(TS_{i}=ReLU(d(g_{0},g_{i}))\);
21:endfor
22:\(g=\frac{1}{\sum_{j=1}^{n}TS_{C_{j}}}\sum_{i=1}^{n}TS_{C_{i}}\cdot\tilde{g}_{C_ {i}}\);
23:\(\omega\leftarrow\omega+\alpha\cdot g\);
24:endfor
25:return\(\omega\).
```  
**Algorithm 2** ETFL  
Algorithm 2 demonstrates our ETFL algorithm, divided into three steps over \(R_{g}\) iterations:  
1. The server receives the initial global model parameters and sends them to the client;
2. The client receives the global model parameters, trains its local model using local data, and uploads it to the server. Meanwhile, the server maintains server model updates based on the global model and reference dataset. Model updates are computedusing the ModelUpdate(\(\omega\),\(D\),\(b\),\(\beta\),\(R\)) function from Algorithm 1, combined with input parameters;
3. The server maintains global model updates by aggregating local model updates and uses them to update the global model with a global learning rate \(\alpha\).
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 2 Materials and Methods
### Study Area and Data Acquisition
#### 2.1.2 Remote Sensing Data  
The phenological observations were not always made in the middle of a forest with a significant cover of the plant species being observed. Therefore, to be able to retrieve remote sensing information that reflects ground observations to some extent, the nearest forest stand [48] within a radius of 20 km [49] from the observed tree and with comparable station characteristics was identified. Given the original observation station characteristics (altitude, slope, and aspect), the substitute location used as the centre for the remote sensing data pixel had characteristics not exceeding altitude \(\pm\) 200 m, slope \(\pm\) 10\({}^{\circ}\), and aspect \(\pm\) 1 class (note that aspects represent the standard 1 to 8 categorisation, i.e., N, NE, E, SE, S, SW, W, and NW, with \(-\)1 for flat areas). In addition, only stands larger than 1 ha (10,000 m\({}^{2}\)) qualified for use as a substitute site. This threshold was based on the assumption that if a tree species is represented as nearly pure in 1 ha, it is likely to be present as a secondary species in the rest of the pixel, therefore contributing substantially to the pixel value (note that most of the species included in this study do not form pure stands over extended areas in Slovenia). From the qualifying forest stands satisfying the aforementioned criteria, the nearest stand to the observation station was used as a substitute stand. The centroid of the substitute stand was used to retrieve remote sensing information from Google Earth Engine for the period 2003 to 2020. The previous routine (the generation of the pixel centre from the observation station) applied for each combination of station and tree species using the ArcGIS desktop software (version 10.7.1) is summarised in Figure 2.  
We opted for MODIS GPP (8-day aggregate) despite its very coarse spatial resolution (500 m) because the seasonality of GPP was more marked in the mixed forests of this study compared to that of vegetation indices, hence leading to better estimates of phenological events, i.e., SOS and EOS. In addition, the proportion of good quality data available for MODIS GPP (non-missing data after filtering out bad quality pixels) was considerably higher than that of vegetation indices, both from MODIS and Landsat 7, making the seasonality easier to retrieve. However, MODIS (250 m spatial resolution, 8-day temporal  
Figure 2: Routine for generating remote sensing pixel centre from observation stations (a tree at a given location).  
resolution) and Landsat 7 ETM+ (30 m spatial resolution, 16-day temporal resolution) derived EVI and NDVI, as well as MODIS LAI (500 m resolution, 4-day temporal resolution) were also tested and reported in this study.
## 3 Results  
### The Land Use Cover Change in the GZA  
This study reclassified the remote sensing data of land use data in the GZA from 2000 to 2019 based on the ArcGIS 10.8, and then carried out land use transfer matrix analysis.  
As shown in Figure 3, the proportion of cropland converted to construction land between 2000 and 2019 was relatively large (Figure 3b), which was mainly observed in most areas of Xi'an City, the central area of Baoji City, and the southern area of Xianyang City, accounting for 76.36% of the total area (Figure 3a). Meanwhile, 3.49% of the areas have changed from grassland to construction land, which mainly occurred in the surrounding area of Baoji and Xi'an City. The proportion of wetland, forest, and unused land converted to urban land was relatively small. In addition, 7.21% of the regions have been transformed from forest to grassland, mainly distributed in most areas of Baoji City. These transformations showed that the urbanization process was fast in this period, and a large number of cultivated land was occupied to develop cities. Table 4 showed that the cropland increased by 79.47 km\({}^{2}\) from 2000 to 2019, mainly due to the conversion of grassland (48.96 km\({}^{2}\)) and forest (30.51 km\({}^{2}\)) to cropland. The total increase in forestland was 190.17 km\({}^{2}\), mainly due to the conversion of grassland (105.57 km\({}^{2}\)) and cropland (84.6 km\({}^{2}\)) to forestland. The increase in grassland was mainly attributed to the transformation from forestland (209.52 km\({}^{2}\)). In addition, the largest increase occurred in construction land. Among these land-use changes, the area converted from cropland to construction land was the largest (2218.05 km\({}^{2}\)), followed by the grassland (101.43 km\({}^{2}\)).  
Figure 3: Land-use change in the GZA from 2000 to 2019. (**a**) Land use spatial transformation map; (**b**) Chord diagram of land use type transformation.
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 2 Materials and Methods
### Preparation of Paclitaxel-Loaded Nanoparticles  
The nanoparticles were prepared by a single emulsion (\(o/w\)) solvent evaporation technique. For this purpose, PTX was dissolved in DCM and then poured into a pre-weighed polymer. The drug-to-polymer ratio was 1:10 (\(w/w\)), with a drug concentration of 2 mg mL\({}^{-1}\). After complete dissolution, the solution was added dropwise to a 1% PVA aqueous solution to form an o/w emulsion and emulsified (10,000 rpm, T25 digital ULTRA-TURRAX(r), IKA, Staufen, Germany). The emulsion obtained was sonicated with 70% amplitude (Vibra Cell 505, SONICS & MATERIALS, INC., Newtown, CT, USA). After the evaporation of the organic solvent, the suspension was centrifuged at 20,000 rpm (Optima XPN-80 Ultracentrifuge, BECKMAN COULTER, Indianapolis, IN, USA) and washed twice to remove the residual emulsifier. The resulting nanoparticles were freeze-dried and stored at 4 \({}^{\circ}\)C for further usage.